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This Author published in this journals
All Journal Bulletin of Electrical Engineering and Informatics International Journal of Advances in Intelligent Informatics Indonesian Journal of Electrical Engineering and Computer Science
Md. Hasan Imam Bijoy
Daffodil International University
Computer Science & IT Electrical & Electronics Engineering

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Journal : Bulletin of Electrical Engineering and Informatics

 1 
Prediction of internet user satisfaction levels in Bangladesh using data mining and analysis of influential factors Md. Hasan Imam Bijoy; Sumiya Alam Akhi; Md. Ali Ashraf Nayeem; Md. Mahbubur Rahman; Md. Jueal Mia
Bulletin of Electrical Engineering and Informatics Vol 11, No 2: April 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v11i2.3617

Abstract

Today the world has already acknowledged as a global village by the inter-net which has technologically evolved into a significant performance instrument for individuals, businesses, and countries seeking to achieve betterment. This study is based on data mining techniques to predict the satisfaction level of internet users from the context of Bangladesh. After conducting a public survey with 18 questions, we were able to acquire 451 responses from participants. Data for user satisfaction was associated with end-user characteristics including certain getting high speed, internet packages, cable type of Wi-Fi connection with targeting various age groups and occupations. The research's most key conceptual breakthrough was the reliability of magnitude predictions of user satisfaction level based on their experience with internet use. The empirical findings indicate that people in Bangladesh have high expectations in existing internet technology, and they are very dissatisfied with their facilities of internet use and to measure satisfaction level related with monthly limit of the Wi-Fi packages and the elements affecting internet speed. Several classifier models were applied to our dataset and among them, Random Forest (RF) performance reaches the top position with 91.53% accuracy.
Bitcoin trading indicator: a machine learning driven real time bitcoin trading indicator for the crypto market Ashikur Rahaman; Abu Kowshir Bitto; Khalid Been Md. Badruzzaman Biplob; Md. Hasan Imam Bijoy; Nusrat Jahan; Imran Mahmud
Bulletin of Electrical Engineering and Informatics Vol 12, No 3: June 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v12i3.4486

Abstract

As opposed to other fiat currencies, bitcoin has no relationship with banks. Its price fluctuation is largely influenced by fresh blocks, news, mining information, support or resistance levels, and public opinion. Therefore, a machine-learning model will be fantastic if it learns from data and tells or indicates if we need to purchase or sell for a little period. In this study, we attempted to create a tool or indicator that can gather tweets in real-time using tweepy and the Twitter application programming interface (API) and report the sentiment at the time. Using the renowned Python module "FBProphet," we developed a model in the second phase that can gather historical price data for the bitcoin to US dollar (BTCUSD) pair and project the price of bitcoin. In order to provide guidance for an intelligent forex trader, we finally merged all of the models into one form. We traded with various models for a very little number of days to validate our bitcoin trading indicator (BTI), and we discovered that the combined version of this tool is more profitable. With the combined version of the instrument, we quickly and with little error root mean square error (RMSE: 1,480.58) generated a profit of $1,000.71 USD.
Sentiment analysis from Bangladeshi food delivery startup based on user reviews using machine learning and deep learning Abu Kowshir Bitto; Md. Hasan Imam Bijoy; Md. Shohel Arman; Imran Mahmud; Aka Das; Joy Majumder
Bulletin of Electrical Engineering and Informatics Vol 12, No 4: August 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v12i4.4135

Abstract

Food delivery methods are at the top of the list in today's world. People's attitudes toward food delivery systems are usually influenced by food quality and delivery time. We did a sentiment analysis of consumer comments on the Facebook pages of Food Panda, HungryNaki, Pathao Food, and Shohoz Food, and data was acquired from these four sites’ remarks. In natural language processing (NLP) task, before the model was implemented, we went through a rigorous data pre-processing process that included stages like adding contractions, removing stop words, tokenizing, and more. Four supervised classification techniques are used: extreme gradient boosting (XGB), random forest classifier (RFC), decision tree classifier (DTC), and multi nominal Naive Bayes (MNB). Three deep learning (DL) models are used: convolutional neural network (CNN), long term short memory (LSTM), and recurrent neural network (RNN). The XGB model exceeds all four machine learning (ML) algorithms with an accuracy of 89.64%. LSTM has the highest accuracy rate of the three DL algorithms, with an accuracy of 91.07%. Among ML and DL models, LSTM DL takes the lead to predict the sentiment.
Co-Authors Abu Kowshir Bitto Aka Das Ashikur Rahaman Fatema Tuj Jannat Hasnur Jahan Imran Mahmud Joy Majumder Khalid Been Badruzzaman Biplob Khalid Been Md. Badruzzaman Biplob Md. Ali Ashraf Nayeem Md. Jueal Mia Md. Mahbubur Rahman Md. Mahfuj Hasan Shohug Md. Shohel Arman Nusrat Jahan Sabina Yesmin Sumiya Alam Akhi