International Journal of Computational Intelligence Studies (23 papers in press)
Handling the Crowd Avoidance Problem in Job Recommendation Systems Integrating FoDRA
by Nikolaos Almalis
Abstract: In this article, we present the basic principles and approaches of the Job Recommender Systems (JRSs). Furthermore, we describe the four different relation types of the job seeking and recruiting problem, derived directly from the formal definition of the JRSs. We use our already published Four Dimensions Recommendation Algorithm (FoDRA) to calculate the suitability of person for a job and then we model a job seeking and recruiting problem with many candidates and many jobs (N-N case). Finally, we execute the algorithm and present the results proposing a solution -the minimum acceptable suitability level-for the crowd avoidance problem that occurred. Our study produces satisfying results and shows that this approach can be considered as an important asset in the domain of Job Seeking and Recruiting.
Keywords: Recommendation system; Job seeking and recruiting; Job recommender; Matching people and jobs; Constraint-based; Information filtering.
Creating classification rules using Grammatical Evolution
by Ioannis Tsoulos
Abstract: A genetic programming based method is introduced for data classification. The fundamental element of the method is the well - known technique of Grammatical Evolution. The method constructs classification programs in a C like programming language in order to classify the input data, producing simple if else rules. The paper introduces the method as well as the conducted experiments on a series of datasets against other well known classification methods.
Keywords: Genetic algorithm; Data classification; Grammatical evolution; Stochastic methods.
Stopping rules for a parallel genetic algorithm
by Ioannis Tsoulos, Alexandros Tzallas, Markos Tsipouras, Vasileios Christou, Dimitrios Tsalikakis
Abstract: A novel method for the implementation of parallel genetic algorithms is introduced to locate the global minimum of a multidimensional function inside a rectangular hyperbox. The algorithm relies on a client - server model and incorporates an enhanced stopping rule. A number of experiments were conducted in order to measure the effects in termination by using the termination rule either on server machine or on clients. The method is tested on a series of well - known test functions as well as neural network training and the results was compared against another parallel genetic algorithm method. The results from the experiments are reported in terms of test error and amount of generations.
Keywords: Genetic algorithm; parallel algorithms; stopping rules; optimization.
Multi Objective Optimization of Thickness and Strain Distribution for Automotive Component in Forming Process
by Ganesh Kakandikar, Vilas Nandedkar
Abstract: Automotive manufacturing industry has emerged as one of the important facet of economy boost in developing countries like India. Globalization, with invited competition, is demanding best quality products from manufacturer. Most of the parts of automotive, which contribute to safety and aesthetics i.e. body parts are manufactured from sheet metal. Metal forming is complex, strain distribution process in formed part from flat blank involving range of processes from simple bending to deep drawing. Ideally the volume of the blank and formed component must remain constant, but decrease/increase in thickness of sheet metal is observed along with strains in major and minor direction. This results in various failures as wrinkling and fracture. The paper presents innovative methodology to distribute uniformly the thickness, preventing thinning/thickening. Multi objective optimization problem has been framed with two contradictory objectives as Thinning and Thickening correlating the process variables. Sealing Cover, automotive component has been selected for study and numerical experimentation, from Vishwadeep Enterprises, Pune. Multi Objective Genetic Algorithm has been applied for process optimization. The results obtained are encouraging and avoids thinning/thickening and results in uniform distribution of thickness in all sections of sealing cover.
Keywords: Optimization; Artificial Intelligence; Genetic Algorithm; Metal Forming.
Application of Computational Intelligence Techniques for Internet of
Things: An Extensive Survey
by Shreyas J, Anand Jumnal, Dilip Kumar S M, Venugopal K R
Abstract: The application of computational intelligence (CI) techniques to Internet of Things (IoT) is gaining popularity due to its capability of providing human-like knowledge, such as cognition, recognition, understanding, learning, and others. This paper attempts to provide an exhaustive survey of the available literature on IoT using CI techniques. In addition, detailed categorization has been provided on the basis of different CI tools and their hybridizations used to tackle different problems of IoT. The potential benefits and utility of CI techniques in IoT are highlighted. The possible mapping of CI techniques to the real-world IoT problems is presented. The advantages and disadvantages of CI algorithms over traditional IoT solutions are discussed. A general evaluation of CI algorithms is presented, which will serve as a guide for using CI algorithms for IoT. Finally, some considerations regarding the recent trends and potential research directions are presented. An extensive bibliography is also included.
