Browsing by Author "Lanre Joseph Olatomiwa"

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    Deployment of an Electronic-based Approach for Fruits Juice Ingredient Analysis
    (International Conference on Science, Engineering and Business for Driving Sustainable Development Goals (SEB4SDG) Landmark University, 2024-04-04) Kufre Esenowo Jack; Lanre Joseph Olatomiwa; Yahaya Asizehi Enesi; Grace Idowu Olaleru; Nnaemeka Emmanuel Ugwuogor; Babawuya Alkali
    This paper considers the deployment of an electronic-based fruits juice ingredient analysis. Most of the fruits juice products available in the market now contain water in large quantities than the active ingredients. This design attempts to respond to the end user's complaints. By putting on this design, it is expected to serve as a quality check and control for our teeming enterprising fruit substance producers. The system was designed and simulated using proteus and implemented using hardware electronic components. The system uses an infrared transmitter, fruit sample handler, and infrared receiver to realize its design. The instrument was calibrated with natural pineapple juice with 60% of water content. The outputs of this device were displayed using a cathode ray oscilloscope and voltmeter respectively. Five different samples of fruit juice were analyzed namely: A, B, C, D, and E. Results showed that all fruit juice contains a reasonable quantity of water which is not regarded as an adulteration since it is the natural content of the fruit. However, water content above 60% may be considered as much. It is recommended that fruit juice producers employ this system for their quality and control checks. Moreover, further research should take into consideration, the colour and viscosity of different fruit juices with a view to seeing how the system can analyze them, while the output should incorporate a microcontroller for an intelligent analysis and digital display.
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    Development of an artificial intelligent agent for library hard materials distribution operations
    (AFIT Journal of Science and Engineering Research, 2024) Kufre Esenewo Jack; Waheed Moses Audu; Lanre Joseph Olatomiwa; Umar Suleiman Dauda; Bello Kontagora Nuhu; Oyewobi S. Stephen
    In recent years, automation has emerged as a technological solution that is gaining grounds across various fields. The task of managing updates like sorting, shelving and documenting large collections of books in the shortest possible time is stressful with limited personnel and can be automated. Other important library tasks such as consultation can, therefore, be undertaken effortlessly. This paper which explores the use of automated solutions for the purpose of book sorting in libraries, proposes the You Only Look Once version 5 (YOLOv5) model to detect books, then performs optical character recognition using EasyOCR on the detected book. After the characters have been extracted, the system then classifies the book into its respective library section using OpenAI. The result from testing the system shows that the book detection model gave an accuracy of 74%, the EasyOCR performed with an accuracy of 91% with variations across different image formats. For simplicity, images used were stored in jpg formats for faster execution time and easy processing. The paper emphasizes the revolutionary impact of AI and machine vision in educational institutions, especially in libraries beyond what can be imagined, pushing for the formation of library robots. This project's benefits include object detection and intelligent book categorization which phase to a new direction for study and advances library automation technologies that boost education.