R. O. Osaseri

JSON APIs are fundamental to modern web and mobile applications which enables seemless communication between client/server applications as well as other integrated systems that enhances a robust usability of an application.The Course Registration Portal is one of such system used to perform reliable, and scalable course registration for students in universities.
Over the years, the use of manual methods which basically, is paper-driven and labour intensive have been time-consuming and subsceptible to errors and misplacement.This project emphasizes on a more automated method for course registration by students and staffs alike.Most universities, across Nigeria have adopted the computerized systemic approach for her academic administrations and this approach birthed systems like the Uniben Portal, the WAeUP Kofa Student Management System (SMS), Computer-Based Tests(CBT) and so on.These automated systems have revolutionized the operations of academic responsibilities by University stakeholders. While putting into consideration the constraints and scope of this research, which is the University of Benin, department of Computer Science, the proposed method for this project is a student course registration portal for the Faculty of Computing, University of Benin. In an attempt to prevent or alleviate unpleasant impromptu behavior in performance of web applications, this project focuses on leveraging Chrome developer tools (DevTools) to analyze, identify and resolve performance bottlenecks that affect responsiveness, reliability and user experience of webapps.The use of the available built-in features of DevTools assists developers and system managers to be able to optimize the API performance on the web portal, and prevent
perceived inefficiency in API usage.However, this project focuses on the optimal performance of the underlying JSON API that
powers the interactions of the proposed automated system whereby, students and staffs of the department can use the system to register courses offered in each level as well as, download the courses registered and manage course administration respectively and the application would display an effective implementation of the whole process through a clean, secure, and
standardized HTTP-based interface(using Node.js), serializes internal objects or data structure into JSON responses using dedicated adapters or serializers, taking advantage of chrome devtools optimization procedure to test API behavior when a particular user sends a request through the client‘s interface to the backend server/database, employ field filtering and
pagination to reduce perceived outrageous payload sizes, implement caching headers or cachecontrol headers to retrieve organized JSON responses to users or the client‘s interface, eliminates or avoids unnecessary duplicate API calls caused by network issues or server errors, minimizes data transfer sizes and be able maintain compatibility with university trends

Color detection is a task that humans perform effortlessly; however, enabling computers to accurately identify colors remains a challenging problem. In many industries, traditional color recognition systems rely heavily on manual processes and paid labor for color-coding items or datasets, which are often time-consuming, repetitive, and proneto human error. To address these limitations, this project presents a deep learning–based color detection program capable of recognizing multiple colors in real time. The system is implemented using Python, a high-level general-purpose programming language, in conjunction with the Open Source Computer Vision Library (OpenCV). By leveraging deep learning techniques, the proposed solution enhances accuracy and efficiency in automated color recognition tasks. The developed system enables computer devices to detect and classify multiple colors in real time, making it suitable for applications across various industries, including pharmaceutical manufacturing, autonomous vehicle development, and robotics. The adoption of this system can significantly reduce production time, minimize reliance on manual labor, and lower operational costs while improving overall productivity

Color detection is a simple task for humans, but for computers it is not easy. In any industry, individual effort has to be implemented when a computer is dealing with colors. Previous system has primarily relied on paid labor input and manually color-coding items or any given data which most of the time could be monotonous and painstaking. Hence, this project developed a deep learning mechanism program for detection of multiple color in real-time using Python which is a high-level general-purpose programming language and Open-Source Computer Library (OpenCV). The Proposed system provides any computer device the ability to recognize multiple colors in real-time which can be useful in various industries such as big pharma, self-driving vehicle manufacturing companies and robotics which will reduce production time and significantly cut down paid labor expenses