International Journal of Computational Intelligence Studies (21 papers in press)
Adaptive Artificial Chemistry for Nash Equilibria Approximation
by Rodica Ioana Lung
Abstract: A simple Artificial Chemistry model designed for computing Nash Equilibria of continuous games, called Adaptive Artificial Chemistry for Nash Equilibria is presented. This method mimicks elementary chemical reactions between molecules representing strategy profiles of a non-cooperative game while using a generative relation for Nash Equilibria in order to direct the search towards the equilibrium. Experimental results - indicating the potential of the proposed method - are performed on Cournot oligopolies for up to 1000 players.
Keywords: Cournot oligopoly; Nash equilibrium; Artificial Chemistry.
A Constraint-Based Job Recommender System Integrating FoDRA
by Nikolaos Almalis
Abstract: We present a framework for a Constraint-Based Recommender System that matches available job positions with job seekers. Our framework utilizes the Four Dimensions Recommendation Algorithm (FoDRA) in which a job attribute (e.g. age of candidate) can be modeled in four classes: exact value (E), a range with lower limit (L), a range with upper limit (U) and a range with both lower and upper limit (LU). FoDRA allows us to better formulate the job seeking and recruiting domain in a computational form. We describe both the system architecture in a high-level and the algorithm formulation of the job seeking and recruiting domain required by FoDRA. Our framework is validated through comparative experiments with real data obtained from the website of Kaggle.
Keywords: Constraint-based; Recommender System; Job Recommender; Job seeking and recruiting; Matching people and jobs; Recommendation algorithm.
Automatic classification of Punjabi poetries using poetic features
by Jasleen Kaur, Jatinderkumar Saini
Abstract: Automatic classification of poetic content is very challenging from the computational linguistic point of view. For library suggestion framework, poetries can be grouped on different measurements, for example, artist, day and age, assumptions, and topic. In this work, content-based Punjabi poetry classifier was built utilizing Weka toolset. Four unique classes were manually populated with 2034 poetries. NAFE, LIPA, RORE, PHSP classes comprises of 505, 399, 529 and 601 number of poems, individually. These poems were passed to different pre-processing substages, for example, tokenization, noise removal, stop word removal, special symbol removal. An aggregate of 31938 tokens was separated, after passing through preprocessing layer, and weighted using term frequency (TF) and term frequency-inverse document frequency (TF-IDF) weighting plan. Depending upon poetic elements of poetry, 2 different poetic features (Orthographic and Phonemic) were experimented to build a classifier using machine learning algorithms. Naive Bayes, Support Vector Machine, Hyper pipes, and K-nearest neighbor algorithms experimented with two poetic features. The results revealed that addition of poetic features does not boost the performance of Punjabi poetry classification task. Using poetic features, the best performing algorithm is SVM and highest accuracy (71.98%) is achieved considering orthographic features.
Keywords: classification; poetry; Punjabi; orthographic; phonemic;.
Computer Aided Breast Cancer Diagnosis: An SVM-Based Mammogram Classification Approach
by Dionyssios Sotiropoulos
Abstract: Computer Aided Diagnosis (CADx) may be considered as one of the major achievements in contemporary medical imagernanalysis and machine learning research. Specifically, radiology diagnostic imaging coupled with highly sophisticated classification algorithms have been proved extremely useful to clinical doctors in assisting the process of differential diagnosis for a wide range of diseases such as cancer. Breast cancer, in particular, constitutes the most common type of cancer amongst the female population whose survival rates are strongly dependent on its early detection. In this context, computer-mediated mammogram categorization may alleviate the need for employing more invasive surgical methods in order to efficiently categorize a breast tumor immediately after its detection. This paper addresses the problem of mammogram classification (benign vs malignant) through the utilization of the state-of-the-art machine learning paradigm of Support Vector Machines (SVMs). We are particularly interested in evaluating the discrimination efficiency of a set of feature extraction algorithms that have been proposed in the relevant literature for describing the textual characteristics present in mammogram masses. Our research focuses on comparing the classification accuracy associated with two well-established feature generation methodologies, namely, Spatial Gray Level Dependence Method (SGLDM) and Run Difference Method (RDM). Our experimentation was conducted on a publicly available mammogram database by parameterizing the underlying kernel function of SVMs on different subsets of features. Our results indicate a moderately high classification accuracy for the linear SVM classifier when trained on the compete set of features.
