Artificial Intelligence, Multidisciplinary, Computer Engineering, Cancer Research
17
Scopus Publications
148
Scholar Citations
6
Scholar h-index
6
Scholar i10-index
Scopus Publications
Machine Learning and Artificial Intelligence Fundamentals for Federated Systems N. Vinaya Kumari, G. S. Pradeep Ghantasala, Pellakuri Vidyullatha, R. Rajesh Sharma Federated Intelligent System for Healthcare A Practical Guide, 2025 Federated learning (FL) is a notable tool for a reading paradigm designed to train models collaboratively through decentralized devices or servers holding a network of information samples without changing them. This approach addresses key challenges in data privacy, security, and utilization. Unlike traditional centralized tool learning, where data is aggregated into a central server, FL allows model training on network data, thereby mitigating privacy risks and reducing latency and bandwidth consumption. This explores critical concepts of tool learning and artificial intelligence as applied to federated systems. It delves into the architectural framework of federated learning, highlighting its core components, including the network training process, aggregation algorithms, and communication protocols. It also covers several types of federated learning, which encompass horizontal FL, vertical FL, and federated transfer learning, emphasizing their applicability based on the nature of data distribution across clients. Furthermore, the discussion extends to the key challenges faced by federated systems, such as dealing with non-independent and identically distributed (IID) statistics, ensuring model robustness against adverse attacks, and maintaining efficient data communication. Solutions to these challenges, including federated averaging, differential privacy, and secure multi-party computation, are also reviewed. This offers a comprehensive assessment of how tool learning and AI principles underpin the federated learning framework, fostering advancements in efficient, privacy-preserving, and collaborative learning systems. Through this examination, the potential of federated learning to revolutionize industries reliant on extensive, distributed datasets is underscored, paving the way for innovative applications in healthcare, finance, and beyond.
The Impact of Generative AI in Gaming: Exploring Immersive Experiences Nalli Vinaya Kumari, G.S. Pradeep Ghantasala, U. Ananthanagu, Pellakuri Vidyullatha, Mudassir Khan Generative AI Disruptive Technologies for Innovative Applications, 2025 The integration of generative AI into video games and digital environments is ushering in a generation of transformation in virtual interactions, growing realism, creativity, and engagement in digital worlds. In this chapter, we discover the various packages of generative AI in video games and display how those superior technologies are revolutionizing sports design, individual development, and immersive storytelling. Generative AI algorithms along with generative adversarial networks (GANs) and variational autoencoders (VAEs) are used to create enormously practical textures, landscapes, and dynamic environments that evolve in reaction to participant actions, supplying an extra-immersive and customized gaming experience. One of the important things to focus on is procedural content material generation, wherein AI-pushed structures autonomously generate full-sized and sundry game worlds, lowering the need for guide content material advent and permitting builders to provide rich, expansive universes at scale. This now not only best hurries up the improvement method but also allows for countless replay ability, as no gaming studies are identical. Moreover, generative AI is instrumental in growing wise, non-playable characters (NPCs) that show off realistic behaviors and adapt to players’ strategies, improving the complexity and intensity of interactions inside the game. The chapter additionally explores the capability of generative AI in digital environments past conventional gaming. Applications in education simulations, academic platforms, and digital reality (VR) stories are examined, highlighting how AI-generated content material can create practical and pleasant situations to familiarize and explore. The integration of generative AI in VR allows the advent of dynamic and interactive digital areas that respond to personal inputs, fostering a more immersive and interactive experience.
