Forthcoming articles


International Journal of Business Intelligence and Data Mining


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International Journal of Business Intelligence and Data Mining (117 papers in press)


Regular Issues


  • OLAP technology and Machine learning as the tools for validation of the Numerical Models of Convective Clouds   Order a copy of this article
    by Elena N. Stankova, Andrey V. Balakshiy, Dmitry A. Petrov, Vladimir V. Korkhov 
    Abstract: In the present work we use the technologies of machine learning and OLAP for more accurate forecasting of such phenomena as a thunderstorm, hail, heavy rain, using the numerical model of convective cloud. Three methods of machine learning: Support Vector Machine, Logistic Regression and Ridge Regression are used for making the decision on whether or not a dangerous convective phenomenon occurs at present atmospheric conditions. The OLAP technology is used for development of the concept of multidimensional data base intended for distinguishing the types of the phenomena (thunderstorm, heavy rainfall and light rain). Previously developed complex information system is used for collecting the data about the state of the atmosphere and about the place and at the time when dangerous convective phenomena are recorded.
    Keywords: OLAP; online analytical processing; machine learning; validation of numerical models; numerical model of convective cloud; weather forecasting; thunderstorm; multidimensional data base; data mining.
    DOI: 10.1504/IJBIDM.2017.10004787
  • Modelling Economic Choice under Radical Uncertainty: Machine Learning Approaches   Order a copy of this article
    by Antov Gerunov 
    Abstract: This paper utilises a novel experimental dataset on consumer choice to investigate and benchmark the performance of alternative statistical models under conditions of extreme uncertainty. We compare the results of logistic regression, discriminant analysis, na
    Keywords: choice; decision-making; social network; machine learning; uncertainty; social network; logistic regression; neural network; random forest; consumer choice; modeling.
    DOI: 10.1504/IJBIDM.2017.10004944
  • Rough Set Theory-Based Feature Selection and FGA-NN Classifier for Medical Data Classification   Order a copy of this article
    by B. Vijayalakshmi, Sugumar Rajendran 
    Abstract: The prediction of heart disease is difficult task, which needs much experience and knowledge. In order to reduce the risk of heart disease prediction, in this paper we proposed a rough set theory-based feature selection and FGA-NN classifier. The overall process of the proposed system consists of two main steps, such as: 1) feature reduction; 2) heart disease prediction. At first, the kernel fuzzy c-means clustering with roughest theory (KFCMRS) algorithm is applied to the high dimensional data to reduce the dimension of the attribute. After that, the medical data classification is done through FGA-NN classifier. To improve the prediction performance, hybridisation of firefly and genetic algorithm (FGA) is utilised with NN for weight optimisation. At last, the experimentation is performed by means of Cleveland, Hungarian, and Switzerland datasets. The experimentation result proves that the FGA-NN classifier outperformed the existing approach by attaining the accuracy of 83%.
    Keywords: Heart disease; FGA-NN; KFCMRS; scaled conjugate gradient; prediction; feature reduction; optimisation.
    DOI: 10.1504/IJBIDM.2017.10005016
  • Students Performance Prediction using Hybrid Classifier Technique in Incremental Learning   Order a copy of this article
    by Roshani Ade 
    Abstract: The performance in higher education is a turning point in the academics for all students. This academic performance is influenced by many factors, therefore it is essential to develop predictive data mining model for student's performance so as to identify the difference between high learners and slow learners student. The knowledge is hidden among the educational data set and it is extractable through data mining techniques. In our paper we used the hybrid classifier approach for the prediction of students performance using Fuzzy ARTMAP and Bayesian ARTMAP classifier. Sensitivity analysis was performed and irrelevant inputs were eliminated. The performance measures used to determine the performance of the techniques include Matthews Correlation Co-efficient (MCC), Accuracy Rate, True Positive, False Positive and Percentage correctly classified instances. The combined result gives the good accuracy for predicting students
    Keywords: Hybrid Classifier; Incremental Learning; Fuzzy ARTMAP; MCC.
    DOI: 10.1504/IJBIDM.2017.10005099
  • Privacy Preserving Data Mining using Hiding Maximum Utility Item First Algorithm By means of Grey wolf optimisation Algorithm   Order a copy of this article
    by M.T. Ketthari, Rajendran Sugumar 
    Abstract: In the privacy preserving data mining, the utility mining casts a very vital part. The objective of the suggested technique is performed by concealing the high sensitive item sets with the help of the hiding maximum utility item first (HMUIF) algorithm, which effectively evaluates the sensitive item sets by effectively exploiting the user defined utility threshold value. It successfully attempts to estimate the sensitive item sets by utilising optimal threshold value, by means of the grey wolf optimisation (GWO) algorithm. The optimised threshold value is then checked for its performance analysis by employing several constraints like the HF, MC and DIS. The novel technique is performed and the optimal threshold resultant item sets are assessed and contrasted with those of diverse optimisation approaches. The novel HMUIF considerably cuts down the calculation complication, thereby paving the way for the enhancement in hiding performance of the item sets.
    Keywords: Data Mining; Privacy Preserving Utility Mining; Sensitive Item sets; optimal threshold; Grey wolf optimisation.
    DOI: 10.1504/IJBIDM.2017.10006335
  • Fuzzy- MCS algorithm based Ontology generation for E Assessment   Order a copy of this article
    by A. Santhanavijayan, S.R. Balasundaram 
    Abstract: Ontologies can lead to important improvements in the definition of a courses knowledge domain, in the generation of an adapted learning path, and in the assessment phase. This paper provides an initial discussion of the role of ontologies in the context of e-learning. Generally, automatic assessment is preferred over manual assessment to avoid bias errors, human errors and also conserves teachers time. Evaluation through objective tests like multiple choice questions has gained a lot of importance in the e-assessment system. Here we have proposed an efficient ontology generation based on soft computing techniques in e-assessment for multiple choice questions. We have employed fuzzy logic incorporated with optimisation algorithm like modified cuckoo search algorithm. Here a set of rules are first designed for creating the ontology. The rules are generated using fuzzy logic and these rules are optimised in order to generate a better ontology structure.
    Keywords: Ontologies; MCS algorithm; Fuzzy; e-learning.
    DOI: 10.1504/IJBIDM.2017.10006336
  • Minimal constraint based cuckoo search algorithm for Removing Transmission Congestion and Rescheduling the Generator units   Order a copy of this article
    by N. Chidambararaj, K. Chitra 
    Abstract: In the paper, a minimal constraint based cuckoo search (CS) algorithm is proposed for solving transmission congestion problem by considering both increase and decrease in generation power. Thus, the proposed algorithm is used to optimise the real power changes of generator while transmission congestion occurred. Then, the power loss, generator sensitivity factor and congestion management cost of the system is evaluated by the proposed algorithm according to the transmission congestion. The proposed method is implemented in MATLAB working platform and their congestion management performance is analysed. The performance of the proposed method is compared with the other existing methods such as fuzzy adaptive bacterial foraging (FABF), simple bacterial foraging (SBF), particle swarm optimisation (PSO), and artificial neural network (ANN)-CS respectively. The congestion management cost is reduced up to 26.169%. Through the analysis of comparison, it is shown that the proposed technique is better and outperforms other existing techniques in terms of congestion management measures.
    Keywords: minimal constraint based CS algorithm; PSO; ANN; real power; congestion management; power loss and congestion management cost.
    DOI: 10.1504/IJBIDM.2017.10006337
  • Effective Discovery of Missing Links in Citation Networks Using Citation Relevancy Check Process   Order a copy of this article
    by Nivash J P, L.D. Dhinesh Babu 
    Abstract: Effective dissemination of knowledge published by eminent authors in reputed journals and ensuring that the referred work is cited properly is the need of the hour. Citation analysis is about the similarity measures of articles or journals which are put forward to scaling as well as clustering procedures. A proper citation relevancy check (CRC) is required to avoid the missing links in the citation networks. Both similar and dissimilar references in the articles have important article citations. The purpose of this work is devise a method to find the most significant articles which can provide useful information to the journal editors and writers. The strategy presented in this paper can assist an author to incorporate most important articles and can help the editor in evaluating the quality of the references. The main benefit in detecting the missing articles is improvement in quality of research along with increased citation count.
    Keywords: Citation network analysis; Missing citations; Citation relevancy check; Increasing citation count.
    DOI: 10.1504/IJBIDM.2017.10006338
  • A Distributed Cross-layer Recommender System Incorporating Product Diffusion   Order a copy of this article
    by Ephina Thendral, C. Valliyammai 
    Abstract: In this era of online retailing, personalisation of web content has become very essential. Recommender system is a tool for extraction of relevant information to render personalisation in web information retrieval systems. With an inclination towards customer oriented service, there is a need to understand the adaptability of customers, to provide products/services of interest at the right time. In this paper, a model for distributed context aware cross layer recommender system incorporating the principle of product diffusion is proposed. The offline-online modelled recommender system learns offline about the adaptation time of users using the principle of product diffusion and then, uses online explore-then-exploit strategy to make effective recommendations to the user at the most probable time of consumption. Also, an algorithm based on product adaptability is proposed for recommending new items to the most probable users. The extensive experiments and results demonstrate the efficiency, scalability, reliability and enhanced retrieval effectiveness of the proposed recommender system model.
    Keywords: Recommender Systems; Personalization; Product Diffusion; Distributed Graph Model; Hadoop; Hbase; Titan graph database; Spark; Cross layer; Distributed processing.
    DOI: 10.1504/IJBIDM.2017.10006339
  • A Critique of Imbalanced Data Learning Approaches for Big Data Analytics   Order a copy of this article
    by Amril Nazir 
    Abstract: Biomedical research becomes reliant on multi-disciplinary, multi-institutional collaboration, and data sharing is becoming increasingly important for researchers to reuse experiments, pool expertise and validate approaches. However, there are many hurdles for data sharing, including the unwillingness to share, lack of flexible data model for providing context information for shared data, difficulty to share syntactically and semantically consistent data across distributed institutions, and expensive cost to provide tools to share the data. In our work, we develop a web-based collaborative biomedical data sharing platform SciPort to support biomedical data sharing across distributed organisations. SciPort provides a generic metadata model for researchers to flflexibly customise and organise the data. To enable convenient data sharing, SciPort provides a central server-based data sharing architecture, where data can be shared by one click through publishing metadata to the central server. To enable consistent data sharing, SciPort provides collaborative distributed schema management across distributed sites. To enable semantic consistency for data sharing, SciPort provides semantic tagging through controlled vocabularies. SciPort is lightweight and can be easily deployed for building data sharing communities for biomedical research.
    Keywords: imbalanced big data learning; large-scale imbalanced data analysis; high-dimensional imbalanced data learning.
    DOI: 10.1504/IJBIDM.2017.10006340
  • A Novel Multi-class Ensemble model based on feature selection using Hadoop framework for classifying imbalanced Biomedical Data   Order a copy of this article
    by THULASI BIKKU, N. Sambasiva Rao, Ananda Rao Akepogu 
    Abstract: Due to the exponential growth of biomedical repositories such as PubMed and Medline, an accurate predictive model is essential for knowledge discovery in Hadoop environment. Traditional decision tree models such as multi-variate Bernoulli model, random forest and multinominal na
    Keywords: Ensemble model; Hadoop; Imbalanced data; Medical databases; Textual Decision Patterns.
    DOI: 10.1504/IJBIDM.2018.10006485
  • An optimised approach to detect the identity of hidden information in gray scale and colour images   Order a copy of this article
    by Murugeswari Ganesan, Deisy Chelliah, Ganesan Govindan 
    Abstract: Feature-based steganalysis is an emerging trend in the domain of Information Forensics, aims to discover the identity of secret information present in the covert communication by analysing the statistical features of cover/stego image. Due to massive volumes of auditing data as well as complex and dynamic behaviours of steganogram features, optimising those features is an important open problem. This paper focused on optimising the number of features using the proposed quick artificial bee colony (qABC) algorithm. Here we tested for three steganalysers, namely subtractive pixel adjacency matrix (SPAM), phase aware projection model (PHARM) and colour filter array (CFA) for the break our steganographic system (BOSS) 1.01 datasets. The significant improvement in the convergence nature of qABC quickly improves the solution and fine tune the search than their real counterparts. The results reveal that qABC method with support vector machine (SVM) classifier outperforms the non-optimised version concerning classification accuracy and reduced number of feature sets.
    Keywords: Steganalysis; Feature Selection; Optimisation; Classification.
    DOI: 10.1504/IJBIDM.2017.10006486
  • An Effective Preprocessing Algorithm for Model Building in Collaborative Filtering based Recommender System   Order a copy of this article
    by Srikanth T, M. Shashi 
    Abstract: Recommender systems suggest interesting items for online users based on the ratings expressed by them for the other items maintained globally as the rating matrix. The rating matrix is often sparse and very huge due to large number of users expressing their ratings only for a few items among the large number of alternatives. Sparsity and scalability are the challenging issues to achieve accurate predictions in recommender systems. This paper focuses on model building approach to collaborative filtering-based recommender systems using low rank matrix approximation algorithms for achieving scalability and accuracy while dealing with sparse rating matrices. A novel preprocessing methodology is proposed to counter data sparsity problem by transforming the sparse rating matrix denser before extracting latent factors to appropriately characterise the users and items in low dimensional space. The quality of predictions made either directly or indirectly through user clustering were investigated and found to be competitive with the existing collaborative filtering methods in terms of reduced MAE and increased NDCG values on bench mark datasets.
    Keywords: Recommender System; Collaborative Filtering; Dimensionality Reduction; Pre- Processing,Sparsity,Scalability,Matrix Factorization.
    DOI: 10.1504/IJBIDM.2017.10006817
  • Error Tolerant Global Search Incorporated With Deep Learning Algorithm to Automatic Hindi Text Summarization   Order a copy of this article
    by J. Anitha, P.V.G.D. Prasad Reddy, M.S. Prasad Babu 
    Abstract: There is an exponential growth in the available electronic information in the last two decades. It causes a huge necessity to quickly understand high volume text data. This paper describes an efficient algorithm and it works by assigning scores to sentences in the document which is to be summarised. It also focuses on document extracts; a particular kind of computed document summary. The proposed approach uses fuzzy classifier and deep learning algorithm. Fuzzy classifier produces score for each sentence and the deep learning (DL) also produces score for each sentence. The combination of score from both fuzzy classifier and DL produces the hybrid score. Finally, the summarised text can be generated based on this hybrid score. In our proposed approach, we have achieved an average precision rate of 0.92 and average recall rate of 0.88 and the compression rate is 10% according to the experimental analysis.
    Keywords: GSA; Fuzzy; summarisation; hybrid; deep learning.
    DOI: 10.1504/IJBIDM.2017.10006978
  • Network Affinity Aware Energy Efficient Virtual Machine Placement Algorithm   Order a copy of this article
    by Ranjana Ramamurthy, S. Radha, J. Raja 
    Abstract: Efficient mapping of virtual machine request to the available physical machine is an optimisation problem in data centres. It is solved by aiming to minimise the number of physical machines and utilising them to their maximum capacity. Another avenue of optimisation in data centre is the energy consumption. Energy consumption can be reduced by using fewer physical machines for a given set of VM requests. An attempt is made in this work to propose an energy efficient VM placement algorithm that is also network affinity aware. Considering the network affinity between VMs during the placement will reduce the communication cost and the network overhead. The proposed algorithm is evaluated using the Cloudsim toolkit and the performance in terms of energy consumed, communication cost and number of active PMs, is compared with the standard first fit greedy algorithm.
    Keywords: Virtualisation; affinity aware; cloud computing; virtual machine placement; network affinity.
