An Overview on Amazon Rekognition Technology

The Covid-19 pandemic has disrupted the daily operations of many businesses due to which they were forced to follow the guidelines set by the local, state, and federal government to reduce the spread of the Covid-19 virus. This project focused on how few businesses that resumed their operations during the onset of Covid-19 integrated Amazon Web Services Rekognition technology to comply with government rules and regulations

Performance evaluation of random forest with feature selection methods in prediction of diabetes

Data mining is nothing but the process of viewing data in different angle and compiling it into appropriate information. Recent improvements in the area of data mining and machine learning have empowered the research in biomedical field to improve the condition of general health care. Since the wrong classification may lead to poor prediction, there is a need to perform the better classification which further improves the prediction rate of the medical datasets. When medical data mining is applied on the medical datasets the important and difficult challenges are the classification and prediction. In this proposed work we evaluate the PIMA Indian Diabtes data set of UCI repository using machine learning algorithm like Random Forest along with feature selection methods such as forward selection and backward elimination based on entropy evaluation method using percentage split as test option. The experiment was conducted using R studio platform and we achieved classification accuracy of 84.1%. From results we can say that Random Forest predicts diabetes better than other techniques with less number of attributes so that one can avoid least important test for identifying diabetes

Synthesis And Application Of Atp Analogs For Phosphorylation-Dependent Kinase-Substrate Crosslinking

Phosphorylation is an important post-translational modification that plays a key role in a variety of signaling cascades and cellular functions. Kinases phosphorylate protein substrates in a highly regulated manner and are promiscuous. Understanding kinase-substrate specificity has been challenging and there is a need for new chemical tools. To this end we developed -phosphate modified ATP photocrosslinking analogs ATP-ArN3 and ATP-BP, that crosslink substrate and kinase in a phosphorylation dependent manner. We have successfully demonstrated that ATP-ArN3 and ATP-BP can be used with natural kinase and substrates using cell lysates in vitro. We used our approach to identify novel kinases of p53. One powerful feature of this methodology is we can obtain an atomic level snapshot of the interactions between proteins, when coupled with analytical techniques like Mass Spectrometry (MS). These tools will help us in validating our understanding of protein-protein interactions, their role in signaling pathways and functioning of the cell

Information Retrieval based on Content and Location Ontology for Search Engine (CLOSE)

This paper mainly focuses on the personalization of the search engine based on data mining technique, such that user preferences are taken into consideration. Clickthrough data is applied on the user profile to mine the user preferences in order to extract the features to know in which users are really interested. The basic idea behind the concept is to construct the content and location ontology2019;s, where content represent the previous search records of the user and location refer to current location of user. SpyNB is the approach used to mining the user preferences from the Clickthrough data. The ranked support vector machine (RVSM) is performed on the searched results in order to display results according to user preferences by considering Clickthrough data

Multi-objective optimization for preemptive & predictive supply chain operation

At present, the manufacturing industry has undergone a tremendous change in its operating principle with respect to the supply chain management system where the demands of consumers are dynamically and exponentially rising. Although Industry 4.0 offers a significant solution to this principle with the aid of its predictive automated operating process, till date there is less number of fault tolerant model that can effectively meet the standard demands of supply chain planning. Therefore, the proposed system introduces an analytical model where predictive optimization is carried out towards bridging the gap between supply and demands in supply chain 4.0. An analytical framework is a design from constraints derived from practical environment in order to offer better applicability of it. The study outcome shows that the proposed model could offer better performance in comparison to the existing optimization method with respect to the better budget control system for offering predictive and preemptive model design

Experimental Studies on Mechanical Properties of Carbon Nanotube Reinforced Aluminum 7075 Composite Material

In the present work multiwalled carbon nanotubes were  added as  reinforcement to aluminum 7075 matrix at 0.5%, 0.75% and 1.25% by weight proportion through stir casting technique. The mechanical properties of the produced composite were studied. The composite has considerably good tensile and wear resistance properties and hence finds its best suited application in aircraft frame and wings structures. Microstructure analysis through SEM showed a uniform distribution of the reinforcement material in the matrix. XRD graphs were taken at selected points during microscopic studies to determine the chemical composition of the matrix alloy, the reinforcement and the composite. The experimental results showed that 1.25% reinforcement in the composite material exhibited a tensile strength of 560 N/mm2 and a compression strength of  649.6 N/mm2 as the highest among the compositions. Thus,  the reinforcement addition at 1.25% improved the tensile and compression strength of the composite material

Synthesis, characterization of Zinc oxide and assessment of electrical DC conductivity properties

Polymer composites are widely used in electrical devices, sensor materials, electromagnetic interference (EMI) shielding, and other applications. Composites may be customized for any intended application by changing the ratios of the polymeric components. Polyaniline (PANI) is not as sensitive as metal oxides toward gasoline species, and its negative solubility in organic solvents limits its packages, however it's miles suitable as a matrix for instruction of engaging in polymer nanocomposites. Therefore, there has been increasing hobby of the researchers for the education of Nano composites based totally on PANI The fuel for the production of nanometal oxide comes from naturally occurring sources. The weight percentages of such generated metal oxides are altered during chemical polymerization with polyaniline. Composites of polyaniline cellulose and varying weight ratios of Zinc oxide (ZnO) are produced. In synthesised Polyaniline cellulose/ZnO composites, the formation of polymers and their interactions with metal oxides are investigated using Ultraviolet (UV)-vis, Fourier-transform infrared spectroscopy (FTIR), and X Ray Diffraction. The surface morphology of the composites is studied using Scanning electron Microscopy. The characteristics of the specified composites are investigated using electrical dc conductivity electrical conductivity

Thesaurus - an ideal tool for vocabulary control in post-coordinate systems : INIS Thesaurus - a case study

The complex conceptual relationships contained in multi-dimensional documents present the problem of inconsistency in subject analysis and vocabulary control for efficient retrieval is stressed. The development of Thesaurus, an important contribution for effective vocabulary control in mechanised IR systems which is among the many developmental efforts in this direction, is highlighted. The development, structure and design of INIS Thesaurus is given in detail. Adaptability of the INIS thesaurus to a computer-based information handling system is examine