DSpace Angular :: Browsing by Author "Abdulsalam, S.O."

Browsing by Author "Abdulsalam, S.O."

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A Chi-Square-SVM Based Pedagogical Rule Extraction Method for Microarray Data Analysis
(Institute of Advanced Engineering and Science - International Journal of Advances in Applied Sciences. 9(2): 93 – 100, 2020) Salawu, M.D.; Arowolo, M.O.; Abdulsalam, S.O.; Isiaka, R.M.; Jimada-Ojuolape, B.; Mudashiru, L.O.; Gbolagade, K.A.
Support Vector Machine (SVM) is currently an efficient classification technique due to its ability to capture nonlinearities in diagnostic systems, but it does not reveal the knowledge learnt during training. It is important to understand of how a decision is reached in the machine learning technology, such as bioinformatics. On the other hand, a decision tree has good comprehensibility; the process of converting such incomprehensible models into an understandable model is often regarded as rule extraction. In this paper we proposed an approach for extracting rules from SVM for microarray dataset by combining the merits of both the SVM and decision tree. The proposed approach consists of three steps; the SVM-CHI-SQUARE is employed to reduce the feature set. Dataset with reduced features is used to obtain SVM model and synthetic data is generated. Classification and Regression Tree (CART) is used to generate Rules as the Last phase. We use breast masses dataset from UCI repository where comprehensibility is a key requirement. From the result of the experiment as the reduced feature dataset is used, the proposed approach extracts smaller length rules, thereby improving the comprehensibility of the system. We obtained accuracy of 93.53%, sensitivity of 89.58%, specificity of 96.70%, and training time of 3.195 seconds. A comparative analysis is carried out done with other algorithms. Keywords: Machine learning, Medical diagnosis, Rule-extraction, SVMs
A Comparative Analysis of Feature Selection and Feature Extraction Models for Classifying Microarray Dataset
(School of Engineering and Computing, University of the West of Scotland - Computing and Information Systems Journal, 22(2), 29 – 38, 2018) Arowolo, M.O; Abdulsalam, S.O.; Isiaka, R.M.; Gbolagade, K.A.
Purpose: The purpose of this research is to apply dimensionality reduction methods to fetch out the smallest set of genes that contributes to the efficient performance of classification algorithms in microarray data. Design/Methodology/Approach: Using colon cancer microarray dataset, One-Way- Analysis of Variance is used as a feature selection dimensionality reduction technique, due to its robustness and efficiency to select relevant information in a high-dimension of colon cancer microarray dataset. Principal Component Analysis (PCA) and Partial Least Square (PLS) are used as feature extraction techniques, by projecting the reduced high-dimensional data into efficient lowdimensional space. The classification capability of colon cancer datasets is carried out using a good classifier such as Support Vector Machine (SVM). The study is analyzed using MATLAB 2015. Findings: The study obtained high accuracies and the performances of the dimension reduction techniques used are compared. The PLS-Based attained 95% accuracy having edge over the other dimension reduction methods (One-Way- ANOVA and PCA). Practical Implications: The major implication of this research is getting the local dataset in the environments which lead to the usage of an open resource dataset. Originality: This study gives an insight and implications of high dimensional data in microarray gene analysis. The application of dimensionality reduction helps in fetching out irrelevant information that halts the performance of a microarray data technology. Keywords: Dimension Reduction, One-Way- ANOVA, PCA, PLS, Classification
A Feature Selection Based on One-Way ANOVA for Microarray Data Classification
(College of Natural Sciences, Al-Hikmah University, Ilorin, Nigeria - Al-Hikmah Journal of Pure and Applied Sciences, 2016) Arowolo, M.O.; Abdulsalam, S.O.; Saheed, Y.K.; Salawu, M.D.
