Advanced Computational Intelligence Techniques for Real-Time Decision-Making in Autonomous Systems

Authors

  • S.D.Govardhan Dhanalakshmi Srinivasan College of Engineering & Technology,
  • R Pushpavalli Paavai Engineering College
  • Tatiraju.V.Rajani Kanth Senior Manager,TVR Consulting Services Private Limited
  • Ponmurugan Panneer Selvam Meenakshi Academy of Higher Education & Research (Deemed to be University),

DOI:

https://doi.org/10.22399/ijcesen.591

Keywords:

Computational Intelligence, Real-Time Decision-Making, Deep Neural Networks, Healthcare Monitoring, Robotic Process Automation

Abstract

This research explores advanced computational intelligence techniques aimed at enhancing real-time decision-making in autonomous systems. The increasing reliance on autonomous technologies across sectors such as transportation, healthcare, and industrial automation demands robust, adaptive, and reliable decision-making frameworks. This study introduces a novel hybrid model that integrates Reinforcement Learning (RL), Deep Neural Networks (DNN), and Fuzzy Logic to enable autonomous systems to make accurate and timely decisions in complex, dynamic environments. The proposed framework leverages RL for adaptive decision-making, DNNs for pattern recognition and prediction, and Fuzzy Logic for handling uncertainty in system states. Experimental evaluations were conducted using high-fidelity simulations across three scenarios: autonomous vehicle navigation, real-time patient monitoring in healthcare, and robotic process automation. Results indicate a 25% improvement in decision accuracy, a 30% reduction in response time, and enhanced robustness against environmental variability compared to conventional decision-making methods. The findings underscore the effectiveness of computational intelligence in supporting critical decisions in real-time, marking a significant step toward more capable and reliable autonomous systems.

References

Gao, Lixin. (2021). On inferring autonomous system relationships in the Internet. IEEE/ACM Transactions on networking 9(6);733-745.

Magoni, D., & Pansiot, J. J. (2001). Analysis of the autonomous system network topology. ACM SIGCOMM Computer Communication Review, 31(3);26-37.

Rai, S., Mukherjee, B., & Deshpande, O. (2005). IP resilience within an autonomous system: Current approaches, challenges, and future directions. IEEE Communications Magazine, 43(10);142-149.

Antsaklis, P. J., Passino, K. M., & Wang, S. J. (1991). An introduction to autonomous control systems. IEEE Control Systems Magazine, 11(4);5-13.

Kammel, S., Ziegler, J., Pitzer, B., Werling, M., Gindele, T., Jagzent, D., ... & Stiller, C. (2008). Team AnnieWAY's autonomous system for the 2007 DARPA Urban Challenge. Journal of Field Robotics, 25(9);615-639.

Kanan, R., Elhassan, O., & Bensalem, R. (2018). An IoT-based autonomous system for workers' safety in construction sites with real-time alarming, monitoring, and positioning strategies. Automation in Construction, 88, 73-86.

Karlin, J., Forrest, S., & Rexford, J. (2008). Autonomous security for autonomous systems. Computer Networks, 52(15);2908-2923.

Dimitropoulos, X., & Riley, G. (2006, May). Modeling autonomous-system relationships. In 20th Workshop on Principles of Advanced and Distributed Simulation (PADS'06) (pp. 143-149). IEEE.

Saleem, M., Khadim, A., Fatima, M., Khan, M. A., Nair, H. K., & Asif, M. (2022, October). ASSMA-SLM: Autonomous System for Smart Motor-Vehicles integrating Artificial and Soft Learning Mechanisms. In 2022 International Conference on Cyber Resilience (ICCR) (pp. 1-6). IEEE.

Conway, L., Volz, R., & Walker, M. (1987, March). Tele-autonomous systems: Methods and architectures for intermingling autonomous and telerobotic technology. In Proceedings. 1987 IEEE International Conference on Robotics and Automation (Vol. 4, pp. 1121-1130). IEEE.

Jo, K., Kim, J., Kim, D., Jang, C., & Sunwoo, M. (2014). Development of autonomous car—Part I: Distributed system architecture and development process. IEEE Transactions on Industrial Electronics, 61(12), 7131-7140.

Bakambu, J. N., & Polotski, V. (2007). Autonomous system for navigation and surveying in underground mines. Journal of Field Robotics, 24(10), 829-847.

Jo, K., Kim, J., Kim, D., Jang, C., & Sunwoo, M. (2015). Development of autonomous car—Part II: A case study on the implementation of an autonomous driving system based on distributed architecture. IEEE Transactions on Industrial Electronics, 62(8), 5119-5132.

