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- ItemDevelopment of an Intrusion Detection System using Mayfly Feature Selection and Artificial Neural Network Algorithms(LAUTECH Journal of Engineering and Technology, 2024-06-24) Sulaiman Olaniyi Abdulsalam; Rasheed Abiodun Ayofe; Michael Favour Edafeajiroke; Jumoke Falilat Ajao; Ronke Seyi BabatundeProtecting the privacy and confidentiality of information and devices in computer networks requires reliable methods of detecting intrusion. However, effective intrusion detection is made more difficult by the enormous dimensions of data available in computer networks. To boost intrusion detection classification performance in computer networks, this study proposed a feature selection mode for the classification task. The proposed model utilized the Mayfly feature selection algorithm and ANN as the classifier. The model was also tested without a Mayfly Algorithm (MA). The efficiency of the model was determined through a comparison of its Accuracy, Specificity, Precision, Negative predictive value, False positive rate, False discovery rate, False negative rate, Sensitivity, and F1- score. The experimental outcomes revealed that the proposed model is more efficient than existing models when implemented on the Canadian Institute for Cybersecurity Intrusion Detection System 2017 (CIC-IDS 2017) dataset. Accuracy of 99.94% (using Data+mayfly+ANN) and 90.17% (using Data+ANN) were attained after experimentation. The proposed model demonstrated superior accuracy compared to existing studies. Its robustness is due to employing mayfly techniques that combine the strengths of PSO, GA, and FA for optimal feature selection. This research presents a dependable dimensionality reduction model useful for intrusion detection and improving security in computer networks.
- ItemA Churn Prediction System for Telecommunication Company Using Random Forest and Convolution Neural Network Algorithms(EAI Endorsed Transactions on Moblie Communications and Applications, 2022-07-27) Sulaiman Olaniyi Abdulsalam; Jumoke Falilat Ajao; Bukola Fatimah Balogun; Micheal Olaolu ArowoloINTRODUCTION: Customer churn is a severe problem of migrating from one service provider to another. Due to the direct influence on the company's sales, companies are attempting to promote strategies to identify the churn of prospective consumers. Hence it is necessary to examine issues that influence customer churn to yield effective solutions to minimize churn. OBJECTIVES: The major purpose of this work is to create a model of churn prediction that assists telecom operatives to envisage clients that are more probably to be prone to churn. METHODS: The experimental strategy for this study leverages the machine learning techniques on the telecom churn dataset, employing an improved Relief-F feature selection algorithm to extract related features from the enormous dataset. RESULTS: The result demonstrates that CNN has a high prediction capability of 94 percent compared to the 91 percent Random Forest classifier. CONCLUSION: The results are of enormous relevance to the telecommunication business in improving churners and loyal clients.
- ItemOptimized Convolution Neural Network-based Model for Detection and Classification of Pulmonary Diseases(LAUTECH Journal of Engineering and Technology, 2024-06-15) Anthony Taiwo Olajide; Ronke Seyi Babatunde; Sulaiman Olaniyi Abdulsalam; Jumoke Falilat Ajao; Rafiu Mope Isiaka; A. J. KehindePelican Optimization Algorithm-based Convolutional Neural Network (POA-CNN) method for the automated identification of pulmonary disorders such as COVID-19 and pneumonia is proposed in this research. The study aims to enhance the efficiency of CNN models in diagnosing lung diseases by using the Pelican Optimization Algorithm (POA), and by addressing drawbacks like a lack of flexibility in hyperparameter modifications. The three primary phases of the model are feature extraction via POA-based hyperparameter optimization, image classification, and image pre-processing. This approach improves existing systems' performance in detecting pulmonary diseases, highlighting the potential of deep learning in identifying and categorizing human diseases. The study uses resizing, grayscale, and augmentation methods to optimize an existing CNN model. A Convolutional Neural Network (CNN) is then applied to classify Pneumonia and COVID-19 cases. The proposed model achieves an accuracy rate of 97.28% and 97.00%, outperforming existing models. This technique is effective in detecting and classifying other pulmonary diseases and can be used to automatically detect and classify these diseases. Higher accuracy findings show how successful the model is, making it a useful tool for pulmonary illness identification.
- ItemOn Gröbner Bases via Alternating Semigroup(FUOYE Journal of Pure and Applied Sciences (FJPAS), 2024) Bakare, GN;; Ajibaye, OK;; Usamot, IF;; Ibrahim, GR;; Adeshola, A. DLet Ω𝜂={1, 2,…, 𝜂} be a finite set ordered in standard way. This paper presents an in-depth investigation into the application of Gröbner bases within the framework of alternating semigroups (𝐴𝜂 𝜍). The study specifically focuses on the exploration of Gröbner bases in the context of 𝐴𝜂 𝜍, which forms a subset of the symmetric inverse semigroup (𝐶𝜂). Key results derived concerning monomial orderings, including Lexicographic (LEX), Graded Lexicographic (GRLEX), and Graded Reverse Lexicographic (GRVLEX) orders. Monomials are degenerated, and computational techniques employed to achieve the outcomes. The broader significance lies in the potential applications of these results in areas such as combinatorial optimization, symbolic computation, and automated theorem proving. By demonstrating the practical utility of monomial degenerations and computational techniques, this research opens new avenues for efficient problem-solving in both theoretical and applied mathematical domains. A notable observation is that every graded lexicographic ordering of 𝑀 (𝐴𝜂 𝜍) is a lexicographic order, whereas the converse does not hold. Additionally, 𝑀 (𝐴𝜂 𝜍) adheres to graded reverse lexicographic order if and only if the index of the first entry of the first monomial is smaller than that of the second monomial. Some classical examples with their graphical representations included to substantiate the theoretical results.
- ItemOptimized HPLC-UV methodology for the simultaneous quantification of multiple preservatives in Jordanian yogurt products(Jordan Journal of Pharmaceutical Science, University of Jordan, Jordan, 2024) Sirhan, A., Alrashdan, Y., Jarrar, Q., Mostafa, A., and Abdulra’uf, L. B.A method based on high-performance liquid chromatography with a UV detector (HPLC-UV) was developed for the simultaneous determination of sorbic acid, benzoic acid, and natamycin in yogurt. The method does not require time-consuming, labor-intensive pre-treatment processes or complicated procedures. Using a C18 150 mm × 4.6 mm, 3.0 µm column (Roc) at 25 °C, the target analytes were separated within 5 minutes with high sensitivity and selectivity. The mobile phase consisted of trifluoroacetic acid (0.1%) in water containing 100 mM sodium acetate, trifluoroacetic acid (0.1%) in acetonitrile, and trifluoroacetic acid (0.1%) in tetrahydrofuran, in a ratio of 70:20:10 (v/v). Using this mobile phase as an extraction mixture, recoveries ranged from 83.0% to 110.2% at spike levels between 2.5 µg/kg and 80.0 µg/kg. The relative standard deviations (RSDs) for these recoveries were below 10%. Intra-day precision and inter-day precision varied from 5.3% to 6.7% and 7.6% to 9.2%, respectively. Additionally, the limits of detection (LOD) were between 0.24 and 0.61 mg/L, and the limits of quantification (LOQ) ranged from 0.80 to 2.0 mg/L for sorbic acid, benzoic acid, and natamycin. Principal component analysis revealed that yogurt type had the greatest positive influence on preservative concentration, while the weight or volume of the yogurt package had the greatest negative influence.