Artificial Intelligence(AI); Trustworthy AI (TAI); eXplainable AI (XAI); autonomous vehicles, Healthcare, IoT

Chamola, Vinay, Hassija, Vikas, Sulthana Abdul Kareem, Razia ORCID: https://orcid.org/0000-0001-5331-1310, Ghosh, Debshishu, Dhingra, Divyansh and Sikdar, Biplab (2023) A review of trustworthy and eXplainable Artificial Intelligence (XAI). IEEE Access, 11. 78994 -79015. ISSN 2169-3536 (Online) (doi:10.1109/ACCESS.2023.3294569)

Naive Bayes; decision trees; random forest; K-Nearest neighbours; backpropagation; DBScan, customer relationship management

A, Razia Sulthana ORCID: https://orcid.org/0000-0001-5331-1310, Jaiswal, Anukriti, P, Supraja and L, SaiRamesh (2023) Customer segmentation using machine learning. In: 2023 Third International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies (ICAECT). IEEE, Piscataway, New Jersey. ISBN 978-1665494014 (doi:10.1109/ICAECT57570.2023.10117924)

Power components, Power electronics module, IGBT, Wire bonds, thermal fatigue, damage, reliability, machine learning, neural network, physics-informed data

Stoyanov, Stoyan ORCID: https://orcid.org/0000-0001-6091-1226, Sulthana, Razia ORCID: https://orcid.org/0000-0001-5331-1310, Tilford, Tim ORCID: https://orcid.org/0000-0001-8307-6403, Zhang, Xiaotian, Hu, Yihua, Yang, Xingyu, Shen, Yaochun and Wang, Yangang (2025) Modelling the fatigue damage in power components using machine learning technology. Power Electronic Devices and Components, 10:100079. ISSN 2772-3704 (Online) (doi:10.1016/j.pedc.2025.100079)

heart disease; data mining; naive Bayes; decision trees; prediction

A, Razia Sulthana ORCID: https://orcid.org/0000-0001-5331-1310, A K, Jaithunbi and P, Supraja (2023) Application of machine learning algorithms in predicting the heart disease in patients. In: 2023 Third International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies (ICAECT). IEEE, Piscataway, New Jersey. ISBN 9781665494014 (doi:10.1109/ICAECT57570.2023.10117653)

image classification; ingredient identification; recipe extraction; Deep Neural Networks; domain ontology; natural language Processing

Abdul Kareem, Razia Sulthana ORCID: https://orcid.org/0000-0001-5331-1310, Tilford, Timothy ORCID: https://orcid.org/0000-0001-8307-6403 and Stoyanov, Stoyan ORCID: https://orcid.org/0000-0001-6091-1226 (2024) Fine-grained food image classification and recipe extraction using a customised Deep Neural Network and NLP. Computers in Biology and Medicine. ISSN 0010-4825 (Print), 1879-0534 (Online) (doi:10.1016/j.compbiomed.2024.108528)

machine learning, biomedical image segmentation, brain tumours, convolutional neural networks

Ilango, Dhakshina and Sulthana, Razia ORCID: https://orcid.org/0000-0001-5331-1310 (2023) Segmentation of brain tumours from MRI images using CNN. In: Inventive Systems and Control Proceedings of ICISC 2023. Lecture Notes in Networks and Systems, 672 . Springer, pp. 693-706. ISBN 9789819916238 ISSN 2367-3370 (Print), 2367-3389 (Online) (doi:10.1007/978-981-99-1624-5_51)

pome fruits, apples, fungal infections, mass spectrometry, machine learning, neural networks

Abdul Kareem, Razia Sulthana ORCID: https://orcid.org/0000-0001-5331-1310, Frost, Nageena, Goodall, Iain, Tilford, Timothy ORCID: https://orcid.org/0000-0001-8307-6403 and Palacios, Ana Paula (2024) Machine learning to detect fungal infections in stored pome fruits via mass spectrometry data: industry, economic, and social implications. Journal of Advances in Information Technology, 15 (10). pp. 1174-1183. ISSN 1798-2340 (Print), 1798-2340 (Online) (doi:10.12720/jait.15.10.1174-1183)

security breach, Naive bayes, logistic regression, machine learning

Sulthana, Razia ORCID: https://orcid.org/0000-0001-5331-1310, Verma, Avani and Jaithunbi, A. K. (2023) A detailed analysis on spam emails and detection using machine learning algorithms. In: Inventive Systems and Control Proceedings of ICISC 2023. Lecture Notes in Networks and Systems, 672 . Springer, pp. 65-76. ISBN 9789819916238 ISSN 2367-3370 (Print), 2367-3389 (Online) (doi:10.1007/978-981-99-1624-5_5)

skin lesion, image segmentation, classification, deep learning, convolution neural network, MobileNet

A., Razia Sulthana ORCID: https://orcid.org/0000-0001-5331-1310, Chamola, Vinay, Hussain, Amir, Hussain, Zain and Albalwy, Faisal (2023) A novel end-to-end deep convolutional neural network based skin lesion classification framework. Expert system with applications. ISSN 0957-4174 (Print), 1873-6793 (Online) (doi:10.1016/j.eswa.2023.123056)

skin lesion; image segmentation; classification; deep learning; convolution neural network; mobile; net

Abdul Kareem, Razia Sulthana ORCID: https://orcid.org/0000-0001-5331-1310, Chamola, Vinay, Hussain, Amir, Hussain, Zain and Albalwy, Faisal ORCID: https://orcid.org/0000-0002-2342-2156 (2023) A novel end-to-end deep convolutional neural network based skin lesion classification framework. Expert Systems with Applications, 246:123056. pp. 1-19. ISSN 0957-4174 (Print), 1873-6793 (Online) (doi:10.1016/j.eswa.2023.123056)