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Stream/Area:
-
Subject:
-
Paper ID:
IJSREM61948
Volume:
10 |
4 |
April 2026
Pages:
1-9
Publication Frequency:
Monthly (12 issues per year)
License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.
Department of Computer Science and Engineering
Sri Shakthi Institute of Engineering and Technology, India
Department of Computer Science and Engineering
Sri Shakthi Institute of Engineering and Technology, India
Department of Computer Science and Engineering
Sri Shakthi Institute of Engineering and Technology, India
The ProConnect Platform is an intelligent web- based collaboration system designed to connect clients and professionals for efficient project management and service delivery. It enables users to create profiles, post projects, and collaborate within a structured and secure environment using modern technologies such as React for the frontend, Node.js and Express for backend services, and MongoDB for scalable data management. The platform supports a role-based system where clients can publish requirements and professionals can explore opportunities, apply for projects, and communicate seamlessly through dynamic dashboards and real- time updates. It also incorporates secure authentication mechanisms to ensure data protection and user privacy. Additionally, ProConnect offers features such as project tracking, user activity management, and performance monitoring, allowing users to view project histories, manage ongoing tasks, and analyze their engagement through a centralized interface. With its user-friendly design and scalable architecture, the platform transforms traditional project handling into a streamlined digital experience, enhancing productivity, transparency, and collaboration while enabling users to build professional connections and achieve better project outcomes.
KEYWORDS
The ProConnect platform successfully demonstrates the implementation of an AI-powered freelancer booking system that enhances collaboration between clients and freelancers. By integrating intelligent profile matching, the platform simplifies the process of identifying suitable candidates based on skills, experience, and project requirements. The inclusion of a structured booking system and real-time project tracking ensures transparency and effective communication throughout the project lifecycle. Additionally, the fraud detection mechanism strengthens platform security by identifying suspicious activities and maintaining a trustworthy environment for users. Overall, the system combines modern web technologies with AI-driven features to deliver a scalable, secure, and user-friendly solution for digital collaboration. The platform not only reduces manual effort but also improves efficiency, accuracy, and user satisfaction. With its modular architecture and adaptability, ProConnect has the potential for future enhancements such as advanced AI models, payment integration, and real-time communication features, making it a reliable and future-ready solution for freelancer-client interaction.As more users interact with the system, the AI models can be refined to provide even more accurate recommendations and stronger fraud detection capabilities. The flexibility of the architecture allows seamless integration of additional features such as automated communication tools, advanced analytics, and enhanced user verification methods.
1. Baltador, L. A., Nancy, P., & Mihaela, R. (2025). Mapping the Freelance Economy: A Cross-Platform Study of Workforce Characteristics, Challenges, and Emerging Trends. Studies in Business and Economics, 20(3), 54-75. 2. Ricci, F., Rokach, L., & Shapira, B. (2022). Recommender Systems Handbook. Springer, 35(2), 1–100. 3. Aggarwal, C. C. (2023). RecommenderSystems: The Textbook. Springer, 20(1), 200–350. Available: https://link.springer.com 4. Koren, Y., Bell, R., & Volinsky, C. (2022). Matrix Factorization Techniques for Recommender Systems. IEEE Computer, 42(8), 30–37. Available: https://ieeexplore.ieee.org 5. Chandola, V., Banerjee, A., & Kumar, V. (2022). Anomaly Detection: A Survey. ACM Computing Surveys, 41(3), 1–58. Available: https://dl.acm.org 6. Phua, C., Lee, V., Smith, K., & Gayler, R. (2023). A Comprehensive Survey of Data Mining-based Fraud Detection. arXiv. Available: https://arxiv.org 7. Resnick, P., & Varian, H. (2022). Recommender Systems. Communications of the ACM, 40(3), 56–58. Available: https://dl.acm.org 8. Zheng, Z., Xie, X., Dai, Q., Chen, X., & Ma, W. (2022). Service Recommendation in Online Platforms. IEEE Transactions on Services Computing, 10(2), 100–120. Available: https://ieeexplore.ieee.org 9. Lops, P., Gemmis, M., & Semeraro, G. (2022). Content-based Recommender Systems: State of the Art and Trends. Springer, 28(1), 73–105. Available: https://link.springer.com 10. Bolton, R. J., & Hand, D. J. (2022). Statistical Fraud Detection: A Review. Statistical Science, 17(3), 235–255. Available: https://projecteuclid.org
Publication Date
4/30/2026
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
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The authors declare no conflict of interest related to this research work.
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