International Journal of Computational Intelligence Studies (9 papers in press)
Combining Multi-objective evolutionary algorithm with averaged one-dependence estimators for Big Data Analytics
by Mrutyunjaya Panda
Abstract: Even though many researchers tried to explore the various possibilities on multi-objective feature selection, still, it is yet to be explored with best of its capabilities in data mining applications rather than going for developing new ones. In this paper, multi-objective evolutionary algorithm ENORA is used to select the features in a multi-class classification problem. The fusion of AnDE (averaged n-dependence estimators) with n=1, a variant of naive Bayes with efficient feature selection by ENORA is performed in order to obtain a fast hybrid classifier which can effectively learn from big data. This method aims at solving the problem of finding optimal feature subset from full data which at present still remains to be a difficult problem. The efficacy of the obtained classifier is extensively evaluated with a range of most popular 21 real world dataset, ranging from small to big ones. The results obtained are encouraging in terms of time; Root means square error; zero-one loss and classification accuracy.
Keywords: MOEA; ENORA; AODE; Classification; 0/1 loss; RMSE; t-test.
Design of a parity preserving reversible full adder/subtractor circuit
by Mohammad Reza Reshadinezhad, Shiva Rahbar Arabani, Majid Haghparast
Abstract: The reversible logical circuits, due to their economized power consumption in comparison with their counterparts with binary circuits have become a major issue of study. A reversible circuit with equal parity of inputs and outputs is considered as a parity preserving circuit. In such circuits any fault effecting only one logical signal, is detectable at the main outputs. A new 5
Keywords: reversible logic; half adder/subtractor; full adder/subtractor; parity preserving; quantum cost; garbage outputs; constant inputs; circuit dimensions.
A Novel Fuzzy Clustering based System for Medical Image Segmentation
by B.S. Harish, S.V. ARUNA KUMAR, P. Shivakumara
Abstract: Segmenting region of interest in medical images is challenging because\r\nmedical image suffers from noise, degradation due environment influence and low resolution due to devices etc. In this paper, we have developed a novel idea based on Weighted Spatial Kernel FCM clustering for segmenting region of interest in the medical images. Unlike, traditional methods ignore spatial information, we propose a new robust system that explores spatial information to remove uncertainty in identifying accurate region in the medical images. Furthermore, the proposed method estimates the weights for spatial information to derive precise membership function to segment the region based on Gaussian kernel as distance metric.We conducted experiments on standard datasets, namely MRI Brian image dataset and evaluated performance of the proposed method using recall, precision and f-measure. Experimental results reveals that, the proposed method performs better compared to existing methods.
Keywords: Clustering; Fuzzy C-Means; Medical Images; Segmentation.
Assessment of Cloud Computing Adoption Models in E-government Environment.
by Khaled Ali, Sherif Mazen, Ehab Hassanein
Abstract: Nowadays many developing countries are experiencing revolution in e-government to deliver fluent and simple services for their citizens. However, governmental sectors face many challenges in using its e-governments services and its infrastructure, improving current services or developing new services; as data and applications increasingly inflating, IT budget costs, software licensing and support and difficulties in migration, integration and management for software and hardware. These challenges may lead to failure of e-governments projects. Therefore, there is a need for a solution to overcome these challenges. Cloud Computing plays a vital role to solve these problems. So, some of governments are now tending to adopt cloud computing in their agencies for utilizing their benefits and overcome on e-government challenges.
This paper demonstrates cloud computing, its characteristics, basic delivery models and deployment models. It analyzes and reviews the literature proposed models of cloud computing adoption in e-government environment. Finally, it compares between these models and classifies it to different classifications from functionally perspective.
Keywords: Cloud computing models; E-government; Influential factors; Cloud readiness; Services readiness.
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
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; it’s 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.