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Review Paper

Hydrological Analysis by Artificial Neural Network: A Review

In this paper, a deep review is conducted on Artificial Neural Network. ANN is used for real-world problems which are related to the hydrological field. Computational Intelligence methods such as Artificial Neural Network are very necessary because conventional methods are very complex and vexatious. Artificial Intelligence operation is based on the transformation of unknown relationship into the known sensible relationship, and hence this transformation helps in modelling real-world problems. Various applications of AI operation are carried out at present time, such as Rainfall-Runoff modelling, Groundwater modelling, water quality modelling, modelling stream flow etc. In recent years, Artificial Neural Network has shown exceptional performance as regression tools, especially when it is used for pattern recognition and function estimation. This paper mainly focuses on various ANN models for solving real and complex hydrological problems with great accuracy, and these are proposed as efficient tools for prediction in hydrology.

Published by: Vikas Poonia, Dr. H. L. Tiwari, Dr. Satanand Mishra

Author: Vikas Poonia

Paper ID: V4I3-1310

Paper Status: published

Published: May 7, 2018

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Research Paper

Accident detection and warning system

This system provides a unique method to curb drunken and drowsy people. This system has an alcohol sensor and eye blinking sensor embedded in the vehicles. Whenever the driver starts the vehicle, the sensors sense the eye blink and measures the content of alcohol in his breath and automatically sends the signal to the buzzer, gsm and LCD. In this system, the outputs of sensors are given to the microcontroller for comparison. If the value reaches the fixed limit then automatically gsm will send the SMS, the buzzer will produce sound and LCD will display the message.

Published by: Avaneesh Kumar Singh, Avinash Singh, Aviral Tripathi, Ayush Chittransh, Himanshu Rajpoot, Rishi Asthana

Author: Avaneesh Kumar Singh

Paper ID: V4I3-1242

Paper Status: published

Published: May 7, 2018

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Survey Report

Cardiovascular health pre-diagnosis system based on bp profile using backpropagation algorithm

Blood pressure profiling during exercise has been found to predict a future diagnosis of heart-related diseases such as hypertension, hypotension, and coronary heart disease. Non-invasive methods have made it easier to measure blood pressure. Devices like stethoscope and sphygmomanometer are most commonly used in clinics and hospitals to measure blood pressure but these devices provide us with single measurement or partial information about a person’s cardiovascular health. Blood pressure does not remain constant; it changes with every instant considering various parameters such as age and gender. So, there is a need to measure BP through a more improved method such as exercise stress testing. This study describes the implementation of Artificial Neural Network to develop an algorithm to perform cardiovascular health pre-diagnosis of a patient. The decision-making is done through a blood pressure (BP) profile generated by conducting exercise stress testing. The parameters considered for profiling were age, gender, height, weight, blood pressure measurement with the risk factors and BMI. The data generated is imparted as training and testing sets to develop an algorithm, which will be able to accurately pre-diagnose cardiovascular health status of a person. Later an expert system can be developed which will assist medical doctors and practitioners to diagnose a patient with heart-related issues with more accuracy and will be able to spread more awareness in people regarding their cardiovascular health status.

Published by: Rahul Kumar Borah, Kratee Jain, Manjunath C R

Author: Rahul Kumar Borah

Paper ID: V4I3-1312

Paper Status: published

Published: May 7, 2018

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Research Paper

Characterization of tin-doped titanium dioxide thin films prepared by SILAR method

The Titanium dioxide and Tin-doped Titanium dioxide thin films were being deposited by Successive Ionic Layer Adsorption and Reaction (SILAR) technique. The prepared samples were characterized using X-ray diffraction, Ultraviolet-visible spectroscopy; photoluminescence and Fourier transform infrared spectroscopy. The XRD pattern of the films confirmed tetragonal structure with the polycrystalline nature. The optical transmittance was increased with the decrease in the optical energy band gap. The optical constants such as extinction coefficient and refractive index were determined. The intensity of the photoluminescence emission was observed at 700 nm for doped films. The Fourier Transform Infrared Spectroscopy confirms that a TiO2 phase has been formed. The field dependent conductivity showed an insignificant rise in photocurrent for TiO2 which was in conformity with its wide band gap nature.

Published by: Suguna Arivazhaga

Author: Suguna Arivazhaga

Paper ID: V4I2-2108

Paper Status: published

Published: May 7, 2018

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Thesis

Finite element analysis of UTM testing of Aluminium Alloy AA6082

Aluminum alloy AA6082 is one of the stronger alloys in its series and has high corrosion resistance properties. These properties combined with its light weight make it extremely useful in the aerospace and automotive industries. It is, hence, essential to test the given grade of aluminum and find out its mechanical properties and failure criteria for further applications in the industry. One of the major sources for testing the mechanical characteristics of a material is the Ultimate Tensile testing Machine (UTM). Tensile testing helps us to ensure a safe and high-quality material and to reduce the chances of failure in the respective field. The various mechanical characteristics provided as an output to the tensile testing experiment along with the interpretation of the flow curves obtained are necessary for the predicting the tensile behavior of the material (including necking and deformation homogeneity). Finite Element Method is a powerful tool used today for the simulation of such experiments and software using this are widely used to predict the mechanical properties of different materials, after validating a particular model. Another advantage is that it reduces the amount of material wastage as the validated model can then be used to find the mechanical properties of the given material under different boundary conditions, thus eliminating the need for those experiments. This project aims at developing and validating the uniaxial tensile test models of the proposed material, varying the strain rates, temperatures and material models, using the commercial FE software ABAQUS 6.14.

Published by: Jay Sachin Kalamkar

Author: Jay Sachin Kalamkar

Paper ID: V4I3-1206

Paper Status: published

Published: May 7, 2018

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Research Paper

Carbon nanotubes based gas sensor

Carbon nanotube(CNT) based Gas sensors are at- tracting huge research interest as it gives high sensitivity, quick response, and stable sensors for industry, biomedicine, and more. The development of nanotechnology has opened a new gateway to build highly sensitive, cheap, portable sensors those who have low power consumption. The extremely high surface to volume ratio and the hollow structure of nanomaterials is perfect for the adsorption of gas molecules.Mainly, the advent of carbon nanotubes has boosted the inventions of gas sensors that exploit CNTs unique morphology, geometry, and properties. Upon exposure to some gases, the changes in carbon nanotubes properties can be determined by many methods. Therefore, carbon nanotube-based gas sensors and their mechanisms are widely studied. In this paper, a broad survey of current carbon nanotubes based gas sensing technology is presented. few experimental works done are reviewed. The types, fabrication, and the sensing mechanisms of the carbon nanotubes based gas sensors are discussed. The challenges of the research up to some extent are also addressed in this paper.

Published by: Ketan Janardhan Rathod, Tejas Prabhu, Vikrant Naik, Dr. Abhay Chopde

Author: Ketan Janardhan Rathod

Paper ID: V4I3-1279

Paper Status: published

Published: May 7, 2018

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