International Journal of Intelligent Systems Technologies and Applications (53 papers in press)
Multi-Agent System Approach for Improve Real-Time Visual Summaries of Geographical Data Streams
by Zina Bouattou, Robert Laurini, Hafida Belbachir
Abstract: Interdisciplinary efforts are needed to support visual exploration and analysis of spatio-temporal data streams from sensor networks, depending on the size and complexity of data that must be analysed before being displayed. For this, several emerging approaches have been proposed known as Visual Summaries of a set of data that will help users find what is most important and interesting to visualize in the mass of available information. In this paper, we present an approach for generating automatically the visual summaries in real time. For this approach, we have adopted chorem-based visual representations of territories issued from both geometric and semantic generalizations. Roger Brunet defined Chorems as schematized visual representations of territories. Our approach relies on a multi-agent framework; an extraction knowledge agent is able to extract important spatiotemporal patterns of data streams coming from a sensor network as interesting regions, and a visualization agent which displays those patterns as simplified maps. The novelty is the multi-agent semantic generalization and its combination with cartographic generalization to generate maps on-the-fly. We validate this model with an example taken from the meteorology field.
Keywords: Geovisualization; sensors networks; important patterns; chorems; multi-agent systems (MAS); visual summaries; automatic generalization.
Automatic Arabic Text Summarization System (AATSS) Based on Morphological Analysis
by Laiali Almazaydeh
Abstract: This Automatic text summarization is a challenging task. Existing techniques suffer from inadequate investigation for Arabic text. The majority of Arabic words are derived according to the morphology that is more complicated than English and other Natural languages. The approach reported in this paper elaborates a morphological analysis to develop a new Automatic Arabic text summarization. The proposed approach is based on three main phases: the text tokenization phase, the trilateral root extraction phase and the aggregate similarity computation phase. The proposed approach has been extensively experimented on samples from the benchmark datasets of EASC. Experimental results show the ability of the proposed system to extract relevant sentences that reflects the summary of Arabic text, as well as a comparable performance with other reported techniques in the literature.
Keywords: Information retrieval; Arabic language; summarization; trilateral rootroot; aggregate similarity.
A Two-Phase Human Activity Classification Design Using Accelerometer Data from Smartphone
by Syed Arsalan Ali, Rooh Ul Amin
Abstract: This paper presents a two phase design for the classification of the human movement activities of Running, Walking and Standing by using accelerometer data logged from smart phone. The first phase of the design is, the two-stage filter applied for the process of raw acceleration data collected from the accelerometer; and the second phase of the design is, the classification of human movements using Logistic Regression with Gradient Descent Algorithm as classifier from the clean data produced by first phase of the design. The raw acceleration data from the tri-axial accelerometer of smart phone contains three axes accelerations. This acceleration data is collected separately for each movement activity and is used for the training and testing of design by using a simulation environment. The activity classification results are promising and show that the proposed design provides an accuracy of more than 96 percent on both train data and the separate independent test data for the classification of human movement activities of Running, Walking and Standing.
Keywords: Pattern recognition; Smartphone accelerometer; Two-Stage filter; Gradient Descent Algorithm; Classifier.
An Online English-Khmer hybrid Machine Translation System
by Suraiya Jabin, Niladri Chatterjee, Suos Samak, Kim Sokphyrum, Javier Sola
Abstract: The present paper describes design of an online hybrid Machine Translation (MT) system involving a low-resource language Khmer. A Statistical Machine Translation system is typically trained on a large parallel corpus in order to compute the translation probabilities, and on an even larger quantities of monolingual data to compute the target language n-gram probabilities that are used to design the language model. The proposed system uses the open source SMT toolkit DoMY CE for training corpus (English-Khmer parallel corpus and also developing the corpus) combined with a post processing step of using parts of speech tagger to further enhance the quality of target language sentence. Web technologies, such as, Python, Apache2 (Web server), HTML, XML, and XSLT for developing an online translation system. Language Model, Translation Model and Decoder Configurations are done in accordance with recent literature using the DOMY toolkit. The parallel corpora have been prepared from various sources, such as ICT books for high school students, translated texts from chemist, education glossaries, law and history articles, country and city names, and the Headley Khmer-English dictionary. Experimental results are presented to demonstrate the success of phrase based SMT methods where English has been used as source and Khmer, a low-resource language as a target language. We used NIST (National Institute of Standards and Technology) and BLEU (BiLingual Evaluation Understudy) metrics have been used evaluate the performance of the system based on the training corpus built. In our experiments the proposed model achieved significantly good BLEU and NIST scores. Khmer being the official language of Cambodia the demand for such an automated system has been on the rise for several decades. The present work is one of the useful efforts towards meeting this demand.
Keywords: Computational Linguistic; English-Khmer Parallel Corpora; English-Khmer translation; Khmer language; Moses toolkit; Statistical Machine Translation; Phrase-based model; Rule-based machine translation system; Hybrid Machine Translation system.
Robust Feature Selection Algorithm based on Transductive SVM Wrapper and Genetic Algorithm: Application on Computer-Aided Glaucoma Classification
by Nawel Zemmal, Mokhtar Sellami, Djamel Zenakhra, Nilanjan Dey, Amira S. Ashour
Abstract: Glaucoma has become a devastating disease after cataract to cause blindness. Thus, early diagnoses for glaucoma can prevent the vision loss. Computer Aided Diagnosis (CAD) systems, which automate the process of ocular disease detection, are urgently needed to alleviate the burden on the clinicians. In the current work, advanced machine learning algorithms are investigated to propose a robust system for Glaucoma diagnosis based on retinal images. Three features extraction methods, namely the Gray-Level Co-Occurrence Matrix (GLCM), Hu moments and Central moments are combined to form the entry feature vector. To select the most relevant features and taking into account at the same time the unlabeled data existing in medical databases, a new scheme of feature selection algorithm is proposed. It is based on Transductive SVM Wrapper and Genetic algorithm, which automatically detect and classify the glaucoma disease using fundus images. The effectiveness of the proposed GA-TSVM is evaluated on a public retinal database RIM-ONE using the classification accuracy, sensitivity, and specificity metrics. The experimental results established that with 16% of labeled data, the proposed system could easily distinguish between the normal and the affected glaucoma cases.
Keywords: Glaucoma disease; Computer Aided Diagnosis; Genetic Algorithm; Feature Extraction and Selection; Semi supervised Learning; Transductive Support Vector Machine (TSVM).
Enhanced Particle Swarm Optimization Algorithms for Multiple-Input Multiple-Output System Modelling using Convolved Gaussian Process Models
by Gang Cao, Edmund Lai, Fakhrul Alam
Abstract: Convolved Gaussian Process (CGP) is able to capture the correlations not only between inputs and outputs but also among the outputs. This allows a superior performance of using CGP than standard Gaussian Process (GP) in the modelling of Multiple-Input Multiple-Output (MIMO) systems when observations are missing for some of outputs. Similar to standard GP, a key issue of CGP is the learning of hyperparameters from a set of input-output observations. It typically performed by maximizing the Log-Likelihood (LL) function which leads to an unconstrained nonlinear and non-convex optimization problem. Algorithms such as Conjugate Gradient (CG) or Broyden-Fletcher-Goldfarb-Shanno (BFGS) are commonly used but they often get stuck in local optima, especially for CGP where there are more hyperparameters. In addition, the LL value is not a reliable indicator for judging the quality intermediate models in the optimization process. In this paper, we propose to use enhanced Particle Swarm Optimization (PSO) algorithms to solve this problem by minimizing the model output error instead. This optimization criterion enables the quality of intermediate solutions to be directly observable during the optimization process. Two enhancements to the standard PSO algorithm which make use of gradient information and the multi-start technique are proposed. Simulation results on the modelling of both linear and nonlinear systems demonstrate the effectiveness of minimizing the model output error to learn hyperparameters and the performance of using enhanced algorithms.