Industry 4.0 Design Principles, Technologies, and Applications Lokaiah Pullagura, Biswajit Brahma, N. Vinaya Kumari, L. Ravikumar, Siva Kumar Gowda Katta, Ronald Chiwariro Computational Intelligence in Industry 4 0 and 5 0 Applications Trends Challenges and Applications, 2025 Industry 4.0, the Fourth Industrial Revolution, redefines manufacturing through principles like interoperability, ensuring seamless communication between devices. Information transparency guarantees the integrity and accessibility of data, with blockchain emerging as a decentralized solution. Decentralized decision-making, employing edge computing, enables real-time responses. Technical assistance integrates augmented reality, empowering workers with real-time guidance. Smart objects, embedded with RFID and NFC, enhance traceability in the supply chain. IoT devices and edge computing revolutionize data collection, minimizing reliance on centralized systems. Big data analytics, powered by artificial intelligence (AI) and machine learning (ML), extracts insights for predictive decision-making. Cloud computing provides scalable infrastructure for Industry 4.0 applications. Cyber-physical systems synchronize physical processes with computational control, creating adaptive manufacturing. Additive manufacturing (3D printing) facilitates rapid prototyping and on-demand production. Predictive maintenance employs ML algorithms to forecast equipment failures, reducing downtime. Digital twins offer real-time simulations for continuous monitoring and optimization. Smart supply chains leverage the IoT and blockchain for transparency and efficiency. Autonomous robots, guided by AI, perform tasks independently, enhancing productivity. Human–machine collaboration, facilitated by collaborative robots, improves flexibility in manufacturing. Blockchain ensures transparency and traceability in the supply chain, reducing fraud. The integration of AI and ML drives pattern recognition and optimization in Industry 4.0. Augmented reality and virtual reality enhance training and maintenance processes. Industry 4.0’s impact spans diverse sectors, shaping a future of intelligent, connected, and efficient manufacturing. As technology advances, Industry 4.0 continues to redefine how we conceptualize and implement industrial processes.
Smart Home Forensics Lokaiah Pullagura, Nalli Vinaya Kumari, Hemanta Kumar Bhuyan Cyber Forensics and Investigation on Smart Devices Volume 1, 2024 The Internet of Things (IoT) has unquestionably exploded into the forefront of everyone's lives, whether they realise it or not. Internet of Things (IoT) technology is now used in medical devices, transportation, and even in our homes. Devices such as these have the ability to access a great deal of personal information. Because of their diminutive size, these devices have made insufficient efforts to build security into their design. Sensors, cameras, and lights are all examples of Internet of Things (IoT) devices that can be used to automate daily tasks around the home. Smartphones and speakers can be used as remote controllers to operate these gadgets. A smart home's IoT devices collect and process data on motion, temperature, lighting control, and other variables, and they store a wider range of data from more diverse users. A wide variety of smart home devices can make extracting meaningful data difficult because of their differing data storage methods. Data from a variety of smart home devices, as well as data that can be used in digital forensics, must be collected and analysed. Google Nest Hub and Samsung Smart Things are the primary sources of forensic smart home data that will be analysed in this study. As a result, we analysed the smart home data collected using companion apps, web interfaces, and APIs to find information that was relevant to our investigation. Various types of data collected by smart homes are also discussed in the paper, and they can be used as crucial evidence in certain forensic cases. IoT devices in a smart home can be hacked, and we'll investigate how, what data can be recovered, and where it resides after it has been hacked as part of our investigation.
LPWAN communication-based ML for IoT networks Lokaiah Pullagura, Nalli. Vinaya Kumari, Siva Kumar Gowda Katta Low Power Wide Area Network for Large Scale Internet of Things Architectures Communication Protocols and Recent Trends, 2024 This chapter presents an overview of the combination of Machine Learning (ML) and Low Power Wide Area Networks (LPWAN) for Internet of Things (IoT) applications. LPWAN provides long-range and low-power connectivity, making it ideal for IoT devices that are usually deployed in remote or hard-to-reach locations, while ML algorithms can analyze large volumes of data collected by these devices to detect patterns, identify anomalies, and predict future outcomes. This chapter discusses different types of LPWAN and IoT sensors, as well as various ML techniques that can be used in IoT applications, including supervised learning, unsupervised learning, and reinforcement learning. In addition, this chapter explores various applications of LPWAN-based IoT and ML, such as smart homes, smart cities, and industrial IoT, along with the challenges and future directions of these technologies. Overall, this chapter provides insights into the potential benefits of combining ML and LPWAN-based IoT applications and how they can improve decision-making and optimize processes in various industries.
