Real-Time AI-Driven Solutions for Smart Parking Systems
Keywords:
AI-Driven Solutions, Smart Parking SystemsAbstract
Smart parking systems have garnered great interest due to the growing urbanization and the necessity for efficient use of limited parking spaces. Traffic congestion and cruising for vacant parking spaces are major issues caused by inadequate parking facilities. Traditionally, parking has been managed and operated manually, leading to several drawbacks such as high operating costs and management difficulties, inefficiency due to lack of parking space information, increased risk of theft or damage to vehicles in underground parking facilities, and ineffective entry and exit management. The scenario change has been aided by technological advancements in various areas such as sensor technology, IoT, real-time artificial intelligence, and big data analysis, incorporating the software. AI applications, research, and subsequent advancements are addressed in the following discussions.
Downloads
References
Tamanampudi, Venkata Mohit. "Automating CI/CD Pipelines with Machine Learning Algorithms: Optimizing Build and Deployment Processes in DevOps Ecosystems." Distributed Learning and Broad Applications in Scientific Research 5 (2019): 810-849.
Pal, Dheeraj Kumar Dukhiram, et al. "AI-Assisted Project Management: Enhancing Decision-Making and Forecasting." Journal of Artificial Intelligence Research 3.2 (2023): 146-171.
Kodete, Chandra Shikhi, et al. "Determining the efficacy of machine learning strategies in quelling cyber security threats: Evidence from selected literatures." Asian Journal of Research in Computer Science 17.8 (2024): 24-33.
Singh, Jaswinder. "The Rise of Synthetic Data: Enhancing AI and Machine Learning Model Training to Address Data Scarcity and Mitigate Privacy Risks." Journal of Artificial Intelligence Research and Applications 1.2 (2021): 292-332.
Alluri, Venkat Rama Raju, et al. "Serverless Computing for DevOps: Practical Use Cases and Performance Analysis." Distributed Learning and Broad Applications in Scientific Research 4 (2018): 158-180.
Machireddy, Jeshwanth Reddy. "Revolutionizing Claims Processing in the Healthcare Industry: The Expanding Role of Automation and AI." Hong Kong Journal of AI and Medicine 2.1 (2022): 10-36.
Tamanampudi, Venkata Mohit. "Autonomous AI Agents for Continuous Deployment Pipelines: Using Machine Learning for Automated Code Testing and Release Management in DevOps." Australian Journal of Machine Learning Research & Applications 3.1 (2023): 557-600.
J. Singh, “How RAG Models are Revolutionizing Question-Answering Systems: Advancing Healthcare, Legal, and Customer Support Domains”, Distrib Learn Broad Appl Sci Res, vol. 5, pp. 850–866, Jul. 2019
S. Kumari, “AI-Enhanced Mobile Platform Optimization: Leveraging Machine Learning for Predictive Maintenance, Performance Tuning, and Security Hardening ”, Cybersecurity & Net. Def. Research, vol. 4, no. 1, pp. 29–49, Aug. 2024
Tamanampudi, Venkata Mohit. "Leveraging Machine Learning for Dynamic Resource Allocation in DevOps: A Scalable Approach to Managing Microservices Architectures." Journal of Science & Technology 1.1 (2020): 709-748.
Downloads
Published
Issue
Section
License
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
License Terms
Ownership and Licensing:
Authors of research papers submitted to Distributed Learning and Broad Applications in Scientific Research retain the copyright of their work while granting the journal certain rights. Authors maintain ownership of the copyright and have granted the journal a right of first publication. Simultaneously, authors agree to license their research papers under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) License.
License Permissions:
Under the CC BY-NC-SA 4.0 License, others are permitted to share and adapt the work, as long as proper attribution is given to the authors and acknowledgement is made of the initial publication in the journal. This license allows for the broad dissemination and utilization of research papers.
Additional Distribution Arrangements:
Authors are free to enter into separate contractual arrangements for the non-exclusive distribution of the journal's published version of the work. This may include posting the work to institutional repositories, publishing it in journals or books, or other forms of dissemination. In such cases, authors are requested to acknowledge the initial publication of the work in this journal.
Online Posting:
Authors are encouraged to share their work online, including in institutional repositories, disciplinary repositories, or on their personal websites. This permission applies both prior to and during the submission process to the journal. Online sharing enhances the visibility and accessibility of the research papers.
Responsibility and Liability:
Authors are responsible for ensuring that their research papers do not infringe upon the copyright, privacy, or other rights of any third party. Scientific Research Canada disclaims any liability or responsibility for any copyright infringement or violation of third-party rights in the research papers.
If you have any questions or concerns regarding these license terms, please contact us at editor@dlabi.org.
