Search Results - (( java implementation path algorithm ) OR ( using sparse machine 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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Blind Source Separation Using Two-Dimensional Nonnegative Matrix Factorization In Biomedical Field
Published 2018“…Theoretically,β and α is parameters that used to vary the NMF2D algorithm in order to yield high SDR value. …”
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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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Semi-supervised learning for feature selection and classification of data / Ganesh Krishnasamy
Published 2019“…The proposed algorithm is compared with the state-of-the-art feature selection algorithms using three different datasets. …”
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3D LiDAR Vehicle Perception and Classification Using 3D Machine Learning Algorithm
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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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Partitional clustering algorithms for highly similar and sparseness y-short tandem repeat data / Ali Seman
Published 2013“…In some cases, they are quite distant and sparseness. This uniqueness of Y-STR data has become problematic in partitioning the data using the existing partitional clustering algorithms. …”
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Robust correlation feature selection based support vector machine approach for high dimensional datasets
Published 2025“…The third step employs the support vector machine algorithm to calculate prediction values. …”
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A hybrid model based on constraint OSELM, adaptive weighted SRC and KNN for large-scale indoor localization
Published 2019“…The understanding is that the original extreme learning machine (ELM) is less robust against noise, while sparse representation classification (SRC) and KNN suffer a high computational burden when using the over-complete dictionary. …”
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DEVELOPMENT OF POINT CLOUD DESCRIPTORS FOR ROBUST 3D RECOGNITION
Published 2015“…Among many surface descriptors proposed in the literature, orientation based descriptors are the most commonly used, however they are represented with large and sparse histograms. …”
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Physics-guided deep neural network to characterize non-Newtonian fluid flow for optimal use of energy resources
Published 2021“…In this research, a novel algorithm (Herschel Bulkley Network) is introduced to simulate the non-Newtonian fluid flow in a pipe using data redundant deep neural network (DNN) for fully developed, laminar, and incompressible flow conditions. …”
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Auto-encoder variants for solving handwritten digits classification problem
Published 2020“…First, we introduce the conventional AE model and its different variant for learning abstract features from data by using a contrastive divergence algorithm. Second, we present the major differences among the following three popular AE variants: sparse AE (SAE), denoising AE (DAE), and contractive AE (CAE). …”
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New Quarter-Sweep-Based Accelerated Over-Relaxation Iterative Algorithms and their Parallel Implementations in Solving the 2D Poisson Equation
Published 2010“…The parallel strategies used in the new algorithms are based on the message latency minimization and processor-independent iterations.…”
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Information fusion and data augmentation with deep features for a deep learning-based baby cry recognition / Zhang Ke
Published 2024“…A sparse autoencoder model is used to fuse the deep features. …”
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