Md. Aktaruzzaman Pramanik

@kfupm.edu.sa

Graduate Student, Department of Information and Computer Science, King Fahd University of Petroleum and Minerals
King Fahd University of Petroleum and Minerals

Md. Aktaruzzaman Pramanik


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https://researchid.co/a_pramanik

RESEARCH, TEACHING, or OTHER INTERESTS

Artificial Intelligence, Software, Computer Science Applications, Human-Computer Interaction
10

Scopus Publications

182

Scholar Citations

7

Scholar h-index

6

Scholar i10-index

Scopus Publications

  • Machine Learning and Explainable AI for Liver Disease Prediction: An Integrated Interpretability Framework
    Niaz Ashraf Khan, Md. Ferdous Bin Hafiz, Md. Aktaruzzaman Pramanik, Sohrab Hossain, Shohag Barman, Noman Hossain
    Biomedical Materials and Devices, 2026
  • Enhancing predictive modelling and interpretability in heart failure prediction: a SHAP-based analysis
    Niaz Ashraf Khan, Md. Ferdous Bin Hafiz, Md. Aktaruzzaman Pramanik
    International Journal of Informatics and Communication Technology, 2025
    Predictive modelling plays a crucial role in healthcare, particularly in forecasting mortality due to heart failure. This study focuses on enhancing predictive modelling and interpretability in heart failure prediction through advanced boosting algorithms, ensemble methods, and SHapley Additive exPlanations (SHAP) analysis. Leveraging a dataset of patients diagnosed with cardiovascular diseases (CVD), we employed techniques such as synthetic minority over-sampling technique (SMOTE) and bootstrapping to address class imbalance. Our results demonstrated exceptional predictive performance, with the gradient boosting (GBoost) model achieving the highest accuracy of 91.39%. Ensemble techniques further enhanced performance, with the voting classifier (VC), stacking classifier (SC), and Blending achieving accuracies of 91.00%. SHAP analysis uncovered key features such as time, Serum_creatinine, and Ejection_fraction, significantly impacting mortality prediction. These findings highlight the importance of transparent and interpretable machine learning models in healthcare decision-making processes, facilitating informed interventions and personalized treatment strategies for heart failure patients.
  • An Analytical Review of Preprocessing Techniques in Bengali Natural Language Processing
    Sovon Chakraborty, Protiva Das, Shakib Mahmud Dipto, Md. Aktaruzzaman Pramanik, Jannatun Noor
    IEEE Access, 2025
    Research in Bengali Natural Language Processing (BNLP) is rapidly expanding. Despite being one of the most widely spoken languages in the world, BNLP research remains insufficient, particularly in Bengali speech recognition. The languages rich morphology, agglutinative structure, and diverse dialects make text and speech processing especially challenging. However, these challenges can be addressed with effective preprocessing techniques. Various organizations in Bangladesh and West Bengal are integrating Natural Language Processing (NLP) into their services, but without a thorough understanding of preprocessing, these implementations remain incomplete. Applying proper preprocessing techniques to the Bengali language will serve as a foundation for developing robust NLP applications. This paper presents a comprehensive review of preprocessing techniques in BNLP based on state-of-the-art research. It covers key areas such as sentiment analysis, Named Entity Recognition, speech recognition, text categorization, and summarization. First, the paper provides an in-depth discussion of Bengali language characteristics and research areas in BNLP. It then explores the challenges faced by researchers in processing Bengali text and speech. Additionally, it details various preprocessing techniques, highlighting their advantages and disadvantages. Finally, the paper examines future directions for BNLP, emphasizing the role of effective preprocessing in advancing the field.
  • A Data-driven Based Bengali Text Sentiment Analysis for Internet Service Users
    Md. Ferdous Bin Hafiz, Samia Gofur, Sohrab Hossain, Niaz Ashraf Khan, Md. Aktaruzzaman Pramanik
    International Conference on Recent Progresses in Science Engineering and Technology Icrpset 2024, 2024