An Optimal Scheduling of Energy Storage Units in Renewable Energy Systems Using Strength-Pareto Evolutionary Algorithm Nalli Vinaya Kumari, Komuravelly Sudheer Kumar, Zaid Alsalami, Harshitha P, Ghazi Mohamad Ramadan International Conference on Intelligent Algorithms for Computational Intelligence Systems Iacis 2024, 2024 Dual-stage optimization scheduling model by hybrid energy storage for grid-connected renewable energy systems, is proposed in this paper which focuses on both intra-day and day-ahead phases. In day-ahead phase, model improves economic efficiency by considering of price values at its peak value difference. In intra-day phase, its motive is to decrease renewable energy usage and it’s related with the day-ahead schedules using accurate, short-term predictions. Strength-pareto evolutionary algorithm 2 (SPEA2) algorithm for optimization is for multiple objective optimizations, achieving optimal and balanced solutions across multiple objectives. This SPEA2 optimization algorithm efficient handles and it enables system to cost-effectively and firmly operate and optimizing scheduling of grid-connected systems by addressing both the aspects of economic and operational. Overall, the model improves economic efficiency, reduces 6.3% the usage of renewable energy, and improves the sustainability power grid through effective integration and utilization of renewable energy sources.
A Study of Suspicious E-Mail Detection Techniques Lokaiah Pullagura, Dontha Madhusudhana Rao, Nalli Vinaya Kumari, Ravi Kumar Lanke, Siva Kumar Gowda Katta, Ronald Chiwariro 2nd International Conference on Intelligent Data Communication Technologies and Internet of Things Idciot 2024, 2024 Electronic mail is a preferred way of written communication, especially in an official or corporate setting, and the influx of emails is ever-growing. While most emails are meant to communicate something useful or important, it is even more common to receive spam or phishing emails regularly. While some of the most used email service providers have filters in their inboxes to filter out unnecessary emails, it is easy for people with malicious intent to bypass the same, thus increasing the need for a more accurate and reliable system for filters. The study aims to survey and review existing work in the space of email classification and recognize the best practices and tools which can be used for analyzing and classifying emails.
Optimizing Athletic Performance through Nutrition and Machine Learning: A Comprehensive Review K. Venkata Narayana, Nalli Vinaya Kumari, G S Pradeep Ghantasala, Rajesh Sharma R, Pellakuri Vidyullatha, Ananthanagu U 2024 IEEE Flagship International BIT Conference Next Generation Applications in Green Energy Technology Bitcon 2024, 2024 Nutrition plays a pivotal function in athletic general overall performance, influencing training, competition, and healing. This paper examines the influence of macronutrients (proteins, carbohydrates, fats), micronutrients (vitamins, minerals), and hydration on general overall performance, integrating insights from sports activities sports era and device learning. Recent advances in periodized vitamins and personalized dietary strategies are more with the resource of the use of wearable generation and information analytics, providing real- time monitoring and optimization of athletes` nutritional status. Current evidence on dietary nutritional dietary supplements for enhancing general overall performance and healing is reviewed. Findings emphasize the importance of tailored vitamins plans based totally mostly on sport-precise wishes and character wishes. Practical pointers for optimizing general overall performance thru strategic vitamins and leveraging device learning tools are provided, underscoring vitamin’s vital function in maximizing athletic capability and long-term health.
