Dr. Kondragunta Rama Krishnaiah is a highly qualified person, an efficient and eminent academician. He is an outstanding administrator; a prolific researcher published 132 research papers in various National and International Journals and a forward-looking educationist. He has guided several students for their M.Tech and Ph.D Degrees and also authored 5 text books. He worked in prestigious K L (Deemed to be) University for 11.5 Years and has contributed his service for NBA accreditation in May 2004, Aug 2007 with ‘record rating’, ISO 9001:2000 in 2004, Autonomous status in 2006, NAAC accreditation of UGC in 2008 and University status in 2009. Later on he worked as Principal at Nova College of Engineering and Technology, Vijayawada for a period of 3.5 Yrs and NVR College of Engineering & Technology, Tenali for a period of 1.5 Yrs. He took charge as the Principal, RKCE, Vijayawada in December 2015.
EDUCATION
Dr. Kondragunta Rama Krishnaiah M.Sc., Ph.D. & M.Tech., Ph.D.
Analysis of different converter topologies for EV applications Bodapati Venkata Rajanna, Kondragunta Rama Krishnaiah, Sakimalla Prabhakar Girija, Shaik Hasane Ahammad, Mohammad Najumunnisa, Syed Inthiyaz, Gouthami Eragamreddy, Giriprasad Ambati, Nitalaksheswara Rao Kolukula International Journal of Power Electronics and Drive Systems, 2026 Electric vehicles (EVs) are gaining global prominence due to their high efficiency, low noise, and minimal carbon emissions. A critical aspect of EV performance lies in the interaction between energy storage systems (ESS) and power converters. Nonetheless, power delivery from storage units tends to be unreliable and needs strong converter units for effective and stable energy transmission. Several forms of direct current-to-direct current conversion systems used in electric vehicles are thoroughly examined in the paper, including both isolated and non-isolated designs such as those with the cuk, flyback, and push-pull architectures. The paper looks at converter categorization, control methods such as proportional-integral and artificial neural networks, as well as the method of modulation using unipolar and bipolar sinusoidal pulse-width modulation (PWM). Additionally, the role of optimization algorithms in improving converter performance is explored. Simulations were conducted using MATLAB/Simulink to evaluate each topology under varying load and input voltage conditions. The results demonstrate that the Push-Pull converter has the best efficiency for high-power applications, while the Cuk and Flyback converters are best for applications requiring continuous current and low-power, compact designs, respectively. This research offers insights for choosing optimal converter structures to improve energy efficiency and reliability of systems in electric vehicles.
Machine learning-driven prognostics for lithium-ion batteries: enhancing RUL prediction and performance in smart energy storage systems Bodapati Venkata Rajanna, Aaluri Seenu, Kondragunta Rama Krishnaiah, Anantha Sravanthi Peddinti, Nelaturi Nanda Prakash, Bandreddi Venkata Seshukumari, Giriprasad Ambati, Shaik Hasane Ahammad, Chakrapani Srivardhan Kumar, Allamraju Shubhangi Rao International Journal of Applied Power Engineering, 2026 In the evolving landscape of energy systems, batteries play a critical role in enabling hybrid and stand-alone renewable energy storage solutions. Precisely estimating battery life and remaining useful operational life will go a long way in enhancing the efficiency of the system with assured reliability in smart power storage devices. This report comprehensively surveys advanced approaches in the management of batteries through state-of-the-art artificial intelligence tools-support vector machines, relevance vector machines (RVM), long short-term memory (LSTM) models, and bayesian filters-that are being used with a view to enhancing remaining useful life (RUL) estimates and making real-time system health monitoring capabilities possible. Modeling approaches surveyed include state estimation, capacity, and thermal management, while discussing their applicability to lithium-ion batteries. The review also explores publicly available battery datasets, feature engineering strategies, and hybrid diagnostic frameworks. A technoeconomic perspective is provided to assess system performance in renewable-integrated power grids. This paper aims to consolidate current knowledge, provide comparative insights into the strengths and limitations of different approaches, and highlight open research challenges to guide future developments in smart AI-enabled battery systems that support sustainable and resilient energy infrastructure.
