Search

Your search keyword '"Mahmud, Tanjim"' showing total 79 results
Start Over Author "Mahmud, Tanjim"

Refine your results

more »

more »

more »

more »
79 results on '"Mahmud, Tanjim"'

1. Exploring the Effectiveness of Region-Based CNNs in Skin Cancer Diagnosis

Author

Mahmud, Tanjim, Barua, Koushick, Chakma, Kanchan, Chakma, Rishita, Sharmen, Nahed, Kaiser, M. Shamim, Hossain, Md. Sazzad, Hossain, Mohammad Shahadat, Andersson, Karl, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Mahmud, Mufti, editor, Kaiser, M. Shamim, editor, Bandyopadhyay, Anirban, editor, Ray, Kanad, editor, and Al Mamun, Shamim, editor

Published
2025
Full Text
View/download PDF

2. A Two-Stage Stacking Ensemble Learning for Employee Attrition Prediction

Author

Barman, Sourav, Biswas, Md. Raju, Marjan, Sultana, Nahar, Nazmun, Imam, Md. Hasan, Mahmud, Tanjim, Kaiser, M. Shamim, Hossain, Mohammad Shahadat, Andersson, Karl, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Mahmud, Mufti, editor, Kaiser, M. Shamim, editor, Bandyopadhyay, Anirban, editor, Ray, Kanad, editor, and Al Mamun, Shamim, editor

Published
2025
Full Text
View/download PDF

3. Deep Learning Solutions for Detecting Bangla Fake News: A CNN-Based Approach

Author

Habiba, Sultana Umme, Mahmud, Tanjim, Naher, Sultana Rokeya, Aziz, Mohammad Tarek, Rahman, Taohidur, Datta, Nippon, Hossain, Mohammad Shahadat, Andersson, Karl, Shamim Kaiser, M., Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Mahmud, Mufti, editor, Kaiser, M. Shamim, editor, Bandyopadhyay, Anirban, editor, Ray, Kanad, editor, and Al Mamun, Shamim, editor

Published
2025
Full Text
View/download PDF

4. Ensemble Learning Approaches for Alzheimer’s Disease Classification in Brain Imaging Data

Author

Mahmud, Tanjim, Aziz, Mohammad Tarek, Uddin, Mohammad Kamal, Barua, Koushick, Rahman, Taohidur, Sharmen, Nahed, Shamim Kaiser, M., Sazzad Hossain, Md., Hossain, Mohammad Shahadat, Andersson, Karl, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Mahmud, Mufti, editor, Kaiser, M. Shamim, editor, Bandyopadhyay, Anirban, editor, Ray, Kanad, editor, and Al Mamun, Shamim, editor

Published
2025
Full Text
View/download PDF

5. Cyberbullying Detection for Low-resource Languages and Dialects: Review of the State of the Art

Author

Mahmud, Tanjim, Ptaszynski, Michal, Eronen, Juuso, and Masui, Fumito

Subjects
Computer Science - Computation and Language
Abstract

The struggle of social media platforms to moderate content in a timely manner, encourages users to abuse such platforms to spread vulgar or abusive language, which, when performed repeatedly becomes cyberbullying a social problem taking place in virtual environments, yet with real-world consequences, such as depression, withdrawal, or even suicide attempts of its victims. Systems for the automatic detection and mitigation of cyberbullying have been developed but, unfortunately, the vast majority of them are for the English language, with only a handful available for low-resource languages. To estimate the present state of research and recognize the needs for further development, in this paper we present a comprehensive systematic survey of studies done so far for automatic cyberbullying detection in low-resource languages. We analyzed all studies on this topic that were available. We investigated more than seventy published studies on automatic detection of cyberbullying or related language in low-resource languages and dialects that were published between around 2017 and January 2023. There are 23 low-resource languages and dialects covered by this paper, including Bangla, Hindi, Dravidian languages and others. In the survey, we identify some of the research gaps of previous studies, which include the lack of reliable definitions of cyberbullying and its relevant subcategories, biases in the acquisition, and annotation of data. Based on recognizing those research gaps, we provide some suggestions for improving the general research conduct in cyberbullying detection, with a primary focus on low-resource languages. Based on those proposed suggestions, we collect and release a cyberbullying dataset in the Chittagonian dialect of Bangla and propose a number of initial ML solutions trained on that dataset. In addition, pre-trained transformer-based the BanglaBERT model was also attempted., Comment: 52 Pages