Optimizing Railway Traffic Management Using Fuzzy Logic-Based Decision-Making Systems Nalli Vinaya Kumari, G S Pradeep Ghantasala, Rajesh Sharma R, Ananthanagu U, Pellakuri Vidyullatha, Dileep Kumar Pandiya 2024 2nd International Conference Computational and Characterization Techniques in Engineering and Sciences Ic3tes 2024, 2024 Railway web page traffic manipulate is a complex and dynamic method that includes making real-time picks based mostly on various uncertain and difficult to understand factors, collectively with fluctuating passenger demand, diverse train schedules, and unpredictable weather conditions. Traditional decision-making strategies often battle to cope with the ones uncertainties, foremost to inefficiencies and potential safety risks. This paper proposes the software program of fuzzy properly judgment-based completely decision-making systems to optimize railway web page traffic manipulate. Fuzzy properly judgment, with its potential to model uncertainty and control difficult to understand data, offers a robust framework for boosting decision-making approaches in railway operations. By simulating various conditions and integrating real-time data, the proposed gadget can dynamically regulate train schedules, manipulate delays, and enhance everyday operational efficiency. The paper may present case studies demonstrating the effectiveness of fuzzy properly judgment in dealing with railway web page traffic, reducing delays, and improving passenger satisfaction. The findings advice that incorporating fuzzy properly judgment into railway web page traffic manipulates systems can purpose greater adaptive and resilient railway operations, ultimately contributing to greater steady and additional reliable rail networks.
Railway Passenger Flow and Sales Prediction Using Advanced Machine Learning Techniques Nalli Vinaya Kumari, G S Pradeep Ghantasala, Rajesh Sharma R, Pellakuri Vidyullatha, Ananthanagu U, Saliha Bathool 2024 IEEE Flagship International BIT Conference Next Generation Applications in Green Energy Technology Bitcon 2024, 2024 Effective passenger waft control is pivotal for the highest quality operation of railway structure, impacting each operational performance and passenger satisfaction. This study delves into the software of system gaining knowledge of strategies to investigate and forecast passenger waft and price tag income inside railway networks. Utilizing historic tour statistics, sensor statistics, and outside influences, we increase predictive fashions that offer treasured insights into passenger conduct and decorate operational decision-making. Our findings spotlight the extensive enhancements in prediction accuracy and operational performance that system gaining knowledge of can deliver, providing sizeable blessings for railway control.
Biomedical data analysis and processing using explainable artificial intelligence and responsive artificial intelligence Mobile Health Advances in Research and Applications Volume II, 2022
HybridViT-CAB: a vision transformer and convolutional attention network for precision weed detection in agricultural systems GR Yenna, NV Kumari, AS Chouhan, S Umar, SR Macha Scientific Reports , 2026 2026
New Era: A Large Language Model DRP VENUTHURUMILLI, DRAS RANI, DRNV KUMARI Geh press , 2025 2025
Augmented Reality and Artificial General Internet of Things Integration NV Kumari, GSP Ghantasala, U Ananthanagu, P Vidyullatha, M Khan Artificial General-Internet of Things (AG-IoT) for Robotics: Advanced … , 2025 2025
Machine Learning and Artificial Intelligence Fundamentals for Federated Systems NV Kumari, GSP Ghantasala, P Vidyullatha, R Rajesh Sharma Federated Intelligent System for Healthcare: A Practical Guide, 153-169 , 2025 2025
Autonomous Maintenance in Railways using AI Techniques for Predictive Preservation NV Kumari, GSP Ghantasala, P Vidyullatha, RR Sharma, A Sungheetha, ... International Conference on Advances and Applications in Artificial … , 2025 2025 Citations: 1
