Search Results - (( data relation based algorithm ) OR ( using optimization based algorithm ))
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Finding the optimal road routes between two locations in East Kuala Lumpur by using A* Algorithm based on GPS data / Ilina Syazwani Musa
Published 2007“…In this paper, we will use A* algorithm in road route in East Kuala Lumpur based on GPS data on the Garmin platform. …”
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SINE COSINE ALGORITHM BASED NEURAL NETWORK FOR RAINFALL DATA IMPUTATION
Published 2024“…SCA has been widely used to address diverse optimization challenges in several domains. …”
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Successor selection for Ant Colony Optimization technique algorithm / Muhammad Iskandar Isman
Published 2017“…ACO algorithm is the best solution because it included the optimization technique to optimized the result based on the data criteria needs. …”
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An ensemble data summarization approach based on feature transformation to learning relational data
Published 2015“…A ensemble clustering is designed, used and evaluated to generate the final classification framework that will take all input generated from the GA based clustering with Feature Selection and Feature Construction algorithms and perform the classification task for the relational datasets. …”
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Optimization of ANFIS with GA and PSO estimating α ratio in driven piles
Published 2020“…A learning fuzzy-based algorithm was used to train the ANFIS model in the MATLAB software. …”
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Ant colony optimization algorithm for rule based classification: Issues and potential
Published 2018“…Furthermore, this review can be used as a source of reference to other researchers in developing new ACO algorithms for rule classification.…”
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Social media mining: a genetic based multiobjective clustering approach to topic modelling
Published 2021“…However, less works have been conducted in applying multiobjective based algorithm for topic extraction. Most of these algorithms are not optimized, even if they are, they are only optimized by using a single objective method and may underperform when solving real-world problems which are typically multi-objectives in nature. …”
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Forecasting of energy-related carbon dioxide emission using ANN combined with hybrid metaheuristic optimization algorithms
Published 2025“…Multilayer perceptrons (MLP) are combined with various nature-inspired optimization algorithms, such as Heap-Based Optimizer (HBO), Teaching-Learning-Based Optimization (TLBO), Whale Optimization Algorithm (WOA), Vortex Search algorithm (VS), and Earthworm Optimization Algorithm (EWA), to create a dependable predictive network that takes the complexity of the problem into account. …”
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Social media mining: a genetic based multiobjective clustering approach to topic modelling
Published 2021“…However, less works have been conducted in applying multiobjective based algorithm for topic extraction. Most of these algorithms are not optimized, even if they are, they are only optimized by using a single objective method and may underperform when solving real-world problems which are typically multi-objectives in nature. …”
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An efficient multi join query optimization for relational database management system using swarm intelligence approaches
Published 2016“…A directed acyclic graph, based on materialized query graph, aids in the optimization of algorithms and solving MJQO by removing non-promising QEP, which decreases the QEP combination space. …”
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An efficient multi join query optimization for relational database management system using swarm intelligence approaches
Published 2016“…A directed acyclic graph, based on materialized query graph, aids in the optimization of algorithms and solving MJQO by removing non-promising QEP, which decreases the QEP combination space. …”
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Fuzzy clustering method and evaluation based on multi criteria decision making technique
Published 2018“…The proposed algorithm is used as a pre-processing method for data followed by Gustafson-Kessel (GK) algorithm to classify credit scoring data. …”
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Deterministic Mutation-Based Algorithm for Model Structure Selection in Discrete-Time System Identification
Published 2011“…System identification is a method of determining a mathematical relation between variables and terms of a process based on observed input-output data. …”
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Towards enhanced remaining useful life prediction of lithium-ion batteries with uncertainty using optimized deep learning algorithm
Published 2025“…The effectiveness of the proposed LSA + LSTM model is assessed using battery aging data from the NASA dataset. In addition, to validate the prediction performance of the proposed LSA + LSTM model, extensive comparisons are performed with other popular optimization-based deep learning methods including artificial bee colony (ABC) based LSTM (ABC + LSTM), gravitational search algorithm (GSA) based LSTM (GSA + LSTM), and particle swarm optimization (PSO) based LSTM (PSO + LSTM) model using different error matrices. …”
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Multilingual Sentiment Analysis From Students Feedback: Optimal Techniques And Resources For Bengali Lingual Data
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Privacy optimization and intrusion detection in modbus/tcp network-based scada in water distribution systems
Published 2021“…Another problematic aspect is related to the intrusion detection solutions that are based on machine learning cluster algorithms to learn systems’ specifications and extract general state-based rules for attacks identification. …”
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An efficient indexing and retrieval of iris biometrics data using hybrid transform and firefly based K-means algorithm title
Published 2019“…The enhanced method combines three transformation methods for analyzing the iris image and extracting its local features. It uses a weighted K-means clustering algorithm based on the improved FA to optimize the initial clustering centers of K-means algorithm, known as Weighted K-means clustering-Improved Firefly Algorithm (WKIFA). …”
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Resource allocation in coordinated multipoint long term evolution-advanced networks
Published 2015“…ORA is formulated based on Lagrangian method and optimized using Particle Swarm Optimization (PSO). …”
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Optimized feature construction methods for data summarizations of relational data
Published 2014“…DARA transforms the data relational representation into a vector space representation and a clustering process is applied to group the data based on their characteristics similarity. …”
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