Department of Computer Science;

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https://repository.lcu.edu.ng/handle/123456789/57

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Browsing Department of Computer Science; by Subject "Accuracy"

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    Employee Attendance Tracking Using Facial Recognition System
    (Lead City University, 2023-12) Bukola Meka OWOLABI
    Traditional pen-and-notebook methods for employee attendance are often susceptible to inaccuracies and falsifications. Biometric systems, despite being more secure, confront issues such as high acquisition costs and inefficiencies in capturing fingerprints, especially when hands are unclean or injured. In this study, a cutting-edge Employee Attendance Tracking System using Facial Recognition is developed, addressing the shortcomings of conventional attendance methods and biometric systems. The proposed system employs an array of Python libraries including Django, face_recognition, OpenCV (cv2), numpy, and PCA. These libraries are utilized for their strengths in image processing, facial recognition, and efficient data management. The primary objective is to create a reliable, cost-effective, and efficient alternative for recording employee attendance, overcoming the limitations of existing methods.The system utilizes advanced image processing techniques to tackle common challenges in facial recognition, such as noise interference, varying lighting conditions, and physical obstructions like occlusions. This is achieved through innovative approaches like noise reduction, illumination normalization, and occlusion handling, significantly improving the accuracy of facial recognition under diverse environmental conditions. A key component of the system is the "Capture_Image" module, which establishes a reference database by capturing and storing employee images. Concurrently, the "Recognize" module employs machine learning algorithms for facial recognition, ensuring accurate and timely recording of attendance. The effectiveness of the system is demonstrated in its ability to adapt to a variety of environments, attributed to its advanced image processing capabilities and robust algorithmic framework. This innovative system is particularly advantageous for institutions, corporate offices, and industries seeking secure, precise, and efficient attendance tracking solutions. It marks a significant advancement in the field of attendance management, offering a blend of enhanced security, accuracy, and operational efficiency. The study recommends further enhancements, such as incorporating advanced algorithms to improve recognition accuracy in different lighting and noise conditions. Keywords: Accuracy, Biometric system, Employee Attendance Tracking, Facial recognition, Machine learning algorithm Word Count: 295 Words
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    Evaluation of Machine Learning-Based Algorithm to Predicting Loan Default in Nigeria
    (Lead City University, Ibadan, 2024-12) Kingsley Oghenekaro EFEKODO
    In the financial sector, accurately predicting loan defaults is critical. Traditional creditworthiness assessment methods, while thorough, often do not capture the dynamic and complex interactions within financial data. This necessitates advanced solutions like machine learning (ML). Traditional credit scoring systems are frequently unable to handle high-dimensional, non-linear data effectively, leading to significant financial losses due to inaccurate predictions of loan defaults. This study aims to harness advanced machine learning techniques to enhance the accuracy of predicting loan defaults, aiming to outperform traditional statistical models. Various machine learning algorithms including Logistic Regression, Decision Trees, Gradient Boosting Classifiers, Random Forest, and Gaussian Naive Bayes were applied to a dataset comprising diverse borrower characteristics and loan details. The selected dataset was an open source containing different datasets for both train and test Demographic data, Performance data and Previous loans data. It contained 3 different datasets for both train and test. The sample submission has 2 outcomes- good (1) or bad (0). The dataset systematically divided into two. 70% for the training set, 30% was the test set. These models underwent rigorous training and validation processes to ensure their robustness and reliability. The Gradient Boosting Classifier emerged as the most effective model, with an accuracy of 78.8%. This model significantly outperformed others by effectively capturing complex patterns in the dataset, thereby substantially reducing both false positives and false negatives. The study confirms that machine learning models, particularly the Gradient Boosting Classifier, offer superior predictive power in the context of loan default risk assessments. Financial institutions should consider integrating these models into their credit evaluation processes to enhance decision-making accuracy and minimize risks. Additionally, future research should explore the integration of more diverse data sources, including non-traditional variables that could affect credit risk assessments, and the application of deep learning techniques to further refine prediction accuracies. Keywords: Accuracy, Classifier, Defaults, Financial, Machine Learning Models, Predicting, Cross- Validation, Data Imputation, Customer Segmentation, Nigerian Lending Market, Class Imbalance Word Count: 300
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    Real Time Credit Card Fraud Detection and Reporting System Using Machine Learning
    (Lead City University, Ibadan, 2023-12) Ahmed Oluwatoyin JOLAOSHO
    In recent years, there has been a significant rise in fraudulent credit card activities, resulting in substantial financial losses for numerous organizations, companies, and government agencies. This study addresses the critical issue of credit card fraud detection in real-time using machine learning algorithms. The primary objective is to develop a robust prototype model capable of promptly identifying fraudulent transactions and notifying users. To achieve this, three algorithms, namely the Random Forest, the Decision Tree classifier and Linear Regression algorithms were used. Furthermore, various sampling techniques were employed to balance the dataset and improve model performance. 12 distinct models were developed, each offering varying levels of accuracy and effectiveness in fraud detection. From the findings, transactions are most commonly made after 12 noon. Also, older individuals above 75 years are more susceptible to fraud, possibly due to their unfamiliarity with evolving transaction methods. Also, transactions in the dataset are predominantly made the Female gender suggesting that transactions involving this gender may be more prone to fraud. Findings also showed that among these models, the Random Forest -SMOTE [Hyperparameter Tuned], emerged as the best classifier with remarkable performance metrics, including a 97% accuracy rate, an F1 score of 95%, and a precision rate of 98%. The study extended its focus to practical implementation by integrating the Random Forest -SMOTE [Hyperparameter Tuned] with Twilio for real-time notification. This integration successfully demonstrated the model's ability to send timely and accurate fraud alerts to users. The analysis and model development for fraud detection has therefore provided valuable insights and a robust solution for real time identifying and responding to fraudulent activities. It is recommended that periodic evaluations of the fraud detection model's performance be performed to ensure its effectiveness in detecting evolving fraud patterns. Keywords: Accuracy, Credit Card Fraud Detection, Decision Tree Classifier, Fraud Detection Model, Fraudulent Transactions, Real-time Notification, Sampling Techniques Word Count: 289 Words