Search Results - (( java implementation path algorithm ) OR ( using simple learning 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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2
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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3
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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5
E4ML: Educational Tool for Machine Learning
Published 2003“…There are various types of machine learning algorithms with certain processes taken by the algorithm.In teaching of the machine learning algorithms, such processes need to be explained especially to the beginner in introductory level.This paper discusses the development the tool that addresses the process by certain algorithm to produce a hypothesis or output based on given data.This tool can also be used in teaching and learning purposes.The explanation of processes by the algorithms is demonstrated through simple simulation.The source of the algorithms was adapted from Mitchell book [1] that cover popular algorithms in machine learning for teaching and learning such as Concept Learning, Decision Tree, Bayesian Learning, Neural Networks, and Instance based Learning.The tool also used several classes of Weka (Waikato Environment for Knowledge Analysis) as a basis for the design and implementation of the new tool that focuses on explaining the processes taken by certain algorithm.…”
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6
Simple Screening Method of Maize Disease using Machine Learning
Published 2019“…Hence, this paper present a simple and efficient machine learning method which is Fuzzy C-Means algorithm to screen leaf disease severity in maize. …”
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Bio-signal identification using simple growing RBF-network (OLACA)
Published 2007“…An enhanced online adaptive centre allocation algorithms (or resource allocation network (RAN)) using simple/stochastic back-propagation method with minimal weight update variant are developed for direct-link radial basis function (DRBF) networks. …”
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Hypertension Prediction in Adolescents Using Anthropometric Measurements: Do Machine Learning Models Perform Equally Well?
Published 2022“…The use of anthropometric measurements in machine learning algorithms for hypertension prediction enables the development of simple, non-invasive prediction models. …”
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A Reference Based Surface Defect Segmentation Algorithm For Automatic Optical Inspection System
Published 2020“…However, present complex algorithms which are accurate require high processing power using a large size of learning dataset without labelling error. …”
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10
Development Of Construction Noise Prediction Method Using Deep Learning Model
Published 2021“…This model could relieve the burden of manual calculations for simple prediction charts, and no previous study has applied this algorithm to construction noise prediction. …”
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A case study of microarray breast cancer classification using machine learning algorithms with grid search cross validation
Published 2023“…In this work, simple machine learning methods are used to classify breast cancer microarray data to normal and relapse. …”
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Hypertension prediction in adolescents using anthropometric measurements: Do machine learning models perform equally well?
Published 2022“…The use of anthropometric measurements in machine learning algorithms for hypertension prediction enables the development of simple, non-invasive prediction models. …”
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Modeling of cardiovascular diseases (CVDs) and development of predictive heart risk score
Published 2021“…Further, it focuses on the development of various forms of local risk prediction models and simple heart risk scores using non-laboratory features and machine learning (ML) algorithms. …”
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14
A direct ensemble classifier for imbalanced multiclass learning
Published 2012“…Researchers have shown that although traditional direct classifier algorithm can be easily applied to multiclass classification, the performance of a single classifier is decreased with the existence of imbalance data in multiclass classification tasks.Thus, ensemble of classifiers has emerged as one of the hot topics in multiclass classification tasks for imbalance problem for data mining and machine learning domain.Ensemble learning is an effective technique that has increasingly been adopted to combine multiple learning algorithms to improve overall prediction accuraciesand may outperform any single sophisticated classifiers.In this paper, an ensemble learner called a Direct Ensemble Classifier for Imbalanced Multiclass Learning (DECIML) that combines simple nearest neighbour and Naive Bayes algorithms is proposed. …”
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Evaluating the performances of Dice, Jaccard, Overlap, and Cosine coefficients in automated marking of algebraic equations / Hasnah Yusof, Wan Rolini Wan Yusoff and Masita Jamal
Published 2006“…The algorithm used in the automated marking was adjusted to fit the DJOC coefficients. …”
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Research Reports -
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Simple quantum circuit for pattern recognition based on nearest mean classifier
Published 2016“…Lett. 114, 140504 (2015)] which uses quantum matrix inverse algorithm to find optimal hyperplane that separated two different classes. …”
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Deep reinforcement learning to multi-agent deep reinforcement learning
Published 2022“…In fact, overall goal in this paper is a comprehensive explanation of the various Deep Reinforcement Learning (DRL) algorithms, and its combination with Multi-Agent methods. …”
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Detection and classification of conflict flows in SDN using machine learning algorithms
Published 2021“…Moreover, applying machine learning algorithms in the identification and classification of conflicting flows has limitations. …”
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Enhanced Q-Learning algorithm for potential actions selection in automated graphical user interface testing
Published 2023“…To overcome this limitation, the Q-Learning algorithm was proposed by several researchers to minimise randomness. …”
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