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All Journal International Journal of Electrical and Computer Engineering International Journal of Public Health Science (IJPHS) International Journal of Integrated Health Sciences Journal of Maternal and Child Health Journal of Public Health and Pharmacy
Rahman, Md Mizanur
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Computer Science & IT Electrical & Electronics Engineering Health Professions Medicine & Pharmacology Public Health

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Does Entitlement Card ensure Utilization of Urban Primary Healthcare Clinic in Bangladesh? Mizan, Sharmin; Rahman, Md Mizanur; Safii, Razitasham binti; Ahmad, Sk Akhtar
Journal of Maternal and Child Health Vol 5, No 2 (2020)
Publisher : Journal of Maternal and Child Health

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (562.777 KB)

Abstract

Background: Although Bangladesh substantial­ly reduced 40% maternal death in the last deca­des, it is still challenging, especially among the ur­ban poor. The achievements are not equitable bet­ween different economic quintiles and bet­ween the rich and the urban poor. This study aims to examine the extent to which the entitle­ment card affects the utilization of maternal and child health care and identifies other factors that influence MCH services.Subjects and Method: This cross-sectional study was carried out in the working areas of the Ur­ban Primary Healthcare Project (UPHCP) in Bang­ladesh. A two-stage cluster sampling tech­ni­que was used to select the participants. A total of 3,949 women aged 15-49 years, having at least one child aged two years or less were selected for this study. The data were collected through face-to-face inter­views. The data were analyzed using multinomial logistic regression.Results: The proportion of utilization of UPH­CC was 49.9%. One-fourth (26.6%) of them fully utilized it and another 23.3% utilized it partially. Stepwise multinomial logistic regression analysis revealed that those who had an Entitlement Card from the UPHC project were 11.75 times (95% CI= 9.481, 14.549; p= 0.001) more likely to fully Utilized and 3.64 times (95% CI= 3.643, 2.911; p= 0.001) likely be utilized partially compared to non-utilizer. Respondents having no formal edu­cation utilized UPHCC fully (AOR=2.32; 95% CI= 1.46, 3.68; p= 0.001) and partially (AOR= 1.76; 95% CI= 1.12, 2.77; p= 0.014) used UPHCC. It was 3.08 (95% CI= 2.03, 4.67; p= 0.001) times for fully and 2.71 (95% CI= 1.82, 4.04; p= 0.001) times for partially utilized UPHCC compared to non-users among the primary level of education. Small family size (≤4) and monthly family in­come in the range of BDT 10,000 above were likely to utilise UPHCC. However, non-Muslims were less likely to Utilized UPHCC.Conclusion: Apart from the entitlement card, other factors such as monthly income BDT> 10,000, small family size, no formal educated mo­ther appeared to be potential predictors for utilization of the Urban Primary Health care clinic.Keywords: entitlement card, maternal care, Utilization, urban primary healthcareCorrespondence: Md Mizanur Rahman. Department of Community Medicine and Public Health, Faculty of Medicine and Health Sciences, Universiti Malaysia Sa­rawak. Email: rmmizanur@unimas.my, rmizanur1958@gmail.com.Journal of Maternal and Child Health (2020), 5(2): 213-225https://doi.org/10.26911/thejmch.2020.05.02.12
Automated tomato leaf disease recognition using deep convolutional networks Sohel, Amir; Rahman, Md Mizanur; Hasan, Md Umaid; Islam, MD Kafiul; Rukhsara, Lamia; Rabeya, Tapasy
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 2: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i2.pp1850-1860

