Browsing by Author "Adewole, K.S."
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Item Comparative Analysis of Predictive Models Created Based on Some Multi-layered Neural Networks Models(Dept. of Computer Science, LAUTECH Ogbomoso, 2018) Ajiboye, A.R.; Mabayoje, M.A.; Adewole, K.S.Multilayer back-propagation neural networks are the network structures build with distinct layers. Due to some inherent challenges of single-layered network in solving some nonlinear problems, it is desirable to have hidden layer(s) with adequate number of neurons; as this would give more processing power to the network. The main objective of this study was to unveil the architecture that can produce most accurate predictive model among the multilayer back-propagation neural networks that is being considered. This study specifically measures and compares the accuracies of the models created using the feed-forward back-propagation, cascade-forward back-propagation and Elman back-propagation neural networks. The required data sets for implementation were retrieved from the online public repositories. Experiments were conducted and repeated using two sets of different data in order to establish the consistencies of the network outputs. Findings from this study are based on a number metrics, and the results show that, among the three architectures being considered, the predictive model created using the feed-forward neural network architecture records the lowest error and found to converge at the lowest epoch.Item Comparative Approach of Back-Propagation Neural Network and Decision Tree on Breast Cancer Classification: An Appraisal(Dept. of Computer Science, LAUTECH Ogbomoso, 2019) Babatunde, R.S.; Adewole, K.S.; Ajiboye, A.R.The use of data mining methods in incorporating decision making has been increasing in the past decades. Data mining simply refers to extracting or mining knowledge from large amount of data. Over the years, medical image processing has benefited immensely from data mining techniques including breast cancer diagnosis. Sonography (also known as ultrasound) has become a great addition to mammography and magnetic resonance imaging (MRI) as imaging techniques dedicated to providing breast cancer screening. This technique is time-consuming and often characterized with low accuracy. Hence, the need to develop a robust classification model with high performance accuracy and reduced false alarm. In this paper, the performance of back propagation neural networks (BPNN) and C4.5 decision tree (DT) for breast cancer prediction was carried out. Filter based feature selection approach using correlation filter was employed for ranking features according to their predictive power. The model was simulated using WEKA data mining tools and extensive comparative study was performed based on the standard evaluation metrics. The performance of the two classifiers was compared based on their predictive accuracy, precision, recall, kappa statistic and other relevant statistical measures. The simulation results shows that C4.5 outperforms BPNN in terms of training time (0.16 secs) and accuracy (94.2857% ) while BPNN has 46.9secs training time and accuracy of 90.9524%. However, the result also reveals that BPNN outperforms C4.5 in terms of error rate, with BPNN having mean absolute error of 0.0542 while C4.5 has mean absolute error of 0.0834. It can therefore be deduced from the comparison that C4.5 can be a good option for prediction task considering the fast training time of the algorithm as well as the high accuracy of prediction.Item Expert System and Decision Support System for Electrocardiogram Interpretation and Diagnosis: Review, Challenges and Research Directions.(MDPI Publishers, Basel Switzerland, 2022) Adewole, K.S.; Mojeed, H.A.; Ogunmodede, James Ayodele; Gabralla, L.A.; FARUK, N; ABDULKARIM, A; et alAbstract: Electrocardiography (ECG) is one of the most widely used recordings in clinical medicine. ECG deals with the recording of electrical activity that is generated by the heart through the surface of the body. The electrical activity generated by the heart is measured using electrodes that are attached to the body surface. The use of ECG in the diagnosis and management of cardiovascular disease (CVD) has been in existence for over a decade, and research in this domain has recently attracted large attention. Along this line, an expert system (ES) and decision support system (DSS) have been developed for ECG interpretation and diagnosis. However, despite the availability of a lot of literature, access to recent and more comprehensive review papers on this subject is still a challenge. This paper presents a comprehensive review of the application of ES and DSS for ECG interpretation and diagnosis. Researchers have proposed a number of features and methods for ES and DSS development that can be used to monitor a patient’s health condition through ECG recordings. In this paper, a taxonomy of the features and methods for ECG interpretation and diagnosis were presented. The significance of the features and methods, as well as their limitations, were analyzed. This review further presents interesting theoretical concepts in this domain, as well as identifies challenges and open research issues on ES and DSS development for ECG interpretation and diagnosis that require substantial research effort. In conclusion, this paper identifies important future research areas with the purpose of advancing the development of ES and DSS for ECG interpretation and diagnosis.Item Frequent Pattern and Association Rule Mining from Inventory Database Using Apriori Algorithm(African Journal of Computing & ICT, 2014-09) Adewole, K.S.; Akintola, A.G.; Ajiboye, A.R.Recently, data mining has attracted a great deal of attention in the information industry and in a Society where data continue to grow on a daily basis. The availability of huge amounts of data and the