S.P.Shiva Prakash

Scopus Publications

OPI-RLI-HO: Optimized Priority Index with Improved Chaotic Single Candidate Optimizer Hand-off Management Scheme for Quality of Service Enhancement in Ultra Dense Network
International Journal of Intelligent Engineering and Systems, 2026
Pose-Specific Adaptive ROI (PSAR) Extraction Model for Yoga Pose Detection
J. H. Meghana, H. K. Chethan, S. P. Shiva Prakash
Lecture Notes in Networks and Systems, 2026
Behavior-Driven Real-Time Risk Assessment for Secure Fusion of Social IoT and Digital Twins
R.S. Anusha, S. P. Shiva Prakash, Kirill Krinkin
IEEE Internet of Things Journal, 2026
The rapid growth of the Social Internet of Things (SIoT) and Digital Twin (DT) technologies in smart homes, healthcare, and industrial automation has created complex networks that demand robust security measures. Integrating these technologies into Social Internet of Digital Twin Things (SIDTT) ecosystems faces security challenges, primarily due to an open attack surface and inadequate real-time risk assessment frameworks. Current solutions often lack comprehensive integration of contextual information and user behavior analytics, leading to potential privacy issues and inefficient security measures. To address these gaps, this research proposes a novel Behavior-Driven Real-Time Risk Assessment framework. It introduces a Context-Aware Federated Access Control and Authorization Framework (CA-FA-CA) that leverages Federated Learning with Long Short-Term Memory (LSTM) networks for user behavior analysis and Isolation Forest for anomaly detection. Additionally, an adaptive risk-based access control that comes under AI (Artificial Intelligence) driven Cybersecurity known as Real-Time Deep Reinforced Risk Score Assessment Framework, which combines Reinforced Generative Adversarial Networks (RRS-GAN) with Deep Q-Network (DQN) and Adaptive Multi-Factor Authentication (AMF), is introduced for dynamically adjusting risk scoring and authentication measures. This comprehensive approach ensures enhanced security and user experience by continuously adapting to real-time data and evolving threats, giving an Accuracy of 98.2% and a throughput of about 1023 TPS, while overcoming the limitations of existing methods in SIDTT ecosystems.
Computational Optimization Techniques to Design RF Circuits
Aijaz M. Zaidi, Ahmed A. Kishk, S.P.Shiva Prakash, Jugul Kishor, Sumer Singh Singhwal, Binod K. Kanaujia, Sembiam R. Rengarajan, Ladislau Matekovits
IEEE Microwave Magazine, 2026
We present a comprehensive review of computational optimization techniques for the design of RF circuits. Important design techniques used to optimize RF circuits, such as genetic algorithms (GAs), particle swarm optimization (PSO), reinforcement learning (RL), Bayesian optimization (BO), and space mapping (SM), are discussed. The basics of the techniques, their merits, and their drawbacks are covered so that new researchers can understand the relevant theories of the techniques. A comparative analysis of design techniques is included. This article also provides insights into the present state and future directions of the optimization techniques.
Extreme Learning Machines and van Emde Boas Tree-based Congestion-Free Model for Social Internet of Things
Iaeng International Journal of Computer Science, 2025
AI-Powered Hybrid Smart Parking: Optimizing Parking Management Across Diverse Applications in Smart Cities
Shalini M.K., Hanumanthappa J., K.S. Santhosh Kumar, S.P. Shiva Prakash
Procedia Computer Science, 2025
An Artificial Intelligence (AI) powered hybrid smart parking system optimizes parking allocation across various applications, including smart hospitals, colleges, offices, and shopping malls. The system uses AI and IoT technologies to enhance the user experience, streamline operations, and improve efficiency. It dynamically allocates parking spaces based on real-time demand, user preferences, and contextual factors. The system accurately predicts parking demand, optimizes space allocation and provides personalized recommendations, reducing congestion and waiting times. The hybrid smart parking algorithm combines machine learning techniques with domain-specific insights to prioritize parking allocation in diverse environments. The study emphasizes the importance of leveraging advanced technologies to address complex urban challenges, such as parking management, and aims to pave the way for sustainable, efficient, and user-centric parking solutions in smart cities. and Random Forest with an overall average score of 0.9400. Machine learning has become crucial for optimizing parking management systems, especially in densely populated cities. To address this challenge, advanced predictive models have been developed to anticipate parking duration based on slot availability, peak hours, and traffic conditions. The Random Forest model outperforms Logistic Regression, Random Forest, and K-Nearest Neighbors in predicting parking length, achieving high accuracy and performance metrics. It maintains user satisfaction and low operational costs, making it a recommended system for further implementation in parking management tasks. The classification report shows a respectable performance with 50% accuracy and a good recall for open slots. The optimization procedure was effective and did not reveal any notable areas for improvement. The optimized route (Slot2, Slot1, Slot3) provides an efficient parking sequence, with high accuracy (95%), good user satisfaction (90%) and suitability for use in smart parking situations.
