Debasish Swapnesh Kumar Nayak

Scopus Publications

Quantum-enhanced neural networks: bridging the quantum algorithm and machine learning
Manisa Manoswini, Debasish Swapnesh Kumar Nayak, Tejaswini Das, Tripti Swarnkar
Quantum Computing, 2026
Using quantum concepts like superposition and entanglement, quantum-enhanced neural networks (QENN) combine machine learning (ML) and quantum computing to improve neural network performance. They are expected to have a revolutionary influence on optimization, pattern recognition, and complex data analysis. In this work, authors discuss the principles of quantum computing, including superposition, entanglement, and quantum gates, before introducing many quantum ML (QML) techniques, including Grover's search, Shor's factoring, and quantum annealing. Following that, it discusses the fundamentals of neural networks for problems like image recognition and natural language processing while addressing issues with big data and high-dimensional features. This work focuses on using quantum processors to speed up traditional ML tasks by combining quantum algorithms with neural networks to generate hybrid models. It investigates quantum kernel techniques, quantum variational algorithms, and QENN, demonstrating their potential to improve conventional neural network frameworks. In addition to this authors also include real-world applications in fields including drug development, materials research, finance, and optimization to show the revolutionary effects of quantum-enhanced machine learning (QEML) on current problems. In conclusion, this chapter provides a thorough review of QML, highlighting the synergy between quantum algorithms and neural networks. It offers unique insights into the field's current state, practical applications, and hopes for defining the future of artificial intelligence through quantum computational paradigms.
CadCNN 1.0: A HYBRID DEEP NETWORK MODEL FOR CARDIAC ARRHYTHMIA DETECTION
Bipasha Patnaik, Santanu Sahoo, Debasish Swapnesh Kumar Nayak, Asit Kumar Subudhi
Biomedical Engineering Applications Basis and Communications, 2026
Cardiac Arrhythmia, which encompasses irregular heart rhythms such as tachycardia, bradycardia, ectopic pulses, represents a significant cause of cardiovascular morbidity and mortality worldwide, caused by disruptions in the heart’s electrical conduction system. Accurate and automated interpretation of the Electrocardiogram (ECG) is consequently crucial for the timely diagnosis and selection of treatment. This study introduces CadCNN 1.0, a novel hybrid Deep Learning (DL) framework that combines one-dimensional Convolutional Neural Networks (1D-CNN) for spatial feature extraction with Bidirectional Long Short-Term Memory (BiLSTM) for temporal sequence modeling. This methodology enables the accurate characterization of ECG waveforms and the model was trained and validated using the MIT-BIH Arrhythmia Database, classifying ECG signals into five categories: Normal (N), Fusion (F), Supraventricular Ectopic Beat (SVEB), Ventricular Ectopic Beat (VEB), and Unknown (Q) Beat. The Gray Wolf Optimization (GWO) algorithm was employed to optimize feature selection and model convergence, resulting in exceptional diagnostic performance with an Accuracy of 99.92%, Sensitivity (Recall) of 99.81%, Specificity of 99.93%, Precision of 99.92%, [Formula: see text]1-score of 99.92% along with MCC of 99.84% with Number of Wolves (Nw) of 10 represents the number of wolves in the population, corresponding to the number of candidate feature subsets simultaneously explored in each iteration, that enhances population diversity and prevents premature convergence and Max Iteration of 10 which denotes the maximum number of optimization cycles during which the wolves update their positions toward the optimal feature subset, ensuring iterative refinement, stability of global optimum. The confusion matrix demonstrates CadCNN 1.0’s discriminative ability to differentiate morphologically equivalent Arrhythmic classes with near-perfect precision, as evidenced by its strong diagonal dominance and minimal off-diagonal misclassifications. Furthermore, the Breast Cancer Dataset performed cross-domain validation, resulting in an Accuracy of 97.50%, Sensitivity (Recall) of 96.49%, Specificity of 97.18%, Precision of 96.49%, [Formula: see text]1-score of 96.49%, MCC of 92.53%. These results underscore the model’s adaptability to heterogeneous biomedical data. CadCNN 1.0 provides a clinically reliable, generalizable, computationally efficient computer-aided diagnostic system that advances automated Arrhythmia detection and enables intelligent, patient-centered healthcare diagnostics by integrating spatial-temporal feature learning, meta-heuristic optimization, and interpretable confusion-matrix validation.
