Article Per Year (5 Year)
p-Index From 2021 - 2026
0.444
P-Index
This Author published in this journals
All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Artificial Intelligence (IJ-AI)
Hoque, Md. Jiabul
Unknown Affiliation
Computer Science & IT Electrical & Electronics Engineering Engineering

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
Revolutionizing malaria diagnosis: deep learning-powered detection of parasite-infected red blood cells Hoque, Md. Jiabul; Islam, Md. Saiful; Khaliluzzaman, Md.; Muntasir, Abdullah Al; Mohsin, Mohammad Abdullah Bin
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 4: August 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i4.pp4518-4530

Abstract

Malaria is a significant global health issue, responsible for the highest rates of morbidity and mortality globally. This paper introduces a very effective and precise convolutional neural network (CNN) method that employs advanced deep learning techniques to automate the detection of malaria in images of red blood cells (RBC). Furthermore, we present an emerging and efficient deep learning method for differentiating between cells infected with malaria and those that are not infected. To thoroughly evaluate the efficiency of our approach, we do a meticulous assessment that involves comparing different deep learning models, such as ResNet-50, MobileNet-v2, and Inception-v3, within the domain of malaria detection. Additionally, we conduct a thorough comparison of our proposed approach with current automated methods for malaria identification. An examination of the most current techniques reveals differences in performance metrics, such as accuracy, specificity, sensitivity, and F1 score, for diagnosing malaria. Moreover, compared to existing models for malaria detection, our method is the most successful, achieving an accurate score of 1.00 in all statistical matrices, confirming its promise as a highly efficient tool for automating malaria detection.
Optimizing potato crop productivity: a meteorological analysis and machine learning approach Hoque, Md. Jiabul; Islam, Md. Saiful; Al Noman, Abdullah; Hoque, Md. Abrarul; Chowdhury, Irfan A.; Saifuddin, Mohammad
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 2: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i2.pp1116-1129

Abstract

Motivated by the critical need to enhance potato production in Bangladesh, particularly in the face of a changing climate, this study investigates the significant impact of weather on potato yield. This research employs various statistical and machine-learning approaches to identify key weather factors influencing potato crops. We utilize ANOVA F regression and random forest (RF) with feature importance analysis to pinpoint crucial monthly weather variables. Additionally, a correlation study employing Pearson's and Spearman's coefficients alongside p-values is conducted to determine the relationships between weather conditions and crop yield. Seaborn's bivariate kernel density estimation is then used to visualize ideal weather conditions for optimal harvests. Furthermore, to predict future yields, the study implements thoroughly trained and validated machine learning models including k-nearest neighbors (KNN), RF, and support vector regressor (SVR). Our analysis reveals that the RF model emerges as the most reliable predictor, achieving a high correlation coefficient (R²=0.9990), and minimal error values (mean absolute percentage error (MAPE)=0.70, mean absolute error (MAE)=0.0803, and root mean square error (RMSE)=0.1114). These findings provide valuable insights to guide informed agricultural decisions and climate-related strategies, particularly for resource-limited countries like Bangladesh.
Co-Authors Al Noman, Abdullah Chowdhury, Irfan A. Hoque, Md. Abrarul Khaliluzzaman, Md. Md. Saiful Islam Mohsin, Mohammad Abdullah Bin Muntasir, Abdullah Al Saifuddin, Mohammad