Comprehensive Analysis of Pose Estimation and Machine Learning Classifiers for Precise Yoga Pose Detection and Classification
Meghana J.H., Chethan H.K., K.S. Santhosh Kumar, S.P. Shiva Prakash
Procedia Computer Science, 2025
Yoga contributes to mental and physical well-being by improving flexibility, strength, balance, and emotional stability when integrated into daily routines. This ancient practice can become more accessible and adaptable to a wider audience when combined with modern artificial intelligence (AI). This study introduces a comprehensive system for detecting and classifying yoga poses using computer vision and machine learning techniques. Central to this work is the application of posture estimation algorithms, such as MediaPipe, PoseNet, and OpenPose, to identify key points on the human body within a single image or video frame. These key points are analyzed in both two-dimensional (2D) and three-dimensional (3D) spaces to construct a skeletal representation of the body, enabling accurate classification of yoga poses. The study focuses on five distinct yoga poses: Downdog, Goddess, Plank, Tree, and Warrior II. To categorize these poses, machine learning classifiers including Support Vector Machines (SVM), Random Forest, K-Nearest Neighbors (KNN), and Naive Bayes utilize the key points extracted from the pose estimation models. This research is distinctive in its thorough evaluation of various conventional classifiers across multiple yoga positions. A comprehensive comparative analysis is essential for identifying the most effective classifiers for posture detection and classification. The dataset used in this study has been carefully curated to encompass a wide array of yoga poses and is divided into training and testing sets at various ratios (90:10, 80:20, 70:30, and 60:40) to ensure robust validation. Results indicate the system’s effectiveness, with SVM and KNN consistently achieving high values for Accuracy PE , Precision PE , Recall PE , and F1-S core PE across all yoga poses. Notably, Random Forest attains up to 100% Accuracy PE in detecting and classifying certain poses, demonstrating its robustness and reliability. This research highlights the potential of integrating pose estimation models with machine learning classifiers to create intelligent systems that can assist practitioners in yoga, marking an innovative step toward merging traditional practices with advanced technology.
IoT-based Efficient Waste Management on University Campus: Enhancing Monitoring and Optimization through Machine Learning
Sumathi M., Hanumanthappa J., K.S. Santhosh Kumar, S.P. Shiva Prakash, Meghana J.
Procedia Computer Science, 2025
The introduction of an IoT-based smart waste management system is proposed to optimize various aspects of university campus waste recycle management processes, such as garbage collection scheduling, waste type identification, and collection routes. This system utilizes advanced data analytics algorithms, wireless communication protocols, and sensors to measure and document variations in trash levels, enabling real-time monitoring of waste accumulation. Through this IoT-based infrastructure, university administrators and waste management staff gain fine-grained visibility and control over garbage collection processes. The implications for sustainability advocacy and environmental stewardship in academic environments are significant. By fostering a culture of environmental awareness and accountability, universities can instill ideals of conservation and resourcefulness in their student populations and staff. The proposed IoT-enabled waste bin system exemplifies the synergy between technological innovation and environmental responsibility, serving as a model for institutions worldwide. Collaboration among universities can leverage IoT technology to drive positive change, mitigate environmental degradation, and promote sustainable practices beyond campus boundaries. Efficient waste management is crucial for nurturing sustainability on college and university campuses, as evidenced by the urgent need for the collection and recycling of compost, general waste, and recyclables. Our models consistently achieve high performance, with perfect scores (1.000) across all split ratios, indicating flawless classification of waste types. Although the Artificial Neural Network (ANN) demonstrates slightly lower accuracy, ranging from 0.973 to 0.997 depending on data splits, it still performs well. As a result, Naive Bayes produces less accurate results, typically surpassing 0.93 but falling short of Random Forest, K-Nearest Neighbors (KNN), and Support Vector Machine (SVM)’s flawless scores, especially at split ratios of 90:10 and 80:20. ANN and Naive Bayes perform adequately with just little compromises in accuracy and recall, while Random Forest, KNN, and SVM stand out as the top-performing models.
