Machine intelligence security : A methodological blend of fuzzy logic in industry 4.0 algorithms
P. Srilathapulipatisrilatha_aids@cbit.ac.inDepartment of Artificial Intelligence and Data Science Chaitanya Bharathi Institute of TechnologyHyderabad, Telangana, IndiaView full profile → , *Shailesh P. BendaleCorresponding authorbendale.shailesh@gmail.comDepartment of Computer Engineering NBN Sinhgad School of EngineeringPune, Maharashtra, IndiaView full profile → , Monisha Singhmonisha.singh@christuniversity.inSchool of Sciences Christ (Deemed to be University)Department of Statistics and Data Science Christ (Deemed to be University)Bengaluru, Karnataka, 560029, IndiaView full profile → , Nitin Chakolenitinbchakole@gmail.comDepartment of Electronics and Communication Engineering Shri Ramdeobaba College of Engineering and ManagementNagpur, Maharashtra, 440013, IndiaView full profile → , Girish M. Dhotegirishdhote@gmail.comDepartment of Mechanical Engineering Yeshwantrao Chavan of EngineeringNagpur, Maharashtra, IndiaView full profile → , Nilesh Shelkenilesh.shelke@sitnagpur.siu.edu.inDepartment of Computer Science & Engineering Symbiosis Institute of Technology, Nagpur Campus Symbiosis International (Deemed University)Symbiosis Institute of Technology Nagpur Campus Symbiosis International Deemed UniversityPune, Maharashtra, 440008, IndiaView full profile →
* Corresponding author · click or hover a name for details
- Published Online:
- 11 Apr 2024
- Article type:
- Research Article
- Language:
- EN
- Article no.:
- JDMSC-1920
- Pages:
- 689–701
Abstract
Keywords
Subject Classifications
References
[1] S. R. Shetty and M. D H, “Multi-Layer Perceptron Based Fuzzy Logic Technique for Detection of Attacks in VANETS,” 2022 IEEE Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI), Gwalior, India, pp. 1-5 (2022), doi: 10.1109/IATMSI56455.2022.10119355.
[2] J. R. -M. I. Senior, V. Medina-Rodríguez, R. E. Quiñonez-Moreno and J. V. -P. I. Member, “PV Fuzzy Model Based on V-I Curves to be Implemented in an Intelligent Sensor,” 2022 IEEE Green Technologies Conference (GreenTech), Houston, TX, USA, pp. 74-79 (2022), doi: 10.1109/GreenTech52845.2022.9772031.
[3] Fath, ArefHashemi, FarshidMadanifar and MasoodAbbasi, “Implementation of multilayer perceptron (MLP) and radial basis function (RBF) neural networks to predict solution gas-oil ratio of crude oil systems”, Petroleum, vol. 6, no. 1, pp. 80-91 (2020).
[4] Khalil Moshkbar-Bakhshayesh and Mohammad B. Ghofrani, “Development of an efficient identifier for nuclear power plant transients based on latest advances of error back -propagation learning algorithm”, IEEE Transactions on Nuclear Science, vol. 61, no. 1, pp. 602-610 (2014).
[5] Fausett and V. Laurene, “Fundamentals of neural networks: architectures algorithms and applications”, Pearson Education India (2006).
[6] DaeinabiAmeneh, Akbar Ghaffar Pour Rahbar and Ahmad Khademzadeh, “VWCA: An efficient clustering algorithm in vehicular ad hoc networks”, Journal of Network and Computer Applications, vol. 34, no. 1, pp. 207-222 (2011).
[7] Ajani, S. N., Khobragade, P., Dhone, M., Ganguly, B., Shelke, N., & Parati, N. Advancements in Computing: Emerging Trends in Computational Science with Next-Generation Computing. International Journal of Intelligent Systems and Applications in Engineering, 12(7s), 546–559 (2023).
[8] Sharma Sparsh and Ajay Kaul, “Hybrid fuzzy multi-criteria decision making based multi cluster head dolphin swarm optimized IDS for VANET”, Vehicular Communications, vol. 12, pp. 23-28 (2018).
[9] O. Elijah et al., “A Survey on Industry 4.0 for the Oil and Gas Industry: Upstream Sector,” in IEEE Access, vol. 9, pp. 144438-144468 (2021), doi: 10.1109/ACCESS.2021.3121302.
[10] S. Singh, B. Yadav and R. Batheri, “Industry 4.0: Meeting the Challenges of Demand Sensing in the Automotive Industry,” in IEEE Engineering Management Review, vol. 51, no. 4, pp. 179-184, Fourthquarter, Dec. (2023), doi: 10.1109/EMR.2023.3292331.
[11] I. Ahmed, G. Jeon and F. Piccialli, “From Artificial Intelligence to Explainable Artificial Intelligence in Industry 4.0: A Survey on What, How, and Where,” in IEEE Transactions on Industrial Informatics, vol. 18, no. 8, pp. 5031-5042, Aug. (2022), doi: 10.1109/TII.2022.3146552.
[12] S. K. Jagatheesaperumal, M. Rahouti, K. Ahmad, A. Al-Fuqaha and M. Guizani, “The Duo of Artificial Intelligence and Big Data for Industry 4.0: Applications, Techniques, Challenges, and Future Research Directions,” in IEEE Internet of Things Journal, vol. 9, no. 15, pp. 12861-12885, 1 Aug. 1 (2022), doi: 10.1109/JIOT.2021.3139827.
[13] R. S. Peres, X. Jia, J. Lee, K. Sun, A. W. Colombo and J. Barata, “Industrial Artificial Intelligence in Industry 4.0 - Systematic Review, Challenges and Outlook,” in IEEE Access, vol. 8, pp. 220121-220139 (2020), doi: 10.1109/ACCESS.2020.3042874.
[14] R. S. Peres, X. Jia, J. Lee, K. Sun, A. W. Colombo and J. Barata, “Industrial Artificial Intelligence in Industry 4.0 - Systematic Review, Challenges and Outlook,” in IEEE Access, vol. 8, pp. 220121-220139 (2020), doi: 10.1109/ACCESS.2020.3042874.
