Accurate and efficient analysis of Electroencephalogram (EEG) signals is crucial for applications like neurological diagnosis and Brain-Computer Interfaces (BCI). Traditional methods often fall short in capturing the intricate temporal dynamics inher...
As digital imaging technology advances, accurate classification of 2D breast cancer images becomes increasingly crucial for early detection and staging. This paper introduces a novel classification approach that integrates deep learning, sparse codin...
Copy move forgery is a type of image forgery in which a portion of the original image is copied and pasted in a new location on the same image. The consistent illumination and noise pattern make this kind of forgery more difficult to detect. In copy-...
Electroencephalography (EEG) signal classification plays a critical role in various biomedical and cognitive research applications, including neurological disorder detection and cognitive state monitoring. However, these technologies face challenges ...
This paper presents an advanced machine learning (ML) framework for precise nerve conduction velocity (NCV) analysis, integrating multiscale signal processing with physiologically-constrained deep learning. Our approach addresses three fundamental li...
Deep learning techniques, particularly Convolutional Neural Networks (CNNs), have been widely recognized as effective tools for facial expression recognition applications. The accuracy of facial expression recognition application requires further enh...
The expansion rate of medical data during the past ten years has rapidly expanded due to the vast fields. The automated disease diagnosis system is proposed using a deep learning (DL) algorithm, which automates and helps speed up the process efficien...
Sleep staging plays a crucial role in the diagnosis and treatment of sleep disorders. Traditional sleep staging requires manual classification by professional technicians based on the characteristic features of each sleep stage. This process is time-...
INTRODUCTION: Sleep disorders significantly disrupt normal sleep patterns and pose serious health risks. Traditional diagnostic methods, such as questionnaires and polysomnography, often require extensive time and are susceptible to errors. This high...
Post-COVID-19, depression rates have risen sharply, increasing the need for early diagnosis using electroencephalogram (EEG) and deep learning. To tackle this, we developed a cloud-based computer-aided depression diagnostic (CCADD) system that utiliz...
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