Search Results - (( data visualization means algorithm ) OR ( variable learning based algorithm ))
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1
Evaluation and Comparative Analysis of Feature Extraction Methods on Image Data to increase the Accuracy of Classification Algorithms
Published 2024“…This process requires human labour to visually inspect and sort fruits, leading to variability and inefficiencies in the sorting process. …”
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High-Resolution Downscaling with Interpretable Relevant Vector Machine: Rainfall Prediction for Case Study in Selangor
Published 2024“…These factors include identifying relevant atmospheric features contributing to rainfall, addressing missing data, and developing a significant model to predict daily rainfall intensity using appropriate machine-learning techniques. 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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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. …”
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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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Improving Classification of Remotely Sensed Data Using Best Band Selection Index and Cluster Labelling Algorithms
Published 2005“…In cluster labelling process, a cluster labelling algorithm based on calculation of minimum-distance (MD) between cluster mean and class mean was developed to label the clusters. …”
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6
Ensemble-based machine learning algorithms for classifying breast tissue based on electrical impedance spectroscopy
Published 2020“…In addition, the ranked order of the variables based on their importance differed across the ML algorithms. …”
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Conference or Workshop Item -
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Visualization of dengue incidences using expectation maximization (EM) algorithm
Published 2017“…Along with the prediction modeling on data using centroid model and distribution model based on K-means and Expectation Maximization (EM) algorithms respectively. …”
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Dynamic Bayesian Networks and Variable Length Genetic Algorithm for Dialogue Act Recognition
Published 2007“…The current dialogue act recognition models, namely cue-based models, are based on machine learning techniques, particularly statistical ones. …”
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Tag cloud algorithm with the inclusion of personality traits
Published 2014“…Since there is no study has tried to create an algorithm that can customize tag cloud visual properties based on personality traits. …”
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Modeling and validation of base pressure for aerodynamic vehicles based on machine learning models
Published 2023“…Based on the identical dataset, the GA-BP and PSO-BP algorithms are also compared to the PCA-BAS-ENN algorithm. …”
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Fuzzy Soft Set Clustering for Categorical Data
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A hybrid spiking neural network model for multivariate data classification and visualization.
Published 2011“…This study proposes a hybrid model of Self-Organizing Map with modified adaptive coordinates (SOM-AC) and Spiking Neural Network (SNN) for multivariate spatial and temporal data visualization and classification. SOM is one of the most prominent unsupervised learning algorithms. …”
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Proceeding -
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One day ahead daily peak hour load forecasting by using invasive weed optimization learning algorithm based Artificial Neural Network
Published 2012“…By using 'seen' and 'unseen' of electrical energy demand data were used to test the performance of the proposed algorithm. Based on result obtained, it shows that IWO learning algorithm is capable to produce accurate prediction load demand. …”
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Student Project -
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Enhanced Adaptive Confidence-Based Q Routing Algorithms For Network Traffic
Published 2004“…These two adaptive routing algorithms enhance the existing Confidence-based Q (CQ) and Confidence-based Dual Reinforcement Q (CDRQ) Routing Algorithms. …”
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15
Enhanced Flipping Technique to Reduce Variability in Image Steganography
Published 2023“…Benchmarking; Discrete cosine transforms; Genetic algorithms; Image coding; Image enhancement; Mean square error; Signal to noise ratio; Bayes method; Cover-image; Data hidden; Embedding capacity; Flipping methods; Least significant bits; Medium; Optimisations; Variability; Visual qualities; Steganography…”
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Dynamic Bayesian networks and variable length genetic algorithm for designing cue-based model for dialogue act recognition
Published 2010“…The model is, essentially, a dynamic Bayesian network induced from manually annotated dialogue corpus via dynamic Bayesian machine learning algorithms. Furthermore, the dynamic Bayesian network's random variables are constituted from sets of lexical cues selected automatically by means of a variable length genetic algorithm, developed specifically for this purpose. …”
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Enhanced bibliographic data retrieval and visualization using query optimization and spectral centrality measure
Published 2019“…This study proposing an enhance bibliographic data retrieval and visualization using hybrid clustering method consists of K-harmonic mean (KHM) and Spectral Algorithm and eigenvector centrality measure. …”
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Weather prediction in Kota Kinabalu using linear regressions with multiple variables
Published 2021“…Numerical weather prediction is the process of using existing numerical data on weather conditions to forecast the weather using machine learning algorithms. This study employs machine learning algorithms, a linear regression model using statistics, and two optimization approaches, the normal equation approach, and gradient descent approach to predict the weather based on a few variables. …”
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Assessment of forest aboveground biomass estimation from superview-1 satellite image using machine learning approaches / Azinuddin Mohd Asri
Published 2022“…In contrast, machine learning is used to calculate the accuracy assessment of dependent between independent variables. …”
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