Search Results - (( variable interaction modified algorithm ) OR ( variable interactive learning algorithm ))
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A variable combinatorial test suite strategy based on modified greedy algorithm
Published 2015“…This proposed will suggest a new CIT strategy based on a modified greedy algorithm (MGA) with addressing the supporting of variable strength interaction to generate a satisfactory test suite size.…”
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Adapting the Elitism on the Greedy Algorithm for Variable Strength Combinatorial Test Cases Generation
Published 2018“…This algorithm is invariably called the modified greedy algorithm (MGA). …”
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Revolutionizing video analytics: a review of action recognition using 3D
Published 2024“…It also addresses the practicalities of implementing action recognition algorithms in real-world situations, which include tools like deep learning frameworks, pre-trained models, open-source libraries, cloud services, GPU acceleration, and evaluation metrics. …”
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Neural Network Multi Layer Perceptron Modeling For Surface Quality Prediction in Laser Machining
Published 2009“…One such method is machine learning, which involves using a computer algorithm to capture hidden knowledge from data. …”
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Short-term electricity price forecasting in deregulated electricity market based on enhanced artificial intelligence techniques / Alireza Pourdaryaei
Published 2020“…This merit is provided by balancing the exploitation of solution structure and exploration of its appropriate weighting factors through the use of Backtracking Search Algorithm (BSA) as an efficient optimization algorithm in learning process of ANFIS approach. …”
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Genetic ensemble biased ARTMAP method of ECG-Based emotion classification
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Support vector machine in precision agriculture: a review
Published 2021“…The Support Vector Machine (SVM) is a Machine Learning (ML) algorithm which may be used for acquiring solutions towards better crop management. …”
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New genetic operator for solving the travelling salesman problem / Fadzilawani Astifar Alias ... [et al.]
Published 2015“…TSP is an NP-complete problem that has many interaction variables with a high degree of freedom. …”
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Predicting sea levels using ML algorithms in selected locations along coastal Malaysia
Published 2024“…In consideration of the distinct behavior of machine learning (ML) algorithms, six well-defined ML used were carried out in this study for predicting sea level on a day-to-day basis. …”
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Predicting sea levels using ML algorithms in selected locations along coastal Malaysia
Published 2025“…In consideration of the distinct behavior of machine learning (ML) algorithms, six well-defined ML used were carried out in this study for predicting sea level on a day-to-day basis. …”
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A centralised multi-objective model predictive control for biventricular assist devices / Vivian Koh Ci Ai
Published 2020“…Therefore, the second objective was to modify a conventional centralised MPC algorithm to cater for the state and time-varying factors of the CVS-BiVAD system. …”
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Improved model for blood glucose control using Multi-Parametric Model Predictive Control (MP-MPC) / Nur Farhana Mohd Yusof
Published 2019“…As a result, we managed to have a good correlation on interactions between the parameters in glucose-insulin intervention. …”
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The Effects Of Segmenting And Computational Thinking In Digital Video Courseware On Knowledge Achievement, Self-Efficacy And Motivation Among Students With Different Thinking Style...
Published 2023“…The researcher found significant main and interaction effects of the learner-paced predefined segment on all dependent variables. …”
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Analysis of daytime and nighttime ground level ozone concentrations using boosted regression tree technique
Published 2017“…Sensitivity testing of the BRT model was conducted to determine the best parameters and good explanatory variables. Using the number of trees between 2,500-3,500, learning rate of 0.01, and interaction depth of 5 were found to be the best setting for developing the ozone boosting model. …”
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Optimization of Lipase Catalysed Synthesis of Sugar Alcohol Esters Using Taguchi Method and Neural Network Analysis
Published 2011“…Various feedforward neural networks were performed using different learning algorithms. The best algorithm was found to be Levenberg–Marquardt (LM) for a network composed of two hidden layers with six and seven neurons in the first and second layers, respectively for xylitol stearate and xylitol palmitate and also seven and five neurons in the first and second layers for xylitol caprate, with hyperbolic tangent sigmoid transfer function. …”
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Production and characterization of biochar derived from oil palm wastes, and optimization for zinc adsorption
Published 2015“…The incremental back propagation algorithm demonstrated the best results and which has been used as learning algorithm for ANN in combination with Genetic Algorithm in the optimization. …”
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Grid-based remotely sensed hydrodynamic surface runoff model using emissivity coefficient / Jurina Jaafar
Published 2015“…It is learned that creating an accurate description of the ground surface is a complex problem, which requires at least site study. …”
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