Browsing by Author "Firoz, Mehedi"

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A study on social media addiction analysis on the people of Bangladesh using machine learning algorithms
(Scopus, 2024-10) Mim, Minjun Nahar; Firoz, Mehedi; Islam, Mohammad Monirul; Hasan, Mahady; Habib, Md. Tarek
: Social media has become a fundamental element of contemporary life, providing countless benefits but also posing substantial concerns. While technology improves connectedness and information exchange, excessive use raises issues about social and personal well-being. The emergence of social media addiction emphasizes its influence on everyday routines and mental health, with many people favoring online activities above vital tasks, resulting in real repercussions. Twitter, Facebook, and Snapchat have a significant impact on emotional well-being, adding to global rates of despair and anxiety. To measure the frequency of social media reliance, we studied data from 1,417 individuals using machine learning methods such as decision tree (DT) classifier, random forest (RF) classifier, support vector classifier (SVC), k-nearest neighbors (K-NN), and multinomial naive Bayes (NB). Understanding the behavioral patterns that drive addiction allows us to create tailored therapies to encourage healthy digital behaviors. This study highlights the critical necessity to address social media addiction as a complicated societal issue. Our major goal is to determine the amount of people who are addicted to social media.
A Study on Social Media Addiction Analysis on the People of Bangladesh Using Machine Learning Algorithms
(Institute of Advanced Engineering and Science (IAES), 2024-10-15) Mim, Minjun Nahar; Firoz, Mehedi; Islam, Mohammad Monirul; Hasan, Mahady; Habib, Md. Tarek
Social media has become a fundamental element of contemporary life, providing countless benefits but also posing substantial concerns. While technology improves connectedness and information exchange, excessive use raises issues about social and personal well-being. The emergence of social media addiction emphasizes its influence on everyday routines and mental health, with many people favoring online activities above vital tasks, resulting in real repercussions. Twitter, Facebook, and Snapchat have a significant impact on emotional well-being, adding to global rates of despair and anxiety. To measure the frequency of social media reliance, we studied data from 1,417 individuals using machine learning methods such as decision tree (DT) classifier, random forest (RF) classifier, support vector classifier (SVC), k-nearest neighbors (K-NN), and multinomial naive Bayes (NB). Understanding the behavioral patterns that drive addiction allows us to create tailored therapies to encourage healthy digital behaviors. This study highlights the critical necessity to address social media addiction as a complicated societal issue. Our major goal is to determine the amount of people who are addicted to social media.
Mental Stress Detection of University Students in Bangladesh Using Machine Learning
(Daffodil International University, 23-02-18) Firoz, Mehedi
These days, there is a huge problem with mental stress, and the problem is particularly widespread among students at educational institutions of higher learning. According to the views that are prevalent now, the historical era that was formerly thought to be the one with the least level of stress is now deemed to be the most difficult time period. Depression, suicide, heart attacks, and strokes are just some of the current health issues that have been linked to the rising levels of mental stress that individuals are exposed to in today's culture. Because of this, we largely extracted the mental stress ratings of university students by using six distinct machine learning algorithms for this study. These examples of machine learning algorithms are as follows: Decision Tree Classifier, Random, Forest Classifier, SVC, KNN Classifier, Multinomial NB, and K-Nearest Neighbors Regressor. The major objective of this inquiry is to determine the number of students who are experiencing difficulties in managing with their emotional stress. The dataset was put together by hand with paper and manual information obtained from a survey. Out of the six different classification strategies, the Decision Tree Classifier and the Random Forest Classifier both had the highest test result of 0.99.
University student's mental stress detection using machine learning
(Daffodil International University, 2023-09-23) Firoz, Mehedi; Islam, Mohammad Monirul; Shidujaman, Mohammad; Islam, Ashraful
University students are especially susceptible to the negative effects of mental stress in today's environment, which is a serious issue overall. A great deal of pressure is now being placed on a period of life that was traditionally considered to be the most carefree. People in today's culture are exposed to increasingly high levels of mental stress, which has been related to a broad variety of health problems, such as depression, suicide, heart attacks, and strokes. Because of this, in order to primarily extract, for the purposes of this research, the mental stress ratings of university students, we applied a total of six distinct machine learning methods. The Decision Tree Classifier, the Random Forest Classifier, the SVC, the KNN Classifier, the Multinomial NB, and the K-Nearest Neighbors Regressor are only some of the machine learning algorithms that are available. This investigation's principal objective is to determine the percentage of students who are struggling to deal with emotional pressure in their lives. The dataset was put together by hand with paper and manual information obtained from a survey. Out of the six distinct classification strategies, the Decision Tree Classifier and the Random Forest Classifier both achieved a test result of 0.99, which is the maximum score that can be achieved.
University Student's Mental Stress Detection Using Machine Learning
(Independent University, Bangladesh, 2023-06) Firoz, Mehedi; Islam, Mohammad Monirul; Shidujaman, Mohammad; Islam, Ashraful; Habib, Md. Tarek
University students are especially susceptible to the negative effects of mental stress in today's environment, which is a serious issue overall. A great deal of pressure is now being placed on a period of life that was traditionally considered to be the most carefree. People in today's culture are exposed to increasingly high levels of mental stress, which has been related to a broad variety of health problems, such as depression, suicide, heart attacks, and strokes. Because of this, in order to primarily extract, for the purposes of this research, the mental stress ratings of university students, we applied a total of six distinct machine learning methods. The Decision Tree Classifier, the Random Forest Classifier, the SVC, the KNN Classifier, the Multinomial NB, and the K-Nearest Neighbors Regressor are only some of the machine learning algorithms that are available. This investigation's principal objective is to determine the percentage of students who are struggling to deal with emotional pressure in their lives. The dataset was put together by hand with paper and manual information obtained from a survey. Out of the six distinct classification strategies, the Decision Tree Classifier and the Random Forest Classifier both achieved a test result of 0.99, which is the maximum score that can be achieved.