Forthcoming articles


International Journal of Computational Intelligence Studies


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International Journal of Computational Intelligence Studies (11 papers in press)


Regular Issues


  • Adaptive Artificial Chemistry for Nash Equilibria Approximation   Order a copy of this article
    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   Order a copy of this article
    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   Order a copy of this article
    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   Order a copy of this article
    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   Order a copy of this article
    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.

  • 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

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   Order a copy of this article
    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   Order a copy of this article
    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   Order a copy of this article
    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   Order a copy of this article
    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.

Special Issue on: ISCSA2017 Computational Intelligence and Applications

  • Air pollution prediction through Internet of Things technology and Big Data Analytics   Order a copy of this article
    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.