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Predicting housing prices using advanced regression techniques

The prices of House increases every year, so there is a need for the system to predict house prices in the future. House price prediction can help the developer to determine the selling price of a house. It also can help the customer to arrange the right time to purchase a house. There are some factors that influence the price of a house which depends on physical conditions, concept, location, and others. House prices vary for each place and in different communities. There are various techniques for predicting house prices. One of the efficient ways is by the use of a regression technique. Regression is a reliable method of identifying which variables have an impact on a topic of interest. Random forests are very accurate and robust to over-fitting. The process of performing a regression allows to confidently determine which factors matter the most, which factors can be ignored and how the factors influence each other. The main objective is to use an advanced methodology for prediction.

Published by: Bharathi A. N., Dr. N. Yuvaraj, Dhivya B.

Author: Bharathi A. N.

Paper ID: V5I1-1282

Paper Status: published

Published: February 11, 2019

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Research Paper

Survey on energy saving strategies in industries

A modern area utilizes more energy than some flip side use parts and as of now, this segment is expanding about 37% of the world's complete conveyed energy. Energy is devoured in the mechanical area by a differing gathering of enterprises including fabricating, agriculture, mining, and development and for a wide scope of exercises, for example, preparing and get together, space molding and lighting. This paper presents an exhaustive writing audit about modern energy sparing by the board, advances, and arrangements. Most recent writings as far as proposition (MS and Ph.D.), diary articles, gathering procedures, web materials, reports, books, handbooks on modern energy the executives, arrangements, and energy investment funds systems have been gathered. Energy-sparing by the board including energy review, preparing projects and housekeeping next to some energy the board rehearses on the planet has been investigated. Energy-sparing innovations, for example, utilization of high proficiency engines (HEMs), Variable Speed Drives (VSDs), economizers, spill avoidance and diminishing weight drop has been checked on. In light of energy sparing innovations results, it has been discovered that in the mechanical segments, a sizeable measure of electric energy, emanations and service bill can be spared utilizing these innovations. Recompense periods for distinctive energy funds measures have been distinguished and observed to be monetarily practical in most cases. At long last, different energy sparing strategies for few chose nations were checked on.

Published by: Ashwini Punse

Author: Ashwini Punse

Paper ID: V5I1-1280

Paper Status: published

Published: February 11, 2019

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Research Paper

Cloud computing using Artificial Intelligence

In this paper, we present The cloud technology can help AI’s by providing the required information for the learning processes while the AI can help cloud by providing information that can offer more data. AI is capable of streamlining the immense capacities of the cloud. It equips cloud technology with enormous powers. It enables the machines to act, react, think and learn in the manner human beings do. AI assists different machines in learning and analyzing the historical data, making decisions and identifying the patterns. Such a process helps in eradicating the chances of human errors. Therefore, AI enhances the process of decision making of various organizations. Cloud technology is spread among a number of servers in various languages with huge data storage and across various geographies. Organizations can make use of this data to make up intelligent and automated solutions for customers and clients. Cloud computing is getting more powerful with AI as its applications are extended across multiple diversified sectors in the economy. Thus, even organizations can make use of AI cloud computing to attain long-term goals for their businesses.

Published by: Kumar Kishan Chandra, Dr. Anand Kumar Pandey, Supriya Raj

Author: Kumar Kishan Chandra

Paper ID: V5I1-1271

Paper Status: published

Published: February 9, 2019

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Review Paper

Survey on clustering attacker activities in IoT data through machine learning techniques

IoT create network and connect "things" and people together by creating relationship between either people-people, people-things or things-things. As the number of device connection is increased, it increases the Security risk. Security is the biggest issue for IoT at any companies across the globe. Privacy and data sharing can again be considered as a security concern for IoT. The IoT has been affected by different botnet activities. As botnets have the cause of serious security risks and financial damage over the years, existing Network forensic techniques cannot identify and track current sophisticated methods of botnets. Machine Learning techniques in order to train and validate a model for defining such attacks, but they still produce high false alarm rates with the challenge of investigating the tracks of botnets. This paper investigates the role of Machine Learning techniques for developing a Network forensic mechanism based on network flow identifiers that can track suspicious activities of botnets. Multivariate Hawkes Process identifies the latent influences between attackers by utilizing the mutually exciting properties. Then cluster the attacker activities based on the inferred weighted influence matrix with resort to the hierarchical, partitioning and graph-based clustering approach.

Published by: Jasmine Joy J., Padmashree A., Karunambika S., Latha R.

Author: Jasmine Joy J.

Paper ID: V5I1-1302

Paper Status: published

Published: February 9, 2019

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Research Paper

Wireless Sensor Network supplemented with MATLAB for improved environment condition and energy conservation

A planned infrastructure or a communicative infrastructure revolves around the cellular network which has a pivotal role to play in the field of wireless communication. But despite these advantages which the wireless network possesses, they are not feasible in drastic environments such as earthquake-hit areas, battlefield or any hostile zones simply because infrastructure remains a vague subject of concern. Recent developments have enhanced the technology fabricating ad-hoc wireless system network which can respond to this type of versatile situation.

Published by: Shipra Vaidya, Karamvir Singh Rajpal, Ravi Kant

Author: Shipra Vaidya

Paper ID: V5I1-1248

Paper Status: published

Published: February 9, 2019

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Survey Report

Providing data integrity in toll booth management system

The Internet of things (IoT) is considered as a network of devices which allows them to interact, connect and exchange data. Stimulators and sensors are considered as smart devices in our environment. Due to the nature of IoT that depends on low computational power devices equipped with a sensor, the security of these systems differs from the security of the traditional system which is been applied in conventional network devices. Simultaneously IoT security brings out new issues. Therefore safety is a fundamental issue in designing IoT. Hybrid encryption is a mode of encryption that makes use of merging two or more encryption systems hence it is considered as a highly secure type of encryption as long as the public and private keys are fully secure. This encryption type provides long security and low consumption. In this paper we have proposed a hybrid encryption algorithm which has been conducted in order to reduce safety risks and enhancing encryption's speed. The main purpose of this hybrid algorithm is maintaining data integrity, confidentiality, non-repudiation in data exchange for IoT. Eventually, the suggested encryption algorithm has been simulated by MATLAB software, and its speed and safety were evaluated in comparison with a conventional encryption algorithm

Published by: Infanta Josephine A., Jenifa, Keerthana, Mounika

Author: Infanta Josephine A.

Paper ID: V5I1-1283

Paper Status: published

Published: February 9, 2019

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