Ai-powered early detection of cardiovascular diseases using deep learning algorithms GSP Ghantasala, NV Kumari, P Vidyullatha, RR Sharma, A Sungheetha, ... International Conference on Advances and Applications in Artificial … , 2025 2025 Citations: 1
Transforming Cancer Diagnostics and Personalized Treatment with Deep Learning Models GSP Ghantasala, NV Kumari, RR Sharma, P Vidyullatha, A Sungheetha, ... International Conference on Advances and Applications in Artificial … , 2025 2025
The Impact of Generative AI in Gaming: Exploring Immersive Experiences NV Kumari, GSP Ghantasala, U Ananthanagu, P Vidyullatha, M Khan Generative AI: Disruptive Technologies for Innovative Applications, 107-130 , 2025 2025 Citations: 1
Industry 4.0 design principles, technologies, and applications L Pullagura, B Brahma, NV Kumari, L Ravikumar, SKG Katta, R Chiwariro Computational Intelligence in Industry 4.0 and 5.0 Applications, 357-388 , 2025 2025 Citations: 6
11 Industry 4.0 Design L Pullagura, B Brahma, NV Kumari, L Ravikumar, SKG Katta, R Chiwariro Computational Intelligence in Industry 4.0 and 5.0 Applications: Trends … , 2025 2025
Optimizing Athletic Performance through Nutrition and Machine Learning: A Comprehensive Review KV Narayana, NV Kumari, GSP Ghantasala, R Sharma, P Vidyullatha 2024 International BIT Conference (BITCON), 1-5 , 2024 2024
Railway Passenger Flow and Sales Prediction Using Advanced Machine Learning Techniques NV Kumari, GSP Ghantasala, R Sharma, P Vidyullatha, S Bathool 2024 International BIT Conference (BITCON), 1-4 , 2024 2024
Addressing Challenges and Opportunities in Precision Medicine Using Deep Learning and Personalized Cancer Treatment GSP Ghantasala, NV Kumari, RR Sharma, P Vidyullatha, A Sungheetha, ... 2024 3rd Edition of IEEE Delhi Section Flagship Conference (DELCON), 1-5 , 2024 2024
Enhancing Team Sports Strategy and Performance Using Machine Learning: A Case Study on Soccer KV Narayana, NV Kumari, GSP Ghantasala, R Sharma, U Ananthanagu, ... 2024 3rd Edition of IEEE Delhi Section Flagship Conference (DELCON), 1-4 , 2024 2024 Citations: 1
Optimizing railway traffic management using fuzzy logic-based decision-making systems NV Kumari, GSP Ghantasala, R Sharma, P Vidyullatha, DK Pandiya 2024 Second International Conference Computational and Characterization … , 2024 2024 Citations: 3
An Optimal Scheduling of Energy Storage Units in Renewable Energy Systems Using Strength-Pareto Evolutionary Algorithm NV Kumari, KS Kumar, Z Alsalami, H P, GM Ramadan 2024 International Conference on Intelligent Algorithms for Computational … , 2024 2024 Citations: 2
Smart home forensics L Pullagura, NV Kumari, HK Bhuyan Cyber Forensics and Investigation on Smart Devices, 1-19 , 2024 2024 Citations: 1
LPWAN communication-based ML for IoT networks L Pullagura, NV Kumari, SKG Katta Low-Power Wide Area Network for Large Scale Internet of Things, 15-27 , 2024 2024
A Study of Suspicious E-Mail Detection Techniques L Pullagura, DM Rao, NV Kumari, RK Lanke, SKG Katta, R Chiwariro 2024 2nd International Conference on Intelligent Data Communication … , 2024 2024 Citations: 6
Computational Intelligence for Industry 4.0 and 5.0 Applications JB Awotunde, K Muduli, B Brahma Boca Raton (FL): CRC Press , 2024 2024 Citations: 5
MOST CITED SCHOLAR PUBLICATIONS
Cancer prediction and diagnosis hinged on HCML in IOMT environment GSP Ghantasala, NV Kumari, R Patan Machine Learning and the Internet of Medical Things in Healthcare, 179-207 , 2021 2021 Citations: 27
[Retracted] Internet of Things‐Based Patient Cradle System with an Android App for Baby Monitoring with Machine Learning VS Kumar, L Pullagura, NV Kumari, S Pooja Nayak, BP Devi, A Alharbi, ... Wireless Communications and Mobile Computing 2022 (1), 1140789 , 2022 2022 Citations: 20
