Search Results - (( optical bat algorithm ) OR ( optical ((rsa algorithm) OR (tree algorithm)) ))
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Accounting Information Systems Genetic Algorithms for All-Optical Shared Fiber-Delay-Line Packet Switches
Published 2009“…In the first algorithm, packet scheduling is formulated as a tree-searching problem. …”
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Assessing Bat Roosts Using the LiDAR System at Wind Cave Nature Reserve in Sarawak, Malaysian Borneo
Published 2017“…Bats that roost in large clusters, specifically Penthetor lucasi were determined through automated counting using connected components labelling, a graph theory algorithm mostly used in image analysis applications. …”
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Seeing trees from space: above-ground biomass estimates of intact and degraded montane rainforests from high-resolution optical imagery
Published 2017“…We used the dimensions of tree crowns detected in the imagery to estimate above-ground biomasses (AGBs) of individual trees and plots. …”
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QoS Forwarding on the Optical Internet Backbone Area Using R-IWDMTC Protocol
Published 2006“…(Extended via Multi-protocol Label Switching (MPLS)) provides connection-oriented setup and multicast tree construction control for Optical Internet data forwarding in the network backbone area. …”
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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“…The algorithms analyzed were generalized linear model (GLM), deep learning (DL), random forest (RF), gradient boosted trees (GBT), and support vector machine (SVM). …”
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Translating conventional wisdom on chicken comb color into automated monitoring of disease-infected chicken using chromaticity-based machine learning models
Published 2024“…The development of the algorithms shows that Logistic Regression, SVM with Linear and Polynomial kernels performed the best with 95% accuracy, followed by SVM-RBF kernel, and KNN with 93% accuracy, Decision Tree with 90% accuracy, and lastly, SVM-Sigmoidal kernel with 83% accuracy. …”
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Delineating mangrove forest zone using spectral reflectance
Published 2020“…To identify individual mangrove species, in-situ measurement was conducted using handheld optical sensors of spectroradiometer to examine the most effective wave bands and spectral regions for discriminating mangrove tree species. …”
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Translating conventional wisdom on chicken comb color into automated monitoring of disease-infected chicken using chromaticity-based machine learning models
Published 2023“…The development of the algorithms shows that Logistic Regression, SVM with Linear and Polynomial kernels performed the best with 95 accuracy, followed by SVM-RBF kernel, and KNN with 93 accuracy, Decision Tree with 90 accuracy, and lastly, SVM-Sigmoidal kernel with 83 accuracy. …”
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Deep convolutional neural network processing of aerial stereo imagery to monitor vulnerable zones near power lines
Published 2018“…© 2018 Society of Photo-Optical Instrumentation Engineers (SPIE).…”
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Deep convolutional neural network processing of aerial stereo imagery to monitor vulnerable zones near power lines
Published 2018“…© 2018 Society of Photo-Optical Instrumentation Engineers (SPIE).…”
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Building extraction for 3D city modelling using infused airborne LiDAR and high-resolution aerial photograph
Published 2021“…The second goal employs a deep learning(DL) algorithm to predict the best sensor for detection, either the LiDAR, optics or the fusion of the LiDAR and high-resolution aerial photography, to know which is most suitable for building detection with little or no user intervention. …”
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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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Simultaneous measurement of multiple soil properties through proximal sensor data fusion: a case study
Published 2019“…After choosing the optimal sensor combination for each soil property, the predictive capability was compared using different data mining algorithms, including support vector machines (SVM), random forest (RF), multivariate adaptive regression splines (MARS), and regression trees (CART). …”
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