Automated arrhythmia detection from electrocardiogram (ECG) signals is crucial and important for the early treatment of cardiac disease (CD). In this investigation, eight machine-learning models have been developed to identify improved ECG arrhythmia...
Intelligent risk assessment in complex systems increasingly relies on methods like trapezoidal fuzzy fault trees. However, conventional techniques often struggle with accurately calculating top-event probabilities and handling model uncertainty, whic...
Brain tumors are the most prevalent and life-threatening cancer; an early and accurate diagnosis of brain tumors increases the chances of patient survival and treatment planning. However, manual tumor detection is a complex, cumbersome and time-consu...
Proceedings of the National Academy of Sciences of the United States of America
Dec 12, 2025
Brain-inspired spiking neural networks (SNNs) have garnered significant research attention in algorithm design and perception applications. However, their potential in the decision-making domain, particularly in model-based reinforcement learning, re...
Human activity recognition (HAR) has numerous applications due to its widespread use of procurement tools, such as smartphones and video cameras, and its ability to capture data on human activity. HAR became a hot scientific area in the computer visi...
Big biological datasets, such as gene expression profiles, often contain redundant features that degrade model performance and limit generalization across independent datasets with complexities like class imbalance and hidden sub-clusters. To overcom...
Vision is a fundamental sense that profoundly impacts daily life and independence. For visually impaired people (VIP), the absence or impairment of this sense presents significant challenges, particularly in navigating their environment and identifyi...
Chromosome research : an international journal on the molecular, supramolecular and evolutionary aspects of chromosome biology
Dec 12, 2025
The segmentation of overlapping chromosomes in metaphase images is a longstanding challenge in cytogenetics, where limited spectral contrast in conventional RGB imaging. In this study, we explore hyperspectral imaging (HSI) as a promising alternative...
Network neuroscience has proven essential for understanding the principles and mechanisms underlying complex brain (dys)function and cognition. In this context, whole-brain network modeling-also known as virtual brain modeling-combines computational ...
This study introduces a novel lightweight image super-resolution reconstruction network aimed at mitigating the challenges associated with computational complexity and memory consumption in existing super-resolution reconstruction networks. The propo...
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