Pramoda Patro

Scopus Publications

Comprehensive Review of Machine Learning Models for Breast Cancer Diagnosis (2018–2023)
Big Data and Computing Visions, 2026
Hybrid data balancing with MLP probabilities-based categorical boosting model for robust intrusion detection system in IoT environment
A. Mallikarjun, Pramoda Patro
Systems and Soft Computing, 2026
The rapid expansion of the Internet of Things (IoT) has led to an estimated 29.3 billion connected devices by 2030, generating over 79.4 zettabytes of data annually. However, IoT networks remain highly vulnerable, with nearly 57% of IoT devices susceptible to cyber threats, including Denial-of-Service (DoS) and data spoofing attacks. Existing Intrusion Detection Systems (IDS) often suffer from class imbalance, leading to biased models and reduced detection accuracy for minority attack classes. To address these challenges, a novel Data-Balanced Machine Learning IDS (DBML-IDS) is proposed, integrating data preprocessing, Support Vector Machine (SVM) Weights-based Synthetic Minority Over-sampling Technique (SVWS) for improved data balancing, and a Multi-Layer Perceptron (MLP) Probabilities-based Categorical Boosting (MLPP-CB) classifier. The CICIoT2023 dataset, consisting of two classes (Normal and Attack), is used for evaluation. The proposed DBML-IDS framework ensures optimal feature distribution, mitigates overfitting, and enhances generalization for real-world IoT threat detection. Experimental results demonstrate that DBML-IDS achieves a superior classification performance with accuracy, precision, recall, and an F1-score of 0.9973, outperforming existing IDS models. These findings highlight the effectiveness of the proposed methodology in securing IoT environments against emerging cyber threats.
Machine Learning-Driven Intrusion Detection Systems for Anomaly Detection in Vehicular Ad-Hoc Networks
T. Ragunthar, S. Annie Christila, B. Jyoshna, L. Meenachi, Subhranginee Das, Pramoda Patro
Transactions on Emerging Telecommunications Technologies, 2026
In Intelligent Transportation Systems, Vehicular Ad‐hoc Networks (VANETs) provide real‐time vehicle‐infrastructure communication. VANETs may be attacked via denial‐of‐service, Sybil, and spoofing due to its open wireless medium and changeable topology. In dynamic vehicle contexts, signature‐based intrusion detection systems (IDSs) fail to identify novel threats, while anomaly‐based techniques have high false alarm rates and limited scalability. The Machine Learning‐based Vehicular Intrusion Detection System (ML‐VIDS) combines unsupervised clustering for zero‐day anomaly detection and supervised learning for known attack categorization. The method improves detection accuracy and edge deployment efficiency via temporal traffic analysis and feature selection. On benchmark VANET datasets, ML‐VIDS beats comparable IDS systems with 97.8% detection accuracy, 6% false positive rate, 15 ms latency, 65% lower computational overhead, and 30 W energy utilization. In next‐generation intelligent transportation systems, ML‐VIDS enables adaptive and resource‐efficient intrusion detection for VANETs to provide strong performance and real‐time security.
Explainable Quantum CNN Models for Detecting and Mitigating Cyber Threats in Distributed Systems
Ananthala Mallikarjun, Pramoda Patro
Lecture Notes in Networks and Systems, 2026
Improving Medical Diagnosis with Augmented Reality Visualization
Thulasi Bikku, G Govinda Rajulu, Pramoda Patro, Kiran Sree Pokkuluri
Virtual Reality Real Emergency Integrating AR VR in Computing and Medical Crisis Management, 2025
Immersive Technologies for Hazardous Material Response Training
Charanjeet Singh, Guna Sekhar Sajja, K. Suresh Kumar, Pramoda Patro, M. Clement Joe Anand
Virtual Reality Real Emergency Integrating AR VR in Computing and Medical Crisis Management, 2025
AR/VR-Based Disaster Simulation for Community Resilience Training
Charanjeet Singh, Guna Sekhar Sajja, K. Suresh Kumar, Pramoda Patro, M. Clement Joe Anand
Virtual Reality Real Emergency Integrating AR VR in Computing and Medical Crisis Management, 2025
Federated Learning Approach for Distributed DDoS Detection in VANETs
Nileshsingh V. Thakur, S. Vijayakumar, L. Meenachi, Subhranginee Das, Pramoda Patro
Transactions on Emerging Telecommunications Technologies, 2025
Vehicular Ad Hoc Networks (VANETs), a crucial part of Intelligent Transportation Systems, provide real‐time vehicle‐RSU communication. VANETs are subject to DDoS assaults, which may undermine safety‐critical systems due to their dynamic and dispersed nature. VANET DDoS detection solutions frequently use centralized designs that need raw data aggregation (DA), which causes scalability, communication cost, and privacy difficulties. Additionally, traditional models fail to adapt to vehicular surroundings' varied and frequently changing traffic patterns. Federated Anomaly Detection with Personalized Autoencoders (FAD‐PAE) is proposed to address these issues. Vehicles and roadside equipment train lightweight Autoencoders (AE) locally to simulate regular traffic behavior, exchanging just model updates via federated learning. Personalized fine‐tuning at each node adapts to local traffic changes, while safe and strong aggregation protects against compromised clients. The VANET‐based cooperative and privacy‐preserving DDoS detection approach reduces false alarms and communication costs. Experimental results show superior detection accuracy (ACC), reaction speed, and flexibility compared to centralized techniques.
