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Research Paper

Artificial Lampyridae Classifier (ALAC) for coronary artery heart disease prediction in diabetes patients

Soft computing techniques and its applications extend its wings in almost all areas which include data mining, pattern discovery, industrial applications, robotics, automation and many more. Soft computing comprises of the core components such as fuzzy logic, genetic algorithm, artificial neural networks, and probabilistic reasoning. In spite of these, recently many bio-inspired computing attracted attention for the researchers to work in that area. Machine learning plays an important role in the design and development of decision support systems, applied soft computing and expert systems applications. This research work aims to build an artificial Lampyridae classifier and also compared with Takagi Sugeno Kang fuzzy classifier and ANN classifier in terms of prediction accuracy, sensitivity, specificity, and Mathew’s correlation coefficient. The significance of MCC is to test the ability of the machine learning classifier in spite of other performance metrics. Implementations are done in Scilab and from the obtained results it is inferred that the built ALC outperforms that that of TSK fuzzy classifier and ANN classifier.

Published by: B. Narasimhan, A. Malathi

Author: B. Narasimhan

Paper ID: V5I2-1543

Paper Status: published

Published: March 27, 2019

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Research Paper

Android mobile hacking using Linux

Backdoors are one of the most complicated types of Android malware. A normal backdoor carries out its functionalities such as installing itself into the system directory, disabling system apps, or gaining access to app’s data, to steal and upload sensitive info, download and ask to install applications and set up mobile botnets when setting proper Android permissions. This project focus on how Android devices are hacked using backdoors and how they can be stopped from doing so. The backdoor application when installed and turned on the mobile allows an attacker to read, write and modify the data. Due to Backdoor attacks Confidentiality, Integrity, and Accountability of the information security are lost. When the application is installed on the victim's mobile and the victim opens the application it creates the meter-preter session which permits the attacker to access functions like webcam, contacts, read SMS, send SMS, read call log, write call log, access storage, install applications.

Published by: Arulpradeep S. P., Vinothkumar P., Nilavarasan G. S., Sai Harshith Kumar S., Naveen P.

Author: Arulpradeep S. P.

Paper ID: V5I2-1528

Paper Status: published

Published: March 27, 2019

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Research Paper

Fault detection monitoring and controlling of induction motor using Zigbee

Our project is basically based on monitoring and controlling of the induction motor by the use of a Zigbee module. Microcontroller and other sensors play an important role in this monitoring and controlling of the induction motor. This type of monitoring helps to take preventive measures against induction motor when a fault occurs.

Published by: Niraj, Tejas Borde, Prasad Mulumkar

Author: Niraj

Paper ID: V5I2-1530

Paper Status: published

Published: March 27, 2019

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Research Paper

Classification of attack types for intrusion detection system using machine learning algorithm: Random forest

In the current era of Big Data, a high volume of data is being grown in vast and the speed of generating the new data is accelerating quickly. Machine Learning algorithms are used for such large datasets to teach computers how to reply to and act like humans. In machine learning with the help of generalization ability, the increase in the size of the training set increases the scope of testing. In this paper, we analyze the results of the attacks classified using Intrusion Detection System, and the training time of Random Forest algorithm is measured by increasing the size of the KDD dataset in intervals thereby observing the changes in the final evaluation metrics obtained

Published by: Dasari Sree Lalitha Chinmayee, C. Visishta, Garbhapu Navya, Sajja Ratan Kumar

Author: Dasari Sree Lalitha Chinmayee

Paper ID: V5I2-1541

Paper Status: published

Published: March 27, 2019

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Research Paper

Design and analysis of velocity profile of air in engine inlet manifold

This project deals with the improvement of the performance and to achieve the even distribution of flow at each cylinder, to select the best turbulence model for the analysis of intake manifold using computational fluid dynamics, to achieve the maximum mass flow rate through the runners. To achieve the even flow of distribution and improve the volumetric efficiency, we divided his analysis into four different parts. In each part, a different iteration of inlet manifold used in Maruti Gypsy’s G13BB engine is used. In this analysis, plenum size is only changed in small values to show the effects of the shape of runners on output. Dividing the work into four different parts provide the greater refinement in the result. Runner shape is determined by the operating conditions of the engine assembly in which the inlet manifold is used. The intake runner diameter influences the point at which peak power is reached while the intake runner length will influence the amount of power available at high and low RPM. To find the best results, we perform the experiments in Ansys Fluent for possible designs and find the effect of curved and branched runners were providing higher volumetric efficiency and even flow of distribution to each cylinder. For designing final intake manifold we select the best design from all four parts and perform the experiment using computational fluid dynamics software Ansys Fluent. This project work brings the design concept and analysis method of “DESIGN AND ANALYSIS OF VELOCITY PROFILE OF AIR IN ENGINE INLET MANIFOLD”.

Published by: Josbin C Mathew, Dharmadurai R., Ajay Krishnan P., Mohammad Shabeeb P. K., Dr. P. Maniiarasan

Author: Josbin C Mathew

Paper ID: V5I2-1523

Paper Status: published

Published: March 26, 2019

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Research Paper

Studies on optimization of fermentative hydrogen production by an isolated strain

Facultative strain of bacteria capable of producing hydrogen was isolated from soil sample rich in decomposed biomass. The isolated strain was characterized and the process parameters, namely pH, temperature and fermentation time were optimized for the maximum hydrogen production. The optimum conditions were found to be: pH-7, temperature-34oC and fermentation time-42hrs. Under these optimum conditions, the isolated strain achieved the highest hydrogen production rate of 2.84 ml H2/L.h

Published by: Manikkandan T. R.

Author: Manikkandan T. R.

Paper ID: V5I2-1392

Paper Status: published

Published: March 26, 2019

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