    The enormous growth of the Internet and ease in social media accessibility has engaged more users in social media platforms where people can share their experiences freely. Sentiment analysis, a widespread contemporary research interest in the field of natural language processing (NLP), is a technique that extracts valuable information from text data and classifies those data into different class labels based on their sentiment. Literature shows that several kinds of research have been conducted on sentiment analysis in different areas, such as helping the telecommunication industry by providing customer reviews for GSM service providers, creating recommender systems for the mobile industry, and so on. However, most of the research has been done on the English language, and under-resourced languages (e.g.) Bengali have been omitted. Hence, this study created a sentiment analysis model utilizing a data-driven approach to identify Bengali sentiments of Internet service users. A large dataset has been created by utilizing the Facebook platform, and several machine learning and deep learning algorithms have been evaluated on the dataset. After a rigorous evaluation, the result shows that LSTM got an accuracy of 87.8%, which proves the suitability of this study. The described procedure is fully automatic and can be leveraged by the Internet service providers to provide insightful suggestions to their customers.
  • A Transformer Based Approach for Analyzing Scrapped E-Commerce Product Reviews from Social Media Platforms with Explainable AI
    Sajida Kabir, Abdullah Masud, Mahadi Hasan, Saieef Sunny, Md. Aktaruzzaman Pramanik, Sovon Chakraborty
    2024 27th International Conference on Computer and Information Technology Iccit 2024 Proceedings, 2024
    Online shopping has gained substantial popularity in Bangladesh, where companies need to understand the sentiments of customers in order to enhance their products and services for their business. Furthermore, it assists customers to make better purchasing decisions. With the advance of Bengali Natural Language Processing (BNLP), numerous Machine Learning algorithms have performed significantly in classifying sentiments from Bengali sequential data. Cultural and dialect variations limit the capacity of these architectures, where most Recurrent Neural Network-based architectures cannot perform better in classifying Bengali data. Bangla BERT architecture is a transformer-based architecture that can perform significantly better in Bengali sentiment analysis. In this research, the authors gather a dataset of 5016 comments associated with product ID, rating, and sentiment towards a particular product. Exploratory Data Analysis reveals that products with poor ratings exhibit good comments from the same customer. Hence, the Bangla BERT model is applied to the customer’s comments from the dataset to achieve better performance with necessary preprocessing and hyperparameter tuning. The experimental result shows that the model reflects a 95.02% F1-score that outperforms state-of-the-art architectures. Finally, an Explainable AI model named LIME is applied to understand the feature extraction procedure of the transformer model.
  • Performance Analysis of Classification Algorithms for Outcome Prediction of T20 Cricket Tournament Matches
    Md. Aktaruzzaman Pramanik, Md. Mahmudul Hasan Suzan, Al Amin Biswas, Mohammad Zahidur Rahman, A. Kalaiarasi
    2022 International Conference on Computer Communication and Informatics Iccci 2022, 2022
    Cricket is the most popular sport in south Asian countries and the second most popular sport globally. And T20 cricket is the most attractive version of this game. Businesses have grown enormously based on cricketing sports events from the last decade. A large number of research have been done to predict the winner of cricket matches or to analyze game statistics. These studies are helping investors and franchisees to decide on which team they can invest in to gain more profit. Also, coaches, sports analysts, and technicians get game facts and ideas about other teams, which help them make decisions and change plans accordingly. In this study, we have shown a comparative analysis of different non-ensemble and ensemble machine learning classifiers. We have collected data of all sea-sons of the Bangladesh Premier League(BPL) T20 tournament. We have prepared two datasets: one contains only pre-match information, and the second includes post-match information. We have applied five base classifiers and five ensemble classifiers for the analysis. We found that K-Nearest Neighbor(KNN) performed best compared to other algorithms while predicting match results before starting the game. The Gradient Boosting classifier seemed more robust than the different classifiers for predicting match outcomes considering all features.
