Hari Mohan
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Assistant Professor Research
Faculty Researcher
Research Areas
Biomedical Subjects
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Biography and Research Information
OverviewAI-generated summary
Dr. Hari Mohan Rai's research focuses on the application of deep learning and machine learning techniques for medical image analysis and disease detection. His work frequently involves developing and evaluating novel hybrid deep convolutional neural network (CNN) models, such as CNN-LSTM and UnetResNext-50, to improve the accuracy and efficiency of diagnostic processes.
His recent publications demonstrate a strong emphasis on using these advanced computational methods for identifying cardiac abnormalities from electrocardiogram (ECG) data and detecting brain tumors from MRI images. He has also contributed to the review of recent advancements in machine learning and deep learning for cancer detection and segmentation, highlighting their role in improving diagnostic techniques across various biomedical imaging datasets.
Dr. Rai's scholarly contributions include over 125 total publications, with 19 recognized by an h-index of 19 and 1,620 total citations. He has also held academic positions in institutions across South Korea, Kazakhstan, and India, and has actively participated in organizing international conferences. His collaborators include Abdul Razaque from Arkansas Tech University, with whom he has co-authored three publications.
Metrics
- h-index: 19
- Publications: 125
- Citations: 1,620
Selected Publications
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Enhanced Lesion Localization and Classification in Ocular Tumor Detection Using Grad-CAM and Transfer Learning (2025)
Collaboration Network
Top Collaborators
- A comprehensive analysis of recent advancements in cancer detection using machine learning and deep learning models for improved diagnostics
- Comparative analysis of machine learning and deep learning models for improved cancer detection: A comprehensive review of recent advancements in diagnostic techniques
- Advancements in traditional machine learning techniques for detection and diagnosis of fatal cancer types: Comprehensive review of biomedical imaging datasets
- Two-headed UNetEfficientNets for parallel execution of segmentation and classification of brain tumors: incorporating postprocessing techniques with connected component labelling
- GAN-SkipNet: A Solution for Data Imbalance in Cardiac Arrhythmia Detection Using Electrocardiogram Signals from a Benchmark Dataset
Showing 5 of 13 shared publications
- Hybrid CNN-LSTM deep learning model and ensemble technique for automatic detection of myocardial infarction using big ECG data
- 2D MRI image analysis and brain tumor detection using deep learning CNN model LeU-Net
- Automatic and accurate abnormality detection from brain MR images using a novel hybrid UnetResNext-50 deep CNN model
- The prediction of cardiac abnormality and enhancement in minority class accuracy from imbalanced ECG signals using modified deep neural network models
- Myocardial Infarction Detection Using Deep Learning and Ensemble Technique from ECG Signals
Showing 5 of 7 shared publications
- Radar-Based Target Tracking Using Deep Learning Approaches with Unscented Kalman Filter
- LightweightUNet: Multimodal Deep Learning with GAN-Augmented Imaging Data for Efficient Breast Cancer Detection
- Advanced Noise-Resistant Electrocardiography Classification Using Hybrid Wavelet-Median Denoising and a Convolutional Neural Network
- The Improved Network Intrusion Detection Techniques Using the Feature Engineering Approach with Boosting Classifiers
- SwinConvNeXt: a fused deep learning architecture for Real-time garbage image classification
Showing 5 of 7 shared publications
- The prediction of cardiac abnormality and enhancement in minority class accuracy from imbalanced ECG signals using modified deep neural network models
- Advancements in traditional machine learning techniques for detection and diagnosis of fatal cancer types: Comprehensive review of biomedical imaging datasets
- Two-headed UNetEfficientNets for parallel execution of segmentation and classification of brain tumors: incorporating postprocessing techniques with connected component labelling
- GAN-SkipNet: A Solution for Data Imbalance in Cardiac Arrhythmia Detection Using Electrocardiogram Signals from a Benchmark Dataset
- Next-Generation Diagnostics: The Impact of Synthetic Data Generation on the Detection of Breast Cancer from Ultrasound Imaging
Showing 5 of 6 shared publications
- Advanced Noise-Resistant Electrocardiography Classification Using Hybrid Wavelet-Median Denoising and a Convolutional Neural Network
- Advanced Segmentation of Gastrointestinal (GI) Cancer Disease Using a Novel U-MaskNet Model
- ViT-DCNN: Vision Transformer with Deformable CNN Model for Lung and Colon Cancer Detection
- Transformative impacts of AI and the IoT on healthcare delivery
- Optimizing Financial Planning Through Advanced Machine Learning Techniques
- IoT-based real-time monitoring and control system for tomato cultivation
- Revolutionizing Skin Cancer Detection: A Comprehensive Review of Deep Learning Methods
- Utilizing Smartwatches and Deep Learning Models for Enhanced Avalanche Victim Identification, Localization, and Efficient Recovery Strategies: An In-depth Study
- Transformative impacts of AI and the IoT on healthcare delivery
- Radar-Based Target Tracking Using Deep Learning Approaches with Unscented Kalman Filter
- SwinConvNeXt: a fused deep learning architecture for Real-time garbage image classification
- Deep Learning for Leukemia Classification: Performance Analysis and Challenges Across Multiple Architectures
- Vehicle tracking and classification for intelligent transportation systems using YOLOv5 and modified deep SORT with HRNN
- LightweightUNet: Multimodal Deep Learning with GAN-Augmented Imaging Data for Efficient Breast Cancer Detection
- Advanced Noise-Resistant Electrocardiography Classification Using Hybrid Wavelet-Median Denoising and a Convolutional Neural Network
- SwinConvNeXt: a fused deep learning architecture for Real-time garbage image classification
- Vehicle tracking and classification for intelligent transportation systems using YOLOv5 and modified deep SORT with HRNN
- Advancements in traditional machine learning techniques for detection and diagnosis of fatal cancer types: Comprehensive review of biomedical imaging datasets
- HRIDM: Hybrid Residual/Inception-Based Deeper Model for Arrhythmia Detection from Large Sets of 12-Lead ECG Recordings
- Transformative impacts of AI and the IoT on healthcare delivery
- Comparative analysis of machine learning and deep learning models for improved cancer detection: A comprehensive review of recent advancements in diagnostic techniques
- Advanced Segmentation of Gastrointestinal (GI) Cancer Disease Using a Novel U-MaskNet Model
- A depth analysis of recent innovations in non-invasive techniques using artificial intelligence approach for cancer prediction
- Agri-Bot: IoT Based Unmanned Smart Vehicle for Multiple Agriculture Operation
- Tumor Detection from Brain Magnetic Resonance Images Using MRDWTA-RBFNNC
- Myocardial Infarction Detection Using Deep Learning and Ensemble Technique from ECG Signals
- Tumor Detection from Brain Magnetic Resonance Images Using MRDWTA-RBFNNC
- Imaging Techniques in Veterinary Disease Diagnosis
- Imaging Techniques in Veterinary Disease Diagnosis
- COVID-19 TravelCover: Post-Lockdown Smart Transportation Management System
- Intelligent Post-Lockdown Management System for Public Transportation
- COVID-19 TravelCover: Post-Lockdown Smart Transportation Management System
- Intelligent Post-Lockdown Management System for Public Transportation
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