High dimensionality of microarray data and expressions of thousands of features in a much smaller number of samples is a challenge affecting the applicability of the analytical results. However Support Vector Machine (SVM) has been commonly used in the classification of microarray datasets, yet the problem of high dimensionality of the feature space of data still exist. This study deals with the reduction of gene expression data into a minimal subset of genes, by introducing feature selection, to greatly reduce computational burden and noise arising from irrelevant genes that can perform a classification of cancer from microarray data using machine learning. Various statistical theory and Machine Learning (ML) algorithms to select important features, remove redundant and irrelevant features have been proposed, but it is unclear how these algorithms respond to conditions like small sample-sizes. This paper presents combination of Analysis of Variance (ANOVA) for feature selection; to reduce high data dimensionality of feature space and SVM algorithms technique for classification; to reduce computational complexity and effectiveness. Computational burden and noise arising from redundant and irrelevant features are eliminated. It reduces gene expression data to a lesser number of genes rather than thousands of genes, which can drop the cost for cancer testing significantly. The proposed approach selects most informative subset of features for classification to obtain a high performance accuracy, sensitivity, specificity and precision. Key words: Gene expressions, Microarray, One-Way-ANOVA, Support Vector Machines
A KNN and ANN Model for Predicting Heart Diseases, Chapter 12
(The Institution of Engineering and Technology - Explainable Artificial Intelligence in Medical Decision Support Systems, 2022) Abdulsalam, S.O.; Arowolo, M.O.; Udofot, E.O.; Sanni, A.M.; Popoola, D.D.; Adebiyi, M.O.
The heart is the single most important organ in the human body. Patients, professions, and medical systems are all bearing the brunt of heart failure’s devastating effects on contemporary society. Since cardiac arrest may well be demonstrated as a better understanding or conceivably go unobserved, particularly in the vast population of clients that have other cardiovascular disorders, the true prevalence of heart failure is likely to be underestimated, accounting for only 1–4% of all hospitalized patients as test procedures in developed nations.A person with heart failure has a heart that is unable to circulate sufficient blood through the body, but the term“heart failure” does not explain why this happens. The clinical picture is confusing since there are several possible causes of heart problems, many of which are diseases in and of themselves. Many cases of heart failure can be avoided if the underlying medical conditions that cause them are identified and treated promptly. The study and prediction of cardiac conditions must be precise because numerous diseases have been connected to the cardiovascular system. The resolution of this problem requires intensive online research on the relevant topic. Since incorrect illness prognoses are a leading cause of death among heart patients, learning more about effective prediction algorithms is crucial. This research utilizes K-nearest neighbor (KNN) and artificial neural network (ANN) to assess cardiovascular diseases using data collected from Kaggle. The highest accuracy (96%) was achieved by ANN trained with the standard scalar. Medical experts, specialists, and academics can all benefit greatly from this study. Based on the results of this study, cardiologists will be able to make more knowledgeable decisions about the inhibition, analysis, and handling of heart disease. Keywords: Heart; Cardio; Disease; Machine learning; Prediction; KNN; ANN; CNN
A Machine Learning Approach to Dropout Early Warning System Modeling
(International Association of Academicians - International Journal of Advanced Studies in Computer Science and Engineering, 8(2): 1 – 12, 2019) Isiaka, R.M.; Babatunde, R.S.; Ajao, J.F.; Abdulsalam, S.O.
ABSTRACT Dropout has been identified as a social problem with an immediate and long term consequences on the socio economic development of the society. A major element of a conceptual framework for dropout monitoring and control system is the prevention component. The component specified the need for automatic dropout early warning system. This paper presents the procedure for building the adaptive model, that is the core element for the realization of the prevention component of the framework. The model development is guided by the knowledge of the domain experts. An experimental approach was used to identify the K-Nearest Neighbors (KNN) as the best of the six algorithms considered for the adaptive models. Other algorithms explored are Logistic Regression (LR), Linear Discriminant Analysis (LDA), Classification and Regression Trees (CART), Gaussian Naive Bayes (NB) and Support Vector Machines (SVM). Historical dataset from a State University in the North Central region of Nigeria was used for building and testing the adaptive model. The average performance of the model on full attributes and the first year academic attributes compared favourably with 0.98 Precision, Recall and F1-Scores. Conversely, the Precision, Recall and F1-Scores on the entry attributes are 0.90, 0.95 and 0.92 respectively. The implication of this is that the academic performance is a very significant factorfor dropping out of the educational system. The developed model can provide an early signal on the students with the propensity to dropout, thereby serving as an advisory system for the students, parents and school managementtowards curtailing the menace. Keywords: dropout, adaptive-model, data mining, machine learning prediction
A Survey of Cloud Computing Awareness, Security Implication and Adoption in Nigeria IT Based Enterprises
(Faculty of Communication and Information Sciences, University of Ilorin, Ilorin, Nigeria - International Journal of Information Processing and Communication, 3(1&2), 210 – 220, 2015) Bashir, S.A.; Adebayo, O.S.; Abdulsalam, S.O.; Sadiku, J.S.; Mabayoje, M.A.