Texture recognization and image smoothing for microcalcification and mass detection in abnormal region GSP Ghantasala, S Kallam, NV Kumari, R Patan 2020 international conference on computer science, engineering and … , 2020 2020 Citations: 20
Breast cancer treatment using automated robot support technology for mri breast biopsy GSP Ghantasala, NV Kumari International Journal of Education, Social Sciences and Linguistics 1 (2 … , 2021 2021 Citations: 14
Identification of normal and abnormal mammographic images using deep neural network GSP Ghantasala, NV Kumari Asian Journal For Convergence In Technology (AJCT) ISSN-2350-1146 7 (1), 71-74 , 2021 2021 Citations: 14
Support vector machine based supervised machine learning algorithm for finding ROC and LDA region NV Kumari, GP Ghantasala Journal of Operating Systems Development & Trends 7 (1), 26-33 , 2020 2020 Citations: 14
Industry 4.0 design principles, technologies, and applications L Pullagura, B Brahma, NV Kumari, L Ravikumar, SKG Katta, R Chiwariro Computational Intelligence in Industry 4.0 and 5.0 Applications, 357-388 , 2025 2025 Citations: 6
A Study of Suspicious E-Mail Detection Techniques L Pullagura, DM Rao, NV Kumari, RK Lanke, SKG Katta, R Chiwariro 2024 2nd International Conference on Intelligent Data Communication … , 2024 2024 Citations: 6
Compulsion for Cyber Intelligence for Rail Analytics in IoRNT NV Kumari, GSP Ghantasala, M Arvindhan Securing IoT and Big Data, 59-83 , 2020 2020 Citations: 6
Computational Intelligence for Industry 4.0 and 5.0 Applications JB Awotunde, K Muduli, B Brahma Boca Raton (FL): CRC Press , 2024 2024 Citations: 5
Optimizing railway traffic management using fuzzy logic-based decision-making systems NV Kumari, GSP Ghantasala, R Sharma, P Vidyullatha, DK Pandiya 2024 Second International Conference Computational and Characterization … , 2024 2024 Citations: 3
Smart Computation Based Smart Card Information Using Rail Management System NV Kumari Journal of Engineering Sciences 10 (12) , 2019 2019 Citations: 3
An Optimal Scheduling of Energy Storage Units in Renewable Energy Systems Using Strength-Pareto Evolutionary Algorithm NV Kumari, KS Kumar, Z Alsalami, H P, GM Ramadan 2024 International Conference on Intelligent Algorithms for Computational … , 2024 2024 Citations: 2
Autonomous Maintenance in Railways using AI Techniques for Predictive Preservation NV Kumari, GSP Ghantasala, P Vidyullatha, RR Sharma, A Sungheetha, ... International Conference on Advances and Applications in Artificial … , 2025 2025 Citations: 1
Ai-powered early detection of cardiovascular diseases using deep learning algorithms GSP Ghantasala, NV Kumari, P Vidyullatha, RR Sharma, A Sungheetha, ... International Conference on Advances and Applications in Artificial … , 2025 2025 Citations: 1
The Impact of Generative AI in Gaming: Exploring Immersive Experiences NV Kumari, GSP Ghantasala, U Ananthanagu, P Vidyullatha, M Khan Generative AI: Disruptive Technologies for Innovative Applications, 107-130 , 2025 2025 Citations: 1
Enhancing Team Sports Strategy and Performance Using Machine Learning: A Case Study on Soccer KV Narayana, NV Kumari, GSP Ghantasala, R Sharma, U Ananthanagu, ... 2024 3rd Edition of IEEE Delhi Section Flagship Conference (DELCON), 1-4 , 2024 2024 Citations: 1
Smart home forensics L Pullagura, NV Kumari, HK Bhuyan Cyber Forensics and Investigation on Smart Devices, 1-19 , 2024 2024 Citations: 1
Human Activity Recognition using CNN and Pretrained Machine Learning Models. VMP Dr. N Vinaya Kumari , MohdSalmanuddin Talha, CH Venkata Vinod ... Emperor Journal of Applied Scientific Research 4 (Issue-07), 11-16 , 2022 2022 Citations: 1
Model for Rail Transportation System To Predicting the Capacity of Train Speed and Length NV Kumari, GSP Ghantasala INTERNATIONAL JOURNAL OF EDUCATION, SOCIAL SCIENCES AND LINGUISTICS 1 (1), 1-10 , 2021 2021 Citations: 1
