http://repository.aust.edu.ng/xmlui/handle/123456789/374 This collection contains Computer Science Student's Theses from 2009-2024 2026-04-22T08:33:45Z http://repository.aust.edu.ng/xmlui/handle/123456789/5135 How Machine Learning Can Evaluate the Influence of Socioeconomic and Climatic Factors in Agricultural Yield: A Case of Nigeria Dappa, Tamuno-Opubo Godwin The major international agencies in charge of nutrition are becoming increasingly concerned about global agricultural production in particular. Food insecurity has emerged in some populated areas, including Africa, as a result of the increased worldwide need for food as a result of record population growth. Climate change and its variability are two additional factors that contribute to world food insecurity. Furthermore, agricultural policy officials, farmers, and decision-makers require advanced technologies in order to make timely strategies or policies that will have an effect on the quality of crop harvests. Machine learning and other new, powerful analytical techniques made possible by big data technologies have already proven useful in a number of industries, including biology, finance, and medicine. The yield of three major crops, including cocoa, sesame, and cashew, at the national level in Nigeria during the course of the years spanning 1990 to 2020 is forecasted in this study using a machine learning-based prediction method. We used climatic, agricultural yield, and socioeconomic data to help policymakers and farmers anticipate the yearly agricultural outputin Nigeria. We employed k-nearest neighbors, a decision tree, and random forest. We also employed a hyper-parameter tweaking technique through cross-validation to enhance the model and avoid overfitting. For sesame, the accuracy of the Decision Tree model was the highest, having a test accuracy of 97.92% for socioeconomic and climatic factors combined, while the KNN model did the best with a test accuracy of 99.71% for climatic components separately. The accuracy of the Random Forest model was 87.54% for climatic elements alone and 87.64% for socioeconomic and economic factors together. For cocoa, the Decision Tree model had an accuracy of 89.49% for socioeconomic and climatic factors combined and 89.51% for climatic components alone, while the KNN model had the best accuracy of 90.71% for climatic elements alone. For socioeconomic and climatic factors taken together, the Random Forest model's accuracy was 87.82%; for climatic components alone, it was 88.83%. For cashew nuts, the accuracy of the KNN model was 78.38% for socioeconomic and climatic components combined and 99.81% for climatic factors alone, compared to 88.27% for socioeconomic and climatic elements combined and 86.58% for climatic factors alone for the Decision Tree model. For both socioeconomic and climatic components combined, the Random Forest model's accuracy was 98.50%, while for climatic factors alone, it was 98.75%. In conclusion, the Random Forest model outperformed the KNN and Decision Tree models across all crop and factor combinations. Our findings indicate that machine learning algorithms can be used to forecast crop yields with reasonable accuracy when socioeconomic and meteorological variables are combined Main Thesis 2023-05-10T00:00:00Z http://repository.aust.edu.ng/xmlui/handle/123456789/5123 Neural Collaborative Filtering and Autoencoder Enabled Deep Learning Models for Recommender Systems Arnold, Kwofie Finding important and useful information is getting harder as much more information is available online. The challenge for content producers is to deliver the appropriate content to the appropriate consumers while making it challenging for users to access that content. The foundation for overcoming these difficulties is provided by recommender systems. Traditional methods like Collaborative Filtering (CF) and Content-Based Recommender Systems have historically been successful in this field of study but are now challenged by problems with data sparsity, cold start, and non-linearity interaction. Evidently, several academic areas, like image detection and natural language processing (El-Bakry, 2008), have shown great interest in deep learning due to outstanding performance and the alluring quality of learning intricate representations. The impact of deep learning is recently showing good advancement when applied to recommender systems research (He, 2008). In this research We dive deep into the Autoencoder and Neural Collaborative Filtering based deep learning models and their implementation on classical collaborative filtering. The research also evaluates the performance of both models and outlines loopholes which can further be improved in future works. Main Thesis 2022-11-15T00:00:00Z http://repository.aust.edu.ng/xmlui/handle/123456789/5122 A Lightweight Convolutional Neural Network for Breast Cancer Detection Using Knowledge Distillation Techniques Modu, Falmata The second most heterogeneous cancer ever discovered is Breast Cancer (BC). BC is a disease that develops from malignant tumors when the breast cells begin to grow abnor-mally. Although it grows in the breast, it can spread to other body parts or organs.through the lymph and blood vessels of the breast. Globally, more than two million new cases and about 600,000 women died from BC in 2020. Early detection increases the chance of survival by 99%. Deep Learning (DL) models have recorded remarkable achievements in disease diagnosis and treatments. However, it requires powerful computing resources. In this work, we propose a lightweight DL model that can detect BC using the knowledge distillation technique. The knowledge of a pre-trained deep neural network is distilled to a shallow neural work that is easily deployable in a low-power computing environment. We have achieved an accuracy of up to 99%. In addition, we recorded 99% reduction in trainable parameters compared to deploying with a deep neural network. Main Thesis 2023-01-23T00:00:00Z http://repository.aust.edu.ng/xmlui/handle/123456789/5120 A Performant Predict Analytics Approach to Recommender Systems Using Deep Learning Methods Obaje, Williams Usman Recently, Massive amounts of data have been generated as a result of the frequency at which the amount of information available digitally is advancing. To enable users to effectively utilise the huge amount of information, recommendation system has been implemented to effectively manipulate a large amount of data in other to communicate necessary output to the user. The reliability of the final recommendations is a common metric used to determine if a recommendation system is effective or not. The RMSE, MSE, and MAE were used as recommendation-based metrics. The recommender system's performance is typically measured using the metrics RMSE, MSE, and MAE. This statistic demonstrates how effectively the Recommender system performs. The performance of the recommendations improves with decreasing RMSE, MAE, and MSE. It offers an erroneous value that illustrates how far off from the real data our model was. It assesses how closely the projections supplied correlate to the quantities that were observed. The final result of evaluating RMSE, MAE, and MSE on 1M Movies Datasets. Taking the mean result of the output, MAE outperformed other matric because it has the lowest mean value of 0.5843. Also, evaluating results of algorithm SVD on the 100k movies dataset MAE outperformed other matric having the lowest output of 0.6593. Furthermore, evaluating the RMSE, MAE, and MSE of algorithm SVD on 5k movies data set, MAE still outperformed other matric having the lowest mean value of 2.8898. Ultimately, it was discovered that the movie data sets with the most customers and reviews performed better than the others with fewer datasets obtainable. Additionally, we suggest a deep learning approach for creating efficient and accurate deep learning collaborative filtering systems (DLCFS). The proposed method and the currently used methods were compared. Main Theses 2022-09-05T00:00:00Z