Search Results - (( java implementation path algorithm ) OR ( program segmentation learning algorithm ))
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Heavy Transportation Shortest Route using Dijkstra’s algorithm (HETRO) / Nurul Aqilah Ahmad Nezer
Published 2017“…The development tools used in developing this project is NetBeans by using Java for the implementation of the coding. The methodology that used for developing this system is the Dijkstra’s algorithm. …”
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Embedded system for indoor guidance parking with Dijkstra’s algorithm and ant colony optimization
Published 2019“…BST inserts the nodes in the way that the Dijkstra’s can find the empty parking in fastest way. Dijkstra’s algorithm initials the paths to finding the shortest path while ACO optimizes the paths. …”
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Path planning for unmanned aerial vehicle (UAV) using rotated accelerated method in static outdoor environment
Published 2021“…In this study, a fast iterative method known as Rotated Successive Over-Relaxation (RSOR) is introduced. The algorithm is implemented in a self-developed 2D Java tool, UAV Planner. …”
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Smart appointment organizer for mobile application / Mohd Syafiq Adam
Published 2009“…The main component of this prototype is the use of Dijkstra algorithm to compute the shortest path from source of appointment to the 6 points of destinations within UiTM Shah Alam. …”
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Accuracy of advanced deep learning with tensorflow and keras for classifying teeth developmental stages in digital panoramic imaging
Published 2022“…Background: This study aims to propose the combinations of image processing and machine learning model to segment the maturity development of the mandibular premolars using a Keras-based deep learning convolutional neural networks (DCNN) model. …”
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STATISTICAL FEATURE LEARNING THROUGH ENHANCED DELAUNAY CLUSTERING AND ENSEMBLE CLASSIFIERS FOR SKIN LESION SEGMENTATION AND CLASSIFICATION
Published 2021“…A profound algorithm comprising of preprocessing in CIELAB color space and Delaunay triangulation based clustering along with Particle Swarm Optimization (PSO) is proposed for the segmentation. …”
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Fostering motivation in TVET students: the role of learner-paced segments and computational thinking in digital video learning
Published 2024“…This study aims to address this gap by examining how learner-paced predefined segments and CT algorithmic thinking can impact TVET students' perceived motivation. …”
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Recognition of isolated elements picture using backpropagation neural network / Melati Sabtu
Published 2005“…There are three main programs work together. The programs are back-propagation neural network program, training and performance program and recognition program. …”
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A review on sentiment analysis model Chinese Weibo text
Published 2020“…For traditional machine learning, there are 2 mainly aspects of innovation: Simultaneous classifier (Adoboost+SVM) and Improvement of classical classification algorithm. …”
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Proceedings -
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Leaf condition analysis using convolutional neural network and vision transformer
Published 2024“…Through the use of a hybrid deep learning model that combines vision transformer and convolutional neural networks for classification, the algorithm can be optimized. …”
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Evolutionary cost-cognizant regression test case prioritization for object-oriented programs
Published 2019“…Therefore, this study proposed a cost-cognizant TCP approach for object-oriented software that uses path-based integration testing to identify the possible execution path extracted from the Java System Dependence Graph (JSDG) model of the source code using forward slicing technique. …”
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Ump Intelligent Chatbot Using Dialogflow
Published 2022“…To create such a chatbot, a machine learning algorithm is used to learn the human language that is mainly used during such conversations. …”
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Undergraduates Project Papers -
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Validation of deep convolutional neural network for age estimation in children using mandibular premolars on digital panoramic dental imaging / Norhasmira Mohammad
Published 2022“…The semi-automated dental staging system developed in this study is based on the Malay children’s population and uses a brain-inspired learning algorithm termed "deep learning". The methodology is comprised of four major steps: image preprocessing, which adheres to the inclusion criteria for panoramic dental radiographs, segmentation, and classification of mandibular premolars according to Demirjian's staging system using the Dynamic Programming-Active Contour (DP-AC) method and Deep Convolutional Neural Network (DCNN), respectively, and statistical analysis. …”
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