Advanced Computational Intelligence Techniques for Real-Time Decision-Making in Autonomous Systems
DOI:
https://doi.org/10.22399/ijcesen.591Keywords:
Computational Intelligence, Real-Time Decision-Making, Deep Neural Networks, Healthcare Monitoring, Robotic Process AutomationAbstract
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. DOI: https://doi.org/10.1109/90.974527
Magoni, D., & Pansiot, J. J. (2001). Analysis of the autonomous system network topology. ACM SIGCOMM Computer Communication Review, 31(3);26-37. DOI: https://doi.org/10.1145/505659.505663
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. DOI: https://doi.org/10.1109/MCOM.2005.1522138
Antsaklis, P. J., Passino, K. M., & Wang, S. J. (1991). An introduction to autonomous control systems. IEEE Control Systems Magazine, 11(4);5-13. DOI: https://doi.org/10.1109/37.88585
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. DOI: https://doi.org/10.1002/rob.20252
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. DOI: https://doi.org/10.1016/j.autcon.2017.12.033
Karlin, J., Forrest, S., & Rexford, J. (2008). Autonomous security for autonomous systems. Computer Networks, 52(15);2908-2923. DOI: https://doi.org/10.1016/j.comnet.2008.06.012
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. DOI: https://doi.org/10.1109/PADS.2006.26
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. DOI: https://doi.org/10.1109/ICCR56254.2022.9995824
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. DOI: https://doi.org/10.1109/ROBOT.1987.1087923
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. DOI: https://doi.org/10.1109/TIE.2014.2321342
Bakambu, J. N., & Polotski, V. (2007). Autonomous system for navigation and surveying in underground mines. Journal of Field Robotics, 24(10), 829-847. DOI: https://doi.org/10.1002/rob.20213
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. DOI: https://doi.org/10.1109/TIE.2015.2410258
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. DOI: https://doi.org/10.21535/jias.v1i2.158
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. DOI: https://doi.org/10.1002/(SICI)1099-114X(199607)20:7<609::AID-ER176>3.0.CO;2-O
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 DOI: https://doi.org/10.25165/j.ijabe.20181101.3557
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 DOI: https://doi.org/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. DOI: https://doi.org/10.1002/9781119857655.ch11
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. DOI: https://doi.org/10.1007/978-3-030-49795-8_34
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 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 DOI: https://doi.org/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 DOI: 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 DOI: 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 DOI: 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 DOI: 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 DOI: 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 DOI: 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 DOI: https://doi.org/10.22399/ijcesen.522
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2024 International Journal of Computational and Experimental Science and Engineering
This work is licensed under a Creative Commons Attribution 4.0 International License.