    DOI: 10.1504/IJBIDM.2018.10007005
  • A Secured Best Data Center Selection in Cloud Computing Using Encryption Technique   Order a copy of this article
    by Prabhu A., M. Usha 
    Abstract: In this work, we have proposed an approach for providing very high security to the cloud system. Our proposed method comprises of three phases namely authentication phase, cloud data centre selection phase and user related service agreement phase. For the purpose of accessing data from the cloud server, we will need a secure authentication key. In the authentication phase, the user authentication is verified and gets the key then encrypts the file using blowfish algorithm. Before encryption the input data is divided into column-wisely with the help of pattern matching approach. In the approach, the encryption and decryption processes are carried out by employing the blowfish algorithm. We can optimally select the cloud data centre to store the data; here the position is optimally selected with the help of bat algorithm. In the final phase, the user service agreement is verified. The implementation will be done by cloud sim simulator.
    Keywords: Authentication key; blowfish; Bat algorithm; pattern match; Cloud Data Center Selection.
    DOI: 10.1504/IJBIDM.2018.10007299
  • Combined Local color curvelet and mesh pattern for image retrieval system   Order a copy of this article
    Abstract: This manuscript presents the content based image retrieval system using new textural features such as colour local curvelet (CLC) based textural descriptor and colour local mesh pattern (CLMP), for the intention of increasing the performance of the image retrieval system. The proposed methods can be able to utilise the distinctive details obtained from spatial coloured textural patterns of various spectral components within the particular local image region. Furthermore, to acquire the benefit of harmonising effect through joint colour texture information, the oppugant colour textural features that obtain the texture patterns of spatial interactions among spectral planes are also integrated in to the creation of CLC and CLMP. Extensive and comparative experiments have been conducted on two benchmark databases, i.e., Corel-1k, MIT VisTex. Retrieval results show that image retrieval using colour local texture features yields better precision and recall than retrieval approaches using either by colour or texture features.
    Keywords: Content based image retrieval system; Curvelet transform; Local mesh pattern; Color local curvelets; Color local mesh pattern.
    DOI: 10.1504/IJBIDM.2017.10007514
    by Malini A, K. . Sundarakantham, C. Mano Prathibhan, A. Bhavithrachelvi 
    Abstract: Testing of mobile applications during the occurrence of interrupts is termed as interrupt testing. Interrupts can occur either internally within the mobile or from other external factors or systems. Interruption in any smart phones may decrease the performance of mobile applications. In this paper, an automated interruption testing model is proposed to analyse the responsiveness of mobile applications during interrupts. This model monitors the applications installed in the mobile devices and evaluates the overall performance of mobile applications during interrupt using fuzzy logic. An enhanced MobiFuzzy evaluation system (MFES) is proposed that is used to dynamically analyse the test results and identify necessary information required for tuning the application. Fuzzy logic will help the developers or testers in tuning the application performance; by automatically categorising the impact
    Keywords: Mobile application testing; Interrupt testing; Application tracker; Performance testing.
    DOI: 10.1504/IJBIDM.2017.10007515
  • Evolution of Singular Value Decomposition in Recommendation Systems : A Review   Order a copy of this article
    by Rachana Mehta, Keyur Rana 
    Abstract: Proliferation of internet and web applications has led to exponential growth of users and information over web. In such information overload scenarios, recommender systems have shown their prominence by providing user with information of their interest. Recommender systems provide item recommendation or generate predictions. Amongst the various recommendation approaches, collaborative filtering techniques have emerged well because of its wide item applicability. Model-based collaborative filtering techniques which use parameterised model for prediction are more preferred as compared to their memory-based counterparts. However, the existing techniques deals with static data and are less accurate over sparse, high dimensional data. In order to alleviate such issues, matrix factorisation techniques like singular value decomposition are preferred. These techniques have evolved from using simple user-item rating information to auxiliary social and temporal information. In this paper, we provide a comprehensive review of such matrix factorisation techniques and their applicability to different input data.
    Keywords: Recommendation System; Collaborative filtering; Matrix factorization;Singular Value Decomposition; Information retrieval;Data mining;Auxiliary information; Latent features;Model learning;Data sparsity.
    DOI: 10.1504/IJBIDM.2017.10007516
  • Investigating Different Fitness Criteria for Swarm-based Clustering   Order a copy of this article
    by Maria P.S. Souza, Telmo M. Silva Filho, Getulio J.A. Amaral, Renata M.C.R. Souza 
    Abstract: Swarm-based optimisation methods have been previously used for tackling clustering tasks, with good results. However, the results obtained by this kind of algorithm are highly dependent on the chosen fitness criterion. In this work, we investigate the influence of four different fitness criteria on swarm-based clustering performance. The first function is the typical sum of distances between instances and their cluster centroids, which is the most used clustering criterion. The remaining functions are based on three different types of data dispersion: total dispersion, within-group dispersion and between-groups dispersion. We use a swarm-based algorithm to optimise these criteria and perform clustering tasks with nine real and artificial datasets. For each dataset, we select the best criterion in terms of adjusted Rand index and compare it with three state-of-the-art swarm-based clustering algorithms, trained with their proposed criteria. Numerical results confirm the importance of selecting an appropriate fitness criterion for each clustering task.
    Keywords: Swarm Optimisation; Fitness criterion; Clustering; Artificial Bee Colony; Particle Swarm Optimisation.
    DOI: 10.1504/IJBIDM.2017.10007517
  • A Combined PFCM and Recurrent Neural Network based Intrusion Detection System for Cloud Environment   Order a copy of this article
    by Manickam M., N. Ramaraj, C. Chellappan 
    Abstract: The main objective of this paper is intrusion detection system for a cloud environment using combined PFCM-RNN. Traditional IDSs are not suitable for cloud environment as network-based IDSs (NIDS) cannot detect encrypted node communication, also host-based IDSs (HIDS) are not able to find the hidden attack trail. The traditional intrusion detection is largely inefficient to be deployed in cloud computing environments due to their openness and specific essence. Accordingly, this proposed work consists of two modules namely clustering module and classification module. In clustering module, the input dataset is grouped into clusters with the use of possibilistic fuzzy C-means clustering (PFCM). In classification module, the centroid from the clusters is given to the recurrent neural network which is used to classify whether the data is intruded or not. For experimental evaluation, we use the benchmark database and the results clearly demonstrate the proposed technique outperformed conventional methods.
    Keywords: cloud computing; intrusion detection system; Possibilistic Fuzzy C-means clustering; recurrent neural network.
    DOI: 10.1504/IJBIDM.2017.10007763
  • Master node fault tolerance in distributed big data processing clusters   Order a copy of this article
    by Ivan Gankevich, Yuri Tipikin, Vladimir Korkhov, Vladimir Gaiduchok, Alexander Degtyarev, A. Bogdanov 
    Abstract: Distributed computing clusters are often built with commodity hardware which leads to periodic failures of processing nodes due to relatively low reliability of such hardware. While worker node fault-tolerance is straightforward, fault tolerance of master node poses a bigger challenge. In this paper master node failure handling is based on the concept of master and worker roles that can be dynamically re-assigned to cluster nodes along with maintaining a backup of the master node state on one of worker nodes. In such case no special component is needed to monitor the health of the cluster while master node failures can be resolved except for the cases of simultaneous failure of master and backup. We present experimental evaluation of the technique implementation, show benchmarks demonstrating that a failure of a master does not affect running job, and a failure of backup results in re-computation of only the last job step.
    Keywords: parallel computing; Big Data processing; distributed computing; backup node; state transfer; delegation; cluster computing; fault-tolerance.
    DOI: 10.1504/IJBIDM.2017.10007764
  • The integration of a newly defined N-gram concept and vector space model for documents ranking   Order a copy of this article
    by Mostafa Salama, Wafaa Salah 
    Abstract: Vector space model (VSM) is used in measuring the similarity between documents according to the frequency of common words among them. Furthermore, the N-gram concept is integrated in VSM to put into consideration the relation between common consecutive words in the documents. This approach does not consider the context and semantic dependency between nonconsecutive words existing in the same sentence. Accordingly, the approach proposed here presents a new definition of the N-gram concept as N non-consecutive words located in the same sentence, and utilises this definition in the VSM to enhance the measurement of the semantic similarity between documents. This approach measures and visualises the correlation between the words that are commonly existing together within the same sentence to enrich the analysis of domain experts. The results of the experimental work show the robustness of the proposed approach against the current ranking models.
    Keywords: N-gram; vector space model; Text Mining.
    DOI: 10.1504/IJBIDM.2017.10007893
    by M. Senthil Kumar, P.S. Manoharan, R. Ramachandran 
    Abstract: This paper presents modelling and simulation of artificial neuro-fuzzy inference system (ANFIS) based maximum power point tracking (MPPT) algorithm for PV system with modified SEPIC converter. The conventional existing MPPT methods are having major drawbacks of high oscillations at maximum power point and low efficiency due to uncertain nature of solar radiation and temperature. These mentioned problems can be solved by the proposed adaptive (ANFIS) based MPPT. The proposed work involves ANFIS and modified single ended primary inductor converter (SEPIC) to extract maximum power from PV panel. The results obtained from proposed methodology are compared with other MPPT algorithms such as perturb and observe (P&O), incremental conductance (INC) and radial basis function network (RBFN). The improvement in voltage rating of modified SEPIC is compared with conventional SEPIC converter. The result confirms the superiority of the proposed system.
    Keywords: ANFIS;INC;Modified SEPIC;P&O;RBFN.
    DOI: 10.1504/IJBIDM.2017.10007894
  • Distributed Algorithms for Improved Associative Multilabel Document Classification considering Reoccurrence of Features and handling Minority Classes   Order a copy of this article
    by Preeti Bailke, S.T. Patil 
    Abstract: Existing work in the domain of distributed data mining mainly focuses on achieving the speedup and scaleup properties rather than improving performance measures of the classifier. Improvement in speedup and scaleup is obvious when distributed computing platform is used. But its computing power should also be used for improving performance measures of the classifier. This paper focuses on the same by considering reoccurrence of features and handling minority classes. Since it is very time consuming to run such complex algorithms on large datasets sequentially, distributed versions of the algorithms are designed and tested on the Hadoop cluster. Base associative classifier is designed based on multi-class, multi-label associative classification (MMAC) algorithm. Since no similar distributed algorithms exist, proposed algorithms are compared with the base classifier and have shown improvement in classifier performance measures.
    Keywords: Multilabel associative classifier; Hadoop; Pig; Feature reoccurrence; Minority Class; Distributed Algorithm.
    DOI: 10.1504/IJBIDM.2017.10007924
  • A survey on time series motif discovery   Order a copy of this article
    by Cao Duy Truong, Duong Tuan Anh 
    Abstract: Time series motifs are repeated subsequences in a long time series. Discovering time series motifs is an important task in time series data mining and this problem has received significant attention from researchers in data mining communities. In this paper, we intend to provide a comprehensive survey of the techniques applied for time series motif discovery. The survey also briefly describes a set of applications of time series motif in various domains as well as in high-level time series data mining tasks. We hope that this article can provide a broad and deep understanding of the time series motif discovery field.
    Keywords: time series; motif discovery; window-based; segmentation-based; motif applications.
    DOI: 10.1504/IJBIDM.2017.10008074
  • Trust Management Scheme for Authentication in Secure Cloud Computing Using Double Encryption Method   Order a copy of this article
    by P. Sathishkumar, V. Venkatachalam 
    Abstract: In cloud computing and banking, the consumer as well as supplier required for their service as protection and confidence. In this document suggest the belief value oriented verification procedure by the aid of encryption procedure, this verification segment bank marketing database are measured to the kernel fuzzy c-means clustering (KFCM) method. Clustered datas are accumulated in the cloud to the confidence data verification procedure. In the verification segment, the consumer verification is confirmed and acquires the verification key then encrypts the file by the double encryption algorithm. Primarily the confidence finest data implemented homomorphic encryption to encrypt the data by blowfish algorithm and then encrypted data are accumulated in cloud data core. This procedure oriented the banking data will be steadily legalised in cloud computing procedure. The outcomes are exemplify the improved encryption time and extremely legitimate the data in the cloud.
    Keywords: Authentication; Cloud Security; Cloud Services; Trust Management; clustering; cloud computing; encryption and decryption.
    DOI: 10.1504/IJBIDM.2017.10008075
  • Trajectory tracking of the robot end-effector for the minimally invasive surgeries   Order a copy of this article
    by Jose De Jesus Rubio, Panuncio Cruz, Enrique Garcia, Cesar Felipe Juarez, David Ricardo Cruz, Jesus Lopez 
    Abstract: The surgery technology has been highly investigated, with the purpose to reach an efficient way of working in medicine. Consequently, robots with small tools have been incorporated in many kind of surgeries to reach the following improvements: the patient gets a faster recovery, the surgery is not invasive, and the robot can access to the body occult parts. In this article, an adaptive strategy for the trajectory tracking of the robot end effector is addressed; it consists of a proportional derivative technique plus an adaptive compensation. The proportional derivative technique is employed to reach the trajectory tracking. The adaptive compensation is employed to reach approximation of some unknown dynamics. The robot described in this study is employed in minimally invasive surgeries.
    Keywords: Trajectory tracking; robot; minimal invasive surgery.
    DOI: 10.1504/IJBIDM.2018.10008077
  • Multi Label Learning Approaches for Multi Species Avifaunal Occurrence Modelling: A Case Study of South Eastern Tamil Nadu   Order a copy of this article
    by Appavu Alias Balamurugan, P.K.A. Chitra, S. Geetha 
    Abstract: Many multi label problem transformation (PT) and algorithm adaptation (AA) methods need to be explored to get good candidate for avifaunal occupancy modelling. This research contrasted eight commonly used state-of-the-art PT and AA multi label methods. The data was created by collecting January 2014December 2014 records from e-bird repository for the study area Madurai district, south eastern Tamil Nadu. The analysis shows that classifier chain (CC) and multi label naive Bayes (MLNB) are the good aspirants for avifauna data. The MLNB did best with 0.019 hamming loss and 90% average precision. To the best of our knowledge this is the first time to use MLNB for avifaunal data and the results of multi label naive Bayes concludes that out of 143 species observed, six species had high occurrence rate and 68 species had low occurrence rate.
    Keywords: Species distribution models; multi species; multi label Learning; Multi Label Naive Bayes; Central part of southern Tamil Nadu.
    DOI: 10.1504/IJBIDM.2018.10008307
  • Analytics on Talent Search Examination Data   Order a copy of this article
    by Anagha Vaidya, Vyankat Munde, Shailaja Shirwaikar 
    Abstract: Learning analytics and educational data mining has greatly supported the process of assessing and improving the quality of education. While learning analytics has a longer development cycle, educational data mining suffers from the inadequacy of data captured through learning processes. The data captured from examination process can be suitably extended to perform some descriptive and predictive analytics. This paper demonstrates the possibility of actionable analytics on the data collected from talent search examination process by adding to it some data pre-processing steps. The analytics provides some insight into the learners characteristics and demonstrates how analytics on examination data can be a major support for bringing the quality in education field.
    Keywords: Learning Analytics; Educational Data Mining; clustering; linear modelling.