Long-Short Term Memory decision model for device argumentation in ambient assisted living
G. S. Madhan Kumar, S. P. ShivaPrakash, Kirill Krinkin, Yulia A. Shichkina
Journal of Ambient Intelligence and Humanized Computing, 2024
Dynamic Data Aggregation Model for Social Internet of Things Devices: Exploring the Static and Mobile Nature
, Meghana J., Hanumanthappa J., S. P. Shiva Prakash, Kirill Krinkin
International Journal of Information Engineering and Electronic Business, 2024
The increasing ubiquity of Social Internet of Things (SIoT) devices necessitates innovative data aggregation techniques to distill meaningful insights from diverse sources.This study introduces a Dynamic Data Aggregation Model for SIoT devices.The model aims to amalgamate static and mobile device data, employing Density-Based Spatial Clustering of Applications with Noise (DBSCAN) for spatial clustering and Recurrent Neural Networks (RNN) for predicting mobile device movement patterns.The purpose is to offer a holistic approach to predictive analytics in the SIoT domain by seamlessly integrating these methodologies.The model begins with data preprocessing, ensuring data quality.It then applies DBSCAN for spatial clustering, enabling a comprehensive understanding of spatial relationships between devices.Simultaneously, RNNs are used for predictive modeling, specifically in forecasting mobile device movement patterns.The integration of DBSCAN clustering and RNNs forms the model's innovative core, providing a unified solution for dynamic data aggregation.Comprehensive testing demonstrates the model's notable accuracy in predicting mobile device movement patterns.By combining the spatial clustering capabilities of DBSCAN with the predictive power of RNNs, the model effectively unifies static and mobile data, advancing predictive analytics in the SIoT context.The proposed model yielded average values of 0.14604 (Mean Squared Error), 2.678385 (Mean Absolute Error), 0.307154 (Root Mean Squared Error), and 1.342317 (Mean Absolute Percentage Error), respectively.The Dynamic Data Aggregation Model proves its efficacy in addressing SIoT challenges.The integration of DBSCAN clustering and RNNs offers a versatile framework for dynamic data analysis, contributing significantly to predictive analytics in SIoT contexts.
OSSIoT: An ontology-based Operational Security model for Social Internet of Things using Machine Learning Techniques
Iaeng International Journal of Computer Science, 2024
Security Framework for Social Internet of Things: A Relativity Strength Approach
K. S. Santhosh Kumar, , Hanumanthappa J., S. P. Shiva Prakash, Kirill Krinkin
International Journal of Information Engineering and Electronic Business, 2024
Class imbalance and concept drift invariant online botnet threat detection framework for heterogeneous IoT edge
Nitish A, Hanumanthappa J, S.P. Shiva Prakash, Kirill Krinkin
Computers and Security, 2024
Enhancing routing efficiency in social internet of things: R-OPTICS and vEBT based congestion free model
Bhavya D, D. S. Vinod, S P Shiva Prakash, Kirill Krinkin
International Journal of Electrical and Computer Engineering, 2024
An Intelligent Bayesian Inference Based Learning Automaton Approach for Traffic Management in Radio Access Network
V. Varsha, S. P. Shiva Prakash, Kirill Krinkin
Wireless Personal Communications, 2024
Gated recurrent unit decision model for device argumentation in ambient assisted living
G. S. Madhan Kumar, S. P. Shiva Prakash, Kirill Krinkin
International Journal of Electrical and Computer Engineering, 2024
Classification of Emotions in Ambient Assisted Living Environment Using Machine Learning Approaches
G. S. Madhan Kumar, S. P. Shiva Prakash, Kirill Krinkin
Handbook of Formal Optimization, 2024
Pose-Critical Keypoint Attention model for Dynamic Yoga Pose Classification
Meghana JH, Chethan HK, S. P. Shiva Prakash
Iet Conference Proceedings, 2024
