Mandela Srikanth, Assistant Professor, IT Department, VISHNU INSTITUTE OF TECHNOLOGY(A), Bhimavaram, West Godavari District Andhra Pradesh, India. Good academic experience in Teaching for Graduates and Post Graduate students. I am currently Pursuing a Ph.D. from Gandhi university I am an author of “Cryptography and Network Security”, ”E-Commerce”, “Problem Solving in C” and “OOPS Through JAVA” Published by Shree Publishing House in the year 2019,2020 and 2021. I published technical papers in various reputed National and International Journals. I published “Two Patents” in the year 2021,2022.
EDUCATION
• Pursing Ph. D. in Computer Science and Engineering at Gandhi Institute of Engineering and Technology University, Gunupur, Odisha.
• Passed with 7.96 CGPA in Pre-Ph.D Computer Science and Engineering at Gandhi Institute of Engineering and Technology University, Gunupur, Odisha.
• Passed with 77.14% of marks in M-Tech (CSE (Software Engineering)) at B.V.C.E.College affiliated to J.N.T. University, Kakinada.
• Passed with 60.30% of marks in B-Tech (IT) at Engineering College, affiliated to J.N.T. University, Kakinada.
• Passed with 58.48% of marks in Diploma (DEEE) at Polytechnic College to state board of Technical education.
• Passed with 73% of marks in 10th at M.M.K.N.M.H.School.
RESEARCH INTERESTS
Machine Learning and Blockchain Technology
26
Scopus Publications
922
Scholar Citations
20
Scholar h-index
34
Scholar i10-index
Scopus Publications
BIG DATA AND MACHINE LEARNING FRAMEWORK FOR CANCER, FINANCIAL, AND STRESS RISK PREDICTION M V B MURALI KRISHNA M, VIJAYA KRISHNA SONTHI, N. SRINIVAS RAO, AVSS SOMASUNDAR, M.SRIKANTH, M CHILAKARAO Journal of Theoretical and Applied Information Technology, 2026 The rapid evolution of the big data in the medical, financial, and behavioral track has offered an opportunity to utilize predictive analytics to contribute to the well-being of the whole picture. However, the existing systems tend to work on the prediction of cancer risks, estimation of financial status, and stress analysis independently, which is limited to provide integrated and tailored risk measurement in the future. To address this limitation, the proposed paper will recommend one single Big Data and Machine Learning Framework to forecast the risks of Cancer, Financial, and Stress. It uses a combination of heterogeneous data, medical indicators, financial data, and behavioral data that are connected to stress and executes the supervised machine learning algorithms such as Logistic Regression, Decision Tree, Random Forest, Linear Regression, and Gradient Boosting. The results of the experiment indicate that, as compared to the Logistic Regression or the Random Forest, Decision Tree model predicted the risk of cancer the most accurately with an accuracy of 83%. Gradient Boosting was the lowest in the Mean Squared Error of 5.25 x 10-6 and better than that of Linear Regression, which is 0.15. In addition, stress risk classification was also effective in the determination of the various levels of stress basing on behavioral and physiological indicators. These results confirm the notion that the proposed integrated framework improves predictive accuracy and makes it possible to consider risks in the comprehensive manner. This model provides decision support tool, which is data-oriented to detect early risks of cancer, financial planning, and stress management to improve holistic well-being.
Estimating the Energy of Low-Quality Images Using Kinetic Energy and a Hybrid Model M. Bhanurangarao, D. V. Naga Raju, Meduri Raghuchandra, Y. Yesu Jyothi, M. Srikanth Advanced Imaging Applications for Interdisciplinary Engineering, 2026 Many applications rely on the correct analysis of low-quality photographs, yet effective interpretation is sometimes hampered by the inherent constraints of degraded image settings. We present a novel approach to estimating the energy content of low-quality images. We create a framework for measuring the dynamic features of these images by applying physical rules, particularly those governing motion. To preprocess the photos for precise energy estimation, we use image enhancement techniques and motion features. Following that, we calculate the energy of the objects' motions in the images using kinetic energy principles, considering characteristics, such as mass and velocity. Our proposed method accurately calculates energy levels from low-quality pictures, as demonstrated experimentally with synthetic and real-world datasets. This methodology has applications in environmental monitoring, robotics, and surveillance, all of which rely on insights into energy dynamics for analysis and decision-making. It also increases our understanding of the dynamics involved in such imaging.
