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Analytical evaluation of Shilajit from different geographical origins

Shilajit is a sticky substance found primarily in the rocks of the Himalayas. It develops over centuries from the slow decomposition of plants. The composition of shilajit largely depends on the type of plants associated with the rocks. Since the drug does not have specific organized morphological structure, it is very difficult to authenticate the drug. The primary component of shilajit is the fulvic acid which contributes to cognitive health. An attempt was thus made to establish an easy method for standardization & hence authentication of shilajit obtained from different geographical sources. Standardization assures that products are reliable in terms of quality, efficacy, performance, and safety. High-performance thin layer chromatography proves to be a good method for quantification of shilajit using a standard marker compound. As none of the methods given in literature were capable of providing good separation, a simple method was developed for detection and quantification of fulvic acid in three raw shilajit samples from different geographical origin and purified Indian shilajit. The developed method was validated for various parameters. Fulvic acid was estimated at 254nm by densitometry by using Merck, reversed phase thin layer chromatography plate silica gel 60 as the stationary phase and a combination of methanol: water (1:1) as a mobile phase. Validation was done using the sample containing the maximum amount of fulvic acid. It was found that purified sample showed the presence of the maximum concentration of fulvic acid. The method was found to be linear, specific, accurate and reproducible.

Published by: Raisa Shaikh, Dr. Swati Patil

Author: Raisa Shaikh

Paper ID: V4I3-1300

Paper Status: published

Published: May 8, 2018

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Research Paper

Analysis and detection of SIM box

Over a past two decades, Telecom industry is growing and mode of communication is changing and advanced day by day for catering individual and corporate needs. With the growth and advancement of technologies, telecom frauds are the major concern for research area and delivering the cent percent revenue in the system. Telecommunications fraud is a problem that affects operators all around the globe and one of the most known frauds is illegal to bypass fraud which is used in International voice traffic, in order to avoid carrier charges and this causes opportunity loss of international interconnect usage charge (IUC) to operators and this is major concern for research scope and impacting the revenue at operator level. As a result, cellular operators around the globe lose billions annually. Moreover, SIM box compromises the cellular network infrastructure by overloading local base stations serving these devices. This paper analyses the fraudulent termination of international traffic so suggest statistical, conventional, modern approach for detection of sim box and processes hundreds of millions of anonymized voice call detail records (CDRs). Their outputs of these models are optimally fused to increase the detection rate of sim box. The operator’s fraud department confirmed that the algorithm succeeds in detecting new fraudulent SIM box.

Published by: Vipin Airn

Author: Vipin Airn

Paper ID: V4I3-1243

Paper Status: published

Published: May 8, 2018

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Research Paper

Dissimilar metal welding of J4-16Cr. austenitic stainless steel with Grade 201LN austenitic stainless steel experimental analysis through optimization Taguchi method

A literature review of studies and research has been made in the field of dissimilar metal welding. It has various industrial applications such as in the field of automobile chemical mechanical thermal power generation, nuclear plant. The main purpose of this paper is to review (a) Dissimilar metal welds and aspect of mechanical and metallurgical point of view. (b) Enhancing GMAW Technique. And second, the main objective of present study is to apply the Taguchi method.In this technique, an orthogonal array, signal to noise ratio (S/N)and analysis to variance (ANOVA) are made to study the welding characteristics of material & optimize the welding parameters.

Published by: Harpreet Singh, Barkat Ali

Author: Harpreet Singh

Paper ID: V4I3-1278

Paper Status: published

Published: May 8, 2018

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Research Paper

Automatic session generator

The manual system of preparing timetables in colleges with a large number of students is taking a lot of time and usually ends with different classes struggling with the same teacher in the same room or at the same time. it occurs. To overcome all these problems, an automated system is proposed to be created. The system will take various inputs like students, subjects and classroom rooms and details of teachers on the basis of these inputs, it will generate a potential timetable, so that optimum utilization of all resources will be done in such a way that any obstacle or rules of the college according to. The list of topics can include electives as well as main topics.

Published by: Shammi Nanda, Amit Kumar Sharma

Author: Shammi Nanda

Paper ID: V4I3-1295

Paper Status: published

Published: May 8, 2018

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Research Paper

Comparative study on Apriori and FP growth algorithms in big data

A dataset is a collection of data. Today the huge amount of the data is being captured by information sensing devices such as mobiles, computers, sensors etc. These huge amounts of the data are now called as big data. Frequent Itemset mining is a tool for identifying the frequently occurring items together. There are many frequent Itemset mining algorithms like apriori, Eclat and FP growth. In the proposed work we use FP growth algorithm and compare it with Apriori algorithm and we show that FP growth algorithm is better when compared to the apriori algorithm.

Published by: Reena Lobo, Venkatesh

Author: Reena Lobo

Paper ID: V4I3-1318

Paper Status: published

Published: May 8, 2018

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Research Paper

A worthwhile performance framework modeling hinge on lambda architecture for batch and stream big data

The amount of data we are now generating is astonishing. Data has also evolved dramatically in recent years, in type, volume, and velocity. The emerging technologies like smartphones and sensors present opportunities for data exploitation, streaming and collecting from heterogeneous device every second. Analyzing these large datasets can unlock multiple behaviors previously unknown, a help optimizes approaches to many applications. However, collecting and handling of these massive datasets present challenges in how to perform optimized the large data. There are several frameworks available for handling the big data applications. The Lambda Architecture is data processing framework that can handle both batch and stream processing. The batch layer is implemented using Pig and hive, the streaming layer is built by using the Spark streaming and Spark SQL. This presents a need for developing the new framework for handling the big data applications particularly using public clouds to minimize cost, resource availability.

Published by: Athira Soman, Smitha Jacob

Author: Athira Soman

Paper ID: V4I3-1281

Paper Status: published

Published: May 8, 2018

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