Search Results - (( data optimization method algorithm ) OR ( framework implementation case algorithm ))
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1
Using artificial intelligence search in solving the camera placement problem
Published 2022“…The various searching algorithms are implemented to seek the maximum coverage of the camera array. …”
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2
Discrete-time system identification using genetic algorithm with single parent-based mating technique
Published 2024“…The methodology encompasses data acquisition, GA program development, SPM technique implementation, and simulation using MATLAB. …”
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3
Using GA and KMP algorithm to implement an approach to learning through intelligent framework documentation
Published 2023“…The main objective of this paper is to propose and implement an intelligent framework documentation approach that integrates case-based learning (CBL) with genetic algorithm (GA) and Knuth-Morris-Pratt (KMP) pattern matching algorithm with the intention of making learning a framework more effective. …”
Conference paper -
4
Dynamic investment model for the restructed power market in the presence of wind source
Published 2014“…The proposed framework has been implemented in the hypothetical restructured power market using the IEEE Reliability Test System. …”
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5
Implementing case-based reasoning approach to framework documentation
Published 2023“…This paper discusses and implements the case-based reasoning (CBR) approach to documenting a framework. …”
Conference paper -
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Low-level hybridization scripting language with dynamic parameterization in PSO-GA / Suraya Masrom
Published 2015“…However, in many cases, implementing the suitable hybrid algorithms for a given optimization problem is a considerably difficult, which in most cases, is time consuming. …”
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7
Quantum Processing Framework And Hybrid Algorithms For Routing Problems
Published 2010“…The framework is used to increase the implementation performance of quantum algorithms. …”
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8
A test case generation framework based on UML statechart diagram
Published 2018“…The results of the experts’ review show that the framework is practical, easy to implement due to it is suitability to generate the test cases. …”
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9
An improved diabetes risk prediction framework : An Indonesian case study
Published 2018“…However,there is the issue of noisy dataset detected as incomplete data and the outlier class problem that affects sampling bias.Existing frameworks were deemed difficult in identifying the critical risk factors of diabetes;some of which were considerably inaccurate and consume substantial computation time.The purpose of this study is to develop a suitable framework for predicting diabetes risks.From a complete blood test,the framework can predict and classify the output of either having diabetes risk or no diabetes risk.A Diabetes Risk Prediction Framework (DRPF) was developed from the literature review and case studies were afterwards conducted in three private hospitals in Semarang.Analyses were conducted to find a suitable component of the framework—due to lack of comparison and analysis on the combination of feature selection and classification algorithm.DRPF comprises four main sections: pre-processing,outlier detection,risk weighting,and learning. …”
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10
Cloud Computing in Smart Cities: Privacy, Ethical and Social Issues
Published 2024“…Ethical concerns arise from the handling of sensitive data, data ownership, and algorithmic biases that could perpetuate discrimination. …”
Conference Paper -
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VLSI floor planning optimization using genetic algorithm and cross entropy method / Angeline Teoh Szu Fern
Published 2012“…These two models are based on topological placement method. DM is optimized using genetic algorithm (GA). …”
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12
Data-driven continuous-time Hammerstein modeling with missing data using improved Archimedes optimization algorithm
Published 2024“…This research introduces the improved Archimedes optimization algorithm (IAOA) for data-driven modeling of continuous-time Hammerstein models with missing data. …”
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An improved method using fuzzy system based on hybrid boahs for phishing attack detection
Published 2022“…Moreover, Butterfly Optimization Algorithm and Harmony Search Algorithm were combined as optimization method led to a new method named BOAHS. …”
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14
Low-level hybridization scripting language with dynamic parameterization in PSO-GA / Suraya Masrom
Published 2015“…However, in many cases, implementing the suitable hybrid algorithms for a given optimization problem is a considerably difficult, which in most cases, is time consuming. …”
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Application of Optimization Methods for Solving Clustering and Classification Problems
Published 2011“…In this study we show how to apply a particular class of optimization methods known as pattern search methods to address these challenges. …”
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17
Fuzzy modeling using Bat Algorithm optimization for classification
Published 2018“…In order to solve it, Bat Algorithm method is implement in to optimization method in fuzzy modeling for classification. …”
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Undergraduates Project Papers -
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Manufacturing process planning optimisation in reconfigurable multiple parts flow lines
Published 2008“…Design/methodology/approach: The genetic algorithm methodology implements a genetic algorithm that is augmented by application specific heuristics in order to guide the search for an optimal solution. …”
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Data-Driven control based on marine predators algorithm for optimal tuning of the wind plant
Published 2022“…Comparative results alongside other existing metaheuristic-based algorithms further confirmed excellence of the proposed method through its superior performance against the slime mould algorithm (SMA), multi-verse optimizer (MVO), sine-cosine algorithm (SCA), grey wolf optimizer (GWO), and safe experimentation dynamics (SED) algorithm.…”
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Conference or Workshop Item -
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Algorithmic Loan Risk Prediction Method Based on PSO-EBGWO-Catboost
Published 2024“…PSO-EBGWO is used to optimize the parameters of the CatBoost model. In this method, the Gray Wolf optimized algorithm (EBGWO) is further optimized by particle swarm optimization (PSO), and when combined with it, the convergence performance of the model is improved, the parameters of the model are reduced, and the model is simplified. …”
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