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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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Development of an effective clustering algorithm for older fallers
Published 2022“…A total of 1279 subjects and 9 variables were selected for clustering after the data pre-possessing stage. …”
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Development of a phantom and metal artifact correction (MAC) algorithm for post-operative spine computed tomography (CT) imaging / Noor Diyana Osman
Published 2014“…The last part is the development of a metal artifact correction (MAC) algorithm and evaluation of the proposed algorithm in artifacts reduction in CT images. …”
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Recommendation System Model For Decision Making in the E-Commerce Application
Published 2024thesis::doctoral thesis -
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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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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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Mortality prediction in critically ill patients using machine learning score
Published 2020“…The algorithm was validated with data obtained from a retrospective study on ICU patients in Malaysia. …”
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Mortality prediction in critically ill patients using machine learning score
Published 2020“…The algorithm was validated with data obtained from a retrospective study on ICU patients in Malaysia. …”
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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“…This is followed by the clustering of the liver tissues using particle swarm optimized spatial FCM 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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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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Classification and prediction analysis for weld bead surface quality using feature extraction and mahalanobis-taguchi system
Published 2025“…Leveraging image feature extraction in combination with the Mahalanobis-Taguchi System (MTS) implemented in MATLAB software, the study constructs a tailored camera jig to ensure consistent image data capture. …”
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Wavelet based fault tolerant control of induction motor / Khalaf Salloum Gaeid
Published 2012“…There are two steps in the fault tolerant control process; in the first step the fault is identified with the feature extraction module, a fault decision module and a feature cluster module. …”
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Machine-learning approach using thermal and synthetic aperture radar data for classification of oil palm trees with basal stem rot disease
Published 2021“…The machine learning algorithm consistently performs well when presented with a well-balanced dataset. …”
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Enhancing obfuscation technique for protecting source code against software reverse engineering
Published 2019“…Obfuscation involves transforming potentially revealing data, renaming useful classes and variables (identifiers) names to meaningless labels or adding unused or meaningless code to an application binary. …”
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Perturbation stochastic model updating of a bolted structure / Mohamad Azam Shah Aziz Shah
Published 2022“…The scheme was developed by introducing a multidimensional analysis consisting of a systematic quasi-random sampling algorithm. …”
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Classification for large number of variables with two imbalanced groups
Published 2020“…This study proposed two algorithms of classification namely Algorithm 1 and Algorithm 2 which combine resampling, variable extraction, and classification procedure. …”
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Anomaly detection of denial-of-service network traffic attacks using autoencoders and isolation forest
Published 2026“…This paper presents an unsupervised network-based anomaly detection framework that integrates deep autoencoders with the Isolation Forest algorithm. The framework analyzes extracted traffic features, including packet length and IP address patterns, to detect deviations from normal behaviour without requiring labelled data. …”
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