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Publications (10 of 254) Show all publications
Chowdhury, N. A., Mahmud, T., Barua, A., Basnin, N., Barua, K., Iqbal, A., . . . Das, S. (2024). A Novel Approach to Detect Stroke from 2D Images Using Deep Learning. In: Mohammad Shamsul Arefin; M. Shamim Kaiser; Touhid Bhuiyan; Nilanjan Dey; Mufti Mahmud (Ed.), Proceedings of the 2nd International Conference on Big Data, IoT and Machine Learning: . Paper presented at 2nd International Conference on Big Data, IoT and Machine Learning (BIM 2023), Dhaka, Bangladesh, September 6-8, 2023 (pp. 239-253). Springer Nature
Open this publication in new window or tab >>A Novel Approach to Detect Stroke from 2D Images Using Deep Learning
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2024 (English)In: Proceedings of the 2nd International Conference on Big Data, IoT and Machine Learning / [ed] Mohammad Shamsul Arefin; M. Shamim Kaiser; Touhid Bhuiyan; Nilanjan Dey; Mufti Mahmud, Springer Nature, 2024, p. 239-253Conference paper, Published paper (Refereed)
Place, publisher, year, edition, pages
Springer Nature, 2024
Series
Lecture Notes in Networks and Systems, ISSN 2367-3370, E-ISSN 2367-3389 ; 867
National Category
Medical Image Processing Computer Sciences
Research subject
Cyber Security
Identifiers
urn:nbn:se:ltu:diva-105238 (URN)10.1007/978-981-99-8937-9_17 (DOI)2-s2.0-85190364778 (Scopus ID)
Conference
2nd International Conference on Big Data, IoT and Machine Learning (BIM 2023), Dhaka, Bangladesh, September 6-8, 2023
Note

ISBN for host publication: 978-981-99-8937-9;

Available from: 2024-04-25 Created: 2024-04-25 Last updated: 2024-04-25Bibliographically approved
Imam, M. H. H., Nahar, N., Bhowmik, R., Omit, S. B., Mahmud, T., Hossain, M. S. & Andersson, K. (2024). A Transfer Learning-Based Framework: MobileNet-SVM for Efficient Tomato Leaf Disease Classification. In: 6th International Conference on Electrical Engineering and Information and Communication Technology (ICEEICT 2024): . Paper presented at 6th International Conference on Electrical Engineering and Information and Communication Technology (ICEEICT), Dhaka, Bangladesh, May 2-4, 2024 (pp. 693-698). IEEE
Open this publication in new window or tab >>A Transfer Learning-Based Framework: MobileNet-SVM for Efficient Tomato Leaf Disease Classification
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2024 (English)In: 6th International Conference on Electrical Engineering and Information and Communication Technology (ICEEICT 2024), IEEE, 2024, p. 693-698Conference paper, Published paper (Refereed)
Place, publisher, year, edition, pages
IEEE, 2024
National Category
Agricultural Science Probability Theory and Statistics
Research subject
Cyber Security
Identifiers
urn:nbn:se:ltu:diva-107505 (URN)10.1109/ICEEICT62016.2024.10534539 (DOI)2-s2.0-85195236963 (Scopus ID)
Conference
6th International Conference on Electrical Engineering and Information and Communication Technology (ICEEICT), Dhaka, Bangladesh, May 2-4, 2024
Note

ISBN for host publication: 979-8-3503-8577-9; 

Available from: 2024-06-17 Created: 2024-06-17 Last updated: 2024-06-17Bibliographically approved
Kabir, S., Hossain, M. S. & Andersson, K. (2024). An Advanced Explainable Belief Rule-Based Framework to Predict the Energy Consumption of Buildings. Energies, 17(8), Article ID 1797.
Open this publication in new window or tab >>An Advanced Explainable Belief Rule-Based Framework to Predict the Energy Consumption of Buildings
2024 (English)In: Energies, E-ISSN 1996-1073, Vol. 17, no 8, article id 1797Article in journal (Refereed) Published
Abstract [en]