RNN-based Intelligent Fault Diagnosis and Alert Model for Construction Equipment Telematics
Mohan Gopal Raje Urs, S.P. Shiva Prakash, Kirill Krinkin
2024 5th International Conference on Innovative Trends in Information Technology Icitiit 2024, 2024
SecureSIoTChain: A relationship enhanced Blockchain Operational Security Framework for the Social Internet of Things
K S Santhosh Kumar, Hanumanthappa J, S.P. Shiva Prakash, Kirill Krinkin
Procedia Computer Science, 2024
Student Performance Prediction: A Co-Evolutionary Hybrid Intelligence model
Vimarsha K, S.P. Shiva Prakash, Kirill Krinkin, Yulia A. Shichkina
Procedia Computer Science, 2024
Review of Phishing Attacks’ Effects on AI-Powered IoT Systems
S. D. Mohana, D. Rafiya Nusrath, S. P. Shiva Prakash, Kirill Krinkin
Lecture Notes in Electrical Engineering, 2024
Redundant data identification in Social Internet of Things using Agglomerative Clustering
J Meghana, J Hanumanthappa, S.P. Shiva Prakash, Kirill Krinkin
2024 5th International Conference on Innovative Trends in Information Technology Icitiit 2024, 2024
Ensemble Technique for Brain Tumor Patient Survival Prediction
D. S. Vinod, S. P. Shiva Prakash, Hussain AlSalman, Abdullah Y. Muaad, Md. Belal Bin Heyat
IEEE Access, 2024
Energy-efficient data transmissions in heterogeneous ultra-dense networks using a hybrid metaheuristic optimization
Varsha. V, S. P. Shiva Prakash, Kirill Krinkin
International Journal of Communication Systems, 2023
On-device context-aware misuse detection framework for heterogeneous IoT edge
Nitish A, Hanumanthappa J, Shiva Prakash S. P, Kirill Krinkin
Applied Intelligence, 2023
CCNSim: An artificial intelligence enabled classification, clustering and navigation simulator for Social Internet of Things
Mohana S.D., S.P. Shiva Prakash, Kirill Krinkin
Engineering Applications of Artificial Intelligence, 2023
KNN-Based Decision Model for Device Argumentation in Ambient Assisted Living
G. S. Madhan Kumar, S. P. Shiva Prakash, Kirill Krinkin
Lecture Notes in Electrical Engineering, 2023
Artificial Neural Network Alert Classifier for Construction Equipments Telematics (CET)
Mohan Gopal Raje Urs, S. P. Shiva Prakash, Kirill Krinkin
Lecture Notes in Networks and Systems, 2023
Relationship LSTM Network for Prediction in Social Internet of Things
S. D. Mohana, S. P. Shiva Prakash, Kiril Krinkin
Lecture Notes in Electrical Engineering, 2023
GMM-EM-ACO Model for Congestion Free Routing in Social Internet of Things
D. Bhavya, D. S. Vinod, S. P. Shiva Prakash, Kiril Krinkin
Lecture Notes in Electrical Engineering, 2023
Lightweight Network Context Change Detection for Online Defense Against Heterogeneous IoT Attacks
Nitish A, Hanumanthappa J, S. P. Shiva Prakash, Kirill Krinkin
2023 International Conference on Computational Intelligence Networks and Security Iccins 2023, 2023
Relationship-Based AES Security Model for Social Internet of Things
K. S. Santhosh Kumar, J. Hanumanthappa, S. P. Shiva Prakash, Kirill Krinkin
Lecture Notes in Electrical Engineering, 2023
Arabic Hate Speech Detection Using Different Machine Learning Approach
Abdullah Y. Muaad, J. Hanumanthappa, S. P. Shiva Prakash, Mohammed Al-Sarem, Fahad Ghabban, J. V. Bibal Benifa, Channabasava Chola
Lecture Notes on Data Engineering and Communications Technologies, 2023
Service Oriented R-ANN Knowledge Model for Social Internet of Things
Mohana S. D., S. P. Shiva Prakash, Kirill Krinkin
Big Data and Cognitive Computing, 2022
Ensemble Method for User Activity classification in Ambient Assisted Living
G S Madhan Kumar, S P Shiva Prakash, Kirill Krinkin
2022 International Conference on Innovative Trends in Information Technology Icitiit 2022, 2022
Semantic Rules for Service Discovery in Social Internet of Things
Mohana S D, S. P. Shiva Prakash, Kirill Krinkin
Proceedings 4th International Conference on Smart Systems and Inventive Technology Icssit 2022, 2022
A review on energy optimization in location-based services for 5G and IoT networks
