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All Journal TELKOMNIKA (Telecommunication Computing Electronics and Control) Indonesian Journal of Electrical Engineering and Computer Science
Mohammad Khairul Islam
University of Chittagong
Computer Science & IT

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An effective feature extraction method for rice leaf disease classification Muhammad Anwarul Azim; Mohammad Khairul Islam; Md. Marufur Rahman; Farah Jahan
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 19, No 2: April 2021
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v19i2.16488

Abstract

Our society is getting more and more technology dependent day by day. Nevertheless, agriculture is imperative for our survival. Rice is one of the primary food grains. It provides sustenance to almost fifty percent of the world population and promotes huge amount of employments. Hence, proper mitigation of rice plant diseases is of paramount importance. A model to detect three rice leaf diseases, namely bacterial leaf blight, brown spot, and leaf smut is proposed in this paper. Backgrounds of the images are removed by saturation threshold while disease affected areas are segmented using hue threshold. Distinctive features from color, shape, and texture domain are extracted from affected areas. These features can robustly describe local and global statistics of such images. Trying a couple of classification algorithms, extreme gradient boosting decision tree ensemble is incorporated in this model for its superior performance. Our model achieves 86.58% accuracy on rice leaf diseases dataset from UCI, which is higher than previous works on the same dataset. Class-wise accuracy of the model is also consistent among the classes.
Crowd Detection in Still Images Using Combined HOG and SIFT Feature Machbah Uddin; Hira Lal Gope; Md. Sayeed Iftekhar Yousuf; Dilshad Islam; Mohammad Khairul Islam
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.pp447-458

Abstract

Person detection and tracking in crowd is a challenging task. We detect the head region and based on this head region we can detect people from crowd. Individual object detection has been improved significantly in recent times but the crowd detection and tracking contains some challenges. Crowd analysis is a highly focused area for law enforcement, urban engineering and traffic management.  There are a lot of incident occurred in crowd area during some fabulous event. In this research low resolution and verities of image orientation is a key factor as well as overlapping person images in crowd misguided the system. An enhanced system of interest point detection based on gradient orientation information as well as improved feature extraction HOG is used for identifying the human head or face from crowd. We have analyzed different types of images in different varieties and found accuracy 88-90%. In a number of applications, such as document analysis and some industrial machine vision tasks, binary images can be used as the input to algorithms that perform useful tasks. These algorithms can handle tasks ranging from very simple counting tasks to much more complex recognition, localization, and inspection tasks. Thus by studying binary image analysis before going on to gray-tone and color images, one can gain insight into the entire image analysis process.
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%.
Performance Evaluation of Different Backoff Algorithms in IEEE 802.15.4 using Double Sensing Md. Mohibur Rahaman; Mohammad Khairul Islam; Kazi Ashrafuzzaman; Mohammad Sanaullah Chowdhury
Indonesian Journal of Electrical Engineering and Computer Science Vol 5, No 2: February 2017
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v5.i2.pp376-382

Abstract

The IEEE 802.15.4 is the standard for Low Rate Wireless Personal Area network (LR-WPAN). It is widely used in many application areas. The standard uses Slotted CSMA/CA mechanism in its contention access period (CAP) for the beacon enabled mode. The protocol has two modes - single sensing (SS) and double sensing (DS). The protocol also adopts a binary exponential backoff (BEB) algorithm. In this paper, we explore the saturation throughput, delay and energy consumption of this standard with double sensing (DS) using the existing BEB algorithm. We also investigate three other backoff schemes - exponential increase exponential decrease (EIED), exponential increase linear decrease (EILD) and exponential increase multiplicative decrease (EIMD). From simulation results, it is found that the EIED, EILD, EIMD perform better than the BEB for higher loads. It shows that the EIED, EILD, EIMD have better throughput and lower delay than the BEB. The EIED outperforms the other schemes in terms of throughput, delay and energy for the higher loads.
Co-Authors Dilshad Islam Farah Jahan Hira Lal Gope Kazi Ashrafuzzaman Machbah Uddin Md. Marufur Rahman Md. Mohibur Rahaman Md. Sayeed Iftekhar Yousuf Mohammad Sanaullah Chowdhury Muhammad Anwarul Azim Shohag Barman