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 Smart Computing

With a drastic change in climate continuously it is very harmful to the people who are living in the disaster-prone areas. In some areas the people are not warned for the consequences of coming specifically in their areas, they are told about the average temperature and humidity of the city while the humidity and temperature vary at different altitude and changes at short distances. The system is a very cost-effective and efficient method for controlling and monitoring the weather, and it sends the data to the cloud so that it can be visible anywhere through internet. The temperature, humidity, and pressure play a significant role in different fields like agricultural, industrial and Logistical Field. Weather forecast is necessary for the growth and development of these industries. The Internet of Things (IoT) is the technology used in developing the proposed system, which is an efficient and advanced method for connecting the sensors to the cloud which can store real-time sensor data and connect the entire world of things in a network. Here things might be anything like electronic gadgets, sensors, and automotive electronic equipment. The system deals with controlling and monitoring the environmental conditions like Temperature, Pressure, Smoke, Relative humidity level and various other gases with sensors and sends the information to the cloud and then plot the sensor data in graphical form. An Intelligent prediction is to be done using machine learning. Machine learning is a branch of Artificial Intelligence (AI) which is a compelling method of Analyzing and predicting the given data-set. The data collected will be analyzed continuously. The real-time data which has to be sent through the sensor can be accessible throughout the world using the internet

School of Information Technology and Engineering

Vellore Campus

Department of Digital Communications

Department of Computer Applications

International Journal of Recent Technology and Engineering Regular Issue

An Intelligent Weather Monitoring System using Internet of Things

Breast cancer is a tumor caused by the excessive growth of cells in the breast tissue or near the region of the breast. Breast cancer is most commonly found among women, and it starts developing when the cell tissues from lump of the breast become abnormal or when there is a calcium deposit in the breast. These affected cells form a large lump that consequently becomes a tumor. Digital infrared images are obtained based on the metabolism of the breast and vascular circulation of the blood flow in and around the breast region, which has more visibility than the normal breast region. In this article, we diagnose breast cancer by processing a thermal image that is acquired from thermal cameras. By analyzing the information, we can implement image processing steps to predict quantitative and qualitative information. In this work, we propose a hybrid clustering algorithm with distance measurements. The clustering step includes adaptive fuzzy k-means clustering with Chebyshev distance with improved classifiers, which include neural networks. The article discusses the experimental results along with the comparison using various metrics, such as accuracy, time, and error rates.

Department of Software and Systems Engineering

Department of Information Security

School of Computer Science and Engineering

Journal of Testing and Evaluation

Diagnosis of Cancer Using Hybrid Clustering and Convolution Neural Network from Breast Thermal Image

Across the globe, right from people who are technologically naive to people who are with high-technical knowledge, use, share, spread, and connect with each other through online social networks. People start believing and sharing the social network content without any proof of its authenticity. In several cases, the reliability of the information that gets shared between the users remains questionable due to the anonymity of the information creators. In this work, a fuzzy detection algorithm is proposed to identify the trustable content in social media. The proposed methodology is evaluated on Twitter social network platform. By computing the predictive measures, the efficiency of the proposed approach is well established. © The Institution of Engineering and Technology 2019

Fulltext

IET Networks

Fuzzy-based fake information detection algorithm to define the user trust on the content of social networks

Data mining is one of the most promising areas of research that has become increasingly popular in health care. The objective of this research article is to elucidate a transdisciplinary approach to classify thyroid levels in patients using data mining techniques. The data set consisting of more than 2 thyroid conditions along with the normal values listed 21 values. The classifier chosen carefully to get the optimized accuracy and falls in different classification category namely J48, random forest, and random tree from tree, decision table from rules, multilayer perceptron from functions, naïve Bayesian from Bayes, and AdaBoost from meta respectively. The J48 classifier displays tree that will assist with better interpretation based on the values and helps to easily determine new value combinations. The Thyropred System graphic is finally presented, which guides the diagnosis of thyroid disease. Thorough consultation with experts along with this prediction system guides the decision of further medication. The J48 classifier provides the best accuracy when compared with the other tested classifiers. © 2019 ASTM International. All rights reserved.

A Transdisciplinary Approach to Classify Thyroid Levels in Patients

Wireless Sensor Networks contain an incredible number of hubs with limited registering, detecting, and wireless communication capacities. These systems have been utilized as a part of a wide zone of utilizations, such as human services, contamination checking, and target-following frameworks. The dynamic clustering of sensors into bunches is a prevalent procedure to expand the system lifetime and increment adaptability. To accomplish this, in this article the sensor hubs are adjusted to ensure a long lifetime and the activities are arranged into rounds that use fixed time intervals. In the first phase, a clustering topology is found, and a group head is picked in view of the outstanding energy level. Then the cluster head screens the network energy threshold value to identify the energy drain rate of all its cluster members. In the second stage, the Long-Lifetime and Low-Latency Data Aggregation Scheduling method is used. This scheduling method assigns schedule openings to group part information parcels. Here, congestion occurrence is completely kept away from the scenario. In the third stage, an Energy-Efficient Distributed Schedule-based convention is used to keep up greatest residual energy level over the network. The experimental outcome shows the steps proposed in this article ensure an increase in the network lifetime and decrease in the energy utilization. © 2019 by ASTM International