The advancement in information technology (IT) infrastructures and the overwhelming pervasive Internet accessibility has tremendously change the way computing is done in IT based enterprises. The recent hype of Cloud Computing that utilizes the existing Internet infrastructure to provide pay-as-you-go services to diverse community of businesses has emerged to alleviate and reduce the cost of computing tremendously. This paper examined the awareness of the Nigerian business enterprisesand their readiness in adopting cloud computing. It was found that the trend of awareness and adoption were very minimal with many being sceptical although few businesses were aware of the cloud technology. The survey also revealed that the stakeholders were precarious of the security-level of the cloud-based computing. Keywords: Cloud-Based Computing, Security Implications, Cloud Awareness, Cloud Adoption, Enterprises, Technology
Agent-Based Faults Monitoring in Automatic Teller Machines
(African Institute of Development Informatics and Policy - Computing, Information Systems and Development Informatics Journal, 3(4), 1 – 6, 2012) Bashir, S.A.; Mohammed, I.K.; Abdulsalam, S.O.
Automated Teller Machine (ATM) has gained widespread acceptance as a convenient medium to facilitate financial transaction without need for human agent. However, ATM deployers are facing challenges in maximizing the uptime of their ATMs as a result of wide gap in fault detection, notification and correction of the ATMs. One way to ameliorate this situation is through intelligent monitoring of ATM by resident software agents that monitor the device real time and report faulty components real time to facilitate quick response. We proposed an architecture for rule-based, intelligent agent based monitoring and management of ATMs. Agents are used to perform remote monitoring on the ATMs and control function such software maintenance. Such agents can detect basic events or correlate existing events that are stored in a database to detect faults. A system administrator can securely modify the monitoring policies and control functions of agents. The framework presented here includes software fault monitor, hardware fault monitor and transaction monitor. A set of utility support agents: caller agent and log agent are used to alert network operator and log error and transaction information in a database respectively. at-1, stuck-at-0 faults in digital circuits validate the point that faulty circuits dissipates more and hence draw more power. Key words: Automated Teller Machine (ATM), Intelligent Agents, Mobile Agents, Event Monitoring
An Adaptive Genetic Algorithm with Recursive Feature Elimination Approach for Predicting Malaria Vector Gene Expression Data Classification Using Support Vector Machine
(Walailak University - Walailak Journal of Science and Technology, 2021) Arowolo, M.O.; Adebiyi, M.O.; Nnodim, C.T.; Abdulsalam, S.O.; Adebiyi, A.A.
As mosquito parasites breed across many parts of the sub-Saharan Africa part of the world, infected cells embrace an unpredictable and erratic life period. Millions of individual parasites have gene expressions. Ribonucleic acid sequencing (RNA-seq) is a popular transcriptional technique that has improved the detection of major genetic probes. The RNA-seq analysis generally requires computational improvements of machine learning techniques since it computes interpretations of gene expressions. For this study, an adaptive genetic algorithm (A-GA) with recursive feature elimination (RFE) (A-GA-RFE) feature selection algorithms was utilized to detect important information from a high-dimensional gene expression malaria vector RNA-seq dataset. Support Vector Machine (SVM) kernels were used as the classification algorithms to evaluate its predictive performances. The feasibility of this study was confirmed by using an RNA-seq dataset from the mosquito Anopheles gambiae. The technique results in related performance had 98.3 and 96.7 % accuracy rates, respectively. Keywords: RNA-seq, Adaptive genetic algorithm, Recursive feature elimination, Malaria vector, Support Vector Machine kernels
Classification of Customer Churn Prediction Model for Telecommunication Industry Using Analysis of Variance
(Institute of Advanced Engineering and Science - International Journal of Artificial Intelligence, 12(3), 1323 – 1329, 2023) Babatunde, R; Abdulsalam, S.O.; Abdulsalam, O.A.; Arowolo, M.O.