Keywords: Enhanced PSO; Convolved Gaussian Process Models; Hyperparameters Learning.
Support Vector Machine based Fault Detection and Diagnosis for HVAC Systems
by Jiaming Li
Abstract: Various faults occurred in the Heating, Ventilation and Air-Conditioning (HVAC) systems usually lead to more energy consumption and worse thermal comfort inevitably. This paper presents a feasible and valid solution of HVAC fault detection and diagnosis problem based on statistical machine learning technology. It learns the consistent nature of different types of faults of HVAC operation based on Support Vector Machine (SVM), and then identify types of fault in all subsystems using the statistical relationships between groups of measurements. In order to speed up the learning process, Principle Component Analysis (PCA) has been applied to compress the training data. Our approach models the dynamical sub-systems and sequence data in HVAC system. The learnt models can then be used for automatic fault detection and diagnosis. The approach has been tested on commercial HVAC systems. It had successfully detected and identified a number of typical AHU faults.
Keywords: Fault detection and diagnosis; FDD; Machine learning; SVM; HVAC system; Principle Component Analysis; PCA.
Efficient Blind Nonparametric Dependent Signal Extraction Algorithm for Determined and Underdetermined Mixtures
by Fasong WANG
Abstract: Blind extraction or separation statistically independent source signals from linear mixtures have been well studied in the last two decades by searching for local extrema of certain objective functions, such as non-Gaussianity (NG) measure. Blind source extraction (BSE) algorithm for extracting statistically dependent source signals from underdetermined and determined linear mixtures is derived using nonparametric NG measure in this paper. After showing that maximization of the NG measure can also separate or extract the statistically weak dependent source signals, the nonparametric NG measure is defined by statistical distances between different distributions of separated signals based on cumulative density function (CDF) instead of traditional probability density function (PDF), which can be estimated by the quantiles and order statistics (OS) using the norm efficiently. The nonparametric NG measure is optimized by a deflation procedure to extract or separate the dependent source signals. Simulation results for synthesis and real world data show that the proposed nonparametric extraction algorithm can extract the desired dependent source signals and yield ideal performance.
Keywords: blind source separation; non-Gaussianity measure; independent component analysis; probability density function; dependent component analysis; underdetermined blind source extraction.
Indic Script Identification from Handwritten Document Images
by Pawan Kumar Singh, Ram Sarkar, Mita Nasipuri
Abstract: Script identification plays an important role in document image processing especially for multilingual environment. This paper hires two conventional textural methods for the recognition of the scripts of the handwritten documents inscribed in different Indic scripts. The first method extracts the well-known Haralick features from Spatial Gray-Level Dependence Matrix (SGLDM) and the second method computes the fractal dimension by using Segmentation-Based Fractal Texture Analysis (SFTA). Finally, a 104-element feature vector is constructed from the features designed by these two methods. The proposed technique is then evaluated on a total dataset comprising of 360 handwritten document pages written in 12 Indian official scripts namely, Bangla, Devanagari, Gujarati, Gurumukhi, Kannada, Malayalam, Manipuri, Oriya, Tamil, Telugu, Urdu and Roman. Experimentations using multiple classifiers reveal that Multi Layer Perceptron (MLP) shows the highest identification accuracy of 96.94%. Encouraging outcome confirms the efficacy of customary textural features to handwritten Indic script identification.
Keywords: Script Identification; Handwritten Indic documents; Textural Features; Spatial Gray-Level Dependence Matrix; Segmentation-Based Fractal Texture Analysis; Statistical Significance Tests.
Combining RSS-SVM with Genetic Algorithm for Arabic Opinions Analysis
by Amel ZIANI, Nabiha Azizi, Djamel Zenakhra, Soraya Cheriguene
Abstract: The Arabic language has drawn the attention of researchers in data mining due to its large-scale users, but it presents challenges because of its richness and complex morphology. That explains the growing importance of the Arabic sentiment analysis and precisely the Arabic opinion detection and classification areas. Thus, the most accurate classification technique used in this area which proven by several previous works is the Support Vector Machine classifier (SVM). This last, is able to increase the rates in opinion mining but with use of very small number of features. Hence, reducing features vector can alternate the system performance by deleting some pertinent ones. To overcome these two constraints (features vector size and considerate all extracted primitives), our idea is to use Random Sub Space algorithm (RSS) at first stage to generate several features vectors with limited size; and to replace the decision tree base classifier of RSS with more accurate classifier which is the SVM. Therefore, each one of the generated features subsets will be the entry of an individual SVM classifier. Despite the obtained high results of this proposed approach, another proposition was implemented in order to enhance the previous algorithm by using the genetic algorithm as subset features generator based on correlation criteria to eliminate the random choice used by RSS and to prevent the use of incoherent features subsets. For that, features extraction stage is considered the main step in opinion mining process. Wherefore, to extract and to calculate the best informative linguistic and statistic features, we struggled with the lack of labeled Arabic dataset. So, a translated SentiWordNet dataset has been used to overcome this problem and to ameliorate the proposed system performance. The experiments results applied on one thousand (1000) labeled reviews collected from Arabic Algerian newspapers and manually annotated are very promising.
Keywords: Arabic opinion mining; SentiWordNet; Machine learning; SVM (Support Vector Machine); RSS (Random Sub Space); GA (Genetic Algorithm).
Facial beauty analysis by age and gender
by Manal El Rhazi, Arsalane Zarghili, Aicha Majda, Anissa Bouzalmat, Ayat Allah Oufkir
Abstract: The face is the first source of information that inspires the attractiveness of a human being, for this reason; several studies were conducted in the aesthetic medicine and the image processing to analyze the aesthetic quality of an adult human face.
This paper proposes an automatic procedure for the analysis of facial beauty. First, we detect the face zone on an image and its features areas, then we present our novel method to extract features corners, and finally we analyze the facial aesthetic quality.
Experimental results show that our method can extract the features corners accurately for the majority of faces presented in the ECVP and FEI images databases, and that there exist a difference in the facial beauty analysis by gender and age, due to anatomic differences in specific facial areas between the categories.
Keywords: Facial features; Plastic surgery; Facial attractiveness; Facial beauty analysis; Image processing; Image analysis.
Enhancement based Background Separation Techniques for Fruit Grading and Sorting
by Jasmeen Gill
Abstract: Image processing plays a remarkable role in the automation of fruit grading and sorting. While grading the fruit, accurate extraction of fruit object from the image (background separation) is the chief concern. For extraction of fruit, appropriate segmentation technique is employed; and to accomplish it accurately, enhancement must be performed prior to segmentation. However, majority of the researchers emphasized over fruit segmentation alone. This communication is intended to show the potential of enhancement techniques when combined with fruit image segmentation. Besides, it presents a comparative analysis of enhancement based background separation techniques for fruit grading and sorting. For this purpose, four main techniques, namely, Contrast limited adaptive histogram equalization (CLAHE) method, Gaussian filter, Median filter and Wiener filter were utilized for enhancement and Basic Global thresholding, Adaptive thresholding, Otsu thresholding and Otsu-HSV thresholding were applied for segmentation. 16 sub-models were developed by combining each enhancement method with every segmentation technique. Afterwards, the image quality of the sub-models was validated using quantitative as well as qualitative analyses. Test results demonstrate that CLAHE/Otsu-HSV model outperformed the others for fruit grading and sorting.
Keywords: Digital image processing; Segmentation; Enhancement; Fruit grading and sorting; Background separation; Otsu-HSV segmentation.