VIT Unitversity

Department of Information Technology

Department of Micro and Nanoelectronics

School of Electronics Engineering

Long-Lifetime and Low Latency Data Aggregation Scheduling for Wireless Sensor Network

Fuzzy role-based access control (FRBAC) is essential for risk-based environments in many futuristic applications, even though role-based access control (RBAC) is the efficient and widely used access control model for enterprise applications. In FRBAC, authorization related information is vague. It brings the fuzziness in mapping among the components of FRBAC such as user, role and permission. Holding the fuzziness in FRBAC, it is challenging for the security engineer to verify the constraints and correctness of access control policy. On verifying the constraints and correctness of access control policy, knowledge representation techniques are much useful in practice. In this scenario, representing FRBAC using fuzzy ontology might be the right choice for semantic web application. The main objective of this article is to represent the access permissions of FRBAC using fuzzy ontology and verify whether the constraints of FRBAC are possible to get implemented in it or not.

International Journal of Information Technology Project Management

Fuzzy Role Based Access Control Design using Fuzzy Ontology

The pervasiveness of interpersonal organizations has made it less demanding than any time in recent memory for clients parts the photographs, recordings, Different communication like audio, video materials anyone in anyplace. Be that as it may, the simple access of client produced media content additionally realizes security concerns. Customary control of connection components, however the solitary connection strategy only through particular bit of substance, can't fulfill the client protection prerequisites in substantial scale media sharing frameworks. Rather, arranging numerous connection for dimensions benefits at mutual video and audio parts are wanted. In unhandy, this adjusts the guideline for interpersonal organizations in data spread. Then again, it concurs the difficult social Websites with their informal organization clients. Propose a versatile media get to control framework to empower such an arrangement in a protected and productive way. The proposed SMAC framework engaged these adaptable encoded message approach quality based encryption calculation just as a complete key administration plot. Here, give normal evidence along with secure for demonstrate the new framework with their security. Moreover, And also lead escalated investigations cell phones to exhibit its productivity.

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

Image Distribution with Scalable Access Control for Privacy in Social Network

Cloud storage services are quickly increasing and more prevalent. CSP-cloud storage providers offer storage as a service to all the users. It is a paid facility that allows association to outsource their confidential data to be stored on remote servers. But, identity privacy and preserving data from an untrusted cloud is a difficult concern, because of the successive change of the members. CSP has to be secured from an illegitimate person who performs data corruption over cloud servers. Thus, there is a need to safeguard information from the individuals who don’t have access by establishing numerous cloud storage encryption plans. Every such plan implemented expects that distributed storage suppliers are protected and can't be hacked; however, practically speaking, a few powers will compel distributed storage suppliers to render client details and secret information on the cloud, in this manner inside and out bypassing stockpiling encryption plans. In this paper, a new scheme is introduced to protect user privacy by a deniable CP_ABE(Cloud Provider_ Attribute Based Encryption) scheme which implements a cloud storage encryption plan. Since coercers cannot specify whether privileged insights are valid or not, the CSP ensures privacy of the user

Secure and Error-Free Data Storage on Cloud via Deniable CP-ABE Scheme

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

Secured Service Chains Tuned Resource Sharing using Nsdp

Big data is a late of huge information stored in it and all we need is to dig into get the important information out of it and create a useful system which can be very helpful in improving the current scenario. There are various applications where big data is being used and even there are few fields that are learning techniques to go with big data and evaluate their work and get an improve decision. This paper particularly concentrates on the e commerce system which is highly trending on the market field. [20] E commerce also known as electronic commerce is a market place which gives you a platform to enjoy various services from both buyers as well as sellers. It is a place with various varieties are provided that can help the consumer to choose from and the buyer can get a platform where he can show case his product and get millions of the customer at the same time and he does not have to look for site all the time, it’s the system that take care of it. Now big data is playing a vital role in e commerce as it reads about user behavior and provides him a suitable product that he may need according to his behavior and query. There are various machine learning algorithms that are working on this and improving the services. [11] Basically in this paper we will read the user information and combine it with the product attributes and get a suitable suggestion for the user that will be most likely to be purchased by him. In the existing system we just look at one part of the case and give suggestion but in this paper we looked at both the sides, that is we looked after the product entities (the attributes and features that it poses) and the user behavior (the information given by the user and its previous history) that will better prediction and improve the system. Moreover for the optimized working of the system we included an enhanced version of HPCA scheduling algorithm for the Hadoop distributed file system also known as HDFS, which is very suitable for the heterogeneous system, the existing algorithm looks after the overall capacity of the node and then the tasks were assigned but here we will consider the health and the left over capacity of the nodes and arrange the queue for the same which will be refreshed all the time after the task is completed by any node.[18] The aim of the paper is to provide fast and most suitable suggestions to the users which can play a vital role in improving the sales of the company and getting the target done soon and faster