ViTat 1.0: An attention based vision transformer model for brain tumor identification
Rasmi Prakash Swain, Shubha Dattatreya Bahinipati, Shweta Kharya, Tejaswini Das, Bipasha Patnaik, Debasish Swapnesh Kumar Nayak
Futuristic Information and Communication A Multimodal Multidisciplinary Signal Analysis, 2026
Deep Network Models for Alzheimer’s Diagnosis Using MRI: A Comparative Analysis of CNN and Hybrid CNN-LSTM Architectures
Sanghamitra Panda, Vikas Singh, Sushree Sasmita Dash, Ashis Kumar Ratha, Sushanta Meher, Debasish Swapnesh Kumar Nayak
Lecture Notes in Networks and Systems, 2026
DeepCCi 1.0: A Deep Learning Model for Cervical Cancer Identification
Pabani Mahanta, Sabyasachi Mohanty, Arpita Priyadarshini, Ashis Kumar Ratha, Sushree Sasmita Dash, Debasish Swapnesh Kumar Nayak
Lecture Notes in Networks and Systems, 2026
Attention enhanced hybrid deep learning model with 1D-CNN and BiLSTM for automated sleep apnea detection
Bipasha Patnaik, Debasish Swapnesh Kumar Nayak, Santanu Sahoo
Discover Applied Sciences, 2025
Sleep Apnea (SA) is an alarming sleep disorder characterized by repeated cessation of breathing during sleep, which frequently results in severe cardiovascular and metabolic impairment. The traditional techniques, such as polysomnography, are time-consuming, costly, and require clinical supervision. To address these challenges, this study proposes a hybrid DL-based framework for automated SA identification with single-lead ECG signals from the PhysioNet Apnea-ECG dataset. The methodology integrates comprehensive signal processing along with feature engineering to improve the morphological, temporal characteristics of ECG signals. The traditional ML models like SVM with Linear, Polynomial, Sigmoid, Radial Basis Kernels, and DL architectures such as 1D-CNN, 1D-CNNN-BiLSTM, and a hybrid 1D-CNN + BiLSTM-Attention mechanism automatically weight temporal features to focus on diagnostically significant ECG segments and improve the hidden feature space representation of prominent patterns. The efficacy of the model is demonstrated in the form of a validated quantitative performance evaluation using 10-fold cross-validation. The model achieved 98.39% Accuracy, 99.02% Precision, 98.29% Sensitivity, 96.53% Specificity, 98.66% F1-Score, 96.67% MCC, 97.78% AUC on PhysioNet-Apnea ECG dataset, indicating strong classification performance with robust generalization across patient’s sub-groups. The strength of the proposed model has been demonstrated using the MIT-BIH Polysomnographic database across various sleep patterns and recording conditions in diagnostic accuracy, efficiency, and generalization across physiological variations. The results have been compared with some of the state-of-the-art methods for establishing the superiority of the proposed model.
Vi-GeN 1.0: GAN-Augmented Vision Transformer Pipeline for Diversified Pneumonia Classification
Archana Dash, Soumyarashmi Panigrahi, Debasish Swapnesh Kumar Nayak, Amiya Prasad Dash, Tripti Swarnkar
Journal of Transformative Technologies and Sustainable Development, 2025
Pneumonia continues to be one of the global causes of morbidity and mortality, necessitating early and proper diagnosis using chest X-rays (CXRs). Deep learning networks, specifically convolutional neural networks (CNNs), have shown excellent results in identifying pneumonia. But their performance is normally hampered by dataset imbalance, feature redundancy, and local receptive fields, reducing generalizability. To overcome such challenges, the study introduces Vi-GeN 1.0, a GAN-augmented Vision Transformer (ViT) pipeline specifically architected for pneumonia classification robustness. Our method uses Wasserstein GAN with Gradient Penalty (WGAN-GP) to synthesize high-quality CXRs, improving dataset diversity and feature learning. The ViT classifier, trained on the real and synthetic data, learns global contextual representations, resulting in better classification performance. Vi-GeN 1.0 model was tested on the Kaggle Chest X-ray Pneumonia dataset, showing 97.3% accuracy, 98.1% sensitivity, 96.5% specificity, and AUC-ROC of 0.985, performing much better than the baseline ViT model (p < 0.01). The realism and feature preservation of the synthetic images are ensured by FID (22.1), SSIM (0.87), and PSNR (28.9 dB) validation. Our results showed that GAN-based augmentation improves pneumonia classification performance effectively, making Vi-GeN 1.0 a valuable candidate for clinical use, especially in low-resource environments. Future research will investigate multi-class classification, domain generalization, and more sophisticated augmentation methods like diffusion models.
