Quantum-Inspired gravitationally guided particle swarm optimization for feature selection and classification Saleem Malik, S Gopal Krishna Patro, Chandrakanta Mahanty, Ayodele Lasisi, Quadri Noorulhasan Naveed, Abdulrajak Buradi, Addisu Frinjo Emma, Saravanapriya Kumar, Azath Mubarakali Scientific Reports, 2025 Population-based metaheuristic optimization algorithms have gained prominence for tackling complex optimization problems. They balance exploration and exploitation, essential for finding optimal solutions. While algorithms like Genetic Algorithms, Particle Swarm Optimization, and Gravitational Search Algorithm have shown success, they have limitations, such as premature convergence and sensitivity to parameters. To address these issues, we have introduced Quantum-Inspired Gravitationally Guided Particle Swarm Optimization (QIGPSO) for addressing complex optimization challenges, particularly in the context of medical data analysis for diagnosing Non-Communicable Diseases (NCDs). The Quantum Particle Swarm Optimization (QPSO) and Gravitational Search Algorithm (GSA) are both used in QIGPSO. It takes advantage of each algorithm's strengths in both global and local search processes. We used an absolute Gaussian random variable to improve the search, changed the position update equations and used a wrapper-based method with Support Vector Machine (SVM) for feature selection and classification. The findings suggest that QIGPSO is effective at identifying key features, achieving high accuracy rates, and lowering the number of incorrect classifications across several NCD datasets. Doctors can use QIGPSO data to make better treatment decisions for their patients. QIGPSO overcomes the limitations of conventional optimization methods by faster convergence while improving exploitation balance.
Hybrid metaheuristic optimization for detecting and diagnosing noncommunicable diseases Saleem Malik, S. Gopal Krishna Patro, Chandrakanta Mahanty, Saravanapriya Kumar, Ayodele Lasisi, Quadri Noorulhasan Naveed, Anjanabhargavi Kulkarni, Abdulrajak Buradi, Addisu Frinjo Emma, Naoufel Kraiem Scientific Reports, 2025 In our data-driven world, the healthcare sector faces significant challenges in the early detection and management of Non-Communicable Diseases (NCDs). The COVID-19 pandemic has further emphasized the need for effective tools to predict and treat NCDs, especially in individuals at risk. This research addresses these pressing concerns by proposing a comprehensive framework that combines advanced data mining techniques, feature selection, and meta-heuristic optimization. The proposed framework introduces novel hybrid algorithms, including the Hierarchical Genetic Multiple Reduct Selection Algorithm (H-GMRA) and the Customized Function-based Particle Swarm Optimization with Rough Set Theory for NCD Feature Selection (CPSO-RST-NFS). These algorithms aim to address the challenges of feature selection, computational complexity, and disease classification accuracy. H-GMRA outperforms traditional methods by identifying minimal feature sets with high dependency ratios. CPSO-RST-NFS combines meta-heuristic optimization with feature selection, resulting in improved efficiency and accuracy. Through extensive experimentation on diverse NCD datasets, this research demonstrates the framework's ability to select informative features, improve classification accuracy, and contribute to better patient outcomes. By bridging the gap between computational efficiency and disease classification accuracy, this work offers valuable insights for healthcare practitioners and data analysts, ultimately advancing the field of NCD research. The proposed framework presents a significant step towards enhancing the early detection and management of NCDs, offering hope for more precise clinical predictions and improved patient care.
Cloud Computing-Optimized Economic Simulation Framework for Infrastructure Investment Saravanapriya Kumar, S. Umamaheswari, VenkataPhanindra Peta, Mouhamd Hashim, TahaRaad AlShaikhli, B. Rex Cyril Proceedings of 2025 10th International Conference on Science Technology Engineering and Mathematics Iconstem 2025, 2025 This paper presents a new concept namely Cloud Computing-Optimized Economic Simulation Framework of the infrastructure investment decisionmaking process that aims at the optimization of the infrastructure and the administrative data modeled in advance through infrastructural investment decisions using intelligent modeling implemented in the cloud. The architecture integrates economic prediction to cloud resource performance, and uses the Gradient Boosting Machine (GBM) to set up correct classification and forecast of high-returning investment routes. The model is run with Python in Jupyter Notebook and interfaced with SimCloud (or CloudSim Express) on which the infrastructure is simulated, to give a dynamic overview of different investment strategies. Findings show that GBM method records a better accuracy as compared to the traditional approaches, hence notable factors that relate to cloud investment scale, latency effect and cost of operation. The simulation results demonstrate high levels of improvements in the estimated contribution to GDP and efficiency which justify the potential of cloudoptimized approaches. This framework offers policymakers and infrastructure developers a strong tool to perform multicriteria investments evaluation which combines technological scalability and economic impact. Extensions can be made in the future with real-time data streams, and adaptive modelling.
