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Interpreting black-box models: a review on explainable artificial intelligence
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 …
methodological development in a broad range of domains. In this rapidly evolving field …
Machine learning for synergistic network pharmacology: a comprehensive overview
Network pharmacology is an emerging area of systematic drug research that attempts to
understand drug actions and interactions with multiple targets. Network pharmacology has …
understand drug actions and interactions with multiple targets. Network pharmacology has …
Deep learning approach for early detection of Alzheimer's disease
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 …
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
Generative adversarial networks (GANs) have rapidly emerged as powerful tools for
generating realistic and diverse data across various domains, including computer vision and …
generating realistic and diverse data across various domains, including computer vision and …
[HTML][HTML] Brain-computer interface: Advancement and challenges
Brain-Computer Interface (BCI) is an advanced and multidisciplinary active research domain
based on neuroscience, signal processing, biomedical sensors, hardware, etc. Since the …
based on neuroscience, signal processing, biomedical sensors, hardware, etc. Since the …
A review of epileptic seizure detection using machine learning classifiers
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 …
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 …
Neuroimaging, in particular magnetic resonance imaging (MRI), has been playing an
important role in understanding brain functionalities and its disorders during the last couple …
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
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 …
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
The unprecedented growth of computational capabilities in recent years has allowed
Artificial Intelligence (AI) models to be developed for medical applications with remarkable …
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
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 …
sustainable development by harnessing the power of cross-domain data fusion from diverse …