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All Journal Makara Journal of Health Research Heca Journal of Applied Sciences Journal of Educational Management and Learning Infolitika Journal of Data Science
Emran, Talha Bin
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Agriculture, Biological Sciences & Forestry Computer Science & IT Decision Sciences, Operations Research & Management Earth & Planetary Sciences Education Engineering Health Professions Languange, Linguistic, Communication & Media Mathematics Medicine & Pharmacology Other

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Depression and Behavioral Changes Associated with Social Media Dependency During COVID-19 Pandemic Among University Students in Bangladesh: A Cross- Sectional Study Karmokar, Sushmita; Islam, Md. Ashraful; Muktadir, Mohammad Hamid Al; Hasan, Rakibul; Tareq, Abu Montakim; Amin, Mohammad Nurul; Emran, Talha Bin
Makara Journal of Health Research Vol. 25, No. 3
Publisher : UI Scholars Hub

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Background: With its rapid spread, the coronavirus disease 2019 (COVID-19) pandemic had a detrimental effect on students’ psychological well-being, depression, and behavioral changes due to indefinite educational leaves, lockdowns, restricted outdoor activities, and excess use of social media. This study aims to assess the relationship of social media exposure with the psychological well-being, depression, and behavioral changes of Bangladeshi university students. Methods: A web-based cross-sectional survey was carried out on 530 students from June 17 to July 10, 2020, to evaluate psychological well-being, depression, behavioral changes, and social media exposure via self-reported measures. Results: The prevalence of factors were as follows: poor psychological well-being was 24.9%; moderate to severe depression was 56.6%; severe behavioral changes was 32.1%; and of moderate to severe addiction to social media exposure was 38.3%. All factors were positively associated with social media exposure. Multivariate logistic regression showed that the addiction of participants to social media was 7.488 times higher with severe behavioral changes (OR: 7.488; 95% CI 4.708–11.909), 2.299 times higher with poor psychological functioning (OR: 2.299; 95% CI 1.421–3.721), 30.54 times higher with severe depressed (OR: 30.54; 95% CI 15.0–62.177) than that of individuals without such symptoms. Conclusions: The above findings imply that the government needs to pay greater attention to improve the overall situation of Bangladeshi university students.
Evaluation of Biochemical and Pathological Parameters at Different Doses of Cisplatin in Experimental Animal Model: Toxicological Study on an Antineoplastic Drug Sultana, Farhana; Islam, Muhammed Mohibul; Amin, Mohammad Nurul; Jahan, Nusrat; Kabir, Asma; Emran, Talha Bin; Sutradhar, Bibek Chandra; Banik, Sujan
Makara Journal of Health Research Vol. 26, No. 1
Publisher : UI Scholars Hub

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Background: This study aimed to assess the effect of cisplatin-induced toxicities on biochemical and pathological parameters such as body, liver, and kidney weights, blood urea nitrogen (BUN), creatinine, alanine aminotransferase (ALT), and blood cells (RBCs and WBCs) in white Swiss albino mice. Methods: Cisplatin’s potential toxic effects on body, liver, and kidney weights were evaluated using standard laboratory methods. Blood biochemical levels such as BUN, creatinine, and ALT levels were determined by an auto-hemolyzer using commercial diagnostic kits. Blood cells (RBCs and WBCs) were counted under a microscope by a hemocytometer. Results: Cisplatin’s potential toxic effects on body, liver, and kidney weights were evaluated using standard laboratory methods. Blood biochemical levels such as BUN, creatinine, and ALT levels were determined by an auto-hemolyzer using commercial diagnostic kits. Blood cells (RBCs and WBCs) were counted under a microscope by a hemocytometer. Conclusions: This study suggested to increase caution when using cisplatin, particularly at high doses. Further investigation shall be performed to minimize its toxic effect and optimize its use.
Cross-Sectional Study on Overweight and Obesity Associated with Fast-Food Consumption in Bangladesh Sultana, Farhana; Siddiqui, Shafayet Ahmed; Islam, Md. Ashraful; Al Muktadir, Mohammad Hamid; Millat, Md. Shalahuddin; Islam, Muhammed Mohibul; Tareq, Abu Montakim; Afroz, Nahida; Rahman, Mahabuba; Amin, Mohammad Nurul; Emran, Talha Bin
Makara Journal of Health Research Vol. 26, No. 2
Publisher : UI Scholars Hub

