D.N. IDEHEN

The Students Industrial Work Experience Scheme (SIWES) serves as a vital bridge between academic learning and practical industry exposure for undergraduate students. Despite its importance, students of the University of Benin often encounter significant challenges in securing suitable Industrial Training (IT) placements, largely due to limited access to reliable information on available opportunities, organizational requirements, and appropriate application procedures. This project presents the design of a web-based Industrial Training Placement System aimed at enhancing and streamlining the placement process for UNIBEN students.
The proposed system provides a centralized platform where students can access verified IT compliant organizations, review key information such as required skills, available roles, stipends, and organizational expectations, and submit placement applications conveniently. The Software Development Life Cycle (SDLC) methodology was adopted, guiding the structured development
process through requirements analysis, system modeling, interface design, and database structuring. The system is expected to significantly reduce the stress associated with manual placement searches, improve access to authentic placement information, and enhance the alignment between students’ areas of study and available industry opportunities. Future enhancements may
include integration with institutional systems and expansion into a mobile application for broader accessibility.

Attendance tracking in classes is a very important activity in any institution, and taking attendance of students using facial recognition is a more efficient and accurate method than the traditional methods which includes paper- based and roll call methods. The facial recognition system is an application of computer vision that can perform two major tasks of identifying and verifying a person from a given database. Facial recognition proves to be more effective in taking attendance than the traditional method, which is inaccurate, time- consuming and vulnerable in most cases of large class environments. This system is designed with a login page for authentication, it also provides a mailing platform where the attendance will be sent to the school authority for record keeping. This study develops a deep-learning-based facial recognition system used to detect the face of students in a class environment for the sole purpose of taking their attendance records.

Cloud computing has revolutionized the way resources are allocated and utilized in the IT
industry. Efficient resource allocation is crucial for optimizing cost, performance, and reliability in cloud environments. This project aims to explore the various resource allocation strategies, challenges, and optimization techniques in the context of cloud computing. We will analyze different cloud service models and deployment models, and evaluate their impact on resource allocation. Additionally, we will develop a resource allocation algorithm and conduct experiments to assess its performance.