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Research Paper

Extended hybrid approach for detecting spammers on Twitter

One of the biggest social networks is Twitter for providing message posting and use direct message services via “tweets”.366 million currently active users on Twitter, Is the second largest space to share common news or message post. These features are also used by spammers on Twitter, spammers are not new on Twitter. spammers must be detected in improving the quality of Twitter message services. In this spammer detected by using metadata, content, interaction, and community-based features methods. Tweet meta information extracted and analyzed based on user-id, tweets,tweet-time and tweet-type. Content Features are extracted based on user posting content with URL, Mention-tags and hash-tags. Interaction and community-based features are analyzed by following and follower information. The proposed approach to introducing network-based features for spammer ratio detection by using unique IP address based user classification. Spammers can be detected by analyzing their tweets based on the extended hybrid approach by using the random forest, decision tree, and Bayesian network on the Twitter dataset that has benign users and spammers.

Published by: Subash G., S. Yuvalatha, V. Lenin Kumar, G. Manimegalai

Author: Subash G.

Paper ID: V5I2-1295

Paper Status: published

Published: March 16, 2019

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Review Paper

Enormous information for Internet of Things

With the quick advancement of the Internet of Things (IoT), Big Data innovations have risen as a basic information examination instrument to convey the learning inside IoT frameworks to all the more likely meet the reason of the IoT frameworks and bolster basic leadership. In spite of the fact that the point of Big Data investigation itself is widely examined, the dissimilarity between IoT areas, (for example, social insurance, vitality, transportation furthermore, others) has disconnected the advancement of Big Data approaches in each IoT area. Along these lines, the common understanding crosswise over IoT areas can propel the advancement of Big Data examine in IoT. In this work, we along these lines direct a study on Big Data innovations in various IoT areas to encourage and animate information sharing over the IoT spaces. In light of our audit, this paper talks about the likenesses and contrasts among Big Data advances utilized in various IoT spaces, proposes how certain Big Data innovation utilized in one IoT area can be re-utilized in another IoT space, furthermore, builds up a reasonable system to plot the basic Big Data advancements over all the audited IoT spaces.

Published by: P. Mounika, N. Nalini

Author: P. Mounika

Paper ID: V5I2-1292

Paper Status: published

Published: March 16, 2019

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Research Paper

A novel prototype to secure network using malware detection framework against malware attack in wireless network

A new and novel proposed algorithm based on malware detection has been used that overcomes the disadvantages of the existing algorithms and helps to eliminate viruses and worms from entrusted environment. In the wireless networks suffers from various spyware programs that prevents access to legitimate users who obtains services from target web server. In our proposed prototype helps to authenticate the sender to make the dynamic rule set to avoid the formations of unavailable networks which any user who obtain web services. In our proposal architecture diagnose malware whether malware based data has been really being sent to the valid user or is it being morphed by the attacker in the middle. The proposed algorithm has been tested against various existing algorithms to study how effectively the algorithm is working, and how effectively it is overcoming the drawbacks of the present malware detection algorithms. The algorithm is projected to serve the purpose of prevention of being used malware based programs to drop it by the user and also to identify that the non-infectious message is reaching only to the valid user.

Published by: G. Jagadish, L. Jaswanth, K. Sowjanya, P. Sri Harsha, M. Nikhil Kumar

Author: G. Jagadish

Paper ID: V5I2-1278

Paper Status: published

Published: March 16, 2019

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Research Paper

Development of particle reinforced composite by plastic and e-waste

The main aim of this project is to develop Particle reinforced composites. Thermoplastic is taken as matrix material. Thermosetting plastics and E-Waste glass powder of 60µm is taken as particles. The formed structure exhibits high strength, low mass density and less weight compare to wood and steel. Due to its superior performance, the composite reinforced materials are mainly used indoors, frames, the automobile industry, especially for vehicle body applications. In this project particle reinforced Composite formed from Plastics and Electronic wastes. A formed specimen is subjected to tensile, hardness and bend test. It exhibits high tensile and bending strength.

Published by: A. Kanagaraj, C. Franciskennathamreth, M. Ajithkumar, V. Anandh, R. Nagaraj

Author: A. Kanagaraj

Paper ID: V5I2-1320

Paper Status: published

Published: March 16, 2019

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Others

Use of housing society

Over the last few years, we have observed a focus on engineering science has been established whose products are likely to create a large market in the near future. It has been known ''Housing Society". First, a user must be registered in the system so that his password can be identified. This impression is securely stored in a central database or a smart card given to the user. The impression is fetched when an individual needs to be verified. Depending on the information, a software system can operate either in a verification (authentication) or a recognition mode.

Published by: Sanket Vikas Sagare, Zishan Usman Baig, Niranjan Bapuso kadam, Faique Shaikh

Author: Sanket Vikas Sagare

Paper ID: V5I2-1252

Paper Status: published

Published: March 15, 2019

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Research Paper

An adaptive approach to prognosticate an individual’s capability for emolument through Machine Learning

One of the major integrant to be considered while granting a loan is the customer‟s ability to pay back the amount to the bank as per the bank's provided a schedule. Our work focuses on the analysis of all the attributes that might affect the customer‟s ability to pay the loan. It is basically a credit scoring mechanism used by the bank to make sure a customer's intentions to apply for a loan are legit using Ensemble Algorithms. Our work gives a probabilistic predictive model or a scorecard to estimate the probability of defaulters in the current global scenario. Our work is due diligence fulfilled by the investors involved with the bank. Our aim is to prognosticate correct credit worth which will cause a significant increment in the profits of commercial institutions.

Published by: Jujjuri Goutham, T. Anitha, S. Joshua Johnson, Routhu Dhanunjay, Vemuri Susmitha, Nagavarapu Sravani

Author: Jujjuri Goutham

Paper ID: V5I2-1309

Paper Status: published

Published: March 15, 2019

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