Soft Neural Network based Block Chain Risk Estimation
by Ganglong Duan, Wenxiu Hu, Yu Tian
Abstract: Financial risk refers to the uncertainty caused by the change of the economic and financial conditions. As a kind of economic phenomenon, the financial risk is objective and can not be eliminated. At present, there are still some imperfect aspects in the research of financial risk assessment. In order to achieve the purpose of comprehensive evaluation of financial risks, the paper analyses the methodology of soft computing and neural networks. The basic function of financial risk monitoring and evaluation system is to forecast the trend of financial activities and risk status, and this is also the fundamental function and objectives of the assessment system. We use BP neural network theory to establish the logistics finance risk evaluation model, using BP neural network structure and training principles to train sample data. The soft computing method is based on the factors of uncertainty and irrationality, which breaks through the limitation of traditional hard computing. There is a consistency between the fuzzy thinking principle of soft computing method and the attribute and structure of the objective world, therefore, soft computing can be used in field of financial risk assessment.
Keywords: Block chain; financial risk; assessment; neural network; soft computing.
Data Flow Tracking based Block Chain Modelling
by Ganglong Duan, Wenxiu Hu, Yu Tian
Abstract: This article carries on the analysis to the recent global financial industry focus block chain, introduces the concept and scope of the technology, and to the financial payment service as an example, analyzes the block chain technology to the center, to trust four characteristics, collective maintenance and safety database etc.. In this area chain based technology in the financial field of application and Research on the current situation, the international payment as an example, analyzes the block chain payment mode and the traditional mode of payment differences, and that the international financial industry attention block chain technology, nature is to build a flat global integrated settlement system. Finally, we summarize the trend of the development of block chain technology innovation, and put forward some key issues that should be paid attention to. Block chain is a kind of computing paradigm to the center of the infrastructure and distributed with new bitcoin digital encryption currency popularity gradually rise, at present it has attracted great attention and concern of government departments, financial institutions, enterprises and capital market. The block chain technology has decentralized, time series data, the collective maintenance, programmable and safe and reliable characteristics, especially suitable for construction of the monetary system, the financial system and macro social system programming. The rapid development of the financial chain block technology applications such as digital currency, smart contracts cause new financial risk, which brings a series of challenges to the existing financial supervision system in China.
Keywords: data stream; tracking algorithm; finance; block chain; technical framework.
AN EFFICIENT MEDICAL IMAGE WATERMARKING TECHNIQUE USING INTEGER WAVELET TRANSFORM AND QUICK/FAST RESPONSE CODES
by K.J. Kavitha
Abstract: Securing the medical images, to make it tamper free is a terribly difficult task. This challenge is with efficiency handled with the assistance, digital watermarking techniques. With the assistance of this growing technology we are able to evaluate validation, dependability, privacy and integrity of the medical images. Several algorithms are enforced on this technology. The Digital Watermarking (DWM) is implemented in two main domains: transform & spatial. The DWM is mostly implemented using the transform techniques such as Singular Valued Decomposition (SVD), Discrete Cosine Transform (DCT), Discrete Wavelet Transform (DWT), combination of DCT and DWT and also with the combination of DWT and SVD. These days the work is extended with Integer Wavelet Transform (IWT). One of the foremost challenges in these technologies is information embedding capability or we say; number of bits hidden in the cover information. This parameter is considered for evaluation of the system since as we know that the more number of bits; the distortion of the medical information is also more and vice versa. The distortion of the information is strictly avoided within the case of medical and military applications. To beat this downside of the technology, these days research work goes on to implement digital watermarking technique with less information embedding capability. One of the possible ways to reduce the number of bits in information is to use Quick /fast response code (QR Code). The QR code consumes a less space compared to the other existing formats available such as barcode. In this paper Associate in nursing approach is planned to implement the digital watermarking technique for the medical images that includes the following techniques: Integer wavelets Transform (IWT), Bit plane methodology and QR code. For the proposed system, the watermarked image is evaluated against a number of the parameters to grasp the potency of technique being utilized. The experiment is dispensed for 2 totally different bit planes and also the results are compared to point out embedding within which of the bit plane range ends up in additional potency and eventually the conclusion is formed.
Keywords: Watermarking; DWT; QR Code.
Automatic Identification of Rhetorical Relations Among Intra-sentence Discourse Segments in Arabic
by Samira Lagrini, Nabiha Azizi, Mohammed Redjimi, Montheer Al Dwairi
Abstract: Identifying discourse relations, whether implicit or explicit, has seen renewed interest and remains an open challenge. We present the first model that automatically identify both explicit and implicit rhetorical relations among intra-sentence discourse segments in Arabic text. We build a large discourse annotated corpora following the rhetorical structure theory framework. Our list of rhetorical relations is organized into three level hierarchy of 23 fine-grained relations, grouped into seven classes.
To automatically learn these relations, we evaluate and reuse features from literature, and contribute three additional features: accusative of purpose, specific connectives and the number of antonym words. We perform experiments on identifying fine-grained and coarse-grained relations. The results show that compared with all the baselines, our model achieves the best performance in most cases, with an accuracy of 91.05%.
Keywords: Discourse relations; Rhetorical structure theory; Arabic language.
Unsupervised Generation of Arabic Words
by Ahmed Khorsi, Abeer Alsheddi
Abstract: Automated word generation might be seen as the reverse process of morphology learning. The aim is to automatically coin valid words in the targeted language. As many other challenges in the field of Natural Language Processing (NLP), the building of the generation engine might be carried out using a supervised or unsupervised approach. The former requires a clean learning data set of a decent size whereas the later needs no more than a plain text. Nonetheless, the unsupervised approaches are usually blamed for their low accuracy. The present article reports the results of an investigation on a context-free generation of classical Arabic words. Unsupervised and relatively simple, The proposed approach reached easily an accuracy of 90%.
Keywords: Arabic language; classical vocabulary; computational linguistics; corpus expansion; linguistic corpora; morphology learning; natural language processing; unsupervised learning; statistical linguistics; word generation.
MODELLING AND SIMULATION OF FUZZY BASED MPPT CONTROL OF GRID CONNECTED PV SYSTEM UNDER VARIABLE LOAD AND IRRADIANCE
by Subhashree Choudhury, Pravat Kumar Rout
Abstract: The Photovoltaic (PV) based distribution generation system has a nonlinear power characteristic curve under random variation in solar irradiance, ambient temperature and electric load. As a result, for the accurate detection and tracking of the maximum power points (MPPs),it is necessary to design an optimal controller with dynamic control capability. Solution to the above issue, this paper presents an intelligent Mamdani based Fuzzy Logic Controller (MFLC) for maximum power point tracking (MPPT) of a PV system. Different test cases with respect to different possible load and irradiance variations in grid connected mode of operation are investigated. To confirm the power quality indices within IEEE standards specification, Fast Fourier Transform (FFT) analysis of voltage and current at the point of common coupling has been done. A detailed comparison has been made in between PV without MPPT, with Incremental Conductance and proposed Fuzzy Logic Control (FLC). The results show an enhance efficiency of energy production from PV and reflects the effectiveness of the proposed scheme justifying its real time application.
Keywords: Photovoltaic (PV) array; Maximum power point Tracking (MPPT); dc-dc boost converter; Incremental Conductance (IC); Mamdani based fuzzy logic controller (MFLC); Fast Fourier Transform (FFT).