GrCRA PCRTAM net based hybrid approach for intelligent control and optimal power management in renewable integrated power distribution systems
S. Arulkumar, Mohammad Arif, Pramoda Patro, M. Siva Ramkumar, M. Sivaramkrishnan, Arunkumar Munimathan, Javed Khan Bhutto, Hadi Hakami, Amanuel Zewdie
Scientific Reports, 2025
Power management in advanced power distribution systems integrated with Photovoltaic (PV) sources, batteries, and Super Capacitors (SCs) plays a vital role in ensuring stable and efficient energy flow. However, these systems often face drawbacks such as increased energy consumption due to inefficient control strategies, higher emissions from backup conventional sources during low PV output, and elevated operational costs from frequent battery cycling and system maintenance, despite efforts to improve efficiency and enhance renewable energy utilization. To overcome these drawbacks, this manuscript proposes an approach for optimal power management in a power distribution system with RES. The suggested method is the combination of both the Greater Cane Rat Algorithm (GrCRA) and Pre-Activated Convolution Residual and Triple Attention Mechanism Network (PCRTAM-Net), termed as the GrCRA-PCRTAM-Net approach. The primary aim of the suggested method is to reduce energy consumption, emissions, and operational cost while maximizing efficiency and renewable energy utilization in an advanced power distribution system. GrCRA optimizes the allocation and scheduling of power resources in advanced power distribution systems. PCRTAM-Net predicts future power demand and renewable energy generation patterns to support optimal power management. Flow Direction Algorithm-Convolutional Neural Network (FDA-CNN), Hippopotamus Optimization Algorithm (HOA), Particle Swarm Optimization (PSO), Spider Wasp Optimizer, and Multi-scale Hypergraph-based Feature Alignment Network (SWO-MHFAN), Golden Jackal Optimization-Progressive Conditional Generative Adversarial Network (GJO-PCGAN) are some of the existing techniques that are compared with the suggested method once it is implemented in MATLAB. An 18.7% overall energy reduction compared to the current methods has been achieved by GrCRA-PCRTAM-Net, which also attained an operational cost of 1505 cents, an emission level of 60.3 ppm, an efficiency of 99.1%, and a reduction in overall energy consumption. This further validates that the hybrid method effectively performed power flow optimization and stability enhancement in power distribution networks with renewable integration.
Machine Learning-Enhanced DDoS Attack Detection and Mitigation in VANET Infrastructure
T. Gayathri, S. Uma Maheswari, S. Ponni Alias Sathya, T. Satyanarayana Murthy, Pramoda Patro
Transactions on Emerging Telecommunications Technologies, 2025
Vehicular ad‐hoc networks (VANETs) are crucial for road safety, traffic management, and intelligent transportation systems, but they are vulnerable to Distributed Denial of Service (DDoS) attacks, which can severely disrupt communication between vehicles and Roadside Units (RSUs). Traditional DDoS detection methods in VANETs are often inefficient due to reliance on centralized architectures and handcrafted features. To address these challenges, we propose the Hybrid Deep Learning with Federated Learning (HDL‐FL) framework, which leverages Convolutional Neural Networks (CNNs) to capture spatial and temporal traffic patterns. By utilizing Federated Learning, HDL‐FL enables distributed, privacy‐preserving training across RSUs and vehicles while reducing communication overhead. Experimental evaluations in simulated VANET environments show that HDL‐FL achieves a 94% improvement in accuracy, a 30% reduction in false positives, and a 99% increase in attack detection rate while also reducing communication overhead by 6.5 s and latency by 160 ms. The framework offers a scalable, robust, and privacy‐preserving solution for securing next‐generation Vehicle‐to‐Everything (V2X) infrastructures, outperforming traditional models in terms of spatio‐temporal accuracy and scalability. For performance validation, the HDL‐FL framework is compared with baseline models, including traditional machine learning approaches such as Support Vector Machine, AI, and IoT.
