Article Per Year (5 Year)
p-Index From 2021 - 2026
0.61
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This Author published in this journals
All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Artificial Intelligence (IJ-AI) International Journal of Public Health Science (IJPHS)
Md. Saiful Islam
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Computer Science & IT Electrical & Electronics Engineering Engineering Health Professions

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 1 
Breakfast Skipping and Obesity Risk among Urban Adults in Bangladesh Shatabdi Goon; Md. Saiful Islam
International Journal of Public Health Science (IJPHS) Vol 3, No 1: March 2014
Publisher : Intelektual Pustaka Media Utama

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (81.664 KB) | DOI: 10.11591/ijphs.v3i1.4670

Abstract

Positive association between skipping breakfast and overweight and obesity is globally observed regardless of cultural diversity among countries. A cross-sectional descriptive study was performed on a total of 426 urban adults, who were randomly selected in a nutrition counseling center of Dhaka city, Bangladesh. The objective of this study was determining the association between breakfast skipping and obesity risk in urban adults of Bangladesh. Results indicated that approximately 35.2% of the sample skipped breakfast. Gender was the only statistically significant sociodemographic variable, with females skipping at two times the rate of males (OR 95% CI: 1.9; 1.3-2.9). Obesity was detected among 39.5% of breakfast skippers and they showed significantly high prevalence (X2=30.15, p<0.05). Skippers were significantly more likely being obese (OR 3.5; 95% CI 2.2-5.5) and obesity was more prevalent in female skippers (X2=8.7, p<0.05), with three times more compared to male skippers (OR 95% CI: 2.8; 1.4-5.9). Breakfast skipping is highly prevalent among urban adult population with significant association of obesity in Bangladesh. Health promotion strategies should be used to encourage all adults to eat breakfast regularly. 
Fish Marketing Status with Formalin Treatment in Bangladesh Shatabdi Goon; Munmun Bipasha; Md. Saiful Islam; Md. Bellal Hossain
International Journal of Public Health Science (IJPHS) Vol 3, No 2: June 2014
Publisher : Intelektual Pustaka Media Utama

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (70.628 KB) | DOI: 10.11591/ijphs.v3i2.4680

Abstract

Fish possess an extremely strong cultural attachment considering irreplaceable animal food source in Bangladeshi diet beset with nutritional importance. It has been getting endangered by abominable practice of formalin in marketing leaded by some deceitful traders counting profit beyond ethical attainment and endangering public health. This paper outlines the formalin extremity with a several years practice in fish marketing involving almost 5% city markets with a petty heterogeneity comprising contrastive and potential strategy with formalin access. Regardless, this formalin corruption affiliated with deleterious health aggravations both for traders and consumers, comes out with impotency in workforce contravening economical influence on overall national prosperity.
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.
Exploring spatial variations and risk factors associated with cesarean section delivery in Bangladesh Islam, Md. Saiful; Bhuiyan, Md. Junayeth; Miah, Md. Sharif; Rashid, Md. Mamunur
International Journal of Public Health Science (IJPHS) Vol 14, No 3: September 2025
Publisher : Intelektual Pustaka Media Utama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijphs.v14i3.24700

Abstract

This study was to explore the spatial variations and risk factors of cesarean delivery in Bangladesh. We used the Bangladesh Demographic and Health Survey (BDHS) 2017-18 data. The Getis-Ord Gi* statistic was applied to assess the hot spots and cold spots of cesarean delivery, and a multilevel logistic regression model was utilized to determine the risk factors related to cesarean delivery in Bangladesh. This study found that one-third of all births (33%; 95% CI = 30.68-34.66) delivered through cesarean section. The hot spots of cesarean delivery were in Dhaka, Khulna, Rajshahi and Rangpur divisions. The cold spots were in Barisal, Rangpur, and Sylhet divisions. Women with higher education (OR =2 .24, 95% CI = 1.49-3.36), overweight/obese women (OR = 2.07, 95% CI = 1.63–2.63) and women from Khulna division (OR = 1.87, 95% CI= 1.32–2.64) were significantly associated with cesarean section. Therefore, concentrating on factors including women’s education, partner's education, partner’s occupation, age at first birth, wealth index, women’s body mass index (BMI) status, media exposure, and divisions might play a crucial role in reducing the unnecessary cesarean section in Bangladesh.
Co-Authors Al Noman, Abdullah Bhuiyan, Md. Junayeth Chowdhury, Irfan A. Hoque, Md. Abrarul Hoque, Md. Jiabul Khaliluzzaman, Md. Md. Bellal Hossain Miah, Md. Sharif Mohsin, Mohammad Abdullah Bin Munmun Bipasha Muntasir, Abdullah Al Rashid, Md. Mamunur Saifuddin, Mohammad Shatabdi Goon Shatabdi Goon