STATISTICAL DATA MINING TECHNIQUE FOR SALIENT FEATURE EXTRACTION
by Jahnavi Reddy
Abstract: Internet based news documents are an important source of information transmission. Large numbers of news documents from various news wire sources are available on the internet. It is almost impossible to view all the news documents generated as a result of a search performed by a user. rnTerm weighting is a useful technique that extracts important features from textual documents, thereby providing a basis for different Text Mining approaches. While several term weighting algorithms, based on manifold statistical measures have been proposed in the past, they are inaccurate in extracting salient terms from internet based digitized news documents. rnThe objective of this work is to study the existing term weighting algorithms for feature extraction and to develop an efficient term weighting algorithm for mining salient features from internet based newswire sources. TF*PDF (Term Frequency * Proportional Document Frequency) is the most popular term weighting algorithm which extracts influential features from news archives. TF*PDF satisfies the basic property of the features in news documents i.e., frequency and thus increases the accuracy when compared to other term weighing algorithms such as Binary, TF (Term Frequency), TF-IDF (Term Frequency-Inverse Document Frequency) and its variants. However, only frequency property is not sufficient for salient topic extraction. To overcome that problem, this paper presents an innovative and effective term weighting algorithm that considers Position, Scattering and Topicality along with Frequency for extracting salient events. Frequency considers the number of occurrences of a term; Position focuses on location of the term; Scattering focuses on the distribution of a term in the entire document. Topicality is the variation in the frequency of usage of a term over a period of time. Experimental evaluation shows that the proposed term weighting algorithm performs better than the existing term weighting algorithms in terms of Coverage Rate.
Keywords: Term Weighting; TF*PDF; FPST.
Neural Network Based Adaptive Selection CFAR for Radar Target Detection in Various Environments
by Budiman Putra Asmaur Rohman, Dayat Kurniawan
Abstract: Constant False Alarm Rate (CFAR), a target detection method commonly used in the radar systems, has an inconsistent performance against various environments. For improving the radar detectability, this paper proposes a novel scheme of radar target detection using neural network based adaptive selection CFAR. The proposed method employs Cell-Averaging, Ordered-Statistic, Greatest-of and Smallest-of CFAR thresholds as the basis of references. The pattern of those threshold values combined with the Cell Under Test signal value will be identified and classified by the neural network to compute the raw threshold. Then, the final threshold is selected depending on the nearest value between raw and four referenced CFARs. The performance of the proposed method is examined against three possible cases of the radar systems including homogeneous background, multiple targets and clutter boundary. The result of this research shows that the proposed method outperforms the classical CFARs due to the adaptive selection algorithm can select properly among referenced CFARs against the given cases particularly in the homogeneous and multiple target environments.
Keywords: neural network; adaptive selection; CFAR; radar; target detection.
Local Search-Based Recommender System for Computing the Similarity Matrix
by Yousef Kilani, Ayoub Alsarhan, Mohammad Bsoul, Subhieh El-Salhi
Abstract: Recommender systems reduce the users' effort in finding their favourite items among a great number of items. In collaborative-based RSs, there are different similarity measures to compute the similarity values between every two users or two items. These measures include: genetic algorithms, Pearson, and Cosine-based similarity techniques. The number of items and personal attributes (e.g environment, sex, job, religion, age, county, education, etc.) that are used by the similarity metric algorithms are increasing significantly which makes the recommendation task more difficult.
In our project, we introduce a new RS that uses the local search algorithms to compute the similarity matrix.
As far as we know we have not found any work in the RS literature that uses local search algorithms techniques.
We name the new recommender system, LSRS.
We consider part of the dataset as training data (e.g. 80%) in order to calculate
the similarity between every two users by LSRS. The remaining dataset is
the testing data (e.g. 20%).
LSRS finds the similarity among the users. It initializes
the similarity value between every two users to a random value between 0-1. It then uses local search to adjust this value by
training the recommender system using the training data.
We experimentally show that LSRS computes the similarity matrix and outperforms the other techniques like the Pearson correlation and cosine similarity and some of the recent genetic-based recommender systems.
Keywords: Collaborative filtering-based recommender systems; similarity matrix; Recommender Systems; local search algorithms; similarity measures.
Similarity searching in ligand based virtual screening using different fingerprints and different similarity coefficients
by BERRHAIL Fouaz, BELHADEF HACENE, HENTABLI HAMZA, FAISSAL SAEED
Abstract: Similarity searching plays an increasingly important role in virtual screening. Its a screening technique that works by comparing the features of the target compound with the features of each compound in the database of compounds. This comparison can be described in three steps: The first step involves the representation of the target compound and the database compounds with an equivalent representation, which is set of binary elements describing the presence or the absence of attributes of compounds (fingerprint). The second step uses similarity coefficient to calculate the score of similarity between two compounds representation. The third step is to rank the database compounds in appropriate order of the similarity score, in order to determine the actives compounds. Many approaches and techniques have been introduced in literature to enhancing and improving similarity based virtual screening. In this work, our primary interests are to investigate the effect of using different combinations of fingerprint and similarity coefficient in ligand-based virtual screening (LBVS). We use in this investigation the MDDR (Drug Data Repot database) to evaluate the different combinations descriptor-coefficient. Some obtained results of combinations with some coefficients demonstrate superiority in performances to these obtained in combination with Tanimoto coefficient.
Keywords: Ligand Based; Virtual Screening; Similarity Searching; Similarity Coefficients; Molecular Descriptors; Fingerprint; Drug Discovery.
Collaborative Approach to Secure Agents in Ubiquitous Healthcare Systems
by Nardjes BOUCHEMAL
Abstract: In sensitive domain such as healthcare, life of people is controlled and it is very important to access fast to health information especially in case of emergency (e.g., allergies and chronic diseases). For that, agent paradigm is very promising for ubiquitous healthcare systems. But, the inherent complexity of information security is bigger because agents are characterized by autonomy, intelligence and not under the control of a single entity. Indeed, the big challenge in agent-based ubiquitous healthcare systems is to assure that emergency workers and doctors can access personal information fast and whenever needed but with high level security. The use of cryptography concepts saturates agents embedded in limited resource devices and obstructs healthcare workers. The idea is to lighten agents with simple cryptography concepts while strengthening the surveillance and make it collaborative. Consequently, all agents of the system are concerned by security and collaborate to maintain it. This paper addresses security challenges in ubiquitous healthcare systems based on agents and presents a collaborative approach. Proposed agents are implemented in JADE-Leap platform designed for restricted devices.
Keywords: Security; U-healthcare; Collaboration; Collective Decision; Ubiquitous Agents;.
Statistical Assessment of Nonlinear Manifold Detection Based Software Defect Prediction Techniques
by Soumi Ghosh, Ajay Rana, Vineet Kansal
Abstract: Prediction of software defects has immense importance for obtaining improved and desired outcome at minimized cost and lesser time. Defect prediction in software system has attracted researchers to work on this topic applying various techniques. But those were not found to be fully effective. Software datasets comprise of redundant or undesired features that hinders effective application of techniques resulting more time consuming and inappropriate prediction of defective areas of software. Hence, it is required to apply proper techniques for accurate software defect prediction. A newer application of Nonlinear Manifold Detection Techniques (Nonlinear MDTs) has been examined and accurate prediction of defects in lesser time and cost by using different classification techniques. In this work, we analyzed and tested the effect of Nonlinear MDTs to find out the best classification technique with higher accuracy for all software datasets. Comparison has been made between the results obtained by using without or with Nonlinear MDTs for estimating better performance of classifier by reducing dimensions. Paired Two-tailed T-Test has been performed for statistically testing and verifying the performance of classifiers using Nonlinear MDTs on all datasets. The outcome revealed that among all Nonlinear MDTs, FastMVU makes most accurate prediction of software defects in case of most of the classification techniques.
Keywords: Dimensionality Reduction; FastMVU; Machine Learning; Manifold Detection; Nonlinear; Promise Repository; Software Defect Prediction.
A game-based virtual machine pricing mechanism in federated clouds
by Ying Hu
Abstract: In a federated cloud environment, diverse pricing schemes among different IaaS service providers (ISPs) form a complex economic landscape that nurtures the market of cloud brokers. Although pricing mechanisms have been proposed in the past few years, few of them address the issue of competitive and cooperative behaviours among different ISPs. In this paper, we employ the learning curve to model the operation cost of ISPs, and introduce a novel algorithm that determines the cooperative pricing mechanism among different ISPs. The cooperation decision algorithm uses the operation cost computed based on the learning curve model and price policies obtained from the competition part as parameters to calculate the final revenue when outsourcing or locally satisfying users resource requests. Extensive experiments are conducted in a real-world federated cloud platform, and the experimental results are compared with three existing pricing mechanisms. Our experimental results show that the proposed pricing mechanism is effective to improve resource utilization as well as reduce the profit loss caused by request rejection.