Published
2023
Full Text
View/download PDF

6. Vulgar Remarks Detection in Chittagonian Dialect of Bangla

Author

Mahmud, Tanjim, Ptaszynski, Michal, and Masui, Fumito

Subjects
Computer Science - Computation and Language
Abstract

The negative effects of online bullying and harassment are increasing with Internet popularity, especially in social media. One solution is using natural language processing (NLP) and machine learning (ML) methods for the automatic detection of harmful remarks, but these methods are limited in low-resource languages like the Chittagonian dialect of Bangla.This study focuses on detecting vulgar remarks in social media using supervised ML and deep learning algorithms.Logistic Regression achieved promising accuracy (0.91) while simple RNN with Word2vec and fastTex had lower accuracy (0.84-0.90), highlighting the issue that NN algorithms require more data., Comment: 5 pages

Published
2023
Full Text
View/download PDF

7. A State-of-the-Art Review of Machine Learning in Cybersecurity Data Science

Author

Aziz, Mohammad Tarek, Mahmud, Tanjim, Datta, Nippon, Maskat Sharif, Md., Khan, Nayeem Uddin Ahmed, Yasmin, Suraiya, Uddin, M. D. Nizam, Hossain, Mohammad Shahadat, Andersson, Karl, Bansal, Jagdish Chand, Series Editor, Deep, Kusum, Series Editor, Nagar, Atulya K., Series Editor, Basha, Syed Muzamil, editor, Taherdoost, Hamed, editor, and Zanchettin, Cleber, editor

Published
2024
Full Text
View/download PDF

8. Deep Learning Approach for Driver Drowsiness Detection in Real Time

Author

Mahmud, Tanjim, Saha, Bappa, Islam, Dilshad, Aziz, Mohammad Tarek, Datta, Nippon, Barua, Koushick, Hossain, Mohammad Shahadat, Andersson, Karl, Bansal, Jagdish Chand, Series Editor, Deep, Kusum, Series Editor, Nagar, Atulya K., Series Editor, Basha, Syed Muzamil, editor, Taherdoost, Hamed, editor, and Zanchettin, Cleber, editor

Published
2024
Full Text
View/download PDF

9. Enhancing Road Safety with AdaBoost-Based Drowsiness Detection for Drivers

Author

Mahmud, Tanjim, Tripura, Sajib, Karim, Md. Adnan Ul, Ara, Israt, Barua, Koushick, Barua, Anik, Hossain, Mohammad Shahadat, Andersson, Karl, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Vasant, Pandian, editor, Panchenko, Vladimir, editor, Munapo, Elias, editor, Weber, Gerhard-Wilhelm, editor, Thomas, J. Joshua, editor, Intan, Rolly, editor, and Shamsul Arefin, Mohammad, editor

Published
2024
Full Text
View/download PDF

10. Early Prediction of Chronic Kidney Disease Using Machine Learning Algorithms with Feature Selection Techniques

Author

Umme Habiba, Sultana, Tasnim, Farzana, Hasan Chowdhury, Mohammad Saeed, Islam, Md. Khairul, Nahar, Lutfun, Mahmud, Tanjim, Kaiser, M. Shamim, Hossain, Mohammad Shahadat, Andersson, Karl, Ghosh, Ashish, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Mahmud, Mufti, editor, Ben-Abdallah, Hanene, editor, Kaiser, M. Shamim, editor, Ahmed, Muhammad Raisuddin, editor, and Zhong, Ning, editor

Published
2024
Full Text
View/download PDF

11. YOLO-V4 Based Detection of Varied Hand Gestures in Heterogeneous Settings

Author

Aziz, Atia Binti, Basnin, Nanziba, Farshid, Mohammad, Akhter, Mohoshena, Mahmud, Tanjim, Andersson, Karl, Hossain, Mohammad Shahadat, Kaiser, M. Shamim, Ghosh, Ashish, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Mahmud, Mufti, editor, Ben-Abdallah, Hanene, editor, Kaiser, M. Shamim, editor, Ahmed, Muhammad Raisuddin, editor, and Zhong, Ning, editor

