Browsing by Author "Akter, Sadia"

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Behavioral segment & customer expectation towards telecommunication operators in Bangladesh
(BRAC University, 8/2/2018) Akter, Sadia; Billah, N.M. Baki
With the help of technological support telecom industries have brought out a revolutionary change, where people are fully depends on networking system. Whereas purchaser ways of life winding up progressively subject to their versatility, the estimated items empowering people to work, convey or engage themselves in an area freeway has risen in like manner. However with the presentation of telecom industry way of life of ordinary citizens has been change, now people are perusing web with their fastest internet data they are getting from their system administrations. Besides this telecom benefits additionally made their client life simpler by propelling MFS benefits through versatile system. Furthermore the chain of providing telecom services is based on B2B, B2B broadcast communications suppliers keep up frameworks that send information, text, sound, voice and video, which take into account coordinate correspondences between organizations. Broadcast communications stages can likewise be used by advertisers keeping in mind the end goal to support an organization's perceive ability inside the business and distinguish organizing openings.
Medical Appointment System MAS
(Department of Technical and Vocational Education(TVE), Islamic University of Technology(IUT), Board Bazar, Gazipur-1704, Bangladesh, 2024-06-30) Akter, Sadia; Akter, Maimuna; Khatun, Sumaiya
Potato Disease Detection Using Machine Learning
(2021 Third International Conference on Intelligent Communication Technologies and Virtual Mobile Networks (ICICV), IEEE, 2021-03-31) Tarik, Marjanul Islam; Akter, Sadia; Al Mamun, Abdullah; Sattar, Abdus
In Bangladesh potato is one of the major crops. Potato cultivation has been very popular in Bangladesh for the last few decades. But potato production is being hampered due to some diseases which are increasing the cost of farmers in potato production. However, some potato diseases are hampering potato production that is increasing the cost of farmers. Which is disrupting the life of the farmer. An automated and rapid disease detection process to increase potato production and digitize the system. Our main goal is to diagnose potato disease using leaf pictures that we are going to do through advanced machine learning technology. This paper offers a picture that is processing and machine learning based automated systems potato leaf diseases will be identified and classified. Image processing is the best solution for detecting and analyzing these diseases. In this analysis, picture division is done more than 2034 pictures of unhealthy potato and potato's leaf, which is taken from openly accessible plant town information base and a few pre-prepared models are utilized for acknowledgment and characterization of sick and sound leaves. Among them, the program predicts with an accuracy of 99.23% in testing with 25% test data and 75% train data. Our output has shown that machine learning exceeds all existing tasks in potato disease detection.
Predicting Student Stress and Smartphone Addiction using Machine Learning
(Daffodil International University, 2025-05-14) Akter, Sumyia; Akter, Sadia
Student stress and smartphone addiction have emerged as critical issues in contemporary academic environments, affecting mental health, academic performance, and overall well-being. This study explores the intricate relationships among behavioral factors, physiological indicators, and smartphone usage patterns using machine learning (ML) techniques. A total of ten regression models—Linear Regression, Decision Tree, Random Forest, Gradient Boosting, Support Vector Regressor (SVR), K-Nearest Neighbors (KNN), ElasticNet, XGBoost, LightGBM, and CatBoost— were evaluated for their ability to predict self-reported stress and addiction levels among students. Performance was measured using MSE, RMSE, R2, and computational efficiency. Results revealed that CatBoost demonstrated superior performance for stress prediction, achieving the lowest MSE (1.634) and highest R2 (0.793), while Linear Regression performed best for addiction prediction with the lowest MSE (0.377) and highest R2 (0.954). Correlation analysis highlighted strong associations between high stress levels and poor academic performance (r = 0.85), reduced sleep duration (r = –0.69), and high smartphone dependency. Notably, nighttime phone usage, frequent device unlocks, and high notification counts were found to significantly influence both stress and addiction levels. Beyond model accuracy, this study provides a comprehensive impact analysis across societal, environmental, ethical, and sustainability dimensions. It emphasizes the urgent need for proactive strategies in educational and mental health domains to mitigate digital overdependence and stress. Furthermore, it advocates for sustainable research practices, including energy-efficient computing and privacy-centered ethical frameworks, aligning technological progress with social responsibility. The findings pave the way for targeted interventions through mobile applications and policy initiatives to enhance student well-being in an increasingly digital academic landscape.