Keywords: Artificial Immune System; Computational Intelligence; Fuzzy Logi; Genetic Algorithms; Internet of Things; Neural Networks; Swarm Intelligence.
Special Issue on: GUCON-2018 Intelligent Models for Emerging Technologies
Empirical Estimation of Various Data Stream Mining Methods
by Ritesh Srivastava, Veena Tayal
Abstract: The online learning is done in order to work in dynamic environments in which concept tend to change with time and the accuracy of classifiers decreases. The current and previous research is done in static environments, but there is a need of real time data streaming due to the potentially larger number of applications available in the scientific and business domains. There are several methods used in learning in the presence of dynamic environments like single classifier methods such as batch and incremental learning approaches, classification methods with explicit drift detection method, windowing techniques and ensemble approaches. This paper, investigates these approaches for determining the best suitable method among them. We utilized light emitting diode (LED) data generator for evaluating the performance of the methods.
Keywords: Concept drifts; online learning; data stream mining; machine learning; classification; drift detection methods.
Special Issue on: BDCA'18 Data Science and Applications
Information Technology performance management by Artificial Intelligence in Microfinance Institutions: An overview
by Kaicer Mohammed
Abstract: This paper presents an overview of the use of new information technology to improve the management of microfinance institutions, experiencing a gap due to the growth of the microfinance sector and the diversity of products and services they offer to the target populations. We will show that artificial intelligence could play a role to ensure reliable management information systems in MFIs.
Keywords: Management of Informatics Technology; Artificial intelligence; Microfinance Institution; Central risk.
Intelligent intrusion detection system using multilayer perceptron optimized by genetic algorithm
by Mehdi Moukhafi, Khalid El Yassini, Bri Seddik
Abstract: This paper presents a neural network-based intrusion detection method for the attacks on a computer network. Neural networks are used to predict unusual activities in the system. In particular, feedforward neural networks with the back propagation training algorithm were employed in this study. we propose a method of intrusion detection based on a combination of GA(Genetic algorithm) and MLP (Multilayer Perceptron) Neural Network to develop a model for intrusion detection system. All tests were realized with the kdd99 data set. The performance of the proposed method of intrusion detection was evaluated on all KDD99 data set, 10% of the KDD99 data set were used for training the GA-MLP model. This system achieves a top accuracy of up to 93.05%.
Keywords: Machine Learning Based Intrusion Detection; Parameters optimization; Genetic algorithm; Multilayer Perceptron Neural Network.
QoE in Video Streaming over Ad-hoc Networks: Comparison and Analysis of AODV and OLSR Routing Protocols
by Hind ZIANI, Nourddine ENNEYA
Abstract: Video Streaming services are easily among of the most consumed services on the internet. Indeed, they are single-handedly accountable for up to 85% of overall internet traffic. And yet, despite the multiple modern infrastructure networks and high-end technologies which remain in constant evolution, network masters still assess Quality levels by its dependent and independent factors. Furthermore, new venues of marketing strategies are constantly witnessing the emergence of ever-novel, ever-revolutionary quality horizons which centralize Human perception. And so, in addition to the Quality of Service (QoS) which is built upon network-oriented metrics, we are now faced with stakes bearing on the Quality of Experience (QoE). In MANET, guaranteeing good quality and performance, be it objective or subjective, is a challenge to be reckoned with. In fact, The extant routing protocols are generally network-oriented and are, as such, chiefly dependent upon objective quality parameters, whence they seldom correlate with the QoE standards as averred by the users perception of the received service. This article purports to analyze and experiment on video transmission, through an Ad-hoc network, based on two emblematic routing protocols -AODV and OLSR- in view of identifying the one most relevant to, and optimal for the subjective quality (QoE) we are focused on.
Keywords: Ad-hoc networks; Video Streaming; Routing protocols; Quality of Experience.