Customer predictive analytics has shown great potential for effective churn models. Thriving in today's telecommunications industry, discerning between consumers who are likely to migrate to a competitor is enormous. Having reliable predictive client behavior in the future is required. Machine learning algorithms are essential to predict customer turnovers, and researchers have proposed various techniques. Churn prediction is a problem due to the unequal dispersal of classes. Most traditional machine learning algorithms are ineffective in classifying data. Client cluster with a higher risk has been discovered. A support vector machine (SVM) is employed as the foundational learner, and a churn prediction model is constructed based on each analysis of variance (ANOVA). The separation of churn data revealed by experimental assessment is recommended for churn prediction analysis. Customer attrition is high, but an instantaneous support can ensure that customer needs are addressed and assess an employee's capacity to achieve customer satisfaction. This study uses an ANOVA with a SVM, classification in analyzing risks in telecom systems It may be determined that SVM provides the most accurate forecast of customer turnover (95%). The projected outcomes will allow other organizations to assess possible client turnover and collect customer feedback. Keywords: Analysis of variance ,Churn, Machine learning , Support vector machine, Telecommunication
Classification of Leukemia Cancer Data using Correlation Based Feature Selection Model: A Comparative Approach
(Faculty of Engineering, Osun State University, Osogbo, Nigeria - UNIOSUN Journal of Engineering and Environmental Sciences, 2025) Babatunde, R.S.; Isiaka, R.M.; Abdulsalam, S.O.; Arowolo, O.M.; Ajao, J.F.
The abundance of data obtained from microarray experiments presents challenges related to the number of variables and the presence of random fluctuations. Despite the efforts that had been made by previous researchers, emphasizing how data mining aids the implementation of models to facilitate informed prediction, gaps are evident which requires improvement over the earlier models. Dimensionality reduction techniques, such as Correlation Based Feature Selection (CBFS), are good candidate solutions to these problems by selecting pertinent features for categorization. This research implements a model for classification of leukemia cancer using CBFS with Support Vector Machine (SVM), k-Nearest Neighbors (KNN), Decision Tree (DT), and Ensemble classifiers. The evaluation of the performance of these machine learning models was carried out using sensitivity, specificity, precision and accuracy. The findings indicate that the CBFS+DT model outperforms the other models in terms of sensitivity (96.75%), specificity (97.18%), precision (97.56%), accuracy (96.75%), and F1 score (96.97%), while also exhibiting a decreased computational time (0.4336). This demonstrates the efficacy of CBFS in improving classification accuracy and reducing computing load. Overall, this study highlights the effectiveness of CBFS in cancer research and underscores the importance of carefully choosing the most pertinent variables to enhance classification outcomes. Keywords: machine learning, classification, feature selection, pattern recognition
Comparative Analysis of Decision Tree Algorithms for Predicting Undergraduate Students’ Performance in Computer Programming
(Faculty of Science, Adeleke University, Ede, Nigeria - Journal of Advances in Scientific Research & Applications, A Multidisciplinary Journal Publication of the Faculty of Science, Adeleke University, Ede, Nigeria, 2015) Abdulsalam, S.O.; Babatunde, A.N.; Hambali, M.A.; Babatunde, R.S.