Abstract

Agriculture is essential for the entire global population. An advanced, robust, and empirically sound agriculture sector is essential for nourishing the global population. Various leaf diseases cause financial hardships for farmers and related businesses. Early identification of foliar diseases in crops would greatly help farmers, leading to a substantial increase in agricultural productivity. The tomato is a widely recognized and nourishing food that is easily accessible and highly favored by farmers. Early diagnosis of tomato leaf diseases is crucial to maximize tomato crop production. This study aims to utilize a deep learning approach to accurately detect and classify damaged leaves and disease patterns in tomato leaf images. By employing a substantial quantity of deep convolutional network models, we achieved a high level of precision in diagnosing the condition. The dataset used in our study work is a self-contained dataset obtained by direct observation of tomato fields in rural areas of Bangladesh. It consists of four classes: healthy, black mold, grey mold, and powdery mildew. In this study work, we utilized various image pre-processing techniques and applied VGG16, InceptionV3, DenseNet121, and AlexNet models. Our results showed that the DenseNet121 model attained the higher accuracy of 97%. This discovery guarantees accurate detection of tomato diseases in a rapid manner, ushering in a new agricultural revolution.
Community-Based Tobacco Smoking Cessation Programmes Among Adolescents in Sarawak: Lesson Learned from Process Evaluation Muhammad , Siddiq; Rahman, Md Mizanur; Lukas, Sabrina Binti; Kana, Kamarudin Bin; Aren, Merikan Bin; Ajeng , Rudy Ngau; Gahamat , Mohd Faiz
Journal of Public Health and Pharmacy Vol. 5 No. 1: MARCH 2025
Publisher : Pusat Pengembangan Teknologi Informasi dan Jurnal Universitas Muhammadiyah Palu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56338/jphp.v5i1.5268

Abstract

Introduction: This study evaluated the effectiveness of community-based quit-smoking interventions using the 5A’s and 3A’s modules. Methods: The study was conducted between 2020 and 2021 in Samarahan and Asajaya District, Sarawak, Malaysia. The study included 519 participants out of 600 individuals, and both facilitators and observers evaluated the process. The process evaluation assessed various components: fidelity, dose delivered, dose received, reach, satisfaction, context, justification for intervention withdrawal, facilitator influence on sessions, and intervention feedback. Results: The study found that most facilitators executed more than 85% of both session modules, achieving at least 75% of the objectives. Most participants of both sessions were positively and actively engaged and would recommend intervention to others. The participants reported positive feedback. However, 26.3% of participants withdrew from the second session due to inconvenient timing. The observer’s fidelity evaluations of both intervention sessions were fully implemented according to plans, achieving over 75% of their objectives. Observers acknowledged active and engaged participants during both intervention sessions and regarded all facilitators as appropriate and positive toward participants. The process evaluation showed that the interventions were administered well, and smoking adolescents demonstrated a willingness to quit smoking due to the outcomes of this intervention. Conclusion: The findings of this study provide valuable insights into the effectiveness of community-based interventions for quitting smoking and highlight the importance of evaluating the process of interventions to understand their relationship with outcomes. The study’s results can inform the development and implementation of future interventions to reduce smoking incidence among adolescents.
Cucumber leaf disease identification in real-time via deep learning based algorithms Rahman, Md Mizanur; Nadim, Mahimul Islam; Akther, Mahinur; Ullah, Ahad; Ahmed, Jakaria; Ahmed, Muhammad Jalal Uddin; Jahan, Israt
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i3.pp3127-3138

Abstract

Cucumber is a cash crop in Bangladesh as it is a side dish grown commercially in cultivable lands year-round. The early prediction of disease-prone crops could save grooming time and minimize losses. The conventional method of examining leaves just through observation of the human eye could only detect the diseases at an advanced stage without a concrete decision of which disease it might be and regular inspection is labour intensive, inaccurate and often unreliable. This study evaluates machine learning-based image analysis for classifying healthy and diseased cucumber leaves by training deep learning models to detect and identify observable traits. Total 1,629 images use as primary dataset and all the data collected from the cucumber field of Bangladesh. To fulfill this purpose, convolutional neural network (CNN), InceptionV3, and EfficientNetB4 are the models implemented in this paper to improve the classification of objects. The dataset was optimized by pre-processing techniques and the leaves are classified into four categories, namely angular leaf spot, downy mildew, powdery mildew, and good leaf. The EfficienNetB4 model achieved the highest train and test accuracy respectively 95% and 87%. A comparative examination of the available models was conducted in this paper to reach a solid decision.
Intention to Consume Alcohol among Dayak Adolescents in Sarawak: An Application of Theory of Planned Behavior Gahamat, Mohd Faiz; Rahman, Md Mizanur; Safii, Razitasham; Daud, Muhammad Siddiq; Ajeng, Rudy Ngau
International Journal of Integrated Health Sciences Vol 11, No 2 (2023)
Publisher : Faculty of Medicine Universitas Padjadjaran