Evaluating the Capability of Large Language Model Chatbots for Generating Plain Language Summaries in Radiology
Pradosh Kumar Sarangi, Pratisruti Hui, Himel Mondal, Debasish Swapnesh Kumar Nayak, M. Sarthak Swarup, et al.
Iradiology, 2025
Background Plain language summary (PLS) are essential for making scientific research accessible to a broader audience. With the increasing capabilities of large language models (LLMs), there is the potential to automate the generation of PLS from complex scientific abstracts. This study assessed the performance of six LLM chatbots: ChatGPT, Claude, Copilot, Gemini, Meta AI, and Perplexity, in generating PLS from radiology research abstracts. Methods A total of 100 radiology abstracts were collected from PubMed. Six LLM chatbots were tasked with generating PLS for each abstract. Two expert radiologists independently evaluated the generated summaries for accuracy and readability, with their average scores being used for comparisons. Additionally, the Flesch–Kincaid (FK) grade level and Flesch reading ease score were applied to objectively assess readability. Results Comparisons of LLM‐generated PLS revealed variations in both accuracy and readability across the models. Accuracy was highest for ChatGPT (4.94 ± 0.18) followed by Claude (4.75 ± 0.31). Readability was highest for ChatGPT (4.83 ± 0.27) followed by Perplexity (4.82 ± 0.29). The Flesch reading ease score was highest for Claude (62.53 ± 10.98) and lowest for ChatGPT (40.10 ± 11.24). Conclusion LLM chatbots show promise in the generation of PLS, but performance varies significantly between models in terms of both accuracy and readability. This study highlights the potential of LLMs to aid in science communication but underscores the need for careful model selection and human oversight.
ARGai 1.0: A GAN augmented in silico approach for identifying resistant genes and strains in E. coli using vision transformer
Debasish Swapnesh Kumar Nayak, Ruchika Das, Santanu Kumar Sahoo, Tripti Swarnkar
Computational Biology and Chemistry, 2025
PerSceptoMed 1.0: An advanced machine learning model for demographic-based antibiotic susceptibility prediction in Pseudomonas aeruginosa infections
Sweta Padma Routray, Saswati Mahapatra, Debasish Swapnesh Kumar Nayak, Swayamprabha Sahoo, Bhabani Shankar Das, Enketeswara Subudhi, Rajashree Panigrahy, Tripti Swarnkar
Engineering Applications of Artificial Intelligence, 2025
Prevalence of multidrug-resistant Klebsiella pneumoniae in urinary tract infections: A retrospective observational study in eastern India
Swayamprabha Sahoo, Jatindra Mohanty, Sweta Routray, Ashirbad Sarangi, Debasish Nayak, Sejal Shah, Jayashankar Das, Enketeswara Subudhi, Tripti Swarnkar
Microbes and Infectious Diseases, 2025
ARGai 2.0: A Feature Engineering Enabled Deep Network Model for Antibiotic Resistance Gene and Strain Identification in E. coli
Debasish Swapnesh Kumar Nayak, Arpita Priyadarshini, Sweta Padma Routray, Santanu Kumar Sahoo, Tripti Swarnkar
International Journal of Online and Biomedical Engineering, 2025
An Artificial Intelligent Approach for Classifying Odia and Non-Odia Handwritten Script using Histogram of Oriented Gradients (HOG) Features
Sasmita Tripathy, Satya Ranjan Pattanaik, Debasish Swapnesh Kumar Nayak
2nd International Conference on Cognitive Green and Ubiquitous Computing IC Cgu 2025, 2025
CiGeN 1.0: A Generative AI Enabled Medical Image Augmentation Pipeline for Deep Network Models
Archana Dash, Debasish Swapnesh Kumar Nayak, Tripti Swarnkar
2025 International Conference on Ambient Intelligence in Health Care Icaihc 2025, 2025
Identification of Key Genetic Drivers in Multidrug Resistance: A Co-Expression Network Analysis of Pseudomonas aeruginosa
