Browsing by Author "Inusah, Fuseini"

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    Agile neural expert system for managing basic education
    (Intelligent Systems with Applications, 2023-01-04) Inusah, Fuseini; Missah, Yaw Marfo; Najim, Ussiph; Twum, Frimpong; 0000-0001-9785-4464; 0000-0002-2926-681X; 0000-0002-6973-7495; 0000-0002-1869-7542
    Inadequate experts in managing resources at the lower level of education to enhance effective teaching and learning for quality education is a significant challenge in developing nations. Many basic schools lack basic educational resources such as sitting places and writing places for learners. Inadequate teaching and learning resources negatively affect the educational policies in a country. It is common to see the media projecting the challenges of a school lacking these resources. The use of an Expert System (ES) in Artificial Intelligence (AI) to assist in effective management is a necessity. In this paper, an agile neural expert system is proposed using differential equations with an initial value problem. The technique combines both rule-based and neural net works in handling the problem. The expertise of the Human Expert (HE) is used in a knowledge-based to assist in managing the resources in schools. This has been possible with the use of Data Mining (DM) techniques and modeling of projected population growth, affecting enrolment in schools and necessitating the provision of re sources to cater to the growing population. For efficiency and effectiveness in planning, provision, and management of the resources, smart notification has been embedded in the system to monitor the availability and provision of the resources by prompting the various actors in the requisition, verification, validation, and approval of resources to be supplied to schools. The system proves a higher efficiency demonstrating speed in decision-making, accuracy in decisions and ease to use.
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    Data Mining and Visualisation of Basic Educational Resources for Quality Education
    (International Journal of Engineering Trends and Technology, 2022-12) Inusah, Fuseini; Missah, Yaw Marfo; Najim, Ussiph; Twum, Frimpong; 0000-0001-9785-4464; 0000-0002-2926-681X; 0000-0002-6973-7495; 0000-0002-1869-7542
    With an increase in educational resources for the growing population, data for Basic Education (BE) is becoming larger, requiring technical data tools to analyze and interpret. This research uses classification and clustering techniques to analyze the data from public schools in Ghana to identify the challenges. Nine (9) data mining algorithms in rapid miner studio 9.10 were used for the analysis to know the most efficient algorithm suitable for the data. These are; Generalized Linear Module (GLM), Naïve Bayes (NB), Logistic Regression (LR), Deep Learning (DL), Decision Tree (DT), Fast Large Margins (FLM), Gradient Boosted Tree (GBT), Random Forest (RF), and Support Vector Machines (SVM). The performance of GBT was seen as more appropriate, and this algorithm's results were presented. Excerpts from the reports are also included in the form of qualitative data. A diagrammatic representation of the interoperability among levels of education for quality education has also been presented. A proposed Neural Network model has been designed for the challenges and solutions. The conclusions draw that addressing the challenges of BE requires educational policy stability and enforcement to maximize resources and minimize the challenges in schools at all levels of education.
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    Integrating expert system in managing basic education: A survey in Ghana
    (International Journal of Information Management Data Insights, 2023-03-13) Inusah, Fuseini; Missah, Yaw Marfo; Najim, Ussiph; Twum, Frimpong; 0000-0001-9785-4464; 0000-0002-2926-681X; 0000-0002-6973-7495; 0000-0002-1869-7542
    Management of basic education in developing countries like Ghana is a major challenge as resources are not ad equately available for effective teaching and learning in schools. Careful planning and prediction using available data is also a major challenge as there are inaccuracies and inconsistencies in the available data. An investigation into the use of an Expert System for easy management of the resources is carried out in this research to know the level of readiness to accept an ES to assist in management. Stakeholders of education are contacted to solicit their views. With 216 districts for managing education in the country, a minimum of 3 participants were selected from each district to constitute a sample for the survey. In all 648 participants data were analyzed. The unstructured interview was also conducted using 9 members of an executive position in management. A thematic analysis was done on the responses and presented in diagrammatic form. The Acceptance Model for Educational Expert System (AMEES) is also presented. The results showed the majority of respondents agree with the use of an Expert System (ES) to assist in managing basic education. The use of data mining techniques to filter the data in an ES and help in easy prediction for decision-making accuracy is a necessity.