An Ensemble Model for the Diagnosis of Brain Tumors through MRIs.

Automatic brain tumor detection in MR Images is one of the basic applications of machine vision in medical image processing, which, despite much research, still needs further development. Using multiple machine learning techniques as an ensemble system is one of the solutions that can be effective in achieving this goal. In this paper, a novel method for diagnosing brain tumors by combining data mining and machine learning techniques has been proposed. In the proposed method, each image is initially pre-processed to eliminate its background region and identify brain tissue. The Social Spider Optimization (SSO) algorithm is then utilized to segment the MRI Images. The MRI Images segmentation allows for a more precise identification of the tumor region in the image. In the next step, the distinctive features of the image are extracted using the SVD technique. In addition to removing redundant information, this strategy boosts the speed of the processing at the classification stage. Finally, a combination of the algorithms Naïve Bayes, Support vector machine and K-nearest neighbor is used to classify the extracted features and detect brain tumors. Each of the three algorithms performs feature classification individually, and the final output of the proposed model is created by integrating the three independent outputs and voting the results. The results indicate that the proposed method can diagnose brain tumors in the BRATS 2014 dataset with an average accuracy of 98.61%, sensitivity of 95.79% and specificity of 99.71%. Additionally, the proposed method could diagnose brain tumors in the BTD20 database with an average accuracy of 99.13%, sensitivity of 99% and specificity of 99.26%. These results show a significant improvement compared to previous efforts. The findings confirm that using the image segmentation technique, as well as the ensemble learning, is effective in improving the efficiency of the proposed method.

Effect of plasma sprayed flyash based composite coatings on corrosion resistance.

With the aim of exploring the possibility of achieving a low-cost thermal spray coating to prevent wear, erosion and corrosion. In the current study, flyash-Al2O3 and flyash-SiC composite coatings were effectively created using the air plasma spray process on substrates of Al6061 alloy. NiCr material is used as the bond coat to improve the bond strength between the coat and the substrate. Taguchi's DoE method is applied to for spray process parameters optimization. In addition, the developed coating is subjected to microstructure analysis and long-term immersion corrosion testing (1 year) in an aqueous environment to assess corrosion properties. The results revealed that the over a certain test period, the developed flyash-SiC coating has greater corrosion resistance than the uncoated and flyash-Al2O3 coated Al6061. It is noticed that the corrosion resistance of the flyash-Al2O3 coating shifts to a negative value with respect to the uncoated substrate. The uncoated sample is extensively pitted and locally corroded, as shown by the SEM image of the corroded surface. Flyash-corroded Al2O3's surface exhibits extensive degradation in the form of peeling, breaking, and cracking of the splats. With flyash-SiC composite coating a very minor corrosion splat deterioration is seen.

Numerical Simulation of the Performance of Sb2Se3 Solar Cell via Optimizing the Optoelectronic Properties Based SCAPS-1D.

Antimony trisulfide (Sb2Se3), a non-toxic and accessible substance, has possibilities as a material for use in solar cells. The current study numerically analyses Sb2Se3 solar cells through the program Solar Cell Capacitance Simulator (SCAPS). A detailed simulation and analysis of the influence of the Sb2Se3 layer's thickness, defect density, band gap, energy level, and carrier concentration on the devices' performance are carried out. The results indicate that a good device performance is guaranteed with the following values in the Sb2Se3 layer: an 800 optimal thickness for the Sb2Se3 absorber; less than 1015 cm-3 for the absorber defect density; a 1.2 eV optimum band gap; a 0.1 eV energy level (above the valence band); and a 1014 cm-3 carrier concentration. The highest efficiency of 30% can be attained following optimization of diverse parameters. The simulation outcomes offer beneficial insights and directions for designing and engineering Sb2Se3 solar cells.