Vellore Institute of Technology (VIT) is a private university located in&nbsp;Tamil Nadu, India. Founded in 1984, as Vellore Engineering College, the institution offers 20 undergraduate, 34 postgraduate, four integrated and four research programs. It has campuses in Vellore, Amravati, Bhopal and Chennai.

VIT is one of the top ranked private universities in India according to NIRF, THE and QS Rankings.&nbsp;Govt. of India has recognized&nbsp;VIT, Vellore as an&nbsp;Institution of Eminence. This has allowed VIT to take independent quality initiatives and move up in world ranking.

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VIT University

Department of Computer Applications

In the current network area, cloud service providers offer infinite storage space and computing power for users to manage their data in the cloud. To enjoy these services, individuals or organizations store their private data on cloud servers. However, in the case of security breaches, users’ private data stored in the cloud are no longer safer. When users outsource their data to cloud servers, they expect complete privacy of their data stored in the cloud storage. To enjoy these services, individuals or organizations store their private data on cloud servers. The semantic-based keyword search over encrypted cloud data becomes of paramount importance. Protecting the privacy and data of users has remained a very crucial problem for cloud servers Additionally, the existing approaches only process them as single words, the flexibility of the encryption policy and the description of users’ rights, and it changes from a one-one to one-many scenario during the encryption and decryption phases, calculation method to measure the semantic similarity between compound concepts. Keyword has been widely used in many scenarios, particularly in cloud computing. In this Project in the proposed scheme we use the trusted authority to generate the trapdoor .The generated trapdoor will be send to the user's e-mail ID, the user will search

Department of Software and Systems Engineering

School of Information Technology and Engineering

Vellore Campus

Department of Smart Computing

Department of Digital Communications

VOLUME-8 ISSUE-10, AUGUST 2019, REGULAR ISSUE International Journal of Innovative Technology and Exploring Engineering

Secured Service Chains Tuned Resource Sharing using Nsdp

In this present work, the effect of anode electrocatalyst materials is investigated by adding NiTiO3 with Pt/C and Pt-Ru/C for the performance enhancement of direct methanol fuel cells (DMFCs). The supportive material NiTiO3/C has been synthesized first by wet chemical method followed by incorporation of Pt and Pt-Ru separately. Experiments are conducted with the combination of four different electrocatalyst materials on the anode side (Pt/C, Pt-NiTiO3/C, PtRu/C, Pt-Ru-NiTiO3/C)and with commercial 20 wt % Pt/C on the cathode side; 0.5 mgpt/cm2 loading is maintained on both sides. The performance tests of the above catalysts are conducted on 5 cm2 active area with various operating conditions like cell operating temperatures, methanol/water molar concentrations and reactant flow rates. Best performing operating conditions have been optimized. The maximum peak power densities attained are 13.30 mW/cm2 (26.6 mW/mgpt)and 14.60 mW/cm2 (29.2 mW/mgpt)for Pt-NiTiO3/C and Pt-Ru-NiTiO3/C at 80 °C, respectively, with 0.5 M concentration of methanol and fuel flow rate of 3 ml/min (anode)and oxygen flow rate of 100 ml/min (cathode). Besides, 5 h short term stability tests have been conducted for PtRu/C and Pt-NiTiO3/C. The overall results suggest that the incorporation of NiTiO3/C supportive material to Pt and Pt-Ru appears to make a promising anode electrocatalysts for the enhanced DMFC performances. © 2019 Hydrogen Energy Publications LLC

Department of Manufacturing Engineering

School of Mechanical Engineering

Department of Design and Automation

Department of Automotive Engineering

World University root node

Fulltext

International Journal of Hydrogen Energy

Experimental investigation on DMFCs using reduced noble metal loading with NiTiO3 as supportive material to enhance cell performances

Organisations that perform business operations in a multi-sourced big data environment are in imperative need to discover meaningful patterns of interest from their diversified data sources. With the advent of big data technologies such as Hadoop and Spark, commodity hardwares play vital role in the task of data analytics and process the multi-sourced and multi-formatted big data in a reasonable cost and time. Though various data analytic techniques exist in the context of big data, recommendation system is more popular in web-based business applications to suggest suitable products, services, and items to potential customers. In this paper, we put forth a big data recommendation engine framework based on local pattern analytics strategy to explore user preferences and taste for both branch level and central level decisions. The framework encourages the practice of moving computing environment towards the data source location and avoids forceful integration of data. Further it assists decision makers to reap hidden preferences and taste of users from branch data sources for an effective customer campaign. The novelty of the framework has been evaluated in the benchmark dataset, MovieLens100k and results clearly confirm the advantages of the proposal.

Journal of Information & Knowledge Management

A Big Data Recommendation Engine Framework Based on Local Pattern Analytics Strategy for Mining Multi-Sourced Big Data

Solid concentration is considered as one of the most important parameters governing the anaerobic digestion process. In this present study, four solid concentration levels (10%, 15%, 20% and 25% of total solids), were tested to evaluate the effect on biogas production from rapeseed oil cake. Four laboratory scale batch reactors with 2 L volume were used for this study with a retention period of 30 days. The performance of reactors was assessed by quantifying the daily biogas production, cumulative biogas production and the degradation of total solids, volatile solids and COD. Biogas production was found to be maximum at a solid concentration of 20%, followed by 25%, 15% and 10%. The results obtained from the study were evaluated using Gompertz, modified Gompertz and Logistic models to determine the kinetic constants of the process. There were smaller differences between measured and predicted biogas production for rapeseed oil cake when using a modified Gompertz model compared with the other two models. © 2019 Taylor & Francis Group, LLC.