Advanced Radio Wave Propagation Models for Next Generation Wireless Networks Using AIML G.Mohan Ram, Divya Vegesna, Subbaraju Pericherla, Tejaswini Kothapalli, Lakshmi Hyma Rudraraju, M. Srikanth Proceedings of the 5th International Conference on Sentiment Analysis and Deep Learning Icsadl 2026, 2026 Exact radio wave propagation modeling is an essential need of designing and optimising the fifth-generation (5 G) and beyond wireless networks in the challenging urban, indoor and high-frequency deployment cases. Although classical propagation models are computationally efficient, they do not provide a good combination of aspects of environmental variability, multipath propagation and frequency-dependent attenuation which occurs at millimeter-wave (mmWave) and terahertz (THz) frequencies. This study aims to eliminate such drawbacks by introducing a superior hybrid radio wave propagation model that combines deterministic ray tracing, statistical channel, and machine learning-based correction that is obtained through empirical means. The proposed model is tested on sub-6 GHz, mmWave and THz frequency bands, in both combination of standardized channel datasets and large-scale real world measurement campaigns in urban, indoor, and rural settings. Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and coefficient of determination <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathrm{R}^{\mathrm{2}}$</tex> are all used to analytically compare performance with traditional models. This has been confirmed in experimental results where the hybrid method yields an average improvement of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$22-35 \%$</tex> on the prediction accuracy as compared to traditional propagation models, and in particularly dense city and indoor scenarios, large improvements are recorded. The results indicate that hybrid propagation modeling is a powerful and scalable solution to the planning of the next-generation wireless network, spectrum allocation, and performance optimization in 5 G and upcoming 6G networks.
Revolutionizing Fashion with Virtual Try-On Experiences J. M. S. V. Ravi Kumar, Srinivasula Bhaskar Prabhat, Chipuri Dinesh Kumar, Nadimpalli Satya Teja Phanendra Varma, Shaik Nagula, M. Srikanth Lecture Notes in Networks and Systems, 2026
Machine learning-augmented blockchain-based graphene field-effect transistor sensor platform for biomarker detection Srinivasa Rao Karumuri, M. Srikanth, J.M.S.V. Ravi Kumar, M. Bhanurangarao Field Effect Transistors, 2025 Detecting biomarkers is essential for the diagnosis, monitoring, and development of drugs for diseases. To enhance precision, scalability, and data safety, we suggest a new platform that integrates graphene field-effect transistor (GFET) sensors with machine learning (ML) algorithms and blockchain technology. ML algorithms examine data for quick pattern identification and improved accuracy, whereas GFET sensors provide outstanding sensitivity for early biomarker detection. All detections are recorded in an immutable and secure manner using blockchain technology. Precise diagnostics, personalized treatment, and constant patient monitoring are all areas where this platform shows tremendous promise in healthcare. Its ability to quickly detect biomarkers enhances its potential uses in pharmaceutical research and clinical trials.
Design of next-generation field-effect transistors using machine learning K. Girija Sravani, M. Srikanth, Manikanta Sirigineedi, Padma Bellapukonda Field Effect Transistors, 2025 Conventional design methodologies are encountering growing complexity with field-effect transistors (FETs), which are essential to contemporary electronic products. A new approach to improving FET design is presented in this research, utilizing machine learning (ML). Collecting and assembling large datasets is the first step in the process. Then comes feature engineering and the use of different ML models, such as neural networks, decision trees, and regression. This model training and validation process rapidly explores the FET design space. ML-driven FET designs can be applied in real-world applications due to advancements in the manufacturing process. Developing a design framework that can adapt to new technological breakthroughs and growing requirements is made possible through continuous data collection and model changes. This study shows how ML improves electronic technology by tackling design issues in semiconductor devices. The suggested method enables the creation of electronic devices that are more powerful, energy efficient, and dependable while also speeding up the design process.
