International Journal on Soft Computing ( IJSC )

International Journal on Soft Computing ( IJSC )

ISSN: 2229 - 6735 [Online] ; 2229 - 7103 [Print]

http://airccse.org/journal/ijsc/ijsc.html

Scope & Topics

Soft computing is likely to play an important role in science and engineering in the future. The successful applications of soft computing and the rapid growth suggest that the impact of soft computing will be felt increasingly in coming years. Soft Computing encourages the integration of soft computing techniques and tools into both everyday and advanced applications. This journal serves as a platform that fosters new applications for all scientists and engineers engaged in research and development in this fast growing field

Topics considered include but are not limited to:

●     Fuzzy Systems ●     Neural Networks ●     Machine learning ●     Probabilistic Reasoning ●     Evolutionary Computing ●     Pattern recognition ●     Hybrid intelligent systems, ●     Software agents ●     Morphic Computing ●     Image processing, ●     E-commerce, e-medicine

●     Rough Sets ●     Symbolic machine learning, ●     Wavelet ●     Signal or Image Processing ●     Vision Recognition ●     Biomedical Engineering ●     Telecommunications ●     Reactive Distributed AI ●     Nano & Micro-systems ●     Data Visualization

Paper Submission

          Authors are invited to submit papers for this journal through E-mail: ijsc@aircconline.com or through Submission System. Submissions must be original and should not have been published previously or be under consideration for publication while being evaluated for this Journal.

Important Dates

●       Submission Deadline : September 07, 2019

●       Notification                  : October 07, 2019

●       Final Manuscript Due   : October 15, 2019

●       Publication Date         : Determined by the Editor-in-Chief

Contact Us

Here's where you can reach us : ijscjournal@yahoo.com or ijsc@aircconline.com

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For other details please visit : http://airccse.org/journal/ijsc/ijsc.html

DEVELOPMENT AND PERFORMANCE EVALUATION OF A LAN-BASED EDGE-DETECTION TOOL

DEVELOPMENT AND PERFORMANCE EVALUATION OF A LAN-BASED EDGE-DETECTION TOOL

Ghassan F. Issa1, Hussein Al-Bahadili1, and Shakir M. Hussain1

1Petra University, Faculty of Information Technology P.O. Box 961343, Amman 11196, Jordan

ABSTRACT

This paper presents a description and performance evaluation of an efficient and reliable edge-detection tool that utilize the growing computational power of local area networks (LANs). It is therefore referred to as LAN-based edge detection (LANED) tool. The processor-farm methodology is used in porting the sequential edge-detection calculations to run efficiently on the LAN. In this methodology, each computer on the LAN executes the same program independently from other computers, each operating on different part of the total data. It requires no data communication other than that involves in forwarding input data/results between the LAN computers. LANED uses the Java parallel virtual machine (JPVM) data communication library to exchange data between computers. For equivalent calculations, the computation times on a single computer and a LAN of various number of computers, are estimated, and the resulting speedup and parallelization efficiency, are computed. The estimated results demonstrated that parallelization efficiencies achieved vary between 87% to 60% when the number of computers on the LAN varies between 2 to 5 computers connected through 10/100 Mbps Ethernet switch.

KEYWORDS

Parallel processing, processor farm methodology, image processing, edge detection, noise removal, LAN 

ORIGINAL SOURCE URL : http://airccse.org/journal/ijsc/papers/2112ijsc10.pdf

http://airccse.org/journal/ijsc/current2012.html

SINGLE REDUCT GENERATION BASED ON RELATIVE INDISCERNIBILITY OF ROUGH SET THEORY

SINGLE REDUCT GENERATION BASED ON RELATIVE INDISCERNIBILITY OF ROUGH SET THEORY

Shampa Sengupta and Asit Kr. Das

M.C.K.V.Institute Of Engineering - 243, G.T.Road (North), Liluah, Howrah 711204,West Bengal

Bengal Engineering And Science University, Shibpur, Howrah 711103, West Bengal

Abstract.

In real world everything is an object which represents particular classes. Every object can be fully described by its attributes. Any real world dataset contains large number of attributes and objects. Classifiers give poor performance when these huge datasets are given as input to it for proper classification. So from these huge dataset most useful attributes need to be extracted that contribute the maximum to the decision. In the paper, attribute set is reduced by generating reducts using the

indiscernibility relation of Rough Set Theory (RST). The method measures similarity among the attributes using relative indiscernibility relation and computes attribute similarity set. Then the set is minimized and an attribute similarity table is constructed from which attribute similar to maximum number of attributes is selected so that the resultant minimum set of selected attributes (called reduct) cover all attributes of the attribute similarity table. The method has been applied on glass dataset collected from the UCI repository and the classification accuracy is calculated by various classifiers. The result shows the efficiency of the

proposed method.

Keywords:

Rough Set Theory, Attribute Similarity, Relative Indiscernibility Relation, Reduct. 

ORIGINAL SOURCE URL : http://airccse.org/journal/ijsc/papers/2112ijsc09.pdf

http://airccse.org/journal/ijsc/current2012.html

AN APPROACH FOR IRIS PLANT CLASSIFICATION USING NEURAL NETWORK

AN APPROACH FOR IRIS PLANT CLASSIFICATION USING NEURAL NETWORK

Madhusmita Swain1, Sanjit Kumar Dash2, Sweta Dash3 and Ayeskanta Mohapatra4

1, 2 Department of Information Technology, College of Engineering and Technology,Bhubaneswar, Odisha, India

3Department of Computer Science and Engineering, Synergy Institute of Engineering and Technology, Dhenkanal, Odisha, India

4Department of Computer Science and Engineering, Hi-tech Institute of Technology,Bhubaneswar, Odisha, India

ABSTRACT

Classification is a machine learning technique used to predict group membership for data instances. To simplify the problem of classification neural networks are being introduced. This paper focuses on IRIS plant classification using Neural Network. The problem concerns the identification of IRIS plant species on the basis of plant attribute measurements. Classification of IRIS data set would be discovering patterns from examining petal and sepal size of the IRIS plant and how the prediction was made from analyzing the pattern to form the class of IRIS plant. By using this pattern and classification, in future upcoming years the unknown data can be predicted more precisely. Artificial neural networks have been successfully applied to problems in pattern classification, function approximations, optimization, and associative memories. In this work, Multilayer feed- forward networks are trained using back propagation learning algorithm.

KEYWORDS

IRIS dataset, artificial neural networks, Back-propagation algorithm

ORIGINAL SOURCE URL : http://airccse.org/journal/ijsc/papers/2112ijsc07.pdf

http://airccse.org/journal/ijsc/current2012.html