Educational data are increasing tremendously with little exploration by the education managers. Hidden knowledge can be discovered from the huge data sets available in educational databases through the use of data mining techniques. Educational data mining (EDM) is an emerging discipline, concerned with developing methods for exploring the unique types of data that reside in educational databases. One of the significant areas of the application of EDM is the development of student models for predicting students’ performances in their educational institutions. The focus of this work is to identify the optimal decision tree algorithms for predicting students’ performance in a computer programming course taken in 200 level based on their ordinary level results in Mathematics and Physics and their 100 level results in Mathematics and Physics courses. One hundred and thirty one (131) students’ records from computer science programme at Kwara State University (KWASU) between 2009 and 2013 were used. The attributes used are students’ ordinary level scores in Mathematics and Physics, 100 level results in Mathematics and Physics courses and the score in a 200 level computer programming course (CSC 203). C4.5 (known as J48 in WEKA) Classification and Regression Tree (CART), and Best-First Tree (BF Tree) decision tree algorithms were used in Waikato Environment for Knowledge Analysis data mining software to generate three classification models employed in predicting students’ performance in CSC 203. The results of these algorithms were compared using 10-fold cross validation method in terms of prediction accuracy and computational time. Our results showed that J48 tree has the highest prediction accuracy of 70.37% and least execution time of 0.02 seconds while CART and BFTree has prediction accuracy of 60.44% and 60.30% respectively and both having execution time of 0.22 seconds based on the data set used in this study. This study also revealed that previous knowledge of Mathematics and Physics both at Ordinary level and 100 level are essential determinants of students’ performance in a computer programming course. Keywords: Data Mining, Educational Data Mining, Decision Tree algorithm, Students’ Performance
Customer Churn Prediction in Banking Industry Using K-Means and Support Vector Machine Algorithms
(Crown Academic Publishing - International Journal of Multidisciplinary Sciences and Advanced Technology, 1(1), 48 – 54, 2020) Abdulsalam, S.O.; Arowolo, M.O.; Jimada-Ojuolape, B.; Saheed, Y.K.
This study proposes a customer churn mining structure based on data mining methods in a banking sector. This study predicts the behavior of customers by using clustering technique to analyze customer’s competence and continuity with the sector using k-means clustering algorithm. The data is clustered into 3 labels, on the basis of the transaction in and outflow. The clustering results were classified using Support Vector Machine (SVM), an Accuracy of 97% was achieved. This study enables the banking administrators to mine the conduct of their customers and may prompt proper strategies as per engaging quality and improve proper conducts of administrator capacities in customer relationship. . Keywords: Customer Churn, Banks, K-Means and SVM
Customer Churn Prediction in Telecommunication Industry Using Classification and Regression Trees and Artificial Neural Network Algorithms
(Institute of Advanced Engineering and Science - Indonesian Journal of Electrical Engineering and Informatics, 10(2), 431 - 440, 2022) Abdulsalam, S.O.; Arowolo, M.O.; Saheed, Y.K.; Afolayan, J.O.
Customer churn is a serious problem, which is a critical issue encountered by large businesses and organizations. Due to the direct impact on the company's revenues, particularly in sectors such as the telecommunications as well as the banking, companies are working to promote ways to identify the churn of prospective consumers. Hence it is vital to investigate issues that influence customer churn to yield appropriate measures to diminish churn. The major objective of this work is to advance a model of churn prediction that helps telecom operatives to envisage clients that are most probable to be subjected to churn. The experimental approach for this study uses the machine learning procedures on the telecom churn dataset, using an improved Relief-F feature selection algorithm to pick related features from the huge dataset. To quantify the model's performance, the result of classification uses CART and ANN, the accuracy shows that ANN has a high predictive capacity of 93.88% compared to the 91.60% CART classifier. Keywords: Telecoms, Relief-F, ANN, CART, Churn
Data Mining for Knowledge Discovery from Financial Institution Database
(Society for Science and Nature - International Journal of Engineering and Management Sciences. 3(4), 409 – 415, 2012) Abdulsalam, S.O.; Adewole, K.S.; Bashir, S.A.; Jimoh, R.G.; Olagunju, M.