Sweta Padma Routray, Saswati Mahapatra, Debasish Swapnesh Kumar Nayak, Enketeswara Subudhi, Tripti Swarnkar
2025 International Conference on Ambient Intelligence in Health Care Icaihc 2025, 2025
DeepEcc 1.0: Ensemble Deep Learning Model Enriched with Whale Optimization for Cervical Cancer Identification
Pabani Mahanta, Anwesha Mishra, Manish Taram, Soumitr Sahu, Aman Jaswal, Debasish Swapnesh Kumar Nayak
2025 IEEE 6th Global Conference for Advancement in Technology Gcat 2025, 2025
YouTube Sentiment Analyzer for Modi (YSAM): Unlocking YouTube Comment Sentiments with Machine Learning and Visualization
Sanghamitra Panda, Sushree Sasmita Dash, Ashis Kumar Ratha, Swapnajit Sahoo, Annimesh Sasmal, Debasish Swapnesh Kumar Nayak
International Conference on Innovations in Intelligent Systems Advancements in Computing Communication and Cybersecurity Isac3 2025, 2025
Machine Learning-Driven Classification Framework Using ViT and Swin Transformers for Feature Extraction of Brain MRI Images
Soumyarashmi Panigrahi, Dibya Ranjan Das Adhikary, Prem Purusottam Jena, Umashankar Biswal, Archana Dash, Debasish Swapnesh Kumar Nayak
International Conference on Artificial Intelligence and Emerging Technologies Icaiet 2025, 2025
Artificial intelligence in improving disease diagnosis: A case study of cardiovascular disease prediction
Abhilash Pati, Santosh Reddy Addula, Amrutanshu Panigrahi, Bibhuprasad Sahu, Debasish Swapnesh Kumar Nayak, Manoranjan Dash
Artificial Intelligence in Medicine and Healthcare, 2025
Diabetes Prediction in Healthcare Utilizing Machine Learning Algorithms
Nrusingha Tripathy, Sarbeswara Hota, Pranati Satapathy, Subrat Kumar Nayak, Debasish Swapnesh Kumar Nayak, Purnya Prava Nayak
2025 International Conference on Ambient Intelligence in Health Care Icaihc 2025, 2025
BCViMer 1.0: A Deep Network Pipeline for the Identification of Breast Cancer Using MRI Data
Tejaswini Das, Ashis Kumar Ratha, Sushree Sasmita Dash, Rakesh Pandey, Sabyasachi Mohanty, Debasish Swapnesh Kumar Nayak
3rd IEEE International Conference on Industrial Electronics Developments and Applications Icidea 2025, 2025
A Prioritized-Recommendation System with Association Rule Mining and Random Forest for Retailing
Debasish Swapnesh Kumar Nayak, Pragyan Nanda, Payal Agasti, Roseleen Anjum, Amit Kumar Harichandan, Suprava Ranjan Laha
Proceedings of the 2025 International Conference on Artificial Intelligence and Emerging Technology Global AI Summit 2025, 2025
Lung Cancer Identification with Deep Networks: Convolutional Neural Network or Transformers!
Satya Ranjan Behera, Sushree Sasmita Dash, Ayush Aryan Dash, Pritam Priyadarshi Mishra, Roseleen Anjum, Debasish Swapnesh Kumar Nayak
Icrteect 2025 2nd International Conference on Recent Trends in Electrical Electronics and Computing Technologies, 2025
Feline Emotion Recognition Using Deep Learning: A Comprehensive EfficientNet-B0 Framework for Multi-Class Sentiment Analysis in Domestic Cats
Anirban Bhattacharya, Ram Krishna Verma, Debasish Swapnesh Kumar Nayak, Sanghamitra Panda, Annimesh Sasmal, Swapnajit Sahoo
2025 1st International Conference on Advancement in Futuristic Technologies Icaft 2025, 2025
GenENet 1.0: A Deep Network Pipeline with Generative Adversarial Networks for Tumor Identification
Tejaswini Das, Sabyasachi Mohanty, Debasish Swapnesh Kumar Nayak, Ashis Kumar Ratha, Satyananda Swain, Tripti Swarnkar
International Conference on Innovations in Intelligent Systems Advancements in Computing Communication and Cybersecurity Isac3 2025, 2025
Facial Emotion Recognition for University Students using CNN: Transforming Learning Environment
Adyasha Swain, Suprava Ranjan Laha, Sasmita Sahoo, Ankit Dalei, Vaibhav Srivastav, et al.