RING THEORY-BASED AGRICULTURE RISK IDENTIFICATION USING HYBRID MODELS Journal of Theoretical and Applied Information Technology, 2025
Predictive Modeling of Chronic Obstructive Pulmonary Disease Progression using a Markov-based Intelligent Health Management System Meduri Raghu Chandra, Bembavarapu Sri Lakshmi Saritha, Bhanurangarao M, M.Srikanth Proceedings of the 6th International Conference on Smart Electronics and Communication Icosec 2025, 2025 Chronic Obstructive Pulmonary Disease (COPD) has come to be one of the most significant health challenges in the world with unforeseen exacerbation and inadequate personal treatment options. The current prognostic instruments, like GOLD staging and the BODE index, do not offer real-time tracking and dynamic disease progressions. This paper attempts to fill this gap in the literature by constructing a Markov-based intelligent health management system to forecast COPD progression and optimise clinical interventions using both real-time physiological and clinical data. A Markov model (Stable COPD, Mild Exacerbation, Severe Exacerbation, Hospitalisation, Death) in five states using discrete time was trained on 1,200 patient records and kept up to date with adaptive Bayesian learning of transition probabilities. Individualised risk scores were included with real-time monitoring inputs (oxygen saturation, respiratory rate, activity levels). High predictive accuracy was observed with the model (r = 0.89, p < 0.001), and early exacerbation detection allowed combination therapy to lessen the progression of mild to severe exacerbations (22% relative reduction, p < 0.001) and increase cost-effectiveness (gain in cost, 25,000/QALY). The presented framework is a new advance, as it integrates stochastic modelling with real-time tracking to monitor COPD to engage in proactive management. Although the system has drawbacks like retrospective validation and use of information provided by wearable devices, the system has great potential to be incorporated into electronic health records and prospective clinical trials in the future. The ability to use it in a wider range of chronic conditions suggests that precision medicine can be expanded and healthcare can become less costly.
ML-based Predictive Maintenance Fault Detection using Optimal Merge Pattern in Solar PV Systems Ponnada Bhargavi, Vuppu Neelima, Pyla Jyothi, M. Srikanth, Jonnapalli Tulasi Rajesh, Pilli Suneetha Proceedings of the 6th International Conference on Electronics and Sustainable Communication Systems Icesc 2025, 2025 As more solar photovoltaic (PV) systems are put in place, we need better ways to find faults to make sure the systems work as well as possible and provide as much energy as possible. This paper shows how to use Optimal Merge Pattern (OMP) in a machine learning (ML)-based predictive maintenance framework to find faults in solar PV systems early and accurately. In changing weather and grid circumstances, traditional techniques of finding faults typically take too long to respond and do not work as well. The suggested system uses sensor data from PV modules and inverters, such as current, voltage, irradiance, and temperature. Optimal Merge Pattern is used to preprocess data by compressing and organising temporal input data streams to cut down on duplication and make learning faster. We use the combined data to train Random Forest and Support Vector Machine (SVM) classifiers to find frequent problems including shading, soiling, hotspot creation, and inverter failure. When compared to baseline approaches that do not use OMP, the system gets $\mathbf{9 6. 8 \%}$ of the classifications right and $\mathbf{2 1 \%}$ fewer false positives. Real-time data from a rooftop solar system shows that the OMP-based method makes predictive maintenance tactics far more responsive. The model is easy to scale up, lightweight, and works well with smart PV monitoring devices that are placed on the edge. This research helps develop smart, automated maintenance solutions that keep systems running longer and cost less to run. In the future, we will concentrate on connecting the model to IoTbased warning systems and adding more types of faults using deep learning to look at PV panels. The suggested paradigm fits with current work on AI-based solutions for sustainable energy.