Integrating Hesitant Fuzzy Sets with Machine Learning for Enhanced Healthcare Predictive Analytics
Journal of Fuzzy Extension and Applications, 2025
Transparent Crop Price Forecasting Framework Using Explainable AI for Multi-Factor Driver Analysis and Stakeholder Decision Support
Pramoda Patro, Vilas Namdeo Nitnaware, Subhranginee Das, Harikishore R, Srinivas G, Akshaya Kumar Dash
Journal of Applied Science and Technology Trends, 2025
Big Data-Based Remote-Sensed Intelligent Visual Analytics for Environmental and Earth Monitoring Systems
Pramoda Patro, Sowmya M, Srinivas N, Subhranginee Das, Arul Kumar V P, Dhanapal R
Journal of Applied Science and Technology Trends, 2025
Enhancing VLSI Placement Efficiency through Comprehensive Parameter Optimization and Cutting-Edge Techniques for Superior Design Quality
International Journal of Research in Industrial Engineering, 2025
AI-enhanced advanced aquaponics in agricultural systems
T. A. Mohanaprakash, A. Bhagyalakshmi, S. Senthilkumar, Pramoda Patro, Nynalasetti Kondala Kameswara Rao, Sampath Boopathi
Utilizing Aeroponics Techniques for Improved Farming, 2025
A machine learning using NLP to predict long-term patient response
K.Suresh Kumar, Sandeep Kumar Davuluri, Lakshman Kumar Kanulla, Bijesh Dhyani, Pramoda Patro
Demystifying the Role of Natural Language Processing Nlp in Mental Health, 2025
Analyzing social media status to predict depression by NLP and ML
Pramoda Patro, Thulasi Bikku, Dr. Harinadh Karimikond, Shrinwantu Raha, J. Hymavathi J.
Demystifying the Role of Natural Language Processing Nlp in Mental Health, 2025
Quantum-Augmented Framework for Noise Reduction and Contrast Enhancement in OCT Imaging for Early Retinal Disease Detection
V. Praveen Kumar, Pramoda Patro
Proceedings of 8th International Conference on Computing Methodologies and Communication Iccmc 2025, 2025
IoT Traffic for Multi-Class Attack Identification Using Kolmogorov-Arnold Network
Ananthala Mallikarjun, Pramoda Patro
International Conference on Software Systems and Information Technology Ssitcon 2025, 2025
Transforming Medical Machine Translation with Next-Generation Large Language Models
Priyanka Suram, Pramoda Patro
Ocit 2025 Proceedings 23rd Oits International Conference on Information Technology, 2025
Precision Image Segmentation-Enhanced CNN for Apple Leaf Disease Detection
Anusha Sirugumalle, Pramoda Patro
Ocit 2025 Proceedings 23rd Oits International Conference on Information Technology, 2025
Two- Channel Convolutional Neural Networks for Rolling Bearing Fault Diagnosis in Unbalanced Datasets
Ramesh Babu Pittala, M. S. Bhuvaneswari, Pramoda Patro
AI and Machine Learning for Mechanical and Electrical Engineering, 2025
LLM-Driven Text Understanding Emotional Analysis
Swathi Lenka, Balajee Maram, Pramoda Patro
Ocit 2025 Proceedings 23rd Oits International Conference on Information Technology, 2025
Optical Remote Sensing and Machine Learning Algorithm Used to Access the Leaf Area Index and Canopy Water Content
Surendra Reddy Vinta, Gireesh Kambala, Anzar Ahmad, Pramoda Patro
Lecture Notes in Networks and Systems, 2025
Edge Computing for AI-Optimized Traffic Management in Autonomous Cars
Anorgul Ashirova, Muzaffar Shojonov, Sirojiddin Khudoyberganov Meylibayevich, Pramoda Patro
2025 2nd International Conference on New Frontiers in Communication Automation Management and Security Iccams 2025, 2025
Toward salable and efficient NoSQL Query Processing by using Big Data
Venkatesh. C. K., Nozimjon Rasulov, Pramoda Patro, D Annie Rose Nirmala
2025 2nd International Conference on New Frontiers in Communication Automation Management and Security Iccams 2025, 2025
Quantum AI for Secure Encryption in Financial Transactions
E. Saranya, Vamsi Krishna Chidipothu, Pramoda Patro, Ravindra Babu. B
2025 2nd International Conference on New Frontiers in Communication Automation Management and Security Iccams 2025, 2025
