[HTML][HTML] Application of data fusion for automated detection of children with developmental and mental disorders: A systematic review of the last decade

SK Khare, S March, PD Barua, VM Gadre, UR Acharya - Information Fusion, 2023 - Elsevier
Mental health is a basic need for a sustainable and developing society. The prevalence and
financial burden of mental illness have increased globally, and especially in response to …

Empowering precision medicine: AI-driven schizophrenia diagnosis via EEG signals: A comprehensive review from 2002–2023

M Jafari, D Sadeghi, A Shoeibi, H Alinejad-Rokny… - Applied …, 2024 - Springer
Schizophrenia (SZ) is a prevalent mental disorder characterized by cognitive, emotional,
and behavioral changes. Symptoms of SZ include hallucinations, illusions, delusions, lack of …

Detection of schizophrenia using hybrid of deep learning and brain effective connectivity image from electroencephalogram signal

S Bagherzadeh, MS Shahabi, A Shalbaf - Computers in Biology and …, 2022 - Elsevier
Detection of mental disorders such as schizophrenia (SZ) through investigating brain
activities recorded via Electroencephalogram (EEG) signals is a promising field in …

Automated steel surface defect detection and classification using a new deep learning-based approach

K Demir, M Ay, M Cavas, F Demir - Neural Computing and Applications, 2023 - Springer
In this study, a new deep learning-based approach has been developed that detects and
classifies surface defects that occur in the steel production process. The proposed …

An effective and robust approach based on r-cnn+ lstm model and ncar feature selection for ophthalmological disease detection from fundus images

F Demir, B Taşcı - Journal of Personalized Medicine, 2021 - mdpi.com
Changes in and around anatomical structures such as blood vessels, optic disc, fovea, and
macula can lead to ophthalmological diseases such as diabetic retinopathy, glaucoma, age …

Automated EEG sentence classification using novel dynamic-sized binary pattern and multilevel discrete wavelet transform techniques with TSEEG database

PD Barua, T Keles, S Dogan, M Baygin… - … Signal Processing and …, 2023 - Elsevier
Electroencephalography (EEG) signal is an important physiological signal commonly used
in machine learning to decode brain activities, including imagined words and sentences. We …

Depression signal correlation identification from different EEG channels based on CNN feature extraction

B Wang, Y Kang, D Huo, D Chen, W Song… - Psychiatry Research …, 2023 - Elsevier
Depression is a mental illness and can even lead to suicide if not be diagnosed and treated.
Electroencephalograph (EEG) is used to diagnose depression and it is more complexity to …

CCPNet136: automated detection of schizophrenia using carbon chain pattern and iterative TQWT technique with EEG signals

M Baygin, PD Barua, S Chakraborty… - Physiological …, 2023 - iopscience.iop.org
Objective. Schizophrenia (SZ) is a severe, chronic psychiatric-cognitive disorder. The
primary objective of this work is to present a handcrafted model using state-of-the-art …

Improved sparse representation based robust hybrid feature extraction models with transfer and deep learning for EEG classification

SK Prabhakar, SW Lee - Expert Systems with Applications, 2022 - Elsevier
Numerous studies in the field of cognitive research is dependent on
Electroencephalography (EEG) as it apprehends the neural correspondences of various …

A new one-dimensional testosterone pattern-based EEG sentence classification method

T Keles, AM Yildiz, PD Barua, S Dogan… - … Applications of Artificial …, 2023 - Elsevier
Electroencephalography (EEG) signals are crucial data to understand brain activities. Thus,
many papers have been proposed about EEG signals. In particular, machine learning …