Reward Based Online Crowdfunding Platform J. M. S. V. Ravi Kumar, Kajuluri Adi Seshu Narayana, Chadalavada Mahesh Babu, Anchala Vishnu Vardhan, Kalluri Yuvaraju, M. Srikanth 2025 International Conference on Data Science Agents and Artificial Intelligence Icdsaai 2025, 2025
Enhancing Tactile Sensor Technology with Neuromorphic Models and Machine Learning Komali Lenka, Pulaparthi Lakshmi Asha Jyothi, Kallakuri N V P S Brahma Ramesh, Dasaradha Ramayya Lanka, M. Srikanth, Jonnapalli Tulasi Rajesh 6th International Conference on Mobile Computing and Sustainable Informatics Icmcsi 2025 Proceedings, 2025
Image Search Engine with Recognition JMSV Ravi Kumar, Bellaganti Dheeraj, Geddada Gowtham, Dokala Vivek Babu, Bonthu Asha Kiran, M. Srikanth 4th International Conference on Sentiment Analysis and Deep Learning Icsadl 2025 Proceedings, 2025
Real-Time Vehicle Detection and Road Condition Prediction for Smart Urban Areas M. Srikanth, Nimmakayala S V S S Jaya Krishna, Settipalli Jaya Sai Krishna, Shaik Irfan, Tamarapalli Gnana Venkat Proceedings of the 4th International Conference on Ubiquitous Computing and Intelligent Information Systems Icuis 2024, 2024
Implementing RF and Microwave Technologies with MLXAI using Spectral Methods R Kondaveti, V Neelima, DDD Suribabu, DR Lanka, NP Tirumani, ... 2026 International Conference on Smart Electronic Devices and Intelligent … , 2026 2026
Graph Signal Processing-based EEG Analysis with Quantum Machine Learning for Early Detection of Alzheimer's Disease PK Babu, CS Syam, J Nikhilesh, B Rishyan, GA Shankar, M Srikanth 2026 9th International Conference on Inventive Computation Technologies … , 2026 2026
An Explainable Multimodal Transformer Framework for Automated Fact-Checking and Fake News Detection using Large Language Models VK Lakshetty, LN Krishna, CS Subrahmanyam, BVSN Lakshmi, ... 2026 9th International Conference on Inventive Computation Technologies … , 2026 2026
Predicting the Trend in Stock Market using Machine Learning JR Kumar, MVJP Naidu, NVS Akhil, S Subrahmanyam, PA Teja, ... 2026 IEEE International Conference on Emerging Computing and Intelligent … , 2026 2026
AI-BASED DECISION MINUTES OF MEETING ASSIGNED A CONFIDENCE SCORE FOR HUMAN REVIEW MSKOARRKACMSAIPTGNDRJR KUMAR IN Patent 202,641,037,729 , 2026 2026
Randomized Algorithm-Based Optimization of Next-Generation M Srikanth, UK Dosanapudi Field-Effect Transistors-Fundamentals, Technologies, and Future Innovations … , 2026 2026
Predictive Modeling of Chronic Obstructive Pulmonary Disease Progression using a Markov-based Intelligent Health Management System MR Chandra, BSL Saritha, B M, M Srikanth 2025 6th International Conference on Smart Electronics and Communication … , 2026 2026
Estimating the Energy of Low-Quality Images Using Kinetic Energy and a Hybrid Model MS M. Bhanurangarao, D. V. Naga Raju, Meduri Raghuchandra, Y. Yesu Jyothi Advanced Imaging Applications for Interdisciplinary Engineering, 93-114 , 2026 2026
BIG DATA AND MACHINE LEARNING FRAMEWORK FOR CANCER, FINANCIAL, AND STRESS RISK PREDICTION MC M V B MURALI KRISHNA M1, VIJAYA KRISHNA SONTHI2, N. SRINIVAS RAO3, AVSS ... Journal of Theoretical and Applied Information Technology 104, 454-60 , 2026 2026
Advanced Radio Wave Propagation Models for Next Generation Wireless Networks Using AIML GM Ram, D Vegesna, S Pericherla, T Kothapalli, LH Rudraraju, ... 2026 5th International Conference on Sentiment Analysis and Deep Learning … , 2026 2026
Intelligent Diagnosis of Leaf and Fruit Disease with Automated Remedy Proposals MC Babu, L Jayaprakash, M Srikanth 2026 International Conference on Data Science, Agents and Artificial … , 2026 2026