Machine Learning for Pest Detection and Crop Protection Optimization
Matyakubov Nurbek, P.Vasantha Lakshmi, K.Venkata Subba Reddy, Pramoda Patro
2025 2nd International Conference on New Frontiers in Communication Automation Management and Security Iccams 2025, 2025
A Data Fusion Technique to Detect and Assess Electromechanical Damage
Arpit Jain, Bala N. Ganesh, Abhishek Choubey, Pramoda Patro, Lakshman Kumar Kanulla
AI and Machine Learning for Mechanical and Electrical Engineering, 2025
Development of a Smart Algorithm to Integrate Fault Detection and Classification of End- to- End Monitoring of Autonomous Transfer Vehicles
P. Prabhakaran, Mudit Joshi, Pramoda Patro, Abhishek Choubey, Santosh Reddy Addula
AI and Machine Learning for Mechanical and Electrical Engineering, 2025
Blockchain architecture, consensus mechanisms, and their applications in watermarking
Gandharba Swain, Anita Pradhan, Satish Muppidi, Pramoda Patro, Monalisa Sahu
Fortressing Pixels Information Security for Images Videos Audio and Beyond, 2025
Federated Learning and Digital Twin-Enabled Predictive Security Architectures With Standardized Policy Enforcement for Zero-Touch 6G Networks
A. Kaliappan, S. Balakrishnan, K. Kavipriya, S. Alagumuthukrishnan, Pramoda Patro, Mohammad Shabaz
IEEE Communications Standards Magazine, 2025
Development of Elliptical Cryptography Technique to Watermark Embedded and Extrusion for Healthcare Records
N. Prajwal Hegde, R. Sivaraman, G. Dharmamoorthy, Ankit Kumar Dubey, Pramoda Patro, S. Suma Christal Mary
Communications in Computer and Information Science, 2025
Breast cancer image classification by using HCNN and LeNet5
Pramoda Patro, Shaik Honey Fathima, R. Harikishore, Aditya Kumar Sahu
Discover Sustainability, 2024
Image Tamper Detection and Correction Using Modified Hamming Code with Eight Data Bits and Four Redundant Bits
Anantha Rao Gottimukkala, Anita Pradhan, Pramoda Patro, J. Hemalatha, Ranjan K. Senapati, Gandharba Swain
International Journal of Safety and Security Engineering, 2024
Classification and diagnosis of heart diseases using fuzzy logic based on IoT
Srinivas Kolli, Pramoda Patro, Rupak Sharma, Amit Sharma
Advances in Fuzzy Based Internet of Medical Things Iomt, 2024
Improving the efficiency of IoMT using fuzzy logic methods
K. Kiran Kumar, S. Sivakumar, Pramoda Patro, RenuVij
Advances in Fuzzy Based Internet of Medical Things Iomt, 2024
Convergence of artificial intelligence and self-sustainability: Ethical considerations and social implications
R. Pitchai, Shiv Kant Tiwari, R. Krishna Kumari, K. Janaki, Pramoda Patro, S. Murugan
Technological Advancements in Data Processing for Next Generation Intelligent Systems, 2024
AI Fuzzy Based Prediction and Prorogation of Alzheimer's Cancer
Srinivas Kolli, Muniyandy Elangovan, M Vamsikrishna, Pramoda Patro
Eai Endorsed Transactions on Pervasive Health and Technology, 2024
IoT-Based Renewable Energy Management Systems in Apartment
Thulasi Bikku, S. Sivakumar, Sudha Arogya Mary Chinthamani, Pramoda Patro
Sustainable Smart Homes and Buildings with Internet of Things, 2024
An Efficient Crop Yield Prediction System Using Machine Learning
Debabrata Swain, Sachin Lakum, Samrat Patel, Pramoda Patro, Jatin
Eai Endorsed Transactions on Internet of Things, 2024
Analysing Public Sentiment Towards Climate Change Using Natural Language Processing
G. Dinesh, Guna Sekhar Sajja, Shivani Naik, Pramoda Patro, M. Clement Joe Anand
Text Mining and Sentiment Analysis in Climate Change and Environmental Sustainability, 2024
Analysing the Textual Analysis of Different NLP Techniques to Classify the Stability of Corporate Reporting
Bilal Asghar, Alhazemi A. Abdulrahman, Rajesh Devaraj, Pramoda Patro, M. Clement Joe Anand
Text Mining and Sentiment Analysis in Climate Change and Environmental Sustainability, 2024