Hadi, G. S., Varianto, R., Trilaksono, B. R., & Budiyono, A. (2014). Autonomous UAV system development for payload dropping mission. Journal of Instrumentation, Automation and Systems, 1(2), 72-77.

Dimitropoulos, X., Krioukov, D., & Riley, G. (2006). Revealing the autonomous system taxonomy: The machine learning approach. arXiv preprint cs/0604015.

Chedid, R., & Saliba, Y. (1996). Optimization and control of autonomous renewable energy systems. International journal of energy research, 20(7), 609-624.

Zhu, X., Chikangaise, P., Shi, W., Chen, W. H., & Yuan, S. (2018). Review of intelligent sprinkler irrigation technologies for remote autonomous system. International Journal of Agricultural & Biological Engineering, 11(1);23-30. DOI: 10.25165/IJABE.V11I1.3557

Maheshwari, R. U., Jayasutha, D., Senthilraja, R., & Thanappan, S. (2024). Development of Digital Twin Technology in Hydraulics Based on Simulating and Enhancing System Performance. Journal of Cybersecurity & Information Management, 13(2):50-65 DOI: 10.54216/JCIM.130204

Paulchamy, B., Uma Maheshwari, R., Sudarvizhi AP, D., Anandkumar AP, R., & Ravi, G. (2023). Optimized Feature Selection Techniques for Classifying Electrocorticography Signals. Brain‐Computer Interface: Using Deep Learning Applications, 255-278.

Paulchamy, B., Chidambaram, S., Jaya, J., & Maheshwari, R. U. (2021). Diagnosis of Retinal Disease Using Retinal Blood Vessel Extraction. In International Conference on Mobile Computing and Sustainable Informatics: ICMCSI 2020 (pp. 343-359). Springer International Publishing.

Maheshwari, U. Silingam, K. (2020). Multimodal Image Fusion in Biometric Authentication. Fusion: Practice and Applications, 79-91. DOI: https://doi.org/10.54216/FPA.010203

R.Uma Maheshwari (2021). encryption and decryption using image processing techniques. International Journal of Engineering Applied Sciences and Technology, 5(12);219-222 DOI: 10.33564/IJEAST.2021.v05i12.037

Priti Parag Gaikwad, & Mithra Venkatesan. (2024). KWHO-CNN: A Hybrid Metaheuristic Algorithm Based Optimzed Attention-Driven CNN for Automatic Clinical Depression Recognition . International Journal of Computational and Experimental Science and Engineering, 10(3)491-506. https://doi.org/10.22399/ijcesen.359

Rakesh Jha, & Singh, M. K. (2024). Analysing the Impact of Social Influence on Electric Vehicle Adoption: A Deep Learning-Based Simulation Study in Jharkhand, India. International Journal of Computational and Experimental Science and Engineering, 10(4);639-646. https://doi.org/10.22399/ijcesen.371

P, P., P, D., R, V., A, Y., & Natarajan, V. P. (2024). Chronic Lower Respiratory Diseases detection based on Deep Recursive Convolutional Neural Network . International Journal of Computational and Experimental Science and Engineering, 10(4);744-752. https://doi.org/10.22399/ijcesen.513

PATHAPATI, S., N. J. NALINI, & Mahesh GADIRAJU. (2024). Comparative Evaluation of EEG signals for Mild Cognitive Impairment using Scalograms and Spectrograms with Deep Learning Models. International Journal of Computational and Experimental Science and Engineering, 10(4);859-866. https://doi.org/10.22399/ijcesen.534

Rama Lakshmi BOYAPATI, & Radhika YALAVAR. (2024). RESNET-53 for Extraction of Alzheimer’s Features Using Enhanced Learning Models. International Journal of Computational and Experimental Science and Engineering, 10(4)879-889. https://doi.org/10.22399/ijcesen.519

M, V., V, J., K, A., Kalakoti, G., & Nithila, E. (2024). Explainable AI for Transparent MRI Segmentation: Deep Learning and Visual Attribution in Clinical Decision Support. International Journal of Computational and Experimental Science and Engineering, 10(4)575-584. https://doi.org/10.22399/ijcesen.479

Venkatraman Umbalacheri Ramasamy. (2024). Overview of Anomaly Detection Techniques across Different Domains: A Systematic Review. International Journal of Computational and Experimental Science and Engineering, 10(4);898-910. https://doi.org/10.22399/ijcesen.522

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Published

2024-11-13

How to Cite

S.D.Govardhan, Pushpavalli, R., Tatiraju.V.Rajani Kanth, & Ponmurugan Panneer Selvam. (2024). Advanced Computational Intelligence Techniques for Real-Time Decision-Making in Autonomous Systems. International Journal of Computational and Experimental Science and Engineering, 10(4). https://doi.org/10.22399/ijcesen.591

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Section

Research Article