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Deep learning-based colorectal cancer classification using augmented and normalised gut microbiome data / Mwenge Mulenga
Published 2022“…First, to investigate the methods used to address limitations associated with microbiome-based datasets in colorectal cancer identification using deep neural network algorithms. …”
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Thesis -
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Keylogger detection analysis using machine learning algorithm / Muhammad Faiz Hazim Abdul Rahman
Published 2022“…Early identification of a keylogger malware attack could prevent hackers from accessing personal user data and reduce the likelihood of infiltration, which could reveal account information, credit cards, usernames, passwords, and other data. …”
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Student Project -
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Classification of Learner Retention using Machine Learning Approaches
Published 2021“…The performance of these algorithms was evaluated based on accuracy, precision, recall, and f-measure. …”
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Automated mold defects classification in paintings: a comparison of machine learning and rule-based techniques.
Published 2025“…By enabling early detection of mold defects, this method can play a crucial role in safeguarding these invaluable artworks for future generations.…”
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Identifying suicidal ideation through twitter sentiment analysis using Naïve Bayes / Annasuha Atie Atirah Alias
Published 2023“…Thus, this project aims to design, develop, and evaluate web-based application utilizing sentiment analysis, specifically employing the Naïve Bayes algorithm, to identify and analyze suicidal ideation within Twitter posts. …”
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Methods for identification of the opportunistic gut mycobiome from colorectal adenocarcinoma biopsy tissues
Published 2024“…•Application of machine learning algorithms to the identification of potential mycobiome biomarkers for non-invasive colorectal cancer screening. …”
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Machine-learning approach using thermal and synthetic aperture radar data for classification of oil palm trees with basal stem rot disease
Published 2021“…In an imbalanced dataset, one of the two classes contains fewer total samples than the other class. The sampling-based method, also known as the data level method, is used to deal with this problem. …”
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Identification of transformer fault based on dissolved gas analysis using hybrid support vector machine-modified evolutionary particle swarm optimisation
Published 2018“…It was found that the proposed hybrid SVM-Modified EPSO (MEPSO)-Time Varying Acceleration Coefficient (TVAC) technique results in the highest correct identification percentage of faults in a power transformer compared to other PSO algorithms. …”
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CORROSION DAMAGE ANALYSIS USING IMAGE PROCESSING
Published 2018“…There is a need to develop a low cost automatic corrosion damage identification. This project focuses on early detection of pipelines and gas tanks corrosion in oil and gas. …”
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Final Year Project -
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Analysis of banana plant health using machine learning techniques
Published 2024“…Recent research emphasizes the imperative nature of addressing diseases that impact Banana Plants, with a particular focus on early detection to safeguard production. The urgency of early identification is underscored by the fact that diseases predominantly affect banana plant leaves. …”
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Outlier Detection Technique in Data Mining: A Research Perspective
Published 2005“…Outlier detection as a branch of data mining has many important applications, and deserves more attention from data mining community. Most methods in the early work that detects outliers independently have been developed in field of Statistics. …”
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Conference or Workshop Item -
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Predictive Modelling of Stroke Occurrence among Patients using Machine Learning
Published 2023“…This study showed that the supervised K-Nearest Neighbors Algorithm (K-NN) model outperforms the other methods, with an accuracy of 95% compared with other models.…”
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Ensemble-based machine learning algorithms for classifying breast tissue based on electrical impedance spectroscopy
Published 2020“…Therefore, we aimed to classify six classes of freshly excised tissues from a set of electrical impedance measurement variables using five ensemble-based machine learning (ML) algorithms, namely, the random forest (RF), extremely randomized trees (ERT), decision tree (DT), gradient boosting tree (GBT) and AdaBoost (Adaptive Boosting) (ADB) algorithms, which can be subcategorized as bagging and boosting methods. …”
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Heart failure detection using Scaled Conjugate Gradient Method and Naïve Bayes
Published 2025“…To achieve this, two algorithms were employed: the Scaled Conjugate Gradient method within an Artificial Neural Network (ANN) framework, and the Naïve Bayes classifier. …”
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