Keywords: Computer Aided Diagnosis;Mammogram Classification;Feature Extraction;Support Vector Machines.
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.
Big data: A distributed storage and processing for online learning systems
by Karim DAHDOUH, Ahmed DAKKAK, Lahcen OUGHDIR
Abstract: The new information and communication technologies have changed the way of teaching and learning. In particular, the big data technology that has recently been developed to overcome the limitations of traditional systems of storage, processing, and analysis. In fact, big data has been used in several fields including health care, public services, and online services such as social media and online learning. It offers a rich set of new technologies in terms of data integration, distributed storage, parallel processing, and data visualization. Furthermore, big data provides many techniques to bring solutions to various educational problems such as the courses recommendation engine, the prediction of learner behaviour, the exponential growth of the learners and pedagogical resources, etc. Today, thanks to the big data ecosystem, it is possible to greatly improve the effectiveness and performance of the online learning services. This article presents the big data paradigm, its components, technologies, and characteristics. It proposes an approach for incorporating big data, online learning systems, and cloud computing in order to enhance the efficiency of the distance learning environment. Also, it provides a methodology to store and process the data produced by online learning platforms using advanced big data technologies and tools. Moreover, It explores the advantages and benefits that big data offer to students, teachers and online learning professionals.
Keywords: computing environments for human learning; big data; cloud computing; e-learning; Online learning; Learner; Learning Management Systems (LMS); NoSQL database; Hadoop; MapReduce; Spark; Cassandra; Hive; Apache Flume; Apache Sqoop
Robust Estimation of IIR System's Parameter using Modified Particle Swarm Optimization Algorithm
by Meera Dash, Trilochan Panigrahi, Renu Sharma
Abstract: This paper introduces a novel method of robust parameter estimation of infinite impulse response (IIR) system. When training signal contains strong outliers, the conventional squared error based cost function fails to provide desired performance. Thus a computationally efficient robust cost functions are used here. It is a fact that the IIR system falls in local minima. Thus the gradient based algorithm which is good for finite impulse response (FIR) system, can not be used for IIR. Therefore the parameters of the IIR system is estimated using modified particle swarm optimization algorithm. The most used and analyzed robust cost functions such as Hubers and saturation nonlinearity function are used in the optimization algorithm. The simulation results show that the proposed robust algorithms are providing better performance than the Wilcoxon norm based robust algorithm and conventional error squared based PSO algorithms.
Keywords: IIR system; impulsive noise; robust estimation; Wilcoxon norm; Hubers cost function; adaptive particle swarm optimization;saturation nonlinearity.
Special Issue on: Advances and Challenges in Nature Inspired Optimisations
Nonlinear time series forecasting using a novel self-adaptive TLBO-MFLANN model
by Sibarama Panigrahi, H. S. Behera
Abstract: Time series forecasting (TSF) is a key field of research in several areas of study including engineering, finance, economics and management science. Conventionally, TSF has been predominantly performed using various linear statistical models. However, to cope up with nonlinear patterns exhibiting in most of the time series produced from real world phenomenon, recently, various artificial neural network (ANN) models have been used. Contrasting to traditional neural networks, higher order neural network (HONN) especially the functional link ANN (FLANN) has the capability to expand the input space with fewer trainable weights which makes it efficient to solve various complex problems. Motivated by this, in this paper, we have proposed a multiplicative FLANN (MFLANN) model for time series forecasting. The multiplicative unit in MFLANN model assists to capture the nonlinear patterns well. In addition, an improved version of teaching learning based optimization (TLBO), called self-adaptive TLBO (SATLBO) has been proposed to train the MFLANN model. The proposed SATLBO uses the gradient descent learning algorithm in the teacher phase while uses the past experience to adapt the learners parameters. This unique integration of SATLBO with gradient descent learning algorithm is used to determine the near optimal weight set of MFLANN. The proposed method is implemented in MATLAB environment and the obtained results are compared with other methods (DE based MFLANN, TLBO based MFLANN, CRO based MFLANN, Jaya based MFLANN and ETLBO-JPSNN) considering 11 benchmark univariate time series datasets. Extensive statistical analysis on obtained results indicated that the proposed SATLBO-MFLANN method is better and statistically significant in comparison to its counterparts.