Efficient of Bitmap Join Indexes for optimizing Star Join Queries in Relational Data Warehouses
by Mohammed YAHYAOUI, Souad AMJAD, Lamia BENAMEUR, Ismail JELLOULI
Abstract: Data warehouses are dedicated to analysis and decision-making applications. They are often schematized as star relational models or variants for on-line analysis. Typically, the analysis process is conducted via OLAP (On-Line Analytical processing) type queries. These queries are usually complex, characterized by multiple selections operations, joins, grouping, and aggregations on large tables. Which require a lot of calculation time and thus a very high response time. The performance of these queries depends directly on the use of the secondary memory. Indeed, each input-output on disk requiring up to ten milliseconds. In order to reduce and minimize the cost of executing these queries, the data warehouse administrator must make a good physical design during the physical design and tuning phase by optimizing access to the secondary memory. We focus on bitmap join indexes that share the same resource, that is, the selection attributes extracted from the business intelligence queries. To optimize star join queries.
Keywords: Data Warehouse; OLAP; Indexes; Optimization Query; Star join query; Bitmap join indexes.
Wolf : A framework for digital workplace - Architecture and models -
by Khadija ELAMRANI, Noureddine Chenfour, Mohamed LAHMER, Ghita Daoudi
Abstract: The main purpose of the digital workplace (DW) is to ensure to the organizations different contributors or actors a portal of digital services, which are accessible through a virtual desktop covering all its business services. During our studies, we were able to identify five major problems. First of all, we note a great confusion in the related definitions because most of them are restricted to the teaching sector. Secondly, most existing DWs are summarized as a simple gateway to pre-existing digital tools collection that covers the organizations business domains, without any means of communication between them. Another problem is the lack of a reference architecture. Moreover, we could not identify any logical or physical model to represent the different DWs entities. Lastly, there is a total absence of a standard or even an appropriate vocabulary.rnFaced with these shortfalls, we propose in this paper a set of fundamentals that is composed by a definition encapsulating the different domains, as well as a naming system and a vocabulary that identify both the entities that compose the virtual desktop and their connections and flows. Based on these fundamentals, we also propose our framework WOLF (Digital Workplace based on Open and Light architecture Framework) that generate automatically customized digital workplaces, and is distinguished from other existing DWs solutions by its generic and extensible character. The generated DW encapsulates all of the organizations domains, services, flows and a collaboration system between the different actors. Our proposed frameworks architecture allows us to classify and organize the various entities into a tree representation whilst data nodes are modelled using XML files.
Keywords: Digital workplace; Digital workspace; Collaboration; Digital work environment.
Special Issue on: CMDM 2017 Computational Intelligence and Data Mining
MC4.5 decision tree algorithm: An improved use of continuous attributes
by Anis Cherfi, Kaouther Nouira, Ahmed Ferchichi
Abstract: C4.5 is one of the top ten data mining algorithms, it is the most widely used decision trees construction techniques. Although effective, it suffer from the problem of complexity when it deals with continuous attributes. It also leads to a certain level of information loss. Therefore, minimizing such loss, and reducing the time complexity is one of the main goals in this paper. With the intention of alleviating these problems, this paper presents a novel algorithm namely MC4.5, which proposes the statistical mean as an alternative to the C4.5 threshold selection process. To demonstrate the effectiveness of the new algorithm, a complete evaluation was launched to prove that MC4.5 complies with the objectives previously mentioned. From the theoretical perspective, we develop an analysis of the complexity to compare algorithms. Empirically, we conduct an experimental study using 30 data sets to prove that, in most cases, the proposed algorithm leads to smaller decision trees with better accuracy comparing to the C4.5 algorithm.
Keywords: Decision tree; MC4.5; C4.5; Statistical mean; Continuous attributes;rnClassification; Information gain.
