Search Results - (( data selection methods algorithm ) OR ( variable extraction using algorithm ))*
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Evaluation and Comparative Analysis of Feature Extraction Methods on Image Data to increase the Accuracy of Classification Algorithms
Published 2024“…The classification algorithm used in this research is the Convolutional Neural Network (CNN) algorithm. …”
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Long-term electrical energy consumption: Formulating and forecasting via optimized gene expression programming / Seyed Hamidreza Aghay Kaboli
Published 2018“…In the developed feature selection approach, multi-objective binary-valued backtracking search algorithm (MOBBSA) is used as an efficient evolutionary search algorithm to search within different combinations of input variables and selects the non-dominated feature subsets, which minimize simultaneously both the estimation error and the number of features. …”
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Edge detection and contour segmentation for fruit classification in natural environment / Khairul Adilah Ahmad
Published 2018“…In this research, the shape and size feature were extracted using aspect ratio of selected morphological features. …”
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CAT CHAOTIC GENETIC ALGORITHM BASED TECHNIQUE AND HARDWARE PROTOTYPE FOR SHORT TERM ELECTRICAL LOAD FORECASTING
Published 2017“…The correlation analysis is used for the identification and selection of the most influential input variable vector (IVV). …”
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The framework of weighted subset-hood Mamdani fuzzy rule based system rule extraction (MFRBS-WSBA) for forecasting electricity load demand
Published 2016“…The objective of this paper is to show the fourth step in the framework which applied the new electricity load forecasting rule extraction by WSBA method. Electricity load demand in Malaysia data is used as numerical data in this framework.These preliminary results show that the WSBA method can be one of alternative methods to extract fuzzy rules for forecast electricity load demand…”
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The framework of weighted subset-hood Mamdani fuzzy rule based system rule extraction (MFRBS-WSBA) for forecasting electricity load demand
Published 2016“…The framework consist of six main steps: (1) Data Collection and Selection; (2) Preprocessing Data; (3) Variables Selection; (4) Fuzzy Model; (5) Comparison with Other FIS and (6) Performance Evaluation. …”
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Improvement on rooftop classification of worldview-3 imagery using object-based image analysis
Published 2019“…The accuracy of each algorithm was evaluated using LibSVM, Bayes network, and Adaboost classifier. …”
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Liver segmentation on CT images using random walkers and fuzzy c-means for treatment planning and monitoring of tumors in liver cancer patients
Published 2017“…The proposed method is based on a hybrid method integrating random walkers algorithm with integrated priors and particle swarm optimized spatial fuzzy c-means (FCM) algorithm with level set method and AdaBoost classifier. …”
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10
Improved measurement of blood pressure by extraction of characteristic features from the cuff oscillometric waveform
Published 2015“…We present a novel approach to improve the estimation of systolic (SBP) and diastolic blood pressure (DBP) from oscillometric waveform data using variable characteristic ratios between SBP and DBP with mean arterial pressure (MAP). …”
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Analysis using data mining techniques: the exploration and review data of diabetes patients / Syarifah Adilah Mohamed Yusoff ... [et al.]
Published 2025“…In this statistical summary procedure, the distribution of attributes and their interactions are crucial for accurately processing the data in accordance with the selected classification or data mining techniques to be performed. …”
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Tree species and aboveground biomass estimation using machine learning, hyperspectral and LiDAR data / Nik Ahmad Faris Nik Effendi
Published 2022“…Besides, Artificial Neural Network (ANN) and Random Forest (RF) algorithm was used to predicted the AGB using different combination of variables. …”
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Recommendation System Model For Decision Making in the E-Commerce Application
Published 2024thesis::doctoral thesis -
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High-Resolution Downscaling with Interpretable Relevant Vector Machine: Rainfall Prediction for Case Study in Selangor
Published 2024“…The Principal Component Analysis (PCA) technique was employed to choose relevant environmental variables as input for the machine learning model, and various imputation methods were utilized to manage missing data, such as mean imputation and the KNN algorithm. …”
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Clinical relevance of VKORC1 (G-1639A and C1173T) and CYP2C9*3 among patients on warfarin
Published 2011“…An initial model was developed using data from one cohort of patients and validated using data from a second cohort. …”
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Detection and Classification of Moving Objects for an Automated Surveillance System
Published 2006“…Finally, classification algorithm that makes use of the extracted moment values from the detected objects successfully categorize objects into pre-defined classes of human and vehicle with 89.08% of accuracy. …”
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Detection and classification of moving objects for an automated surveillance system
Published 2006“…Finally, classification algorithm that makes use of the extracted moment values from the detected objects successfully categorize objects into pre-defined classes of human and vehicle with 89.08% of accuracy. …”
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Wavelet based fault tolerant control of induction motor / Khalaf Salloum Gaeid
Published 2012“…The fault detection algorithm identifies the time and location of each fault. …”
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Detection and classification of moving objects for an automated surveillance system
Published 2006“…Finally, classification algorithm that makes use of the extracted moment values from the detected objects successfully categorize objects into pre-defined classes of human and vehicle with 89.08% of accuracy. …”
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Oil palm female inflorescences anthesis stages identification using selected emissivities through thermal imaging and Machine Learning
Published 2022“…This research studies different Machine Learning (ML) classification and ensemble techniques for the assessment of the four pollination stages consist of pre-anthesis I, pre-anthesis II, pre-anthesis III, and anthesis using thermal imaging. Different ML algorithms such as Random Forest (RF), k Nearest Neighbor (kNN), Support Vector Machine (SVM), Artificial Neural Network (ANN) as well as an ensemble method are used on data extracted from thermal images collected during infield oil palms pollination stages monitoring. …”
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