Keywords: FLANN; Multiplicative FLANN; TLBO; Self-adaptive TLBO; Hybrid model; Time series forecasting
Impact of C-Factor on PSO for Solar PV based BLDC Motor Drive Control
by manoj kumar merugumalla, prema kumar navuri
Abstract: The constriction factor (C-factor) based particle swarm optimization algorithm is proposed for the solar photovoltaic array powered brushless direct current (BLDC) motor drive control. The rotor position sensors are completely eliminated. Instead, it is determined by measuring the changes in back-emf. The CPSO algorithm is used for the tuning of proportional-integral-derivative (PID) controller parameters for the speed control of the drive. The BLDC motor drive is modelled in MATLAB/SIMULINK and trapezoidal back emf waveforms are modelled as a function of rotor position using matlab code. This paper deals with rise time, settling time, peak overshoot and steady-state error under varying conditions and examines the effectiveness of the BLDC motor drive with the proposed algorithm. The comparison of simulation results of proposed algorithm with other PSO algorithms demonstrates the prominence of the constriction factor for the drive controller.
Keywords: Brushless direct current motor ;Solar photo voltaic array;
Particle swarm optimization ; PID controller ; Constriction factor ; Inertia weight
Design of Fractional Order PID Controller for Heat Flow System using Hybrid Particle Swarm Optimization and Gravitational Search Algorithm
by Rosy Pradhan, Santosh Kumar Majhi, Bibhuti Bhusan Pati
Abstract: This paper uses the hybrid Particle Swarm Optimization (PSO) and Gravitational Search Algorithm (GSA) for the design of fractional order proportional-integrator-derivative (FOPID) controller for a heat flow system. The social behavior of PSO is combined with the motion technique of GSA. The objective of the algorithm is to obtain the optimal controller parameters for the heat flow system. To obtain the optimal computation, different performance indices such as IAE (Integral Absolute Error), ISE (Integral Squared Error), ITAE (Integral Time Absolute Error), ITSE (Integral Time Squared Error) are considered for the optimization. The performance of the hybrid PSO-GSA is compared with the IMC-PID, Fractional Oder Filter-PID, PSO-FOPID. The proposed method performs comparatively better than the other published methods. Simulink/Matlab environment is used for simulation purpose.
Keywords: FOPID; performance indices; PSO; PSO-GSA
Suitability and Importance of Deep Learning Feature Space in the Domain of Text Categorization
by Rajendra Kumar Roul
Abstract: One of the important features of Multilayer ELM (ML-ELM) is its capability of non-linearly mapping the features to an extended dimensional space and thereby builds the input features linearly separable. This paper studies the significance of deep learning feature space using ML-ELM for classification of text data which are the follow-up of my earlier work. The previous approach discusses a new feature selection technique named Combined Cohesion Separation and Silhouette Coefficient (CCSS) to generate a good feature vector and then used it for classification of text data using ML-ELM, which is a deep learning classifier. This approach has been extended here that has two main aspects. The first aspect is to compare the performance of CCSS approach with the traditional feature selection techniques and the second aspect is to test the performance of different conventional classification techniques on the higher dimensional feature space of ML-ELM. Results of the experiment on different benchmark datasets justify that the proposed CCSS technique is comparable with the existing feature selection techniques and the ML-ELM feature space is more promising compared to the traditional TF-IDF vector space for classification of text data.
Keywords: Classification, Cohesion, Deep learning, Extreme learning machine, Multilayer ELM, Separation, Silhouette coefficient
Special Issue on: ISCSA2017 Computational Intelligence and Applications
An Independent-domain Natural Language Interface for Multimodel Databases
by Bais Hanane
Abstract: Databases are gaining prime importance in the world of modern computing. Retrieving information stored in databases required the knowledge of the database Query languages such as Structured Query Language (SQL). However, learning this language can be difficult for non-expert users.Hence, the using of natural language is a very easy and convenient method that can provide powerful improvements to the use of data stored in databases. In this paper, we present the architecture of an intelligent natural language interface for a multimodel database. This interface functions independently of database domain, language and model. The using of machine learning approach helps our system to improve automatically its knowledge base through experience.
Keywords: Databases, Natural Language Processing (NLP), Intermediate XML Logical Query (IXLQ), Extended Context Free Grammar (ECFG), intelligent interface.