    DOI: 10.1504/IJBIDM.2018.10008308
  • A fast clustering approach for large multidimensional data   Order a copy of this article
    by Hajar Rehioui, Abdellah Idrissi 
    Abstract: Density-based clustering is a strong family of clustering methods. The strength of this family is its ability to classify data of arbitrary shapes and to omit the noise. Among them density-based clustering (DENCLUE), which is one of the well-known powerful density-based clustering methods. DENCLUE is based on the concept of the hill climbing algorithm. In order to find the clusters, DENCLUE has to reach a set of points called density attractors. Despite the advantages of DENCLUE, it remains sensitive to the growth of the size of data and of the dimensionality, in the fact that the density attractors are calculated of each point in the input data. In this paper, in the aim to overcome the DENCLUE shortcoming, we propose an efficient approach. This approach replaces the concept of the density attractor by a new concept which is the hyper-cube representative. The experimental results, provided from several datasets, prove that our approach finds a trade-off between the performance of clustering and the fast response time. In this way, the proposed clustering methods work efficiently for large of multidimensional data.
    Keywords: Large Data; Dimensional Data; Clustering; Density based clustering; DENCLUE.
    DOI: 10.1504/IJBIDM.2017.10008309
  • CBRec: a book recommendation system for children using the matrix factorisation and content-based filtering approaches   Order a copy of this article
    by Yiu-Kai Ng 
    Abstract: Promoting good reading habits among children is essential, given the enormous influence of reading on students development as learners and members of the society. Unfortunately, very few (children) websites or online applications recommend books to children, even though they can play a significant role in encouraging children to read. Given that a few popular book websites suggest books to children based on the popularity of books or rankings on books, they are not customised/personalised for each individual user and likely recommend books that users do not want or like. We have integrated the matrix factorisation approach and the content-based approach, in addition to predicting the grade levels of books, to recommend books for children. Recent research works have demonstrated that a hybrid approach, which combines different filtering approaches, is more effective in making recommendations. Conducted empirical study has verified the effectiveness of our proposed children book recommendation system.
    Keywords: Book recommendation; matrix factorisation; content analysis; children.
    DOI: 10.1504/IJBIDM.2018.10008310
  • Enhancing Purchase Decision using Multi-word Target Bootstrapping with Part-of-Speech Pattern Recognition Algorithm   Order a copy of this article
    by M. Pradeepa Sivaramakrishnan, C. Deisy 
    Abstract: In this research work, multi-word target related terms are extracted automatically from the customer reviews for sentiment analysis. We used LIDF measure and have proposed a novel measure called, TCumass in iterative multi-word target (IMWT) bootstrapping algorithm. In addition, part-of-speech pattern recognition (PPR) algorithm has been proposed to identify the appropriate target and emotional words from multi-word target related terms. This article aims to bring out both implicit and explicit targets with their corresponding polarities in an unsupervised manner. We proposed two models namely, MWTB without PPR and MWTB with PPR. Thus, the present research illustrates the comparison between the proposed works and the existing multi-aspect bootstrapping (MAB) algorithm. The experiment has been done based on different data sets and thereafter the performance evaluated using different measures. From this study, the result expounds that MWTB with PPR model performs well, having achieved the precise targets and emotional words.
    Keywords: Bootstrapping; emotional polarity; multi-word target; Part-of-Speech (POS); sentiment analysis.
    DOI: 10.1504/IJBIDM.2018.10008334
  • Probabilistic Variable Precision Fuzzy Rough Set Technique for Discovering Optimal Learning Patterns in E-learning   Order a copy of this article
    by Bhuvaneshwari K.S, D. Bhanu, S. Sophia, S. Kannimuthu 
    Abstract: In e-learning environment, optimal learning patterns are discovered for realising and understanding the effective learning styles. The value of uncertain and imprecise knowledge collected has to be categorised into classes known as membership grades. Rough set theory is potential in categorising data into equivalent classes and fuzzy logic may be applied through soft thresholds for refining equivalence relation that quantifies correlation between each class of elucidated data. In this paper, probabilistic variable precision fuzzy rough set technique (PVPFRST) is proposed for deriving robust approximations and generalisations that handles the types of uncertainty namely stochastic, imprecision and noise in membership functions. The result infers that the degree of accuracy of PVPFRST is 21% superior to benchmark techniques. Result proves that PVPFRST improves effectiveness and efficiency in identifying e-learners styles and increases the performance by 27%, 22% and 25% in terms of discrimination rate, precision and recall value than the benchmark approaches.
    Keywords: Inclusion degree; Probabilistic fuzzy information system; fuzzy membership grade; Crispness coefficient; Probabilistic variable precision fuzzy rough set; Inclusion function.
    DOI: 10.1504/IJBIDM.2018.10008496
  • Inferring the Level of Visibility from Hazy Images   Order a copy of this article
    by Alexander A. S. Gunawan, Heri Prasetyo, Indah Werdiningsih, Janson Hendryli 
    Abstract: In our research, we would like to exploit crowdsourced photos from social media to create low-cost fire disaster sensors. The main problem is to analyse how hazy the environment looks like. Therefore, we provide a brief survey of methods dealing with visibility level of hazy images. The methods are divided into two categories: single-image approach and learning-based approach. The survey begins with discussing single image approach. This approach is represented by visibility metric based on contrast-to-noise ratio (CNR) and similarity index between hazy image and its dehazing image. This is followed by a survey of learning-based approach using two contrast approaches that is: 1) based on theoretical foundation of transmission light, combining with the depth image using new deep learning method; 2) based on black-box method by employing convolutional neural networks (CNN) on hazy images.
    Keywords: Hazy image; visibility level; single image approach; learning based approach; social media.
    DOI: 10.1504/IJBIDM.2018.10008497
  • The Complexity of Cluster-Connectivity of Wireless Sensor Networks   Order a copy of this article
    by H.K. Dai, H.C. Su 
    Abstract: Wireless sensor networks consist of sensor devices with limited computational capabilities and memory operating in bounded energy resources; hence, network optimisation and algorithmic development in minimising the total energy or power while maintaining the connectivity of the underlying network are crucial for their design and maintenance. We consider a generalised system model of wireless sensor networks whose node set is decomposed into multiple clusters, and show that the decision and the associated minimisation problems of the connectivity of clustered wireless sensor networks appear to be computationally intractable completeness and hardness, respectively, for the non-deterministic polynomial-time complexity class. An approximation algorithm is devised to minimise the number of end nodes of inter-cluster edges within a factor of 2 of the optimum for the cluster-connectivity.
    Keywords: wireless sensor network; connectivity; spanning tree; nondeterministic polynomial-time complexity class; approximation algorithm.
    DOI: 10.1504/IJBIDM.2018.10008498
  • Efficient Moving Vehicle Detection for Intelligent Traffic Surveillance System Using Optimal Probabilistic Neural Network   Order a copy of this article
    by Smitha J.A, N. Rajkumar 
    Abstract: The vehicle detection system plays an essential role in the traffic video surveillance system. Video communication of these traffic cameras over real-world limited bandwidth networks can frequently suffer network congestion. The objective of this paper is to develop an effective method for moving vehicle detection problems that can find high quality solutions (with respect to detection accuracy) at a high convergence speed. To achieve this objective, we propose a method that hybridises the cuckoo search (CS) with Opposition-based learning (OBL), where OBL is improve the performance of the CS algorithm while optimising the weights of the standard PNN model. The proposed system mainly consists of two modules such as: 1) design novel OCS-PNN model; 2) moving vehicle detection using OCS-PNN model. The algorithm is tested on three standard video dataset. For instance, the proposed method achieved the maximum precision of 94%, F-measure of 94% and similarity of 94%.
    Keywords: Moving vehicle detection; Probabilistic neural network; oppositional; Cuckoo search; Traffic video surveillance system; OCS-PNN.
    DOI: 10.1504/IJBIDM.2018.10008660
  • The Mediation Roles of Purchase intention and brand trust in Relationship between social marketing activities and brand loyalty   Order a copy of this article
    by Nasrin Yazdanian, Saman Ronagh, Parya Laghaei, Fatemeh Mostafshar 
    Abstract: The rise of social media significantly challenges the way of firms managing about introducing their brands. The literature on social media marketing activities (SMMA) has promoted specially in the field of luxury marketing. Building on the basic of web 2.0 social media applications have simplified and facilitated extraordinary growth in customer interaction in modern times. The objective of this study is to examine the role of affecting factors which influence Iranian luxury brands customers attitude toward purchase intention and brand loyalty. A questionnaire was used for collecting data from a sample of 114 luxury brand customers in social media in Tehran, capital and metropolitan city of Iran. Structural equation modelling was applied to examine the impact of social media marketing activities on brand loyalty. The mediating role of purchase intention and brand trust is considered too. The results indicated that entertainment does not have positive impact on purchase intention, brand trust and brand loyalty. The results of this research enable luxury brands managers to forecast the future purchasing behaviour of their customers and provide a guide to managing their strategies and marketing activities in competitive environment.
    Keywords: Luxury brands; Social Media Marketing Activities; brand trust ; loyalty; purchase intention.
    DOI: 10.1504/IJBIDM.2018.10008661
  • Application of a hybrid data mining model to identify the main predictive factors influencing hospital length of stay   Order a copy of this article
    by Ahmed Belderrar, Abdeldjebar Hazzab 
    Abstract: Length of hospital stay is one of the most appropriate measures that can be used for management of hospital resources and assistant of hospital admissions. The main predictive factors associated with the length of stay are critical requirements and should be identified to build a reliable prediction model for hospital stays. A hybrid integration approach consisting of fuzzy radial basis function neural network and hierarchical genetic algorithms was proposed. The proposed approach was applied on a data set collected from a variety of intensive care units. We achieved an acceptable forecast accuracy level with more than 80.50%. We found 14 common predictive factors. Most notably, we consistently found that the demographic characteristics, hospital features, medical events and comorbidities strongly correlates to the length of stay. The proposed approach can be used as an effective tool for healthcare providers and can be extended to other hospital predictions.
    Keywords: data mining; hospital management; length of hospital stay; hybrid prediction model; predictive factors.
    DOI: 10.1504/IJBIDM.2018.10008777
  • Genetic Algorithm based Intelligent Multiagent Architecture for Extracting Information from Hidden Web Databases   Order a copy of this article
    by Weslin D, T. Joshva Devadas 
    Abstract: Though there are enormous amount of information available in the web, only very small portion of the available information is visible to the users. Due to the non-visibility of huge information, the traditional search engines cannot index or access all information present in the web. The main challenge in the mining of the relevant information from a huge hidden web database is to identify the entry points to access the hidden web databases. The existing web crawlers cannot retrieve all information from the hidden web databases. To retrieve all the relevant information from the hidden web, this paper proposes an architecture that uses genetic algorithm and intelligent agents for accessing hidden web databases. The proposed architecture is termed as genetic algorithm based intelligent multi-agent system (GABIAS). The experimental results show that the proposed architecture provides better precision and recall than the existing web crawlers.
    Keywords: Genetic Algorithm (GA); Hidden Web; Intelligent Agent; Web Crawler.
    DOI: 10.1504/IJBIDM.2018.10008837
  • Efficient Clustering Technique for K-Anonymization with Aid of Optimal KFCM   Order a copy of this article
    by Chitra Ganabathi G., P. Uma Maheswari 
    Abstract: The k-anonymity model is a simple and practical approach for data privacy preservation. To minimise the information loss due to anonymisation, it is crucial to group similar data together and then anonymises each group individually. So that in this paper proposes a novel clustering method for conducting the k-anonymity model effectively. The clustering will be done by an optimal kernel based fuzzy c-means clustering algorithm (KFCM). In KFCM, the original Euclidean distance in the FCM is replaced by a kernel-induced distance. Here the objective function of the kernel fuzzy c-means clustering algorithm is optimised with the help of modified grey wolf optimisation algorithm (MGWO). Based on that, the collected data is grouped in an effective manner. The performance of the proposed technique is evaluated by means of information loss, time taken to group the available data. The proposed technique will be implemented in the working platform of MATLAB.
    Keywords: Privacy preservation; k-anonymity; Kernel Fuzzy C-Means; Grey wolf optimization; information loss.
    DOI: 10.1504/IJBIDM.2018.10008933
  • Optimal Decision Tree Fuzzy Rule Based Classifier (ODT-FRC) For Heart Disease Prediction Using Improved Cuckoo Search Algorithm   Order a copy of this article
    by Subhashini Narayan, Jagadeesh Gobal 
    Abstract: Heart disease is a major cause for anomaly in developed countries and one of the basic diseases in developing countries. Then there is a necessary to insert an alternative expressively caring network for predicting heart disease of a patient. The clinical alternative expressively caring networks contain three method of preprocessing such as preprocessing, generate decision rule and rule weighting, classification. Initially, the Cleveland data, Hungarian data and Switzerland data are loud in the reliable information from the database in preprocessing. On this process, underline quantity reduction method will be associated to reduce the components space exploiting orthogonal neighbourhood safeguarding projection (OLPP) computation. While, the combinations of cuckoo search algorithm, fuzzy and decision tree classifier can create a hybrid classifier. Here, fuzzy and decision tree algorithm will be sufficiently combined with cuckoo search (CS) algorithm and which will guide for accurate grouping.
    Keywords: preprocessing; cuckoo search; fuzzy; decision tree; classification.
    DOI: 10.1504/IJBIDM.2018.10008934
  • A Novel Attribute Based Dynamic Clustering with Schedule Based Rotation Method (ADC-SBR) for Outlier Detection   Order a copy of this article
    by Karthikeyan .G, P. Balasubramanie 
    Abstract: Detection of outliers in bank transactions has gained popularity in the recent years. The existing outlier detection techniques are unable to process the high volume of data. Hence, to address this issue, an efficient attribute based dynamic clustering-schedule based rotation (ADC-SBR) method is proposed. The similarity between transactions within a cluster is estimated using Jaccard coefficient based labelling approach and the optimal cluster head is chosen by the similarity-based cluster head selection (SbCHS) method. The outlier detection is performed in two levels. The node level outlier detection is performed using linear regression model and the cluster level outlier detection is performed by deviation based ranking. An own dataset with bank transactions is used for the experimental analysis. The suggested method is implemented in Apache Spark and is compared with existing algorithms for the metrics. The comparison results prove that the proposed method is optimal for all metrics than existing algorithms.
    Keywords: Attribute based Dynamic Clustering (ADC) - Schedule based Rotation (SBR); Jaccard coefficient; Linear Regression method; Deviation based ranking; Similarity based Cluster Head Selection (SbCHS).
    DOI: 10.1504/IJBIDM.2018.10009135
  • Mining Multilingual and Multiscript Twitter Data: Unleashing the Language and Script Barrier   Order a copy of this article
    by Bidhan Sarkar, Nilanjan Sinhababu, Manob Roy, Pijush Kanti Dutta Pramanik, Prasenjit Choudhury 
    Abstract: Micro-blogging sites like Twitter have become an opinion hub where views on diverse topics are expressed. Interpreting, comprehending and analysing this emotion-rich information can unearth many valuable insights. The job is trivial if the tweets are in English. But lately, increase in native languages for communication has imposed a great challenge in social media mining. Things become more complicated when people use Roman scripts to write non-English languages. India, being a country with a diverse collection of scripts and languages, encounters the problem severely. We have developed a system that automatically identifies and classifies native tweets, irrespective of the script used. Converting all tweets to English, we get rid of the script vs language problem. The new approach we formulated consists of Script Identification, Language analysis, and Clustered mining. Considering English and the top two Indian languages, we found that the proposed framework gives better precision than the prevailing approaches.
    Keywords: Twitter Mining; Language Classification; Script Identification; Indic language; Preprocessing; Naive Bayes; Support Vector Machine; LDA.