A Lightweight Learning Model for Myocardial Infarction Detection and Positioning Kondragunta Rama Krishnaiah, Abdul Rahiman Sheik Proceedings of the 5th International Conference on Sentiment Analysis and Deep Learning Icsadl 2026, 2026 Nowadays, myocardial infarction becomes more complex for people if it is not detected in the early stages. Myocardial infarction (MI) is known as a heart attack that can block or reduce the blood flow to the heart muscle (myocardium). It mainly forms the blood clot in a coronary artery. In this work, a Lightweight Learning Model (LLM) is presented to detect myocardial infarction, which is the most dangerous form of heart disease. It is very important to detect MI in the early stages to prevent the person's death. The existing models are highly complex on large datasets and perform poorly. The proposed LLM demonstrates low computational overhead for processing large datasets and accurately detects blood clots. Advanced preprocessing and feature extraction techniques have been introduced to refine the actual ECG input samples. These techniques also capture the highly discriminative patterns associated with ischemic myocardial damage. The LLN also identifies the accurately affected regions based on the clots within them. The experiments are conducted on benchmark datasets, such as the PTB-XL ECG dataset, available on Kaggle. The results show that the proposed approach achieves high classification rate based on healthy and MI affected ECG samples.
A Hyper-Active Security System for Protection of Cloud Data Pasupula Nagabhushanam, Allacheruvu Brahmaiah, Kondragunta Rama Krishnaiah Proceedings of the 2026 International Conference on AI Driven Smart Systems and Ubiquitous Computing Icauc 2026, 2026 Cloud computing is the domain that provides online services to various users. Nowadays, many cloud service providers (CSPs) provide Software as a Service (SaaS) with Quality of Service (QoS). These applications can be used by various clients from various parts of the world. Cloud computing provides privacy, security, and regulatory compliance. Many existing algorithms have failed to address advanced threats and intelligent attack patterns. These limitations are addressed by introducing the new Hyper-Active Security System (HASS) to protect cloud data. The proposed approach continuously monitors cloud applications and dynamically detects security threats using intelligent behavior analysis and rapid-response systems. HASS mainly consists of three layers: the first layer focuses on advanced anomaly detection, dynamic data access, and intelligent threat mitigation to provide fast data protection. The performance of the proposed approach improved through continuous risk analysis and a policy-aware approach that detects highly advanced threats. Experiments are conducted using two datasets, UNSW-NB15 and CICIDS 2017, which show the strength of the proposed approach.
Advanced Evaluation of Manuscript using Deep Intelligent Learning Model K. Koteswara Rao, D. Komali, Kondragunta Rama Krishnaiah Proceedings of the 2026 6th International Conference on Image Processing and Capsule Networks Icipcn 2026, 2026 Presently, many researchers, scholars, and students are seeking to publish their manuscripts in high-quality, peer-reviewed journals. Currently, the evaluation of manuscripts and books has become more complex due to a manual process that takes longer to analyze them. In this work, an Advanced Deep Intelligent Learning Model (DILM) is used to evaluate an automated manuscript. The DILM introduced the Deep Discourse Coherence Analyzer (DDCA), an advanced grammatical analysis tool for quality measurement of manuscripts. The proposed DILM first segments the input data using document structure segmentation (DSS), which divides the document into section-wise segments. The EquNormalization is a pre-processing technique that removes missing values in text and equations, yielding more effective text. A feature extraction technique, such as contextual embeddings, is used to capture significant features from the meaning and context of the given input document. Finally, the multi-criteria decision layer is introduced to classify the document based on the manuscript's quality. In this work, the main aim is to show whether the manuscript is accepted or not based on the quality score. The results show that the proposed approach obtains high performance.
