Search Results - optical ((cloud algorithm) OR (((mining algorithm) OR (learning algorithm))))
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The Parallel Fuzzy C-Median Clustering Algorithm Using Spark for the Big Data
Published 2024“…A comparative study is done to validate the proposed algorithm by implementing the other contemporary algorithms for the same dataset. …”
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Development of personal finance mobile application with Optical Character Recognition (OCR) technology for automated receipt management and expense tracking
Published 2023“…OCR is the short form for Optical Character Recognition. It is a technology that was powered by Machine Learning algorithms like Convolutional Neural Networks (CNN) whereby it allows extraction of text from images or documents in a blink of eye. …”
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Final Year Project / Dissertation / Thesis -
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Depth value approximation of 2D complex-shape objects for 3D modelling using optical flow and trigonometry
Published 2015“…Colour information of the feature points can be extracted as well. Data enhancement algorithms were implemented to perform noise filtering and inverse perspective mapping (IPM). …”
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Near-sea-level langley calibration algorithm
Published 2014“…The key advantages of the proposed algorithm are its objectivity, computational efficiency and the ability to detect short intervals of cloud transits. …”
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A Reference Based Surface Defect Segmentation Algorithm For Automatic Optical Inspection System
Published 2020“…Results show that the proposed algorithm required a learning dataset size as small as 5 samples and was resistant to learning labelling error up to 50%.…”
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Retrieval Of Environmental Parameters Over Water Areas Using MODIS Data
Published 2012“…In this study, new and simple algorithms to retrieve cirrus cloud, sediment and aerosol optical depth (AOD) from Moderate Resolution Radiospectrometer (MODIS) imagery is suggested and demonstrated. …”
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Estimating Forest Aboveground Biomass Density Using Remote Sensing and Machine Learning : A RSME Approach
Published 2025“…Integrated with the random forest algorithm in the Google Earth Engine for AGB density modeling at a spatial resolution of 1 km, the methodology incorporates GEDI Level 4, Sentinel-1 radar, Sentinel-2 optical imagery, and elevation/slope maps. …”
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Machine learning approach for automated optical inspection of electronic components
Published 2019“…The factor that affecting the confidence level of the supervised machine learning algorithm is discussed. …”
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Final Year Project / Dissertation / Thesis -
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Optimized techniques for landslide detection and characteristics using LiDAR data
Published 2018“…The locations of landslides were detected accurately by employing two Machine learning classifiers, namely, SVM and RF, decision rule and hierarchal rules sets were developed by applying decision tree (DT) algorithm to provide improved landslide inventory. …”
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Feature Extraction and Matching from images / lntan Syaherra Ramli
Published 2023“…Next, the feature points from two images are matched using Optical Flow Lucas Kanade. The corresponding feature points are used to find the 30 point cloud for computer graphic application. …”
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Monograph -
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The analysis of tropospheric aerosol concentration and distribution using NOAA AVHRR
Published 2005“…The results suggests that the application of DTA algorithm on NOAA AVHRR imagery whenever available and cloud free could be used as an indicator to air quality assessment for this region.…”
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Improving Prediction Accuracy and Extraction Precision of Frequency Shift from Low-SNR Brillouin Gain Spectra in Distributed Structural Health Monitoring
Published 2023“…Brillouin scattering; Concretes; Curve fitting; Data handling; Extraction; Fiber optic sensors; Fiber optics; Learning algorithms; Machine learning; Structural health monitoring; BOTDA; Brillouin frequency shift extraction; Brillouin frequency shifts; Brillouin gain spectrum; Correlation techniques; Distributed fiber-optic sensors; Frequency shift; Generalized linear model; Low signal-to-noise ratio; Prediction accuracy; Signal to noise ratio; algorithm; fiber optics; noise; signal noise ratio; Algorithms; Fiber Optic Technology; Noise; Signal-To-Noise Ratio…”
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Comparative analysis on the deployment of machine learning algorithms in the distributed brillouin optical time domain analysis (BOTDA) fiber sensor
Published 2023“…This paper demonstrates a comparative analysis of five machine learning (ML) algorithms for improving the signal processing time and temperature prediction accuracy in Brillouin optical time domain analysis (BOTDA) fiber sensor. …”
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Generalized linear model for enhancing the temperature measurement performance in Brillouin optical time domain analysis fiber sensor
Published 2023“…Curve fitting; Deterioration; Fiber optic sensors; Forecasting; Learning algorithms; Machine learning; Signal to noise ratio; Temperature measurement; Temperature sensors; Theorem proving; Brillouin frequency shifts; Brillouin gain spectrum (BGS); Brillouin optical time domain analysis; Distributed temperature sensing; Generalized linear model; Low signal-to-noise ratio; Temperature prediction; Temperature resolution; Time domain analysis…”
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