    DOI: 10.1504/IJBIDM.2018.10009136
    by Geetha Narayanan, P.T. Vanathi 
    Abstract: Learning-to-rank has been an exciting topic of research exclusively in hypothetical and the productions in the information retrieval practices. Usually, in the learning-based ranking procedures, it is expected the training and testing data are recovered from the identical data delivery. However those existing research methods do not work well in case of multiple documents retrieved from the cross domains (different domains). In this case ranking of documents would be more difficult where the contents are described in multiple documents from different cross domains. The main goal of this research method is to rank the documents gathered from the multiple domains with improved learning rate by learning features from different domains. The feature level information allocation and instance level information relocation are achieved with four learners namely RankNet, ranking support vector machine (SVM), RankBoost and AdaRank. The estimation results presented that the AdaRank algorithm achieves good performance.
    Keywords: Learning-to-rank; knowledge transfer; RankNet; Ranking SVM; RankBoost;AdaRank.
    DOI: 10.1504/IJBIDM.2018.10009137
  • An Automated Ontology Learning for benchmarking classifier models through Gain-Based Relative-Non-Redundant (GBRNR) Feature Selection : A case-study with Erythemato   Order a copy of this article
    by S. Sivasankari, Shomona Gracia Jacob 
    Abstract: Erythemato-squamous disease (ESD) is one of the complex diseases in the dermatology field, the diagnosis of which is challenging, due to common morphological features and often leads to inconsistent results. Besides, diagnosis has been done on the basis of inculcated visible symptoms pertinent with the expertise of the physician. Hence, ontology construction for prediction of Erythemato-squamous disease through data mining techniques was believed to yield a clear representation of the relationships between the disease, symptoms and course of treatment. However, the classification accuracy required to be high in order to obtain a precise ontology. This required identifying the correct set of optimal features required to predict ESD. This paper proposes the Gain based Relative-Non-Redundant Attribute selection approach for diagnosis of ESD. This methodology yielded 98.1% classification accuracy with Adaboost algorithm that executed J48 as the base classifier. The feature selection approach revealed an optimal feature set comprising of 19 selected features.
    Keywords: Ontology; Feature Selection; Classifier; Web Ontology Language; Gain Base;Erythemato-Squamous.
    DOI: 10.1504/IJBIDM.2018.10009138
  • Fuzzy C Means clustering and Elliptic Curve Cryptography Using Privacy Preserving in Cloud   Order a copy of this article
    by Sasidevi Jayaraman, Sugumar Rajendran, Shanmuga Priya P 
    Abstract: Cloud computing is the distribution of computing devices which reduce the cost for IT infrastructure. In this projected approach, the databases are measured to collecting method generate the transitional datasets. These datasets acquire the facts increase to pick the responsive data to the encryption and decryption procedure, the responsive data preferred procedure depend upon the entry value. The facts increase is integrated to get the superior bound limitation for the combined maintaining outflow. Responsive data to the elliptic curve cryptography (ECC) system to encrypt the data to isolation procedure. Encrypted data storage system is utilised to protected cloud data standards. Encrypting every transitional data sets are neither competent nor rate effectual one. From the trial outcome, the isolation defending charge of transitional datasets can be appreciably condensed by our method above obtainable ones where the entire datasets are encrypted.
    Keywords: cloud computing; intermediate datasets; privacy preserving; Encryption and Decryption; cryptography and clustering.
    DOI: 10.1504/IJBIDM.2018.10009139
  • Optimal Page Ranking System For Web Page Personalization Using MKFCM And GSA   Order a copy of this article
    by Pranitha P., M.A.H. Farquad, G. Narshimha 
    Abstract: In this personalised web search (PWS), we utilise a kernel-based FCM for clustering a web pages. For effective personalised web search, queries are optimised using GSA with respect to clustered query sessions. In offline processing, initially preprocess the input information taken from consumer visited web pages and are transformed in to numerical matrix. These matrices are gathered with the help of kernel-based FCM method after produce a vector for consumer query and detect a minimum distance as centroid values these values are input to the GSA algorithm. It will engender these links given top N web pages from cluster. In online processing, the user query is engaged as input then extract some web pages from Google, Bing, Yahoo also extract content and snippet from web pages. Finally, detect a sum of contents and snippets and web pages would be considered in descending order.
    Keywords: Kernelbased Fuzzy c-means; Clustering; offline; online; preprocessing; Google; Bing; Yahoo.
    DOI: 10.1504/IJBIDM.2018.10009140
    by Arul Easwaramoorthy, Venugopal Manikandan 
    Abstract: Malwares enters into the victim system by injecting the code into victim system executable files or well-known files or folders. In this paper, the proposed dynamic runtime protection technique (DRPT) will ensure for protection of all the modes of the malware entering into the system. In the affected system, the behaviours of the injected file are monitored and controlled and the malware spreads either through online or offline modes via files. The DRPT unpack the malware, continuously monitors and analyses the windows application programming interface (API) calls in the imported and exported dynamic link library (DLLs) of the malwares to find the injection code. DRPT also protects against the malware spread into the other files and the stealing of information from the victim machine. The DRPT tested with 1,517 executable files, among which 811 malicious files have been taken with different malware families. The result of DRPT shows true positive of 94.20% and false positive of 0.05%.
    Keywords: Malware; DRPT; DLL; API.
    DOI: 10.1504/IJBIDM.2018.10009141
  • Privacy Preserving-Aware Over Big Data in Clouds Using GSA and Map Reduce Framework   Order a copy of this article
    by Sekar K., Mokkala Padmavathamma 
    Abstract: This paper proposes a privacy preserving-aware-based approach over Big data in clouds using GSA and MapReduce framework. It consists of two modules such as; MapReduce module and evaluation module. In MR module, convolution process is applied to the dataset and creates a new kernel matrix. The convolution process is correctly done; the utility and privacy information of the data is well secured. Once the convolution process is over, the privacy-persevering framework over big data in cloud systems is performed based on the evaluation module. In Evaluation module, the neural-network is trained based on the Gravitational Search Algorithm with Scaled conjugate gradient (GSA-SCG) algorithm which is improving the utility of the privacy data. Finally, the reduced privacy datas are stored in the service provider (CSP). The MapReduce framework is to ensure the private data, which is in charge for anonymising original data sets as per privacy requirements.
    Keywords: Map reduce; privacy preserving; big data; Cloud service provider; cloud system; GSA; convolution; entropy.
    DOI: 10.1504/IJBIDM.2018.10009361
  • Secure Hash Algorithm based Multiple Tenant User Security over Green Cloud Environment   Order a copy of this article
    by Ram Mohan, S. Padmalal, B. Chitra 
    Abstract: This paper proposes a green cloud multi-tenant trust authentication with secure hash algorithm-3 (GreenCloud-MTASHA3) scheme to eliminate the unauthorised tenant access. GreenCloud-MTASHA3 scheme provide security over the multiple tenant requests by referring the confidentiality, integrity and availability rate. Confidentiality refers to limiting the unauthorised tenants green cloud data access using the additive homomorphic privacy property in proposed scheme. Additive homomorphic privacy property-based encryption function is developed to improve the privacy preserving level. To attain the integrity level between the tenant requests and green cloud server machine in GreenCloud-MTASHA3 scheme an encrypted trust data management process is carried out. Trustworthiness of tenant request is measured to maintain the consistency level on security with minimal computational time. The proposed scheme attains the confidentiality, integrity and availability rate on communicating task. Experiment is conducted on factors such as secure computation confidence, authorised tenant computational time and space taken on storing encrypted data.
    Keywords: Green Cloud; Security; Confidentiality; Secure Hash Algorithm; Computational Time; Multi-Tenant; Integrity; Privacy Level; Cryptographic System.
    DOI: 10.1504/IJBIDM.2018.10009362
  • Frequent Pattern Mining for Parameterised Automatic Variable Key based cryptosystems   Order a copy of this article
    by Shaligram Prajapat 
    Abstract: Huge amount of information is exchanged electronically in most enterprises and organisations. In particular, in all financial and e-business set ups the amount of data stored or exchanged is growing enormously over public network among variety of computing devices. Securing this gargantuan sized input is challenging. This paper provides a framework for securing information exchange using parametric approaches with AVK approach and investigating strength of this cryptosystem using mining algorithms on symmetric key-based cryptosystem. This work demonstrates association rule application as one of the component of cryptic mining system used to process the encrypted data for extracting use full patterns and association. The degree of identified patterns may be use full to rank the degree of safety and class of cryptic algorithm, during auditing of security algorithms.
    Keywords: Mining algorithms; symmetric key cryptography; AVK.
    DOI: 10.1504/IJBIDM.2018.10009363
  • A hybrid framework for Job Scheduling on Cloud using Firefly and BAT algorithm   Order a copy of this article
    by Hariharan B., Dassan Paul Raj 
    Abstract: Nowadays cloud computing is an emerging field, requires more algorithm and techniques for the various process of cloud computing. Here, we have considered the job scheduling process in cloud computing platform that needs a good algorithm to schedule the jobs requested from various users of cloud computing environment. Here, the request can be from any platform so scheduling is indispensable one when a number of users need the particular jobs. In this research, we have intended to develop a hybrid algorithm for job scheduling in cloud computing environment. Accordingly, multiple criteria will be taken for scheduling various jobs located in various servers. Then, the job scheduling will be done based on a hybrid optimisation algorithm. Additionally, different jobs with different constraints will be considered and the cloud computing environment is simulated with the help of cloudsim tool.
    Keywords: Cloud Computing; Firefly Algorithm; BAT algorithm; Job Scheduling; FF-BAT Algorithm.
    DOI: 10.1504/IJBIDM.2018.10009440
  • An effective Feature Selection for Heart Disease Prediction with Aid of Hybrid Kernel SVM   Order a copy of this article
    by Keerthika T., K. Premalatha 
    Abstract: In todays modern world cardiovascular disease is the most lethal one. This disease attacks a person so instantly that it hardly gets any time to get treated with. So, diagnosing patients correctly on timely basis is the most challenging task for the medical fraternity. In order to reduce the risk of heart disease, effective feature selection and classification based prediction system is proposed. An efficient feature selection is applied on the high dimensional medical data, for selecting the features fish swarm optimisation algorithm is used. After that, selected features from medical dataset are fed to the HKSVM for classification. The performance of the proposed technique is evaluated by accuracy, sensitivity, specificity, precision, recall and f-measure. Experimental results indicate that the proposed classification framework have outperformed by having better accuracy of 96.03% for Cleveland dataset when compared existing SVM method only achieved 91.41% and optimal rough fuzzy classifier achieved 62.25%.
    Keywords: Hybrid Kernel Support Vector Machine; feature selection; Fish swarm Optimization; SVM; optimal rough fuzzy; Cleveland; Hungarian and Switzerland.
    DOI: 10.1504/IJBIDM.2017.10009441
  • Brain Tumour Detection using Self-Adaptive Learning PSO-Based Feature Selection Algorithm in MRI images   Order a copy of this article
    by A.R. Kavitha, C. Chellamuthu 
    Abstract: In this paper, we propose a brain tumour classification scheme to classify the breast tissues as normal or abnormal. At first, we segment the region of interest (ROI) from the medical image using modified region growing algorithm (MRGA). Feature matrix is generated using gray-level co-occurrence matrix (GLCM) to the entire detailed coefficient from 2D-DWT of the region of interest (ROI). To derive the relevant features from the feature matrix, we take the self-learning particle swarm optimisation (SLPSO) algorithm. In SLPSO, four upgrading strategies are utilised to adaptively redesign the velocity of every particle to guarantee its differences and robustness. The relevant features are used in a feed forward neural network (FFNN) classifier for classification. The method yield very encouraging result in terms of classification accuracy using a neural network. In experimental result most cases, the classification accuracy improved on previously reported results.
    Keywords: Region of interest; modified region growing; co-occurrence matrix; GLCM; 2D-DWT; SLPSO; features; feed forward neural network; classification.
    DOI: 10.1504/IJBIDM.2017.10009881
    by Vellingiriraj EK, P. Balasubrmanie 
    Abstract: The ancient Tamil characters recognition is the complex task because there is no sufficient training information is available. Various researchers attempted to perform accurate recognition of ancient Tamil characters. In our preceding work, hybrid multi-neural learning based prediction and recognition system (HMNL-PRS) is introduced for the prediction process which lacks from inaccurate recognition. In this proposed research work, this is overcome by proposing the Brahmi character prediction and conversion system (BC-PCS) methodology. Here, the modified graph based segmentation algorithm (MGSA) is used to segment the characters. And then the statistical and structural features are extracted based on which classification is done using hybridised support vector machine based fuzzy neural network. In the MATLAB simulation environment, the proposed research work is implemented and it is confirmed that the proposed research work direct to give the excellent result compared to the preceding research methodology in terms of recognition rate.
    Keywords: Brahmi characters; accurate recognition; segmentation; graph based approach; Classification.
    DOI: 10.1504/IJBIDM.2018.10009882
  • Benchmarking Tree based Least Squares Twin Support Vector Machine Classifiers   Order a copy of this article
    by Mayank C, S.S. Bedi 
    Abstract: Least square twin support vector machine is an emerging learning method applied in classification problem. This paper present a tree-based least square twin support vector machine (T-LSTWSVM) for classification. Classification procedure depends on the correlation of input feature as well as output feature. UCI benchmark data sets are used to evaluate the test set performance of tree-based least square twin support vector machine (T-LSTWSVM) classifiers with multiple kernel functions such as linear, polynomial and radial basis function (RBF) kernels. This method applies on two main types of classification problems such as binary class problem as well as multi-class problem. The evaluation and accuracy is calculated in terms of distance metric. It was observed that multi-class classification problem performed excellently by tree-based method.
    Keywords: Binary Tree; Classification; Hyper plane; Kernel Function; Machine Learning; Support Vector Machine (SVM); Least Square Twin SVM.
    DOI: 10.1504/IJBIDM.2018.10009883
  • An Utility Based Approach for Business Intelligence to Discover Beneficial Itemsets With or Without Negative Profit in Retail Business Industry   Order a copy of this article
    by C. SIVAMATHI, S. Vijayarani 
    Abstract: Utility mining is defined as discovery of high utility itemsets from the large databases. It can be applied in business Intelligence for business decision-making such as arranging products in shelf, catalogue design, customer segmentation, cross-selling etc. In this work a novel algorithm MAHUIM (matrix approach for high utility itemset mining) is proposed to reveal high utility itemsets from a transaction database. The proposed algorithm uses dynamic matrix structure. The algorithm scans the database only once and does not generate candidate itemsets. The algorithm calculates minimum threshold value automatically, without seeking from the user. The proposed algorithm is compared with the existing algorithms like HUI-Miner, D2HUP and EFIM. For handling negative utility values, MANHUIM algorithm is proposed and this is compared with HUINIV. For performance analysis, four benchmark datasets like Connect, Foodmart, Chess and Mushroom are used. The result shows that the proposed algorithms are efficient than the existing ones.
    Keywords: Utility mining; High utility itemset mining; individual item utility; transaction utility; Minimum utility threshold; Negative utility; Pruning strategy; Profitable transactions.
    DOI: 10.1504/IJBIDM.2018.10009884
  • Automated Optimal Test Data Generation for OCL Specification Using Harmony Search Algorithm   Order a copy of this article
    by A. Jali 
    Abstract: Exploring software testing possibilities at an early software life cycle is increasingly necessary to avoid the propagation of defects to the subsequent phases. This requirement demands technique that can generate automated test cases at the initial phases of software development. Thus, we propose a novel framework for automated test data generation using formal specifications written in object constraint language (OCL). We also defined a novel fitness function named exit-predicate-wise branch coverage (EPWBC) to evaluate the generated test data. Another focus of the proposed approach is to optimise the test case generation process by applying, harmony search (HS) algorithm. The experimental results indicate that the proposed framework outperforms the other OCL-based test case generation techniques. Furthermore, it has been inferred that OCL based testing adopting HS algorithm forms an excellent combination to produce more test coverage and an optimal test suite thereby improving the quality of a system.
