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Open CV based border security surveillance robot

At present, the surveillance of International border areas is a headache for border guarding forces. The border guarding forces patrolling the border seriously, but it is not possible to monitor the border at each and every moment. Robot becomes an essential requirement of the surveillance when a trespasser enters the restricted area and it report’s nearby board security control unit. The use of robots in the border for surveillance reduces the risk factor of soldiers. The spy robot uses Raspbian operating system with remote monitoring and control algorithm through the Internet of Things (IoT) has been developed which reduces manual error and prevents the entry of unknown persons. The spy robot system comprises of Raspberry Pi (small single-board computer), Pi camera and sensors. PIR sensor detects the living objects and sends information regarding the detection of the living object is sent to the users through the web server and pi camera capture the moving object which gets transferred through mail simultaneously. The user controls the robot with wheel drive control buttons on the webpage. This surveillance system using a spy robot can be modified in different ways for the use in industries, banks, and shopping malls.

Published by: Jeevanantham R., Hemanth Thethan S, Akash K. P., S. Padmavathi

Author: Jeevanantham R.

Paper ID: V5I2-1193

Paper Status: published

Published: March 8, 2019

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Research Paper

A non-intrusive approach for drowsy and drunk driving using computer vision techniques

This paper presents a holistic, non-intrusive approach for drunk and drowsy detection of the driver using computer vision techniques of facial landmark detection and motion detection. The driver's continuous real-time video feed is observed with the help of a smartphone camera. A single scalar quantity, Eye Aspect Ratio (EAR) which characterizes persistent eye blinks continuously analyses this feed. Simultaneously the system checks the body and the head movements using the differential imaging technique, which operates in real-time. A severity score indicating the fitness to drive is generated cumulatively using both methods. The driver is notified with the sound of an alarm if the results are positive based on a threshold value of the severity score.

Published by: Madhu, Khushboo Mishra, Shubham Karki, S. R. Dhore

Author: Madhu

Paper ID: V5I2-1176

Paper Status: published

Published: March 8, 2019

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Research Paper

Importance of healthcare dietitians’ communication skills

The nutritional professional competency and the communication competency together are very important for a dietitian to provide quality nutritional care to the patients. Dietitians provide nutrition counseling to patients and it is very important that Dietitians should master the art of communication. This article stresses the need and importance of possessing good communication skills by dietitians who are an integral part of the patients’ treatment care.

Published by: S. Ramesh, B. Manimegalai, Annie Valsan

Author: S. Ramesh

Paper ID: V5I2-1200

Paper Status: published

Published: March 8, 2019

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Research Paper

Extraction of Neem (Azadirachta Indica) oil using blends of hexane, ethyl acetate and acetone by sonication

Neem oil is a very beneficial oil for human utility in various fields and is commonly extracted by a solvent like hexane and ethanol. This work stresses on investigating the usability of blends of pure solvents while adopting a new technology of sono assisted extraction for the extraction of oil from neem seeds. The prepared blends have been analyzed on the basis of total pressure, bubble point temperature, molecular weight for the different compositions solvents that is to be mixed to form the blends. In this work, it is proposed to use ethyl acetate + acetone, hexane + acetone and hexane + ethyl acetate binary blends with the comparison to unary pure solvents and also recording the solvent recovery in each case after distillation. The reusability of the solvents recovered after the first sets of extraction is also a topic of major concern.

Published by: Priyanuj Bhuyan, Saikat Sen, Vivek Bihari

Author: Priyanuj Bhuyan

Paper ID: V5I2-1204

Paper Status: published

Published: March 8, 2019

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Research Paper

Hear global on reality index base

As urban environments grow and become even more complex, businesses need highly accurate location intelligence technology to stay ahead. Building a scalable network that detects and identifies objects as fast as your brain starts with the vision of the vehicle. Forward-facing cameras and radar will soon be standard equipment in all cars. This project aims at every car learning from every car, car Parking place identification and continuing to enable an autonomous world with the help of tracking device (precise, end-to-end tracking and accurate, real-time, and historical locations for devices, people, and things). By combining open data with proprietary sources and technologies car sensor data and AI, the HEAR location platform offers a uniquely complete location data set along. The raw images captured by cameras may contain noises, the lighting of workspaces, a flickering of light sources. The preprocessing includes filtering out the noises, images conversions into different color spaces, blurring the image, edge detection, line detection, circle detection. The current project paper comprises of the development of image processing based parking space management. This project will implement based on the theories of Background subtraction algorithm. The usage of this algorithm will be used as a mapping method to reduce the error of detecting the vehicle. The explanation of algorithms such as Background subtraction algorithm and the implementation of Open CV as software to manipulate the image program will be used throughout the project

Published by: Pureti Anusha, Koncha Tejaswini, Mula Srilakshmi, Dhulipalla Sai kumar, Darla Srujana

Author: Pureti Anusha

Paper ID: V5I2-1179

Paper Status: published

Published: March 8, 2019

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Research Paper

Intelligent model for predicting water quality

Over the decades, water pollution has been a real threat to the living species. The real-time monitoring of drinking water is nothing less than a challenging task. This paper aims to design and develop a low-cost system for the real-time monitoring of water quality using Internet of Things (IoT) and Machine Learning (ML). The physical and chemical parameters of the water such as temperature, level, moisture, humidity, and visibility are measured using respective sensors. ESP8266, the core controller is employed to process the measured values from the sensors. The data acquired from Sensors are sent to the Django server. Random Forest (RF) and K-Nearest Neighbours (KNN) algorithm are used in the analysis and prediction of water quality.

Published by: Ashwini K., J. Janice Vedha, D. Diviya, M. Deva Priya

Author: Ashwini K.

Paper ID: V5I2-1160

Paper Status: published

Published: March 8, 2019

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