Improving Customer Trust through Fraud Prevention E-Commerce Model
The advancement of E-commerce brings trust concerns in society, due to the lack of physical inspection of items by customers. Online fraud is badly menacing the customers and e-commerce boom in society because E-commerce has removed the barriers of physical contact between merchants and customers in the business environment thus making the online transaction to be vulnerable. This has brought some challenges and of most importance is customers’ trust amidst fraudulent transactions. The ...
Hybrid Quantum-classical Strategies for Hydrogen Variational Quantum Eigensolver Optimization
This research focuses on enhancing the optimization process of the classical ground state energy for hydrogen molecules, which is known to be -1.1373 Ha. Various well-known optimization algorithms such as Gradient Descent Optimizer, Adagrad Optimizer, Adam Optimizer, RMSProp Optimizer, and Momentum Optimizer are employed to explore their convergence behavior and their proximity to the precise ground state energy. The analysis extends to examining potential energy curves to gain insights into ...
Application of RFM Model on Customer Segmentation in Digital Marketing
Customers play a pivotal role in the success of any business. The ability to attract and retain the right clientele, who consistently engage with a company's products and services, hinges on a thorough understanding of their purchasing behavior. Successful businesses tailor their offerings to meet the unique requirements and preferences of their customers. Utilizing marketing analysis tools, such as the RFM model, facilitates the segmentation of customers based on distinct parameters, enablin...
Designing a Hybrid Genetic Algorithm Trained Feedforward Neural Network for Mental Health Disorder Detection
This research delves into the innovative application of feed-forward neural networks (FNNs) specifically the multi-layer perceptron (MLP). MLP is a flexible algorithm due to its ability to adapt to different realworld problems amongst other features, and this makes it a preferred machine learning algorithm in the early detection of mental health disorders. MLP’s number of layers and the number of neurons per layer changes to accommodate these abilities. MLP was chosen for this work becau...
Development of a Real-time Phishing Detection Website via a Triumvirate of Information Retrieval, Natural Language Processing. and Machine Learning Modules
Phishing attacks are a serious risk to people and businesses because they try to trick users into sending critical information by using phony emails and websites. Users remain susceptible to these assaults because sophisticated phishing websites are frequently difficult for existing preventive measures to identify and block. As a result, a reliable detection system that can recognize and stop phishing assaults is required. The goal of the research is to create a detection system that efficien...
UNMASKING FRAUDSTERS: Ensemble Features Selection to Enhance Random Forest Fraud Detection
Fraud detection is used in various industries, including banking institutes, finance, insurance, government agencies, etc. Recent increases in the number of fraud attempts make fraud detection crucial for safeguarding financial information that is confidential or personal. Many types of fraud problems exist, including card-not-present fraud, fake Marchant, counterfeit checks, stolen credit cards, and others. We proposed an ensemble feature selection technique based on Recursive feature el...
Enhanced Brain Tumor Image Classification Using Convolutional Neural Network with Attention Mechanism
Detecting brain tumors early is crucial for precise diagnosis and the development of effective treatment strategies, given the severity of the condition involving the uncontrolled growth of abnormal cell clusters in the brain.This research introduces an innovative Convolutional Neural Network (CNN) model augmented with an attention mechanism for the classification of brain tumor images, utilizing a comprehensive dataset of 3,000 Magnetic Resonance Imaging (MRI) scans sourced from Kaggle. Rigo...
Exploring the Fusion of Graph Theory and Diverse Machine Learning Models in Evaluating Cybersecurity Risk
The frequency and severity of cyber- attacks have surged, causing detrimental impacts on businesses and their operations. To counter the ever-evolving cyber threats, there's a growing need for robust risk assessment systems capable of ef ectively pinpointing and mitigating potential vulnerabilities. This paper introduces an innovative risk assessment technique rooted in both Machine Learning and graph theory, which of ers a method to evaluate and foresee companies' susceptibility to ...
Cybershield: Harnessing Ensemble Feature Selection Technique for Robust Distributed Denial of Service Attacks Detection
As Modern-day data increases in terms of dimensions (Instances X attributes), Single Feature Selection Techniques (SFST) often have certain biases and fail to provide optimal performance in machine learning models. To overcome the challenges associated with using SFST on large datasets, an Ensemble Feature Selection Technique (EFST) is proposed that includes multi-filter-based feature ranking selection and a wrapper-based feature subset selection methods for feature extraction on the Cana...
Graduate/Research Assistant
Vortex Energy Dubai
May 2020 to Present
Postgraduate
Delta State University Abraka
January 2022 to Present