The prediction of building energy consumption is beneficial to utility companies, users, and facility managers to reduce energy waste. However, due to various drawbacks of prediction algorithms, such as, non-transparent output, ad hoc explanation by post hoc tools, low accuracy, and the inability to deal with data uncertainties, such prediction has limited applicability in this domain. As a result, domain knowledge-based explainability with high accuracy is critical for making energy predictions trustworthy. Motivated by this, we propose an advanced explainable Belief Rule-Based Expert System (eBRBES) with domain knowledge-based explanations for the accurate prediction of energy consumption. We optimize BRBES’s parameters and structure to improve prediction accuracy while dealing with data uncertainties using its inference engine. To predict energy consumption, we take into account floor area, daylight, indoor occupancy, and building heating method. We also describe how a counterfactual output on energy consumption could have been achieved. Furthermore, we propose a novel Belief Rule-Based adaptive Balance Determination (BRBaBD) algorithm for determining the optimal balance between explainability and accuracy. To validate the proposed eBRBES framework, a case study based on Skellefteå, Sweden, is used. BRBaBD results show that our proposed eBRBES framework outperforms state-of-the-art machine learning algorithms in terms of optimal balance between explainability and accuracy by 85.08%.

Place, publisher, year, edition, pages
MDPI, 2024
Keywords
accuracy, building energy, explainability, trust, uncertainties
National Category
Computer Sciences
Research subject
Cyber Security; Pervasive Mobile Computing
Identifiers
urn:nbn:se:ltu:diva-105059 (URN)10.3390/en17081797 (DOI)
Funder
Vinnova, 2022-01188
Note

Validerad;2024;Nivå 2;2024-04-11 (signyg);

Full text license: CC BY

Available from: 2024-04-11 Created: 2024-04-11 Last updated: 2024-04-24Bibliographically approved
Mahmud, T., Barua, K., Habiba, S. U., Sharmen, N., Hossain, M. S. & Andersson, K. (2024). An Explainable AI Paradigm for Alzheimer’s Diagnosis Using Deep Transfer Learning. Diagnostics, 14(3), Article ID 345.
Open this publication in new window or tab >>An Explainable AI Paradigm for Alzheimer’s Diagnosis Using Deep Transfer Learning
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2024 (English)In: Diagnostics, ISSN 2075-4418, Vol. 14, no 3, article id 345Article in journal (Refereed) Published
Abstract [en]

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 potential of combining deep transfer learning with explainable AI in the realm of Alzheimer’s disease diagnosis, paving the way for more interpretable and clinically relevant AI models in healthcare.

Place, publisher, year, edition, pages
Multidisciplinary Digital Publishing Institute (MDPI), 2024
Keywords
Alzheimer’s disease, explainable AI (XAI), grad-CAM, saliency maps, transfer learning
National Category
Computer Sciences
Research subject
Cyber Security
Identifiers
urn:nbn:se:ltu:diva-104357 (URN)10.3390/diagnostics14030345 (DOI)001160341800001 ()2-s2.0-85184719181 (Scopus ID)
Note

Validerad;2024;Nivå 2;2024-04-09 (marisr);

Full text licence: CC BY

Available from: 2024-02-22 Created: 2024-02-22 Last updated: 2024-04-09Bibliographically approved
Rahman, M. A., Begum, M., Mahmud, T., Hossain, M. S. & Andersson, K. (2024). Analyzing Sentiments in eLearning: A Comparative Study of Bangla and Romanized Bangla Text using Transformers. IEEE Access, 12, 89144-89162
Open this publication in new window or tab >>Analyzing Sentiments in eLearning: A Comparative Study of Bangla and Romanized Bangla Text using Transformers
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2024 (English)In: IEEE Access, E-ISSN 2169-3536, Vol. 12, p. 89144-89162Article in journal (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers Inc., 2024
Keywords
Accuracy, eLearning, Electronic learning, Ensemble Learning, Natural Language Processing, Reviews, Sentiment analysis, Social networking (online), Transformers, Video on demand, Web sites
National Category
Media and Communication Technology Computer Sciences
Research subject
Cyber Security
Identifiers
urn:nbn:se:ltu:diva-108307 (URN)10.1109/ACCESS.2024.3419024 (DOI)2-s2.0-85197070858 (Scopus ID)
Note

Validerad;2024;Nivå 2;2024-07-08 (joosat);

Full text: CC BY-NC-ND License;