imminent need for turning such data into useful information and knowledge is the major focus of data mining. The information and knowledge obtained from large data can be used for applications ranging from market analysis, fraud detection, production control, customer retention, and science exploration. A record in such data typically consists of the transaction date and the items bought in the transaction. Successful organizations view such databases as important pieces of the marketing infrastructure. This paper considers the problem of mining association rules between items in a large database of sales transactions in order to understand customer-buying habits for the purpose of improving sales. Apriori algorithm was used for generating strong rules from inventory database. It was found that for a transactional database where many transaction items are repeated many times as a superset in that type of database, Apriori is suited for mining frequent itemsets. The algorithm was implemented using PHP, and MySQL database management system was used for storing the inventory data. The algorithm produces frequent itemsets completely and generates the accurate strong rules.Item A NOVEL APPROACH TO OUTLIERS REMOVAL IN A NOISY NUMERIC DATA SET FOR EFFICIENT MINING(Department of Computer Science, University of Ilorin., 2016) Ajiboye, A.R.; Adewole, K.S.; Babatunde, R.S.; Oladipo, I.D.Data pre-processing is a key task in the data mining process. The task generally consumes the largest portion of the total data engineering effort while unveiling useful patterns from datasets. Basically, data mining is about fitting descriptive or predictive models from data. However, the presence of outlier sometimes reduces the reliability of the models created. It is, therefore, essential to have raw data properly pre-processed before exploring them for mining. In this paper, an algorithm that detects and removes outliers in a numeric dataset is proposed. In order to establish the effectiveness of the proposed algorithm, the clean data obtained through the implementation of the proposed approach is used to create a prediction model. Similarly, the clean data obtained through the use of one of the existing techniques is also used to create a prediction model. Each of the models created is simulated using a set of untrained data and the error associated with each model is measured. The resulting outputs from the two approaches reveal that, the prediction model created using the output from the proposed algorithm has an error of 0.38, while the prediction model created using the cleaned data from the clustering method gives an error of 0.61. Comparison of the errors associated with the models created using the two approaches shows that, the proposed algorithm is suitable for cleaning numeric dataset. The results of the experiment also unveils that, the proposed approach is efficient and can be used as an alternative technique to other existing cleaning methods.Item Student Web Self-Service Portal for a Tertiary Institution(Covenant University, Nigeria., 2016) Akintola, A.G.; Adewole, K.S.; Mabayoje, M.A.; Oke, J.O.The optimum condition for students to study is in an environment where they can access virtually all they need to know about courses, lecturers, locate places (for fresh and prospective students), validate staff identity, access past examination questions easily, identify vacant student hostels within and outside the school premises. Some of the reasons for students’ failure can be attributed to finding accommodation within or outside the school premises. Also, fresh students find it difficult to locate specific places in the university environment and some students find it very difficult to interact with staff. This paper presents a student self-service portal to address some of these challenges. Unified Modeling Language (UML) was used to model the system. The model was implemented using Microsoft C#, Microsoft ASP.net, Microsoft SQL Server, and Google Map. The proposed system was tested and the result obtained during the execution shows that the system is capable of addressing some of the challenges confronted by students.Item TSDL: A Framework for Tip Spam Detection in Location Based Social Network.(Nigeria Computer Society, 2017) Adewole, K.S.; Isiaka, R.M.; Jimoh, R.G.; Ajiboye, A.R.In Web 2.0 systems, Location Based Social Network (LBSN) has become increasingly popular because of its ability for locating places, such as restaurants, hotels and nearby facilities that can render services to the users. Among such LBSN is Apontador, a popular social network introduced in Brazil a couple of years ago. However, despite the numerous opportunities offered by Apontador LBSN, it has become attractive social network for tips spam distribution. In this work, an intelligent machine learning framework is proposed to detect tips spam in Apontador LBSN. The proposed framework explored three classification algorithms: Random Forest, Multilayer Perceptron, and Decorate. Bio-inspired feature identification was studied using Evolutionary algorithm (EA) and Particle Swamp Optimization (PSO) to identify the discriminating features for tip spam detection in LBSN. Three experiments were conducted based on sixty (60), twenty-two (22), and ten (10) features to ascertain the performance of the proposed framework for tip spam detection. Based on the various experiments conducted, Random Forest classification algorithm produced accuracy and F-measure of 90.5% and ROC of 96.7% using 60 features extracted from Apontador LBSN. The algorithm also outperformed the two other classifiers during EA evaluation. However, Decorate classifier produced the best results during the PSO evaluation, achieving F-measure and ROC of 86.3% and 92.9% respectively. The experimental results show that the proposed framework improved in performance for tips spam detection in Apontador LBSN.