Search Results - (( using codification mining algorithm ) OR ( basic extraction tree algorithm ))

Extracting crown morphology with a low-cost mobile LiDAR scanning system in the natural environment by Wang, Kai, Zhou, Jun, Zhang, Wenhai, Zhang, Baohua

“…Finally, morphological parameters of the canopy, such as crown height, crown diameter, and crown volume, are extracted using statistical and voxel methods. To verify the algorithm, a total of 43 trees in multiple plots of the campus were tested and compared. …”
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Automatic extraction of digital terrain model and Building Footprint from airborne LiDAR data using rule-based learning techniques by Jifroudi, Hamidreza Maskani

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An automatic visual inspection of oil tanks exterior surface using unmanned aerial vehicle with image processing and cascading fuzzy logic algorithms by Mohammed A. H., Ali, Muhammad, Baggash, Jaloliddin, Rustamov, Abdulghafor, Rawad Abdulkhaleq Abdulmolla, Najm Al-Deen N., Abdo, Mubarak H. G., Abdo, Talep S., Mohammed, Ameen A., Hasan, Ali N., Abdo, Sherzod, Turaev, Yusoff, Nukman

“…For the former, an image processing algorithm was developed to improve or remove noise, adjust the brightness of the captured image, and extract features to identify defects in oil tanks. …”
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Twofold Integer Programming Model for Improving Rough Set Classification Accuracy in Data Mining. by Saeed, Walid

“…The accuracy for rules and classification resulted from the TIP method are compared with other methods such as Standard Integer Programming (SIP) and Decision Related Integer Programming (DRIP) from Rough Set, Genetic Algorithm (GA), Johnson reducer, HoltelR method, Multiple Regression (MR), Neural Network (NN), Induction of Decision Tree Algorithm (ID3) and Base Learning Algorithm (C4.5); all other classifiers that are mostly used in the classification tasks. …”
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Deep learning-based breast cancer detection and classification using histopathology images / Ghulam Murtaza by Ghulam , Murtaza

“…The trained EBrC-Net is used to extract discriminative features. The extracted features are evaluated through six machine learning (ML) classifiers namely softmax, k-nearest neighbor (kNN), support vector machine, linear discriminant analysis, decision tree, and naive Bayes. …”
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A deep learning approach: The impact of sentiment analysis of Bangladeshi workers over the world by Tomal, Md Raihanul Islam, Kader, Tanveer, Kohbalan, Moorthy, Mazlina, Abdul Majid

“…TF-IDF vectorization was used for feature extraction, followed by basic machine learning algorithms such as Decision Tree, Support Vector Machine, and Naive Bayes. …”
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