Published
2024
Full Text
View/download PDF

12. A BERT-Based Chatbot to Support Cancer Treatment Follow-Up

Author

Bappy, Arup Dutta, Mahmud, Tanjim, Kaiser, M. Shamim, Shahadat Hossain, Mohammad, Andersson, Karl, Ghosh, Ashish, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Mahmud, Mufti, editor, Ben-Abdallah, Hanene, editor, Kaiser, M. Shamim, editor, Ahmed, Muhammad Raisuddin, editor, and Zhong, Ning, editor

Published
2024
Full Text
View/download PDF

13. Machine Learning-Driven Job Recommendations: Harnessing Genetic Algorithms

Author

Aziz, Mohammad Tarek, Mahmud, Tanjim, Uddin, Mohammad Kamal, Hossain, Samien Naif, Datta, Nippon, Akther, Sharmin, Hossain, Mohammad Shahadat, Andersson, Karl, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Yang, Xin-She, editor, Sherratt, Simon, editor, Dey, Nilanjan, editor, and Joshi, Amit, editor

Published
2024
Full Text
View/download PDF

14. Enhancing Diagnosis: An Ensemble Deep Learning Model for Brain Tumor Detection and Classification

Author

Mahmud, Tanjim, Barua, Anik, Barua, Koushick, Basnin, Nanziba, Hossain, Mohammad Shahadat, Andersson, Karl, Kaiser, M. Shamim, Hossain, Md. Sazzad, Monju, Mahabuba, Sharmen, Nahed, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Arefin, Mohammad Shamsul, editor, Kaiser, M. Shamim, editor, Bhuiyan, Touhid, editor, Dey, Nilanjan, editor, and Mahmud, Mufti, editor

Published
2024
Full Text
View/download PDF

15. A Novel Approach to Detect Stroke from 2D Images Using Deep Learning

Author

Chowdhury, Nezat Akter, Mahmud, Tanjim, Barua, Anik, Basnin, Nanziba, Barua, Koushick, Iqbal, Aseef, Hossain, Mohammad Shahadat, Andersson, Karl, Kaiser, M. Shamim, Hossain, Md. Sazzad, Das, Sudhakar, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Arefin, Mohammad Shamsul, editor, Kaiser, M. Shamim, editor, Bhuiyan, Touhid, editor, Dey, Nilanjan, editor, and Mahmud, Mufti, editor

Published
2024
Full Text
View/download PDF

17. A Novel Approach to Detect Stroke from 2D Images Using Deep Learning

Author

Chowdhury, Nezat Akter, primary, Mahmud, Tanjim, additional, Barua, Anik, additional, Basnin, Nanziba, additional, Barua, Koushick, additional, Iqbal, Aseef, additional, Hossain, Mohammad Shahadat, additional, Andersson, Karl, additional, Kaiser, M. Shamim, additional, Hossain, Md. Sazzad, additional, and Das, Sudhakar, additional

Published
2024
Full Text
View/download PDF

18. Exploring Deep Transfer Learning Ensemble for Improved Diagnosis and Classification of Alzheimer’s Disease

Author

Mahmud, Tanjim, Barua, Koushick, Barua, Anik, Das, Sudhakar, Basnin, Nanziba, Hossain, Mohammad Shahadat, Andersson, Karl, Kaiser, M. Shamim, Sharmen, Nahed, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Liu, Feng, editor, Zhang, Yu, editor, Kuai, Hongzhi, editor, Stephen, Emily P., editor, and Wang, Hongjun, editor

Published
2023
Full Text
View/download PDF

19. Reason Based Machine Learning Approach to Detect Bangla Abusive Social Media Comments

Author

Mahmud, Tanjim, Das, Sudhakar, Ptaszynski, Michal, Hossain, Mohammad Shahadat, Andersson, Karl, Barua, Koushick, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Vasant, Pandian, editor, Weber, Gerhard-Wilhelm, editor, Marmolejo-Saucedo, José Antonio, editor, Munapo, Elias, editor, and Thomas, J. Joshua, editor

Published
2023
Full Text
View/download PDF

25. Enhancing Cybersecurity: Hybrid Deep Learning Approaches to Smishing Attack Detection.

Author

Mahmud, Tanjim, Prince, Md. Alif Hossen, Ali, Md. Hasan, Hossain, Mohammad Shahadat, and Andersson, Karl

