Nanoparticles of Li, Mg and Sr doped and undoped zinc oxide was prepared by simple precipitation method. The structural, optical, and magnetic properties of the samples were investigated by the Powder X-ray Diffraction (XRD), Scanning Electron Microscope (SEM), Transmission Electron Microscope (TEM), Fourier Transform Infrared (FTIR) spectroscopy, Ultra-violet Visible spectroscopy (UV-vis) spectra, Photoluminescence (PL) and Vibrational Sample Magnetometer (VSM). The Powder X-ray diffraction data confirm the formation of hexagonal wurtzite structure of all doped and undoped ZnO. The SEM photograph reveals that the pores availability and particles size in the range of 10 nm-50 nm. FTIR and UV-Visible spectra results confirm the incorporation of the dopant into the ZnO lattice nanostructure. The UV-Visible spectra indicate that the shift of blue region (lower wavelength) due to bandgap widening. Photoluminescence intensity varies with doping due to the increase of oxygen vacancies in prepared ZnO. The pure ZnO exist paramagnetic while doped (Li, Mg and Sr) ZnO exist ferromagnetic property. The photocatalytic activity of the prepared sample also carried out in detail.
<p>The objective of this research was to introduce a new system for automated detection of breast masses in mammography images. The system will be able to discriminate if the image has a mass or not, as well as benign and malignant masses. The new automated ROI segmentation model, where a profiling model integrated with a new iterative growing region scheme has been proposed. The ROI region segmentation is integrated with both statistical and texture feature extraction and selection to discriminate suspected regions effectively. A classifier model is designed using linear fisher classifier for suspected region identification. To check the system’s performance, a large mammogram database has been used for experimental analysis. Sensitivity, specificity, and accuracy have been used as performance measures. In this study, the methods yielded an accuracy of 93% for normal/abnormal classification and a 79% accuracy for bening/malignant classification. The proposed model had an improvement of 8% for normal/abnormal classification, and a 7% improvement for benign/malignant classification over Naga <em>et al.</em>, 2001. Moreover, the model improved 8% for normal/abnormal classification over Subashimi <em>et al.</em>, 2015. The early diagnosis of this disease has a major role in its treatment. Thus the use of computer systems as a detection tool could be viewed as essential to helping with this disease.</p>
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