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Machine learning techniques for diagnosis of alzheimer disease, mild cognitive disorder, and other types of dementia
Alzheimer's disease (AD) is one of the most common form of dementia which mostly affects
elderly people. AD identification in early stages is a difficult task in medical practice and …
elderly people. AD identification in early stages is a difficult task in medical practice and …
Self-supervised learning for electroencephalography
Decades of research have shown machine learning superiority in discovering highly
nonlinear patterns embedded in electroencephalography (EEG) records compared with …
nonlinear patterns embedded in electroencephalography (EEG) records compared with …
An attention-aware long short-term memory-like spiking neural model for sentiment analysis
LSTM-SNP model is a recently developed long short-term memory (LSTM) network, which is
inspired from the mechanisms of spiking neural P (SNP) systems. In this paper, LSTM-SNP …
inspired from the mechanisms of spiking neural P (SNP) systems. In this paper, LSTM-SNP …
Predicting blood–brain barrier permeability of molecules with a large language model and machine learning
ETC Huang, JS Yang, KYK Liao, WCW Tseng… - Scientific Reports, 2024 - nature.com
Predicting the blood–brain barrier (BBB) permeability of small-molecule compounds using a
novel artificial intelligence platform is necessary for drug discovery. Machine learning and a …
novel artificial intelligence platform is necessary for drug discovery. Machine learning and a …
Facial expression recognition with contrastive learning and uncertainty-guided relabeling
Facial expression recognition (FER) plays a vital role in the field of human-computer
interaction. To achieve automatic FER, various approaches based on deep learning (DL) …
interaction. To achieve automatic FER, various approaches based on deep learning (DL) …
A hybrid time-distributed deep neural architecture for speech emotion recognition
In recent years, speech emotion recognition (SER) has emerged as one of the most active
human–machine interaction research areas. Innovative electronic devices, services and …
human–machine interaction research areas. Innovative electronic devices, services and …
Emotion classification from EEG with a low-cost BCI versus a high-end equipment
The assessment of physiological signals such as the electroencephalography (EEG) has
become a key point in the research area of emotion detection. This study compares the …
become a key point in the research area of emotion detection. This study compares the …
Unlocking travel narratives: a fusion of stacking ensemble deep learning and neural topic modeling for enhanced tourism comment analysis
User-generated comments are crucial in the domain of hotel bookings, especially in the fast-
changing online planning and booking industry. Our research presents a sophisticated …
changing online planning and booking industry. Our research presents a sophisticated …
Effect of action units, viewpoint and immersion on emotion recognition using dynamic virtual faces
Facial affect recognition is a critical skill in human interactions that is often impaired in
psychiatric disorders. To address this challenge, tests have been developed to measure and …
psychiatric disorders. To address this challenge, tests have been developed to measure and …
From intricacy to conciseness: A progressive transfer strategy for EEG-based cross-subject emotion recognition
Z Cai, L Wang, M Guo, G Xu, L Guo… - International Journal of …, 2022 - World Scientific
Emotion plays a significant role in human daily activities, and it can be effectively recognized
from EEG signals. However, individual variability limits the generalization of emotion …
from EEG signals. However, individual variability limits the generalization of emotion …