Icoicc 2025 3rd International Conference on Intelligent and Cloud Computing, 2025
Optimized Deep Learning Architecture for Maize Disease Detection Using Efficient Channel Attention and Transfer Learning
Lucy Dash, Suprava Ranjan Laha, Binod Kumar Pattanayak, Debasish Swapnesh Kumar Nayak, Pranashi Chakraborty, Salankara Sarkar
2025 Global Conference on Information Technology and Communication Networks Gitcon 2025, 2025
aiGeneR 3.0: an enhanced deep network model for resistant strain identification and multi-drug resistance prediction in Escherichia coli causing urinary tract infection using next-generation sequencing data
Debasish Swapnesh Kumar Nayak, Abhilash Pati, Amrutanshu Panigrahi, Mudassir Khan, Bayan Alabdullah, Santanu Kumar Sahoo, Bibhuprasad Sahu, Abrar Almjally, Saurav Mallik, Tripti Swarnkar
Frontiers in Genetics, 2025
DnnARs: An Artificial Intelligence Technique for Prediction of Antimicrobial Resistant Strains in E. coli Bacteria Causing Urine Tract Infection
Debasish Swapnesh Kumar Nayak, Arpita Priyadarshini, Pabani Mahanta, Tejaswini Das, Santanu Kumar Sahoo, Tripti Swarnkar
SN Computer Science, 2024
Radiologic Decision-Making for Imaging in Pulmonary Embolism: Accuracy and Reliability of Large Language Models—Bing, Claude, ChatGPT, and Perplexity
Pradosh Kumar Sarangi, Suvrankar Datta, M. Sarthak Swarup, Swaha Panda, Debasish Swapnesh Kumar Nayak, Archana Malik, Ananda Datta, Himel Mondal
Indian Journal of Radiology and Imaging, 2024
Genomic characterization and distribution of XDR Klebsiella pneumoniae ST15 carrying blaOXA-181 on ColKP3 plasmid from an ICH patient in eastern India: A global comparative analysis
Swayamprabha Sahoo, Sweta Padma Routray, Jatindra Nath Mohanty, Animesh Pattnaik, Debasish Swapnesh Kumar Nayak, Sejal Shah, Jayashankar Das, Enketeswara Subudhi, Tripti Swarnkar
Gene Reports, 2024
Radiological Differential Diagnoses Based on Cardiovascular and Thoracic Imaging Patterns: Perspectives of Four Large Language Models
Pradosh Kumar Sarangi, Aparna Irodi, Swaha Panda, Debasish Swapnesh Kumar Nayak, Himel Mondal
Indian Journal of Radiology and Imaging, 2024
Cybersecurity Challenges in IoT-Based Healthcare Systems A Survey
Suprava Ranjan Laha, Debasish Swapnesh Kumar Nayak
Intelligent Security Solutions for Cyber Physical Systems, 2024
HFai: Deep Networks for Detecting Heart Failure Progression
Bipasha Patnaik, Debasish Swapnesh Kumar Nayak, Santanu Kumar Sahoo
3rd International Conference on Communication Control and Intelligent Systems Ccis 2024, 2024
Multichannel ECG-Based Heart Disease Detection with ResNet-50 and Decision-Tree Classifier
Bipasha Patnaik, Debasish Swapnesh Kumar Nayak, Santanu Kumar Sahoo
2nd International Conference on Signal Processing Communication Power and Embedded Systems Scopes 2024, 2024
A Blockchain-Enhanced Framework for Efficient Paging and Energy Optimization in 5G Networks
Satyananda Swain, Sushanta Meher, Debasish Swapnesh Kumar Nayak, Bijayini Mohanty
2nd International Conference on Signal Processing Communication Power and Embedded Systems Scopes 2024, 2024
ARGai: AI Fusion Approaches for Classification of S. aureus Strains and Identification of Antibiotic Resistant Genes
Debasish Swapnesh Kumar Nayak, Archana Dash, Sweta Padma Routray, Santanu Sahoo, Tripti Swarnkar
Proceedings of the 2024 International Conference on Artificial Intelligence and Emerging Technology Global AI Summit 2024, 2024
Deep Learning Techniques for Identification of Pneumonia: A CNN Approach
Ruchika Das, Debasish Swapnesh Kumar Nayak, Chinmayee Priyadarshini Rout, Lambodar Jena, Tripti Swarnkar
