Search Results - (( variables classification modeling algorithm ) OR ( program implementation path algorithm ))
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1
Simulation of shortest path using a-star algorithm / Nurul Hani Nortaja
Published 2004“…The steps to calculate a shortest path using A • algorithm is shown by using appropriate examples and related figures. …”
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Classification for large number of variables with two imbalanced groups
Published 2020“…This study proposed two algorithms of classification namely Algorithm 1 and Algorithm 2 which combine resampling, variable extraction, and classification procedure. …”
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Formulating new enhanced pattern classification algorithms based on ACO-SVM
Published 2013“…ACO originally deals with discrete optimization problem.In applying ACO for solving SVM model selection problem which are continuous variables, there is a need to discretize the continuously value into discrete values.This discretization process would result in loss of some information and hence affects the classification accuracy and seeking time.In this algorithm we propose to solve SVM model selection problem using IACOR without the need to discretize continuous value for SVM.The second algorithm aims to simultaneously solve SVM model selection problem and selects a small number of features.SVM model selection and selection of suitable and small number of feature subsets must occur simultaneously because error produced from the feature subset selection phase will affect the values of SVM model selection and result in low classification accuracy.In this second algorithm we propose the use of IACOMV to simultaneously solve SVM model selection problem and features subset selection.Ten benchmark datasets were used to evaluate the proposed algorithms.Results showed that the proposed algorithms can enhance the classification accuracy with small size of features subset.…”
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Vision-based robot indoor navigation
Published 2022“…A navigation robot is built to test the workability and efficiency of the algorithms. In general, the algorithm can provide the nearest path to navigate around the environment without manual assistance. …”
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Implementation of Autonomous Vehicle Navigation Algorithms Using Event-Driven Programming
Published 2012“…By using FSM to describe the behaviour of a navigating mobile robot, an equivalent algorithm can be developed. The algorithm can be relatively easy translated to a suitable program with event-driven programming technique. …”
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Optimization Of Two-Dimensional Dual Beam Scanning System Using Genetic Algorithms
Published 2008“…This algorithm has been tested and implemented successfully via a dual beam optical scanning system.…”
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Implementation of an autonomous mobile robot navigation algorithm using C language
Published 2009“…The problem can basically be divided into positioning and path planning. This report basically discusses the study and also work that has been done from previous of the chosen topic, which is Implementation of an Autonomous Mobile Robot Navigation Algorithm using 'C' language. …”
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Feature selection and model selection algorithm using incremental mixed variable ant colony optimization for support vector machine classifier
Published 2013“…In order to enhance SVM performance, these problems must be solved simultaneously because error produced from the feature subset selection phase will affect the values of the SVM parameters and resulted in low classification accuracy.Most approaches related with solving SVM model selection problem will discretize the continuous value of SVM parameters which will influence its performance.Incremental Mixed Variable Ant Colony Optimization (IACOMV) has the ability to solve SVM model selection problem without discretising the continuous values and simultaneously solve the two problems.This paper presents an algorithm that integrates IACOMV and SVM.Ten datasets from UCI were used to evaluate the performance of the proposed algorithm.Results showed that the proposed algorithm can enhance the classification accuracy with small number of features.…”
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Implementation of autonomous vehicle navigation algorithms using event-driven programming
Published 2012“…By using FSM to describe the behaviour of a navigating mobile robot, an equivalent algorithm can be developed. The algorithm can be relatively easy translated to a suitable program with event-driven programming technique. …”
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GPS boundary navigation of DrosoBots using MATLAB simulation
Published 2010“…To implement this, region recognition and several path planning algorithms have been utilized. …”
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Proceeding Paper -
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An improved algorithm in test case generation from UML activity diagram using activity path
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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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14
Development of obstacle avoidance technique in web-based geographic information system for traffic management using open source software
Published 2014“…Pg Routing as an extension of Postgre SQL/Post GIS database is an open source library that implements the Dijkstra shortest path algorithm. However, the functionality to avoid obstacles in that analysis is still limited. …”
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Mixed variable ant colony optimization technique for feature subset selection and model selection
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Development of Path Loss Models for Smooth and Convex Surfaces Terrains in Malaysian Environment
Published 2004“…This program consists of four algorithms which are conversion formulas, smooth terrain, single convex surface terrain and double convex surfaces terrain.…”
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Development of a syncope classification algorithm from physiological signals acquired in tilt-table test
Published 2023“…There are 8 set of feature selection model has built and a total of 24 set of classifiers with 3 different type of classification techniques were developed. …”
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Empirical Analysis of Intra vs. Inter-Subject Variability in VR EEG-Based Emotion Modelling
Published 2018“…Secondly, the data will then be tested and trained with KNN and SVM algorithms. We conduct subject-dependent as well as subject-independent classifications in order to compare intra-against inter-subject variability, respectively in VR EEG-based emotion modeling. …”
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