Search Results - (( variable detection method algorithm ) OR ( using vectorization machine algorithm ))
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Extremal region detection and selection with fuzzy encoding for food recognition
Published 2019“…Classification was performed using a Support Vector Machine (SVM) with a linear kernel. …”
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Thesis -
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Artificial immune system based on real valued negative selection algorithms for anomaly detection
Published 2015“…Classifier algorithms, namely the Support Vector Machine and K-Nearest Neighbours were used for benchmarking the performance of the Real-Valued Negative Selection Algorithms. …”
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Predictive Modelling of Stroke Occurrence among Patients using Machine Learning
Published 2023“…Advanced machine learning algorithms, including logistic regression, decision trees, random forests, and support vector machines, were utilized to analyses the dataset and develop a predictive model. …”
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Article -
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Development of sorting system for oil palm in vitro shoots using machine vision approach
Published 2014“…Ultimately, the sorting algorithm performance came to be evaluated by support vector machine algorithm. …”
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Automated detection and evaluation of ischemic stroke on ct brain imaging using machine learning techniques
Published 2025“…This study investigates the application of machine learning algorithms for the detection of ischemic stroke using CT brain images. …”
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Monograph -
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Pattern Recognition for Human Diseases Classification in Spectral Analysis
Published 2022“…On the other hand, classification methods are techniques or algorithms used to group samples into a predetermined category. …”
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Classification Of Cervical Cancer Stage From Pap Smear Tests
Published 2019“…Then, classification methods for the classification of classes in these cells such as K-Nearest Neighborhood (KNN) and Support Vector Machine (SVM) were explored. …”
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Final Year Project -
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Diagnostic power of resting-state fMRI for detection of network connectivity in Alzheimer's disease and mild cognitive impairment: a systematic review
Published 2021“…We conducted a systematic review aimed at determining the diagnostic power of rs-fMRI to identify FC abnormalities in the DMN of patients with AD or MCI compared with healthy controls (HCs) using machine learning (ML) methods. Multimodal support vector machine (SVM) algorithm was the commonest form of ML method utilized. …”
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Detection and Classification of Moving Objects for an Automated Surveillance System
Published 2006“…The applied post processing module capable to remove noise and shadow from the detected objects with less than 1% of error. 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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Thesis -
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Detection and classification of moving objects for an automated surveillance system
Published 2006“…The applied post processing module capable to remove noise and shadow from the detected objects with less than 1% of error. 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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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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Improvement on rooftop classification of worldview-3 imagery using object-based image analysis
Published 2019“…Then, the classifier (support vector machine (SVM) and data mining (DM) algorithm, decision tree (DT) were applied on each fusion image and their accuracy were evaluated. …”
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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“…The applied post processing module capable to remove noise and shadow from the detected objects with less than 1% of error. 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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Thesis -
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Rough Neural Networks Architecture For Improving Generalization In Pattern Recognition
Published 2004“…A novel feature extraction algorithm was developed to extract the feature vectors. …”
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The influence of sentiments in digital currency prediction using hybrid sentiment-based Support Vector Machine with Whale Optimization Algorithm (SVMWOA)
Published 2021“…Support Vector Machine (SVM) technique is used with the Whale Optimization Algorithm (WOA) which is inspired by the swarm optimization algorithms. …”
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Proceeding Paper -
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Using the bees algorithm to optimise a support vector machine for wood defect classification
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Computer-aided diagnosis of diabetic subjects by heart rate variability signals using discrete wavelet transform method
Published 2015“…These features are ranked by using various ranking methods, namely, Bhattacharyya space algorithm, t-test, Wilcoxon test, Receiver Operating Curve (ROC) and entropy. …”
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Article -
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Optimizing support vector machine parameters using continuous ant colony optimization
Published 2012“…Hence, in applying Ant Colony Optimization for optimizing Support Vector Machine parameters, which are continuous parameters, there is a need to discretize the continuous value into a discrete value.This discretization process results in loss of some information and, hence, affects the classification accuracy and seek time.This study proposes an algorithm to optimize Support Vector Machine parameters using continuous Ant Colony Optimization without the need to discretize continuous values for Support Vector Machine parameters.Seven datasets from UCI were used to evaluate the performance of the proposed hybrid algorithm.The proposed algorithm demonstrates the credibility in terms of classification accuracy when compared to grid search techniques.Experimental results of the proposed algorithm also show promising performance in terms of computational speed.…”
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Conference or Workshop Item