    Keywords: specification-based testing; OCL;object constraint language; HS; harmony search; EPWBC; exit-predicate-wise branch coverage;Optimal Test Case Generation.
    DOI: 10.1504/IJBIDM.2018.10009885
  • Enhancing the JPEG image steganography security by RSA and attaining high payload using advanced DCT replacement method and modified quantisation table   Order a copy of this article
    by J. Hemalatha, M.K. Kavitha Devi, S. Geetha 
    Abstract: Steganography deal with hiding information science, which offers an ultimate security in defence, profitable usages, thus sending the imperceptible information, will not be bare or distinguished by others. The aim of this paper is to propose a novel steganographic method in JPEG images to highly enrich a data security by RSA algorithm and attains higher payload by modified quantisation table. The goals of this paper are to be recognised through: 1) modify the quantisation table of the JPEG-JSTEG tool, hiding secret message with its middle frequency to offer great embedding capacity; 2) for challenge, secure RSA algorithm is used to prevent data from extraction. A broad experimental evaluation compares the performance of our proposed work with existing JSTEG was conducted. This algorithm resulted in greater PSNR values and steganogram histogram is more similar. Experimental results reported that the proposed system is a state-of-the model, contributing abundant payload and beating the statistical revealing. Besides, our method has better in all the parameters than JPEG-JSTEG method.
    Keywords: RSA; Information Forensics; Robustness; DCT; JPEG; Quantization Table.
    DOI: 10.1504/IJBIDM.2019.10010131
  • Characteristic of Enterprise Collaboration System and Its Implementation Issues in Business Management   Order a copy of this article
    by Tanvi Bhatia, Sudhanshu Joshi, Tanvi Bhatia, Sadhna Sharma, Durgesh Samadhiya, Rajiv Ratn Shah 
    Abstract: Collaboration is an extremely useful area for the most of the enterprise systems particularly within Web 2.0 and Enterprise 2.0. The collaboration provides help in enterprise collaboration system (ECS) to achieve the desired goal by unifying completed tasks of employees or people working on a similar or the same task. Thus, the collaboration systems have witnessed significant attention. The ECS provides consistent and off-the-shelf support to processes and managements within organisations. Management techniques of the ECS may be useful to a community which manages ECS systems for collaboration. In this context, this paper focuses on enterprise collaboration system and answers critical questions related to ECS including: 1) what does collaboration really means for an enterprise system; 2) how can the collaboration help to improve internal processes and management of the system; 3) how it is helpful to improve interactions with customers and partners?
    Keywords: Enterprise Collaboration System; Web 2.0; Enterprise 2.0; Management Techniques; Enterprise System.
    DOI: 10.1504/IJBIDM.2019.10010132
  • Unsupervised Key Frame Selection using Information Theory and Color Histogram Difference   Order a copy of this article
    by Janya Sainui, Masashi Sugiyama 
    Abstract: Key frame selection is one of the important research issues in video content analysis, as it helps effective video browsing and retrieval as well as efficient storage. Key frames would typically be as different from each other as possible but, at the same time, cover the entire content of the video. However, the existing methods still lose some meaningful frames due to an inaccurate evaluation of the differences between frames. To address this issue, in this paper, we propose a novel method of key frame selection which incorporates an information theoretic measure, called quadratic mutual information (QMI), with the colour histogram difference. Here, these two criteria are used to produce an appropriate frame difference measure. Through the experiments, we demonstrate that the proposed key frame selection method generates a more coverage of the entire video content with minimum redundancy of key frames compared with the competing approaches.
    Keywords: Key frame selection; Similarity measure; Information theory ; Quadratic mutual information ; Color histogram di?erence.
    DOI: 10.1504/IJBIDM.2018.10010173
  • Building Acoustic Model for Phoneme Recognition using PSO-DBN   Order a copy of this article
    by B.R. Laxmi Sree, M.S. Vijaya 
    Abstract: Deep neural networks has shown its power in generous classification problems including speech recognition. This paper proposes to enhance the power of deep belief network (DBN) further by pre-training the neural network using particle swarm optimisation (PSO). The objective of this work is to build an efficient acoustic model with deep belief networks for phoneme recognition with much better computational complexity. The result of using PSO for pre-training the network drastically reduces the training time of DBN and also decreases the Phoneme error rate (PER) of the acoustic model built to classify the phonemes. Three variations of PSO namely, the basic PSO, second generation PSO (SGPSO) and the New model PSO (NMPSO) are applied in pre-training the DBN to analyse their performance on phoneme classification. It is observed that the basic PSO is performing comparably better to other PSOs considered in this work, most of the time.
    Keywords: Phoneme Recognition; Deep Neural Networks; Particle Swarm Optimisation; Acoustic Model; Tamil Speech Recognition; Deep Learning. Deep Belief Networks.
    DOI: 10.1504/IJBIDM.2018.10010711
  • Data Cubes Retrieval and Design in OLAP Systems: From Query Analysis to Visualisation Tool   Order a copy of this article
    by Rahma Djiroun, Kamel Boukhalfa, Zaia Alimazighi 
    Abstract: Business intelligence systems provide an effective solution from large volumes of data for multidimensional online computing and analysis. Usually, in a decision-making process, organisations and enterprises, require several internal and/or external cubes which are often heterogeneous. Most of the time, the structure of these cubes is unknown to the decision-makers. To analyse a phenomenon, the decision-maker seeks among sets of cubes, in a collection, the cube which responds better to his need. In this context, we propose an approach that enables decision-makers to express their needs via a query expressed in a natural language, returns top-K relevant cubes and designs/constructs new cubes when no, or few deployed cubes are relevant. We propose a tool called RD-cubes-query implementing our approach in a ROLAP architecture. We use this tool in some experiments to validate our approach.
    Keywords: cubes design; cubes search; online analytical processing; OLAP; top-K; query analysis; visualisation tool.
    DOI: 10.1504/IJBIDM.2018.10010712
  • Improved Artificial Neural Network (ANN) With Aid of Artificial Bee Colony (ABC) For Medical Data Classification   Order a copy of this article
    by Balasaheb Tarle, Sudarson Jena 
    Abstract: The ultimate aim of the proposed method is to establish a model for classification of medical data. Various methods have been generated to health related data to detect upcoming health fitness usage including detecting person's spending and illness related issues for diseased persons. In order to achieve promising results in medical data classification, we have planned to utilise orthogonal local preserving projection and optimal classifier. Initially, the pre-processing will be applied for extracting useful information and to convert suitable sample from raw medical datasets. Here, orthogonal local preserving projection (OLPP) is used to reduce the feature dimension. Once the feature reduction is formed, the prediction will be done based on the optimal classifier. In the optimal classifier, artificial bee colony algorithm will be used with neural network. The effectiveness of our proposed is measured in terms of accuracy, sensitivity and specificity. Here, Switzerland dataset achieves the maximum accuracy value 95.935%.
    Keywords: orthogonal local preserving projection; classifier; neural network; artificial bee colony algorithm.
    DOI: 10.1504/IJBIDM.2017.10010713
  • Efficient search for top-k discords in streaming time series   Order a copy of this article
    by Giao Bui Cong, Duong Tuan Anh 
    Abstract: The problem of anomaly detection in streaming time series has received much attention recently. The problem addresses finding the most anomalous subsequence (discord) over a time-series stream, which might arrive at high speed. The fact that finding top-k discords is more useful than finding the most unusual subsequence since users might make a choice among the top-k discords instead of choosing only one. Hence, an efficient method of search for top-k discords in streaming time series is proposed in the paper. The method uses a lower bound threshold, a lower bounding technique on a common dimensionality reduction transform, and a state-of-the-art technique of the distance computation between two time-series subsequences to prune off unnecessary distance calculations. The three techniques are arranged in a cascading fashion to speed up the performance of the method. Furthermore, the proposed method can return a set of top-k discords on the fly. The experimental results show that the proposed method can acquire quality discords nearly identical to those obtained by HOT SAX, a well-known method of anomaly detection. Remarkably, our proposed method demonstrates a fast response in handling time-series streams at high speed.
    Keywords: anomaly detection; discord; streaming time series.
    DOI: 10.1504/IJBIDM.2018.10010853
  • Mining Big data streams using Business analytics tools: A bird   Order a copy of this article
    by Arunkumar PM, S. Kannimuthu 
    Abstract: Big data evolves as the prominent field in modern computing era. Big data analytics and its impact on extracting business intelligence is becoming indispensable for plethora of applications. The non-proprietary software revolution paved the way for illustrious evolution of tools like Weka, rapid miner, orange and R. Traditional data mining techniques hardly adapts to the requirements of rapid data analysis. The data stream processing algorithms that handle multitude of data endow with greater challenge in real time. Big data mining requires further improvisation in traditional tools to address the challenges of Massive data processing. This paper highlights the importance of data stream mining and explores two important open source frameworks, namely massive online analysis (MOA) and scalable advanced massive online analysis (SAMOA). The implications of both the tools augurs well for further deliberations in big data research community. Business information system (BIS) models can reach unprecedented heights with the proliferation of these business analytics tools.
    Keywords: Big Data; Data mining; Data streams; Massive online analysis; Business Intelligence.
    DOI: 10.1504/IJBIDM.2019.10010854
  • A novel dynamic approach to identifying suspicious customers in money transactions   Order a copy of this article
    by Abdul Khalique Shaikh, Amril Nazir 
    Abstract: Money laundering activity causes a negative impact on the development of the national economy. Anti-money laundering (AML) solutions within financial institutions facilitate to control it in a suitable way. However, one of the fundamental challenges in AML solution is to identify real suspicious transactions. To identify these types of transactions, existing research uses pre-defined rules and statistical approaches that help to detect the suspicious transactions. However, due to the fixed and predetermined rules, it is highly probable that a normal customer can be identified as suspicious customers. To overcome the above limitations, a novel dynamic approach to identifying suspicious customers in money transactions is proposed that is based on dynamic analysis of customer profile features to identify suspicious transactions. The experiment has been executed with real bank customers and their transactions data and the results of the experiment provide promising outcomes in terms of accuracy.
    Keywords: AML; anti-money laundering; suspicious transactions; money transaction; dynamic AML analysis; data analysis.
    DOI: 10.1504/IJBIDM.2019.10010869
  • Anomaly detection for elderly home care   Order a copy of this article
    by Kurnianingsih Kurnianingsih, Lukito Edi Nugroho, Widyawan Widyawan, Lutfan Lazuardi, Anton Satria Prabuwono, Mahardhika Pratama 
    Abstract: In this paper, we propose a model for detecting anomalies in elderly home care. Two scenarios are investigated in detecting anomalies: 1) the elderly person's vital signs and their surrounding environment; 2) the mobility patterns of the elderly. We evaluated our proposed model by employing the isolation forest which detects anomalies using an isolation approach on a random forest of decision trees. We compare isolation forest on unlabeled data with statistical methods on labelled data. Subsequently, to show the reliability of the isolation concept, we compare it with a distance measure concept. The experiment shows that isolation forest has higher detection accuracy and lower error prediction for two attributes in the first scenario: skin temperature and heart rate, whereas, in the second scenario, multi-covariance determinant has a slightly better accuracy compared to isolation forest (3.9% difference in accuracy) and has a small number of prediction errors compared to isolation forest.
    Keywords: anomaly detection; isolation forest; elderly home care.
    DOI: 10.1504/IJBIDM.2018.10011101
  • Multi-Document Based Text Summarization Through Deep Learning Algorithm   Order a copy of this article
    by G. PadmaPriya, K. Duraiswamy 
    Abstract: The proposed approach is provided an effort in terms of deep leaning algorithm to retrieve an effective text summary for a set of documents. Basically, the proposed system consists of two phases such as training phase and the testing phases. The training phase is used for exploiting the three different algorithms to make the text summarisation process an effective one. Similar to every training phase, the proposed training phases is also possessed of known data and attributes. After that, the testing phase is implemented to test the efficiency of the proposed approach. For experimentation, we used four documents sets which are selected from the DUC (2002). The experimental evaluation showed expected results as, the average precision of 78%, the average recall of 1 and the average f-measure of 84%.
    Keywords: Particle Swarm Optimisation; Text Summarization ; Deep Learning Algorithm.
    DOI: 10.1504/IJBIDM.2018.10011144
  • Grey-Wolf Optimizer Based Feature Selection for Feature-Level Multi-Focus Image Fusion   Order a copy of this article
    by Sujatha K, D. Shalini Punithavathani, J. Janet, S. Venkatalakshmi 
    Abstract: This paper proposes optimal ensemble-individual-features (OEIF) for multi-focus image fusion through combining the decision information of individual features. This proposed system consists of three stages. In the first stage, the different types of features such as spatial, texture and frequency are extracted from every block on input blurred images. In the second step, grey wolf optimiser (GWO)-based features validation method is proposed to find suitable features from source images. This method is based on an iterative process, in which each individual represents a candidate solution for validating/invalidating the features. In the final step, the ensemble decision based on optimal individual features is utilised to fuse blurred images. We prove that OEIF method is better in comparison to the noisy feature-based individual pixel-level and the feature-level fusion methods with different multi-focus images and it reveals that OGWO-based proposed method performs better visual quality than other methods.
    Keywords: Multi-focus image fusion; grey wolf optimiser; feature validation; spatial; texture; frequency.
    DOI: 10.1504/IJBIDM.2018.10011145
  • Online Products Recommendation System using Genetic Kernel Fuzzy C-Means and Probabilistic Neural Network   Order a copy of this article
    by Manohar E, D. Shalini Punithavathani 
    Abstract: The purchaser's review plays a significant role in choosing the purchasing activities for online shopping as a customer desires to obtain the opinion of other purchasers by observing their opinion through online products. However, most appropriate product selection from the best website is a challenging problem for online users. Accordingly, this paper proposes a hybrid recommendation system for identifying customer preferences and recommending the most appropriate product. To do this, first the dataset is collected and prepared in the pre-processing step. Genetic kernel fuzzy C-means (GAKFCM) is used for usage cluster formation after the pre-processing step. The different features are extracted from each cluster-based user interest level. The user interest levels are used as features for classifier to extract user knowledge discovery. Based upon the user interest level, the product recommendation is done using probabilistic neural network (PNN). The simulation results show high precision rate which clearly indicates that the proposed method is very useful and appealing.
    Keywords: website; web-log; ranking; rating; review; products; Genetic Kernel Fuzzy C-Means; probabilistic neural network.
    DOI: 10.1504/IJBIDM.2018.10011146
  • Hybridising Neural Network and Pattern Matching under Dynamic Time Warping for Time Series Prediction   Order a copy of this article
    by Thanh Son Nguyen 
    Abstract: Pattern matching-based forecasting models are attractive due to their simplicity and the ability to predict complex nonlinear behaviours. Euclidean measure is the most commonly used metric for pattern matching in time series. However, its weakness is that it is sensitive to distortion in time axis; so, this can influence on forecasting results. The dynamic time warping (DTW) measure is introduced as a solution to the weakness of Euclidean distance metric. In addition, artificial neural networks (ANNs) have been widely used in the time series forecasting. They have been used to capture the complex relationships with a variety of patterns. In this work, we propose an improved hybrid method which is an affine combination of neural network model and DTW-based pattern matching model for time series prediction. This method can take full advantage of the individual strengths of the two models to create a more effective approach for time series prediction. Experimental results show that our proposed method outperforms neural network model and DTW-based pattern matching method used separately in time series prediction.
    Keywords: time series; pattern matching; artificial neural network; time series prediction; dynamic time warping; k-nearest neighbour.
