Search Results - (( java location based algorithm ) OR ( parameter applying learning algorithm ))
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A harmony search-based learning algorithm for epileptic seizure prediction
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Group formation using genetic algorithm
Published 2009“…However, due to lack of programming skills especially in Java programming language and the inability to have meetings frequently among the group members,most of the students’ software project cannot be delivered successfully.To solve this problem, systematic group formation is one of the initial factors that should be considered to ensure that every group consists of quality individuals who are good in Java programming and also to ensure that every group member in a group are staying closer to each other.In this research, we propose a method for group formation using Genetic Algorithms, where the members for each group will be generated based on the students’ programming skill and location of residential colleges.…”
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Multi-floor indoor location estimation system based on wireless local area network
Published 2007“…The most probable match is selected and returned as estimated location based on Bayesian filtering algorithm. Estimated location is reported as physical location and symbolic location. …”
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Differential evolution for neural networks learning enhancement
Published 2008“…These elements will affect the speed of neural network learning. DE has been applied successfully to improve ANN learning from previous studies. …”
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Autism Spectrum Disorder Classification Using Deep Learning
Published 2021“…In the future, different types of deep learning algorithms need to be applied, and different datasets can be tested with different hyper-parameters to produce more accurate ASD classifications.…”
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Hybrid Harmony Search Algorithm with Grey Wolf Optimizer and Modified Opposition-based Learning
Published 2019“…Then, a modified version of opposition-based learning technique has been applied on the hybrid algorithm to improve the HS exploration because HS easily gets trapped into local optima. …”
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Machine learning model for performance prediction in mobile network management / Muhammad Hazim Wahid
Published 2022“…One of the major challenges when applying machine learning is to identify the best algorithm from a variety of algorithms to solve a problem. …”
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Multi-Objective Hybrid Algorithm For The Classification Of Imbalanced Datasets
Published 2019“…For this purpose, the normal distributions are applied to each class. The parameters of this distribution are optimized by applying the proposed MOHA. …”
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Development of an intelligent prediction tool for rice yield based on machine learning techniques
Published 2006“…As the next step, correlation and regression analysis is applied for analyzing the impact of various parameters on the rice yield. and also for predicting the yield.…”
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Improved Manta Ray Foraging Optimizer-based SVM for Feature Selection Problems: A Medical Case Study
Published 2024“…Support Vector Machine (SVM) has become one of the traditional machine learning algorithms the most used in prediction and classification tasks. …”
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Prediction of biochemical oxygen demand in Mexican surface waters using machine learning / Maximiliano Guzmán-Fernández ... [et al.]
Published 2021“…Two groups were formed and used as input to four machine learning algorithms. Random forest algorithm obtained the best performance. …”
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Intrusion Detection Systems, Issues, Challenges, and Needs
Published 2021“…However, these algorithms suffer from many lacks especially when apply to detect new type of attacks, and need for new algorithms such as JAYA algorithm, teaching learning-based optimization algorithm (TLBO) algorithm is arise. …”
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Particle swarm optimization for neural network learning enhancement
Published 2006“…To overcome this problem, Genetic Algorithm (GA) has been used to determine optimal value for BP parameters such as learning rate and momentum rate and also for weight optimization. …”
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Wavelet network based online sequential extreme learning machine for dynamic system modeling
Published 2013“…This attains good performance at extremely fast learning. The initial kernel parameters of WN played a big role to ensure fast and better learning performance. …”
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Multi-step time series prediction using recurrent kernel online sequential extreme learning machine / Liu Zongying
Published 2019“…However, the problems with traditional offline and online learning algorithms in machine learning algorithms are usually faced with parameter dependency, concept drift handling problem, connectionless of neural net and unfixed reservoir. …”
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