Solving flexible job-shop problem with sequence dependent setup time and learning effects using an adaptive genetic algorithm
by Ameni Azzouz, Meriem Ennigrou, Lamjed Ben SAID
Abstract: For the most scheduling problems studied in literature, job processing times are assumed to be known and constant over time. However, this assumption is not appropriate for many realistic situations where the employees and the machines execute the same task in a repetitive manner. They learn how to perform more efficiently. As a result, the processing time of a given job is shorter if it is scheduled later, rather than earlier in the sequence. In this paper, we consider the Flexible Job Shop Problem (FJSP) with two kinds of constraint, namely, the sequence-dependent setup times (SDST) and the learning effects. Makespan is specified as the objective function to be minimized. To solve this problem, an Adaptive Genetic Algorithm (AGA) is proposed. Our algorithm uses an adaptive strategy based on : (1) the current specificity of the search space, (2) the preceding results of already used operators and (3) their associated parameter settings. We adopt this strategy in order to maintain the balance between exploration and exploitation. Experimental studies are presented to assess and validate the benefit of the incorporation of the learning process to the SDST-FJSP over the original problem.
Keywords: scheduling problem; Genetic algorithm; Adaptive strategy; Learning effects.
Contributions to the Automatic Processing of the User-Generated Tunisian Dialect on the Social Web
by Jihene Younes, Hadhemi Achour, Emna Souissi, Ahmed Ferchichi
Abstract: With the growing use of social media in the Arab world, Arabic dialects are rapidly spreading on the web, leading to a growing interest from NLP researchers. These dialects are however, still under-resourced languages and the lack of available dialectal resources is a major obstacle to their study and processing. In this paper, we focus on the automatic processing of the user-generated Tunisian dialect (TD) on the social web and propose an approach that aids to automatically generate TD language resources (LRs), useful for any NLP research work dealing with this dialect. This approach exploits the large amounts of textual productions on the social web in order to extract and generate dialectal content. It is based on two main NLP components, namely the TD Identification and the TD transliteration. A machine learning approach using Conditional Random Fields (CRF), is proposed for implementing these two components and reached an accuracy of 87.45 for the TD identification and 90.49 for the automatic generation of dialectal contents by transliteration.
Keywords: Tunisian Dialect; language resources; corpora; lexica; identification; transliteration; natural language processing; machine learning.
A Co-evolutionary Decomposition-based Algorithm for the Bi-level knapsack optimization problem
by Abir Chaabani, Lamjed Ben Said
Abstract: Bi-level optimization problems (BOPs) are a class of challenging problems with two levels of optimization tasks. These problems allow to model a large number of real-life situations in which a first decision maker, hereafter the leader, optimizes his objective by taking the follower\'s response to his decisions explicitly into account. In this way, evaluating a solution in the upper level requires finding an optimal solution to the lower level problem. This fact makes BOPs difficult to handle and have kept researchers and practitioners busy alike. Recently, a new research field, called EBO (Evolutionary Bi-Level Optimization) has appeared thanks to the promising results obtained by the use of EAs (Evolutionary Algorithms) to solve such kind of problems. In this context,two recently proposed EBO called CODBA and CODBA-II were proposed to solve combinatorial BOPs. The proposed approaches were able to improve the quality of generated bi-level solutions regarding to the recently proposed methods within this research area. In fact, a wide range of applications fit the bi-level programming framework and real-life implementations still scarce. For this reason, we propose in this paper a Co-evolutionary Decomposition-based Bi-level Algorithm for the bi-level knapsack optimization problem. The computational performance of the proposed algorithm turned out to be quite efficient on both computation time and solution quality regarding to other competitive EAs.
Keywords: Bi-level combinatorial optimization; evolutionary methods; bi-level\r\nknapsack problem.
Web service selection based on QoS and user profile
by Ilhem Feddaoui, Faîçal Felhi, Jalel Akaichi
Abstract: The Web Services are from different sources, heterogeneous, and of large volume. The user is in a crucial situation to select the best Web services. The Web service selection process aims to discovery the desired Web services; as it allows to select the best Web services to users' query. In particular, various Web services have the same functionalities, so we need another factor to select the desired Web services, which is the Quality of Service (QoS). The QoS has an important role in the Web service selection process, it aims to classify the Web service that have same functionality. This paper focuses on different concepts of the QoS. We present a new approach that is composed by two services; its role is primarily the best Web service selection in relation with users' query and profile. In our approach, a better knowledge of user behavior is important because users can participate in research design and construction. The experiment shows that our method can accurately recommend the needed Web services in a faster time.
