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Bethany Gosala
Bethany Gosala
Researcher of Computer Science, DST-CIMS, ISc, Banaras Hindu University
bhu.ac.in üzerinde doğrulanmış e-posta adresine sahip
Başlık
Alıntı yapanlar
Alıntı yapanlar
Yıl
Wavelet transforms for feature engineering in EEG data processing: An application on Schizophrenia
B Gosala, PD Kapgate, P Jain, RN Chaurasia, M Gupta
Biomedical Signal Processing and Control 85, 104811, 2023
462023
Automatic classification of UML class diagrams using deep learning technique: convolutional neural network
B Gosala, SR Chowdhuri, J Singh, M Gupta, A Mishra
Applied Sciences 11 (9), 4267, 2021
442021
Detecting design patterns: a hybrid approach based on graph matching and static analysis
J Singh, SR Chowdhuri, G Bethany, M Gupta
Information Technology and Management 23 (3), 139-150, 2022
102022
A deep learning based model to study the influence of different brain wave frequencies for the disorder of depression
B Gosala, ER Gosala, M Gupta
International Conference on Multi-disciplinary Trends in Artificial …, 2023
22023
Mining Design Patterns using String Encoding
J Singh, SR Chowdhuri, B Gosala, A Pande, M Gupta
Journal of Scientific Research 64 (2), 2020
12020
GCN-LSTM: A hybrid graph convolutional network model for schizophrenia classification
B Gosala, AR Singh, H Tiwari, M Gupta
Biomedical Signal Processing and Control 105, 107657, 2025
2025
A Transformer Based Emotion Recognition Model for Social Robots Using Topographical Maps Generated from EEG Signals
G Bethany, M Gupta
International Conference on Human-Computer Interaction, 262-271, 2024
2024
Hybrid Convolutional Neural Networks for Multi-Emotion Classification Using GAMEEMO
B Gosala, B Jagwani, M Gupta
International Conference on Advanced Communications and Machine Intelligence …, 2024
2024
Multi-domain Feature Extraction Methods for Classification of Human Emotions from Electroencephalography (EEG) Signals
PD Kapagate, G Bethany, P Jain, M Gupta
International Conference on Advanced Network Technologies and Intelligent …, 2023
2023
Machine Learning and Deep Learning Techniques to Classify Depressed Patients from Healthy, by Using Brain Signals from Electroencephalogram (EEG)
G Bethany, GE Raj, M Gupta
Data Modelling and Analytics for the Internet of Medical Things, 171-185, 2023
2023
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