Search Results - (( basic visualization mining algorithm ) OR ( learning object optimization algorithm ))
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Using text mining algorithm to detect gender deception based on Malaysian chatroom lingo / Dianne L.M. Cheong and Nur Atiqah Sia Abdullah@Sia Sze Yieng
Published 2006“…Based on this designed text algorithm, a prototype in Visual Basic is developed. …”
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Research Reports -
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Using text mining algorithm to detect gender deception based on Malaysian chat room lingo / Dianne L. M. Cheong and Nur Atiqah Sia Abdullah @ Sia Sze Yieng
Published 2006“…Based on this algorithm. a prototype in Visual Basic is developed It was tested with /6 documents; each consists of 5 e-mails exchanges of respective individuals. …”
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Enhancing predictive crime mapping model using association rule mining for geographical and demographic structure
Published 2014“…This project proposed a data mining technique called Association Rule Mining. Basically Association Rule Mining is to investigate the rules according to the predefined parameter. …”
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Deep reinforcement learning approaches for multi-objective problem in Recommender Systems
Published 2022“…The current major existing multi-objective recommendation approaches utilize collaborative filtering method as rating predictor to replenish the missing ratings and combined with evolutionary algorithm for only bi-objective optimization. …”
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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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Modeling time series data using Genetic Algorithm based on Backpropagation Neural network
Published 2018“…There are three objectives in this research. The first objective is to study the effects of varying the architecture designs and parameter values of the backpropagation neural network (BPNN) learning algorithm. …”
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A novel quasi-oppositional modified Jaya algorithm for multi-objective optimal power flow solution
Published 2018“…This study introduces a novel meta-heuristic optimization algorithm known as quasi-oppositional modified Jaya (QOMJaya) to solve different multi-objective optimal power flow (MOOPF) problems. …”
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Automated bilateral negotiation with incomplete information in the e-marketplace.
Published 2011“…The reason is that, SRT algorithm is sensitive to the accuracy of the learned preferences while MGT algorithm can generate Pareto-optimal offers even with an approximation of the learned preferences.…”
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Improved intrusion detection algorithm based on TLBO and GA algorithms
Published 2021“…The proposed method combined the New Teaching-Learning-Based Optimization Algorithm (NTLBO), Support Vector Machine (SVM), Extreme Learning Machine (ELM), and Logistic Regression (LR) (feature selection and weighting) NTLBO algorithm with supervised machine learning techniques for Feature Subset Selection (FSS). …”
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Multi-objective deep reinforcement learning for recommendation systems
Published 2022“…However, other non-accuracy metrics such as novelty and diversity should not be overlooked. Existing multi-objective (MO) RSs employed collaborative filtering and combined with evolutionary algorithms to handle bi-objective optimization. …”
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Deriving Optimal Operation Rule for Reservoir System Using Enhanced Optimization Algorithms
Published 2025“…This involves their application to various facets of the reservoir operating system, particularly in determining optimal rule curves. This study delves into the exploration of different algorithms, including Artificial Bee Colony (ABC), Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Firefly Algorithm (FA), Invasive Weed Optimization (IWO), Teaching Learning-Based Optimization (TLBO), and Harmony Search (HS). …”
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Hyperparameter tuning in deep learning using NSGA-III: a Multi-Objective perspective
Published 2025“…The proposed Multi-Objective NSGAIII-DL framework is benchmarked against six baseline optimization strategies—manual search, grid search, random search, Bayesian optimization, particle swarm optimization, and standard genetic algorithms using MNIST and CIFAR-10 datasets. …”
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Optimal power flow based on fuzzy linear programming and modified Jaya algorithms
Published 2017“…Finally, two novel hybrid optimization algorithms namely, FLP-QOJaya algorithm for single objective OPF problems and MFLP-QOMJaya algorithm for MOOPF problems are proposed. …”
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An application of teaching–learning-based optimization for solving the optimal power flow problem with stochastic wind and solar power generators
Published 2023“…This paper proposes the implementation of metaheuristic algorithm namely, teaching–learning-based optimization (TLBO) algorithm to solve optimal power flow (OPF) problem. …”
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Rao-SVM machine learning algorithm for intrusion detection system
Published 2020“…In the proposed IDS, Rao Optimization Algorithm, Support Vector Machine (SVM), Extreme Learning Machine (ELM), and Logistic Regression (LR) (feature selection and weighting) were combined with NTLBO algorithm with supervised ML techniques (for feature subset selection (FSS). …”
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Multi-Objective Multi-Exemplar Particle Swarm Optimization Algorithm with Local Awareness
Published 2024“…Many machine learning algorithms excel at handling problems with conflicting objectives. …”
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Using GA and KMP algorithm to implement an approach to learning through intelligent framework documentation
Published 2023“…GA assists in optimizing the search process and performs machine learning. …”
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Greedy-assisted teaching-learning-based optimization algorithm for cost-based hybrid flow shop scheduling
Published 2025“…However, limited attention has been given to CHFS when considering holistic cost models using efficient algorithms. This paper presents a novel Greedy-Assisted Teaching-Learning-Based Optimization (GTLBO) algorithm for CHFS. …”
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Multiobjective deep reinforcement learning for recommendation systems
Published 2022“…However, other non-accuracy metrics such as novelty and diversity should not be overlooked. Existing multi-objective (MO) RSs employed collaborative filtering and combined with evolutionary algorithms to handle bi-objective optimization. …”
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