Due to its importance for the investment decision-making and risk management, describing and predicting stock represents a key topic in stock analysis. Stock or shares are valued and analyzed by stock investors using fundamental analysis and technical analysis. Stock investors describe and predict stock manually based on individual experiences. Employing manual procedure in analyzing stock, most especially technical analysis is always very cumbersome and inefficient; because of much time that is consumed in examining the past records of a respective firm, an investor is willing to invest in. This paper presents a better way of describing and predicting stock, especially technical analysis, by employing data mining techniques. A database was developed employing 360 records of daily activity summary (equities) and 78 records of weekly activity summary (equities) spanning through 18 months that is, from January 2007 to June 2008. These data were obtained from the daily official list of the prices of all shares traded on the stock exchange published by the Nigerian Stock Exchange being the financial institution that runs Nigerian stock market; using banking sector of Nigerian economy with three banks namely:- First Bank of Nigeria Plc, Zenith Bank Plc, and Skye Bank Plc. A data mining software tool was developed and employed in identifying patterns and relationships from the database to generate new knowledge about the data set in the database through the use of data mining techniques that employ regression analysis. KEYWORDS: Data Mining, Stock Exchange, Financial Institution, Decision-Making, Risk Management, Regression Analysis
Design and Implementation of Electronic Election Campaign System
(College of Natural Sciences, Al-Hikmah University, Ilorin - Al-Hikmah Journal of Pure and Applied Sciences. 2(2), 122 – 129, 2016) Salau-Ibrahim, T.T.; Abdulsalam, S.O.
Researches have been conducted over the years till date on the importance of election campaign in the fields of electoral studies, political studies and the use of information systems to assist the process. The use of computer technology in developing information systems to support several aspects of election campaign has also continued to evolve. System analysis best practices on the use of qualitative research methodology, Unified Modelling Language (UML) to enhance communication as well as rapid prototyping tools were explored and those well suited were used effectively. The emphasis of this work is to deduce how to provide computer support to improve gathering, analysis of data and information from face-to-face canvassing as well as conducting effective get-out-the-vote (GOTV) activities before and on election day. The system will be web based in order to ease communication and user interaction with the system. As one might expect for web based systems, Hyper Text Markup Language (HTML) will be used to display results in the web browser that will be visible to the user. Therefore, in this work, PHP Hypertext Preprocessor (PHP) was used for coding the core logic of the system and this runs on Apache web server, JavaScript for validation and printing facility, cascading style sheet (CSS) and lastly MySQL for database all residing on Apache web server. The present application will assist campaign managers, staff, as well as volunteers to carry out their duties more accurately and effectively. Keywords: Information Systems, System Analysis, Software Engineering, Election Campaign, Campaign Violence, face-to- face canvassing
Development of a Bank Customers’ Credit Rating System Using Neural Networks Algorithms
(Faculty of Science, Gombe State University, Nigeria - Bima Journal of Science and Technology, 9(1A), 233 – 244, 2025) Abdulsalam, S.O.; Yusuf, I.; Awotunde, S.O.; Edafeajiroke, M.F.
Credit rating systems are vital for financial institutions to evaluate creditworthiness, manage risk, and minimize losses from defaults. Traditional methods, relying on historical financial data, lack adaptability and exclude borrowers with limited credit histories. Machine learning (ML) techniques, such as Backpropagation Neural Networks (BPNN) and Feedforward Propagation Neural Networks (FPNN), offer improved predictive accuracy. This study developed an advanced credit rating system using BPNN and FPNN, trained on Nigerian bank data incorporating financial and non-financial indicators. BPNN’s iterative weight-adjustment capability through backpropagation enhanced performance, while FPNN served as a simpler baseline. The methodology included data collection, preprocessing, model training, and evaluation using accuracy, precision, recall, and F1-score. Results demonstrated BPNN’s superiority, achieving 82.5% accuracy compared to FPNN’s 81.2%, alongside higher precision, recall, and F1 scores. The study underscores BPNN’s effectiveness in refining credit risk assessment, making it a more reliable tool for Nigerian banks. By leveraging ML, financial institutions can enhance decision-making, reduce risks, and expand credit access to underserved populations. Keywords: Credit Rating System, Back Propagation Neural Networks (BPNN), Feed-Forward Propagation Neural Network (FPNN), Development of Banks' Customers Credit Rating System, Machine Learning in Banking system
Development of an Intrusion Detection System in a Computer Network
(Council for Innovative Research - International Journal of Computers and Technology, 12(5), 3479 – 3485, 2014) Babatunde, R.S.; Adewole, K.S.; Abdulsalam, S.O.; Isiaka, R.M.