  • Students' Adaptability Level Prediction in Online Education using Machine Learning Approaches
    Mahmudul Hasan Suzan, Nishat Ahmed Samrin, Al Amin Biswas, Aktaruzzaman Pramanik
    2021 12th International Conference on Computing Communication and Networking Technologies Icccnt 2021, 2021
    Online Education has become a buzzword since the COVID-19 hit the World. Most of the educational institutions went online to continue educational activities while developing countries like Bangladesh took a significant period of time to ensure online education at every education level. Students of several levels also faced many difficulties when they got introduced to online education. It is important for the decisionmakers of educational institutions to be informed about the effectiveness of online education so that they can take further steps to make it more beneficial for the students. Our main motivation is to contribute to this matter by analyzing the relevant factors associated with online education. In this work, we have collected students' information of all three different levels(School, College, and University) by conducting both online and physical surveys. The surveys form consists of an individual's socio-demographic factors. To get an idea about the effectiveness of online education we have applied several machine learning algorithms named Decision Tree (DT), Random Forest (RF), Naive Bayes (NB), Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and also Artificial Neural Network(ANN) on our dataset to predict the adaptability level of the students to online education. Among used algorithms, the Random Forest classifier achieved the best accuracy of 89.63% and outperformed other algorithms.
  • Lemon Leaf Disease Classification Using CNN-based Architectures with Transfer Learning
    Aktaruzzaman Pramanik, Akib Zabed Khan, Al Amin Biswas, Mahbubur Rahman
    2021 12th International Conference on Computing Communication and Networking Technologies Icccnt 2021, 2021
    Various pest-affected and citrus diseases of lemon leaf have become very severe in the temperate weather areas of southeast Asian countries. As a result, the cultivation of lemon and other citrus fruits has been badly affected. An efficient classification of these kinds of diseases can decrease the rate of loss by choosing proper pesticides in time. In this paper, we have applied some Transfer Learning-based Deep Learning models (DenseNet-201, ResNet-50, ResNet-152V2, and Xception) for a cost-effective classification of lemon leaf diseases. We have used our image dataset that is collected from the field level. Among the models we have used, Xception achieved a very higher overall accuracy of 94.34% and outperformed the other previous works.
  • Modeling Traffic Congestion in Developing Countries Using Google Maps Data
    Md. Aktaruzzaman Pramanik, Md Mahbubur Rahman, A. S. M. Iftekhar Anam, Amin Ahsan Ali, M. Ashraful Amin, A. K. M. Mahbubur Rahman
    Advances in Intelligent Systems and Computing, 2021
  • Bangla Documents Classification using Transformer Based Deep Learning Models
    Md Mahbubur Rahman, Md. Aktaruzzaman Pramanik, Rifat Sadik, Monikrishna Roy, Partha Chakraborty
    2020 2nd International Conference on Sustainable Technologies for Industry 4 0 Sti 2020, 2020
    Document classification or categorization assign documents to a predefined domain category. The improvement of document classification techniques has been noticeable worldwide recently. Many transformer-based models have been introduced for different languages, which shows significant improvement in this area of Natural Language Processing. In this paper, we have classified Bangla text documents with the most recent transformer or attention mechanism-based models. We have applied the BERT (Bidirectional Encoder Representations from Transformers) and ELECTRA (Efficiently Learning an Encoder that Classifies Token Replacements Accurately) model for Bangla text classification. Both of them are pre-training text encoders and we have applied fine-tuning approach for the downstream(classification) task. Here, we have used three different Bangla text datasets for our experiment. Both of the models provide outstanding performance for two out of three datasets we have used.