RECENT SCHOLAR PUBLICATIONS
Quantum-Inspired gravitationally guided particle swarm optimization for feature selection and classification S Malik, SGK Patro, C Mahanty, A Lasisi, QN Naveed, A Buradi, AF Emma, ... Scientific Reports 15 (1), 34155 , 2025 2025 Citations: 8
Hybrid metaheuristic optimization for detecting and diagnosing noncommunicable diseases S Malik, SGK Patro, C Mahanty, S Kumar, A Lasisi, QN Naveed, ... Scientific Reports 15 (1), 7816 , 2025 2025 Citations: 9
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Hybrid nature inspired feature selection algorithms for classification on non communicable diseases JB K. Saravanapriya Periyar University Created and maintained by INFLIBNET Centre , 2021 2021
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Performance Analysis of Quick Reduct And Multiple Optimal Reduct Algorithm Based on Dependency Ratio for Feature Selection SRJB K. Saravanapriya 2018
Performance Analysis of Classification Algorithms on Diabetes Dataset JB K. Saravanapriya1* International Journal of Computer Sciences and Engineering 5 (9), 15-20 , 2017 2017 Citations: 7
Comparative Study of Data Mining Classification Algorithms in Heart Disease Prediction SK Durgadevi. A International Journal of Recent Research in Mathematics Computer Science and … , 2016 2016
A Study on Free Open Source Data Mining Tools K Saravanapriya International Journal of Engineering and Computer Science 3 (12), 9450-9452 , 2014 2014 Citations: 10
MOST CITED SCHOLAR PUBLICATIONS
A novel gaussian based particle swarm optimization gravitational search algorithm for feature selection and classification S Kumar, B John Neural Computing and Applications 33 (19), 12301-12315 , 2021 2021 Citations: 21
A Study on Free Open Source Data Mining Tools K Saravanapriya International Journal of Engineering and Computer Science 3 (12), 9450-9452 , 2014 2014 Citations: 10
Hybrid metaheuristic optimization for detecting and diagnosing noncommunicable diseases S Malik, SGK Patro, C Mahanty, S Kumar, A Lasisi, QN Naveed, ... Scientific Reports 15 (1), 7816 , 2025 2025 Citations: 9
Quantum-Inspired gravitationally guided particle swarm optimization for feature selection and classification S Malik, SGK Patro, C Mahanty, A Lasisi, QN Naveed, A Buradi, AF Emma, ... Scientific Reports 15 (1), 34155 , 2025 2025 Citations: 8
Performance Analysis of Classification Algorithms on Diabetes Dataset JB K. Saravanapriya1* International Journal of Computer Sciences and Engineering 5 (9), 15-20 , 2017 2017 Citations: 7
A Rough Set Pooled Fitness Function Based Particle Swarm Optimization Algorithm using Golden Ratio Principle for Feature Selection JB K. Saravanapriya International Journal of Engineering and Advanced Technology (IJEAT) 9 (1 … , 2019 2019 Citations: 3
Introduction to Block Chain Technology S K POWER OF TECHNOLOGY EMERGING TRENDS IN BUSINESS 1, 12-21 , 2023 2023
Policy Analysis: Ransomware Impact on the Financial Sector Amidst the Covid -19 Pandemic KS Yaw Amoah Adum-Attah Journal of Computing and Intelligent Systems 5 (1), 187 - 200 , 2021 2021
Artificial Intelligence: The Impact Of A.I on Transportation and Road Management in Ghana SAA K. Saravanapriya Journal of Computing and Intelligent Systems 5 (1), 220 - 229 , 2021 2021
Hybrid nature inspired feature selection algorithms for classification on non communicable diseases JB K. Saravanapriya Periyar University Created and maintained by INFLIBNET Centre , 2021 2021
Data Mining Classification Techniques for the Diagnosis of Diabetes Mellitus – A Review JB K. Saravanapriya International Journal of Computational Intelligence and Informatics (IJCII … , 2019 2019
Performance Analysis of Quick Reduct And Multiple Optimal Reduct Algorithm Based on Dependency Ratio for Feature Selection SRJB K. Saravanapriya 2018
Comparative Study of Data Mining Classification Algorithms in Heart Disease Prediction SK Durgadevi. A International Journal of Recent Research in Mathematics Computer Science and … , 2016 2016
