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Review Paper

A review on tracking of student performance using decision tree

The main objective of this paper is an attempt to use data mining methodologies to study and track the student’s academic performance in the subject, is to help in enhancing the educational institutions by evaluating and classifying student data to study the main attributes that may affect the student performance in the subject. This paper focused on improving student academic performance based on their semester marks, class assignments, and extra curriculum activity. Tracking students’ performance will help the learner to know about their performance and it gives a chance to improve their performance in future. The dataset used for the tracking students ‘academic performance include semester marks, class assignments, extra curriculum activity. This paper is mostly focused on the C4.5 algorithm to track the student performance.

Published by: Anjali Sharma, Nigita Pradhan, Sneha Gupta, Ong Tshering Lepcha, Arvind Lal

Author: Anjali Sharma

Paper ID: V4I3-1256

Paper Status: published

Published: May 7, 2018

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Research Paper

Big data analytics in precision agriculture and constant monitoring of soil and weather

Agriculture has been one of the zones where innovation has not been utilized to the fullest and the usage of Precision Agriculture (PA) is still in its beginning times. One of the most important parameters in the field of agriculture that needs to be monitored constantly is the moisture content of the soil. To maximize the productivity in this field the condition and development of the crops are the most critical variables and these elements rely upon the levels of moisture content in the soil. Different crops need different levels of moisture content, therefore it is very important to monitor and forecasts it. In this paper, a mathematical model is created to compute the surface soil dampness by utilizing both precipitation and evaporation rate obtained by the electromagnetic sensors installed in the ground. Evaporation can be considered to be a linear combination of dynamic evaporation and thermodynamic evaporation which happens due to radiation. Soil moisture content is inversely proportional to the evaporation rate and it is directly proportional to the precipitation rate. Therefore a linear regression model is the best fit to determine the soil moisture content. Constant monitoring can be made possible by the soil mapping software with the sensors which keep reading the data periodically.

Published by: Nikhil Chandra P, Nikhil U, Manjunath C R, Sahana Shetty

Author: Nikhil Chandra P

Paper ID: V4I3-1313

Paper Status: published

Published: May 7, 2018

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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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