Nowadays, the aluminum metal matrix composites (AMCs) play an empirical role to improve the mechanical performance by various applications. Therefore, the secondary processes need to enhance the surface morphology of intermetallic phases with the appropriate reinforcing particles. In this research, Al7075- and ceramic-based nanosilicon carbide (SiC) were utilized to compose the metal matrix composites. These composites were subjected to friction welding for intermetallic surface modification with the various forging pressure and rotating speed. Initially, the AMCs were prepared with three (8–12) kinds of SiC weight proportions by the design of Taguchi L9 orthogonal array. As per the weight proportions, nine samples were prepared and then conducted the friction welding with 10–20 MPa of forge pressure and 1,650–2,050 rpm of rotational speed of spindle. Then, the entire nine specimens were allowed to conduct the tensile and microhardness test. During the mechanical test, the overall welded zone had higher mechanical properties than the base metal. Then, the artificial neural network was utilized to predict the output responses as per the designed concept of trial and error method. The overall predicted responses are nearly closed to the experimental values.

The main aim of this research is to determine the erratic user behaviour over social media using machine learning classifiers by comparing Novel K-Nearest Neighbour algorithm and Support Vector Machine algorithm. Classification is performed using K-Nearest Neighbour with sample size (N=10) and Support Vector Machine sample size (N=10), and results were compared based on the accuracy of both algorithms. The KNN is used to determine the accuracy of erratic user behaviour with the help of social media network reviews with twitter data. The accuracy achieved for KNN is (95.30%) and SVM is (92.67%). The statistical significance between K-Nearest Neighbour & Support Vector Machine is (p=0.0094) where (p<0.05).K-Nearest Neighbour algorithm helps in determining with more accuracy in erratic user behaviour over social media networks, and here KNN algorithm shows better accuracy than SVM algorithm.

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