An Ensemble Pre-Trained Intelligence Model for Early Detection of Colorectal Cancer Shabash Shaik, P. Vidhyavathi, Kondragunta Rama Krishnaiah Proceedings of the 2026 International Conference on AI Driven Smart Systems and Ubiquitous Computing Icauc 2026, 2026 Colorectal cancer (CRC) is one of the deadliest cancers that can cause the loss of lives if not identified in the early stages. CRC usually starts in the colon (large intestine) and spreads to other parts of the digestive system. The growth of a polyp inside the colon or rectum may sometimes convert to cancer if not removed in the early stages. In this paper, the advanced pre-trained model HistoNet50 is introduced to detect and identify accurate CRCs from high-quality image datasets. Pre-processing techniques, such as Vignetting correction and ConvNeXt features (feature extraction), are used to refine images for accurate detection and classification of CRC. The final classification approach, Adaptive Deep Boosting Networks (ADBN), is presented for binary classification, such as whether the disease is present or not. In this work, we used the Colorectal Histology MNIST dataset for the experimental analysis. Results show that the proposed approach accurately detects cancer regions and achieves superior performance across various performance metrics.
An Advanced Extraction Technique for Location-based Data from Google Maps using Python Allacheruvu Brahmaiah, Ch. Sowmya, Kondragunta Rama Krishnaiah Proceedings of the 2026 6th International Conference on Image Processing and Capsule Networks Icipcn 2026, 2026 Nowadays, location-based data can be used in many applications, such as planning, navigation, and geospatial analysis. Google Maps provides rich information about places, including businesses, restaurants, ratings, and reviews of hotels and other tourist destinations. However, extracting an effective data structure from these maps is challenging. The maps can be used to travel to various important places around the world. In this work, an Advanced Extraction Technique (AET) for location-based data from Google Maps using Python was developed to automatically collect the data, achieving accuracy and scalability. The proposed approach combines intelligent parsing and data preprocessing techniques to accurately extract the pertinent geographic and business features. The proposed AET also developed robust data handling and improved data consistency. The experiments are conducted on location intelligence data from Google Maps, gathered from Kaggle, to measure the performance of the proposed approach. Finally, the proposed approach achieves superior performance.
MicroEthics: Harnessing Small Data Intelligence to Recode Big Data Ethics for a Responsible Digital Future Rahul Jalinder Jadhav, Kondragunta Rama Krishnaiah, Suhalekhya Immadisetty, Jayendra Gopal Thatipudi, Lalitha Palthiya, Shrikant Upadhyay Proceedings of the 2026 International Conference on AI Driven Smart Systems and Ubiquitous Computing Icauc 2026, 2026 The exponential growth of big data has revolutionized AI, yet it has simultaneously triggered ethical concerns involving over-collection, privacy erosion, opaque decision-making, and algorithmic bias. Here, the paper proposes changing the paradigm of data maximalism to small data intelligence (SDI)-based MicroEthics, which is more ethical-minimalist, transparent, and stakeholder-trustful. The proposed system does not use massive datasets but rather uses ethically curated low volume data with context, processed on interpretable AI models embedded on federated learning environments. There is a custom lightweight decision engine, Symbolic Decision Matrix Engine (SDME), that makes decisions without compromising privacy or explanations. In several fields such as healthcare, finance, and digital governance, the MicroEthics was compared to the conventional big data pipelines. The findings demonstrate a federated model accuracy of 93.2 with privacy compliance score of 0.97 that is better than centralized big data systems with privacy compliance score of 0.71. MicroEthics Ethical Risk Scores were 0.91, which is significantly more than 0.65 of the traditional models. Stakeholder ratings ensured increased transparency (0.93) and user trust (0.91) using MicroEthics than the big data methods. This paper confirms that MicroEthics is a practical ethical framework that unites the utility of AI with human values. Not only does it show similar performance, but it goes higher in terms of ethical compliance and reliability than those that are already in place. The solution is scalable, modular and easily applicable in areas where moral accountability and data sensitivity are paramount. The results promote the concept of a responsible transition to ethical AI frameworks that do not rely on the quantity of data, but on data virtue.