Coulomb's Law–Inspired Lung Cancer Risk Prediction Using Daily Habit Interaction Modeling DS Rao, KVVN Babu, R Kondaveti, KV Nageswari, M Srikanth, R Bokka 2026 International Conference on Machine Learning and Autonomous Systems … , 2026 2026
Revolutionizing Fashion with Virtual Try-On Experiences M Srikanth Smart Computing Paradigms: Human-Centric Systems for Sustainable Development … , 2026 2026
Revolutionizing Fashion with Virtual Try-On Experiences SNMS J. M. S. V. Ravi Kumar, Srinivasula Bhaskar Prabhat, Chipuri Dinesh ... Smart Computing Paradigms: Human-Centric Systems for Sustainable Development , 2026 2026
An ML-based Intelligent System for House Cost Estimation and Space Optimization DS Rao, K Merum, M Srikanth, ML Narayana, MC Naik 2025 5th International Conference on Ubiquitous Computing and Intelligent … , 2026 2026 Citations: 5
Coulomb's Law–Inspired Lung Cancer Risk Prediction Using Daily Habit Interaction Modeling M Srikanth 2026 International Conference on Machine Learning and Autonomous Systems … , 2026 2026
A Hybrid Air-Water Pollution Monitoring System Using Outlier Detection And Adaptive Feature Selection For Aqi-Based Environment Assessment M SRIKANTH, S NAVINRAJ, S GOKULRAJ, S KUMAR, RR ME 2026
A Hybrid Intelligence Framework for EmotionAware Deepfake Detection and Misinformation Risk Reduction M Srikanth, V Niharika, K Govardhani, P Lallisri, V Nandini, JR Kumar IEEE Conference , 2026 2026 Citations: 2
Hybrid Machine Learning Framework for Environment-Based Fish Disease Detection in Sustainable Aquaculture Systems M Srikanth 2025 2nd International Conference on Electronic Circuits and Signaling … , 2025 2025
RING THEORY-BASED AGRICULTURE RISK IDENTIFICATION USING HYBRID MODELS M SRIKANTH, MC NAIK, RNVJ MOHAN Journal of Theoretical and Applied Information Technology 103 (22) , 2025 2025
MOST CITED SCHOLAR PUBLICATIONS
A new approach for authorship verification using information retrieval features S Kumar, S Rajeswari, M Srikanth, TR Reddy Innovations in Computer Science and Engineering: Proceedings of the Sixth … , 2019 2019 Citations: 55
Predict Early Pneumonitis in Health Care Using Hybrid Model Algorithms RNS M Srikanth, Ramisetty Upendra Journal of Artificial Intelligence, Machine Learning and Neural Network … , 2023 2023 Citations: 42
Tackle Outliers for Predictive Small Holder Farming Analysis MCN M.Srikanth, R.N.V.Jagan Mohan IEEE-10.1109/ICSMDI57622.2023.00024 , 2023 2023 Citations: 42
Query Response Time in Blockchain Using Big Query Optimization RNVJM M.Srikanth Apple Academy Press and CRC Press , 2022 2022 Citations: 38
Stop spread corona based on voice, face and emotional recognition using machine learning, query optimization and Block chain Technology RNVJM M.Srikanth IFERP 63 (6), 3512-3520 , 2020 2020 Citations: 36
Blockchain based Crop Farming Application Using Peer-to-Peer M Srikanth, RNVJ Mohan, MC Naik 2022 Citations: 33
Machine Learning for Query Processing System and Query Response Time using Hadoop RNVJM M.Srikanth IJMTST , 2020 2020 Citations: 32
Blockchain-based consensus for a secure smart agriculture supply chain M Srikanth, RNVJ Mohan, MC Naik European Chemical Bulletin 12 (4), 8669-8678 , 2023 2023 Citations: 31
Block chain enable for Smallholder’s farmer’s Crop Transaction Using Peer-to-Peer MCN M.Srikanth, R.N.V.Jagan Mohan Indo-American Journal of Agricultural and Veterinary Sciences 10 (3), 33-43 , 2022 2022 Citations: 28
Block-level based query data access service availability for query process system M Srikanth, RNVJ Mohan 2020 International conference on computer science, engineering and … , 2020 2020 Citations: 28
Auction Algorithm: Peer-To-Peer System Based on Hybrid Technologies for Smallholder Farmers to Control Demand and Supply MCN M.Srikanth, R.N.V.Jagan Mohan International Journal of Research In Science & Engineering (IJRISE) 3 (1), 9–23 , 2023 2023 Citations: 27