Keywords: cloud computing; pricing mechanism; resource market; game theory.
Real Time Path Planning for High Speed UGVs
by Ajith Gopal, Elsmari Wium
Abstract: The application of a modified A-Star (A*) global search algorithm and
trajectory planner based on the tentacles algorithm approach are investigated for
real time path and trajectory planning on an unmanned ground vehicle operating
at a speed of 40km/h. The fundamental assumption made is that for high speed
applications, the requirement for an optimal path is secondary to the requirement
for short processing times, provided that a solution, if it exists, is found. The
proposed solution is benchmarked against the original A* algorithm and shows
a reduction in search space of up to 84% and a reduction in processing time of
up to 97%. Results for the trajectory planner are also presented, though no direct
comparative evaluation against the original tentacles algorithm was executed. The
combined path and trajectory processing time of the proposed solution translates
to less than 2mm of travel distance before a reaction to a change in the environment
can be processed.
Keywords: Path Planning; Trajectory Planning; UGV; Real Time; A-Star.
Detection of Glaucoma based on Cup-to-Disc Ratio using Fundus Images
by Imran Qureshi, Muhammad Attique, Muhammad Sharif, Tanzila Saba
Abstract: Glaucoma is a permanent damage of optic nerves which cause of partial or complete visual loss. This work presents a glaucoma detection scheme by measuring CDR from fundus photographs. The proposed system consists of image acquisition, feature extraction and glaucoma assessment steps. Image acquisition discusses the transformation of a RGB fundus image into grey form and enhancing the contrast of fundus features. While, boundary of optic disc and cup were segmented in feature extraction step. Finally, a cup-to-disc ratio of an exploited image will compute to assess glaucoma in the image. The proposed system is tested on 398 fundus images from four publicly available datasets, obtaining an average value of sensitivity 90.6%, specificity 97% and accuracy 96.1% in glaucoma diagnosis. The achieved results show the suitability of proposed art for glaucoma detection.
Keywords: Cup-to-disc ratio (CDR); fundus images; glaucoma; image processing; optic disc; segmentation.
Special Issue on: Inventive Systems and Internet of Things
Mixed Integer Programming for Vehicle Routing Problem with Time Windows
by Divya Aggarwal, Vijay Kumar
Abstract: Being a key element in logistics distribution, Vehicle Routing Problem becomes an importance research topic in management and computation science. Vehicle Routing Problem with time windows is a specialization of Vehicle Routing Problem. In this paper, a brief description of vehicle routing problem is presented. A Mixed Integer Programming is utilized to solve the vehicle routing problem with time windows. A novel mathematical model of MIP is formulated and implemented using IBM CPLEX. A novel constraint is designed to optimize the number of vehicle used. The proposed model is used to optimize both transportation cost and number of vehicle used simultaneously. The proposed model is tested on two well-known instances of Solomons benchmark test problem. Experimental results illustrate that the proposed formulation provides promising solutions in reasonable computation time. The sensitivity analysis of customer nodes is also studied.
Keywords: Vehicle Routing Problem; Time Windows; Solomon’s Instance; CPLEX; MIP.
EFFICIENT MULTIMEDIA CONTENT STORAGE AND ALLOCATION IN MULTIDIMENSIONAL CLOUD COMPUTING RESOURCES
by Sivaraman Eswaran, Manickachezian R
Abstract: Optimal management of the cloud resources for multimedia contents is the important aim of this research. In our previous work, Multiple Kernel Learning with Support Vector Machine (MKL-SVM) is introduced which can achieve a balanced resource usage with multimedia user request. However existing work do not concentrate on caching mechanism which might lead to more computational overhead. To solve this problem, new method is proposed namely Improved Storage and Scheduling of Multimedia Contents in Cloud Storage (ISS-MCCS). In this work, Fuzzy Neural Network Classification (FNNC) is utilized for handling the server clusters with unevenness. Then task scheduling is done using Hybrid Genetic-Cuckoo Search Algorithm (HGCSA) where Hybrid fuzzy weighting scheme is used for the fitness evaluation. Finally Adaptive Replacement Cache (ARS) is integrated to optimize memory. The overall assessment of the research work is done in cloudsim environment which proves it can manage the multimedia contents with efficiently.
Keywords: Multimedia contents; multiple QoS; optimized scheduling; efficient load balancing; adaptive replacement; fuzzy neural network classification.
Context Aware Reliable Sensor Selection in IoT
by K. R. Remesh Babu Raman, M. Vishnu Prathap, Philip Samuel
Abstract: Internet of Things (IoT) is a computing concept where physical objects with embedded sensors connect to the Internet and can identify themselves to other devices. Today the number of devices connected to the Internet is increasing rapidly and they want to communicate each other for different purposes. The future Internet will comprise of billions of intelligent communicating objects having capabilities for sensing, actuating, and data processing. Each object in this Cyber Physical Systems (CPS) will have one or more embedded sensors that will capture huge amount of data. Managing these data in cloud and obtaining the relevant data from appropriate sensors are important concerns. For information retrieval context awareness is important. Usually users need information from these sensors depending upon several factors like location, accuracy level, etc. The proposed method senses reliable data from a sensor environment that satisfies user contexts. It also contains different functionalities like user addition, location sensing, context specification, user context counting, and selection of current best results.
Keywords: Cloud Computing; Context Awareness; IoT; Sensor selection; Cloud of Things; Resource management.
Lagrangian Relaxation for Distribution Networks with Cross-Docking Center
by Manpreet Singh Bhangu, Rimmi Anand, Vijay Kumar
Abstract: This paper proposes a cross-docking in logistics network that aims to reduce the transportation costs. The proposed strategy eliminates need of inventory for storing commodities. It consists of two main stages. During first echelon, the number and location of cross-dock warehouses are determined. In second echelon, the allocation of warehouse as cross-dock to distribution centers is determined. Each warehouse has limited capacity and distribution center can supplied by only one cross-dock. In this paper, the problem is mathematical formulated using a mixed-integer programming model. This formulation is used the concept of cross-docking allocation and commodities distribution. The Lagrangian relaxation approach is proposed to solve logistics network problem. Experimental results reveal that the proposed approach provides optimal solution in a reasonable time.
Keywords: Facility location; Network design; Mixed-integer programming; Lagrangian relaxation; Merge-in-Transit.
Factors Influencing Regression Testing on Cloud and On-Premises: An Analysis
by Suma V, Narasimha Murthy M S
Abstract: Since, the evolution of software, it has laid its impact in all domains of operations wherein software industry has taken up the major role. Hence, it is required to upgrade software as the market demands so that it can sustain in the industrial environment. However, one of the most important criteria for software industries to survive is development of high quality software which can completely satisfy customers. In order to achieve the above mentioned goal, it becomes mandatory for the software organizations to adopt themselves to the market dynamics. Since, cloud computing has not taken wide popularity as one of the promising technology in the current situation, IT services have marched themselves to serve the needs of the society through cloud based technology. Testing applications using cloud has further become one of the dominating areas of operations. The main objective of this paper is therefore to analyse the effectiveness, and efficiency of testing applications in cloud environment. This paper further put forth a case study which involves an empirical investigation carried out in leading software company follows cloud technology in their day to day flow of developmental activities. A comprehensive analysis is conducted upon sampled data which is collected in two different domains namely health care and telecom domain of the company under investigation. From the analysis, it is observed that testing applications in cloud model is a good practice against conventional mode of software testing (on-premises). The results also depict testing applications in cloud environment improves the performance of various parameters of testing process. Further, this inference paves way to carry out further research to formulate effective strategies to test applications in cloud.