Subjects
CONVOLUTIONAL neural networks, PHISHING, INTERNET security, HEURISTIC, DEEP learning
Abstract

Smishing attacks, a sophisticated form of cybersecurity threats conducted via Short Message Service (SMS), have escalated in complexity with the widespread adoption of mobile devices, making it increasingly challenging for individuals to distinguish between legitimate and malicious messages. Traditional phishing detection methods, such as feature-based, rule-based, heuristic, and blacklist approaches, have struggled to keep pace with the rapidly evolving tactics employed by attackers. To enhance cybersecurity and address these challenges, this paper proposes a hybrid deep learning approach that combines Bidirectional Gated Recurrent Units (Bi-GRUs) and Convolutional Neural Networks (CNNs), referred to as CNN-Bi-GRU, for the accurate identification and classification of smishing attacks. The SMS Phishing Collection dataset was used, with a preparatory procedure involving the transformation of unstructured text data into numerical representations and the training of Word2Vec on preprocessed text. Experimental results demonstrate that the proposed CNN-Bi-GRU model outperforms existing approaches, achieving an overall highest accuracy of 99.82% in detecting SMS phishing messages. This study provides an empirical analysis of the effectiveness of hybrid deep learning techniques for SMS phishing detection, offering a more precise and efficient solution to enhance cybersecurity in mobile communications. [ABSTRACT FROM AUTHOR]

Published
2024
Full Text
View/download PDF

26. Face Detection and Recognition System

Author

Mahmud, Tanjim, Tripura, Sajib, Salma, Umme, Fardoush, Jannat, Naher, Sultana Rokeya, Sikder, Juel, Aziz, Md Faisal Bin Abdul, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Peng, Sheng-Lung, editor, Hsieh, Sun-Yuan, editor, Gopalakrishnan, Suseendran, editor, and Duraisamy, Balaganesh, editor

Published
2021
Full Text
View/download PDF

27. Fabric Defect Detection System

Author

Mahmud, Tanjim, Sikder, Juel, Chakma, Rana Jyoti, Fardoush, Jannat, Kacprzyk, Janusz, Series Editor, Pal, Nikhil R., Advisory Editor, Bello Perez, Rafael, Advisory Editor, Corchado, Emilio S., Advisory Editor, Hagras, Hani, Advisory Editor, Kóczy, László T., Advisory Editor, Kreinovich, Vladik, Advisory Editor, Lin, Chin-Teng, Advisory Editor, Lu, Jie, Advisory Editor, Melin, Patricia, Advisory Editor, Nedjah, Nadia, Advisory Editor, Nguyen, Ngoc Thanh, Advisory Editor, Wang, Jun, Advisory Editor, Vasant, Pandian, editor, Zelinka, Ivan, editor, and Weber, Gerhard-Wilhelm, editor

Published
2021
Full Text
View/download PDF

28. Decision Support System for House Hunting: A Case Study in Chittagong

Author

Mahmud, Tanjim, Sikder, Juel, Naher, Sultana Rokeya, Kacprzyk, Janusz, Series Editor, Pal, Nikhil R., Advisory Editor, Bello Perez, Rafael, Advisory Editor, Corchado, Emilio S., Advisory Editor, Hagras, Hani, Advisory Editor, Kóczy, László T., Advisory Editor, Kreinovich, Vladik, Advisory Editor, Lin, Chin-Teng, Advisory Editor, Lu, Jie, Advisory Editor, Melin, Patricia, Advisory Editor, Nedjah, Nadia, Advisory Editor, Nguyen, Ngoc Thanh, Advisory Editor, Wang, Jun, Advisory Editor, Arai, Kohei, editor, Kapoor, Supriya, editor, and Bhatia, Rahul, editor

Published
2021
Full Text
View/download PDF

33. An Explainable AI Paradigm for Alzheimer’s Diagnosis Using Deep Transfer Learning

Author

Mahmud, Tanjim, Barua, Koushick, Habiba, Sultana Umme, Sharmen, Nahed, Hossain, Mohammad Shahadat, Andersson, Karl, Mahmud, Tanjim, Barua, Koushick, Habiba, Sultana Umme, Sharmen, Nahed, Hossain, Mohammad Shahadat, and Andersson, Karl

