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Identification of seismic activity levels in coal mines is important to avoid accidents such as rockburst. Creating an early warning system that can save lives requires an automated way of predicting. This study proposes a prediction algorithm for the AAIA'16 Data Mining Challenge: Predicting Dangerous Seismic Events in Active Coal Mines that is based on transient activity features along with average...
The aim of this study, determination of blood vessels on high dimensional retinal images using decomposed pixel classification method. In this work intensity histograms are obtained by using vectoral directions. Local minimum and local maximum points are found by using these histograms to detect location of vessels. In addition to this, vessel thickness information are detected by determining a threshold...
This paper proposes a new method for automated clustering of high dimensional datasets. The method is based on a recursive binary division strategy that successively divides an original dataset into distinct clusters. Each binary division is carried out using a model-free expectation maximization scheme that exploits the posterior probability computation capability of the quasi-supervised learning...
The aim of this study, nuclei base automatic detection of cancerous regions via determination of DNA-rich regions in high definition histology images. In the study; DNA-rich regions were determined using k-means clustering and some mathematical morphology operations, the diseased regions were diagnosed using morphological characteristics via quasi-supervised learning.It's observed that quasi-supervised...
In this study, the separation of the stimulus effects from the baseline was investigated in electroencephalography data recorded under different visual stimuli using quasi-supervised learning. The data feature vectors were constructed using independent component analysis and wavelet transform, and then, these feature vectors were separated using quasi-supervised learning. Experiment results showed...
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