Keywords: Software Engineering; Software Testing; Cloud Computing; Regression Testing; Software Quality; Total Customer Satisfaction.
A Hybrid Test Prioritization Technique for Combinatorial Testing
by Preeti Satish, Krishnan Rangarajan
Abstract: IoT systems comprise of multiple devices connected together, to perform an intelligent task in real time. Such systems have to be meticulously tested in order to avoid hazards situations. Combinatorial testing technique can effectively test such complex IoT systems with reduced effort as it generates fewer test cases with adequate coverage. It basically tests the interactions that exist between values of different parameters and practically faults upto six-way interactions have been found successfully. Prioritization of combinatorial tests deals with finding an ideal order of the test cases so that faults are detected early. Recent approaches to prioritization problem are either coverage based or parameter-value weight based for two-way or three-way interaction strengths separately. In this paper, we present a hybrid prioritization technique for combinatorial testing that combines both weight based and interaction coverage based approaches. We derive a combined weight to each test case considering user given weights denoting the importance of values of parameters and the interaction coverage up to six-way interactions. To the best of our knowledge, no research has been carried out yet, that accounts for higher order combinations up to six-way at a time. To demonstrate the effectiveness of our algorithm, we have conducted initial synthetic experiments on various covering arrays, and measured the effectiveness with t-Rate of fault of detection metric. The results are promising in covering the combinations early.
Keywords: IoT systems; combinatorial testing; prioritization; combinatorial coverage; weight based; hybrid technique; interaction testing; interaction strength.
Intelligent systems for Redundancy Removal with Proficient Run Length Coding and statistical analysis using regression
by V.R. PRAKASH, S. Nagarajan
Abstract: The surveillance video aspect has been one of the key technologies in various tactical monitoring. However, the quantum of analysis with proper implication of video quality subjected to enormous amount of time might degrade its error metrics. So in order to analyse this quantum has been made with the hierarchical order wherein four videos where taken and its peak errors where being analysed. The significance of the work is dealt with feature extraction and then comparison with input and extracted texture followed by feature analysis with cosine angle distance. Finally, a multiple regression analysis has been developed with PSNR as dependant variable where video size and execution time are taken as independent variable. The significance of regression has been based on prediction equation has been done in order to near optimality of PSNR value for varying video size and execution time.
Keywords: Proficient Run Length Coding; Regression analysis.
An Intelligent Inventive System for Personalized Web Page Recommendation based on Ontology Semantics
by Gerard Deepak, Ansaf Ahmed, Skanda B
Abstract: Owing to the information diversity in the Web and its dynamically changing contents, extraction of relevant information from the web is a huge challenge. With the World Wide Web transforming into a more organized Semantic Web, the incorporation of Semantic techniques to retrieve relevant information is highly necessary. In this paper, a dynamic ontology alignment technique for recommending relevant Web Pages is proposed. The strategy focuses on knowledge tree construction by computing the semantic similarity between the query terms as well as the ontological entities. Furthermore, the semantic similarity is again computed between nodes of the constructed knowledge tree and URLs in the URL repository to recommend relevant Web Pages. The dynamic ontology alignment by computing their respective semantic similarity constitutes Ontology Semantics. Personalization is achieved by prioritization of Web Pages by Content Based Analysis of the users Web Usage Data. An overall accuracy of 87.73 % is achieved by the proposed approach.
Keywords: Ontologies; Personalized; Semantic Strategy; Web Page Recommendation System; Web Search.
Input Data Transition Aware Adaptive Frequency Scaling based Energy Efficient MAC Architecture for Low Power DSP Applications
by Haripriya D, Govindaraju C, Sumathi M
Abstract: In this paper, input transition aware adaptive frequency scaling based low power 8 bit Multiplier-Accumulator (MAC) architecture for Digital Signal Processing (DSP) has been presented. As the data transition causes dynamic power dissipation in the electronic circuits, it becomes mandatory to minimize the data transition to minimize the dynamic power. A novel adaptive frequency scaling scheme based on the number of input data transitions is implemented and the dynamic power has been minimized to a greater extent. The proposed input data transition aware adaptive frequency scaling is very effective method to minimize the dynamic power consumption without degrading the performance of the system. The input data transition detector circuit in the proposed low power MAC detects the transition and applies the adaptive frequency scaling based on the number of transitions, so that a notable amount of dynamic power is reduced by the proposed scheme. The dynamic power consumed by the conventional MAC is 77.05mW when all inputs are switching and it is only 2.370mW for the proposed MAC with the same simulation parameters. The proposed MAC consumes 96.92% less power than the conventional MAC for the same set of inputs and simulation environment at 27
Keywords: Adaptive Frequency Scaling; Digital Signal Processing; Dynamic Power; Energy Efficient; Low Power; Multiply and Accumulate; Transition Aware.
SURVEY ON TECHNIQUES OF FAULT DETECTION- ROOKIES VANTAGE POINT
by Vijaya Bharathi Manjeti, Koteswara Rao Kodepogu
Abstract: Cutting edge frameworks are turning out to be exceedingly configurable to fulfil the shifting needs of clients and clients. Programming product offerings are consequently turning into a typical pattern in programming improvement to decrease cost by empowering deliberate, expansive scale reuse. A few shortcomings may be uncovered just if a specific mix of components is chosen in the conveyed items. Yet, testing all mixes is typically not possible by and by, because of their to a great degree extensive numbers. Combinatorial testing is a method to produce littler test suites for which all mixes of t elements are ensured to be tried. In this paper, we display a few hypotheses depicting the likelihood of irregular testing to recognize connection blames and contrast the outcomes with combinatorial testing. For instance, an irregular testing turns out to be considerably more viable as the quantity of elements increments and focalizes toward equivalent adequacy with combinatorial testing. Be that as it may, when imperatives are available among elements, then irregular testing can passage subjectively more awful than combinatorial testing. Subsequently, with a specific end goal to have a reasonable effect, future research ought to concentrate on combinatorial testing.
Keywords: fault; framework; vantage.
Effect of Magnetizing Core on Impedance and Induced EMF of Two Coils Wound on Single Iron Core
by SUDA KRISHNARJUNA RAO, VAJRALA NARSI REDDY, SINGAMSETTY NAGENDRA KUMAR
Abstract: A copper wire wound on a iron rod is called as iron cored inductor. Basically iron cored inductor having two components resistance and inductance. These two parameters depend up on the permeability of the core. In this paper variation of induced EMF in the secondary coil placed on the same core at different magnetic fields are presented. A detailed study of dependence of resistance and inductance is also presented in this paper.
Keywords: Magnetization; Iron cored inductor; Induced EMF.
Special Issue on: Computational Intelligence informatics and Information Security
Image Preprocessing of Icing Transmission Line based on Fuzzy Clustering
by Wang Jing, Zhang Hangbo, Han Ming, Yang Zhengyan
Abstract: Propose a kind of electric transmission lines icing image segmentation algorithm based on quick bi-circulating level set to improve effect of electric transmission lines icing image segmentation algorithm. Firstly, carry out pretreatment to graying of electric transmission lines icing image and decrease noise influence in image; secondly, improve fuzzy c mean value clustering algorithm and propose SPKFCM algorithm by increasing space penalty function, which is used for automatic initialization of quick bi-circulating level set algorithm; finally, it is shown that proposed algorithm has better segmentation effect and segmentation efficiency by experiment contrast on electric transmission lines icing image.
Keywords: Quick bi-circulation; Level set; Electric transmission lines; Icing image; Segmentation.