    DOI: 10.1504/IJBIS.2018.10011147
  • REFERS: Refined & Effective Fuzzy E-commerce Recommendation System   Order a copy of this article
    by Sankar Pariserum Perumal, Ganapathy Sannasi, Kannan Arputharaj 
    Abstract: Online shopping culture is gaining traction globally and some of the biggest beneficiaries of this e-commerce shift are Amazon, eBay, etc. Recommendation systems guide online users in a personalised manner to choose what they want and their interest on each product present in the catalogue list. In such a scenario, the existing systems need complete information for making recommendations, which is not always possible in real applications. Therefore, a novel refined and effective fuzzy e-commerce recommendation system has been proposed in this paper that combines the benefits of difference in importance within the rating factors by a single user and new similarity measure approach that aims at improved recommendation list to the e-commerce user. The proposed methodology has been implemented using a new similarity measure on experimental datasets and the refined scores for such e-commerce website-based unlocked mobile phones are compared in this work against classic similarity measures.
    Keywords: Fuzzy recommendation system; degree of similarity measure; rating factor importance; collective expert rating.
    DOI: 10.1504/IJBIDM.2019.10011148
  • Decision tree classifier for university single rate tuition fee system   Order a copy of this article
    by Taufik F. Abidin, Samsul Rizal 
    Abstract: The regulation about single rate tuition fee for undergraduate study at state universities in Indonesia was enacted in 2013. The tuition fee is calculated based on the needs of each academic program and the regional cost index. The fee is grouped into several categories and set differently for each university. For Syiah Kuala University, located in Banda Aceh, Indonesia, the tuition fee is grouped into five different categories. This paper describes the construction of J48 decision tree classifier and evaluates its performance during training and testing phases when compared to ID3 and Naive Bayes classifiers to determine the category. The results show that the J48 decision tree classifier outperforms the other two classifiers in both phases. In the training phase, the F-measure and ROC for the J48 decision tree classifier are 0.889 and 0.973, respectively, and in the testing phase, the F-measure and ROC are 0.911 and 0.987, respectively.
    Keywords: Decision tree classifier; multi-class classification; university single rate tuition fee system.
    DOI: 10.1504/IJBIDM.2019.10011149
  • Using Diverse Set of Features to Design a Content-Based Video Retrieval System Optimized by Gravitational Search Algorithm   Order a copy of this article
    by S. Padmakala, Ganapathy Sankar Anandha Mala, K.M. Anandkumar 
    Abstract: This paper explains about the content based video retrieval approach (CBVR) using four varieties of features and 12 distance measurements, which is optimized by gravitational search algorithm (GSA). Initially, CBVR technique extracts five kinds of features such as color, texture, shape, image and audio features that belong to each frame. Consequently, it emerges particular distance measurements for every sort of features to compute the similarity between query frame and remaining in the database frame. In this paper, we have used GSA to find the nearly optimal combination between the features and their respective similarity measurements. At last, from the video database, the query based videos are recovered. For experimentation, here we used two types of databases such as sports video and UCF sports action datasets. The experimental results demonstrate that the proposed CBVR method shows better performance when contrasted with other existing methods.
    Keywords: video retrieval; distance measurements; color; texture; shape; audio; CBVR; similarity; combinations.
    DOI: 10.1504/IJBIDM.2018.10012001
  • Weighted Neuro-Fuzzy Hybrid Algorithm for Channel Equalization in Time Varying Channel   Order a copy of this article
    by Zeeshan A Abbasi, Zainul Abdin Jaffery 
    Abstract: In MIMO-OFDM communication systems, accurate and specific channel estimation and equalisations are plays a major role. In this paper, we use weighted neuro-fuzzy hybrid (WNFH) channel estimation algorithm for channel equalisation. The pilot is designed based on combination of neural network and fuzzy logic system. Scaled conjugate gradient (SCG) is mutual with group search optimiser (GSO) algorithm along with; the training procedure of neural network is prepared using the hybrid training algorithm. In the transmitter section, the projected system contains quadrature amplitude modulation (QAM) and transmitter. By considering the channel prediction error to recover the performance of symbol detection the minimum mean-square error (MMSE) estimation design is accomplished. To reduce the MMSE of channel estimation and the calculated pilot sequences present great superiority in MIMO-OFDM system. Experimentation outcome shows that the channel assessment is supportive.
    Keywords: MIMO-OFDM; Group Search Optimizer; Scaled Conjugate Gradient; Channel Estimation.
    DOI: 10.1504/IJBIDM.2019.10012002
  • Discrete Weibull regression for modeling football outcomes   Order a copy of this article
    by Alessandro Barbiero 
    Abstract: We propose the use of the discrete Weibull distribution for modeling football match results, as an alternative to existing Poisson and generalized Poisson models. The number of goals scored by the two teams playing a football match are regarded as a pairwise observation and are modelled first through two independent discrete Weibull variables, and then through two dependent discrete Weibull variables, using a copula approach that accommodates non-null correlation. The parameters of the bivariate discrete Weibull distributions are assumed to depend on covariates such as the attack and defense abilities of the two teams and the 'home effect'. Several discrete Weibull regression models are proposed and then applied to the 2015-2016 Italian Serie A. Even if the interpretation of parameters is less immediate than in the case of bivariate Poisson models, nevertheless these models represent a suitable alternative, which can be applied also in other fields than sport data analysis.
    Keywords: count data; count regression model; Frank copula; Poisson distribution; sport analytics.
    DOI: 10.1504/IJBIDM.2018.10012003
  • BAIT: Behavior aided Intruder Testimony Technique for Attacker Intention Prediction in Business Data Handling   Order a copy of this article
    by NarasimhaMallikarjunan Kambaraj, S. Mercy Shalinie, A. Bhuvaneshwaran 
    Abstract: During business transactions there are lot of opportunity for data theft and data misinterpretation. Mostly, the legitimate users act like malicious users and try to misuse their privileges. So, it is very important to know their intentions and different strategies they apply for business data theft. In this paper, we develop an Information analytics based technique for inferring Attacker Intent Objectives and Strategies (AIOS). The input to the model is the alert logs in real-world attack-defense scenario and output are the discovered attack strategies or patterns. The implementation of this model is done on a real-world attack-defense scenario to increase the learning efficiency of the technique. Experimental results on expected impact and attack path shows that the technique provides better results than conventional Intrusion Detection Systems.
    Keywords: Attacker behavior Analysis; Information analytics; Attacker Intent; Objectives and Strategies (AIOS); Category of Attacker; Pattern Mining.
    DOI: 10.1504/IJBIDM.2018.10012004
  • Diamond Search Depend Integrated Projection Error Measurement (DIPEM) Motion Estimation Algorithm in Video Compression   Order a copy of this article
    by H. Rajasekhar, B. Prabhakara Rao 
    Abstract: In the preceding video compression method, some limitations required to be enhanced, i.e. the compression ratio has to be improved. To enhance the conventional method negative aspect, an innovative video compression method employing Diamond Search depend Integrated Projection Error Motion Estimation (DIPEM) algorithm is anticipated. To begin with, the input video frames are processed by employing watershed algorithm and then the video frames motion vectors are evaluated by diamond search dependent integrated projection error motion inference algorithm. Subsequent the motion vectors assessment, encoding and decoding process are performed by JPEG-LS method. The execution result depicts the efficiency of anticipated method, in compressing more number of videos and the performance is assessed with conventional video compression techniques. The comparison result demonstrates that our anticipated method acquires high-quality compression ratio and PSNR than the conventional techniques.
    Keywords: Video Compression; TDS algorithm; Watershed algorithm; Motion Vectors; JPEG-LS; Integral Projection Error Measures; Diamond Search pattern.
    DOI: 10.1504/IJBIDM.2019.10012005
  • Prediction of Process Parameters in Electrical Discharge Machining Using Response Surface Methodology and ANN: An Experimental Study   Order a copy of this article
    by T.M. Chenthil Jegan, R. Chitra, V.S. Thangarasu 
    Abstract: In the present work, the process parameters of Electro Discharge Machining are predicted by Response Surface Methodology and Artificial Neural Network (ANN) in AA6061. AA6061 is extensively used in aircraft and aerospace applications. In order to reduce the depletion of the material during machining, prediction of appropriate machining parameter is essential. Current, Pulse On, Pulse Off and Flushing Pressure are considered as input parameters for prediction. Experiments were conducted with those parameters in five different levels and data collected related to process responses for optimization. Material removal rate and surface roughness measured for each experimental run were compared, utilized to fit a quadratic mathematical model in Response Surface Methodology. ANN with back propagation algorithm was used to develop the relationship between input parameters and predominant output responses. The performance of the developed model is analyzed ANOVA and regression plot. The results proved that ANN model is better for empirical modelling.
    Keywords: EDM; Design of Experiments; Response Surface Methodology; Artificial Neural Network Material Removal Rate; Surface Roughness.
    DOI: 10.1504/IJBIDM.2018.10012006
  • Implementation of Multi Node Hadoop Virtual Cluster on Open Stack Cloud Environments   Order a copy of this article
    by Karthikeyan Saminathan, R. Manimegalai 
    Abstract: Nowadays computing plays a vital role in information technology and all other fields. Yes, the Cloud Computing is one of the biggest milestone in most leading next generation technology and booming up in IT filed and business sectors. In our day to day life the data is being generated is enormous amount such as Tera (TB), Peta(PB), Zeta(ZB) bytes. Hadoop Map Reduce is the popular distributed computing paradigm to process data intensive jobs in cloud. Completion time goals or deadline of map reduce jobs set by users are becoming crucial in existing cloud based data processing environments like Hadoop. In this paper proposed a real-time implementation of single node Hadoop cluster on Open stack private cloud and handles the huge data sets in parallel Virtual Machines and compares its average execution time for different size inputs.
    Keywords: Cloud –Data intensive- Hadoop - Map Reduce- Open Stack-Cluster.
    DOI: 10.1504/IJBIDM.2019.10012007
  • Research on Aircraft Landing Schedule using Opposition Based Genetic Algorithm with Cauchy Mutation   Order a copy of this article
    by C. Nithyanandam, Gabriel Mohankumar 
    Abstract: Optimal scheduling of airport runway operation plays a significant responsibility in the aircraft transportation. Arrival runways are a crucial resource in the air traffic system. Arrival delays encompass an immense impact on airline operations in addition to cost. An imperative responsibility is the planning of airport operations like arrival and departure of aircraft. At this juncture, this paper describes the technique of the execution time in addition to the penalty cost of the every aircrafts. These experimentations demonstrate whenever aircrafts landing on the runway in the mean while no congestion on to facilitate particular path, if it is happening subsequently it seems to be problematic. In order towards eradicating these problems, neural network and genetic algorithms through Cauchy mutations are utilised in the direction of eradicating the congestion occur during the runway as well as in addition to proposed technique towards reducing the penalty cost to be charged.
    Keywords: Artificial Neural Network (ANN); Aircraft Selection; Aircraft Landing Problem Opposition Genetic Algorithms with Cauchy Mutation; Runway Selection; Scheduling.
    DOI: 10.1504/IJBIDM.2018.10012008
  • ScrAnViz: A Tool for Analytics and Visualization of Unstructured Data   Order a copy of this article
    by Sriraghav Kameswaran, V.S. Felix Enigo 
    Abstract: Existing big data visualization tools are meant for visualizing structured data. But survey shows that about 80-90% of potentially usable business information is in unstructured format. Analyzing unstructured data is challenging due to lack of structure and relational form. In this paper, we have proposed a tool called ScrAnViz that can structure data, perform analysis and provide visualization thereby helps in decision making for business people and end users. An attribute based opinion mining algorithm has been developed and implemented. Performance analysis shows that the algorithm has reduced the search time by three times than the traditional document level sentiment analysis systems.
    Keywords: Unstructured data; Data Analytics; Sentiment Analysis; Opinion Mining; Data visualization.
    DOI: 10.1504/IJBIDM.2019.10012009
  • Link prediction in multilayer networks   Order a copy of this article
    by Deepak Malik, Anurag Singh 
    Abstract: Link prediction has gained popularity in recent years in large networks. Researchers have proposed various methods for finding the missing links. These methods include common neighbour, Jaccard coefficient, etc. based on the proximity of the nodes. These methods have limitations as they treat all common nodes equal from a pair of nodes. A new method is proposed, common neighbours common neighbour (CNCN). Its performance is better than the existing methods in a single layer network. These methods are based on the topological features of the network. The proposed method finds the different behaviour of common nodes for a pair of nodes. The link prediction is also useful in the multiplex networks. The link predictions in the multiplex networks are more useful than the single layer network as several layers may give more information about a node than the single layer network. Two methods are proposed using dynamic and static weights.
    Keywords: common neighbours; complex network; link prediction.
    DOI: 10.1504/IJBIDM.2018.10012010
    by P. Velvizhy, A. Pravi, M. Selvi, S. Ganapathy, A. Kannan 
    Abstract: Opinion Mining is an ongoing research area in E-commerce which aims at analyzing the people's opinions, sentiments and emotions. Moreover, the existing E-commerce systems allow the users to share their feedback in the form of textual reviews regarding the products and services. It also allows the consumers to give ratings for products that help in future recommendation of products. In this research work, a computational framework for efficiently predicting the consumer review ratings on the products has been proposed. The proposed framework integrates Dimensionality Reduction, Genetic Algorithm, Fuzzy C-Means and Adaptive Neuro-Fuzzy Inference techniques to overcome the limitations of the existing systems. Experiments have been conducted in this work using Amazon dataset consisting of reviews for different products. This system provides better performance and prediction accuracy for review ratings when it is compared with the related work.
    Keywords: sentiment analysis; review ratings prediction; dimensionality reduction; genetic algorithm; data mining; fuzzy c means.
    DOI: 10.1504/IJBIDM.2019.10012011
  • A Technique for Semantic Annotation and Retrieval of E-Learning Objects   Order a copy of this article
    by Balavivekanandhan A 
    Abstract: The primary objective of my research is to design and develop semantic annotation and retrieval model for e-learning document. In training phase, the documents from different domains are taken and the informative words from each document are obtained based on balanced mutual information and frequency of contents in each document. We then use the informative words to identify the superordinates and the objects. The superordinates, the informative words and the objects from each document will give the relation and properties of each document. The relation and properties of each document are then used to cluster the documents. In the testing phase, we give a query or a document as input to the system to retrieve the relevant documents. If a document is given as input, the relation and properties of that document are first identified and it is used to retrieve the relevant documents.
    Keywords: e-learning; document clustering; balanced mutual information; one way matching; cluster based matching.
    DOI: 10.1504/IJBIDM.2018.10012012
    by R. Bhavani, V. Prakash, K. Chitra 
    Abstract: Web pages are heterogeneous and complex and there exists complicated associations within one web pages and linking to the others. The high interactions between terms in pages demonstrate vague and ambiguous meanings. Efficient and effective clustering methods are needed to discover latent and coherent meanings in context are necessary. This paper proposes an efficient clustering approach for fair semantic web content retrieval based on tri-level ontology construction model with hybrid dragonfly algorithm. Initially the query processing phase, by making use of systematic adaptive hierarchy method (SAHM) efficient ontology selection process is carried out by means of matching keywords retrieved form user query. Secondly, Fuzzy Sensitive Near-neighbour Influence (FSNI) based clustering approach relied on the ontology driven fuzzy linguistic measure, applied to estimate the uncertainty that may be relevant to the semantic content which belongs to the user quires. The proposed FSNI clustering approach with HDA algorithm performance is be evaluated and compared with existing clustering approaches in terms of retrieval accuracy and surfing time.
