Abstract
A major step forward in pharmaceutical production has been the merging of Artificial Intelligence (AI) with the Internet of Things (IoTs), which successfully connects the digital and physical realms. Improving the production process and quality control through the integration of AI algorithms into IoT sensors leads to higher overall efficiency. Digital Twins (DT) are made possible by the creation and implementation of new Industry 4.0 technologies, which help to make manufacturing smarter and more agile. DTs are digital representations of real-world systems that mimic their operation and dynamics. Complete DT implementation in pharmaceutical production has not yet occurred, despite the pharmaceutical sector is currently undergoing a digital transformation to incorporate industry 4.0 and has adopted Quality-by-Design (QbD) programs. Therefore, it is crucial to assess the pharmaceutical industry's progress in implementing DT solutions. Data obtained from digital twins can also provide a full image of a product or process's lifetime, which can help with supply chain management, product quality control, and workflow optimization for manufacturing individual parts. This research proposes a Digital Twin enabled Data Pattern Analysis with Diminutive Difference Trigger using Probabilistic Regression (DT-DPA-DDT-PR) model for accurate monitoring of pharmaceutical production operations. The proposed model when contrasted with traditional methods performs better in pharmaceutical production monitoring.
References
Dave, Akashbhai. "Intelligent Resource Management and Secure Live Migration in Cloud Environments: A Unified Approach using Particle Swarm Optimization, Machine Learning, and Blockchain on XenServer." Journal of Applied Science and Technology Trends 6, no. 2 (2025): 393-407.
Haris, Raseena M., Mahmoud Barhamgi, Ahmed Badawy, Armstrong Nhlabatsi, and Khaled M. Khan. "Enhancing Security and Performance in Live VM Migration: A Machine Learning‐Driven Framework with Selective Encryption for Enhanced Security and Performance in Cloud Computing Environments." Expert Systems 42, no. 2 (2025): e13823.
Ramesh, Jayroop, Zahra Solatidehkordi, Khaled El-Fakih, and Raafat Aburukba. "Minimizing Virtual Machine Live Migration Latency for Proactive Fault Tolerance Using an ILP Model With Hybrid Genetic and Simulated Annealing Algorithms." IEEE Access (2024).
Haris, Raseena M., Khaled M. Khan, Armstrong Nhlabatsi, and Mahmoud Barhamgi. "A Machine Learning-Based Optimization Approach for Pre-copy Live Virtual Machine Migration." Cluster Computing 27, no. 2 (2024): 1293-1312.
Gupta, Ambika, Suyel Namasudra, and Prabhat Kumar. "A Secure VM Live Migration Technique in a Cloud Computing Environment Using Blowfish and Blockchain Technology." The Journal of Supercomputing 80, no. 19 (2024): 27370-27393.
Zolfaghari, Rahmat. "Energy-Performance Aware Virtual Machines Migration In Cloud Network by Using Prediction and Fuzzy Approaches." Engineering Applications of Artificial Intelligence 131 (2024): 107825.
Kavitha, Thummuluru, and Thatimakula Sudha. "Dynamic Multi-Objective Framework for Migrating Live Virtual Machines in the Cloud." GAMANAM: Global Advances in Multidisciplinary Applications in Next-Gen And Modern Technologies 1, no. 3 (2025): 172-182.
Alubaidan, Haya A., and Sumayh S. Aljameel. "A Prediction Model for Improving Virtual Machine Live Migration Performance in Cloud Computing Using Artificial Intelligence Techniques." International Journal of Computers and Applications 46, no. 12 (2024): 1069-1087.
Singh, Mandeep, Gurpreet Singh Panesar, and Sanjay Taneja. "Optimization, and Future Prospects of Live Virtual Machine Migration in Cloud Computing: A Comprehensive Survey on Techniques, Challenges, and Emerging Directions." In 2025 3rd International Conference on Artificial Intelligence and Machine Learning Applications Theme: Healthcare and Internet of Things (AIMLA), IEEE, 2025, 1-6.
Zhu, Xinying, Ran Xia, Hang Zhou, Shuo Zhou, and Haoran Liu. "An Intelligent Decision System for Virtual Machine Migration Based on Specific Q-Learning." Journal of Cloud Computing 13, no. 1 (2024): 122.
Haris, Raseena M., Mahmoud Barhamgi, Armstrong Nhlabatsi, and Khaled M. Khan. "Optimizing Pre-copy Live Virtual Machine Migration in Cloud Computing Using Machine Learning-Based Prediction Model." Computing 106, no. 9 (2024): 3031-3062.
Wang, Guikun, Bin Wen, Jingtao He, and Qingbin Meng. "A New Approach to Reduce Energy Consumption in Priority Live Migration of Services Based on Green Cloud Computing." Cluster Computing 28, no. 3 (2025): 207