Machine Learning-Based Earthquake Prediction Using Global Seismic Data Allacheruvu Brahmaiah, Kondragunta Rama Krishnaiah, Kattika Koteswara Rao, Nanduri Srinivas, Shahanaz Shaik, Venneti Murali Krishna Proceedings of the IEEE International Conference on AI Engineering and Innovations Aiei 2026, 2026 As earthquakes may endanger people and their possessions, using accurate predictions can help in addressing risks. Lately, global earthquakes that have occurred since 2020 have been explored by researchers using the KNN method for studying and forecasting patterns. Using the model, scientists can predict if earthquakes are likely to occur in different regions globally during the next month, six months and a year. MSE, RMSE, Accuracy Percentage and MAPE are used to evaluate the model, which have values of 0.198, 0.445, 98.57% and 1.16%, respectively. Based on the results, it appears that machine learning with KNN can show the frequency of earthquakes over time. The findings suggest that AI can help forecast earthquakes and assess the dangers that follow. While the model accurately predicts, future advancements using deep learning and quick service will help it in warning systems.
7G Wireless Networks: Vision, Technologies, and Roadmap Kondragunta Rama Krishnaiah, Sai Kalyana Deepthi, Kampa Kanthi kumar, Swathi Yalavarthy, Charles Jeya Rao, Sathish Kuppani Lecture Notes in Networks and Systems, 2026
Evaluating the Effectiveness of Firewalls to Prevent DDoS Attacks Gowtham Mamidisetti, C. V. Subhaskara Reddy, Nihal Singh, Kondragunta Rama Krishnaiah, B. Parvathi, Raghavendra Kulkarni 2025 2nd International Conference on New Frontiers in Communication Automation Management and Security Iccams 2025, 2025
Implementing Reinforcement Learning for Autonomous Vehicle Navigation R. Santhoshkumar, D. Srinivasarao, M. Naga Triveni, Rehana Tabassum, Kondragunta Rama Krishnaiah, K. Nagaraju 2025 2nd International Conference on New Frontiers in Communication Automation Management and Security Iccams 2025, 2025
Application of Transfer Learning techniques in Facial Recognition Systems Y. Mallikarjuna Achari, Raghavendra Kulkarni, M Vishnuvardhan Reddy, Kasetty Lakshminarasimha, Kondragunta Rama Krishnaiah, T. Kanakamma 2025 2nd International Conference on New Frontiers in Communication Automation Management and Security Iccams 2025, 2025
Evolving Predictive Models for Customer Churn in Telecommunications M. Shahimoon, Haroon Rasheed, D. Raghunatha Rao, Kondragunta Rama Krishnaiah, Kishor Kumar Gajula, T. Kanakamma 2025 2nd International Conference on New Frontiers in Communication Automation Management and Security Iccams 2025, 2025
RECENT SCHOLAR PUBLICATIONS
Comparative forecasting of CO₂ emissions in India, Russia, Canada, and Japan using statistical, machine learning and deep learning models A Brahmaiah, KK Rao, KR Krishnaiah, N Srinivas Theoretical and Applied Climatology 157 (5), 290 , 2026 2026
Machine Learning–Based Earthquake Prediction Using Global Seismic Data A Brahmaiah, KR Krishnaiah, KK Rao, N Srinivas, S Shaik, VM Krishna 2026 IEEE International Conference on AI Engineering and Innovations (AIEI), 1-5 , 2026 2026
Advanced Evaluation of Manuscript using Deep Intelligent Learning Model KK Rao, D Komali, KR Krishnaiah 2026 6th International Conference on Image Processing and Capsule Networks … , 2026 2026
An Advanced Extraction Technique for Location-based Data from Google Maps using Python A Brahmaiah, C Sowmya, KR Krishnaiah 2026 6th International Conference on Image Processing and Capsule Networks … , 2026 2026
