Browsing by Author "Najim, Ussiph"
Now showing 1 - 7 of 7
Results Per Page
Sort Options
- ItemAgile 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; 0000-0002-1869-7542Inadequate 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.
- ItemCold Boot Attack on Encrypted Containers for Forensic Investigations(KSII Transactions On Internet And Information Systems,, 2022-09) Twum, Frimpong; Lagoh, Emmanuel Mawuli; Missah, Yaw Marfo; Najim, Ussiph; Ahene, Emmanuel; 0000-0002-1869-7542; 0000-0002-2926-681X; 0000-0002-6973-7495; 0000-0002-0810-1055
- ItemData 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; 0000-0002-1869-7542With 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.
- ItemImplementation of a Data Protection Model dubbed Harricent_RSECC((IJACSA) International Journal of Advanced Computer Science and Applications, 2022) Twum, Frimpong; Amankona, Vincent; Missah, Yaw Marfo; Najim, Ussiph; Opoku, Michael; 0000-0002-1869-7542; 0000-0001-8658-7575; 0000-0002-2926-681X; 0000-0002-6973-7495Every organization subsists on data, which is a quintessential resource. Quite a number of studies have been carried out relative to procedures that can be deployed to enhance data protection. However, available literature indicates most authors have focused on either encryption or encoding schemes to provide data security. The ability to integrate these techniques and leverage on their strengths to achieve a robust data protection is the pivot of this study. As a result, a data protection model, dubbed Harricent_RSECC has been designed and implemented to achieve the study’s objective through the utilization of Elliptic Curve Cryptography (ECC) and Reed Solomon (RS) codes. The model consists of five components, namely: message identification, generator module, data encoding, data encryption and data signature. The result is the generation of the Reed Solomon codewords; cipher texts; and generated hash values which are utilized to detect and correct corrupt data; obfuscates data; and sign data respectively, during transmission or storage. The contribution of this paper is the ability to combine encoding and encryption schemes to enhance data protection to ensure confidentiality, authenticity, integrity, and non-repudiation.
- ItemIntegrating 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; 0000-0002-1869-7542Management 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.
- ItemPredicting Bank Operational Efficiency Using Machine Learning Algorithm: Comparative Study of Decision Tree, Random Forest, and Neural Networks(Advances in Fuzzy Systems, 2020) Appiahene, Peter; Missah, Yaw Marfo; Najim, Ussiph; 0000-0002-6098-4537; 0000-0002-2926-681X; 0000-0002-6973-7495%e financial crisis that hit Ghana from 2015 to 2018 has raised various issues with respect to the efficiency of banks and the safety of depositors’ in the banking industry. As part of measures to improve the banking sector and also restore customers’ confidence,efficiency and performance analysis in the banking industry has become a hot issue. %is is because stakeholders have to detect the underlying causes of inefficiencies within the banking industry. Nonparametric methods such as Data Envelopment Analysis (DEA) have been suggested in the literature as a good measure of banks’ efficiency and performance. Machine learning algorithms have also been viewed as a good tool to estimate various nonparametric and nonlinear problems. %is paper presents a combined DEA with three machine learning approaches in evaluating bank efficiency and performance using 444 Ghanaian bank branches, Decision Making Units (DMUs). %e results were compared with the corresponding efficiency ratings obtained from the DEA. Finally, the prediction accuracies of the three machine learning algorithm models were compared. %e results suggested that the decision tree (DT) and its C5.0 algorithm provided the best predictive model. It had 100% accuracy in predicting the 134 holdout sample dataset (30% banks) and a P value of 0.00. %e DT was followed closely by random forest algorithm with a predictive accuracy of 98.5% and a P value of 0.00 and finally the neural network (86.6% accuracy) with a P value 0.66. %e study concluded that banks in Ghana can use the result of this study to predict their respective efficiencies. All experiments were performed within a simulation environment and conducted in R studio using R codes.
- ItemRainfall Prediction Using Machine Learning Algorithms for the Various Ecological Zones of Ghana(IEEE Access, 2022) Appiah-Badu, Nana Kofi Ahoi; Missah, Yaw Marfo; Amekudzi, Leonard K.; Najim, Ussiph; Twum, Frimpong; Ahene, Emmanuell; 0000-0002-3029-4498; 0000-0002-2186-3425; 0000-0002-2926-681X; 0000-0002-6973-7495; 0000-0002-1869-7542; 0000-0002-0810-1055Accurate rainfall prediction has become very complicated in recent times due to climate change and variability. The efficiency of classification algorithms in rainfall prediction has flourished. The study contributes to using various classification algorithms for rainfall prediction in the different ecological zones of Ghana. The classification algorithms include Decision Tree (DT), Random Forest (RF), Multilayer Perceptron (MLP), Extreme Gradient Boosting (XGB) and K-Nearest Neighbour (KNN). The dataset, consisting of various climatic attributes, was sourced from the Ghana Meteorological Agency spanning 1980 – 2019. The performance of the classification algorithms was examined based on precision, recall, f1-score, accuracy and execution time with various training and testing data ratios. On all three training and testing ratios: 70:30, 80:20 and 90:10, RF, XGB and MLP performed well, whereas KNN performed least across all zones. In terms of the execution time of the models, Decision Tree is consistently portrayed as the fastest, whereas MLP used the most run time.