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15850/ijihs.v11n2.3353

Abstract

Objectives: To explore the application of a model that integrates various factors that influence Dayak adolescents' intentions to consume alcohol in Sarawak, Malaysia.Methods: A cross-sectional quantitative study was conducted from September 2019 to February 2022. Through multistage stratified cluster sampling, 12 districts were selected from 12 divisions. Respondents were selected randomly and were interviewed using a questionnaire.Results: Structural equation modeling was used to test the Theory of Planned Behavior (TPB) and explore the relationship between various variables and respondents' intention to consume alcohol. The findings suggest that attitude (β=.22, p<.001), subjective norm (β = .33, p < .001), and perceived behavior control (β =−.41, p<.001) influenced the intention to consume alcohol. In contrast, alcohol consumption was associated with intention (β=.15, p < .001), attitude (β=.20, p<.001), and perceived behavior control (β=−.32, p<.001).Conclusion: The findings demonstrated that the TPB model can be used to explore various variables that influence the intention to consume alcohol among Dayak adolescents, with attitude, subjective norm, and perceived behavior control as the variable influencing the intention. This highlights the need for paying attention to those variables when developing age-appropriate strategies that address various social levels to curb alcohol consumption. Given the concerning rates of risky drinking and dependency, school-based health initiatives and focused screening for Dayak adolescents are crucial.
Supportive work environment for people with Down syndrome in Malaysia: a cross-sectional study Rahman, Md Mizanur; Ting, Chuong Hock; Safii, Razitasham; Saimon, Rosalia; Chen, Yoke Yong; Puteh, Sharifa Ezat Wan; Adenan, Abg Safuan
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.v14i3.25124

Abstract

Understanding organizational culture, knowledge of employment rights, and positive attitudes towards people with disabilities is crucial for creating inclusive workplaces. This Malaysian study compared the perspectives of employers, employees, and community members with disabilities using a cross-sectional design and convenience sampling of 595 respondents. Data on demographics, organizational culture, legislative knowledge, and attitudes were collected via a validated survey and analyzed using descriptive statistics, one-way analysis of variance (ANOVA), and multiple linear regression in JAMOVI and SPSS, with a p-value<.05 indicating significance. The study found a moderately supportive organizational culture for employing people with disabilities, with the highest scores in supportive work environments and inclusive culture. Employers and employees perceived greater top management commitment and inclusivity than community members with Down syndrome. Legislative knowledge and positive attitudes significantly shaped perceptions of a supportive and inclusive workplace. Muslim participants reported greater support and disability-accommodating human resource (HR) practices than those of other religions. The findings underscore the need for targeted training and awareness programs on disability rights to enhance inclusivity among all stakeholders in Malaysia.
Co-Authors Adenan, Abg Safuan Ahmad, Sk Akhtar Ahmed, Jakaria Ahmed, Muhammad Jalal Uddin Ajeng , Rudy Ngau Ajeng, Rudy Ngau Akther, Mahinur Aren, Merikan Bin Chen, Yoke Yong Daud, Muhammad Siddiq Gahamat , Mohd Faiz Gahamat, Mohd Faiz Hasan, Md Umaid Islam, Md Kafiul Jahan, Israt Kana, Kamarudin Bin Lukas, Sabrina Binti Mizan, Sharmin Muhammad , Siddiq Nadim, Mahimul Islam Puteh, Sharifa Ezat Wan Rabeya, Tapasy Rukhsara, Lamia Safii, Razitasham Safii, Razitasham binti Saimon, Rosalia Sohel, Amir Ting, Chuong Hock Ullah, Ahad