Sunil Kumar Prabhakar

Publication Activity (10 Years)

Years Active: 2016-2024
Publications (10 Years): 22

Top Topics

Text Classification

Comprehensive Analysis

Top Venues

Expert Syst. Appl.

Comput. Intell. Neurosci.

Publications

Sunil Kumar Prabhakar, Dong-Ok Won
SCMs: Systematic Conglomerated Models for Audio Cough Signal Classification. Algorithms 17 (7) (2024)
Sunil Kumar Prabhakar, Dong-Ok Won
Performance comparison of bio-inspired and learning-based clustering analysis with machine learning techniques for classification of EEG signals. Frontiers Artif. Intell. 6 (2023)
Sunil Kumar Prabhakar, Dong-Ok Won
Phonocardiogram signal classification for the detection of heart valve diseases using robust conglomerated models. Expert Syst. Appl. 221 (2023)
Sunil Kumar Prabhakar, Seong-Whan Lee
Holistic Approaches to Music Genre Classification using Efficient Transfer and Deep Learning Techniques. Expert Syst. Appl. 211 (2023)
Sunil Kumar Prabhakar, Dong-Ok Won
Multiple robust approaches for EEG-based driving fatigue detection and classification. Array 19 (2023)
Sunil Kumar Prabhakar, Harikumar Rajaguru, Kwangsub So, Dong-Ok Won
A Framework for Text Classification Using Evolutionary Contiguous Convolutional Neural Network and Swarm Based Deep Neural Network. Frontiers Comput. Neurosci. 16 (2022)
Sunil Kumar Prabhakar, Young-Gi Ju, Harikumar Rajaguru, Dong-Ok Won
Sparse measures with swarm-based pliable hidden Markov model and deep learning for EEG classification. Frontiers Comput. Neurosci. 16 (2022)
Sunil Kumar Prabhakar, Harikumar Rajaguru, Semin Ryu, In cheol Jeong, Dong-Ok Won
A Holistic Strategy for Classification of Sleep Stages with EEG. Sensors 22 (9) (2022)
Sun-Hee Kim, Hyung-Jeong Yang, Thi Anh Ngoc Nguyen, Sunil Kumar Prabhakar, Seong-Whan Lee
WeDea: A New EEG-Based Framework for Emotion Recognition. IEEE J. Biomed. Health Informatics 26 (1) (2022)
Sunil Kumar Prabhakar, Seong-Whan Lee
ENIC: Ensemble and Nature Inclined Classification with Sparse Depiction based Deep and Transfer Learning for Biosignal Classification. Appl. Soft Comput. 117 (2022)
Sunil Kumar Prabhakar, Seong-Whan Lee
Improved Sparse Representation based Robust Hybrid Feature Extraction Models with Transfer and Deep Learning for EEG Classification. Expert Syst. Appl. 198 (2022)
Sunil Kumar Prabhakar, Dong-Ok Won
Medical Text Classification Using Hybrid Deep Learning Models with Multihead Attention. Comput. Intell. Neurosci. 2021 (2021)
Sunil Kumar Prabhakar, Harikumar Rajaguru, Dong-Ok Won
Performance Analysis of Hybrid Deep Learning Models with Attention Mechanism Positioning and Focal Loss for Text Classification. Sci. Program. 2021 (2021)
Sunil Kumar Prabhakar, Harikumar Rajaguru, Seong-Whan Lee
A Framework for Schizophrenia EEG Signal Classification With Nature Inspired Optimization Algorithms. IEEE Access 8 (2020)
Sunil Kumar Prabhakar, Harikumar Rajaguru, Sun-Hee Kim
Schizophrenia EEG Signal Classification Based on Swarm Intelligence Computing. Comput. Intell. Neurosci. 2020 (2020)
Sunil Kumar Prabhakar, Harikumar Rajaguru, Seong-Whan Lee
Eigen Vector Method with Swarm and Non Swarm Intelligence Techniques for Epileptic Seizure Classification. BCI (2020)
Sunil Kumar Prabhakar, Seong-Whan Lee
Transformation Based Tri-Level Feature Selection Approach Using Wavelets and Swarm Computing for Prostate Cancer Classification. IEEE Access 8 (2020)
Sunil Kumar Prabhakar, Seong-Whan Lee
An Integrated Approach for Ovarian Cancer Classification With the Application of Stochastic Optimization. IEEE Access 8 (2020)
Sunil Kumar Prabhakar, Harikumar Rajaguru, Seong-Whan Lee
Metaheuristic-Based Dimensionality Reduction and Classification Analysis of PPG Signals for Interpreting Cardiovascular Disease. IEEE Access 7 (2019)
Sunil Kumar Prabhakar, Harikumar Rajaguru, Seong-Whan Lee
A Comprehensive Analysis of Alcoholic EEG Signals with Detrend Fluctuation Analysis and Post Classifiers. BCI (2019)
Sunil Kumar Prabhakar, Harikumar Rajaguru
Conceptual analysis of epilepsy classification using probabilistic mixture models. BCI (2017)
Sunil Kumar Prabhakar, Harikumar Rajaguru
Expectation Maximization Based PCA and Hessian LLE with Suitable Post Classifiers for Epilepsy Classification from EEG Signals. SoCPaR (2016)