Neural networks have been successfully employed in various domains such as
classification, regression and clustering, etc. Generally, the back propagation (BP) based …

Abstract Machine learning methods are extensively used for processing and analysing
speech signals by virtue of their performance gains over multiple domains. Deep learning …

Alzheimer's disease (AD) is the most prevalent neurodegenerative disorder and the most
common cause of Dementia. Neuroimaging analyses, such as T1 weighted magnetic …

Automated segmentation of multiple organs and tumors from 3D medical images such as
magnetic resonance imaging (MRI) and computed tomography (CT) scans using deep …

As one gets older, the likelihood of this happening increases. Minor cognitive impairment
may serve as an early warning sign of dementia, according to popular belief. Some of the …

Alzheimer's disease, which is characterized by a continual deterioration of cognitive abilities
in older people, is the most common form of dementia. Neuroimaging data, for example …

While graph convolution-based methods have become the de-facto standard for graph
representation learning, their applications to disease prediction tasks remain quite limited …

Recently, food spoilage caused by pathogens has been increasing. Therefore, applying
control strategies is essential. Bacteriophages can potentially reduce this problem due to …

Ensuring the prediction accuracy of a learning algorithm on a theoretical basis is crucial and
necessary for building the reliability of the learning algorithm. This paper analyzes prediction …

Short-term electric load forecasting (STLF) plays a pivotal role in modern power system
management, bolstering forecasting accuracy and efficiency. This enhancement assists …