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Shohag Barman
University of Chittagong

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Fire Detection in Still Image Using Color Model Hira Lal Gope; Machbah Uddin; Shohag Barman; Dilshad Islam; Mohammad Khairul Islam
Indonesian Journal of Electrical Engineering and Computer Science Vol 3, No 3: September 2016
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v3.i3.pp618-625

Abstract

Fire incidence is one of the major disasters of human society. This paper proposes a still image-based fire detection system. It has many advantages like lower cost, faster response, and large coverage. The existing methods are not able to detect fire region adequately. The proposed method overcome and addresses the issue. A binary contour image of flame that is capable of classifying fire or no fire in image for fire detection is proposed in this study. The color of fire area can range from red yellow to almost white. So, here it is challenges the detected area is actually fire or no fire. Our propose method consists of five parts. Firstly, the digital image is taken from dataset and the digital image is sampled and mapped as a grid of dots or picture elements. We convert image to separate RGB Color range Matrix. We define some rules to select yellow color range of the image later on converted the image to binary range. Finally, binary contour image of flame information that detect the fire. We have analyzed different types of fire images in different varieties and found accuracy 85-90%.
Clustering Techniques for Software Engineering Shohag Barman; Hira Lal Gope; M M Manjurul Islam; Md Mehedi Hasan; Umme Salma
Indonesian Journal of Electrical Engineering and Computer Science Vol 4, No 2: November 2016
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v4.i2.pp465-472

Abstract

Software industries face a common problem which is the maintenance cost of industrial software systems. There are lots of reasons behind this problem. One of the possible reasons is the high maintenance cost due to lack of knowledge about understanding the software systems that are too large, and complex. Software clustering is an efficient technique to deal with such kind of problems that arise from the sheer size and complexity of large software systems. Day by day the size and complexity of industrial software systems are rapidly increasing. So, it will be a challenging task for managing software systems. Software clustering can be very helpful to understand the larger software system, decompose them into smaller and easy to maintenance. In this paper, we want to give research direction in the area of software clustering in order to develop efficient clustering techniques for software engineering. Besides, we want to describe the most recent clustering techniques and their strength as well as weakness. In addition, we propose genetic algorithm based software modularization clustering method. The result section demonstrated that proposed method can effectively produce good module structure and it outperforms the state of the art methods. 
Co-Authors Dilshad Islam Hira Lal Gope M M Manjurul Islam Machbah Uddin Md Mehedi Hasan Mohammad Khairul Islam Umme Salma