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Background: This study aimed to investigate the risk factors and status of fast-food consumption among students in Bangladesh. Methods: This cross-sectional study was conducted from March to November 2020. A total of 654 samples were collected from several schools, colleges, and universities during this study period. Results: About 60.1% and 39.9% of the students were male and female, respectively. Of the students, 53.1% considered fast food as unhealthy (p < 0.001), but only 47.7% were leading a sedentary lifestyle. A significant outcome of overweight and pre-obesity was observed for student institutions, consumption frequency, daily fast-food consumption, and sedentary lifestyle (p < 0.001). In addition, positive association was observed for fast-food consumption more than three times and less than three times per week (OR and 95% CI: 11.13 [7.52–16.47], p < 0.001), higher social class and lower class (OR and 95% CI: 2.18 [1.31–3.62], p = 0.003), fast food preference and other foods (OR and 95% CI: 1.55 [1.11–2.15], p = 0.009), and sedentary and heavily active lifestyle (OR and 95% CI: 5.71 [2.02–16.10], p = 0.001) using logistic regression. Conclusions: Overweight and obesity are serious public health concerns, which are highly associated with fast-food consumption along with lifestyle, economy, and fast-food preference among students in Dhaka City, Bangladesh.
QSAR Classification of Beta-Secretase 1 Inhibitor Activity in Alzheimer's Disease Using Ensemble Machine Learning Algorithms Noviandy, Teuku Rizky; Maulana, Aga; Emran, Talha Bin; Idroes, Ghazi Mauer; Idroes, Rinaldi
Heca Journal of Applied Sciences Vol. 1 No. 1 (2023): June 2023
Publisher : Heca Sentra Analitika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60084/hjas.v1i1.12

Abstract

This study focuses on the development of a machine learning ensemble approach for the classification of Beta-Secretase 1 (BACE1) inhibitors in Quantitative Structure-Activity Relationship (QSAR) analysis. BACE1 is an enzyme linked to the production of amyloid beta peptide, a significant component of Alzheimer's disease plaques. The discovery of effective BACE1 inhibitors is difficult, but QSAR modeling offers a cost-effective alternative by predicting the activity of compounds based on their chemical structures. This study evaluates the performance of four machine learning models (Random Forest, AdaBoost, Gradient Boosting, and Extra Trees) in predicting BACE1 inhibitor activity. Random Forest achieved the highest performance, with a training accuracy of 98.65% and a testing accuracy of 82.53%. In addition, it exhibited superior precision, recall, and F1-score. Random Forest's superior performance was a result of its ability to capture a wide variety of patterns and its randomized ensemble approach. Overall, this study demonstrates the efficacy of ensemble machine learning models, specifically Random Forest, in predicting the activity of BACE1 inhibitors. The findings contribute to ongoing efforts in Alzheimer's disease drug discovery research by providing a cost-effective and efficient strategy for screening and prioritizing potential BACE1 inhibitors.
Explainable Deep Learning Approach for Mpox Skin Lesion Detection with Grad-CAM Idroes, Ghazi Mauer; Noviandy, Teuku Rizky; Emran, Talha Bin; Idroes, Rinaldi
Heca Journal of Applied Sciences Vol. 2 No. 2 (2024): September 2024
Publisher : Heca Sentra Analitika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60084/hjas.v2i2.216

Abstract

Mpox is a viral zoonotic disease that presents with skin lesions similar to other conditions like chickenpox, measles, and hand-foot-mouth disease, making accurate diagnosis challenging. Early and precise detection of mpox is critical for effective treatment and outbreak control, particularly in resource-limited settings where traditional diagnostic methods are often unavailable. While deep learning models have been applied successfully in medical imaging, their use in mpox detection remains underexplored. To address this gap, we developed a deep learning-based approach using the ResNet50v2 model to classify mpox lesions alongside five other skin conditions. We also incorporated Grad-CAM (Gradient-weighted Class Activation Mapping) to enhance model interpretability. The results show that the ResNet50v2 model achieved an accuracy of 99.33%, precision of 99.34%, sensitivity of 99.33%, and an F1-score of 99.32% on a dataset of 1,594 images. Grad-CAM visualizations confirmed that the model focused on relevant lesion areas for its predictions. While the model performed exceptionally well overall, it struggled with misclassifications between visually similar diseases, such as chickenpox and mpox. These results demonstrate that AI-based diagnostic tools can provide reliable, interpretable support for clinicians, particularly in settings with limited access to specialized diagnostics. However, future work should focus on expanding datasets and improving the model's capacity to distinguish between similar conditions.
Does Online Education Make Students Happy? Insights from Exploratory Data Analysis Noviandy, Teuku Rizky; Idroes, Ghalieb Mutig; Hardi, Irsan; Emran, Talha Bin; Zahriah, Zahriah; Rahimah, Souvia; Lala, Andi; Idroes, Rinaldi
Journal of Educational Management and Learning Vol. 1 No. 2 (2023): December 2023
Publisher : Heca Sentra Analitika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60084/jeml.v1i2.124

