Search Results - (( using vector methods algorithm ) OR ( a visualization using algorithm ))
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Extremal region detection and selection with fuzzy encoding for food recognition
Published 2019“…Overall, the propose method generates a compact and discriminative visual dictionary for food recognition using only a single feature type, small numbers of interest regions, and low-dimensional feature vectors. …”
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Thesis -
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Visualization of dengue incidences using expectation maximization (EM) algorithm
Published 2017“…R-GIS(R software) and clustering algorithm were used for year 2014 with several weeks to develop the relation between the visualization and prediction of reported incidences. …”
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Improved abnormal detection using self-adaptive social force model for visual surveillance
Published 2017“…Using the attained flow vectors from this stage, interaction force estimation is done based on SFM theory. …”
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4
Sauvola Segmentation and Support Vector Machine-Salp Swarm Algorithm Approach for Identifying Nutrient Deficiencies in Citrus Reticulata Leaves
Published 2024“…Next, file sizes are reduced using lossless compression methods, achieving a 96.99% reduction. …”
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5
B-spline curve fitting with different parameterization methods
Published 2020“…This research is only focused on B-spline curve and four parameterization methods. In addition, uniformly spaced and averaging knot vector generations are used in generating the knot vector. …”
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An improved algorithm for iris classification by using support vector machine and binary random machine learning
Published 2018“…The first objective of this study is to improve a new algorithm technique for classification. The new algorithm come from a combination of an ideas of k-NN algorithm and ensemble concept. …”
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7
An improved plant identification system by Fuzzy c-means bag of visual words model and sparse coding
Published 2020“…This demonstrate the intensity of the correlation between that aspect of data and a specific cluster. In the classic Bag of visual words model, the Fuzzy c-means algorithm is replaced with K-means and the accuracy of SIFT matching is increased. …”
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Efficient selective encryption schemes to secure video data and moving objects information for HEVC/H.265 using advanced encryption standard / Mohammed Ahmed Mohammed Saleh
Published 2016“…Those approaches named as, Encryption for Absolute Coefficient Level, Encryption of Intra Prediction Mode, and Encryption of Motion Vector Difference (MVD). In the first and second methods, the visual video information is secured by encrypting limited transformed coefficients using AES algorithm. …”
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9
Place recognition using semantic concepts of visual words
Published 2011“…Once obtained the semantic concepts, the corresponding of these concepts in a query image are formed as a vector of similarity density, which it can be exploited in the place recognition using the support vector machine (SVM) classifier. …”
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Predicting 30-day mortality after an acute coronary syndrome (ACS) using machine learning methods for feature selection, classification and visualization
Published 2021“…Feature selection methods such as Boruta, Random Forest (RF), Elastic Net (EN), Recursive Feature Elimination (RFE), learning vector quantization (LVQ), Genetic Algorithm (GA), Cluster Dendrogram (CD), Support Vector Machine (SVM) and Logistic Regression (LR) were combined with RF, SVM, LR, and EN classifiers for 30-day mortality prediction. …”
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Decoding of visual activity patterns from fMRI responses using multivariate pattern analyses and convolutional neural network
Published 2017“…General linear model (GLM) is used to find the unknown parameters of every individual voxel and the classification is done using multi-class support vector machine (SVM). …”
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Visualization on colour based flow vector of thermal image for movement detection during interactive session
Published 2018“…Conventional system might cause false information with the present of shadow. Thus, methods employed in this work are Canny edge detector method, Lucas Kanade and Horn Shunck algorithms, to overcome the major problem when using thresholding method, which is only intensity or pixel magnitude is considered instead of relationships between the pixels. …”
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Fast shot boundary detection based on separable moments and support vector machine
Published 2021“…Moreover, for the proposed SBD algorithm, a comparative study is performed with state-of-the-art algorithms. …”
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Feature extraction from oil palm in vitro shoot images
Published 2010“…The feature extraction techniques are discussed in this article. The algorithms and methods developed are robust and advanced enough to be used in combination with a machine vision algorithm and automation system. …”
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Subspace Techniques for Brain Signal Enhancement
Published 2009“…Next, the validity and the effectiveness of the algorithms to detect the P100's (used in objective assessment of visual pathways) are evaluated using real patient data collected from a hospital. …”
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Classification of labour pain using electroencephalogram signal based on wavelet method / Sai Chong Yeh
Published 2020“…Supervised and unsupervised machine learning algorithms particularly the Support Vector Machine (SVM) and Density Based Spatial Clustering of Application with Noise (DBSCAN) are used in this study. …”
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18
The evolution and trend of chain code scheme
Published 2008“…This paper explains some chain code concepts and their applications that are be the background of the development of vertex chain code cells algorithm. This algorithm is able to visualize and to transcribe a binary image into vertex chain code easily. …”
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Classification of heart disease with machine learning: a comparison of grid search, random search, and Bayesian Optimization
Published 2026“…This study systematically evaluates the effect of hyperparameter optimization on the performance of predictive models by comparing three main techniques: Grid Search, Random Search, and Bayesian Optimization. Four commonly used machine learning algorithms: Logistic Regression (LR), Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and Gradient Boosting were tested on benchmark datasets from the UC Machine Learning Repository. …”
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