Keywords: Web service; Query; User profile; QoS.
An effective Genetic Algorithm for solving the Capacitated Vehicle Routing Problem with Two-dimensional Loading Constraint
by Ines Sbai, Olfa Limam, Saoussen Krichen
Abstract: In this article, we focus on the symmetric capacitated vehicle routing problem where customer demand is composed of two-dimensional weighted items. The objective consists in designing a set of trips, starting and terminating at a central depot, that minimise the total transportation cost with a homogenous fleet of vehicles based on a depot node. Items in each vehicle trip must satisfy the two-dimensional orthogonal packing constraint. The capacitated vehicle routing problem with two-dimensional loading constraint is an NP-hard problem of high complexity. Given the importance of this problem, many solution approaches have been developed. However, it still a challenging problem. Then, we propose to use a new heuristic based on an adaptive genetic algorithm in order to find better solution. Our algorithm is tested with 150 benchmark instances and compared with state-of-the-art approaches. Results shown that our proposed approach is competitive in terms of the quality of the solutions found.
Keywords: Capacitated Vehicle Routing Problem; Loading; Genetic Algorithm;rn2L-CVRP.
A Multi-Level Study for Trust Management Models Assessment in VANETs
by Ilhem Souissi, Nadia Ben Azzouna, Lamjed Ben Said
Abstract: Nowadays, trust management is one of the key elements to ensure a high security level in ad hoc networks. Trust assessment can be perceived at three levels. First, the data perception trust need to be assessed in order to ensure a high quality of raw sensed data. Second, the trust relationship assessment is required to detect the selfish and malicious entities and to maintain the data integrity. Finally, the data fusion trust is essential to preserve the performance of the fusion process. In this paper, we intend to point out the need to integrate the data perception trust, the communication trust and the data fusion trust in order to preserve the information trustworthiness in VANETs. We further browse the literature to identify recent advancements with regard to each type of trust.
Keywords: Data Perception Trust; Communication Trust; Data Fusion Trust; VANETs.
Special Issue on: Intelligent Systems for Cyber Security Current Trends, Applications and New Challenges
Intrusion Detection using Data Mining
by Shubha Puthran, Ketan Shah
Abstract: Intrusion Detection plays very important role in securing Information Servers. Classification and Clustering Data Mining algorithms are very effective to deal with Intrusion Detection. However, classification (supervised) results with false negative detection and Clustering (unsupervised) results with false positive detection. This paper introduces a unique framework consisting of Pre-processing unit, Intrusion detection using quad split(IDTQS), Intrusion Detection using Correlation based quad split (IDTCA) and Intrusion Detection using Clustering (IDTC). In this proposed framework, IDTQS and IDTCA shows accuracy improvement for University of New South Wales (UNSW) dataset is in the range 4%-34% for DoS, Probe, R2L, U2R and Normal classes. IDTC Clustering algorithm performs with 97% accuracy. Training and testing time is improved by 14% for IDTCA in comparison with IDTQS.
Keywords: Quad split; Decision Tree; Correlated Attributes; UNSW dataset.
An Integrated Approach for Multimodal Biometric Recognition System using Pearson Type-II (Beta) Distribution
by Naga Jagadesh Bommagani, A.V.S.N Murty
Abstract: Biometric recognition plays an important role in personnel identity authentication. Usually biometric recognition protocols which involve single source of information are called unimodal systems. Such systems suffer from the problems like noisy sensor data, performance, collectability and non-universality. To have an accurate recognition it is needed to develop a system with multimodal biometrics. Hence, in this paper a new approach is proposed with the combination of multiple biometric traits such as face, fingerprint and palm vein. Region of Interest (ROI) is used to consider the valuable information from the images. The 2D Discrete Cosine Transform is used for extracting the feature vector from face, fingerprint and palm vein and fusion at feature extraction level. Here the feature vector is modelled with Pearson Type-II distribution and the model parameters are estimated using the EM algorithm. The initialization of model parameters is done through moment method of estimators and K-means algorithm. The performance of the proposed algorithm is carried by experimentation with CASIA biometric database. Through experimentation the proposed model performs more effectively than the algorithm with Gaussian mixture model.
