JSSATE Noida

About: JSSATE Noida is a based out in . It is known for research contribution in the topics: Computer science & Deep learning. The organization has 567 authors who have published 586 publications receiving 2633 citations.

Arduino-based smart irrigation using water flow sensor, soil moisture sensor, temperature sensor and ESP8266 WiFi module

Abstract: Emergence of Controlled Environment Agriculture (CEA) ranging from computer controlled water irrigation system to lightning and ventilation has changed the conventional scenario of farming. This paper proposes and demonstrate an economical and easy to use arduino based controlled irrigation system. The designed system deals with various environmental factors such as moisture, temperature and amount of water required by the crops using sensors like water flow sensor, temperature sensor and soil moisture sensor. Datas are collected and received by arduino which can be linked to an interactive website which show the real time values along with the standard values of different factor required by a crop. This allows user to control irrigation pumps and sprinklers from far distance through a website and to meet the standard values which would help the farmer to yield maximum and quality crops. Studies conducted on laboratory prototype suggested the designed system to be applicable which can be implemented.

Real time sentiment analysis of tweets using Naive Bayes

Abstract: Twitter1 is a micro-blogging website which provides platform for people to share and express their views about topics, happenings, products and other services. Tweets can be classified into different classes based on their relevance with the topic searched. Various Machine Learning algorithms are currently employed in classification of tweets into positive and negative classes based on their sentiments, such as Baseline, Naive Bayes Classifier, Support Vector Machine etc. This paper contains implementation of Naive Bayes using sentiment140 training data using Twitter database and propose a method to improve classification. Use of SentiWordNet along with Naive Bayes can improve accuracy of classification of tweets, by providing positivity, negativity and objectivity score of words present in tweets. For actual implementation of this system python with NLTK and python-Twitter APIs are used.

Portrayals of Male and Female Scientists in Television Programs Popular Among Middle School-Age Children:

Abstract: This content analysis examined portrayals of scientist characters in 14 television programs popular among or likely to have been viewed by middle school-age children. While male scientists significantly outnumbered and appeared in significantly more scenes than did female scientists, males and females were depicted similarly in reference to professional position, marital status, and parental status. Gender-stereotyped behavior was largely absent in portrayals of scientist characters. Additionally, both male and female scientists were portrayed most often with the wishful identification attribute of intelligence. Implications for middle school-age children's perceptions of scientists and for cultivating girls' interest in science careers are discussed.