A Study on Power Balance Control Strategies of Mining Variable Speed Magnetic Coupling Based on Fuzzy Self-adaptive PID
by Wang Lei, Jia Zhen Yuan, Zhang Li, Wang Xin Ming, Li Liang
Abstract: This paper studied fuzzy self-adaptive PID control strategies that are applicable to mining variable speed magnetic coupling based on analysis on the magnetic coupling mathematical model fitted by a large number of data simulation and calculation by combining traditional PID control strategies and fuzzy control methods and then carried out experiments for further verification. It can be proved by experimental analysis that such control strategy can not only solve multi-motor driving power balance problems effectively, but adapt to working condition changes and revise control parameters automatically, which can provide theoretical support for project practices.
Keywords: magnetic coupling; PID control; fuzzy control; power balance.
Exploration on Predicting Breast Cancer Stage with the Aid of Redesigned ANN Incorporated with Enhanced Social Spider Optimization (ESSO) Technique
by Ramani Selvanambi, Jaisankar Natarajan
Abstract: The core intention of this work is to predict the breast cancer stage as benignant or malignant from the given input dataset with parameters such as instance Clump Thickness, Uniformity of Cell Size, Uniformity of Cell Shape, Marginal Adhesion, Single Epithelial Cell Size, Bare Nuclei, Bland Chromatin, Normal Nucleoli and Mitoses. Predicting the cancer stage helps to determine the best way to contain and eliminate the breast cancer. In this process various techniques are used, one of the classification methods used is Artificial Neural Network (ANN) which it is trained with several training algorithms and the selected training algorithm is Levenberg-Marquardt. This training algorithm performs in the better way and gives minimum error value. In order to obtain the better prediction the default structure of ANN is redesigned using optimization techniques. In the default structure, one hidden layer of the network includes ten neurons. To improve the structural design (hidden layer and neuron) diverse optimization techniques are used for example Cuckoo Search (CS), Particle Swarm Optimization (PSO), Social Spider Optimization (SSO) and Enhanced Social Spider Optimization (ESSO). Our results shows the Enhanced Social Spider Optimization (ESSO) is employed better and evaluates the metrics as Accuracy 97%, Sensitivity 98%, and Specificity 95% compared with other techniques. The accuracy is fine tuned in this work were contrasted with existing work and the stage of breast cancer is predicted.
Keywords: Artificial Neural Network(ANN); Breast cancer; Levenberg-Marquardt (LM) algorithm; Feed Forward Back Propagation(FFBN); Enhanced Social Spider Optimization(ESSO) algorithm.
Prediction of oil Production based on SVM Optimized Multi Objective Particle Swarm Optimization
by Rong Wang-Yin, Rui Zhou, An Xian-Shao, Jing Yu-Pang
Abstract: The unstable availability and capacity exist in energy efficiency optimization regarding to renewable energy supply for cellular network due to several factors, such as climate change, high oil resource consumption and energy safety etc. The energy supply sustainability problem is modeled as optimization solution problem aiming at maximizing NP-hard network energy residue ratio (ERR). The network ERR maximization algorithm has been put forward through analysis of relationship between energy efficiency and energy depletion rate (EDR). This algorithm is worked by adopting cross-layer manner, which first maximizes the link energy efficiency by the power control at physical (PHY) layer and then maximizes network ERR by the access control at media access control (MAC). The simulation results show that the network ERR maximization algorithm performs excellently in improving network lifetime as well as increasing the number of users served by renewable energy.
Keywords: energy efficiency; cellular network; renewable energy; energy residue rate; energy depletion rate.
An Improved Adaptation Algorithm for Signer-independent Sign Language Recognition
by Wang Min, Wang Ya, Zhu Xiao-Juan
Abstract: Sign language recognition is a technology that can present sign language in a understandable form, in order to achieve barrier-free communication between deaf and normal. In order to solve the differences in sign language data issues and the lack of training samples of manpower caused by low recognition of non-specific language, presents MLLRMAP adaptive progressive non-specific integrated manpower language recognition framework. This approach optimizes the division MLLR regression class to provide more accurate initial MAP model, which give full play to the rapidity and the MAP MLLR progressive. Then introduced MCE model parameter estimation algorithm to compensate for the limitations of the model parameters adaptive method to further reduce the system error rate and accelerate the recognition speed. Meanwhile, for the MCE algorithm computationally intensive problems proposed improvements. Experimental results show that the adaptive sign language data required for this algorithm is less than traditional MLLR and MAP methods, while improved average recognition rate by 15.6%.
Keywords: machine learning; signer-independent sign language recognition; mllr algorithm; map algorithm; model parameter estimate.
Design of Remote Control System for Intelligent Irrigation based on Zigbee and GPRS
by Zhang Hongbo
Abstract: The sensor nodes of WSNs (Wireless Sensor Networks)-based agricultural monitoring system are required to collect the environmental data periodically, including temperature and humidity, and additionally the any time monitoring shall be required to be realized among the monitored area with minimized quantity of sensor nodes. To meet the mentioned goal, a SPMSC (Solar Power-Moving Schedule of mobile -based optimal Coverage) is proposed. In accordance with the content of SPMSC, every sensor node is able to obtain energy through the solar panel. The moving schedule of sensor nodes is determined through predicting the quantity of solar power as to realize the optimal coverage project with the minimized quantity of sensor nodes and the energy consumption. As the simulation result indicates, the proposed SPMSC, compared with other similar projects, has the 4% decrease of sensor node quantity and the 10% increase of the network lifetime.
Keywords: WSNs (Wireless Sensor Networks); Agricultural monitoring; Intelligence algorithm; Coverage; Network lifetime; Irrigation system.
Modification of AES using Genetic Algorithms for High-Definition Image Encryption
by Sayantani Basu, Marimuthu K, Rajkumar S, Niranchana R
Abstract: Genetic Algorithms (GAs) have proved to be a powerful tool in cryptography. The standard AES algorithm incurs high computation costs and has problems in pattern formation when applied to image encryption. It is also prone to the differential and side channel attacks. In this paper, a modification of AES has been proposed for High-Definition (HD) Image Encryption that uses a genetic approach for the S-Box Generation and Key Expansion processes. S-Boxes have been evolved to have high nonlinearity and low transparency. Also, in the key generation, the process has been randomized by using only genetic operators. As a result of applying genetic modifications to AES, the proposed algorithm has been found to be more secure against the differential and side-channel attacks and shows reduced pattern appearance when applied for encryption of HD images.
Keywords: AES; GA; image encryption; s-box; key generation.
A Study On Flow Based Classification Models Using Machine Learning Techniques
by Chokkanathan Kothandapani
Abstract: Network traffic management facing a lot ofchallenges in the recent years because of the continuous and fast development in network scale, number of consumers and type of known and unknown applications over the network. Traffic Classification is a key factor for providing the Quality of network Services (QoS) and also playing a vital role in handling the delay and congestion during the network transmission. In spite of many algorithms existing in the field of network traffic classification, Machine learning algorithms are playing key role. In this paper I would like to discuss about flow based classification models such as port based, payload based, statistical based and behavioral based classifications which are frequently used for identifying traffic classes. Techniques (Methods): Widely used techniques such as port based, pay-load based, behavioral based and statistical based classification models are discussed and sample data sets have been produced with graphical notations to strengthen the analysis process. Findings: This analysis provides the capability to distinguish and isolate the traffic flows into real-time and non-real-time traffic flow according to the process category. Since we are experiencing the integrated environment of multiple fields its very difficult to classify the traffic. So the comprehensive study on these classification models will provide the clarity on how the traffic is classified with different attribute sets and to understand the quality of service and issues of each classification model. Applications:In a wide-range of network environment, traffic classification is more important to provide the differentiated service quality based on the various types of applications and also for monitoring the security over the traffic. In a network traffic analysis, network classification is the first step and core element for network intrusion detection system.There are many network servicing areas where we need to identify and classify the trafficsuch as routing, firewall access-control, policy based routing, traffic billing need to be differentiated and their quality of service has to be assured.
Keywords: Network Classifications Models; Network Traffic Analysis; Quality of Service; Intrusion Detection System.
