Search Results - (( data visualization based algorithm ) OR ( variable estimation learning algorithm ))
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1
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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2
Assessment of forest aboveground biomass estimation from superview-1 satellite image using machine learning approaches / Azinuddin Mohd Asri
Published 2022“…The suitable independent variables (hL, DBH, and CPA) were vital to estimating the dependent variable (Sc) and producing a carbon stock map for the final result. …”
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Thesis -
3
HAPTIC VISUALIZATION USING VISUAL TEXTURE INFORMATION
Published 2011“…The design of the proposed framework involves haptic texture rendering algorithm and query algorithm. The proposed framework integrates computer haptic and content based image retrieval (CBIR) where haptic texture rendering is performed based on extracted cloth data. …”
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4
VISUALIZATION OF GENETIC ALGORITHM BASED ON 2-D GRAPH TO ACCELERATE THE SEARCHING WITH HUMAN INTERVENTIONS.
Published 2012“…The aim of the proposed approach is to study the benefit of using visualization techniques to explorer Genetic Algorithm data based on gene values. …”
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5
Evaluating enhanced predictive modeling of foam concrete compressive strength using artificial intelligence algorithms
Published 2025Subjects:Article -
6
SLIDING WINDOW TRAINING ALGORITHMS USING MLP-NETWORK FOR CORRELATED AND LOST PACKET DATA
Published 2012“…This thesis gives a systematic investigation of various MLP learning mainly Sliding Window (SW) learning mode which is treated as the adaptation of offline algorithms into online application Consequently this thesis reviews various offline algorithms including: batch backpropagation, nonlinear conjugate gradient, limited memory and full-memory Broyden, Fletcher, Goldfarb and Shanno algorithms and different forms of the latest proposed bimary ensemble learning. …”
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7
Improving forest above-ground biomass estimation by integrating individual machine learning models
Published 2024“…Machine learning algorithms have been proven to have great potential in forest AGB estimation with remote sensing data. …”
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8
Dynamic Bayesian Networks and Variable Length Genetic Algorithm for Dialogue Act Recognition
Published 2007“…In the selection phase, a new variable length genetic algorithm is applied to select the lexical cues. …”
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9
E-Hadith DV: Hadith scriptures in Syafie Madhhab retrieval using data visualization / Nor Faezahtul Salme Norhisam
Published 2020“…Therefore, a progressive web-based information retrieval visualization system that utilizes Term Frequency-Inverse Document Frequency (TFIDF) algorithm and data visualization are suggested to solve these problems. …”
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10
Performance of correlational filtering and deep learning based single target tracking algorithms / ZhongMing Liao and Azlan Ismail
Published 2023“…This paper closely follows the tracking framework of target tracking algorithms and discusses in detail the traditional visual target tracking methods, the mainstream single target tracking algorithms based on correlation filtering, and the video single target tracking algorithms based on deep learning. …”
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11
Algorithm Development of Bidirectional Agglomerative Hierarchical Clustering Using AVL Tree with Visualization
Published 2024thesis::doctoral thesis -
12
Tag clouds algorithm with the inclusion of personality traits
Published 2015“…Therefore, the main objective of this study is to develop an algorithm that can adapt visual features of tag cloud layout styles based on personality traits of the user. …”
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13
An efficient real-time terrain data organization and visualization algorithm based on enhanced triangle-based level of detail technique
Published 2006“…The massive volume of data involved in the development of a real-time terrain visualization and navigation system is inevitable. …”
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14
Robust Data Fusion Techniques Integrated Machine Learning Models For Estimating Reference Evapotranspiration
Published 2022“…Observations of the results of this study revealed that the solar radiation (Rs) is the most essential variable for estimating ET0 in Peninsular Malaysia. …”
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15
A case study on quality of sleep and health using Bayesian networks
Published 2012“…The network scores computation is implemented to estimate the fitting of the resulting network of each structural learning algorithm in order to choose the best-fitted network. …”
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16
Modelling monthly pan evaporation utilising Random Forest and deep learning algorithms
Published 2023Article -
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Improving Classification of Remotely Sensed Data Using Best Band Selection Index and Cluster Labelling Algorithms
Published 2005“…Using Landsat Thematic Mapper (TM) and ModisIAster Airborne Simulator (hMSTER) images as the test datasets, the BBSI algorithm was compared to the Optimum Index Factor (OIF) algorithm in selection of the best three-band combination for image visualization. …”
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18
Ray Casting for Iso-surface in Volumetric Data
Published 2005“…Volume data visualization is an active field of research and development. …”
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19
Visual Simultaneous Localization and Mapping Using Direct-Based Method for Unmanned Aerial Vehicle (UAV)
Published 2023“…The data evaluation was based on three different environments in the university campus. …”
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20
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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