    Keywords: Systematic adaptive hierarchy method (SAHM); linear projection based Self Organized Map (SOM); Additive Normalized-Point wise Mutual Information (AN-PMI); Hybrid dragonfly algorithm (HDA); Tri-level o.
    DOI: 10.1504/IJBIDM.2019.10012013
    by Bolanle Ojokoh, Oluwatosin Olatunbosun Aboluje, Tobore Igbe 
    Abstract: In this paper, Pearson's correlation coefficient is employed for collaborative filtering due to its ability to manipulate numerical data as well as determine linear relationship among existing users. Its steps involve a user-user representation, similarity generation and prediction generation with a goal to produce a predicted opinion of the active user about a specific item. Concept of parental control is also incorporated for enhancement. Evaluation of the system was done using precision, recall, F-measure, discounted cumulative gain (DCG), idealised discounted cumulative gain (IDCG), normalised discounted cumulative gain (nDCG) and mean absolute error (MAE). Three hundred fortysix datasets were used, out of which 126 were gathered from local video shops and 220 were extracted from internet movie database (IMDb). These were used for the experiments and the results generated through mining of data obtained from profiles and ratings of system users prove the system's average ranking quality of the collaborative filtering algorithm is 95.9%.
    Keywords: Movies; Recommendation; Collaborative Filtering; Information Filtering; Correlation Coefficient; Evaluation.
    DOI: 10.1504/IJBIDM.2018.10012014
  • Location based Personalized Recommendation systems for the Tourists in India   Order a copy of this article
    by Madhusree Kuanr, Sachi Nandan Mohanty 
    Abstract: This study examines the collaborative filtering in recommender system by categorising users according to their choices of place, food, local item purchase, etc. The proposed system will store the opinions of the local users about the sites, foods and products for purchase available in those sites. It uses collaborative filtering technique to find the similar users to a given querying user. The system recommends the best sites along with good foods and products available on those sites according to the recent data. Two hundred (male = 110, female = 90) married individuals from Bhubaneswar, Odisha (India) participated in this survey. Cosine similarity is used in the proposed system to find the similar users of a given input query user. The results revealed that collaborative filtering is the more reliable technique for personalised recommender systems. Experimental results show performance of the proposed system in terms of precision, recall and F-measure values.
    Keywords: collaborative filtering; recommender systems; user profile generation; India.
    DOI: 10.1504/IJBIDM.2019.10012396
  • Stability analysis of Feature Ranking Techniques in the presence of noise: A comparative study   Order a copy of this article
    by Milad Dehghani, Mojtaba Khorram Niaki, Mostafa Rezapour 
    Abstract: Noisy data is one of the common problems associated with real-world data, and may affects the performance of the data models, consequent decisions and the performance of feature ranking techniques. In this paper, we show how stability performance can be changed if different feature ranking methods against attribute noise and class noise are used. We consider Kendalls Tau rank correlation and Spearman rank correlation to evaluate various feature ranking methods stability, and quantify the degree of agreement between ordered lists of features created by a filter on a clean dataset and its outputs on the same dataset corrupted with different combinations of the noise level. According to the results of Kendall and Spearman measures, Gini index (GI) and information gain (IG) have the best performances respectively. Nevertheless, both Kendall and Spearman measures results show that ReliefF (RF) is the most sensitive (the worst) performance.
    Keywords: Attribute noise; Class noise; Filter-based feature ranking; Threshold-based feature ranking; Stability; Kendall’s Tau rank correlation; Spearman rank correlation.
    DOI: 10.1504/IJBIDM.2019.10012557
  • Topic-driven top-k similarity search by applying constrained meta-path based in content-based schema-enriched heterogeneous information network   Order a copy of this article
    by Phu Pham, Phuc Do 
    Abstract: In this paper, we propose a model of TopCPathSim in order to address the problem related to topic-driven similarity searching based on constrained meta-path (or also called restricted meta-path) between same-typed objects within the content-based heterogeneous information networks (HINs). The topic distributions over content-based objects such as: paper/article on the bibliographic network or users comments/reviews on the social networks, etc. are obtained by using the LDA topic model. We conduct the experiments on the real DBLP, Aminer and ACM datasets which demonstrate the effectiveness of our proposed model. Throughout experiments, our proposed model gains about 73.56% in accuracy. The output results also show that the combination of probabilistic topic model with constrained meta-path is promising to leverage the output quality of topic-oriented similarity searching in content-based HINs.
    Keywords: constrained meta-path; content-based heterogeneous information network; topic-driven similarity search; LDA; topic modelling.
    DOI: 10.1504/IJBIDM.2019.10012558
  • Deep learning framework for early detection of intrusion in Virtual Environment   Order a copy of this article
    by Madhu Priya G, S. Mercy Shalinie, P. Mohana Priya 
    Abstract: Today's business enterprise adapts cloud based services as its architectural design. Intelligence technique incorporated into the architecture gives massive tangible and intangible benefits in terms of performance and reliability. Such cloud based business architecture faces many threats towards its availability. DDoS attack is the most prominent threat as its impact is more in the virtual resource based cloud infrastructure. Therefore, there is a need for a Business Intelligence based framework to detect early the attack by monitoring the virtual network traffic. The proposed intelligence framework uses a deep learning framework, Continuous Discriminative-Deep Belief Network (CD-DBN). CD-DBN dynamically captures attack patterns from the network data, analyzes the data and detects the intrusion to the cloud. The observed result shows that the earlier detection approach guarantees the availability of cloud services to the legitimate users and enhances the cloud resource usage.
    Keywords: Deep Learning; Restricted Boltzmann Machine; Deep Belief Network; Cloud Environment; Virtualization; Hypervisor; Intrusion Detection; Availability threat; DDoS attack; SysBench benchmark suite.
    DOI: 10.1504/IJBIDM.2018.10012559
  • Analysing Thyroid Disease using Density Based Clustering Technique   Order a copy of this article
    by Tanupriya Choudhury, Veenita Kunwar, A. Sai Sabitha, Abhay Bansal, Tanupriya Choudhury 
    Abstract: Data mining in medicine has been used to predict unknown patterns in health data and to obtain diagnostic results. Healthcare industry generates large amounts of complex data about patients, diseases and treatments. Data mining in healthcare provides benefits like detecting fraud, availing medical facilities for patients at low cost, ensuring high quality patient care and making healthcare policies. Disease detection has become essential due to increased number of health issues occurring day by day. The thyroid has become one such concern with numerous cases being detected yearly. It causes improper functioning of the thyroid gland. In this paper, clustering technique has been used to detect and understand factors influencing thyroid disease. DBSCAN algorithm has been used as it can handle clusters of varying shapes and sizes and is noise resistant. PCA has also been done for finding high dimension data patterns and to reduce dimension. The experimental setup has been implemented in RapidMiner.
    Keywords: Data mining; Clustering; Thyroid disease; DBSCAN; Principal component analysis.
    DOI: 10.1504/IJBIDM.2019.10013037
  • A fuzzy approach to prioritize DEA ranked association rules   Order a copy of this article
    by Shekhar Shukla, B.K. Mohanty, Ashwani Kumar 
    Abstract: Association rule mining discovers interesting information from large databases. Frequency, reliability and domain knowledge form the multiple criteria for evaluation these association rules. Data envelopment analysis (DEA) is a popular technique used to rank association rules based on the previously mentioned multiple criteria. A decision maker might be interested to have a priority list of these ranked rules based on business and situational requirements. We present an approach to prioritise DEA ranked association rules based on the preference and desirability of the decision maker for different criteria. A modified generalised fuzzy evaluation method (MGFEM) obtains vector-valued fuzzy scores of a group of decision makers and aggregate them to form a preference. A fuzzy logic-based decision support mechanism prioritises these rules based on the decision makers desirability using the membership function and preference obtained from MGFEM. An example of DEA ranked association rules is presented to explain this innovative approach for prioritisation.
    Keywords: Association Rules; Data Envelopment Analysis (DEA); Multiple Criteria; Fuzzy Logic; Modified Generalised Fuzzy Evaluation Method.
    DOI: 10.1504/IJBIDM.2019.10013038
  • A Simple Transform Domain Based Low Level Primitives Preserving Texture Synthesis   Order a copy of this article
    by S. Anuvelavan, M. GANESH, P. Ganesan 
    Abstract: In this work, a new patch-based texture synthesis scheme with orthogonal polynomials model coefficients is presented. The proposed scheme has four phases. In the first phase, a block matching technique that identifies a best match, to synthesis in the output image of bigger size is designed in terms of ordered orthogonal polynomials model coefficients. In case of successful match of block, called patch-hit, the proposed scheme finds candidate blocks with triangular search, in the next phase. In the patch selection phase, the proposed scheme considers a subset of orthogonal polynomials model coefficients among the blocks, for the purpose of synthesis which consumes less memory and time. This synthesised output is smoothened in the final phase, by preserving the low level contents between the synthesised patches. The performance of the proposed scheme is measured with energy, contrast, correlation, homogeneity and entropy between the original and synthesised images and is also compared with existing texture synthesis schemes. The results are encouraging.
    Keywords: Texture Synthesis; Orthogonal Polynomials; Patch-Hit; Candidate Block; Patch Selection.
    DOI: 10.1504/IJBIDM.2018.10013005
  • Optimal Region growing and Multi-kernel SVM for fault detection in Electrical Equipments using Infrared Thermography Images   Order a copy of this article
    by C. Shanmugam, E. Chandira Sekaran 
    Abstract: Infrared thermography (IRT) has played an essential part in observing and examining thermal defects of electrical equipment without ending, which has vital enormity for the dependability of electrical recorded. This paper dissected the electrical parts are faulted or non-faulted with the help of segmentation and classification model. The features are calculated from the input thermal images and regions of interest (ROI) is segmented by utilising optimal region growing (ORG) technique and faults are classified using multi kernel support vector machine (MKSVM). In the tests, the classification performances from different input features are assessed. For enhancing the performance of the segmentation investigation optimisation procedure that is whale optimisation (WO) is used. Before classifying, the extracted electrical components are fused by using feature level fusion (FLF) procedure to fused vector in all images. These multi Kernel classification performance indices, including sensitivity, specificity and accuracy are utilised to recognise the most appropriate input feature and the best arrangement of classifiers. The performance of SVM is contrasted with a neural network. The correlation comes about demonstrating that our technique can accomplish a superior performance with accuracy at 98.21%.
    Keywords: Feature extraction; Whale optimisation,Support vector machine; optimisation; Classification and fault detection,Infrared thermography.
    DOI: 10.1504/IJBIDM.2019.10013039
  • ComRank: community-based ranking approach for heterogeneous information network analysis and mining   Order a copy of this article
    by Phu Pham, Phuc Do 
    Abstract: In this paper, we propose the ComRank model to address this problem of ranking a specific typed of object, over the generated topic-driven communities in the information networks. The topic-driven communities are generated by applying the latent topic modelling of LDA. Our proposed ComRank model is directly generated ranking results for specific typed object in the different network communities. We apply our approach to construct the scholastic recommendation system, which support the researchers to find the appropriate citations or potential authors for cooperating while doing scientific researches. The ComRank model is tested with the real-world dataset of DBLP bibliographic network. The experimental results demonstrated that our proposed model can generate the meaningful ranking results within detected topic-driven communities.
    Keywords: information network; heterogeneous network; bibliographic network; community detection; community-based ranking; path-based ranking.
    DOI: 10.1504/IJBIDM.2019.10013040
  • Comparison between optimised Genetic based Honda algorithm and Honda algorithm for Collision Avoidance System   Order a copy of this article
    by Kgmanjunath Sastry, N. Jaisankar 
    Abstract: As the drivers mental state is among the primary factors responsible for collision, most of the sectors now install an automated approach to prevent the collision. The automatic approaches halt the vehicle unit on sensing the state of collision. Anti-collision system is an automatic approach that monitors the various factors of a vehicle unit on a gradual basis. The system is powered with Honda algorithm that computes the minimum safe distance from these factors. As the efficiency of algorithm is subjected to accuracy of sensor readings, the environmental tragedies and precision of sensors can deliver false readings that affects the performance of algorithm. In this paper genetic algorithm is implemented to optimise these values. The optimised modification of readings discards the probability of false reading obtained due to environmental noise. The evaluation parameters indicate that GA performs with almost same accuracy for optimisation.
    Keywords: Crossover Selection Warning range Collision avoidance Vehicle Velocity.
    DOI: 10.1504/IJBIDM.2019.10013083
  • AGS: A Precise and Efficient AI Based Hybrid Software Effort Estimation Model   Order a copy of this article
    by Vignaraj Vikraman, S. Srinivasan 
    Abstract: To predict the amount of effort to develop software is a tedious process for software companies. Hence, predicting the software development effort remains a complex issue drawing in extensive research consideration. The success of software development process considerably depends on proper estimation of effort required to develop that software. Effective software effort estimation techniques enable project managers to schedule software life cycle activities properly. The main objective of this paper is to propose a novel approach in which an artificial intelligence (AI)-based technique, called AGS algorithm, is used to determine the software effort estimation. AGS is hybrid method combining three techniques, namely: adaptive neuro fuzzy inference system (ANFIS), genetic algorithm and satin bower bird optimisation (SBO) algorithm. The performance of the proposed method is assessed using a well standard dataset with real-time benchmark with many attributes. The major metrics used in the performance evaluation are correlation coefficient (CC), kilo lines of code (KLoC) and complexity of the software. The experimental result shows that the prediction accuracy of the proposed model is better than the existing algorithmic models.
    Keywords: Software Effort Estimation; AI; ANFIS; Lines of code (LoC); Genetic Algorithm (GA); Satin Bower Bird Optimiser (SBO); Correlation Co-efficient (CC); Kilo Lines of Code (KLoC),Software Complexity.
    DOI: 10.1504/IJBIDM.2019.10013150
  • High dimensional sentiment classification of product reviews using evolutionary computation   Order a copy of this article
    by Sonu Lal Gupta, Anurag Singh Baghel 
    Abstract: Feature selection is an important process in text classification. In general, traditional feature selection approaches are based on exhaustive search hence become inefficient due to a large search space. Further, this task becomes more challenging as the number of features increases. Recently, evolutionary computation (EC)-based search techniques have received a lot of attention in solving feature selection problem in high-dimensional feature space. This paper proposes a particle swarm optimisation (PSO)-based feature selection approach which is capable of generating the desired number of high-quality features from a large feature space. The proposed algorithm is tested on a large dataset and compared with several existing state-of-the-art algorithms used for feature selection. The accuracy of the underlying classifier has been considered as a measure of performance. Our obtained results demonstrated that the proposed PSO-based feature selection approach outperforms the other traditional feature selection algorithms in all the considered classifiers.
    Keywords: sentiment classification; feature selection; particle swarm optimisation; PSO; evolutionary computation; support vector machine; SVM; naïve Bayes; NB; mutual information; MI; chi-square; CHI.
    DOI: 10.1504/IJBIDM.2019.10013337
  • Using bagging to enhance clustering procedures for planar shapes   Order a copy of this article
    by Elaine Cristina De Assis, Renata Souza, Getulio José Amorim Do Amaral 
    Abstract: Partitional clustering algorithms find a partition maximizing or minimizing some numerical criterion. Statistical shape analysis is used to make decisions observing the shape of objects. The shape of an object is the remaining information when the effects of location, scale and rotation are removed. This paper introduces clustering algorithms suitable for planar shapes. Four numerical criteria are adapted to each algorithm. In order to escape from local optima to reach a better clustering, these algorithms are performed in the framework of Bagging procedures. Simulation studies are carried to validate these proposed methods and two real-life data sets are also considered. The experiment quality is assessed by the corrected Rand index and the results the application of the proposed algorithms showed the effectiveness of these algorithms using different clustering criteria and the union of the Bagging method to the cluster algorithms provided substantial gains in of the quality of the clusters.
