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All Journal TELKOMNIKA (Telecommunication Computing Electronics and Control) Indonesian Journal of Electrical Engineering and Computer Science Health Dynamics
Dilshad Islam
Chattogram Veterinary and Animal Sciences University
Computer Science & IT Dentistry Health Professions Immunology & microbiology Medicine & Pharmacology Nursing Public Health

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An efficient automated vehicle license plate recognition system under image processing Dilshad Islam; Tanjim Mahmud; Tanjia Chowdhury
Indonesian Journal of Electrical Engineering and Computer Science Vol 29, No 2: February 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v29.i2.pp1055-1062

Abstract

An automated vehicle license plate recognition system using image processing techniques identifies vehicle numbers without human interference. This system has significant impact because of its good application in various fields like car parking, access control, speed control, automatic toll collection, border security, traffic violence detection and surveillance applications. This paper presents a methodology that is quite simple but at the same time very much efficient and this system consists of four sequential modules which are preprocessing, number plate extraction, number plate character segmentation and character recognition. Preprocessing aims to improve the image quality that is captured in various illumination conditions and stick out outstanding information that we need, which is favorable to subsequent processing including extraction, segmentation and recognition. After preprocessing various morphological operations are applied to extract the desired license plate region. Then for segmentation the bounding box method is applied that segments each letter and number present on the license plate region. Finally, template matching is applied in identifying all segmented characters present in the license plate image. The experimental results showed that the proposed system can recognize license plate characters efficiently with higher accuracy. Using MATLAB software, the proposed method attains recognition accuracy of 94.17%.
Convolutional neural network-based real-time drowsy driver detection for accident prevention Nippon Datta; Tanjim Mahmud; Manoara Begum; Mohammad Tarek Aziz; Dilshad Islam; Md. Faisal Bin Abdul Aziz; Khudaybergen Kochkarov; Temur Eshchanov; Valisher Sapayev Odilbek Uglu; Sobir Parmanov; Mohammad Shahadat Hossain; Karl Andersson
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 23, No 3: June 2025
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v23i3.26059

Abstract

Drowsy driving significantly threatens road safety, contributing to many accidents globally. This paper presents a convolutional neural network (CNN)-based real-time drowsy driver detection system aimed at preventing such accidents, particularly for deployment in Android applications. We propose a lightweight CNN architecture that effectively identifies drowsiness and microsleep episodes by categorizing driver facial expressions into four distinct categories: close-eye expressions, open-eye expressions, yawns, and no yawns. Our model, which employs facial landmark detection and various pre-processing techniques to enhance accuracy, achieves an impressive 96.6% accuracy. This performance surpasses several popular CNN architectures, including VGG16, VGG19, MobileNetV2, ResNet50, and DenseNet121. Notably, our proposed model is highly efficient, with only 0.4 million parameters and a memory requirement of 1.51 MB, making it ideal for real-time applications. The comparative analysis highlights the superior balance between accuracy and resource efficiency of our model, demonstrating its potential for practical deployment in reducing accidents caused by driver fatigue.
Influence of Infant and Young Child Feeding Practices (IYCF) on Nutritional Status of Children Aged 6-59 Months in Moheshkhali, Cox’sbazar, Bangladesh: A Cross-Sectional Study M Abdullah Al Masum; Taslima Ahmed; Dilshad Islam; Sadia Tuz Zohra
Health Dynamics Vol 1, No 11 (2024): November 2024
Publisher : Knowledge Dynamics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33846/hd11102

Abstract

Background: This study addresses the critical issue of malnutrition among under-five children in Moheshkhali, Cox's Bazar, Bangladesh. Despite the alarming rates of stunting, wasting, and underweight in this region, there is a significant lack of localized data on socioeconomic factors, IYCF practices, and overall health status. Understanding these variables is essential for developing effective interventions. Methods: The research involved a cross-sectional study design, where anthropometric measurements were conducted to assess the nutritional status of children aged 6 to 59 months. Maternal education, age, and Body Mass Index (BMI) were also evaluated to explore their impacts on child nutrition. Data were analyzed using standard statistical methods to draw correlations and identify trends. Results: The findings revealed a high prevalence of malnutrition among under-five children, with notable gender disparities in stunting and underweight. The analysis indicated a strong relationship between maternal health indicators—particularly BMI—and child nutritional outcomes. Moreover, inadequate adherence to IYCF practices was observed, with many mothers failing to implement optimal breastfeeding and complementary feeding strategies. Conclusion: The study underscores the urgent need for targeted interventions aimed at improving maternal education and IYCF practices in Moheshkhali. A multi-sectoral approach involving government and non-government organizations is essential to address the malnutrition crisis effectively. Investing in preventive measures and promoting healthy feeding practices are crucial for ensuring the well-being of both mothers and their children.
Co-Authors Karl Andersson Khudaybergen Kochkarov M Abdullah Al Masum Manoara Begum Md. Faisal Bin Abdul Aziz Mohammad Shahadat Hossain Mohammad Tarek Aziz Nippon Datta Sadia Tuz Zohra Sobir Parmanov Tanjia Chowdhury Tanjim Mahmud Tanjim Mahmud Taslima Ahmed Temur Eshchanov Valisher Sapayev Odilbek Uglu