Abstract

This study investigates the impact of online education on student happiness. Utilizing a dataset of 5715 students sourced from Bangladesh, we employed an exploratory data analysis to analyze the quantitative data. The key finding is that there is a prevalent trend of dissatisfaction with online education among Bangladeshi students, regardless of demographic factors like age, gender, education level, preferred device for access, or type of academic institution. The dissatisfaction trend highlights the need of continuous improvements and targeted interventions are essential to ensure online education not only enables academic success, but also supports the overall wellbeing and happiness of students in the context of a developing country.
Ensemble Machine Learning Approach for Quantitative Structure Activity Relationship Based Drug Discovery: A Review Noviandy, Teuku Rizky; Maulana, Aga; Idroes, Ghazi Mauer; Emran, Talha Bin; Tallei, Trina Ekawati; Helwani, Zuchra; Idroes, Rinaldi
Infolitika Journal of Data Science Vol. 1 No. 1 (2023): September 2023
Publisher : Heca Sentra Analitika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60084/ijds.v1i1.91

Abstract

This comprehensive review explores the pivotal role of ensemble machine learning techniques in Quantitative Structure-Activity Relationship (QSAR) modeling for drug discovery. It emphasizes the significance of accurate QSAR models in streamlining candidate compound selection and highlights how ensemble methods, including AdaBoost, Gradient Boosting, Random Forest, Extra Trees, XGBoost, LightGBM, and CatBoost, effectively address challenges such as overfitting and noisy data. The review presents recent applications of ensemble learning in both classification and regression tasks within QSAR, showcasing the exceptional predictive accuracy of these techniques across diverse datasets and target properties. It also discusses the key challenges and considerations in ensemble QSAR modeling, including data quality, model selection, computational resources, and overfitting. The review outlines future directions in ensemble QSAR modeling, including the integration of multi-modal data, explainability, handling imbalanced data, automation, and personalized medicine applications while emphasizing the need for ethical and regulatory guidelines in this evolving field.
Embrace, Don’t Avoid: Reimagining Higher Education with Generative Artificial Intelligence Noviandy, Teuku Rizky; Maulana, Aga; Idroes, Ghazi Mauer; Zahriah, Zahriah; Paristiowati, Maria; Emran, Talha Bin; Ilyas, Mukhlisuddin; Idroes, Rinaldi
Journal of Educational Management and Learning Vol. 2 No. 2 (2024): November 2024
Publisher : Heca Sentra Analitika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60084/jeml.v2i2.233

Abstract

This paper explores the potential of generative artificial intelligence (AI) to transform higher education. Generative AI is a technology that can create new content, like text, images, and code, by learning patterns from existing data. As generative AI tools become more popular, there is growing interest in how AI can improve teaching, learning, and research. Higher education faces many challenges, such as meeting diverse learning needs and preparing students for fast-changing careers. Generative AI offers solutions by personalizing learning experiences, making education more engaging, and supporting skill development through adaptive content. It can also help researchers by automating tasks like data analysis and hypothesis generation, making research faster and more efficient. Moreover, generative AI can streamline administrative tasks, improving efficiency across institutions. However, using AI also raises concerns about privacy, bias, academic integrity, and equal access. To address these issues, institutions must establish clear ethical guidelines, ensure data security, and promote fairness in AI use. Training for faculty and AI literacy for students are essential to maximize benefits while minimizing risks. The paper suggests a strategic framework for integrating AI in higher education, focusing on infrastructure, ethical practices, and continuous learning. By adopting AI responsibly, higher education can become more inclusive, engaging, and practical, preparing students for the demands of a technology-driven world.
Co-Authors Al Muktadir, Mohammad Hamid Amin, Mohammad Nurul Banik, Sujan Ghazi Mauer Idroes Hardi, Irsan Hasan, Rakibul Idroes, Ghalieb Mutig Islam, Md. Ashraful Islam, Muhammed Mohibul Jahan, Nusrat Kabir, Asma Karmokar, Sushmita Lala, Andi Maria Paristiowati Maulana, Aga Millat, Md. Shalahuddin Mukhlisuddin Ilyas Muktadir, Mohammad Hamid Al Nahida Afroz Rahman, Mahabuba Rinaldi Idroes Siddiqui, Shafayet Ahmed Souvia Rahimah Sultana, Farhana Sutradhar, Bibek Chandra Tareq, Abu Montakim Teuku Rizky Noviandy TRINA EKAWATI TALLEI Zahriah, Zahriah Zuchra Helwani, Zuchra