Multi-Centrality and Path-Based Analysis for Essential Cancer Protein Detection in PPI Networks∗
Trilochan Rout, Sujata Chakravarty, Raj Kumar Mohanta, Rednam S S Jyothi, V.S. Damodharan Varadarajan, Pramoda Patro
2nd International Conference on Signal Processing Communication Power and Embedded Systems Scopes 2024, 2024
Intelligent data classification using optimized fuzzy neural network and improved cuckoo search optimization
Iranian Journal of Fuzzy Systems, 2023
Dual image-based reversible fragile watermarking scheme for tamper detection and localization
Aditya Kumar Sahu, Monalisa Sahu, Pramoda Patro, Gupteswar Sahu, Soumya Ranjan Nayak
Pattern Analysis and Applications, 2023
AN INTELLIGENT MORE METHOD FOR PRIVACY - PRESERVING TRAINING TECHNIQUE IN CLOUD ENVIRONMENT
Journal of Theoretical and Applied Information Technology, 2023
A NOVEL HOMOMORPHIC AND MATRIX OPERATION FOR RANDOMIZATION ENCRYPTION SCHEMES FOR PRIVACY IN CLOUD COMPUTING ARCHITECTURE
Journal of Theoretical and Applied Information Technology, 2023
Multi-Key Privacy-Preserving Training and Classification using Supervised Machine Learning Techniques in Cloud Computing
R. Hari Kishore, A. Chandra Sekhar, Pramoda Patro, Debabrata Swain
Proceedings of the 2nd IEEE International Conference on Advances in Computing Communication and Applied Informatics Accai 2023, 2023
Functional overview of integration of AIML with 5G and beyond the network
Kolli Srinivas, J Aswini., Pramoda Patro, Devendra Kumar
2023 International Conference on Computer Communication and Informatics Iccci 2023, 2023
Modified Imperialist Competitive Algorithm (MICA) For Smart Heart Disease Prediction in IoT System
Thangarasan, Dhana Sony J, Premkumar M, Pramoda Patro, Samson Isaac J, Maniraj P
2nd IEEE International Conference on Distributed Computing and Electrical Circuits and Electronics Icdcece 2023, 2023
Predicting the Impacts of the Environment on the Probability and Severity of Electrical Injuries by AIML
Araddhana Arvind Deshmukh, K Parish Venkata Kumar, B.Anjanee Kumar, Pramoda Patro, Pavitar Parkash Singh, N. Ashokkumar
2023 International Conference on New Frontiers in Communication Automation Management and Security Iccams 2023, 2023
Similarity and wavelet transform based data partitioning and parameter learning for fuzzy neural network
Pramoda Patro, Krishna Kumar, G. Suresh Kumar, Gandharba Swain
Journal of King Saud University Computer and Information Sciences, 2022
An Analysis of Drug-Drug Interactions (DDIs) Using Machine Learning Techniques in the Drug Development Process
Pramoda Patro, Krishna Kumar, Debabrata Swain, Hanumantha Rao Sama
Machine Vision for Industry 4 0 Applications and Case Studies, 2022
Qualitative texture analysis on detection of plant disease
Anu Yadav, Piyush Kumar Yadav, Srilatha Toomula, Sushma Jaiswal, Pramoda Patro
Proceedings of the 5th International Conference on Electronics Communication and Aerospace Technology Iceca 2021, 2021
A hybrid approach estimates the real-time health state of a bearing by accelerated degradation tests, Machine learning
Pramoda Patro, R. Azhagumurugan, R Sathya, Krishna Kumar, T. Rajasanthosh Kumar, M. Vijaya Sekhar Babu
2021 2nd International Conference on Smart Technologies in Computing Electrical and Electronics Icstcee 2021 Proceedings, 2021
Automatic Digitization of Engineering Diagrams using Intelligent Algorithms
Premanand Ghadekar, Shaunak Joshi, Debabrata Swain, Biswaranjan Acharya, Manas Ranjan Pradhan, Pramoda Patro
Journal of Computer Science, 2021
Neuro fuzzy system with hybrid ant colony particle swarm optimization (Haso) and robust activation
Pramoda Patro
Journal of Advanced Research in Dynamical and Control Systems, 2020
Applications of three layer CNN in image processing
Journal of Advanced Research in Dynamical and Control Systems, 2018

Pramoda Patro

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Scopus Publications