RECENT SCHOLAR PUBLICATIONS

  • Machine Learning and Explainable AI for Liver Disease Prediction: An Integrated Interpretability Framework
    NA Khan, MF Bin Hafiz, MA Pramanik, S Hossain, S Barman, N Hossain
    Biomedical Materials & Devices, 1-14 , 2025
    2025
    Citations: 1
  • An Analytical Review of Preprocessing Techniques in Bengali Natural Language Processing
    S Chakraborty, P Das, SM Dipto, MA Pramanik, J Noor
    IEEE Access , 2025
    2025
    Citations: 7
  • Enhancing predictive modelling and interpretability in heart failure prediction: a SHAP-based analysis
    NA Khan, MFB Hafiz, MA Pramanik
    International Journal of Informatics and Communication Technology (IJ-ICT … , 2025
    2025
    Citations: 13
  • A Transformer Based Approach for Analyzing Scrapped E-Commerce Product Reviews from Social Media Platforms with Explainable AI
    S Kabir, A Masud, M Hasan, S Sunny, MA Pramanik, S Chakraborty
    2024 27th International Conference on Computer and Information Technology … , 2024
    2024
  • A Data-driven Based Bengali Text Sentiment Analysis for Internet Service Users
    MFB Hafiz, S Gofur, S Hossain, NA Khan, MA Pramanik
    2024 International Conference on Recent Progresses in Science, Engineering … , 2024
    2024
  • Performance Analysis of Classification Algorithms for Outcome Prediction of T20 Cricket Tournament Matches
    MA Pramanik, MMH Suzan, AA Biswas, MZ Rahman, A Kalaiarasi
    2022 International Conference on Computer Communication and Informatics … , 2022
    2022
    Citations: 16
  • Lemon Leaf Disease Classification Using CNN-based Architectures with Transfer Learning
    MA Pramanik, AA Biswas, MAZ Khan, MM Rahman
    12th International Conference on Computing Communication and Networking … , 2021
    2021
    Citations: 28
  • Students’ Adaptability Level Prediction in Online Education using Machine Learning Approaches
    MMH Suzan, NA Samrin, AA Biswas, MA Pramanik
    12th International Conference on Computing Communication and Networking … , 2021
    2021
    Citations: 49
  • Modeling Traffic Congestion in Developing Countries using Google Maps Data
    MA Pramanik, MM Rahman, ASM Anam, AA Ali, MA Amin, AKM Rahman
    Future of Information and Communication Conference 1363, 513-531 , 2021
    2021
    Citations: 11
  • Bangla Documents Classification using Transformer Based Deep Learning Models
    MM Rahman, MA Pramanik, R Sadik, M Roy, P Chakraborty
    2nd International Conference on Sustainable Technologies for Industry 4.0 … , 2020
    2020
    Citations: 57

MOST CITED SCHOLAR PUBLICATIONS

  • Bangla Documents Classification using Transformer Based Deep Learning Models
    MM Rahman, MA Pramanik, R Sadik, M Roy, P Chakraborty
    2nd International Conference on Sustainable Technologies for Industry 4.0 … , 2020
    2020
    Citations: 57
  • Students’ Adaptability Level Prediction in Online Education using Machine Learning Approaches
    MMH Suzan, NA Samrin, AA Biswas, MA Pramanik
    12th International Conference on Computing Communication and Networking … , 2021
    2021
    Citations: 49
  • Lemon Leaf Disease Classification Using CNN-based Architectures with Transfer Learning
    MA Pramanik, AA Biswas, MAZ Khan, MM Rahman
    12th International Conference on Computing Communication and Networking … , 2021
    2021
    Citations: 28
  • Performance Analysis of Classification Algorithms for Outcome Prediction of T20 Cricket Tournament Matches
    MA Pramanik, MMH Suzan, AA Biswas, MZ Rahman, A Kalaiarasi
    2022 International Conference on Computer Communication and Informatics … , 2022
    2022
    Citations: 16
  • Enhancing predictive modelling and interpretability in heart failure prediction: a SHAP-based analysis
    NA Khan, MFB Hafiz, MA Pramanik
    International Journal of Informatics and Communication Technology (IJ-ICT … , 2025
    2025
    Citations: 13
  • Modeling Traffic Congestion in Developing Countries using Google Maps Data
    MA Pramanik, MM Rahman, ASM Anam, AA Ali, MA Amin, AKM Rahman
    Future of Information and Communication Conference 1363, 513-531 , 2021
    2021
    Citations: 11
  • An Analytical Review of Preprocessing Techniques in Bengali Natural Language Processing
    S Chakraborty, P Das, SM Dipto, MA Pramanik, J Noor
    IEEE Access , 2025
    2025
    Citations: 7
  • Machine Learning and Explainable AI for Liver Disease Prediction: An Integrated Interpretability Framework
    NA Khan, MF Bin Hafiz, MA Pramanik, S Hossain, S Barman, N Hossain
    Biomedical Materials & Devices, 1-14 , 2025
    2025
    Citations: 1
  • A Transformer Based Approach for Analyzing Scrapped E-Commerce Product Reviews from Social Media Platforms with Explainable AI
    S Kabir, A Masud, M Hasan, S Sunny, MA Pramanik, S Chakraborty
    2024 27th International Conference on Computer and Information Technology … , 2024
    2024
  • A Data-driven Based Bengali Text Sentiment Analysis for Internet Service Users
    MFB Hafiz, S Gofur, S Hossain, NA Khan, MA Pramanik
    2024 International Conference on Recent Progresses in Science, Engineering … , 2024
    2024