MicroEthics: Harnessing Small Data Intelligence to Recode Big Data Ethics for a Responsible Digital Future RJ Jadhav, KR Krishnaiah, S Immadisetty, JG Thatipudi, L Palthiya, ... 2026 International Conference on AI-Driven Smart Systems and Ubiquitous … , 2026 2026
A Hyper-Active Security System for Protection of Cloud Data P Nagabhushanam, A Brahmaiah, KR Krishnaiah 2026 International Conference on AI-Driven Smart Systems and Ubiquitous … , 2026 2026
An Ensemble Pre-Trained Intelligence Model for Early Detection of Colorectal Cancer S Shaik, P Vidhyavathi, KR Krishnaiah 2026 International Conference on AI-Driven Smart Systems and Ubiquitous … , 2026 2026
Effect of injection parameters on compression ignition engines: A review of methyl ester oil applications KR Krishnaiah European Journal of Sustainable Development Research 10 (1) , 2026 2026
A high-efficiency transformerless buck-boost inverter with fuzzy logic control for grid-connected solar PV systems BV Rajanna, KR Krishnaiah, V Ramaiah, SH Ahammad, M Najumunnisa, ... Bulletin of Electrical Engineering and Informatics 14 (6), 4304-4315 , 2025 2025
Predictive Modelling for Atmospheric Conditions: A Machine learning and Deep learning Approach with Comprehensive Error Metrics and Forecast Visualization A Brahmaiah, KR Krishnaiah 2025 5th International Conference on Artificial Intelligence and Signal … , 2025 2025
Injection parameter optimization for efficient biodiesel blends in compression ignition engine H Harish, Y Pasha, H Venu, K Rama Krishnaiah CT&F-Ciencia, Tecnología y Futuro 15 (1), 31-46 , 2025 2025
Bidirectional power converter for electrical vehicle with battery charging and smart battery management system KR Krishnaiah International Journal of Power Electronics and Drive System (IJPEDS) 16 (4 … , 2025 2025
Evaluating the Effectiveness of Haptic Feedback in Virtual Learning Environments KR Krishnaiah 2025 2nd IEEE International Conference on New Frontiers in Communication … , 2025 2025
Implementing Reinforcement Learning for Autonomous Vehicle Navigation KR Krishnaiah 2025 2nd IEEE International Conference on New Frontiers in Communication … , 2025 2025
Application of Transfer Learning techniques in Facial Recognition Systems KR Krishnaiah 2025 2nd IEEE International Conference on New Frontiers in Communication … , 2025 2025
Evolving Predictive Models for Customer Churn in Telecommunications KR Krishnaiah 2025 2nd IEEE International Conference on New Frontiers in Communication … , 2025 2025
Evaluating the Effectiveness of Firewalls to Prevent DDoS Attacks KR Krishnaiah 2025 2nd IEEE International Conference on New Frontiers in Communication … , 2025 2025
Enhancing the Security of the Internet of Things by the Application of Robust Cryptographic Algorithms KR Krishnaiah 2025 2nd IEEE International Conference on New Frontiers in Communication … , 2025 2025
A Comparative Study of Classical and Quantum Computing: Concepts, Circuits, and Real-World Applications KR Krishnaiah International Journal of Research Publication and Reviews 6 (8), 3002 - 3008 , 2025 2025
A Hybrid Software Engineering and Machine Learning Approach for Diabetes Prediction in Health Informatics KR Krishnaiah, H Harish Journal of Neonatal Surgery 14 (25s), 132–142 , 2025 2025
MOST CITED SCHOLAR PUBLICATIONS
Security evaluation of pattern classifiers under attack M Vidyadhari, K Kiranmai, KR Krishniah, DS Babu International Journal of Research 3, 1043-1048 , 2016 2016 Citations: 5
