This study explores a semi-supervised classification approach using random forest as a base classifier to classify the low-back disorders (LBDs) risk associated with the industrial jobs. Semi-supervised classification approach uses unlabeled data together with the small number of labelled data to create a better classifier. The results obtained by the proposed approach are compared with those o...

this research was performed to evaluate the potentials of landsat mss data for map-ping land features in arid zones of southeastern esfahan, iran. databases of the area were formed using all available relevant maps and reports which were supported by fieldwork. a supervised image classification approach was used and thirty-two training areas were applied. separability of the spectral classes wa...

the growing population and increasing socio-economic necessitiescreates a pressure on land use/land cover. nowadays, land use change detection using remote sensing data provides quantitative and timely information for management and evaluation of natural resources. this study investigates the land use changes in part of hableh rood watershed of iran using landsat 7 and 8 (sensor etm+ and oli) i...

the growing population and increasing socio-economic necessities creates a pressure on land use/land cover. nowadays, land use change detection using remote sensing data provides quantitative and timely information for management and evaluation of natural resources. this study investigates the land use changes in part of hableh rood watershed of iran using landsat 7 and 8 (sensor etm+ and oli) ...

conclusions in this study, we proposed a new computer aided diagnostic tool for the detection and classification of breast cancer. the obtained results showed that the proposed method is more reliable in diagnostic to assist the radiologists in the detection of abnormal data and to improve the diagnostic accuracy. results after classification with the ensemble supervised algorithm, the performa...

We present Self-Classifier – a novel self-supervised end-to-end classification learning approach. learns labels and representations simultaneously in single-stage manner by optimizing for same-class prediction of two augmented views the same sample. To guarantee non-degenerate solutions (i.e., where all are assigned to class) we propose mathematically motivated variant cross-entropy loss that h...

brain mr images tissue segmentation is one of the most important parts of the clinical diagnostic tools. pixel classification methods have been frequently used in the image segmentation with two supervised and unsupervised approaches up to now. supervised segmentation methods lead to high accuracy but they need a large amount of labeled data, which is hard, expensive and slow to obtain. moreove...