Interpreting black-box models: a review on explainable artificial intelligence

V Hassija, V Chamola, A Mahapatra, A Singal… - Cognitive …, 2024 - Springer
Recent years have seen a tremendous growth in Artificial Intelligence (AI)-based
methodological development in a broad range of domains. In this rapidly evolving field …

Machine learning for synergistic network pharmacology: a comprehensive overview

F Noor, M Asif, UA Ashfaq, M Qasim… - Briefings in …, 2023 - academic.oup.com
Network pharmacology is an emerging area of systematic drug research that attempts to
understand drug actions and interactions with multiple targets. Network pharmacology has …

Deep learning approach for early detection of Alzheimer's disease

HA Helaly, M Badawy, AY Haikal - Cognitive computation, 2022 - Springer
Alzheimer's disease (AD) is a chronic, irreversible brain disorder, no effective cure for it till
now. However, available medicines can delay its progress. Therefore, the early detection of …

Ten years of generative adversarial nets (GANs): a survey of the state-of-the-art

T Chakraborty, UR KS, SM Naik, M Panja… - Machine Learning …, 2024 - iopscience.iop.org
Generative adversarial networks (GANs) have rapidly emerged as powerful tools for
generating realistic and diverse data across various domains, including computer vision and …

[HTML][HTML] Brain-computer interface: Advancement and challenges

MF Mridha, SC Das, MM Kabir, AA Lima, MR Islam… - Sensors, 2021 - mdpi.com
Brain-Computer Interface (BCI) is an advanced and multidisciplinary active research domain
based on neuroscience, signal processing, biomedical sensors, hardware, etc. Since the …

A review of epileptic seizure detection using machine learning classifiers

MK Siddiqui, R Morales-Menendez, X Huang… - Brain informatics, 2020 - Springer
Epilepsy is a serious chronic neurological disorder, can be detected by analyzing the brain
signals produced by brain neurons. Neurons are connected to each other in a complex way …

Application of deep learning in detecting neurological disorders from magnetic resonance images: a survey on the detection of Alzheimer's disease, Parkinson's …

MBT Noor, NZ Zenia, MS Kaiser, SA Mamun… - Brain informatics, 2020 - Springer
Neuroimaging, in particular magnetic resonance imaging (MRI), has been playing an
important role in understanding brain functionalities and its disorders during the last couple …

[HTML][HTML] A comparative analysis of machine learning classifiers for stroke prediction: A predictive analytics approach

N Biswas, KMM Uddin, ST Rikta, SK Dey - Healthcare Analytics, 2022 - Elsevier
Stroke is the third leading cause of death in the world. It is a dangerous health disorder
caused by the interruption of the blood flow to the brain, resulting in severe illness, disability …

Explainable artificial intelligence in Alzheimer's disease classification: A systematic review

V Viswan, N Shaffi, M Mahmud, K Subramanian… - Cognitive …, 2024 - Springer
The unprecedented growth of computational capabilities in recent years has allowed
Artificial Intelligence (AI) models to be developed for medical applications with remarkable …

Deep learning for cross-domain data fusion in urban computing: Taxonomy, advances, and outlook

X Zou, Y Yan, X Hao, Y Hu, H Wen, E Liu, J Zhang… - Information …, 2025 - Elsevier
As cities continue to burgeon, Urban Computing emerges as a pivotal discipline for
sustainable development by harnessing the power of cross-domain data fusion from diverse …