Search Results - optical ((svm algorithm) OR (((tree algorithm) OR (_ algorithm))))

Comparative analysis on the deployment of machine learning algorithms in the distributed brillouin optical time domain analysis (BOTDA) fiber sensor by Nordin N.D., Zan M.S.D., Abdullah F.

“…The algorithms analyzed were generalized linear model (GLM), deep learning (DL), random forest (RF), gradient boosted trees (GBT), and support vector machine (SVM). …”

Translating conventional wisdom on chicken comb color into automated monitoring of disease-infected chicken using chromaticity-based machine learning models by Bakar M.A.A.A., Ker P.J., Tang S.G.H., Baharuddin M.Z., Lee H.J., Omar A.R.

“…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. …”

Translating conventional wisdom on chicken comb color into automated monitoring of disease-infected chicken using chromaticity-based machine learning models by Bakar, Mohd Anif A. A., Ker, Pin Jern, Tang, Shirley G. H., Baharuddin, Mohd Zafri, Lee, Hui Jing, Omar, Abdul Rahman

“…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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Accounting Information Systems Genetic Algorithms for All-Optical Shared Fiber-Delay-Line Packet Switches by Liew, S.Y., Wong, E.S.K.

“…In the first algorithm, packet scheduling is formulated as a tree-searching problem. …”
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Optimized techniques for landslide detection and characteristics using LiDAR data by Mezaal, Mustafa Ridha

“…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 by Wenjun, Ji, Adamchuk, Viacheslav I., Song, Chao Chen, Mat Su, Ahmad S., Ismail, Ashraf, Qianjun, Gan, Zhou, Shi, Biswas, Asim

“…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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Combining data mining algorithm and object-based image analysis for detailed urban mapping of hyperspectral images by Hamedianfar, Alireza, Mohd Shafri, Helmi Zulhaidi, Mansor, Shattri, Ahmad, Noordin

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Spectral feature selection and classification of roofing materials using field spectroscopy data by Samsudin, Sarah Hanim, M. Shafri, Helmi Z., Hamedianfar, Alireza, Mansor, Shattri

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LSB algorithm based on support vector machine in digital image steganography by Hussain, Hanizan Shaker, Din, Roshidi, Mustapha, Aida, Mansor, Fawwaz Zamir

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Seeing trees from space: above-ground biomass estimates of intact and degraded montane rainforests from high-resolution optical imagery by Phua, Mui How, Ling, Zia-Yiing, David Anthony Coomes, Wilson Wong, Alexius Korom, Satoshi Tsuyuki, Keiko Ioki, Yasumasa Hirata, Hideki Saito, Gen Takao

“…To this purpose, preprocessed IKONOS imagery was segmented using a watershed algorithm; stem diameter values were then estimated from the cross-sectional crown areas of these trees using regression relationships obtained from ground-based measurements. …”
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QoS Forwarding on the Optical Internet Backbone Area Using R-IWDMTC Protocol by Addie Irawan, Hashim, R. Badlishah, Ahmad

“…(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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Gesture recognition system for Nigerian tribal greeting postures using support vector machine / Segun Aina …[et al.] by Aina, Segun, V. Sholesi, Kofoworola, R. Lawal, Aderonke, D. Okegbile, Samuel, I. Oluwaranti, Adeniran

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The formulation of a transfer learning pipeline for the classification of the wafer defects by Lim, Shi Xuen

“…Thus, this study will aim to explore 17 types of TL models, and classify the features extracted using 3 different ML algorithms, namely Support Vector Machine (SVM), k-Nearest Neighbor (kNN) and Random Forest (RF). …”
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Design Of A Predictive Model For TCM Pulse Diagnosis In Malaysia Using Machine Learning by Ong, Jia Ying

“…The machine learning algorithms applied in this project are k-nearest neighbors (KNN), naïve Bayes, random forest, gradient boosting and support vector machine (SVM). …”
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Integrated approach using data mining-based decision tree and object-based image analysis for high-resolution urban mapping of WorldView-2 satellite sensor data by Hamedianfar, Alireza, Mohd Shafri, Helmi Zulhaidi

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A preliminary lightweight random forest approach-based image classification for plant disease detection by Mashitah Ibrahim, Muzaffar Hamzah, Mohammad Fadhli Asli

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Delineating mangrove forest zone using spectral reflectance by Abdul Whab @ Abdul Wahab,, Zulfa

“…Species identification with spectral library derived from in-situ measurements using SID algorithm and derived from Landsat 8 using SAM algorithm was done. …”
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Comparison of fusion of different algorithms in mapping of Melaleuca forest in Marang district, Malaysia by Saberioon, Mohammadmehdi, Mardan, Makhdzir, Nordin, Laili, Mohd Sood, Alias

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Detection of vegetation encroachment in power transmission line corridor from satellite imagery using support vector machine: A features analysis approach by Mahdi Elsiddig Haroun F., Mohamed Deros S.N., Bin Baharuddin M.Z., Md Din N.

“…Color; Electric lines; Electric power transmission; Optical radar; Satellite imagery; Space-based radar; Synthetic aperture radar; Transmissions; Vector spaces; Vegetation; Classification accuracy; Environmental challenges; Gray level co occurrence matrix(GLCM); Light detection and ranging; Power interruptions; Statistical moments; Support vector machine algorithm; Vegetation density; Support vector machines…”

Deep convolutional neural network processing of aerial stereo imagery to monitor vulnerable zones near power lines by Qayyum, A., Saad, N.M., Kamel, N., Malik, A.S.

“…© 2018 Society of Photo-Optical Instrumentation Engineers (SPIE).…”
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