Cited by Ensemble graph neural networks for structural classification of HIV inhibiting molecules

Smart healthcare: Integration of AI and brain cells for advanced healthcare applications DOI

Oroos Arshi, Aryan Chaudhary

Open Health, Journal Year: 2024, Volume and Issue: 5(1)

Published: Jan. 1, 2024

Abstract The combination of brain cells and artificial intelligence (AI) is a paradigm shift in the healthcare industry that provides previously unheard-of chances for creativity change variety fields. This work an attempt to offer thorough examination confluence AI healthcare, clarifying important ideas, methods, applications will influence medical practice research going forward. Theis article overview looks at wide methods algorithms advancing personalized medicine, therapy optimization, disease diagnostics. It also touches upon how interact, brain–computer interfaces (BCIs) can transform neuroscience human–machine interaction. highlights revolutionary on delivery patient care by outlining application domains BCI across fields talking about integration reinforcement learning with BCIs. showcases practical ranging from prognostication diagnostics prosthetics rehabilitation. suggests new trends development opportunities field integration, as well future directions this field.

Language: Английский

Citations

A novel healthcare decision support system using IoT and ANFIS DOI

Naveen Kumar Dewangan,

Neeti Pandey,

Ritu Gautam

et al.

International Journal of Information Technology, Journal Year: 2024, Volume and Issue: unknown

Published: Aug. 31, 2024

Language: Английский

Citations

Sentimatrix: sentiment analysis using GNN in healthcare DOI

Bharti Khemani,

Sachin Malave,

Shruti Patil

et al.

International Journal of Information Technology, Journal Year: 2024, Volume and Issue: unknown

Published: Sept. 23, 2024

Language: Английский

Citations

A review of epilepsy detection and prediction methods based on EEG signal processing and deep learning DOI

Xizhen Zhang,

Xiaoli Zhang,

Qiong Huang

et al.

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

Published: Nov. 15, 2024

Epilepsy is a chronic neurological disorder that poses significant challenges to patients and their families. Effective detection prediction of epilepsy can facilitate patient recovery, reduce family burden, streamline healthcare processes. Therefore, it essential propose deep learning method for efficient epileptic electroencephalography (EEG) signals. This paper reviews several key aspects EEG signal processing, focusing on prediction. It covers publicly available datasets, preprocessing techniques, feature extraction methods, learning-based networks used in these tasks. The literature categorized based independence, distinguishing between patient-independent non-patient-independent studies. Additionally, the evaluation methods are classified into general classification indicators specific criteria, with findings organized according cycles reported various review reveals important insights. Despite availability public they often lack diversity types collected under controlled conditions may not reflect real-world scenarios. As result, tend be limited fully represent practical conditions. Feature network designs frequently emphasize fusion mechanisms, recent advances Convolutional Neural Networks (CNNs) Recurrent (RNNs) showing promising results, suggesting new models warrant further exploration. Studies using data generally produce better results than those relying data. Metrics typically perform though future research should focus latter more accurate evaluation. kept 1 h, most studies concentrating intervals 30 min or less.

Language: Английский

Citations

Ensemble graph neural networks for structural classification of HIV inhibiting molecules DOI

Tejas Pradhan,

Aniket Ghorpade,

Shruti Patil

et al.

International Journal of Information Technology, Journal Year: 2024, Volume and Issue: unknown

Published: Dec. 3, 2024

Language: Английский

Citations