Journal of Applied Artificial Intelligence <p>Journal of Applied Artificial Intelligence (JAAI) is an international and interdisciplinary scholarly peer reviewed journal on artificial intelligence published by Saba Publishing.<br />JAAI devoted entirely to Artificial Intelligence and welcomes papers in the overall field including, but not limited to, machine learning and cognition, deep learning, supervised learning, unsupervised learning, classification, regression, clustering, big and streaming data, optimization algorithms, feature selection and extraction, pattern recognition, bio-informatics, uncertain information processes, recommender systems, E-service personalization, distributed and parallel processing, computer vision, neural networks, natural language processing, heuristic search, multi-objective optimization, multi-agent systems, advances in social network systems, reasoning under uncertainty, forecasting and predication models as well as other hot topics.</p> <p><strong>Editor in Chief: <a href="" target="_blank" rel="noopener">Dr Nibras Abdullah</a></strong><br /><strong>ISSN (online)</strong>: <a href="" target="_blank" rel="noopener">2709-5908</a><br /><strong>Frequency:</strong> Semiannual</p> Saba Publishing en-US Journal of Applied Artificial Intelligence 2709-5908 Development of a Predictive Model of Student Attrition Rate <p>Enrollment in courses is a key performance indicator in educational systems for maintaining academic and financial viability. Today, a lot of factors, comprising demographic and individual features like age, gender, academic background, financial capabilities, and academic degree of choice, contribute to the attrition rates of students at various higher education institutions. In this study, we developed prediction models for students' attrition rate in pursuing a computer science degree as well as those who have a high chance of dropping out before graduation using machine learning methodologies. This approach can assist higher education institutions in creating effective interventions to lower attrition rates and raise the likelihood that students will succeed academically. Student data from 2015 to 2022 were collected from the Federal University Lokoja (FUL), Nigeria. The data was preprocessed using existing WEKA machine learning libraries where our data was converted into attribute-related file form (ARFF). Further, the resampling techniques were used to partition the data into the training set and testing set, and correlation-based feature selection was extracted and used to develop the students' attrition model to identify the students' risk of attrition. Random Forest and decision tree machine learning algorithms were used to predict students' attrition. The results showed that Random Forest has 79.45% accuracy while the accuracy of Random tree stood at 78.09%. This is an improvement over previous results, where an accuracy of 66.14%. and 57.48% were recorded for random forest and Random tree respectively. This improvement was because of the techniques demonstrated in this study. It is recommended that applying techniques to the classification model will improve the performance of the model.</p> Godwin Sani Francisca Oladipo Emeka Ogbuju Friday J. Agbo Copyright (c) 2022 Journal of Applied Artificial Intelligence 2022-12-31 2022-12-31 3 2 1 12 10.48185/jaai.v3i2.601 The Sentiment Analysis of EndSARS Protest in Nigeria <p>The extraction of public opinions from online communication platforms can serve several purposes in corporate institutions, state politics, and governance. The analysis of these opinions may be useful for both immediate business decision making and professional planning. This analysis is becoming relevant in managing social movements and digital activism by applying computational technology. There is a need to deploy this opinion mining technology to the recent largest digital activism in Nigeria known as the #EndSARS movement. In this work, we proposed the EndSARS live analytics framework which holds a promising solution to social unrest and may serve as a panacea to curbing the menace of vandalism resulting from unresolved protest issues. Using a dataset of 12,357 tweets, we demonstrated that computational technology can be relevant to addressing online protests. The result of the analysis shows the eight basic emotions expressed during the protest and approaches the government may adopt to address future activisms.</p> Emeka Ogbuju Ikechukwu Mpama Temitope Martha Oluwafemi Fati Oiza Ochepa Joshua Agbogun Victoria Yemi-Peters Folusho O. Owoeye Mercy Idoko Bello Taoheed Copyright (c) 2022 Journal of Applied Artificial Intelligence 2022-12-31 2022-12-31 3 2 13 23 10.48185/jaai.v3i2.560 A Framework for Predictive - Diagnosis of Prevalent Illness among University Students <p>The issue of identifying the prevalence of sickness that is linked to the population of a nation, state, neighborhood, organization, or school has not been taken into consideration by the majority of prior studies on the prediction of illness among populations. They frequently merely choose any sickness based on assumption, while those that determined the prevalence of the condition before developing their framework utilized survey data or data from web repositories, which removes idiosyncrasies from those data. In order to increase performance, this research suggests an enhanced data analytics framework for the predictive diagnosis of common illnesses affecting university students. In order to do this, exploratory data analysis (EDA) using a multivariate analytic technique was conducted using a high-level model methodology using CRISP-DM stages. When the suggested strategy was evaluated on support vector machines, ensemble gradient boosting, random forest, decision tree, K-neighbors, and linear regression machine learning models, experimental findings revealed that it outperformed current methods.</p> <p>In comparison to other reviewed frameworks that used survey datasets, standardized or online repositories' dataset, the framework with emphasis on the ensemble Gradient Boosting classifier and regression had accuracy of 100% and mean absolute error of 0.18, respectively. It is also steady due to its ability to manage both small and big data sets without impacting the model's performance. The enhanced results through localized dataset demonstrate the benefit of including local data sources in the process of developing models for the diagnosis and prognosis of prevalent illnesses of any area with people.</p> Dauda Olorunkemi Isiaka Joshua Babatunde Agbogun Taiwo Kolajo Copyright (c) 2022 Journal of Applied Artificial Intelligence 2022-12-31 2022-12-31 3 2 24 38 10.48185/jaai.v3i2.667 Convolutional Neural Networks for Defect Detection on LV cables <p>A convolutional neural network (CNN) is a machine learning algorithm that is particularly well-suited for tasks such as object recognition, image captioning, and speech recognition. CNNs are particularly effective at detecting features in images that are not easily observable by other machine learning algorithms, such as defects in manufacturing. By analyzing large collections of images, CNNs are able to find patterns that are indicative of defects. Power cables are an important part of manufacturing, as they allow machines to be operated and communicated with. Recently, great importance has been offered in making electricity generation, transmissions, distribution and storage smart. However, the shift to smart grid should include intelligent methods of detecting the reliability of electrical connections. Several types of electrical cables are used to transmission and distribution of electrical energy. Due to excellent electrical and mechanical properties, cross-linked polyethylene (XLPE) cables are widely used in power systems. Poor manufacturing techniques in the production and installation of cable joints will cause insulation defects. Some suggest, the use of interdigital capacitive (IDC) for online monitoring on XLPE cables. Others suggest The use of a continuous wave (CW) terahertz (THz) imaging technology could help display and detect interior faults in cross-linked polyethylene (XLPE) plates used for power line insulation. In this paper, I developed models which predominantly use locally collected custom dataset to forecast individual power cable physical safety status. The model is aimed at replacing the physical inspection with computer vision and image processing techniques to classify defective power cable from non-defective ones. The project is implemented using the Python programming language, the Tensorflow library, and a Convolutional neural network.The Convolutional Neural Network (CNN)-based method is purposefully chosen and applied in this project for power cable defect classification. The project culminates by recommending the use of same or additional datasets and provide modalities to detect power cable defect from live video.</p> Tesfaye Mengistu GELAN Copyright (c) 2022 Journal of Applied Artificial Intelligence 2023-01-09 2023-01-09 3 2 39 46 10.48185/jaai.v3i2.620