Available from: 2024-07-08 Created: 2024-07-08 Last updated: 2024-07-08Bibliographically approved
Mahmud, T., Hasan, I., Aziz, M. T., Rahman, T., Hossain, M. S. & Andersson, K. (2024). Enhanced Fake News Detection through the Fusion of Deep Learning and Repeat Vector Representations. In: 2nd International Conference on Intelligent Data Communication Technologies and Internet of Things (IDCIoT 2024): . Paper presented at 2nd International Conference on Intelligent Data Communication Technologies and Internet of Things (IDCIoT 2024), Bengaluru, India, January 4-6, 2024 (pp. 654-660). IEEE
Open this publication in new window or tab >>Enhanced Fake News Detection through the Fusion of Deep Learning and Repeat Vector Representations
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2024 (English)In: 2nd International Conference on Intelligent Data Communication Technologies and Internet of Things (IDCIoT 2024), IEEE, 2024, p. 654-660Conference paper, Published paper (Refereed)
Place, publisher, year, edition, pages
IEEE, 2024
National Category
Computer Sciences Information Systems, Social aspects
Research subject
Cyber Security; Wood and Bionanocomposites
Identifiers
urn:nbn:se:ltu:diva-105236 (URN)10.1109/IDCIoT59759.2024.10467839 (DOI)2-s2.0-85190156897 (Scopus ID)
Conference
2nd International Conference on Intelligent Data Communication Technologies and Internet of Things (IDCIoT 2024), Bengaluru, India, January 4-6, 2024
Note

ISBN for host publication: 979-8-3503-2753-3;

Available from: 2024-04-25 Created: 2024-04-25 Last updated: 2024-04-25Bibliographically approved
Mahmud, T., Barua, A., Barua, K., Basnin, N., Hossain, M. S., Andersson, K., . . . Sharmen, N. (2024). Enhancing Diagnosis: An Ensemble Deep Learning Model for Brain Tumor Detection and Classification. In: Mohammad Shamsul Arefin; M. Shamim Kaiser; Touhid Bhuiyan; Nilanjan Dey; Mufti Mahmud (Ed.), Proceedings of the 2nd International Conference on Big Data, IoT and Machine Learning: . Paper presented at 2nd International Conference on Big Data, IoT and Machine Learning (BIM 2023), Dhaka, Bangladesh, September 6-8, 2023 (pp. 409-424). Springer Nature
Open this publication in new window or tab >>Enhancing Diagnosis: An Ensemble Deep Learning Model for Brain Tumor Detection and Classification
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2024 (English)In: Proceedings of the 2nd International Conference on Big Data, IoT and Machine Learning / [ed] Mohammad Shamsul Arefin; M. Shamim Kaiser; Touhid Bhuiyan; Nilanjan Dey; Mufti Mahmud, Springer Nature, 2024, p. 409-424Conference paper, Published paper (Refereed)
Place, publisher, year, edition, pages
Springer Nature, 2024
Series
Lecture Notes in Networks and Systems, ISSN 2367-3370, E-ISSN 2367-3389 ; 867
National Category
Radiology, Nuclear Medicine and Medical Imaging Medical Image Processing
Research subject
Cyber Security
Identifiers
urn:nbn:se:ltu:diva-105239 (URN)10.1007/978-981-99-8937-9_28 (DOI)2-s2.0-85190390293 (Scopus ID)
Conference
2nd International Conference on Big Data, IoT and Machine Learning (BIM 2023), Dhaka, Bangladesh, September 6-8, 2023
Note

ISBN for host publication: 978-981-99-8937-9;

Available from: 2024-04-25 Created: 2024-04-25 Last updated: 2024-04-25Bibliographically approved
Khan, A. S., Khan, F. T., Mahmud, T., Khan, S. K., Sharmen, N., Hossain, M. S. & Andersson, K. (2024). Integrating BERT Embeddings with SVM for Prostate Cancer Prediction. In: 6th International Conference on Electrical Engineering and Information and Communication Technology (ICEEICT 2024): . Paper presented at 6th International Conference on Electrical Engineering and Information and Communication Technology (ICEEICT), Dhaka, Bangladesh, May 2-4, 2024 (pp. 574-579). IEEE
Open this publication in new window or tab >>Integrating BERT Embeddings with SVM for Prostate Cancer Prediction
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2024 (English)In: 6th International Conference on Electrical Engineering and Information and Communication Technology (ICEEICT 2024), IEEE, 2024, p. 574-579Conference paper, Published paper (Refereed)
Place, publisher, year, edition, pages
IEEE, 2024
National Category
Language Technology (Computational Linguistics) Cancer and Oncology Probability Theory and Statistics
Research subject
Cyber Security
Identifiers
urn:nbn:se:ltu:diva-107506 (URN)10.1109/ICEEICT62016.2024.10534547 (DOI)2-s2.0-85195238253 (Scopus ID)
Conference
6th International Conference on Electrical Engineering and Information and Communication Technology (ICEEICT), Dhaka, Bangladesh, May 2-4, 2024
Note

