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Data Privacy and Security Analysis for Mental Health Chatbot Applications
This paper analyzed 10 notable apps collected from the Google Play Store, examining their functionalities, data safety policies and required permissions to assess whether user data privacy is adequately protected. We also performed some security test and found several vulnerabilities and discrepancies.
Al Amin Biswas, Md. Sabab Zulfiker, Md. Mahfujur Rahman, Md. Rafsan Jani, Md. Musfique Anwar
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Data Privacy and Security Analysis for Mental Health Chatbot Applications
Deep Learning-Based Classification of Conference Paper Reviews: Accept or Reject?
This paper proposed an automated classification system for paper reviews. Here, the Bi-GRU-LSTM-CNN model attained the highest accuracy of 95.33%.
T. T. Prama, Al Amin Biswas, Md. Musfique Anwar
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Deep Learning-Based Classification of Conference Paper Reviews: Accept or Reject?
Predicting Participants’ Performance in Programming Contests Using Deep Learning Techniques
This paper proposed a framework that predicts the performance of any particular contestant in the upcoming competitions as well as predicts the rating after that contest based on their practice and the performance of their previous contests.
Md. Mahbubur Rahman, Badhan Chandra Das, Al Amin Biswas, Md. Musfique Anwar
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Predicting Participants’ Performance in Programming Contests Using Deep Learning Techniques
A Comparative Study of Machine Learning Algorithms to Detect Cardiovascular Disease with Feature Selection Method
This paper describes different machine learning (ML) algorithms to predict heart disease incorporating a Cardiovascular Disease dataset.
Md. Jubier Ali, Badhan Chandra Das, Suman Saha, Al Amin Biswas, Partha Chakraborty
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A Comparative Study of Machine Learning Algorithms to Detect Cardiovascular Disease with Feature Selection Method
Feature Ranking Based Carrot Disease Recognition Using MIFS Method
This paper proposed an optimal solution to identify diseased carrots. The Random Forest classifier with the top nine features has outperformed all the applied classifiers with the highest accuracy of 94.17%. Lastly, we have found that the features selection technique has helped to minimize the computational cost.
Al Amin Biswas, Md. Sabab Zulfiker, Aditya Rajbongshi, Md. Jueal Mia, Anup Majumder
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Feature Ranking Based Carrot Disease Recognition Using MIFS Method
Sunflower Diseases Recognition Using Computer Vision-Based Approach
This paper proposed an approach for sunflower disease recognition. The highest average accuracy of 90.68% has been obtained for the Random Forest classifier.
Aditya Rajbongshi, Al Amin Biswas, Jahanur Biswas, Rashiduzzaman Shakil, Bonna Akter, Mala Rani Barman
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Sunflower Diseases Recognition Using Computer Vision-Based Approach
Predicting Insomnia Using Multilayer Stacked Ensemble Model
A multilayer stacking model has been employed in this study to predict the appearance of insomnia in a person.
Md. Sabab Zulfiker, Nasrin Kabir, Al Amin Biswas, Partha Chakraborty
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Predicting Insomnia Using Multilayer Stacked Ensemble Model
Forecasting the Trends and Patterns of Crime in Bangladesh using Machine Learning Model
Various machine learning regression models are used to forecast the trends and patterns of crime in Bangladesh.
Al Amin Biswas, Sarnali Basak
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Forecasting the Trends and Patterns of Crime in Bangladesh using Machine Learning Model