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In the present study we have measured the excitation functions for the nuclear reactions $^{100}$Mo($\alpha$,n)$^{103}$Ru, $^{nat}$Mo($\alpha$,x)$^{97}$Ru, $^{nat}$Mo($\alpha$,x)$^{95}$Ru, $^{nat}$Mo($\alpha$,x)$^{96g}$Tc, $^{nat}$Mo($\alpha$,x)$^{95g}$Tc and $^{nat}$Mo($\alpha$,x)$^{94g}$Tc in the energy range 11-32 MeV. We have used the stacked foil activation technique followed by off-line gamma ray spectroscopy technique to measure the excitation functions. In this study we have also documented detailed uncertainty analysis for these nuclear reactions and their corresponding covariance matrix are also presented. The excitation functions are compared with the available experimental data from EXFOR data library and the theoretical prediction from TALYS nuclear reaction code. The present measurements are found to be consistent with the available experimental data.

The excitation functions of 66Ga, 67Ga, 65Zn and 64Cu radioisotopes produced via alpha-induced reaction on nat Cu were measured using a stacked foil activation method. The gamma-ray activity produced by the above mentioned radionuclides was measured using the HPGe detector. The covariance analysis was performed to quantify the measured cross-section uncertainties as well as the correlation between different alpha energy cross-sections. A covariance matrix and cross-sections for the nat Cu(α, x)66Ga, nat Cu(α, x)67Ga, nat Cu(α, x)65Zn and nat Cu(α, x)64Cu nuclear reactions in the projectile energy range of 15–37 MeV are reported in the present work. The measured reaction cross-sections are compared with the existing experimental data and theoretically simulated results from the TALYS code.

With the incremental growth of Web based platforms, Automated Review sentiment analysis has become main focus, with the very primary purpose is to get the implicit information by the reviews for the better acknowledgement of the customers. Conventional methods have made tremendous growth on the basic level and are not very much reliable including the sentiment analysis, inspite the rapid growth of data scale and processing high dimension of data faster, effectively and accurately becomes the biggest drawbacks. The primary focus of the paper is the use of Support vector machines, embedding & deep neural networks which are suitable for the high dimensional data analysis, and proposes the structure of sentiment analysis using deep learning. This will act as the generalized automated review system for various industries Such as E-Commerce platform, YouTube comments, movie review rating etc. with more high precision and has potential to replace orthodox star-based rating System. Experiments which were carried on different data scales and various domains reveals that the proposed method can be used to solve the problem with better performance and higher Accuracy.