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Hybrib NSGA-II optimization for improving the three-term BP network for multiclass classification problems
Published 2015“…This paper presents a hybrid of the multiobjective evolutionary algorithm to gain a better accuracy of the fi nal solutions.The aim of using the hybrid algorithm is to improve the multiobjective evolutionary algorithm performance in terms of the enhancement of all the individuals in the population and increase the quality of the Pareto optimal solutions.The multiobjective evolutionary algorithm used in this study is a nondominated sorting genetic algorithm-II (NSGA-II) together with its hybrid, the backpropagation algorithm (BP), which is used as a local search algorithm to optimize the accuracy and complexity of the three-term backpropagation (TBP) network. …”
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Hybrid NSGA-II Optimization for Improving the Three-Term BP Network for Multiclass Classification Problems
Published 2015“…The aim of using the hybrid algorithm is to improve the multiobjective evolutionary algorithm performance in terms of the enhancement of all the individuals in the population and increase the quality of the Pareto optimal solutions. …”
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Enhanced grey wolf optimisation algorithm for feature selection in anomaly detection
Published 2022“…The second modification develops a new position update mechanism using the Bat Algorithm movement. The third modification improves the controlled parameter of the MBGWO algorithm using indicators from the search process to refine the solution. …”
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BAT-BP: A new BAT based back-propagation algorithm for efficient data classification
Published 2016“…The performance of the proposed Bat-BP algorithm is then compared with Artificial Bee Colony using BPNN (ABC-BP), Artificial Bee Colony using Levenberg-Marquardt (ABC-LM) and BPNN algorithm. …”
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A Novel Wrapper-Based Optimization Algorithm for the Feature Selection and Classification
Published 2023“…The performance of the proposed SCSO algorithm was compared with six state-of-the-art and recent wrapper-based optimization algorithms using the validation metrics of classification accuracy, optimum feature size, and computational cost in seconds. …”
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An enhanced soft set data reduction using decision partition order technique
Published 2017“…Also, the accuracy of original soft-set optimal and sub-optimal results have been improved using an intelligent SSR-BPSO-BBO algorithm. The computational cost of search domain (space) has been enhanced using proposed Markov Chain Model.…”
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Improving ant swarm optimization with embedded vaccination for optimum reducts generation
Published 2013“…This paper proposes a hybrid ant swarm optimization algorithm by using immunity to discover better fitness value in optimizing rough reducts set. …”
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A modified weighted support vector machine (WSVM) to reduce noise data in classification problem
Published 2021“…To overcome SVM drawback for noise data problem, WSVM using KPCM algorithm was used but WSVM using kernel-based learning algorithm such as KPCM algorithm suffer from training complexity, expensive computation time and storage memory when noise data contaminate training data. …”
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A modified weighted support vector machine (WSVM) to reduce noise data in classification problem
Published 2021“…To overcome SVM drawback for noise data problem, WSVM using KPCM algorithm was used but WSVM using kernel-based learning algorithm such as KPCM algorithm suffer from training complexity, expensive computation time and storage memory when noise data contaminate training data. …”
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Balancing Exploitation And Exploration Search Behavior On Nature-Inspired Clustering Algorithms
Published 2018“…In the OGC framework, the exhibited explorative search behavior of the Gravitational Clustering (GC) algorithm has been addressed by (i) eliminating the agent velocity accumulation, and (ii) integrating an initialization method of agents using variance and median to subrogate the exploration process. …”
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Stage of maturity banana fruit classification using image processing / Nadia Kasim ... [et al.]
Published 2019Subjects: “…Analytical methods used in the solution of physical problems…”
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Investigation of rectangular shape object detection and classification using python
Published 2022“…This work will implement and uses both hardware and software elements to provide the best solution for the purpose of fishcake quality sustainability. …”
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Underwater Image Recognition using Machine Learning
Published 2024“…Machine Learning is the branch of Artificial Intelligence in which a computer is fed with data and based on that data it tries to find out solution on its own. It encompasses the procedure for feeding algorithms information to create the algorithms realize patterns in the data and then increase the performance of the algorithms. …”
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Algorithmic design issues in adaptive differential evolution schemes: Review and taxonomy
Published 2018“…The performance of most metaheuristic algorithms depends on parameters whose settings essentially serve as a key function in determining the quality of the solution and the efficiency of the search. …”
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Poverty risk prediction based on socioeconomic factors using machine learning approach
Published 2025“…The findings indicated that the Logistic Regression outperformed the other algorithm with 99.06% using cross-validation and 98.42% using the splitting method, and with the best value of precision, recall, and F1-score. …”
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An improved genetic-fuzzy system for classification and data analysis
Published 2017“…In the second variant classifier, we further improve the first variant classifier by enhancing the selection method of the antecedent conditions of the rules generated in the initial population of genetic algorithm. Unlike the method applied in the original classifier and the first variant classifier, which uses a random selection of the antecedent conditions, we proposed a feature-based selection method to favor the antecedent conditions associated with the most relevant features. …”
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Minimizing the number of stunting prevalence using the euclid algorithm clustering approach
Published 2023“…The algorithm used is Euclid. The Euclid algorithm can cluster stunting prevalence data into 4 clusters with the very little category at 79%, the little category at 67%, the many categories at 51%, and the very much category at 21%. …”
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