Search Results - (( java implementation path algorithm ) OR ( basic information selection 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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Intelligent agent for e-commerce using genetic algorithm / Kok Sun Sun
Published 2000“…This system is achieved using the Genetic Algorithm which is capable of performing information retrieval and learning algorithm. …”
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Butterfly traffic pattern in selection algorithm on hierarchical torus network
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Proceeding Paper -
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Adaptive routing algorithms and implementation for TESH network
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Enhanced Distributed Learning Classifier System For Simulated Mobile Robot Behaviours
Published 2010“…The main problem in robotic system is in selecting the correct behaviour. The aim of this research is to overcome the behaviour selection problem. …”
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Static image of hand gesture for numerical sign language recognition system using backfrofagation neural network / Erman Ibrahim
Published 2007“…The usefiil information can be used as a basic idea towards project methodology whereby a detail development process presented. …”
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An ensemble feature selection method to detect web spam
Published 2018“…In addition, it improves classification metrics in comparison to basic feature selection methods.…”
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Improved Multi-Verse Optimizer In Text Document Clustering For Topic Extraction
Published 2021“…To achieve this aim: First, A new feature selection method for TDC, that is, binary multi-verse optimizer algorithm (BMVO) is proposed to eliminate irrelevantly, redundant features and obtain a new subset of more informative features. …”
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Hybridflood algorithms minimizing redundant messages and maximizing efficiency of search in unstructured P2P networks
Published 2012“…The amount of digital information is expected to grow exponentially. There are many challenges posed by such a huge amount of information for existing search systems. …”
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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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Medical diagnosis using data mining techniques / Shaiful Nizam Zamri
Published 2003“…Secondly, this report will review the literature part which started with basic knowledge of data mining and knowing what the basic information about data mining. …”
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ANALYSIS OF CUSTOMER SERVICE BUSINESS PROCESS USING DATA MINING
Published 2020“…Hence, this paper contribute to justify by the basic concepts of data mining, described the selected types and models of algorithms, and the process of data mining by using R Tools.…”
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Final Year Project -
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Metaheuristic-Based Neural Network Training And Feature Selector For Intrusion Detection
Published 2019“…However, the training datasets usually compose feature sets of irrelevant or redundant information, which impacts the performance of classification, and traditional learning algorithms such as backpropagation suffer from known issues, including slow convergence and the trap of local minimum. …”
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