This paper is aimed to process properties of the conventional hardness and microstructure of Al-Sic nano composites with grapheme flakes. It is planned to use various combinations with weight percentage of grapheme and Sic and to produce the Aluminum matrix composites by Compression Process. The use of AL-Blast furnace slag reinforced Composite materials is finding applications day by day in engineering fields such as aero-space, automotive, aircrafts constructions, etc. Glass fibre polymer composites are reinforced with granulated blast furnace slag (GBFS) ball milled to size of 5 nm in different weight fractions, i.e. (0%, 5%, 10% and 15%) and fabricated by using hand lay-up technique. These fabricated slabs are cut into required dimensions and the tests for mechanical properties like Tension test, Compression test, Flexural test Impact test and Hardness test were performed. Tension test, compression test and Flexural test were performed on Computerized Universal Testing Machine (UTM), Impact test was performed for Izod and Charpy specimens and Hardness test was performed on Brinell's Hardness Testing Machine. From the experimental results obtained, it was noticed that the mechanical properties were enhanced when the blast furnace slag percent was increased and also among these percentages of blast furnace slag, the specimen having 5% was possessing highest properties.

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Author : DrDVRao, M.Sukun, G.V.Ganesh Reddy , S.K.Altaf, E.Sai kumar

Title : Modeling, Analysis and Fabrication of Piston Using Al-Si/Fly ash/Granite Dust Reinforced Composite

Volume/Issue : 2022;04(4)

Page No : 1-4

There has been a recent uptick in the installation of high-tech video surveillance equipment in public areas. One of the primary uses for gathered video features is safety monitoring, made possible by the implementation of deep learning and machine learning techniques. In this work, our primary focus is on anomaly detection in situations with a large number of people, both indoors and outdoors. In this study, we describe the VGG 16 architecture for the detection of abnormalities occurring in surveillance cameras using classifiers. The VGG 16 architecture was implemented with state-of-the-art classifiers like random forest, J48, decision tree, and SVM. Experiments revealed that the suggested model of using VGG 16 has a relatively low computational burden while still producing satisfactory results, with an accuracy of 80.86% for the random forest classifier.

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Author : KALLEPALLI ROHIT KUMAR,2DR.NISARG GANDHEWAR

Title : ANOMALY DETECTION USING VGG 16 ARCHITECTURE

Volume/Issue : 2022;04(4)

Page No : 5-12