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All Journal International Journal of Electrical and Computer Engineering JOURNAL OF EDUCATION SCIENCE Indonesian Journal of Electrical Engineering and Computer Science
Md. Sadekur Rahman
Daffodil International University
Computer Science & IT Education Electrical & Electronics Engineering

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Prediction of addiction to drugs and alcohol using machine learning: A case study on Bangladeshi population Md. Ariful Islam Arif; Saiful Islam Sany; Farah Sharmin; Md. Sadekur Rahman; Md. Tarek Habib
International Journal of Electrical and Computer Engineering (IJECE) Vol 11, No 5: October 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v11i5.pp4471-4480

Abstract

Nowadays addiction to drugs and alcohol has become a significant threat to the youth of the society as Bangladesh’s population. So, being a conscientious member of society, we must go ahead to prevent these young minds from life-threatening addiction. In this paper, we approach a machinelearning-based way to forecast the risk of becoming addicted to drugs using machine-learning algorithms. First, we find some significant factors for addiction by talking to doctors, drug-addicted people, and read relevant articles and write-ups. Then we collect data from both addicted and nonaddicted people. After preprocessing the data set, we apply nine conspicuous machine learning algorithms, namely k-nearest neighbors, logistic regression, SVM, naïve bayes, classification, and regression trees, random forest, multilayer perception, adaptive boosting, and gradient boosting machine on our processed data set and measure the performances of each of these classifiers in terms of some prominent performance metrics. Logistic regression is found outperforming all other classifiers in terms of all metrics used by attaining an accuracy approaching 97.91%. On the contrary, CART shows poor results of an accuracy approaching 59.37% after applying principal component analysis.
ENGAGING STUDENTS TO TAKE OWNERSHIP OF THEIR LEARNING THROUGH A STEPPED TEACHING MODEL BASED ON THE QUR’AN: EVALUATION BY TEACHERS TRAINED USING THIS MODEL Khalid Been Md. Badruzzaman Biplob; Yousuf M. Islam; Md. Sadekur Rahman
JOURNAL OF EDUCATION SCIENCE Vol 1, No 1 (2015): April 2015
Publisher : Universitas Ubudiyah Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.3314/jes.v1i1.17

Abstract

Getting students to learn and be confident has always challenged teachers and educationists. With the introduction of the subject of Instructional Design, teaching is now looked upon as a stepped process through which students must be taken through. To this end many teaching models have been proposed and used by teachers all over the world. At the same time, it has been noticed that students who take “ownership” of their learning are most likely to become independent learners. Also, with the huge rise in demand for tertiary level education all over the world and more so in developing countries like Bangladesh developing successful models to manage the wave of new students has become even more important. In Bangladesh, the increased demand is coming from rural students who have had a limited access to proper primary and secondary education available in the rural areas of Bangladesh. This has added to the challenge of being able to deliver teaching that can turn around students who have poor study and language skills. In this paper we propose a stepped teaching model based on verses from the Qur’an that talk about the brain. We apply this model to training 54 newly recruited teachers who have joined Daffodil International University in the semester of spring, 2015. The idea is the teachers should evaluate the model and if perceived effective use the model in their own teaching. We demonstrate the model in action with these teachers and share the evaluation done by them.Keywords: Inductive teaching, instructional design, ownership, Qur’an, stepped teaching model,teaching models, tertiary level education
An investigative design of optimum stochastic language model for bangla autocomplete Md.Iftakher Alam Eyamin; Md. Tarek Habib; Muhammad Ifte Khairul Islam; Md. Sadekur Rahman; Md. Abbas Ali Khan
Indonesian Journal of Electrical Engineering and Computer Science Vol 13, No 2: February 2019
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v13.i2.pp671-676

Abstract

Word completion and word prediction are two important phenomena in typing that have extreme effect on aiding disable people and students while using keyboard or other similar devices. Such autocomplete technique also helps students significantly during learning process through constructing proper keywords during web searching. A lot of works are conducted for English language, but for Bangla, it is still very inadequate as well as the metrics used for performance computation is not rigorous yet. Bangla is one of the mostly spoken languages (3.05% of world population) and ranked as seventh among all the languages in the world. In this paper, word prediction on Bangla sentence by using stochastic, i.e. N-gram based language models are proposed for autocomplete a sentence by predicting a set of words rather than a single word, which was done in previous work. A novel approach is proposed in order to find the optimum language model based on performance metric. In addition, for finding out better performance, a large Bangla corpus of different word types is used.
Co-Authors Farah Sharmin Khalid Been Md. Badruzzaman Biplob Md. Abbas Ali Khan Md. Ariful Islam Arif Md. Tarek Habib Md.Iftakher Alam Eyamin Muhammad Ifte Khairul Islam Saiful Islam Sany Yousuf M. Islam