MACHINE LEARNING

This study develops a machine learning model to predict abnormalities in commercial airplanes using real-world Automatic Dependent Surveillance-Broadcast (ADS-B) data, focusing on altitude changes exceeding 100 feet in 10 seconds. Following the methodology established by Passarella et al. (2024), this research implements and compares 25 different machine learning algorithms, ultimately selecting Quadratic Discriminant Analysis (QDA) as the optimal approach. The dataset comprises 167,844 records, including 84,074 normal and 83,770 abnormal instances, with features such as altitude, velocity, heading, latitude, and longitude. The theoretical foundation covers the comprehensive taxonomy of machine learning methods, from supervised learning algorithms like Support Vector Machines and Decision Trees to unsupervised approaches such as K-Means clustering. The QDA model achieves superior performance with 93-97% accuracy, 0.96-0.97 ROC-AUC, validated through stratified 5-fold cross-validation. Visualizations, including altitude plots and ROC curves, enhance interpretability for aviation professionals. This research demonstrates that QDA's ability to model non-linear decision boundaries with class-specific covariance matrices makes it particularly suitable for complex aviation data patterns, supporting enhanced flight safety and operational efficiency.

Manual pavement inspection methods are slow, subjective, and often inconsistent, leading to delayed maintenance and increased road deterioration. This study was carried out to develop an automated, image-based system capable of detecting and classifying visible defects in flexible pavements using machine learning. The objectives of the study were to review existing pavement inspection techniques, collect and preprocess pavement image data, and design and train a model capable of identifying pavement failures accurately. The study was with the aim of improving the speed, objectivity, and reliability of pavement condition assessments. A dataset of pavement images was obtained from the Edo State Ministry of Works, field surveys, and public sources. The images were annotated in YOLO format and augmented by flipping, rotation, cropping, and brightness adjustment. The YOLOv8 object detection model, implemented in Python using TensorFlow, PyTorch, and OpenCV, was trained on Google Colab with an NVIDIA T4 GPU. Training was performed at varying epochs (50, 100, and 200) and hyperparameters to optimize detection performance. The model’s accuracy was evaluated using mean Average Precision (mAP) and recall metrics to assess its ability to detect cracks, potholes, and rutting in flexible pavements. Results showed that the model achieved a mean Average Precision (mAP₅₀) of 0.68 and recall above 0.80 for visible defects such as potholes and alligator cracking, at a confidence level of 0.5. The model was less effective in detecting faint, low-contrast linear cracks. This study concluded that YOLOv8-based models can effectively automate pavement distress detection, providing a faster and more reliable alternative to manual inspection. It is recommended that future work expand the dataset and explore enhanced training strategies to improve the detection of subtle linear cracks.

This project work provides an overview on the application of linear algebra to artificial intelligence including natural language processing and machine learning. We discuss how linear algebra operations such as matrices, linear transformations, eigen values and eigen vectors, are used to optimize AI models, analyze complex data structures and enable efficient computation. Beginning with an overview of fundamental concepts in linear algebra, such as vectors, matrices, and linear transformations, the study delves into specific applications of these concepts in AI. One key area of focus is machine learning, where linear algebra forms the backbone of algorithms for tasks such as regression analysis, and principal component analysis for dimensionality reduction. This work also showcases the versatility of linear algebra by delving deep into the various reaches of linear algebra into many other fields and areas of study such as economics, physics and engineering.

Malaria remains a global health crisis, particularly in low-resource regions, where traditional diagnostic methods face challenges such as human error, resource constraints, and delayed detection. This project addresses these limitations by leveraging supervised machine learning (ML) to enhance malaria diagnosis and outbreak prediction. The motivation stems from the urgent need for scalable, accurate, and cost-effective solutions to reduce the disease’s burden, which claims over 600,000 lives annually. The objective is to develop robust ML models capable of automating malaria diagnosis using blood smear images and patient metadata while improving outbreak forecasting through environmental and epidemiological data analysis. Methodologically, the study employs supervised learning algorithms, including convolutional neural networks (CNNs) for image- based detection and random forests for tabular data. Datasets were preprocessed to handle class imbalance and missing values, followed by hyperparameter tuning and cross-validation to optimize performance. Results demonstrated that CNNs achieved 96% accuracy in classifying infected blood cells, outperforming traditional methods like microscopy. Random Forest models yielded 92% recall and 89% precision in predicting malaria risk from clinical data, highlighting their utility in early diagnosis. Additionally, stratified k-fold cross-validation ensured model generalizability across diverse datasets. This work underscores the transformative potential of supervised ML in malaria control, offering tools that enhance diagnostic speed, accuracy, and accessibility. By bridging technological innovation with public health needs, the project contributes to global efforts toward malaria eradication, particularly in endemic regions