Predicting fake online reviews: a comprehensive study of supervised and semi-supervised learning models KR Krishnaiah Turkish Journal of Computer and Mathematics Education 14 (3), 392-399 , 2023 2023 Citations: 4
VAGUE-Near Rings, Near Fields and Boolean Rings SN KV Rama Rao, K RamaKrishnaiah Zeichen Journal 9 (2), 36 – 44 , 2023 2023 Citations: 3
An innovative approach to continuous sign language recognition: Combining cnn and bilstm models with iterative training KR Krishnaiah, AH Prasad Turkish Journal of Computer and Mathematics Education (TURCOMAT) 14 (1), 321-333 , 2023 2023 Citations: 2
Upper Total Signed Unidomination Number of a Rooted Product Graph of a Path with a Star PV Durgavathi, KR Krishnaiah 2021 Citations: 1
Comparative forecasting of CO₂ emissions in India, Russia, Canada, and Japan using statistical, machine learning and deep learning models A Brahmaiah, KK Rao, KR Krishnaiah, N Srinivas Theoretical and Applied Climatology 157 (5), 290 , 2026 2026
Machine Learning–Based Earthquake Prediction Using Global Seismic Data A Brahmaiah, KR Krishnaiah, KK Rao, N Srinivas, S Shaik, VM Krishna 2026 IEEE International Conference on AI Engineering and Innovations (AIEI), 1-5 , 2026 2026
Advanced Evaluation of Manuscript using Deep Intelligent Learning Model KK Rao, D Komali, KR Krishnaiah 2026 6th International Conference on Image Processing and Capsule Networks … , 2026 2026
An Advanced Extraction Technique for Location-based Data from Google Maps using Python A Brahmaiah, C Sowmya, KR Krishnaiah 2026 6th International Conference on Image Processing and Capsule Networks … , 2026 2026
MicroEthics: Harnessing Small Data Intelligence to Recode Big Data Ethics for a Responsible Digital Future RJ Jadhav, KR Krishnaiah, S Immadisetty, JG Thatipudi, L Palthiya, ... 2026 International Conference on AI-Driven Smart Systems and Ubiquitous … , 2026 2026
A Hyper-Active Security System for Protection of Cloud Data P Nagabhushanam, A Brahmaiah, KR Krishnaiah 2026 International Conference on AI-Driven Smart Systems and Ubiquitous … , 2026 2026
An Ensemble Pre-Trained Intelligence Model for Early Detection of Colorectal Cancer S Shaik, P Vidhyavathi, KR Krishnaiah 2026 International Conference on AI-Driven Smart Systems and Ubiquitous … , 2026 2026
Effect of injection parameters on compression ignition engines: A review of methyl ester oil applications KR Krishnaiah European Journal of Sustainable Development Research 10 (1) , 2026 2026
A high-efficiency transformerless buck-boost inverter with fuzzy logic control for grid-connected solar PV systems BV Rajanna, KR Krishnaiah, V Ramaiah, SH Ahammad, M Najumunnisa, ... Bulletin of Electrical Engineering and Informatics 14 (6), 4304-4315 , 2025 2025
Predictive Modelling for Atmospheric Conditions: A Machine learning and Deep learning Approach with Comprehensive Error Metrics and Forecast Visualization A Brahmaiah, KR Krishnaiah 2025 5th International Conference on Artificial Intelligence and Signal … , 2025 2025
Injection parameter optimization for efficient biodiesel blends in compression ignition engine H Harish, Y Pasha, H Venu, K Rama Krishnaiah CT&F-Ciencia, Tecnología y Futuro 15 (1), 31-46 , 2025 2025
Bidirectional power converter for electrical vehicle with battery charging and smart battery management system KR Krishnaiah International Journal of Power Electronics and Drive System (IJPEDS) 16 (4 … , 2025 2025
Evaluating the Effectiveness of Haptic Feedback in Virtual Learning Environments KR Krishnaiah 2025 2nd IEEE International Conference on New Frontiers in Communication … , 2025 2025
