Ganiyu Adesola Aderounmu

Handwritten digit classification represents a foundational task in computer vision and has been widely adopted in applications ranging from Optical Character Recognition (OCR) to biometric authentication. Despite the availability of large benchmark datasets, the development of models that achieve both high accuracy and computational efficiency remains a central challenge. In this study, the performance of three representative machine learning paradigms—Chi-Squared Automatic Interaction Detection (CHAID), Generative Adversarial Networks (GANs), and Feedforward Deep Neural Networks (FFDNNs)—was systematically evaluated on the Modified National Institute of Standards and Technology (MNIST) dataset. The assessment was conducted with a focus on classification accuracy, computational efficiency, and interpretability. Experimental results demonstrated that deep learning approaches substantially outperformed traditional Decision Tree (DT) methods. GANs and FFDNNs achieved classification accuracies of approximately 97%, indicating strong robustness and generalization capability for handwritten digit recognition tasks. In contrast, CHAID achieved only 29.61% accuracy, highlighting the limited suitability of DT models for high-dimensional image data. It was further observed that, despite the computational demand of adversarial training, GANs required less time per epoch than FFDNNs when executed on modern GPU architectures, thereby underscoring their potential scalability. These findings reinforce the importance of model selection in practical deployment, particularly where accuracy, computational efficiency, and interpretability must be jointly considered. The study contributes to the ongoing discourse on the role of artificial intelligence (AI) in pattern recognition by providing a comparative analysis of classical machine learning and deep learning approaches, thereby offering guidance for the development of reliable and efficient digit recognition systems suitable for real-world applications.

A contrived dataset of substation automation for cybersecurity research in the smart grid networks based on IEC61850

Mobile wireless networks enable the connection of devices to a network with minimal or no infrastructure. This comes with the advantages of ease and cost-effectiveness, thus largely popularizing the network. Notwithstanding these merits, the open physical media, infrastructural-less attributes, and pervasive deployment of wireless networks make the channel of communication (media access) vulnerable to attacks such as traffic analysis, monitoring, and jamming. This study designed a virtual local area network (VLAN) model to circumvent virtual jamming attacks and other intrusions at the Media Access Control (MAC) layer of the Carrier Sense Multiple Access with Collision Avoidance (CSMA/CA) protocol. A Torpor VLAN (TVLAN) Data Frame Encapsulation and the algorithm for T-VLAN security in CSMA/CA were formulated and presented. A simulation experiment was conducted on the model using OMNeT++ software. The performance metrics used to evaluate the model were packet delivery ratio, network throughput, end-to-end channel delay, and channel load. The simulation results show that the TVLAN defence mechanism did not increase the channel load arbitrarily during TVLAN defence. similarly, the system throughput was shown to be 82% during TVLAN defence. Nevertheless, the network delay of the system during TVLAN defence was significantly high but the channel load was 297 when the TVLAN security mechanism was launched. These results demonstrate the model’s ability to provide a survivability mechanism for critical systems when under attack and add a security layer to the CSMA/CA protocol in wireless networks. Such a remarkable performance is required of a CSMA/CA infrastructure for improving the cybersecurity posture of a wireless network.

Diabetes is one of the major health issues that affect more than 10.5 percent of the adult population across the globe. This study applied deep learning techniques of deep belief network (DBN), long short-term memory (LSTM) and recurrent neural network (RNN) with Bayesian optimization on a diabetes dataset to forecast patients with diabetes. A splitting ratio of 80:20 was used for model performance evaluation. DBN model had the lowest mean absolute error in comparison to the other two models with 95.79% accuracy, 0.0331 mean absolute error, 0.0709 mean squared error, 0.1204 loss function, 0.9458 precision, 0.1819 RMSE, and 0.5307 recall. The results from this study validate that the DBN model can be used on a larger dataset to reduce variance and model overfitting, thereby achieving a better accuracy score.

In this paper, investigation was made to evaluate the effectiveness of the different classifiers suitable to predict the probability of cyber threat or fraudulent intent applicant during Mobile Money Service on-boarding or service activation process with the goal of determining the best machine learning model for the predictive model solution. Experimental work was carried out by formulating cyber threat predictive models, using six (6) supervised machine learning algorithms, Logistic regression, Naïve Bayes, Shallow Neural Network (SNN), Deep Neural Network (DNN), Classification and Regression Trees (CART) and Random Forest (RF) of different configurations. Each model was simulated with Synthetic Minority Operation Techniques (SMOTE) and without SMOTE (No-SMOTE) application on 25,000 dataset records of mobile money applicants. Twenty-four (24) different configurations of the formulated predictive models were simulated and evaluated using Python programming language. Simulation results of the predictive models proved that the Random Forest model multiclass configurations with SMOTE dataset outperformed all other configurations. The results also showed that the multiclass experiments with SMOTE has a better performance than the binary configuration without SMOTE of the predictive models. The study concluded that using the Random Forest based predictive machine learning model will increase the security level of Mobile Money solution by detecting and preventing anomalous customer registrations during mobile money for the unbanked on-boarding process.

Mobile Money Services (MMS), enabled by the wide adoption of mobile phones, offered an opportunity for financial inclusion for the unbanked in developing nations. Meanwhile, the risks of cybercrime are increasing, becoming more widespread, and worsening. This is being aggravated by the inadequate security practises of both service providers and the potential customers' underlying criminal intent to undermine the system for financial gain. Predicting potential mobile money cyber threats will afford the opportunity to implement countermeasures before cybercriminals explore this opportunity to impact mobile money assets or perpetrate financial cybercrime. However, traditional security techniques are too broad to address these emerging threats to Mobile Financial Services (MFS). Furthermore, the existing body of knowledge is not adequate for predicting threats associated with the mobile money ecosystem. Thus, there is a need for an effective analytical model based on intelligent software defence mechanisms to detect and prevent these cyber threats. In this study, a dataset was collected via interview with the mobile money practitioners, and a Synthetic Minority Oversampling Technique (SMOTE) was applied to handle the class imbalance problem. A predictive model to detect and prevent suspicious customers with cyber threat potential during the onboarding process for MMS in developing nations using a Machine Learning (ML) technique was developed and evaluated. To test the proposed model's effectiveness in detecting and classifying fraudulent MMS applicant intent, it was trained with various configurations, such as binary or multiclass, with or without the inclusion of SMOTE. Python programming language was employed for the simulation and evaluation of the proposed model. The results showed that ML algorithms are effective for modelling and automating the prediction of cyber threats on MMS. In addition, it proved that the logistic regression classifier with the SMOTE application provided the best classification performance among the various configurations of logistic regression experiments performed. This classification model will be suitable for secure MMS, which serves as a key deciding factor in the adoption and acceptance of mobile money as a cash substitute, especially among the unbanked population.