Proceedings of 2nd International Conference on Advancements in Smart Secure and Intelligent Computing Assic 2024, 2024
An Investigation into Opportunities and Challenges for Forex Decision Making Using Machine Learning
Ibanga Kpereobong Friday, Debasish Swapnesh Kumar Nayak, Rashmi Ranjan Panigrahi, Saikat Gochhait, Tripti Swarnkar
Lecture Notes in Electrical Engineering, 2024
ResNet-50: The Deep Networks for Automated Breast Cancer Classification using MR Images
Tejaswini Das, Debasish Swapnesh Kumar Nayak, Anindita Kar, Lambodar Jena, Tripti Swarnkar
Proceedings of 2nd International Conference on Advancements in Smart Secure and Intelligent Computing Assic 2024, 2024
Web-Based Tools Validation for Antimicrobial Resistance Prediction: An Empirical Comparative Analysis
Sweta Padma Routray, Swayamprabha Sahoo, Debasish Swapnesh Kumar Nayak, Sejal Shah, Tripti Swarnkar
SN Computer Science, 2024
Optimizing XGBoost for Enhanced Electrical Theft Detection: A Fusion of Particle Swarm and Jaya Optimization Techniques
Sashikanta Prusty, Debasish Swapnesh Kumar Nayak, Meena Moharana, Sushree Gayatri Priyadarsini Prusty, Manmohan Sahoo, Jyotirmayee Rautaray
2024 IEEE 4th International Conference on Sustainable Energy and Future Electric Transportation Sefet 2024, 2024
Modified Deep Neural Network Approach to Identify Heart Disease using IoMT: Artificial Neural Networks or Convolutional Neural Networks!
Debasish Swapnesh Kumar Nayak, Arpita Priyadarshini, Pabani Mahanta, Soumyarashmi Panigrahi, Sushanta Meher, Satyananda Swain
2024 International Conference on Intelligent Computing and Sustainable Innovations in Technology IC Sit 2024, 2024
Enhanced Seizure Prediction Using XGBoost and EEG Data: A Machine Learning Approach with increasing Model Metrics
Sanghamitra Panda, Sushree Sasmita Dash, Rakesh Pandey, Swapnajit Sahoo, Annimesh Sasmal, Debasish Swapnesh Kumar Nayak
2nd International Conference on Signal Processing Communication Power and Embedded Systems Scopes 2024, 2024
Weighted Bayesian Belief Network for diabetics: a predictive model
Shweta Kharya, Sunita Soni, Abhilash Pati, Amrutanshu Panigrahi, Jayant Giri, Hong Qin, Saurav Mallik, Debasish Swapnesh Kumar Nayak, Tripti Swarnkar
Frontiers in Artificial Intelligence, 2024
aiGeneR 1.0: An Artificial Intelligence Technique for the Revelation of Informative and Antibiotic Resistant Genes in Escherichia coli
Debasish Swapnesh Kumar Nayak, Saswati Mahapatra, Sweta Padma Routray, Swayamprabha Sahoo, Santanu Kumar Sahoo, Mostafa M. Fouda, Narpinder Singh, Esma R. Isenovic, Luca Saba, Jasjit S. Suri, Tripti Swarnkar
Frontiers in Bioscience Landmark, 2024
A neural network framework for predicting Adenocarcinoma cancer using high-throughput gene expression data
Subhra Mohanty, Debasish Swapnesh Kumar Nayak, Tripti Swarnkar
Aip Conference Proceedings, 2023
ReCuRandom: A hybrid machine learning model for significant gene identification
Debasish Swapnesh Kumar Nayak, Abhilash Pati, Amrutanshu Panigrahi, Santanu Sahoo, Tripti Swarnkar
Aip Conference Proceedings, 2023
A Comparative Analysis of Machine Learning Models for Colon Cancer Classification
Rashmi Ranjan Swain, Debasish Swapnesh Kumar Nayak, Tripti Swarnkar
2023 International Conference in Advances in Power Signal and Information Technology Apsit 2023, 2023
Enhancing Fault Tolerance and Load Balancing in Cloud computing for improved e-healthcare Systems Performance
Aliva Priyadarshini, Sateesh Kumar Pradhan, Suprava Ranjan Laha, Debasish Swapnesh Kumar Nayak
2023 2nd International Conference on Ambient Intelligence in Health Care Icaihc 2023, 2023