Keywords: Multimodal biometric recognition; Discrete Cosine Transform; EM algorithm; Pearson mixture model.
IbPaKdE: Identity-Based Pairing free Authenticated Key and Data Exchange Protocol for Wireless Sensor Networks
by Lakshmana Rao Kalabarige
Abstract: The security vulnerabilities in key distribution approaches of WSN
reveals important credentials. The secure distribution of keys without having
permanent storage of important credentials in the permanent memory part
(ROM) of a sensor node is a challenging task. Further, the design of a key
distribution approach with less computational complexity, energy efficient, low
communication overhead, and low memory overhead are some challenging
tasks for a resource constrained sensor nodes. This piece of work addresses
all these challenges by combining Identity-Based Cryptography(IBC) with
Symmetric Key Cryptography(SKC). The proposed Identity-Based Pairing free
Authenticated Key and Data Exchange Protocol(IbPaKdE) avails advantages
of both IBC and SKC to address the above challenges. This approach does
not require prior communication for the establishment of secret keys and it
supports pairing free key distribution. The proposed IbPaKdE uses IBC strategy
for secure exchange of keys and SKC to provide security to the data to be
transmitted. The on demand establishment of keys eliminates the permanent
storage of important credentials in the sensor nodes. Simulation results of the
proposed approach is compared with hashed identity based secure key and data
exchange(HISKDE). The results show that the IbPaKdE incur better results than
HISKDE in terms of energy efficiency, reduces memory, communication and
computational, overheads of a sensor node.
DCT statistics and pixel correlation based blind image steganalysis for identification of covert communication
by Madhavi B. Desai, S.V. Patel, Vipul H. Mistry
Abstract: In the last decade, the interconnection of systems through networks, access to information, different computer technologies and the combination of all these aspects have increased the use of image steganography techniques for illegal acts. Furtherance of image steganography techniques exploited to send secret information on social network builds the requisite of blind image steganalysis. Blind image steganalysis is one in which no prior information is available about the data hiding method used to embed the message. Existing image steganalysis methods are either domain specific or the one which requires a very high dimensional feature set. Considering the types of image steganography methods, embedding rates, image types and feature dimensionality there is an utmost need of a low dimensional blind image steganalysis method. This paper proposes a blind steganalysis method with a 32-D feature set comprising of DCT Statistics and Pixel Correlation (DSPC) algorithm with the aim to the reduced time complexity of feature extraction as well as the complexity of classifier. The experimental results evidence that the proposed feature set gives better results against state-of-art high dimensional image steganalysis methods. The performance of the proposed algorithm is evaluated using experiments with varying embedding message size, message types and image formats using Ensemble classifier. The algorithm is implemented in Matlab and all the experiments are performed on standard image datasets i.e. BSDS300, CorelDraw.
Keywords: Blind Image Steganalysis; Binary Similarity Measures; DCT Transform; Ensemble Classifier; Feature Extraction; Statistical Features; and SVM.
Adaptive QoS Constraint-based Service Differentiated Routing In Wireless Sensor Network
by Yogita Patil
Abstract: Achieving the best quality of service (QoS) as per user requirement is one of the important challenges. The time-critical applications in Wireless Sensor Networks (WSN) demand energy efficient transmission of data with limited resource availability. To resolve these issues AQSDR has been proposed. The proposed protocol support packet differentiation and selection of sensor node based on energy, delay, and congestion for path establishment to transfer normal data packets providing energy conservation. The multipath is chosen for transmission of emergency packets satisfying delay requirements of non-delay tolerant applications. AQSDR support adaptive path selection, according to application requirement. The proposed technique in this work outperforms when compared with the existing protocol in terms of minimized energy consumption, delay, control overhead, packet drop ratio, and high throughput.
Keywords: Clustering; Congestion Index; Delay; Energy Efficiency; Multipath; Service differentiation; WSN.