ISBN for host publication: 979-8-3503-8577-9; 

Available from: 2024-06-17 Created: 2024-06-17 Last updated: 2024-06-17Bibliographically approved
Chakraborty, A., Chakraborty, A., Khan, F. T., Mahmud, T., Hossain, M. S. & Andersson, K. (2024). Optimizing Tandem Solar Cell Efficiency: A Perovskite-CIGS Approach. In: 2024 Second International Conference on Emerging Trends in Information Technology and Engineering (ICETITE): . Paper presented at 2nd International Conference on Emerging Trends in Information Technology and Engineering (ic-ETITE 2024), Vellore, India, February 22-23, 2024. IEEE
Open this publication in new window or tab >>Optimizing Tandem Solar Cell Efficiency: A Perovskite-CIGS Approach
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2024 (English)In: 2024 Second International Conference on Emerging Trends in Information Technology and Engineering (ICETITE), IEEE, 2024Conference paper, Published paper (Refereed)
Abstract [en]

Tandem solar cells combining perovskite and CIGS have rapidly gained prominence as highly efficient photovoltaic devices. Recent advancements in enhancing their efficiency in-volve adjusting the thickness of distinct layers with tunable bandgaps, demonstrating superior performance compared to al-ternative tandem solar cells. The upper cell, employing perovskite material chosen for its excellent light absorption coefficient and optoelectronic properties, collaborates with the bottom CIGS cell, characterized by a tunable bandgap and low thermal requirements, contributing significantly to overall efficiency improvement. Through numerical simulations conducted with wxAMPS software, our proposed model, which incorporates a 3μm thick perovskite absorber layer as the upper cell and a 3.5μm thick CIGS absorber layer as the lower cell, demonstrated outstanding power conversion efficiency of 40.0209%, Voc of 1.8675 V, Jsc of 23.2146 mA/cm2, and FF of 92.3152

Place, publisher, year, edition, pages
IEEE, 2024
Keywords
absorption coefficient, CIGS, optoelectronic, Perovskite, Tandem solar cell, thermal budget, tunable bandgap, wxAMPS
National Category
Materials Chemistry Other Electrical Engineering, Electronic Engineering, Information Engineering Condensed Matter Physics
Research subject
Cyber Security
Identifiers
urn:nbn:se:ltu:diva-105565 (URN)10.1109/ic-ETITE58242.2024.10493502 (DOI)2-s2.0-85192511418 (Scopus ID)
Conference
2nd International Conference on Emerging Trends in Information Technology and Engineering (ic-ETITE 2024), Vellore, India, February 22-23, 2024
Note

ISBN for host publication: 979-8-3503-2820-2

Available from: 2024-05-22 Created: 2024-05-22 Last updated: 2024-05-22Bibliographically approved
Dey, P., Mahmud, T., Nahar, S. R., Hossain, M. S. & Andersson, K. (2024). Plant Disease Detection in Precision Agriculture: Deep Learning Approaches. In: 2nd International Conference on Intelligent Data Communication Technologies and Internet of Things (IDCIoT 2024): . Paper presented at 2nd International Conference on Intelligent Data Communication Technologies and Internet of Things (IDCIoT 2024), Bengaluru, India, January 4-6, 2024 (pp. 661-667). IEEE
Open this publication in new window or tab >>Plant Disease Detection in Precision Agriculture: Deep Learning Approaches
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2024 (English)In: 2nd International Conference on Intelligent Data Communication Technologies and Internet of Things (IDCIoT 2024), IEEE, 2024, p. 661-667Conference paper, Published paper (Refereed)
Place, publisher, year, edition, pages
IEEE, 2024
National Category
Computer Sciences
Research subject
Cyber Security
Identifiers
urn:nbn:se:ltu:diva-105235 (URN)10.1109/IDCIoT59759.2024.10467525 (DOI)2-s2.0-85190101884 (Scopus ID)
Conference
2nd International Conference on Intelligent Data Communication Technologies and Internet of Things (IDCIoT 2024), Bengaluru, India, January 4-6, 2024
Note

ISBN for host publication: 979-8-3503-2753-3;

Available from: 2024-04-25 Created: 2024-04-25 Last updated: 2024-04-25Bibliographically approved
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ORCID iD: ORCID iD iconorcid.org/0000-0003-0244-3561

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