Search Results - (( evolution optimization learning algorithm ) OR ( features extraction method algorithm ))
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Class binarization with self-adaptive algorithm to improve human activity recognition
Published 2018“…Therefore, feature selection using Relief-f with self-adaptive Differential Evolution (rsaDE) algorithm is proposed to select the most significant features. …”
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Stock market turning points rule-based prediction / Lersak Photong … [et al.]
Published 2021“…Finally, rule-based optimisation techniques such as Particle Swarm Optimization (PSO), Differential Evolution (DE) and Grey Wolf Optimizer (GWO) were used to minimise the amount of time employed in the stock market turning points prediction. …”
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Comparison between Lamarckian Evolution and Baldwin Evolution of neural network
Published 2006“…Hybrid genetic algorithms are the combination of learning algorithms(Back propagation), usually working as evaluation functions, and genetic algorithms. …”
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Differential evolution for neural networks learning enhancement
Published 2008“…To overcome this problem, Differential Evolution (DE) has been used to determine optimal value for ANN parameters such as learning rate and momentum rate and also for weight optimization. …”
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Broadening selection competitive constraint handling algorithm for faster convergence
Published 2020“…In this study, the BSCCH algorithm has been coupled with Differential Evolution algorithm as a proof of concept because it is found to be an efficient algorithm in the literature for constrained optimization problems. …”
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Dual optimization approach in discrete Hopfield neural network
Published 2024“…Therefore, this research contributes to the improvement of the learning and retrieval phases by integrating the Hybrid Differential Evolution Algorithm and Swarm Mutation respectively. …”
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Evaluation and Comparative Analysis of Feature Extraction Methods on Image Data to increase the Accuracy of Classification Algorithms
Published 2024“…The feature extraction methods evaluated were Grayscale Pixel Values, Mean Pixel Value of Channels, and Extracting Edge Features. …”
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Navigating challenges and opportunities of machine learning in hydrogen catalysis and production processes: Beyond algorithm development
Published 2025“…This review uniquely focuses on harnessing the synergy between ML and computational modeling (CM) or optimization tools, as well as integrating multiple ML techniques with CM, for the synthesis of diverse hydrogen evolution reaction (HER) catalysts and various hydrogen production processes (HPPs). …”
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Genetic algorithm based ensemble framework for sentiment analysis
Published 2018“…Prior to developing the whole multilayered ensemble framework, two separate experiments were performed to evaluate and study the different methods of feature extraction and selection. Methods of feature extraction can be separated into word based and phrase based. …”
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Extracting feature from images by using K-Means clustering algorithm / Abdul Hakim Zainal Abidin
Published 2016“…This research purposed clustering algorithm to improve process extracting feature in images to get meaningful information because it can speed up the time to process of extracting meaningful information in images due to the efficient of the algorithm that has high performance to process the image. …”
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Using the evolutionary mating algorithm for optimizing deep learning parameters for battery state of charge estimation of electric vehicle
Published 2023“…This paper presents the application of a recent metaheuristic algorithm namely Evolutionary Mating Algorithm (EMA) for optimizing the Deep Learning (DL) parameters to estimate the state of charge (SOC) of a battery for an electric vehicle in the real environment. …”
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Improved whale optimization algorithm for feature selection in Arabic sentiment analysis
Published 2019“…Whale Optimization Algorithm (WOA) is one of the recent metaheuristic optimization algorithm that mimics the whale hunting mechanism. …”
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Artificial neural network learning enhancement using Artificial Fish Swarm Algorithm
Published 2011“…Artificial Neural Network (ANN) is a new information processing system with large quantity of highly interconnected neurons or elements processing parallel to solve problems.Recently, evolutionary computation technique, Artificial Fish Swarm Algorithm (AFSA) is chosen to optimize global searching of ANN.In optimization process, each Artificial Fish (AF) represents a neural network with output of fitness value.The AFSA is used in this study to analyze its effectiveness in enhancing Multilayer Perceptron (MLP) learning compared to Particle Swarm Optimization (PSO) and Differential Evolution (DE) for classification problems.The comparative results indeed demonstrate that AFSA show its efficient, effective and stability in MLP learning.…”
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Conference or Workshop Item -
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A New Co-Evolution Binary Particle Swarm Optimization With Multiple Inertia Weight Strategy For Feature Selection
Published 2019“…Hence, a new co-evolution binary particle swarm optimization with a multiple inertia weight strategy (CBPSO-MIWS) is proposed in this work. …”
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Cancellable face template algorithm based on speeded-up robust features and winner-takes-all
Published 2020“…This research presents a cancell able face image algorithm built upon the speeded-up robust features method to extract and select features. …”
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Performances Of Metaheuristic Algorithms In Optimizing Tool Capacity Allocations
Published 2014“…In this research, the algorithms studied includes Genetic Algorithm, Particle Swarm Optimization Algorithm, Differential Evolution Algorithm, Harmony Search Algorithm, Teaching-LearningBased Optimization Algorithm and Black Hole Algorithm. …”
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Register Transfer Level Implementation Of Pooling - Based Feature Extraction For Finger Vein Identification
Published 2017“…Feature extraction techniques based on global feature extraction such as Principle Component Analysis (PCA) were implemented. …”
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PMT : opposition based learning technique for enhancing metaheuristic algorithms performance
Published 2020“…To evaluate the PMT’s performance and adaptability, the PMT was applied to four contemporary metaheuristic algorithms, Differential Evolution, Particle Swarm Optimization, Simulated Annealing, and Whale Optimization Algorithm, to solve 15 well-known benchmark functions as well as 2 real world problems based on the welded beam design and pressure vessel design. …”
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An improved negative selection algorithm based on the hybridization of cuckoo search and differential evolution for anomaly detection
Published 2018“…These randomly generated detectors suffer from not been able to adequately cover the non-self space, which diminishes the detection performance of the V-Detectors algorithm. This research therefore proposed CSDE-V-Detectors which entail the use of the hybridization of Cuckoo Search (CS) and Differential Evolution (DE) in optimizing the random detectors of the V-Detectors. …”
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