Search Results - (( knowledge classification problem algorithm ) OR ( java implementation path 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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Overview of metaheuristic: classification of population and trajectory
Published 2010“…Some algorithms can be defined if the developer of the system has problem specific knowledge to the solution. …”
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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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Decision tree as knowledge management tool in image classification
Published 2008“…Expert System has been grown so fast as a science that study how to make computer capable of solving problems that typically can only be solved by expert.It has been realized that the biggest challenge of developing expert system is the process include expert’s knowledge into the system.This research tries to model expert’s knowledge management using case based reasoning method.The knowledge itself is not inputted directly by the expert, but rather the system will learn the knowledge from what the expert did to the previous cases.This research takes image classification as the problem to be solved.As the knowledge development technique, we build decision tree by using C4.5 algorithm.Variables used for building the decision tree are the image visual features.…”
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Comparison Performance of Qualitative Bankruptcy Classification based on Data Mining Algorithms
Published 2018“…Knowledge discovery using data mining techniques are commonly applied in bankruptcy classification and prediction. …”
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New Learning Models for Generating Classification Rules Based on Rough Set Approach
Published 2000“…Recently, different models were used to generate knowledge from vague and uncertain data sets such as induction decision tree, neural network, fuzzy logic, genetic algorithm, rough set theory, and others. …”
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Feature and Instances Selection for Nearest Neighbor Classification via Cooperative PSO
Published 2014“…Data reduction is an essential task in the data preparation phase of knowledge discovery and data mining (KDD). The reduction method contains two techniques, namely features reduction and data reduction which are commonly applied to a classification problem. …”
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Evaluation of fall detection classification approaches
Published 2012“…This paper presents the comparison of different machine learning classification algorithms using Waikato Environment for Knowledge Analysis (WEKA) platform for classifying falling patterns from ADL patterns. …”
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Nearest neighbour group-based classification
Published 2010“…This can be seen as a simplification of the well studied, but computationally complex, non-sequential compound classifica�tion problem. In this paper, we extend three variants of the nearest neighbour algorithm to develop a number of non-parametric group-based classification techniques. …”
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Semi-supervised learning for feature selection and classification of data / Ganesh Krishnasamy
Published 2019“…Furthermore, HSS-ELM maintains almost all the advantages of the traditional ELM such as the significant training efficiency and straightforward implementation for multiclass classification problems. The proposed algorithm is tested on publicly available datasets. …”
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An enhanced classification framework for intrusions detection system using intelligent exoplanet atmospheric retrieval algorithm
Published 2022“…Regardless of the methods implemented by this system, eliminating false alerts is still a huge problem. To describe data traffic passing through the network, a database of the network security layer (NSL) knowledge discovery in database (KDD) dataset is used. …”
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An improvement of back propagation algorithm using halley third order optimisation method for classification problems
Published 2020“…Back Propagation (BP) has proven to be a robust algorithm for different connectionist learning problems which commonly available for any functional induction that provides a computationally efficient method. …”
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Comparing the knowledge quality in rough classifier and decision tree classifier
Published 2008“…Theoretically, different classifiers will generate different sets of rules via knowledge even though they are implemented to the same classification problem.Hence, the aim of this paper is to investigate the quality of knowledge produced by Rc and DTc when similar problems are presented to them.In this case, four important performance metrics are used as comparison, the accuracy of classification, rules quantity, rules length and rules coverage.Five dataset from UCI Machine Learning are chosen and then mined using Rc toolkit namely ROSETTA while C4.5 algorithm in WEKA application is chosen as DTc rule generator. …”
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Hyper-heuristic framework for sequential semi-supervised classification based on core clustering
Published 2020“…Hence, the algorithm must overcome the problem of dynamic update in the internal parameters or countering the concept drift. …”
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Recognition of human motion from qualitative normalised templates
Published 2007“…This paper proposes a Qualitative Normalised Templates (QNTs) framework for solving the human motion classification problem. In contrast to other human motion classification methods which usually include a human model, prior knowledge on human motion and a matching algorithm, we replace the matching algorithm (e.g. template matching) with the proposed QNTs. …”
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