Computer Vision-Based Asset Management in Project Management: Using Machine Learning for Real-Time Resource Tracking
Published 01-12-2023
Keywords
- Computer Vision,
- Machine Learning
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
How to Cite
Abstract
Abstract
The increasing complexity of project management necessitates the adoption of innovative technologies to streamline processes and enhance decision-making. This paper explores the application of computer vision in tracking physical assets within project management, emphasizing how machine learning models can provide real-time insights into resource allocation. The study discusses the integration of computer vision with machine learning techniques to develop a robust asset management framework. By employing image recognition algorithms and data analytics, organizations can effectively monitor asset utilization, predict potential shortages, and optimize resource allocation. Additionally, the paper highlights several case studies demonstrating the practical implementation of these technologies in diverse project management environments. The findings suggest that computer vision-based asset management not only improves operational efficiency but also contributes to significant cost savings, thereby positioning organizations to succeed in an increasingly competitive landscape.
Downloads
References
- Gayam, Swaroop Reddy. "Deep Learning for Predictive Maintenance: Advanced Techniques for Fault Detection, Prognostics, and Maintenance Scheduling in Industrial Systems." Journal of Deep Learning in Genomic Data Analysis 2.1 (2022): 53-85.
- George, Jabin Geevarghese. "Advancing Enterprise Architecture for Post-Merger Financial Systems Integration in Capital Markets laying the Foundation for Machine Learning Application." Australian Journal of Machine Learning Research & Applications 3.2 (2023): 429-475.
- Yellepeddi, Sai Manoj, et al. "AI-Powered Intrusion Detection Systems: Real-World Performance Analysis." Journal of AI-Assisted Scientific Discovery 4.1 (2024): 279-289.
- Nimmagadda, Venkata Siva Prakash. "Artificial Intelligence for Supply Chain Visibility and Transparency in Retail: Advanced Techniques, Models, and Real-World Case Studies." Journal of Machine Learning in Pharmaceutical Research 3.1 (2023): 87-120.
- Putha, Sudharshan. "AI-Driven Predictive Maintenance for Smart Manufacturing: Enhancing Equipment Reliability and Reducing Downtime." Journal of Deep Learning in Genomic Data Analysis 2.1 (2022): 160-203.
- Sahu, Mohit Kumar. "Advanced AI Techniques for Predictive Maintenance in Autonomous Vehicles: Enhancing Reliability and Safety." Journal of AI in Healthcare and Medicine 2.1 (2022): 263-304.
- Kondapaka, Krishna Kanth. "AI-Driven Predictive Maintenance for Insured Assets: Advanced Techniques, Applications, and Real-World Case Studies." Journal of AI in Healthcare and Medicine 1.2 (2021): 146-187.
- Kasaraneni, Ramana Kumar. "AI-Enhanced Telematics Systems for Fleet Management: Optimizing Route Planning and Resource Allocation." Journal of AI in Healthcare and Medicine 1.2 (2021): 187-222.
- Pattyam, Sandeep Pushyamitra. "Artificial Intelligence in Cybersecurity: Advanced Methods for Threat Detection, Risk Assessment, and Incident Response." Journal of AI in Healthcare and Medicine 1.2 (2021): 83-108.
- Alluri, Venkat Rama Raju, et al. "Automated Testing Strategies for Microservices: A DevOps Approach." Distributed Learning and Broad Applications in Scientific Research 4 (2018): 101-121.
- M. Abadi et al., "TensorFlow: A system for large-scale machine learning," in Proceedings of the 12th USENIX Symposium on Operating Systems Design and Implementation (OSDI 16), 2016, pp. 265-283.
- Y. Zhang and Q. Yang, "A survey on multi-task learning," IEEE Transactions on Knowledge and Data Engineering, vol. 34, no. 12, pp. 5586-5609, Dec. 2022.
- Y. Wang, Q. Chen, and W. Zhu, "Zero-shot learning: A comprehensive review," IEEE Transactions on Neural Networks and Learning Systems, vol. 30, no. 7, pp. 2172-2188, Jul. 2019.
- D. Bahdanau, K. Cho, and Y. Bengio, "Neural machine translation by jointly learning to align and translate," in Proceedings of the 3rd International Conference on Learning Representations (ICLR), 2015.
- M. I. Jordan and T. M. Mitchell, "Machine learning: Trends, perspectives, and prospects," Science, vol. 349, no. 6245, pp. 255-260, 2015.
- J. Devlin, M. W. Chang, K. Lee, and K. Toutanova, "BERT: Pre-training of deep bidirectional transformers for language understanding," in Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, 2019, pp. 4171-4186.
- A. Vaswani et al., "Attention is all you need," in Proceedings of the 31st International Conference on Neural Information Processing Systems (NeurIPS), 2017, pp. 5998-6008.
- R. Collobert and J. Weston, "A unified architecture for natural language processing: Deep neural networks with multitask learning," in Proceedings of the 25th International Conference on Machine Learning, 2008, pp. 160-167.