ANTIBIOLOG: An Advanced Tool for Combat Antibiotic Resistance With Enhanced Multinomial Logistic Regression
Sweta Padma Routray, Amlan Deep Nayak, Debasish Swapnesh Kumar Nayak, Swayamprabha Sahoo, Tripti Swarnkar
Proceedings of 2023 IEEE 9th International Women in Engineering Wie Conference on Electrical and Computer Engineering Wiecon Ece 2023, 2023
An IOT-Based Soil Moisture Management System for Precision Agriculture: Real-Time Monitoring and Automated Irrigation Control
Suprava Ranjan Laha, Binod Kumar Pattanayak, Saumendra Pattnaik, Debashree Mishra, Debasish Swapnesh Kumar Nayak, Bibhuti Bhusan Dash
Proceedings of the 4th International Conference on Smart Electronics and Communication Icosec 2023, 2023
Efficient Technique for Glioblastoma Multiforme Detection: Deep Learning or Transfer Learning
Subhashree Mohapatra, Sonali Kumari, Sangita Kabi, Raja Sahu, Debasish Swapnesh Kumar Nayak, Tripti Swarnkar
IEEE Region 10 Humanitarian Technology Conference R10 Htc, 2023
En-MinWhale: An Ensemble Approach Based on MRMR and Whale Optimization for Cancer Diagnosis
Amrutanshu Panigrahi, Abhilash Pati, Bibhuprasad Sahu, Manmath Nath Das, Debasish Swapnesh Kumar Nayak, Ghanashyam Sahoo, Shashi Kant
IEEE Access, 2023
Quality Control Pipeline for Next Generation Sequencing Data Analysis
Debasish Swapnesh Kumar Nayak, Jayashankar Das, Tripti Swarnkar
Smart Innovation Systems and Technologies, 2022
Prediction of Smoking Addiction Among Youths Using Elastic Net and KNN: A Machine Learning Approach
Shreerudra Pratik, Debasish Swapnesh Kumar Nayak, Rajendra Prasath, Tripti Swarnkar
Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 2022
An Enhanced Web-based Tools for Multiple Sequence Alignment: A Comparative Approach
Swayamprabha Sahoo, Sweta Padma Routray, Debasish Swapnesh Kumar Nayak, Tripti Swarnkar
Ican 2022 3rd International Conference on Computing Analytics and Networks Proceedings, 2022
Deep learning approaches for high dimension cancer microarray data feature prediction: A review
Debasish Swapnesh Kumar Nayak, Subhashree Mohapatra, David Al-Dabass, Tripti Swarnkar
Computational Intelligence in Cancer Diagnosis Progress and Challenges, 2022
IRGM: An Integrated RNN-GRU Model for Stock Market Price Prediction
Ibanga Kpereobong Friday, Julius Femi Godslove, Debasish Swapnesh Kumar Nayak, Sashikanta Prusty
Proceedings 2022 International Conference on Machine Learning Computer Systems and Security Mlcss 2022, 2022
Predicting Pediatric Appendicitis using Ensemble Learning Techniques
Abhilash Pati, Amrutanshu Panigrahi, Debashish Swapnesh Kumar Nayak, Ghanashyam Sahoo, Debabrata Singh
Procedia Computer Science, 2022
A Comparative Study using Next Generation Sequencing Data and Machine Learning Approach for Crohn's Disease (CD) Identification
Debasish Swapnesh Kumar Nayak, Sweta Padma Routray, Swayamprabha Sahooo, Santanu Kumar Sahoo, Tripti Swarnkar
Proceedings 2022 International Conference on Machine Learning Computer Systems and Security Mlcss 2022, 2022
Interpretation of Optimized Hyper Parameters in Associative Rule Learning using Eclat and Apriori
Debaniranjan Mohapatra, Jogeswar Tripathy, Kalyan Kumar Mohanty, Debasish Swapnesh Kumar Nayak
Proceedings 5th International Conference on Computing Methodologies and Communication Iccmc 2021, 2021
Gene selection and enrichment for microarray data—A comparative network based approach
Debasish Swapnesh Kumar Nayak, Saswati Mahapatra, Tripti Swarnkar
Advances in Intelligent Systems and Computing, 2018

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