Use deep transfer learning for efficient time-series updating of subsurface flow surrogate model DOI
Wenhao Fu, Piyang Liu, Kai Zhang

et al.

Engineering Applications of Artificial Intelligence, Journal Year: 2025, Volume and Issue: 153, P. 110873 - 110873

Published: April 18, 2025

Language: Английский

Machine Learning Techniques to Predict Mental Health Diagnoses: A Systematic Literature Review DOI Open Access

Ujunwa Madububambachu,

Augustine Ukpebor,

U.H. Ihezue

et al.

Clinical Practice and Epidemiology in Mental Health, Journal Year: 2024, Volume and Issue: 20(1)

Published: July 26, 2024

Introduction This study aims to investigate the potential of machine learning in predicting mental health conditions among college students by analyzing existing literature on diagnoses using various algorithms. Methods The research employed a systematic review methodology application deep techniques from 2011 2024. search strategy involved key terms, such as “deep learning,” “mental health,” and related conducted reputable repositories like IEEE, Xplore, ScienceDirect, SpringerLink, PLOS, Elsevier. Papers published between January, 2011, May, 2024, specifically focusing models for diagnoses, were considered. selection process adhered PRISMA guidelines resulted 30 relevant studies. Results highlights Convolutional Neural Networks (CNN), Random Forest (RF), Support Vector Machine (SVM), Deep Networks, Extreme Learning (ELM) prominent conditions. Among these, CNN demonstrated exceptional accuracy compared other diagnosing bipolar disorder. However, challenges persist, including need more extensive diverse datasets, consideration heterogeneity condition, inclusion longitudinal data capture temporal dynamics. Conclusion offers valuable insights into students. While show promise, addressing limitations incorporating dynamics are crucial further advancements.

Language: Английский

Citations

6
An Approach to Binary Classification of Alzheimer’s Disease Using LSTM DOI Creative Commons
Ahmad Waleed Salehi, Preety Baglat, Gaurav Gupta

et al.

Bioengineering, Journal Year: 2023, Volume and Issue: 10(8), P. 950 - 950

Published: Aug. 9, 2023

In this study, we use LSTM (Long-Short-Term-Memory) networks to evaluate Magnetic Resonance Imaging (MRI) data overcome the shortcomings of conventional Alzheimer's disease (AD) detection techniques. Our method offers greater reliability and accuracy in predicting possibility AD, contrast cognitive testing brain structure analyses. We used an MRI dataset that downloaded from Kaggle source train our network. Utilizing temporal memory characteristics LSTMs, network was created efficiently capture sequential patterns inherent scans. model scored a remarkable AUC 0.97 98.62%. During training process, Stratified Shuffle-Split Cross Validation make sure findings were reliable generalizable. study adds significantly body knowledge by demonstrating potential specific field AD prediction extending variety methods investigated for image classification research. have also designed user-friendly Web-based application help with accessibility developed model, bridging gap between research actual deployment.

Language: Английский

Citations

14
Diagnosis of Schizophrenia in EEG Signals Using dDTF Effective Connectivity and New PreTrained CNN and Transformer Models DOI
Afshin Shoeibi, Marjane Khodatars,

Hamid Alinejad-Rorky

et al.

Lecture notes in computer science, Journal Year: 2024, Volume and Issue: unknown, P. 150 - 160

Published: Jan. 1, 2024

Language: Английский

Citations

5
Early Diagnosis of Schizophrenia in EEG Signals Using One Dimensional Transformer Model DOI
Afshin Shoeibi, Mahboobeh Jafari,

Delaram Sadeghi

et al.

Lecture notes in computer science, Journal Year: 2024, Volume and Issue: unknown, P. 139 - 149

Published: Jan. 1, 2024

Language: Английский

Citations

5
A review of visual sustained attention: neural mechanisms and computational models DOI Creative Commons
Huimin Huang, Rui Li, Junsong Zhang

et al.

PeerJ, Journal Year: 2023, Volume and Issue: 11, P. e15351 - e15351

Published: June 13, 2023

Sustained attention is one of the basic abilities humans to maintain concentration on relevant information while ignoring irrelevant over extended periods. The purpose review provide insight into how integrate neural mechanisms sustained with computational models facilitate research and application. Although many studies have assessed attention, evaluation humans’ not sufficiently comprehensive. Hence, this study provides a current both visual attention. We first models, measurements, propose plausible pathways for Next, we analyze compare different that previous reviews systematically summarized. then automatically detecting vigilance states Finally, outline possible future trends in field

Language: Английский

Citations

11
Fine-grained video super-resolution via spatial-temporal learning and image detail enhancement DOI
Chia‐Hung Yeh,

Hsin-Fu Yang,

Yuyang Lin

et al.

Engineering Applications of Artificial Intelligence, Journal Year: 2024, Volume and Issue: 131, P. 107789 - 107789

Published: Jan. 1, 2024

Language: Английский

Citations

4
Using artificial intelligence methods to study the effectiveness of exercise in patients with ADHD DOI Creative Commons
Dan Yu,

Jia hui Fang

Frontiers in Neuroscience, Journal Year: 2024, Volume and Issue: 18

Published: April 23, 2024

Attention Deficit Hyperactivity Disorder (ADHD) is a prevalent neurodevelopmental disorder that significantly affects children and adults worldwide, characterized by persistent inattention, hyperactivity, impulsivity. Current research in this field faces challenges, particularly accurate diagnosis effective treatment strategies. The analysis of motor information, enriched artificial intelligence methodologies, plays vital role deepening our understanding improving the management ADHD. integration AI techniques, such as machine learning data analysis, into study ADHD-related behaviors, allows for more nuanced disorder. This approach facilitates identification patterns anomalies activity are often characteristic ADHD, thereby contributing to precise diagnostics tailored Our focuses on utilizing techniques deeply analyze patients' information cognitive processes, aiming improve ADHD On dataset, model improved accuracy 98.21% recall 93.86%, especially excelling EEG processing with rates 96.62 95.21%, respectively, demonstrating capturing behaviors physiological responses. These results not only reveal great potential diagnostic developing personalized plans, but also open up new perspectives complex neurological logic In addition, suggests innovative approaches treatment, provides solid foundation future exploring similar disorders, providing valuable insights. scientifically important outcomes quality life, points way future-oriented medical clinical practice.

Language: Английский

Citations

4
Investigating the effects of Gaussian noise on epileptic seizure detection: The role of spectral flatness, bandwidth, and entropy DOI
Nuri İkizler, Güneş Ekim

Engineering Science and Technology an International Journal, Journal Year: 2025, Volume and Issue: 64, P. 102005 - 102005

Published: Feb. 21, 2025

Language: Английский

Citations

0
STL Net: A spatio-temporal multi-task learning network for Autism spectrum disorder identification DOI
Yongjie Huang, Yanyan Zhang, Man Chen

et al.

Biomedical Signal Processing and Control, Journal Year: 2025, Volume and Issue: 106, P. 107678 - 107678

Published: March 1, 2025

Language: Английский

Citations

0
An intellectual autism spectrum disorder classification framework in healthcare industry using ViT-based adaptive deep learning model DOI

R Parvathy,

Rajesh Arunachalam,

Sukumaran Damodaran

et al.

Biomedical Signal Processing and Control, Journal Year: 2025, Volume and Issue: 106, P. 107737 - 107737

Published: March 3, 2025

Language: Английский

Citations

0