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The effect of different distance measures in detecting outliers using clustering-based algorithm for circular regression model by Nur Faraidah, Muhammad Di, Siti Zanariah, Satari

“…Then, a stopping rule for the cluster tree based on the mean direction and circular standard deviation of the tree height is proposed. …”
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Comparative study of clustering-based outliers detection methods in circular-circular regression model by Siti Zanariah Satari, Nur Faraidah Muhammad Di, Yong Zulina Zubairi, Abdul Ghapor Hussin

“…A stopping rule for the cluster tree based on the mean direction and circular standard deviation of the tree height was used as the cutoff point and classifier to the cluster group that exceeded the stopping rule as potential outliers. …”
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Comparative study of clustering-based outliers detection methods in circularcircular regression model by Siti Zanariah, Satari, Nur Faraidah, Muhammad Di, Yong Zulina, Zubairi, Abdul Ghapor, Hussin

“…A stopping rule for the cluster tree based on the mean direction and circular standard deviation of the tree height was used as the cutoff point and classifier to the cluster group that exceeded the stopping rule as potential outliers. …”
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Comparative study of clustering-based outliers detection methods in circular-circular regression model by Siti Zanariah, Satari, Nur Faraidah, Muhammad Di, Yong Zulina, Zubairi, Abdul Ghapor, Hussin

“…A stopping rule for the cluster tree based on the mean direction and circular standard deviation of the tree height was used as the cutoff point and classifier to the cluster group that exceeded the stopping rule as potential outliers. …”
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Three-dimension coronary artery tree curvature confirmation by Khaleel, Hasan Hadi, O. K. Rahmat, Rahmita Wirza, Dimon, Mohd Zamrin, Mahmod, Ramlan, Mustapha, Norwati

“…We propose to use the standard deviation technique to compare the output features of the 3D coronary artery trees. …”
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Footwear quality evaluation using decision tree and logistic regression models by Tan, Swee Choon

“…The analysis showed that Decision Tree with Gini algorithm (three branches) in the first method prevails against the other methods with misclassification rate of 0.1307. …”
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Analysis of short term load forecasting techniques / Tan Vy Luoh by Tan, Vy Luoh

“…In this report, three common numerical STLF techniques including Multiple Linear Regression (MLR), Curve Fitting and Bagged Tree Regression are proposed to forecast one-day ahead load profile with a yearly historical load data. …”
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Parameter estimation and outlier detection for some types of circular model / Siti Zanariah binti Satari by Satari, Siti Zanariah

“…Then, a stopping rule for the cluster tree based on the mean direction and circular standard deviation of the tree height is proposed. …”
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A hybrid interpretable deep structure based on adaptive neuro‑fuzzy inference system, decision tree, and K‑means for intrusion detection by Jia, Lu, Yin Chai, Wang, Chee Siong, Teh, Xinjin, Li, Liping, Zhao, Fengrui, Wei

“…The proposed algorithm was trained, validated, and tested on the NSL-KDD (National security lab–knowledge discovery and data mining) dataset. …”
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Implementation of machine learning algorithms for streamflow prediction of Dokan dam by Sarmad Dashti Latif, Mr.

“…This study aims at comparing the application of deep learning algorithms and conventional machine learning algorithms for predicting reservoir inflow. …”

Developing machine learning algorithms for meteorological temperature and humidity forecasting at Terengganu state in Malaysia by Hanoon M.S., Ahmed A.N., Zaini N., Razzaq A., Kumar P., Sherif M., Sefelnasr A., El-Shafie A.

Identifying multiple outliers in linear functional relationship model using a robust clustering method by Adilah Abdul Ghapor, Yong Zulina Zubairi, Al Mamun, Sayed Md., Siti Fatimah Hassan, Elayaraja Aruchunan, Nurkhairany Amyra Mokhtar

“…A new robust cut-off point using the median and median absolute deviation for the tree heights to classify the potential outliers are proposed in this study. …”
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Enhanced Adaptive Neuro-Fuzzy Inference System Classification Method for Intrusion Detection by Jia, Liu

“…On the KDDTest+ dataset, the proposed method also outperforms single CART and ANFIS in terms of various metrics other than precision. Since the CART tree is a binary tree, it can only represent the relationship between data through a split based on a single attribute at a single tree node. …”
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A comparative analysis of classical machine learning and deep learning techniques for predicting lung cancer survivability by Huang S., Arpaci I., Al-Emran M., K?l?�arslan S., Al-Sharafi M.A.

An improved hybrid learning approach for better anomaly detection by Mohamed Yassin, Warusia

“…Therefore, anomaly detection is often associated with high false alarm with only moderate accuracy of detection rates. In recent years, data mining approach for intrusion detection have been proposed and used such as neural networks, clustering, genetic algorithms, decision trees, and support vector machines. …”
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Assessment of near-infrared and mid-infrared spectroscopy for early detection of basal stem rot disease in oil palm plantation by Liaghat, Shohreh

“…Comparing the results achieved from analyzing the reflectance spectra (VIS-NIR and MIR) of leaf and trunk samples with SVM and NNclassifiers demonstrated that mid-infrared absorbance data of trunk samples with the average overall classification accuracies of 97% (standard deviation = 1%) for SVM and 97% (standard deviation = 3%)for NN resulted in better performance in classifying four classes of Ganderma infestation. …”
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3D multimodal cardiac data reconstruction using computerized tomographic angiography and x-ray angiography registration by Moosavitayebi, Seyed Rohollah

“…Over the last decade, some algorithms have been developed to register coronary arteries from the above modalities. …”
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