A hybrid approach to improve the quality of software fault prediction using Na
by Sathyaraj Ragupathi, Prabu S
Abstract: This paper considers an improvisation in software fault prediction research area using supervised classification algorithms and it mainly focuses to increase the performance of fault prediction. In this paper, we propose a hybrid prediction model using na
Keywords: Naïve Bayes; K-NN; software fault prediction; classification; ensemble.
Labeled Decision Making Method based on Neural Network Model and Pruning Algorithm
by Jian-hai Du
Abstract: Aiming at the advantages in commonly used method of generating decision tree, a method of generating cost-sensitive decision tree based on the correlation degree of neural network attributes is proposed through quoting the correlation degree of neural network attributes and cost-sensitive learning. This method reduces the condition attributes by using rough set theory, and takes the correlation degree and cost performance of attributes as the bases of split node to build the cost-sensitive decision tree by using modified information gain method during the process of building decision tree. It is shown in test result that such method is superior to commonly used method of generating decision tree in classification accuracy and the number of nodes generated.
Keywords: neural network; rough set; cost-sensitive; labeled tree reduction; decision-making.
Design of Water Quality Monitoring System based on WSN and ZigBee
by Zhang Yuan, Qi Lan
Abstract: In the Thesis, based on actual demand, a set of water quality monitoring system on the basis of wireless sensor network, ZigBee technology and GPRS technology is designed to realize a large scale of real-time water quality monitoring. Wireless sensor network is applied to city macrozone water quality monitoring system, in the meantime, wireless transmission technologies such as ZigBee and GPRS are combined to realize collection and transmission of water quality data, and technologies such as sleep mode management, automatic alarm of exceeding water area, self-adaptively high-speed collection, preferred transmission and network cluster node topology control optimization are adopted to improve energy utilization efficiency of the system, which reduces the cost of system maintenance and use and meets with actual application demands. System networking and deployment are convenient comparatively with great expandability and application prospects.
Keywords: Water quality monitoring; Wireless sensor network; ZigBee; GPRS; Data transmission.
Special Issue on: IRICT 2017 Reliable and Intelligent Information and Communication Technology
Interest emotion recognition approach using self-organizing map and motion estimation
by Kenza Belhouchette, Mohamed Berkane, Hacene Belhadef
Abstract: Recognizing human facial expressions and emotions by computer is an interesting and challenging problem. Its usefulness may appear in various fields such as e-learning. Although several approaches have been proposed to recognize emotions based on facial expressions, the recognition rate, amount of used resources and calculation time remain factors for improvement. Our work presents a new approach for recognizing basic emotions (joy, sadness, anger, disgust, surprise and fear) in image sequences. We introduced interest emotion and created its corresponding action units (AUs) based on psychological foundations. Our approach is mainly characterized by minimizing used data and consequently optimizing the computing time and improving the recognition rate. The proposed approach was divided into three steps. The first step is face detection using the method developed by Viola and Jones. The second step concerns the extraction of facial features. At this level, we exploited the Facial Action Coding System proposed by Paul Ekman, which is based on AUs. To detect AUs, we extracted face strategic points (inner, outer and centre points of the eyebrow; centre points of the lower and upper eyelids; right, left, top and bottom corners of the mouth; and left and right external nose wing) using an active appearance model and a block-matching approach. At the last step, we classified the results by using the Kohonen self-organizing map.
Keywords: Emotion; Interest; neural network; Kohonen; action units; facial expression; bloc matching.
Arabic sign language recognition using vision and hand tracking features with HMM
by Ala Addin Sidig, Hamzah Luqman, Sabri Mahmoud
Abstract: Sign language employs signs made by hands and facial expressions to convey meaning. Sign language recognition facilitates the communication between community and hearing-impaired people. This work proposes a recognition system for Arabic sign language using four types of features, namely Modified Fourier Transform, Local Binary Pattern, Histogram of Oriented Gradients, and a combination of Histogram of Oriented Gradients and Histogram of Optical Flow. These features are evaluated using Hidden Markov Model on two databases. The best performance is achieved with Modified Fourier Transform and Histogram of Oriented Gradients features with 99.11% and 99.33% accuracies, respectively. In addition, two algorithms are proposed, one for segmenting sign video streams captured by Microsoft Kinect V2 into signs and the second for hand detection in video streams. The obtained results show that our algorithms are efficient in segmenting sign video streams and detecting hands in video streams.
Keywords: Arabic Sign language; Sign language recognition; video segmentation; Histogram of Oriented Gradients; Hands detection; Hidden Markov Model.
Quality of Service (QoS) Task Scheduling Algorithm for time-cost trade off Scheduling Problem in Cloud Computing Environment
by DANLAMI GABI, Abdul Samad Ismail, Anazida Zainal, Zailmiyah Zakaria
Abstract: As cloud computing environment is evolving, managing trade-offs between time and cost when executing large-scale tasks to guarantee customers minimum running time and cost of computation is not always feasible. Many heuristics and metaheuristics have been proposed to resolve this problem. The metaheuristics are considered promising, since they can schedule large-scale tasks as well optimise the best-known trade-offs among conflicting objectives and return solution in just one run. However, they are characterised with certain limitations that need to be resolve, which include local trapping, poor convergence and imbalance between global and local search to enhanced their solution findings. In this paper, we first present a multi-objective task scheduling model upon which a dynamic Multi-Objective Orthogonal Taguchi-Based Cat Swarm Optimisation (dMOOTC) Algorithm is proposed to solve the model. In the proposed algorithm, Taguchi Orthogonal method is incorporated into the local search of a conventional Cat Swarm Optimisation (CSO) to overcome local trapping and ensure it diversity. Pareto-Optimisation strategy incorporated within the algorithm is used to balance solution of the global search and local search. The efficiency of the proposed algorithm is studied by simulation with CloudSim tool. Thirty independent simulation runs where conducted and results is evaluated based on the following metrics, i.e., execution time, execution cost and Performance Improvement Rate Percentage (PIR%). The results of the simulations showed the proposed dMOOTC algorithm can select the best known optimal trade-off values that can minimised the execution time and execution cost than single objective conventional Cat Swarm Optimization (CSO), Multi-Objective Particle Swarm Optimization (MOPSO), Enhanced Parallel CSO (EPCSO) and Orthogonal Taguchi Based-Cat Swarm Optimization (OTB-CSO) algorithms.
Keywords: Multi-Objective; Quality of Service; Task Scheduling; Cat Swarm Optimisation; Pareto-Optimisation.
Data stream management system for video on demand hybrid storage server
by Ola Al-Wesabi, Nibras Abdullah
Abstract: The storage device is one of the main components of video on demand (VOD) server. The VOD storage system is responsible for storing and streaming large videos. Hence, the VOD server requires a large storage capacity and rapid video retrieval from this storage to quickly stream these videos to users. The hybrid storage system, which combines hard disk drive (HDD) and solid-state-drive (SSD) components in the server, has become popular because of such requirements. HDD is becoming economical and is providing a high storage capacity for numerous videos. Moreover, the SSD can act as a buffer for fast retrieval and streaming of videos to users. The combination of both storage modes is relatively weak in terms of optimizing fast access prior and in supporting the production of a high number of simultaneous streams. This paper presents the proposed VOD storage server system, namely, enhanced hybrid storage system (EHSS) based VOD server to improve the performance of the VOD server. The design of the EHSS and its streaming management scheme produce high performance and satisfy the performance requirements of a VOD server in terms of I/O throughput and access latency. The experimental results show that the proposed VOD server-based EHSS with the proposed DSC scheme provides better performance than the VOD server-based FADM because it enhances the average response times for the various scales of intensive workload by 69.89%.
Keywords: Data stream controller (DSC); Hard disk drives (HDDs); Cache hit ratio; Hybrid storage; I/O response time; Solid-state-drive (SSD); Throughput; Video on demand (VOD).