Special Issue on: IWCIA2017 Innovative Computational Intelligence for Knowledge Representation and Learning
Fast Training of Adaptive Structural Learning Method of Deep Learning for Multi Modal Data
by Shin Kamada
Abstract: Recently, deep learning has been applied in the techniques of artificial intelligence. Especially, their new architectures performed good results in the field of image recognition. However, the method is required to train not only image data, but also numerical data, text data, and other binary data. Multi modal data consists of two or more kinds of data such as a pair of image and text of giving an explanation of the image. The arrangement of multi modal data in the traditional method is formed in the squared array with no specification. In this paper, the method can modify the squared array of the multi modal data, according to the similarity of input-output pattern of adaptive structural learning method of Deep Belief Network. Some experimental results show that the computational time of deep learning decreases.
Keywords: Multi Modal Data; Automatically Data Arrangement Method; Deep Learning; Adaptive Learning Method; Restricted Boltzmann Machine; Deep Belief Network; Shorting Learning Time.
Characteristics of Contrastive Hebbian Learning with Pseudorehearsal for Multilayer Neural Networks on Reduction of Catastrophic Forgetting
by Motonobu Hattori, Shunta Nakano
Abstract: Neural networks encounter serious catastrophic forgetting or catastrophic interference when information is learned sequentially. One of the methods which can reduce catastrophic forgetting is pseudorehearsal, in which pseudopatterns are learned with training patterns. This method has shown superior performance for multilayer neural networks trained by the backpropagation algorithm. However, the backpropagation algorithm is biologically implausible because it requires passage of error signals backward from output neurons to input ones. That is, the learning cannot by executed locally. On the other hand, Contrastive Hebbian Learning (CHL) is a learning method using Hebbian rule for synaptic weight changes. Since Hebbian learning can be performed locally between two neurons and doesnt need to take into account error information computed at output neurons, it is much more biologically plausible than the backpropagation algorithm. In this paper, we examine characteristics of multilayer neural networks trained by CHL with pseudorehearsal when information is applied sequentially, and how catastrophic forgetting can be reduced.
Keywords: Contrastive Hebbian Learning; Pseudorehearsal; Multilayer Neural Networks; Catastrophic Forgetting; Pseudopatterns; Hebbian Rule; Additional Learning.
Search Performance Analysis of Qubit Convergence Measure for Quantum-Inspired Evolutionary Algorithm Introducing on Maximum Cut Problem
by Yoshifumi Moriyama, Ichiro Iimura, Shigeru Nakayama
Abstract: The quantum-inspired evolutionary algorithm (QEA) and QEA with a pair-swap strategy (QEAPS), where each gene is represented by a quantum bit (qubit), and the qubit is updated by a unitary transformation in both algorithms. QEA and QEAPS can automatically shift the evolution from a global search to a local search and have shown superior search performance to the classical genetic algorithm. However, the population get into a locally optimal solution and the solution search stagnates when the probability amplitudes of qubit excessively converge to '0> or '1>. In this study, we have proposed a measure that can confirm convergence state of qubits. From the results of the computational experiment in the maximum cut problem, we have clarified that the proposed measure can estimate the state of the qubit, and the quality of the obtained solution is improved by applying the method for maintenance of diversity.
Keywords: quantum-inspired evolutionary algorithm; QEA; QEA with pair-swap strategy; QEAPS; qubit convergence measure; Noah's ark strategy; population-based incremental learning; PBIL; univariate marginal distribution algorithm; UMDA; estimation of distribution algorithms; EDAs; maximum cut problem.
A Generative Model Approach for Visualising Convolutional Neural Networks
by Masayuki Kobayashi, Masanori Suganuma, Tomoharu Nagao
Abstract: Convolutional neural networks (CNN) have continued to achieve outstanding performance in a variety of computer vision tasks. CNNs have advanced significantly deeper and deeper, continuing to show substantial improvements for various tasks. Despite their successes, their models are often considered as black-box predictors, and their uninterpretable natures are major problems. In this paper, we introduce a new visualisation framework based on generative adversarial networks (GAN) to provide insight into how CNNs work. Following the standard GAN training, we train the generator and the discriminator to produce natural images that activate a particular unit in the pre-trained CNN. We apply our method to the AlexNet and CaffeNet and visualise the neuron activations. Our method is very simple, yet produces comparatively recognisable visualisations. We also attempt to use our visualisation as indications of models trust and verify the potential of our visualisations.
Keywords: Convolutional Neural Network; Generative Adversarial Networks; Visualisation; Activation Maximisation; Interpretability.
