Early infant crying provides critical insights into neurodevelopment, with atypical acoustic features linked to conditions such as preterm birth. However, previous studies have focused on limited and specific acoustic features, hindering a more compr...
Alzheimer's disease (AD) constitutes a neurodegenerative disorder predominantly observed in the geriatric population. If AD can be diagnosed early, both in terms of prevention and treatment, it is very beneficial to patients. Therefore, our team prop...
In recent years, with the advancement of deep learning, Convolutional Neural Networks (CNNs) have been widely applied in speaker recognition, making CNN-based speaker embedding learning the predominant method for speaker verification. Time Delay Neur...
Grassland sheep counting is essential for both animal husbandry and ecological balance. Accurate population statistics help optimize livestock management and sustain grassland ecosystems. However, traditional counting methods are time-consuming and c...
This study aims to determine the optimum extraction conditions that maximize the biological activities of Agaricus campestris and Agaricus bisporus species. In the study, a total of 64 extraction experiments were carried out at different temperatures...
The detection of brain tumors is crucial in medical imaging, because accurate and early diagnosis can have a positive effect on patients. Because traditional deep learning models store all their data together, they raise questions about privacy, comp...
Brain-computer interfaces (BCIs) harness electroencephalographic signals for direct neural control of devices, offering significant benefits for individuals with motor impairments. Traditional machine learning methods for EEG-based motor imagery (MI)...
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 methods are notorious for relying on extensive labeled datasets to train and assess their performance. This can cause difficulties in practical situations where models should be trained for new applications for which very little data is...
BACKGROUND: The development of automatic emotion recognition models from smartphone videos is a crucial step toward the dissemination of psychotherapeutic app interventions that encourage emotional expressions. Existing models focus mainly on the 6 b...
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