Search Results - (( processes specification method algorithm ) OR ( variable selection based algorithm ))
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1
Taguchi?s T-method with Normalization-Based Binary Bat Algorithm
Published 2025“…Therefore, a variable selection technique using a swarm-based Binary Bat algorithm is proposed. …”
Conference paper -
2
Model selection approaches of water quality index data
Published 2016“…Automatic model selection by using algorithm can avoid huge variability in model specification process compared to manual selection.With the employment of algorithm, the right model selected is then also used for forecasting purposes. …”
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3
Development of fault detection, diagnosis and control system identification using multivariate statistical process control (MSPC)
Published 2006“…In this research work, an FDD algorithm is developed using MSPC and correlation coefficients between process variables. …”
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Monograph -
4
Deterministic Image Enhancement Based on Fuzzy Method
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Proceedings -
5
Systematic design of chemical reactors with multiple stages via multi-objective optimization approach
Published 2015“…Following the identification of path-dependent design variables, several (possibly conflicting) design objectives will be selected and solutions of the corresponding problem will be generated from multi-objective optimization algorithm. …”
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Conference or Workshop Item -
6
Multi-Objective Multi-Exemplar Particle Swarm Optimization Algorithm with Local Awareness
Published 2024“…Multi-Objective Optimization (MOO) algorithms play a crucial role in this process by enabling them to navigate these trade-offs effectively. …”
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7
Edge detection and contour segmentation for fruit classification in natural environment / Khairul Adilah Ahmad
Published 2018“…Therefore, this research has designed fuzzy learning algorithm that is able to classify fruits based on their shape and size features using Harumanis dataset. …”
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Thesis -
8
Extremal region detection and selection with fuzzy encoding for food recognition
Published 2019“…The second algorithm reduces the quantity of interest regions by using the Extremal Region Selection (ERS) algorithm. …”
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9
Improvement on rooftop classification of worldview-3 imagery using object-based image analysis
Published 2019“…To overcome such challenges, this study attempts to comprehend and improve the remote sensing technology for rooftop classification using the Worldview-3 (WV-3) image and object-based image analysis (OBIA) method. The improvement process involved segmentation, feature selection and classification techniques. …”
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Thesis -
10
Neural Network Multi Layer Perceptron Modeling For Surface Quality Prediction in Laser Machining
Published 2009“…On the other hand, when we use computers to reduce uncertainty, the computer itself can become an expert in a specific field through a variety of methods. One such method is machine learning, which involves using a computer algorithm to capture hidden knowledge from data. …”
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Book Chapter -
11
A single measurement of combined Direct and indirect MPPT algorithm for mismatch PV module application / Mohd Najib Mohd Hussain
Published 2016“…The system also offer a flexibility of using different electrical specification of PV module in string connection solving the issue of the selection of PV module should be in similar specification. …”
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Thesis -
12
Shear strength test on unsaturated soil using natural microscopic surface tension force / Basharudin Abdul Hadi
Published 2017“…There were two sets of algorithms in developing the UPLRB ANN model and every algorithm set consisted of model inputs data preparation, neural network 29 30 base of the specimen until the flow ceased..…”
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Book Section -
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SWAT and ANN model hydrological assessment using Malaysia soil data / Khairi Khalid
Published 2017“…There were two sets of algorithms in developing the UPLRB ANN model and every algorithm set consisted of model inputs data preparation, neural network script and neural network error checking measures. …”
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Thesis -
15
SWAT and ANN model hydrological assessment using Malaysia soil data / Khairi Khalid
Published 2017“…The evolution of distributed watershed models has been established for more accurate representation of the hydrological system by considering the spatial variability of model parameters and inputs. The research aims to evaluate the performance and hydrological response of the Soil Water Assessment Tool (SWAT) process-based model in tropical river basin using Malaysia soil data. …”
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Book Section -
16
Variable block based motion estimation using hexagon diamond full search algorithm (HDFSA) via block subtraction technique
Published 2015“…The variable block matching developed based on four stages which is the video and frame selection, threshold calculation, block size selection and search pattern. …”
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Thesis -
17
A meta-heuristics based input variable selection technique for hybrid electrical energy demand prediction models
Published 2017“…The focus of the paper is to propose a hybrid approach for the selection of the most influential input variables for the training and testing of neural network based hybrid models. …”
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Article -
18
Comparison between specifications of linear regression and spatial-temporal autoregressive models in mass appraisal valuation for single storey residential property
Published 2013“…The experiments were replicated on three different treatments based on removal of outliers and transformation of variables with high value of skewness. …”
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Thesis -
19
Robust variable selection methods for large- scale data in the presence of multicollinearity, autocorrelated errors and outliers
Published 2016“…We develop Robust Forward Selection algorithm based on RFCH correlation coefficient (RFS.RFCH) because FS.Winso is not robust to multivariate outliers. …”
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20
Evaluation of machine learning classifiers in faulty die prediction to maximize cost scrapping avoidance and assembly test capacity savings in semiconductor integrated circuit (IC)...
Published 2019“…The model training flow will have 2 classifier groupings which are control group and auto machine learning (ML) where feature selection with redundancy elimination method to be applied on input data to reduce the number of variables to minimum prior modeling flow. …”
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