An Enhanced and Naive Clustering Algorithm for Text Classification Based on Weight M Srikanth IJMETMR 1 (12), 7 , 2014 2014 Citations: 27
One-pot rapid synthesis of face-centered cubic silver nanoparticles using fermented cow urine, a nanoweapon against fungal and bacterial pathogens M Prabhu, S Mutnuri, SK Dubey, MM Naik Journal of Bionanoscience 8 (4), 265-273 , 2014 2014 Citations: 27
Real-Time Vehicle Detection and Road Condition Prediction for Smart Urban Areas M Srikanth, NSVSSJ Krishna, SJS Krishna, S Irfan, TG Venkat 12-13 December 2024, 1025-1029 , 2025 2025 Citations: 26
Increasing the reliability of intercropping in agriculture using machine learning M Srikanth, RNVJ Mohan, MC Naik Algorithms in Advanced Artificial Intelligence, 150-157 , 2024 2024 Citations: 26
A New Way to Improve Crop Quality and Protect the Supply Chain is to use a Trajectory Network and Game Theory MCN M.Srikanth, R.N.V.Jagan Mohan Mathematical Statistician and Engineering Applications 71 (4), 10600-10610 , 2022 2022 Citations: 24
AI-Optimised Model for Resource Management in Aquaculture-Agriculture Systems M Srikanth, VGS Varma, SA Aziz, JJ Chowdary, V Bharath 12-13 December 2024, 4th International Conference on Ubiquitous Computing … , 2025 2025 Citations: 22
Enhancing Tactile Sensor Technology with Neuromorphic Models and Machine Learning K Lenka, PLA Jyothi, KNVPSB Ramesh, DR Lanka, M Srikanth, JT Rajesh 2025 6th International Conference on Mobile Computing and Sustainable … , 2025 2025 Citations: 21
The Early Detection Of Alzheimer's Illness Using Machine Learning And Deep Learning Algorithms PB Manikanta Sirigineedi, M.Srikanth Journal of Pharmaceutical Negative Results 13 (9), 4852-4859 , 2022 2022 Citations: 21
Publications
• M.Srikanth published a paper titled: Protecting tribal peoples nearby patient care centres use a hybrid techniques based on a distribution network, International Journal of Health Sciences, June 2022.
• M.Srikanth published a paper titled: Blockchain based Crop Farming Application Using Peer-to-Peer, Journal of Xidian, May 2022.
• M.Srikanth published a paper titled “Stop spread corona based on voice, face and emotional recognition using machine learning, query optimization and Block chain Technology” in Solid State Technology [Scopus- Indexed] on Oct 2020
• M.Srikanth published a paper titled” Machine Learning for Query Processing System and Query Response Time using Hadoop” in IJMTST on August 2020.
• Srikanth Mandela published a paper titled “Block-level based Query Data Access Service Availability for Query Process System” in IEEE on July 2020.
• M.Srikanth published a paper titled “Query Response Time in Blockchain Using Big Query Optimization” in ICRTIB-2019 –Apple Academic Press on Dec 2019.
• M. Srikanth published a paper title “A new Approach for Authorship verification using information retrieval features” in Springer-ICSE [Scopus- Indexed], Volume 74, pp- 23-29.
• Srikanth Mandela published a paper titled “An Enhanced and Naive Clustering Algorithm for Text Classification Based on Weight” in International Journal & Magazine of Engineering, Technology, Management and Research Dec, 2012.
RESEARCH OUTPUTS (PATENTS, SOFTWARE, PUBLICATIONS, PRODUCTS)
• M.Srikanth published a patent titled “System and Method for Managing Farming Using Blockchain” Application No: 202231022056 in Intellectual Property India in Apr 2022.
• M.Srikanth published a patent titled “A Modern Analysis of Autism Spectral Disorder of Electronic Health Records” Application No: 202041005601 in Intellectual Property India in Jan 2021.
