Zainal Abedin

Work place: Department of Computer Science and Engineering, University of Science and Technology Chittagong, Bangladesh



Research Interests: Models of Computation, Mathematics of Computing, Combinatorial Optimization, Natural Language Processing, Computational Learning Theory


Zainal Abedin received the BSc and MSc degree in Computer Science & Engineering from Chittagong University of Engineering and Technology (CUET), Chattagram, Bangladesh. He is a faculty member of the Department of Computer Science and Engineering at University of Science and Technology Chittagong. He published a good number of articles in international conferences and journals. His research interest includes Machine Vision with Deep Learning, Optimization of Machine learning model, Natural Language Processing, Signal Processing for Health care application and block chain technology.

Author Articles
Classification of Leaf Disease Using Global and Local Features

By Prashengit Dhar Md. Shohelur Rahman Zainal Abedin

DOI:, Pub. Date: 8 Feb. 2022

Leaf disease of plants causes great loss in productivity of crops. So proper take care of plants is mandatory. Plants can be affected by various diseases. So Early diagnosis of leaf disease is a good practice. Computer vision-based classification of leaf disease can be a great way in diagnosing diseases early. Early detection of diseases can lead to better treatment. Vision based technology can identify disease quickly. Though deep learning is trending and using vastly for recognition task, but it needs very large dataset and also consumes much time. This paper introduced a method to classify leaf diseases using Gist and LBP (Local Binary Pattern) feature. These manual feature extraction process need less time. Combination of gist and LBP features shows significant result in classification of leaf diseases. Gist is used as global feature and LBP as local feature. Gist can describe an image very well as a scene. LBP is robust to illumination changes and occlusions and computationally simple. Various diseases of different plants are considered in this study. Gist and LBP features from images are extracted separately. Images are pre-processed before feature extraction. Then both feature matrix is combined using concatenation method. Training and testing is done on different plants separately. Different machine learning model is applied on the feature vector. Result from different machine learning algorithms is also compared. SVM performs better in classifying plant’s leaf dataset.

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Bengali News Headline Categorization Using Optimized Machine Learning Pipeline

By Prashengit Dhar Zainal Abedin

DOI:, Pub. Date: 8 Feb. 2021

Bengali text based news portal is now very common and increasing day by day. With easy access of internet technology, reading news through online is now a regular task. Different types of news are represented in the news portal. The system presented in this paper categorizes the news headline of news portal or sites. Prediction is made by machine learning algorithm. Large number of collected data are trained and tested. As pre-processing tasks such as tokenization, digit removal, removing punctuation marks, symbols, and deletion of stop words are processed. A set of stop words is also created manually. Strong stop words leads to better performance. Stop words deletion plays a lead role in feature selection. For optimization, genetic algorithm is used which results in reduced feature size. A comparison is also explored without optimization process. Dataset is established by collecting news headline from various Bengali news portal and sites. Resultant output shows well performance in categorization.

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