Mining Non-Redundant Recurrent Rules from a Sequence Database
by SeungYong Yoon, Hirohsia Seki
Abstract: Many methods have been studied for mining sequential patterns from arnsequence database. In particular, Lo et al. have proposed the notionrnof "recurrent rules" and an algorithm called NR^3 for mining them.rnRecurrent rules can express temporal constraints such as ``Whenever arnseries of precedent events occurs, eventually a series of consequentrnevents occurs,'' and they are useful in various domains, includingrnsoftware specification and verification. Although the algorithm NR^3rnand its successor BOB for mining non-redundant recurrent rules havernbeen given by Lo et al., mining recurrent rules still requiresrnconsiderable computational costs. In this paper, we propose a newrnalgorithm, called LF-NR^3, to make NR^3 more efficient, which is basedrnon a familiar program transformation "loop fusion". We apply the looprnfusion technique to NR^3, thereby simplifying the operations of thernoriginal mining algorithm. We also make use of a hash-based datarnstructure to reduce loads of the manipulation of sequences repeatedlyrnrequired in mining recurrent rules. We show the effectiveness of ourrnproposed method based on experimental results of some datasets used inrnthe literature.
Keywords: sequential pattern mining; recurrent rule; sequencerndatabase; program transformation; vertical data format.
Time Series Classification using MACD-Histogram-based Recurrence Plot
by Keiichi Tamura, Takumi Ichimura
Abstract: Time series classification is one of the most active research topics in time series data mining, because they cover a broad range of applications in many different domains. Representation for time series is a technique that converts time series to feature vectors representing the characteristics of time series. The performance of classifying time series depends on this representation. Chaotic time series analyses have been well-studied. Moreover, recurrence plotting underlying chaos theory is one of the most robust time series representation for time series. In this study, we propose new time series representation utilizing the recurrence plot technique. Moving average convergence divergence (MACD) histogram is the acceleration of time that can represent local-variation in time series. Therefore, a recurrence plot that is made from MACD histogram, which is called a MACD-Histogram-based recurrence plot (MHRP), can handle time series very well. Recurrence plots are referred to as gray-scale images and we utilize stacked auto-encoders as a classifier for MHRPs. To evaluate the performance of the proposed classifier, experiments using the UCR time series classification archive was conducted. The experimental results showed that the proposed classifier outperforms other methods.
Keywords: Time series classification; Time series mining; Recurrence plot; Chaotic time series analysis; MACD histogram.
Acquisition of Characteristic Sets of Block Preserving Outerplanar Graph Patterns by a Two-Stage Evolutionary Learning Method for Graph Pattern Sets
by Fumiya Tokuhara, Tetsuhiro Miyahara, Tetsuji Kuboyama, Yusuke Suzuki, Tomoyuki Uchida
Abstract: Knowledge acquisition from graph structured data is an important task in machine learning and data mining. Block preserving outerplanar graph patterns are graph structured patterns having structured variables and are suited to represent characteristic graph structures of graph data modeled as outerplanar graphs. We propose a learning method for acquiring characteristic sets of block preserving outerplanar graph patterns by a two-stage evolutionary learning method for graph pattern sets as individuals, from positive and negative outerplanar graph data, in order to represent characteristic graph structures more concretely.
Keywords: evolutionary method; genetic programming; outerplanar graphs; sets of graph structured patternsrn.
Special Issue on: ISCSA2017 Computational Intelligence and Applications
Air pollution prediction through Internet of Things technology and Big Data Analytics
by Safae Sossi Alaoui, Brahim Aksasse, Yousef Farhaoui
Abstract: Air pollution is one of the biggest and serious challenges facing our planet nowadays. In fact, the need to develop models to predict this issue is considered so crucial. Indeed, our work aimed at building an accurate model to predict air quality of US country by using a dataset collected from connected devices of Internet of Things (IoT), namely from wireless sensor networks (WSN). Therefore, the huge amount of data captured by these sensors (approximately 1.4 million observations) brings about a highly complex data that necessitates new form of advanced analytic; its about Big Data Analytics. In this paper, we examine the possibility to make a fusion between the two new concepts Big Data and Internet of Things; in the context of predicting Air pollution that occurs when harmful substances; like NO2, SO2, CO and O3, are introduced into Earth's atmosphere.
Keywords: Internet of Things (IoT); Wireless sensor networks (WSN); Air pollution; Air Quality Index (AQI); Big Data Analytics; Apache Spark.