Search Results - (( java implementation learning algorithm ) OR ( parameter centered learning algorithm ))
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1
Plagiarism Detection System for Java Programming Assignments by Using Greedy String-Tilling Algorithm
Published 2008“…The prototype system, known as Java Plagiarism Detection System (JPDS) implements the Greedy-String-Tiling algorithm to detect similarities among tokens in a Java source code files. …”
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A harmony search-based learning algorithm for epileptic seizure prediction
Published 2016“…The learning phase of wavelet neural network entails the task of finding the optimal set of parameter, which includes wavelet activation function, translation centers, dilation parameter, synaptic weight values, and bias terms. …”
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An Educational Tool Aimed at Learning Metaheuristics
Published 2020“…Implemented with Java, this tool provides a friendly GUI for setting the parameters and display the result from where the learner can see how the selected algorithm converges for a particular problem solution. …”
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Adoption of machine learning algorithm for analysing supporters and non supporters feedback on political posts / Ogunfolajin Maruff Tunde
Published 2022“…The method was implemented using Java and the results of the simulation were evaluated using five standard performance metrics: accuracy, AUC, precision, recall, and f-Measure. …”
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A Feature Ranking Algorithm in Pragmatic Quality Factor Model for Software Quality Assessment
Published 2013“…The methodology used consists of theoretical study, design of formal framework on intelligent software quality, identification of Feature Ranking Technique (FRT), construction and evaluation of FRA algorithm. The assessment of quality attributes has been improved using FRA algorithm enriched with a formula to calculate the priority of attributes and followed by learning adaptation through Java Library for Multi Label Learning (MULAN) application. …”
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Prediction of Ground Surface Deformation Induced by Earthquake on Urban Area Using Machine Learning
Published 2023“…Overall, the four machine learning algorithms have outstanding performance, with a coefficient determinant of more than 0.9. …”
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RECURSIVE LEARNING ALGORITHMS ON RBF NETWORKS FOR NONLINEAR SYSTEM IDENTIFICATION
Published 2010“…The proposed training algorithms discussed in this thesis are derived for fixed size RBF network and being compared with Extreme Learning Machine (ELM) as the ELM technique just randomly assigned centers and width of the hidden neurons and update the output connected weights. …”
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Hybridization of metaheuristic algorithm in training radial basis function with dynamic decay adjustment for condition monitoring / Chong Hue Yee
Published 2023“…In this research work, the motivation is to develop an autonomous learning model based on the hybridization of an adaptive ANN and a metaheuristic algorithm for optimizing ANN parameters so that the network could perform learning and adaptation in a more flexible way and handle condition classification tasks more accurately in industries, such as in power systems. …”
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9
Web-based clustering tool using fuzzy k-mean algorithm / Ahmad Zuladzlan Zulkifly
Published 2019“…This project will use fuzzy k-means clustering algorithm to cluster the data because it is easy to implement and have many advantages. …”
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Activity recognition using optimized reduced kernel extreme learning machine (OPT-RKELM) / Yang Dong Rui
Published 2019“…One of the major research problems is the computation resources required by machine learning algorithm used for classification for HAR. …”
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A Mininet emulation study for SDN fat tree data center sleep mode routing algorithms
Published 2025“…The proposed sleep mode method obtained enhancement in the performance parameter with uses less energy in the network. ? 2024 by the authors; licensee Learning Gate.…”
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Features selection for intrusion detection system using hybridize PSO-SVM
Published 2016“…Hybridize Particle Swarm Optimization (PSO) as a searching algorithm and support vector machine (SVM) as a classifier had been implemented to cope with this problem. …”
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13
Personalized one-shot local adaptation federated learning for mortality prediction in multi-center Intensive Care Unit
Published 2024“…Step 3 automatically evolves the best-fitting parameters for the highly personalized model at each center using an adapted genetic algorithm. …”
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Talkout : Protecting mental health application with a lightweight message encryption
Published 2022“…The investigation of lightweight message encryption algorithms is conducted with systematic quantitative literature and experiment implementation in Java and Android running environment. …”
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Super-opposition spiral dynamic-based fuzzy control for an inverted pendulum system
Published 2022“…An improvement on the spiral dynamic algorithm (SDA), this method uses a concept centered on opposition-based learning, which is used to evaluate the fitness of agents at the opposite location to the current solution. …”
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Mathematical simulation for 3-dimensional temperature visualization on open source-based grid computing platform
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Predicting Water Quality with Artificial Intelligence: A Review of Methods and Applications
Published 2024“…We compared these articles in terms of parameters, modelling algorithms, time scale scenarios, and performance measurement indicators. …”
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A joint Bayesian optimization for the classification of fine spatial resolution remotely sensed imagery using object-based convolutional neural networks
Published 2022“…This paper presents a novel approach for combining convolutional neural networks (CNN) with OBIA based on joint optimization of segmentation parameters and deep feature extraction. A Bayesian technique was used to find the best parameters for the multiresolution segmentation (MRS) algorithm while the CNN model learns the image features at different layers, achieving joint optimization. …”
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Predictive analysis of porous media–cooled photovoltaic panels using gradient-boosting machine learning models
Published 2026“…Four advanced gradient-boosting algorithms, CatBoost, XGBoost, LightGBM, and GBM, were evaluated using five progressively complex models that incorporate key cooling parameters: solar radiation, channel height, coolant type, porosity, flow rate, and ambient conditions. …”
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