Search Results - (( quality classification problem algorithm ) OR ( simulation optimization learning algorithm ))
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BAT-BP: A new BAT based back-propagation algorithm for efficient data classification
Published 2016“…One of the nature inspired meta-heuristic Bat algorithm is becoming a popular method in solving many complex optimization problems. …”
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A New Machine Learning-based Hybrid Intrusion Detection System and Intelligent Routing Algorithm for MPLS Network
Published 2024“…The dataset development for both algorithms is carried out via simulations in Graphical Network Simulator 3 (GNS3). …”
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Opposition-based learning simulated kalman filter for Numerical optimization problems
Published 2016“…Simulated Kalman Filter (SKF) optimization algorithm is a population-based optimizer operated mainly based on Kalman filtering. …”
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Classification model for water quality using machine learning techniques
Published 2015“…This article proposes a suitable classification model for classifying water quality based on the machine learning algorithms. …”
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Investigating the Performance of Deep Reinforcement Learning-Based MPPT Algorithm under Partial Shading Condition
Published 2024“…These DRL-based algorithms optimize the local and global maximum power point (MPP) using deep Q-learning and deep deterministic policy gradient (DDPG). …”
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An adaptive ant colony optimization algorithm for rule-based classification
Published 2020“…Various classification algorithms have been developed to produce classification models with high accuracy. …”
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Deriving Optimal Operation Rule for Reservoir System Using Enhanced Optimization Algorithms
Published 2025Subjects:Article -
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A Hybrid of Ant Colony Optimization Algorithm and Simulated Annealing for Classification Rules
Published 2013“…In the first proposed algorithm, SA is used to optimize the rule's discovery activity by an ant. …”
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Development of self-learning algorithm for autonomous system utilizing reinforcement learning and unsupervised weightless neural network / Yusman Yusof
Published 2019“…From the simulation result, by using these sensors information the AUTOWiSARD algorithm can successfully differentiate and classify states without supervision, while the Q-learning algorithm is able to produce and optimized states-actions policy. …”
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Hybrib NSGA-II optimization for improving the three-term BP network for multiclass classification problems
Published 2015“…The outcome positively demonstrates that the hybrid algorithm is able to improve the classification performance with a smaller number of hidden nodes and is effective in multiclass classifi cation problems.Furthermore, the results indicate that the proposed hybrid method is a potentially useful classifi er for enhancing the classification process ability when compared with the multiobjective genetic algorithm based on the TBP network (MOGATBP) and certain other methods found in the literature.…”
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Hybrid NSGA-II Optimization for Improving the Three-Term BP Network for Multiclass Classification Problems
Published 2015“…The outcome positively demonstrates that the hybrid algorithm is able to improve the classification performance with a smaller number of hidden nodes and is effective in multiclass classifi cation problems. …”
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Enhancing simulated kalman filter algorithm using current optimum opposition-based learning
Published 2019“…Simulated Kalman filter (SKF) is a new population-based optimization algorithm inspired by estimation capability of Kalman filter. …”
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Grid-Based Classifier as a Replacement for Multiclass Classifier in a Supervised Non-Parametric Approach
Published 2009“…The quality of a given classification technique is measured by the computational complexity, execution time of algorithms, and the number of patterns that can be classified correctly despite any distribution. …”
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Opposition- based simulated kalman filters and their application in system identification
Published 2017“…Among the various kinds of optimization algorithms, Simulated Kalman Filter (SKF) is a new population-based optimization algorithm inspired by the estimation capability of Kalman Filter. …”
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Pressure vessel design simulation using hybrid harmony search algorithm
Published 2019“…Recently the development of optimization algorithm is rapidly increased. Among several optimization algorithms, Harmony Search (HS) has been recently proposed for solving engineering optimization problems. …”
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Conference or Workshop Item -
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Development of a Bioinspired optimization algorithm for the automatic generation of multiple distinct behaviors in simulated mobile robots
Published 2006“…Experiments were conducted within a 10% noise environment with different task environment complexities to investigate whether the MOEA is effective for controller synthesis. A simulated Khepera robot is evolved by a Pareto-frontier Differential Evolution (POE) algorithm, and learned through a 3-layer feed-forward artificial neural network, attempting to simultaneously fulfill two conflicting objectives of maximizing robot phototaxis behavior while minimizing the neural network's hidden neurons by generating a Pareto optimal set of controllers. …”
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Research Report -
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Intent-IQ: customer’s reviews intent recognition using random forest algorithm
Published 2025“…In order to overcome this problem, a classification model for intent recognition is developed. …”
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The potential of a novel support vector machine trained with modified mayfly optimization algorithm for streamflow prediction
Published 2023“…Balancing; Forecasting; Stream flow; Support vector machines; Exploitation and explorations; Machine learning models; Optimisations; Optimization algorithms; Prediction modelling; Simulated annealing integrated with mayfly optimization; Streamflow prediction; Support vector regression models; Support vector regressions; Support vectors machine; Simulated annealing; algorithm; mayfly; optimization; prediction; streamflow; support vector machine; Jhelum River…”
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An improved artificial bee colony algorithm for training multilayer perceptron in time series prediction
Published 2014“…The simulation results of the MLP trained with improved algorithms were compared with that when trained with the standard BP, ABC, Global ABC and Particle Swarm Optimization algorithm. …”
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