Jahangirnagar University, Savar, Dhaka, Bangladesh

Jahangirnagar University, Savar, Dhaka, Bangladesh

Syed Ahmmed College, Rajshahi, Bangladesh

Sukhan Pukur High School, Rajshahi, Bangladesh

February 01, 2020 to Present

February 01, 2014 to January 31, 2020

February 01, 2012 to January 31, 2014

An Explainable Machine Learning-Based Employee Attrition Predictive System.

Changes in the board of directors’ number of meetings: why and so what?

Identification of bacterial key genera associated with breast cancer using machine learning techniques.

Boosting Heart Attack Prediction Performance: An Ensemble Learning Perspective. Journal of Science and Technology.

Phishing Website Identification: Unleashing The Potential of Machine Learning and Stacking Ensembles Techniques.

Forecasting Monthly Export of Readymade Garments by Removing Seasonal Impact.

Identifying the Socioeconomic and Demographic factors affecting the Maternal health care and delivery types of Santal women’s of Dinajpur, Bangladesh.

A study on the effects of different factors on Academic achievement among university students in Dinajpur District, Bangladesh: A Statistical Study.

Socio-Economic and Demographic Factors Influencing Fertility Preference in Bangladesh: Evidence from BDHS 2007-2018.

Forecasting the Remittance Inflow Based on Time Series Model in Bangladesh.

Forecasting the Production of Jute Based on Time Series Model in Bangladesh.

Forecasting the Production of Sugar Cane Based on Time Series Model in Bangladesh.

Factors Influencing Women's Waiting Time to First Birth in Bangladesh: An Application Of Cox Proportional Hazard Model.

Time Series Modeling and Forecasting of CPI of Bangladesh.

Prevalence of comprehensive knowledge about HIV/AIDS among ever married men and women in Bangladesh.

Socioeconomic and Demographic Determinants: Malnutrition of 6-59 months old rural santal children and Food security status of their families in Dinajpur.

Predicting Mpox Outbreaks Using Machine Learning, Deep Learning, and Explainable AI for Public Health Interventions. 

8th INTERNATIONAL CONFERENCE ON THE ROLE OF STATISTICS AND DATA SCIENCE IN 4IR (ICRSDS4IR) Department of Statistics, University of Rajshahi, Bangladesh,   December 26 – 28, 2024.

Optimizing Food Cart Revenue Prediction Using Machine Learning and Explainable AI Techniques.  

8th INTERNATIONAL CONFERENCE ON THE ROLE OF STATISTICS AND DATA SCIENCE IN 4IR (ICRSDS4IR) Department of Statistics, University of Rajshahi, Bangladesh,  December 26 – 28, 2024.

Prediction of Wind Speed Using Real Data: An analysis of Statistical Machine Learning Techniques.

Forecasting Day-ahead Solar Radiation Using Machine Learning Approach.

A Comparative Study of GARCH and Deep Learning Models in Predicting Bitcoin Daily Returns. Accepted Paper.  

International Journal of Statistical Sciences, ISSN 1683-5603.

Funded by: HSTU

Position: Associate Professor

Description: At post-graduate and undergraduate’s level, Department of Statistics, Faculty of Science, Hajee Mohammad Danesh Science and Technology University, Bangladesh.

Funded by: Institute of Research and Training (IRT), HSTU, 2024-25.

Position: Principal Investigator

Description: This research addresses the critical public health challenge of low birth weight (LBW) by creating an intelligent and interpretable prediction system. Leveraging Bangladesh Demographic and Health Survey (BDHS) data, we first identify significant socioeconomic and demographic determinants of LBW. We then engineer a sophisticated stacking ensemble model, SmartFusion-LR5, which outperforms standard machine learning and deep learning models with 93% accuracy and a 94% AUC. Beyond raw predictive power, we integrate Explainable AI (XAI) techniques to ensure transparency, revealing the global and local impact of features like maternal age and household wealth index. The final framework offers a practical, scalable solution for early LBW risk assessment, enabling proactive, data-informed healthcare strategies to improve neonatal outcomes in Bangladesh and similar regions.

Funded by: Institute of Research and Training (IRT), HSTU, 2023-24.

Position: Principal Investigator

Description: This project develops and validates a dynamic prediction framework to assess the prognostic accuracy of time-varying biomarkers for patients with Primary Biliary Cholangitis (PBC). Utilizing a landmarking approach and the R package dynamicLM, we model the 5-year risk of clinical events while accounting for competing risks. The research evaluates scenarios involving both baseline covariates with time-varying effects and truly time-dependent covariates. Our analysis demonstrates that the Cause-Specific Cox (CSC) model consistently outperforms a null model across landmark times of 0 to 3 years, showing superior predictive accuracy (lower Brier Scores) and discriminative ability (higher AUC). However, calibration plots reveal a tendency to overestimate risk in mid-ranges and underestimate it for high-risk patients, indicating a need for model recalibration for clinical use. This work underscores the critical value of dynamic prediction models that incorporate evolving patient data, providing a more precise tool for guiding timely interventions and improving long-term patient outcomes in chronic diseases.

Funded by: Institute of Research and Training (IRT), HSTU, 2022-23.

Position: Principal Investigator

Description: his project addresses the critical business challenge of employee attrition by developing a predictive machine learning framework. The objective is to identify employees at high risk of churn and uncover the key factors driving turnover. Using the IBM employee attrition dataset, we trained and evaluated multiple classification models, including Logistic Regression, Lasso Regression, and Random Forest. Our comparative analysis revealed that the Lasso Classifier delivered the most robust overall performance, achieving the highest accuracy, recall, and ROC score, while also producing the fewest false negatives—a critical consideration for ensuring at-risk employees are not overlooked. The analysis pinpointed that young, single, low-paid employees in roles like lab technicians and sales representatives, who work extensive overtime, are most likely to leave. This data-driven solution provides organizations with a powerful tool for proactive talent management, enabling targeted retention strategies, optimized resource allocation, and a significant reduction in the high costs associated with employee turnover.

Funded by: Institute of Research & Training, HSTU, 2014-15.

Position: Principal Investigator

Description: This project focuses on forecasting the agriculture and forestry sector's contribution to Bangladesh's gross domestic product (GDP) using time series analysis. While Auto-Regressive Integrated Moving Average (ARIMA) models have been widely applied in financial market forecasting for decades, their use in agriculture and forestry GDP forecasting has been limited. The study analyzed published secondary data for Bangladesh’s agriculture and forestry sector from 1979-80 to 2012-13. Through the Box-Jenkins methodology, the ARIMA (0, 2, 1) model was identified as the best-fit model based on several statistical criteria such as adjusted R-squared, Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), and Mean Absolute Percentage Error (MAPE). The model’s forecasts closely matched the original data series, indicating accuracy and reliability for short-term forecasting. This validated model can predict agricultural and forestry production trends effectively, both during and beyond the estimation period. The study’s findings are valuable for policymakers as they provide a robust quantitative tool to support decision-making in the agriculture and forestry sectors, enhancing strategic planning and sustainable development efforts in Bangladesh.

URL: https://scholar.google.com/citations?user=08pZ5psAAAAJ&hl=en&oi=ao

URL: https://www.researchgate.net/profile/Md-Hassan-61?ev=hdr_xprf