Search Results - (( subset selection modified algorithm ) OR ( wave optimization search algorithm ))*

Aco-based feature selection algorithm for classification by Al-mazini, Hassan Fouad Abbas

“…However, the MGCACO algorithm has three main drawbacks in producing a features subset because of its clustering method, parameter sensitivity, and the final subset determination. …”
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An ensemble learning method for spam email detection system based on metaheuristic algorithms by Behjat, Amir Rajabi

“…In order to address the challenges that mentioned above in this study, in the first phase, a novel architecture based on ensemble feature selection techniques include Modified Binary Bat Algorithm (NBBA), Binary Quantum Particle Swarm Optimization (QBPSO) Algorithm and Binary Quantum Gravita tional Search Algorithm (QBGSA) is hybridized with the Multi-layer Perceptron (MLP) classifier in order to select relevant feature subsets and improve classification accuracy. …”
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Fuzzy clustering method and evaluation based on multi criteria decision making technique by Sameer, Fadhaa Othman

“…For the third problem a modified of Kohonen Network (MKN) algorithm was proposed to select the initial centres of clusters. …”
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Mutable composite firefly algorithm for gene selection in microarray based cancer classification by Fajila, Mohamed Nisper Fathima

“…This leads to the classification accuracy and genes subset size problem. Hence, this study proposed to modify the Firefly Algorithm (FA) along with the Correlation-based Feature Selection (CFS) filter for the gene selection task. …”
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Enhanced AI-based anomaly detection method in the intrusion detection system (IDS) / Kayvan Atefi by Atefi, Kayvan

“…Experiments demonstrate and prove that the proposed EBPSO method produces better accuracy mining data and selecting subset of relevant features comparing other algorithms. …”
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Healthcare Data Analysis Using Water Wave Optimization-Based Diagnostic Model by Kaur, Arvinder, Kumar, Yugal

“…In the proposed diagnostic model, a water wave optimization (WWO) algorithm was implemented for improving the diagnosis accuracy. …”
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Enhanced weight-optimized recurrent neural networks based on sine cosine algorithm for wave height prediction by Alqushaibi, A., Abdulkadir, S.J., Rais, H.M., Al-Tashi, Q., Ragab, M.G., Alhussian, H.

“…Therefore, the wind plays an essential role in the oceanic atmosphere and contributes to the formation of waves. This paper proposes an enhanced weight-optimized neural network based on Sine Cosine Algorithm (SCA) to accurately predict the wave height. …”
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A generalized laser simulator algorithm for optimal path planning in constraints environment by Aisha, Muhammad

“…The suggested algorithm outperforms the competition in terms of improving path cost, smoothness, and search time. …”
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Improved Multi-Verse Optimizer In Text Document Clustering For Topic Extraction by Abasi, Ammar Kamal Mousa

“…To achieve this aim: First, A new feature selection method for TDC, that is, binary multi-verse optimizer algorithm (BMVO) is proposed to eliminate irrelevantly, redundant features and obtain a new subset of more informative features. …”
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Modelling of optimized hybrid debris flow using airborne laser scanning data in Malaysia by Lay, Usman Salihu

“…Cuckoo search), and evaluator or model inducing algorithms (e.g SVM) were utilized for feature subset selection, which further compared to select the optimal conditioning factors subset. …”
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Enhancing predictive performance in statistical modeling: Innovative hybrid best subset feature selection for rice production in Malaysia by Chuan, Zun Liang, Abraham Lim, Bing Sern, Ren Sheng, Tham, David Lau, King Luen, Tan, Chek Cheng

“…The proposed feature selection method integrates mathematics techniques, specifically the modified Taguchi-based VIseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) multicriteria decision-making (MCDM) algorithm and three performance metrics. …”
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Study Of EMG Feature Selection For Hand Motions Classification by Abdullah, Abdul Rahim, Mohd Saad, Norhashimah, Too, Jing Wei

“…Thus, this paper employs two recent feature selection methods namely competitive binary gray wolf optimizer (CBGWO) and modified binary tree growth algorithm (MBTGA) to evaluate the most informative EMG feature subset for efficient classification. …”
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Design Of Feature Selection Methods For Hand Movement Classification Based On Electromyography Signals by Too, Jing Wei

“…For this purpose, the feature selection (FS) method is applied to evaluate the best feature subset from a large available feature set. …”
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Artificial Neural Network Based Particle Swarm Optimization for Microgrid Optimal Energy Scheduling by Abdolrasol M., Mohamed R., Hannan M., Al-Shetwi A., Mansor M., Blaabjerg F.

Attacks detection in 6G wireless networks using machine learning by Saeed, Mamoon M., Saeed, Rashid A., Gaid, Abdulguddoos S. A., Mokhtar, Rania A., Khalifa, Othman Omran, Ahmed, Zeinab E.

“…The second stage involves the feature selection approach. Correlation Feature Selection algorithm (CFS) is used to implement the suggested hybrid strategy. …”
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Shunt active power filter with modified synchronous reference frame technique and fuzzy logic current controller for harmonic mitigation by Suleiman, Musa

“…Furthermore, the ability to select an optimized subset of fuzzy rules is a major challenge in designing the control system. …”
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Comparison of mabsa, PSO and GWO of PI-PD controller for dc motor by Nur Naajihah, Ab Rahman

“…The swarm intelligence group selected Particle Swarm Optimization (PSO), Grey Wolf Optimization (GWO), and Modified Adaptive Bats Sonar Algorithms (MABSA) to optimize the parameters of the PI-PD controller. …”
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A Reinforced Active Learning Algorithm for Semantic Segmentation in Complex Imaging by Usmani, U.A., Watada, J., Jaafar, J., Aziz, I.A., Roy, A.

“…An agent learns the strategy of selecting a subset of small image regions, which are more knowledgeable than the whole set of images from an unlabeled data pool. …”
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Information Theoretic-based Feature Selection for Machine Learning by Muhammad Aliyu, Sulaiman

“…Three major factors that determine the performance of a machine learning are the choice of a representative set of features, choosing a suitable machine learning algorithm and the right selection of the training parameters for a specified machine learning algorithm. …”
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Hybrid metaheuristics for QOS-aware service composition / Hadi Naghavipour by Hadi , Naghavipour

“…As a result of this mapping study, five major hybridization strategies were identified in which two-third of solutions have been based on modifying algorithm operators or integration with another metaheuristic. …”
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