Abstract

Alzheimer’s disease (AD) is a progressive neurodegenerative disorder that affects millions of individuals worldwide, causing severe cognitive decline and memory impairment. The early and accurate diagnosis of AD is crucial for effective intervention and disease management. In recent years, deep learning techniques have shown promising results in medical image analysis, including AD diagnosis from neuroimaging data. However, the lack of interpretability in deep learning models hinders their adoption in clinical settings, where explainability is essential for gaining trust and acceptance from healthcare professionals. In this study, we propose an explainable AI (XAI)-based approach for the diagnosis of Alzheimer’s disease, leveraging the power of deep transfer learning and ensemble modeling. The proposed framework aims to enhance the interpretability of deep learning models by incorporating XAI techniques, allowing clinicians to understand the decision-making process and providing valuable insights into disease diagnosis. By leveraging popular pre-trained convolutional neural networks (CNNs) such as VGG16, VGG19, DenseNet169, and DenseNet201, we conducted extensive experiments to evaluate their individual performances on a comprehensive dataset. The proposed ensembles, Ensemble-1 (VGG16 and VGG19) and Ensemble-2 (DenseNet169 and DenseNet201), demonstrated superior accuracy, precision, recall, and F1 scores compared to individual models, reaching up to 95%. In order to enhance interpretability and transparency in Alzheimer’s diagnosis, we introduced a novel model achieving an impressive accuracy of 96%. This model incorporates explainable AI techniques, including saliency maps and grad-CAM (gradient-weighted class activation mapping). The integration of these techniques not only contributes to the model’s exceptional accuracy but also provides clinicians and researchers with visual insights into the neural regions influencing the diagnosis. Our findings showcase the po, Validerad;2024;Nivå 2;2024-04-09 (marisr);Full text licence: CC BY

Published
2024
Full Text
View/download PDF

34. Analyzing Sentiments in eLearning: A Comparative Study of Bangla and Romanized Bangla Text using Transformers

Author

Rahman, Md Akash, Begum, Manoara, Mahmud, Tanjim, Hossain, Mohammad Shahadat, Andersson, Karl, Rahman, Md Akash, Begum, Manoara, Mahmud, Tanjim, Hossain, Mohammad Shahadat, and Andersson, Karl

Abstract

In the modern world, learning is becoming increasingly critical due to rapid technological breakthroughs, which highlight the need for continuous skill development in both the personal and professional spheres. As a result, eLearning is a cutting-edge approach to education that delivers lessons, courses, and instructional materials remotely via digital technology and the Internet. It makes learning more flexible and accessible by enabling users to interact with teachers online and access classes or other content. Sentiment analysis is an eLearning technique that evaluates user opinions, typically via written feedback, to improve the overall quality of instruction in a course. Sentiment analysis for e-learning feedback has been extensively studied in several languages, except Bangla and Romanized Bangla. The three datasets produced were one for Bangla, one for Romanized Bangla, and one for a combination of Bangla and Romanized. Three datasets contained 3178 Bangla, 3090 Romanized Bangla, and 6268 Bangla and Romanized Bangla texts. The feedback has been divided into three categories: positive, negative, and neutral. The validation of the datasets was conducted using Krippendorff’s alpha and Cohen’s kappa metrics, ensuring the reliability and consistency of the dataset annotations. Several techniques were used to train the preprocessed datasets, including transformers, deep learning, machine learning, ensemble learning, and hybrid approaches. Transformer-based algorithms, such as XLM-RoBERTa, outperformed the others in terms of accuracy, achieving the highest values of 89.46% and 85.81% for the Bangla and Combined datasets. At 89.59%, ANN demonstrated exceptional performance on the Romanized Bangla dataset., Validerad;2024;Nivå 2;2024-07-08 (joosat);Full text: CC BY-NC-ND License

Published
2024
Full Text
View/download PDF

36. Face Detection and Recognition System

Author

Mahmud, Tanjim, primary, Tripura, Sajib, additional, Salma, Umme, additional, Fardoush, Jannat, additional, Naher, Sultana Rokeya, additional, Sikder, Juel, additional, and Aziz, Md Faisal Bin Abdul, additional

Published
2021
Full Text
View/download PDF

Catalog

Books, media, physical & digital resources
See catalog results