S. P. Shiva Prakash, Varsha V.
International Journal of Embedded and Real Time Communication Systems, 2021
Hybrid Machine Learning Model for Context-Aware Social IoT Using Location-Based Service Under Both Static and Mobility Conditions
D. P. Abhishek, Nidhi Dinesh, S. P. Shiva Prakash
Advances in Intelligent Systems and Computing, 2020
Congestion Avoidance and Delay Minimization in Energy Aware Routing of Dynamic ieee 802.11s WMN: Wireless Mesh Networks Under Mobility Conditions
S.P. Shiva Prakash, T.N. Nagabhushan, Kirill Krinkin
Cognitive Analytics Concepts Methodologies Tools and Applications, 2020
Walking and Transition Irregularity Detection Using ANN Approach for Wireless Body Area Network
S. P. Shiva Prakash, Apurwa Agrawal
Lecture Notes on Data Engineering and Communications Technologies, 2019
Performance Enhancement of Optimized Link State Routing Protocol for Health Care Applications in Wireless Body Area Networks
V. Chetan Kumar, S.P. Shiva Prakash, Sergey Balandin
Conference of Open Innovation Association Fruct, 2018
Cluster based approach to minimize delay in energy aware routing for ieee 802.11s Wireless Mesh Networks under mobility conditions
S. P. Shiva Prakash, T. N. Nagabhushan, Kirill Krinkin
Conference of Open Innovation Association Fruct, 2017
On improving energy saving in ieee 802.11s wireless mesh networks under mobility conditions
S.P. Shiva Prakash, T.N. Nagabhushan, Kirill Krinkin
Proceedings 2016 2nd International Conference on Cognitive Computing and Information Processing Ccip 2016, 2016
Congestion avoidance and delay minimization in energy aware routing of dynamic ieee 802.11s WMN: Wireless mesh networks under mobility conditions
S.P. Shiva Prakash, T.N. Nagabhushan, Kirill Krinkin
International Journal of Embedded and Real Time Communication Systems, 2016
An approach to detect intruder in energy-aware routing for wireless mesh networks
P. H. Annappa, Udaya Kumar K. Shenoy, S. P. Shiva Prakash
Advances in Intelligent Systems and Computing, 2016
Energy optimized path selection in cluster based routing for Wireless Mesh Network
S.P. Shiva Prakash, T.N. Nagabhushan, Kirill Krinkin
Proceedings 1st International Conference on Computational Intelligence and Networks Cine 2015, 2015
Minimum battery draining rate aware optimized link state routing in wireless mesh network
T.N. Nagabhushan, S.P. Shiva Prakash, Kirill Krinkin
Conference of Open Innovation Association Fruct, 2013
Energy aware power save mode management in wireless mesh networks
S. P. Shiva Prakash, T. N. Nagabhushan, Kirill Krinkin
Conference of Open Innovation Association Fruct, 2013
Process mining approach for traffic analysis in wireless mesh networks
Kirill Krinkin, Eugene Kalishenko, S. P. Shiva Prakash
Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 2012
Power-saving routing algorithms in wireless mesh networks: A survey
T.N. Nagabhushan, S.P. Shiva Prakash, Kirill Krinkin
Conference of Open Innovation Association Fruct, 2012
Remote monitoring and discrete data capture of joint pain and other parameters via the NokiaN900 device: Enhancing patient/physician interaction
T N Nagabhushan, S.P. Shiva Prakash, Suresh Chande, Prabhu Shankar
Conference of Open Innovation Association Fruct, 2012

S.P.Shiva Prakash

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Scopus Publications