Search Results - (( evolution visualization learning algorithm ) OR ( program segmentation learning algorithm ))
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A review on monocular tracking and mapping: from model-based to data-driven methods
Published 2022“…This article starts by introducing the basic sensor systems and analyzing the evolution of monocular tracking and mapping algorithms through bibliometric data. …”
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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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An adaptive HMM based approach for improving e-Learning methods
Published 2023“…The evolution of web based interaction and information processing has provided an important platform to conduct e-learning activities. …”
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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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Exploration of COVID‑19 data in Malaysia through mapper graph
Published 2024“…Still, the hierarchical clustering does not further subdivide Selangor data into two to three separate clusters as the Mapper algorithm does. This research provides valuable insights for comprehending the pandemic timeline in Malaysia via the Mapper algorithm, which serves as a highly compact data visualization technique.…”
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Using the evolutionary mating algorithm for optimizing the user comfort and energy consumption in smart building
Published 2023“…A comparative analysis is conducted with other well-known algorithms such as Particle Swarm Optimization (PSO), Differential Evolution (DE), Ant Colony Optimization (ACO), Biogeography-Based Optimization (BBO), Teaching-Learning Based Optimization (TLBO), and Beluga Whale Optimization (BWO). …”
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Critical review of object detection techniques for traffic light detection in intelligent transportation systems
Published 2025“…Despite progress in deep learning and optimization algorithms, traffic light detection still faces persistent challenges under varying conditions such as illumination changes, occlusions, and visual clutter. …”
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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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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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