Precision agriculture has emerged as a vital approach for improving agricultural efficiency and sustainability by leveraging advanced technologies. This project focuses on developing a machine learning model that utilizes historical data for precision agriculture applications. The system aims to assist farmers in making data-driven decisions by predicting suitable crops, forecasting crop yields, and detecting potential crop diseases. The proposed solution integrates wireless sensor networks to collect environmental parameters such as rainfall, temperature, humidity, Potassium, Nitrogen and soil pH, which are combined with historical agricultural data. Machine learning models, including Voting Classifier (Support Vector Machines, Gaussian Naïve Bayes, Random Forest) and Linear regression model, are trained and deployed to analyze the data for actionable insights. The Voting classifier model achieved a 99.3% accuracy in predicting suitable crops, while the Linear regression model provided yield forecasts with an R² score of 0.91. The Convolutional Neural Network (CNN) detected crop diseases with an accuracy of 93.8%. The system’s implementation demonstrates the effectiveness of combining historical data with machine learning techniques to enhance precision agriculture practices. By providing accurate and timely information, this solution helps optimize crop selection, improve resource allocation, and mitigate the risk of crop diseases. Recommendations for integration with mobile applications, weather data integration and farmer education and training are proposed to further enhance the system’s usability and impact. This project offers a promising step toward sustainable and smart farming practices, contributing to food security and agricultural productivity.

Hospital readmissions create challenges for healthcare systems, increasing costs and putting pressure on resources. This project introduces a machine learning-based system designed to predict patient readmissions, helping medical personnel and hospitals take early action to improve patient care and manage resources more effectively. By analyzing electronic health record (EHR) data, the model assesses a patient’s risk and provides explanations for key factors influencing the prediction. The system was trained on a dataset containing patient details such as age, medical history, lab results, and past hospital visits. It was developed using Python for machine learning, Express.js for the backend, and TypeScript with React for the frontend, ensuring smooth data processing and an easy-to-use interface. Strong security features like authentication, encryption, and error handling were added to protect patient information. The result shows that the model was able to achieve 63.87% accuracy, with recall scores of 72% and 55% in different areas. These results highlight the model’s ability to predict readmissions while also showing areas where improvements can be madethrough better data processing and tuning. By using predictive analytics, this system helps healthcare professionals make informed decisions, reduce avoidable readmissions, and improve hospital efficiency. This project demonstrates how AI-powered solutions can transform healthcare by enabling proactive patient management and better decision-making.

This research explores the application of Artificial Intelligence (AI) and Machine Learning (ML) for the predictive maintenance of transmission lines, specifically targeting fault detection, failure prediction, and maintenance optimization. Synthetic data was used to simulate parameters such as current, voltage, and temperature. Data preprocessing techniques, including cleaning and normalization, were performed. A supervised learning approach, the Random Forest Classifier, was applied using Python to mimic real-world fault scenarios. Model performance was evaluated using standard metrics: accuracy, precision, recall, and F1-score.The findings demonstrate that AI-based predictive maintenance has the potential to improve power system reliability and efficiency by reducing downtime and optimizing maintenance scheduling. The study also addresses key challenges, such as data availability and model generalization, proposing solutions like data augmentation and hybrid model design. Ultimately, this research provides a framework for developing scalable, data-driven predictive maintenance systems, advancing smart grid
technologies and sustainable power system management.

