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Full-Text Articles in Physical Sciences and Mathematics
Effect Of Label Noise On The Machine-Learned Classification Of Earthquake Damage, Jared Frank, Umaa Rebbapragada, James Bialas, Thomas Oommen, Timothy C. Havens
Effect Of Label Noise On The Machine-Learned Classification Of Earthquake Damage, Jared Frank, Umaa Rebbapragada, James Bialas, Thomas Oommen, Timothy C. Havens
Michigan Tech Publications
Automated classification of earthquake damage in remotely-sensed imagery using machine learning techniques depends on training data, or data examples that are labeled correctly by a human expert as containing damage or not. Mislabeled training data are a major source of classifier error due to the use of imprecise digital labeling tools and crowdsourced volunteers who are not adequately trained on or invested in the task. The spatial nature of remote sensing classification leads to the consistent mislabeling of classes that occur in close proximity to rubble, which is a major byproduct of earthquake damage in urban areas. In this study, …
K-Nn-Based Classification Of Sleep Apnea Types Using Ecg, Oğuz Han Ti̇muş, Emi̇ne Doğru Bolat
K-Nn-Based Classification Of Sleep Apnea Types Using Ecg, Oğuz Han Ti̇muş, Emi̇ne Doğru Bolat
Turkish Journal of Electrical Engineering and Computer Sciences
Obstructive sleep apnea syndrome (OSAS) is a common sleep disorder that yields cardiovascular diseases, excessive daytime sleepiness, and poor quality of life if not treated. Classification of OSAS from electrocardiograms (ECGs) is a noninvasive method and much more affordable than traditional methods. This study proposes a pattern recognition system for automated apnea diagnosis based on heart rate variability (HRV) and ECG-derived respiratory signals. The k-nearest neighbor (k-NN) classifier has been used to develop the models for classifying the sleep apnea types. For comparison purposes, classification models based on multilayer perceptron, support vector machines, and C4.5 decision tree (C4.5 DT) have …
Multiclass Semantic Segmentation Of Faces Using Crfs, Khalil Khan, Nasir Ahmad, Khalil Ullah, Irfanud Din
Multiclass Semantic Segmentation Of Faces Using Crfs, Khalil Khan, Nasir Ahmad, Khalil Ullah, Irfanud Din
Turkish Journal of Electrical Engineering and Computer Sciences
Multiclass semantic image segmentation is widely used in a variety of computer vision tasks, such as object segmentation and complex scene understanding. As it decomposes an image into semantically relevant regions, it can be applied in segmentation of face images. In this paper, an algorithm based on multiclass semantic segmentation of faces is proposed using conditional random fields. In the proposed model, each node corresponds to a superpixel, while the neighboring superpixels are connected to nodes through edges. Unlike previous approaches, which rely on three or four classes, the label set is extended here to six classes, i.e. hair, eyes, …
Classification Of Eeg Signals Of Familiar And Unfamiliar Face Stimuli Exploiting Most Discriminative Channels, Abdurrahman Özbeyaz, Sami̇ Arica
Classification Of Eeg Signals Of Familiar And Unfamiliar Face Stimuli Exploiting Most Discriminative Channels, Abdurrahman Özbeyaz, Sami̇ Arica
Turkish Journal of Electrical Engineering and Computer Sciences
The objective of the study is to classify electroencephalogram signals recorded in a familiar and unfamiliar face recognition experiment. Frontal views of familiar and unfamiliar face images were shown to 10 volunteers in different sessions. In contrast to previous studies, no marker button was used during the experiment. Participants had to decide whether the displayed face was familiar or unfamiliar at the instant of stimulus presentation. The signals were analyzed in the preprocessing, channel selection, feature extraction, and classification stages. The novel two-feature extraction and eight-channel selection methods were applied to the analyses. Sixteen classification results were compared and the …
Dtreesim: A New Approach To Compute Decision Tree Similarity Using Re-Mining, Gözde Bakirli, Derya Bi̇rant
Dtreesim: A New Approach To Compute Decision Tree Similarity Using Re-Mining, Gözde Bakirli, Derya Bi̇rant
Turkish Journal of Electrical Engineering and Computer Sciences
A number of recent studies have used a decision tree approach as a data mining technique; some of them needed to evaluate the similarity of decision trees to compare the knowledge reflected in different trees or datasets. There have been multiple perspectives and multiple calculation techniques to measure the similarity of two decision trees, such as using a simple formula or an entropy measure. The main objective of this study is to compute the similarity of decision trees using data mining techniques. This study proposes DTreeSim, a new approach that applies multiple data mining techniques (classification, sequential pattern mining, and …
Enhanced Breast Cancer Classification With Automatic Thresholding Using Support Vector Machine And Harris Corner Detection, Mohammad Taheri
Enhanced Breast Cancer Classification With Automatic Thresholding Using Support Vector Machine And Harris Corner Detection, Mohammad Taheri
Electronic Theses and Dissertations
Image classification and extracting the characteristics of a tumor are the powerful tools in medical science. In case of breast cancer medical treatment, the breast cancer classification methods can be used to classify input images as benign and malignant classes for better diagnoses and earlier detection with breast tumors. However, classification process can be challenging because of the existence of noise in the images, and complicated structures of the image. Manual classification of the images is timeconsuming, and need to be done only by medical experts. Hence using an automated medical image classification tool is useful and necessary. In addition, …