    Keywords: Statistical Shape Analysis; Partitional Clustering Methods; Bagging Procedure.
    DOI: 10.1504/IJBIDM.2019.10013537
  • Impact of Clustering on quality of Recommendation in Cluster based Collaborative Filtering: an Empirical Study   Order a copy of this article
    by MONIKA SINGH, Monica Mehrotra 
    Abstract: In memory nearest neighbour computation is a typical approach for collaborative filtering (CF) due to its high recommendation accuracy. However, this approach fails on scalability; which is the declined performance of the same due to the rapid increase in the number of users and items in archetypal merchandising applications. One of the popular techniques to attenuate scalability issue is cluster-based collaborative filtering (CBCF), which uses clustering approach to group most similar users/items from complete dataset. In this work we present a detailed analysis of the impact of clustering in CF approach. Specifically, we study how the extent of clustering impacts collaborative filtering systems in terms of quality of predictions, quality of recommendations, throughput and coverage. Based on the empirical results obtained from two datasets, Movielens100K and Jester; we conclude that with increasing number of clusters the quality of predictions, the quality of recommendations and the throughput are enhanced but the coverage provided by clustered subsystems declines.
    Keywords: Recommender Systems; Collaborative Filtering; Clustering; Prediction; Nearest neighbors; Clustering based collaborative filtering; Average recommendation time; Coverage; Quality of predictions and Qua.
    DOI: 10.1504/IJBIDM.2019.10013538
    by Lakshmi R, S. Baskar 
    Abstract: In this paper, two new similarity measures, namely distance of term frequency-based similarity measure (DTFSM) and presence of common terms-based similarity measure (PCTSM), are proposed to compute the similarity between two documents for improving the effectiveness of text document clustering. The effectiveness of the proposed similarity measures is evaluated on reuters-21578 and WebKB datasets for clustering the documents using K-means and K-means++ clustering algorithms. The results obtained by using the proposed DTFSM and PCTSM are significantly better than other measures for document clustering in terms of accuracy, entropy, recall and F-measure. It is evident that the proposed similarity measures not only improve the effectiveness of the text document clustering, but also reduce the complexity of similarity measures based on the number of required operations during text document clustering.
    Keywords: Document Clustering; Similarity Measures; Accuracy; Entropy; Recall; F-Measure; K-means clustering Algorithm.
    DOI: 10.1504/IJBIDM.2018.10013539
  • XML web quality analysis by employing MFCM clustering Technique and KNN classification   Order a copy of this article
    by M. Gopianand, P. Jaganathan 
    Abstract: The great accomplishment of web search engine is keyword search which is the most trendy search representation for regular consumers. It is permits that the consumer can create the queries without the knowledge of query language and the database schema. So, it is also considered as a user friendly method. The quality of XML web has to be accurate if the exact queries have to be answered. Here we have proposed a method to access the quality of the XML web by analysing the keyword present in the XML web based on the respective keyword search. In our proposed method we collect number of XML documents and are clustered based on the keyword depending on the type of XML files. Modified fuzzy C means (MFCM) is used for clustering. Once the clustering based on the respective keyword is done, we classify the XML web based on quality of the data by utilising KNN classifier.
    Keywords: XML web; K nearest neighbor; Error value; Classification accuracy; feature vectors.
    DOI: 10.1504/IJBIDM.2018.10014525
  • Analysis and Prediction of Heart Disease Aid of Various Data Mining Techniques: A Survey   Order a copy of this article
    by V. Poornima, D. Gladis 
    Abstract: In recent times, health diseases are expanding gradually because of inherited. Particularly, heart disease has turned out to be the more typical nowadays, i.e., life of individuals is at hazard. The data mining strategies specifically decision tree, Na
    Keywords: Data mining; Heart Disease Prediction; performance measure; Fuzzy; and clustering.
    DOI: 10.1504/IJBIDM.2018.10014620
  • Signal-Flow Graph Analysis and Implementation of Novel Power Tracking Algorithm Using Fuzzy Logic Controller   Order a copy of this article
    by S. VENKATESAN, Manimaran Saravanan, Subramanian Venkatnarayanan, Senior Member IEEE 
    Abstract: This paper discussed merits of novel modified perturb and observe (P&O) maximum power point tracker (MPPT) algorithm for stand-alone solar PV system using interleaved LUO converter with fuzzy logic controller (FLC). The merits of FLC based system are compared with existing system. Analytical expressions of the proposed converter are derived through signal flow graph. The proposed interleaved LUO converter based PV system with fuzzy controller reduces considerable amount of ripple content and also proposed MPPT algorithm creates less hunting around maximum power point. Simulations at different illumination levels are carried-out using MATLAB/Simulink. It also experimentally verified with a typical 40 W solar PV panel. The result confirms the superiority of the proposed system with fuzzy controller.
    Keywords: Fuzzy Logic Controller; Interleaved LUO Converter; Maximum Power Point Tracking (MPPT); Modified P&O algorithm; Photovoltaic(PV) system.
    DOI: 10.1504/IJBIDM.2018.10014621
  • SoLoMo Cities: Socio-Spatial City Formation Detection and Evolution Tracking Approach   Order a copy of this article
    by Sara Elhishi, Mervat Abu-Elkheir, Ahmed Aboul-Fotouh 
    Abstract: The tremendous growth of telecommunication devices coupled with the huge number of social media users has revealed a new kind of development that turning our cities into information-rich smart platforms. We analyse the role of LBSN check-ins using social community detection methods to extract city structured communities, which we call SoLoMo cities, using a modified version of Louvain algorithm, then we track these communities evolution patterns through a pairwise consecutive matching process to detect behavioural events changing citys communities. The findings of the experiments on the Brightkite dataset can be summarised as follows: online users check-in activities reveal a set of well-formed physical land spaces of citys communities, the concentration of online social interactions and the formation of those cities are positively correlated with a percentage of 89%. Finally, we were able to track the evolution of the discovered communities through detecting three community behaviour events: survive, grow and shrink.
    Keywords: location-based social networks; LBSN; social; spatial analysis; community detection; evolution; tracking; Brightkite.
    DOI: 10.1504/IJBIDM.2019.10014746
    by Betty P, Mohanageetha D, Jeena Jacob 
    Abstract: Biometric authentication has received greater significance due to its high uniqueness and performance. The ability of quick and convenient authentication is required due to its widespread demand. Extraction of feature is the primary and important task for effective authentication. Dissimilar chrominance texture pattern (DiCTP) technique is used in this paper for effective feature extraction. Patterns of two sequences are generated from the inter channel information of the image which extracts the coloured texture information of the input. Unique information is generated from RGB and BRG planes of the image which produces a part of diversified chromatic feature vectors. The local binary pattern (LBP) code is generated and added along with the feature vector, which aids to inculcate the greyscale information of the image. The experimental results are formulated using the CASIA Face Image Database Version 5 (DB1) and Indian Face database (DB2) which give considerable improvements over the existing methodology.
    Keywords: Biometric Authentication; Dissimilar Chrominance Texture Pattern ; Content Based Image Retrieval.
    DOI: 10.1504/IJBIDM.2018.10014922
  • Discovery of Rare Association Rules in the Distribution of Lawsuits in the Federal Justice System of Southern Brazil   Order a copy of this article
    by Lucia Gruginskie, Guilherme Vaccaro, Leonardo Chiwiakwosky, Attilla Blesz Jr 
    Abstract: In the context of data mining, infrequent association rules may be beneficial for analysing rare or extreme cases with very low support values and high confidence. In researching risky situations or allocating specific resources, such rules may have a much greater impact than rules with high support value. The objective of this study is to obtain association rules from the database of lawsuits filed in the Federal Court of Southern Brazil in 2016, including both frequent and rare rules. By finding these rules, especially rare ones, the information collected can assist in the decision-making process, in this case, such as training clerks or establishing specialised courts.
    Keywords: Association Rules; Rare Rules; Distribution of lawsuits; Brazilian Federal Justice; Data mining.
    DOI: 10.1504/IJBIDM.2019.10015160
  • Integral Verification and Validation for Knowledge Discovery Procedure Models (March, 2018)   Order a copy of this article
    by Anne Antonia Scheidler, Markus Rabe 
    Abstract: This paper explains why the knowledge discovery in database (KDD) procedure models lacks verification and validation (V&V) mechanisms and introduces an approach for integral V&V. Based on a generic model for knowledge discovery, a structure named 'KDD triangle model' is presented. This model has a modular design and can be adapted for other KDD procedure models. This has the benefit of allowing existing projects for improving their quality assurance in knowledge discovery. In this paper, the different phases of the developed triangle model for KDD are discussed. One special focus is on the phase results and related testing mechanisms. This paper also describes possible V&V techniques for the developed integral V&V mechanism to ensure direct applicability of the model.
    Keywords: knowledge discovery in databases; data mining; procedure model; verification and validation; quality assurance.
    DOI: 10.1504/IJBIDM.2019.10015983
  • A Multiclass Classification Approach for Incremental Entity Resolution on Short Textual Data   Order a copy of this article
    by Denilson Pereira, João A. Silva 
    Abstract: Several web applications maintain data repositories containing references to thousands of real-world entities originating from multiple sources, and they continually receive new data. Identifying the distinct entities and associating the correct references to each one is a problem known as entity resolution. The challenge is to solve the problem incrementally, as the data arrive, especially when those data are described by a single textual attribute. In this paper, we propose a new approach for incremental entity resolution. The method we have implemented, called AssocIER, uses an ensemble of multiclass classifiers with self-training and detection of novel classes. We have evaluated our method in various real-world datasets and scenarios, comparing it with a traditional entity resolution approach. The results show that AssocIER is effective and efficient to solve unstructured data in collections with a large number of entities and features, and is able to detect hundreds of novel classes.
    Keywords: Entity Resolution; Associative Classification; Incremental Learning; Novel Class Detection; Self-training.
    DOI: 10.1504/IJBIDM.2019.10015984
  • Method for Improvement of Transparency: Use of Text Mining Techniques for Reclassification of Governmental Expenditures Records in Brazil   Order a copy of this article
    by Gustavo De Oliveira Almeida, Kate Revoredo, Claudia Cappelli, Cristiano Maciel 
    Abstract: Many countries have transparency laws requiring availability of data. However, often data is available but not transparent. We present the Transparency Portal of Brazilian Federal Government case and discuss limitations of public acquisitions data stored in free text format. We employed text-mining techniques to reclassify descriptive texts of measurement units related to products and services. The solution presented in KNIME and JAVA aggregated measurements in the original (n = 69,372 with 78% reduction in number of descriptions, 94% items classified) and in cross validation sample (n = 105,266 with 88% reduction, classifying 78% of items). In addition, we tested computational time for processing of texts for a wide range of data input sizes, suggesting the stability and scalability of the solution to process larger datasets. Finally, we produced analysis identifying probable input errors, suppliers and purchasing units with abnormal transactions and factors affecting procurement prices. We present suggestions for future research and improvements.
    Keywords: e-government; data mining; open government; text mining; transparency; KNIME; knowledge discovery; techniques; Brazil.
    DOI: 10.1504/IJBIDM.2019.10015985
  • Data Mining in Credit Insurance Information System for Bank Loans Risk Management in Developing Countries   Order a copy of this article
    by Fouad J. Al Azzawi 
    Abstract: The task of credit risk insurance in our time is critical since loans are taken by everyone and everywhere and it is quite difficult to accurately estimate the possible losses that are incurred by failing to pay those loans. This work proposes an information system module for the banking system to improve the risk management operation that distributes losses on some fair basis, as well as accepting the maximum number of loan requests. Insuring the risk associated with stumbled loans, the bank will partially or completely shift losses under this contract to the insurance company, thus minimising its own losses. The proposed module could find out for what price the bank can buy such insurance policy. The proposed module also could be a key valuable motivation for different development countries to update their strategy of current insurance market to outsource part of the states insurance functions to independent insurance industry. Data mining techniques and mathematical induction have been used and successfully implemented this model. An optimal classification solution module for predicting risky loan requests have been successfully employed. New mathematical model has been developed for calculating the cost of insurance policy in crisis economy.
    Keywords: Data mining; Credit insurance; information systems; Bank loans; risk management; developing countries.
    DOI: 10.1504/IJBIDM.2019.10016599
  • Fibonacci Retracement Pattern Recognition for Forecasting Foreign Exchange Market   Order a copy of this article
    by Mohd Fauzi Ramli, AHMAD KADRI JUNOH, Mahyun Ab Wahab, Wan Zuki Azman Wan Muhamad 
    Abstract: Fibonacci retracement implicates a forecast of future movements in foreign exchange rates (forex) of the previous movement inductive analysis. Fibonacci ratios are used to forecast the retracements level of 0.382, 0.500 and 0.618 and to determine the current trend which provide the mathematical foundation for the Elliott wave theory. K-nearest neighbour (KNN) and linear discriminant analysis (LDA) algorithm are the pattern recognition method for nonlinear feature mining of Elliott wave patterns. Results show that LDA is better than KNN in terms of classification accuracy data which are 99.43%. Among of three levels of Fibonacci retracement results, the 38.2% shows the best forecasting for Great Britain Pound pair to US Dollar currency as major pair by using mean absolute error (MAE), root mean square error (RMSE) and pearson correlation coefficient (r) as the statistical measurements which are 0.001884, 0.000019 and 0.992253 for uptrend and 0.001685, 0.000019 and 0.998806 for downtrend.
    Keywords: forex; forecast; fibonacci retracement; elliott wave; golden ratio.
    DOI: 10.1504/IJBIDM.2019.10016710
  • Enhanced R package based Cluster Analysis Fault Identification Models for 3 Phase Power system Network   Order a copy of this article
    by Nithiyananthan K, Pratap Nair M, Raman Raghuraman, TanYong Sing, Syahrel Emran Bin Siraj 
    Abstract: The main objective of this research work is to develop an R based fault identification model for power system in a cluster analysis environment Cluster Analysis based Data Mining Techniques model has been implemented to locate the 3-phase transmission lines fault in IEEE 30 bus power system Power World version 18 software was used to simulate the IEEE 30 bus power system and the 3-phase transmission lines fault The bus voltages at fault were collected and import to Statistical Package for the Social Sciences (SPSS) to identify the faults at buses Through Cluster Analysis using Squared Euclidean Distance method, fault has been identified at each bus Then the data also imported to R statistical package to compute the cophenetic distance of dendrogram and check the accuracy of clustering. The proposed innovative successful model was able to locate the fault at each bus by bus nominal voltage comparison method.
    Keywords: Power system transmission lines faults; Data Mining; Cluster Analysis; R; SPSS,IEEE 30 bus system; dendrogram,Data preprocessor,Power World simulator,Squared Euclidean Distance.
    DOI: 10.1504/IJBIDM.2019.10016711
  • Effective Optimization of Honda Algorithm for Rear End Collision Avoidance System with Genetic Algorithm   Order a copy of this article
    by K.G. Manjunath, N. Jaisankar 
    Abstract: Honda is one of the most popular algorithm for rear end collision avoidance system, but does not intend to avoid all the accidents, this due to that it gives a smaller range of warning which cannot avoid the accidents at high speed, to overcome this issue we proposing the optimisation of warning range with a genetic algorithm to minimise the probability of collision. The results show that proposed algorithm avoids 7% more accident than the standard Honda algorithm. We presented the MATLAB implementation of proposed work with examples to show the efficiency of our work.
    Keywords: mutation; warning range; collision avoidance; vehicle velocity.
    DOI: 10.1504/IJBIDM.2019.10016987