M
Mirza Muntasir Nishat
Researcher at Islamic University of Technology
Publications - 44
Citations - 406
Mirza Muntasir Nishat is an academic researcher from Islamic University of Technology. The author has contributed to research in topics: Computer science & Medicine. The author has an hindex of 4, co-authored 19 publications receiving 48 citations.
Papers
More filters
Journal ArticleDOI
Design and Simulation of a DC - DC Boost Converter with PID Controller for Enhanced Performance
TL;DR: The proposed closed loop implementation of the converter maintains constant output voltage despite changes in input voltage and significantly reduces overshoot thereby improving the efficiency of the Collider, enhancing overall performance of the system.
Proceedings ArticleDOI
Computer Aided Diagnosis of Thyroid Disease Using Machine Learning Algorithms
Md. Asfi-Ar-Raihan Asif,Mirza Muntasir Nishat,Fahim Faisal,Md. Fahim Shikder,Mahmudul Hasan Udoy,Rezuanur Rahman Dip,Ragib Ahsan +6 more
TL;DR: In this paper, a comprehensive study of investigating the performance of different machine learning algorithms in the diagnosis of thyroid disease has been presented, which provided conclusive evidence that Multilayer Perceptron (MLPC) was the most proficient algorithm with an accuracy of 99.70% after hyperparameter optimization.
Journal ArticleDOI
Development of Genetic Algorithm (GA) Based Optimized PID Controller for Stability Analysis of DC-DC Buck Converter
Mirza Muntasir Nishat,Fahim Faisal,Anik Jawad Evan,Md. Moshiour Rahaman,Md. Sadman Sifat,H. M. Fazle Rabbi +5 more
TL;DR: An analysis is carried out where the performance of the buck converter is illustrated in terms of rise time, settling time and percentage of overshoot by deploying GA based PID controller and the overall comparative study is presented.
Proceedings ArticleDOI
Performance Investigation of Different Boosting Algorithms in Predicting Chronic Kidney Disease
Mirza Muntasir Nishat,Fahim Faisal,Rezuanur Rahman Dip,Md. Fahim Shikder,Ragib Ahsan,Md. Asfi-Ar-Raihan Asif,Mahmudul Hasan Udoy +6 more
TL;DR: In this article, the performance of different boosting algorithms in predicting chronic kidney disease (CKD) more accurately was investigated and a broad comparative investigation was conducted in terms of accuracy, precision, sensitivity, F1 score, ROC-AUC of each algorithm.
Journal ArticleDOI
An Investigation for Enhancing Registration Performance with Brain Atlas by Novel Image Inpainting Technique using Dice and Jaccard Score on Multiple Sclerosis (MS) Tissue
TL;DR: It is evident that the proposed inpainting algorithm performs satisfactorily with a view to reducing the bias in the registration step, and the overall performance of the technique is evaluated by utilizing Dice and Jaccard scores.