This research explores the application of Artificial Intelligence (AI) and Machine Learning (ML) for the predictive maintenance of transmission lines, specifically targeting fault detection, failure prediction, and maintenance optimization.
Synthetic data was used to simulate parameters such as current, voltage, and temperature. Data preprocessing techniques, including cleaning and normalization, were performed. A supervised learning approach, the Random Forest Classifier, was
applied using Python to mimic real-world fault scenarios. Model performance was evaluated using standard metrics: accuracy, precision, recall, and F1-score.The findings demonstrate that AI-based predictive maintenance has the potential to improve power system reliability and efficiency by reducing downtime and optimizing maintenance scheduling. The study also addresses key challenges, such as data availability and model generalization, proposing solutions like data augmentation and
hybrid model design. Ultimately, this research provides a framework for developing scalable, data-driven predictive maintenance systems, advancing smart grid technologies and sustainable power system management

Predicting the subsurface temperature distribution within sedimentary and petroleum‐bearing formations is essential for accurate hydrocarbon maturation modeling, well‐bore stability, and drilling‐fluid design. Traditional approaches—relying on sparse bottom‐hole temperature (BHT) measurements and one‐dimensional conductive models—often misestimate true formation
temperatures by 5–10 °C in heterogeneous settings such as the Niger Delta. To address these limitations, this study develops a data‐driven workflow that employs machine learning algorithms trained on routinely acquired drilling logs to produce continuous, high‐resolution temperature profiles. First, we assembled a dataset from Niger Delta wells comprising wireline logs (gamma‐ray, four‐pad resistivities, density, neutron porosity, sonic velocity), drilling parameters (equivalent circulating density, rate of penetration, exposure time), and corrected BHT readings. After replacing sentinel values (–9999) with NaNs and depth‐referencing all curves to true vertical depth (TVD), each log was clipped to its 1st–99th percentile range to mitigate extreme outliers. Features were standardized to zero mean and unit variance. Derived attributes—such as depth‐derivatives and moving‐window averages—were also explored to enhance lithofacies and thermal signal detection. We compared three regression models: a multilayer perceptron neural network (ANN), a Random Forest (RF) ensemble, and Support Vector Regression (SVR). Hyperparameters were tuned via grid search with k-fold cross‐validation, and models were evaluated on a hold‐out subset (20 % of depths). Error metrics (RMSE, MAE, R²) and well‐log–style scatter and depth‐track plots quantified predictive performance and bias. The ANN achieved an RMSE of 0.16 °F and R² = 0.97, producing smooth temperature gradients. The RF delivered an RMSE of 0.25 °F and R² = 0.985, with feature‐importance analysis highlighting mid‐pad resistivities and drilling parameters as primary predictors. SVR, with an RMSE of 0.47 °F and R² = 0.955, was less competitive but still captured over 95 % of temperature variance. Well‐log plots demonstrated that both ANN and RF closely track actual temperature profiles across lithologic transitions. This end‐to‐end pipeline—from data cleaning and feature engineering to model training, validation, and interpretation—demonstrates that machine learning can offer accurate, cost‐effective alternatives to physics‐based thermal models. Future work will explore hybrid physics–ML models, temporal drilling‐data integration, expanded feature fusion (e.g; seismic attributes), and explainable‐AI techniques, with the goal of operationalizing these tools in real‐time drilling workflows

The exponential rise in digital banking transactions has heightened the risk of fraudulent and anomalous bank transfers, necessitating intelligent and automated mechanisms for its detection. Traditional rule-based systems often fail to capture evolving fraud patterns, motivating the adoption of machine learning-based anomaly detection techniques. This study aims to develop and evaluate robust unsupervised learning models for detecting anomalies in bank transaction datasets. Specifically, the research applies three state-of-the-art algorithms; Isolation Forest, Local Outlier Factor (LOF), and One-Class Support Vector Machine (OCSVM) to identify irregular transaction behaviors indicative of potential fraud. The methodology involves comprehensive data preprocessing, scaling, encoding, and feature selection to improve model learning. Real-world bank transfer datasets from kaggle were utilized, for this training. Each model’s performance was assessed using standard evaluation metrics, including; Silhouette Score, Anomaly Ratio, and Average Decision Score. Results show that OCSVM performed best (Silhouette = 0.635, strong decision scores), reliably flagging about 5% of records as anomalies. Analysis of flagged transactions revealed consistent patterns high transaction amounts or balance-change ratios, very short or very long intervals between transactions, and activity at unusual hours making the alerts interpretable and practically useful.