Department of Chemistry

School of Advanced Sciences

Energy Sources, Part A: Recovery, Utilization, and Environmental Effects

Effect of solid concentration on biogas production through anaerobic digestion of rapeseed oil cake

E-commerce and online shopping have seen a surge in demand that surpasses offline shopping. To keep up with this demand and stay relevant in the market, companies need to provide best service in the minimum time. The conventional trend is to get the product to the nearest warehouse after it is ordered or stock the warehouses to their full capacity to cater to customer demands. Those methods are ineffective because if the product is called after it is ordered it will lead to a waiting time of a couple of days, also special transportation needs to be arranged to get a less number of parcels to the destination which raises costs and pollution. Stocking up of warehouses is also ineffective as you waste space that could have been used for other products but also the products that no one needs have to be sent back which doubles the transportation cost. What we propose is a method to estimate the demand and strategically get and order products on trend analysis to save time, money and the environment. The algorithm we try to reach that end is Krill Herd Algorithm and Particle Swarm Optimisation which are a part of the genetic algorithms. © BEIESP.

Department of Software Systems

School of Computer Science and Engineering

International Journal of Recent Technology and Engineering

Warehouse stock prediction using krill herd algorithm

In the present competitive environment, a farmer needs better education, business expertise and good knowledge of technologies and tools to be successful in agriculture. Farmers usually select crop for cultivation according to their traditional knowledge and past experience in farming, but a farmer’s predictions may go wrong due to natural disaster. Thus, decision-making tool need to be developed to help farmers to take decision on crop cultivation. In this paper, decision-making tool was developed for selecting the suitable crop that can be cultivated in a given agricultural land. In the present study, 26 input variables were identified and categorized into six broad heads of main variables such as soil, water, season, input, support and infrastructure. Each main variable has several sub-variables. The priority weights for the variables were determined using the dominance-based rough set approach. In order to convert sub-variable sequences to main variable sequences, evaluation scores of each main variable were calculated by applying the weights of sub-variables and by using simple additive method. Finally, the evaluation scores were applied to Johnson’s reduct algorithm and classification rules were generated. The developed tool predicts each site in the datasets into one of the three crops such as paddy, groundnut and sugarcane. In order to validate the performance of the tool, the same datasets were predicted again by agriculture experts. The results obtained from the tool showed 92% agreement with the results obtained from the experts. Thus, the tool is a feasible tool for cultivating the suitable crops in the agricultural sites. © 2017, The Natural Computing Applications Forum.

Neural Computing and Applications

Decision-making tool for crop selection for agriculture development

In this paper, rough, fuzzy and soft set approaches have been integrated to develop a multi-class classification model to assist the farmers in taking decision on crop cultivation for a given agriculture land. The model is divided into three major sections, namely weight calculation of variables, conversion of continuous data to fuzzified values and classification rule generation. Dominance-based rough set approach is used for the calculation of relative weights of variables. Fuzzy proximity relation is applied to convert the continuous data into fuzzified values. Bijective soft set approach is used to generate classification rules for five agriculture crops, namely paddy, groundnut, sugarcane, cumbu and ragi. The developed model has been tested with agriculture dataset which showed 92% accuracy for the validation dataset and proved to be confident and robust for agriculture development. Further, the performance of the proposed model is compared with three popular classifiers such as naïve Bayes, support vector machine and J48. The obtained experimental results showed high predictive performance, and the potential of the proposed model is compared with the other classifiers. © 2018, Springer-Verlag GmbH Germany, part of Springer Nature.

Soft Computing

Hybrid rough fuzzy soft classifier based multi-class classification model for agriculture crop selection

Employment opportunities provided to men and women empower them in social and economic aspects. In developing countries like India and other Asian countries, people are not aware of these resources and thus fail to grab them. The study on various employment opportunities both from governmental and non-governmental sectors insist on eradication of unemployment problem in the society to greater extent. The framework provides mechanism to amplify job opportunities to the middle and lower class people using a mobile application. The district chosen for the study is Vellore, Tamil Nadu, India. Analysis on the unemployment problem has been done and a solution for the same is provided through a mobile application (Mobile app). Collecting various job openings and taking them to the appropriate person in the nearby location through mobile app is the task. The prototype of the model is framed using intelligent pattern matching technique with locality based classification. A job recommendation system can be built based on the proposed framework; so that it can help the people in the locality to get job alerts based on their qualification and skillset.

SSRN Electronic Journal

Framework of an Intelligent Job Recommendation System

Internet of Things for Industry 4.0: Design, Challenges and Solutions

AI for Internet of Things

In this article, Adomian decomposition method have been applied for getting analytical approximate solutions of time-fractional nonlinear coupled Boussinesq-Burgers equations. The solutions are compared with the exact solution at α = 1 for different values of x and t. © 2019 Author(s).