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Adoption of machine learning algorithm for analysing supporters and non supporters feedback on political posts / Ogunfolajin Maruff Tunde
Published 2022“…The method was implemented using Java and the results of the simulation were evaluated using five standard performance metrics: accuracy, AUC, precision, recall, and f-Measure. …”
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Study and Implementation of Data Mining in Urban Gardening
Published 2019“…Using the J48 tree algorithm implemented through WEKA API on a Java Servlet, data provided is processed to derive a health index of the plant, with the possible outcomes set to “Good,” “Okay”, or “Bad”. …”
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Embedded system for indoor guidance parking with Dijkstra’s algorithm and ant colony optimization
Published 2019“…This study proposes a car parking management system which applies Dijkstra’s algorithm, Ant Colony Optimization (ACO) and Binary Search Tree (BST) in structuring a guidance system for indoor parking. …”
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Computational Thinking (Algorithms) Through Unplugged Programming Activities: Exploring Upper Primary Students’ Learning Experiences
Published 2021“…From the responses gathered through interviewing nine of these participants, four main themes (Good Learning Quality, Much Knowledge, Easy and Useful) related to their learning experiences have been derived. …”
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E4ML: Educational Tool for Machine Learning
Published 2003“…There are various types of machine learning algorithms with certain processes taken by the algorithm.In teaching of the machine learning algorithms, such processes need to be explained especially to the beginner in introductory level.This paper discusses the development the tool that addresses the process by certain algorithm to produce a hypothesis or output based on given data.This tool can also be used in teaching and learning purposes.The explanation of processes by the algorithms is demonstrated through simple simulation.The source of the algorithms was adapted from Mitchell book [1] that cover popular algorithms in machine learning for teaching and learning such as Concept Learning, Decision Tree, Bayesian Learning, Neural Networks, and Instance based Learning.The tool also used several classes of Weka (Waikato Environment for Knowledge Analysis) as a basis for the design and implementation of the new tool that focuses on explaining the processes taken by certain algorithm.…”
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Virtual reality in algorithm programming course: practicality and implications for college students
Published 2024“…The analysis of learning problems shows the unavailability of interactive learning media that can support various learning styles of students in programming algorithm materials. …”
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Mobile machine vision for railway surveillance system using deep learning algorithm
Published 2021“…In this paper, object detection model is developed and implemented with deep learning algorithm. Object classification model is produced through the model training with Deep Neural Networks (DNN). …”
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Proceedings -
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Novice programmers’ emotion and competency assessments using machine learning on physiological data / Fatima Jannat
Published 2022“…There is also growing interest in modeling machine learning and deep learning algorithms that can learn from user’s data, understand and react to that individual’s affective state. …”
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Evaluation of the Transfer Learning Models in Wafer Defects Classification
Published 2022“…The key metrics for the evaluation are classification accuracy, classification precision and classification recall. 855 images were used to train and test the algorithms. Each image went through the embedding process by the evaluated algorithms. …”
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Using GA and KMP algorithm to implement an approach to learning through intelligent framework documentation
Published 2023Conference paper -
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State Revenue Prediction Dashboard using Machine Learning Algorithm
Published 2022“…This report serves as documentation for the State Revenue Prediction Dashboard utilizing Machine Learning algorithm project. State revenue is used to finance the operation of the government. …”
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Optimisation of fed-batch fermentation process using deep reinforcement learning
Published 2023“…Fed-batch fermentation process has always been a challenge for optimisation because it is highly non-linear and complex. Deep reinforcement learning is a self-learning algorithm through trial and error and experience, without any prior knowledge. …”
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The impact of virtual reality on programming algorithm courses on student learning outcomes
Published 2024“…In the control class, students apply traditional learning, while the experimental class uses VR-based learning. …”
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Quantifying usability prioritization using K-means clustering algorithm on hybrid metric features for MAR learning
Published 2023Conference Paper -
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A Reference Based Surface Defect Segmentation Algorithm For Automatic Optical Inspection System
Published 2020“…However, present complex algorithms which are accurate require high processing power using a large size of learning dataset without labelling error. …”
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Optimising neural network training efficiency through spectral parameter-based multiple adaptive learning rates
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Study of machine learning in computer vision using Raspberry Pi
Published 2024text::Final Year Project -
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Investigation of cross-entropy-based streamflow forecasting through an efficient interpretable automated search process
Published 2024“…However, the highly imbalanced flow distribution often hinders the machine learning algorithm's performance. In this paper, streamflow forecasting was approached through the formulation of two distinct machine learning problems: categorical streamflow forecast and regression streamflow forecast. …”
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Evaluation of optimal MLP structure for heart disease diagnosis / Salbiah Ab Hamid
Published 2010“…The weight of each value in hidden layers will be considered during the learning process. LM algorithm is used to minimize the error during training and testing process. …”
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