The development of network technologies and application has promoted network attack both in number and severity. The last few years have seen a dramatic increase in the number of attacks, hence, intrusion detection has become the mainstream of information assurance. A computer network system should provide confidentiality, integrity and assurance against denial of service. While firewalls do provide some protection, they do not provide full protection. This is because not all access to the network occurs through the firewall. This is why firewalls need to be complemented by an intrusion detection system (IDS).An IDS does not usually take preventive measures when an attack is detected; it is a reactive rather than proactive agent. It plays the role of an informant rather than a police officer. In this research, an intrusion detection system that can be used to deny illegitimate access to some operations was developed. The IDS also controls the kind of operations performed by users (i.e. clients) on the network. However, unlike other methods, this requires no encryption or cryptographic processing on a per-packet basis. Instead, it scans the various messages sent on a network by the user. The system was developed using Microsoft Visual Basic. Indexing terms: Intrusion detection system, illegitimate, misuse, network.
Development of an Intrusion Detection System using ANOVA Feature Selection and Support Vector Machine Algorithms
(Journal Press India - COMPUTOLOGY: Journal of Applied Computer Science and Intelligent Technologies, 4(1), 89 – 108, 2024) Edafeajiroke, M.F.; Abdulsalam, S.O.; Shuaib, M.U.; Babatunde, R.S.
The escalating sophistication and frequency of cyber-attacks have necessitated the development of more advanced intrusion detection systems (IDS). This research presents the development of an innovative IDS employing Analysis of Variance (ANOVA) for feature selection and a Support Vector Machine (SVM) algorithm for intrusion detection. The aim is to enhance detection accuracy while reducing computational overhead. ANOVA was used to identify significant features from vast and complex network traffic data, simplifying the high dimensional data and improving the detection system’s efficiency. The selected features are then classified using the Radial Basis Function (RBF) SVM algorithm, renowned for its high accuracy and robustness in handling high-dimensional data. A comparative analysis with existing IDS models demonstrates the improved efficiency and accuracy of the proposed model. This work provides an advanced methodology for cyber security, contributing to the ever-evolving battle against cyber threats. Keywords: Intrusion detection; Machine learning; Feature selection; Support vector machine; ANOVA.
Development of an Intrusion Detection System using Mayfly Feature Selection and Support Vector Machine Algorithms
(Faculty of Engineering, Osun State University, Osogbo, Nigeria - UNIOSUN Journal of Engineering and Environmental Sciences, 7(1), 446 – 452, 2025) Abdulsalam, S.O.; Adewale, T.; Saka, K.K.; Abdulrauf, U.T.
Numerous security strategies have been used to control the threats associated with computer and network security. Methods such as access control, software and hardware firewall restrictions, and the encryption of private information. Nevertheless, these methods are insufficient because they all have serious drawbacks. As a result, using additional defense mechanisms, such as intrusion detection systems (IDS), becomes crucial. This research developed an effective intrusion detection system using mayfly feature selection and support vector machine algorithm. The SVM classifier achieved an accuracy of approximately 99.9% the precision is 99.75% sensitivity 100%, Fscore 99.87% While the training time display an average time of 1.4630sec. The results of this study suggest that security professionals and researchers should consider adopting ensemble methods like AdaBoost, especially when combined with robust base learners such as SVM, in the development of intrusion detection systems for IoT networks Keywords: Intrusion detection system, Support vector machine, Mayfly optimization algorithm, NSL KDD dataset
Development of an Intrusion Detection System using Mayfly Feature Selection and Support Vector Machine Algorithms
(Faculty of Engineering, Osun State University, Osogbo, Nigeria - UNIOSUN Journal of Engineering and Environmental Sciences, 2025) Abdulsalam, S.O.; Adewale, T.; Saka, K.K.; Abdulrauf, U.T.

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