Christ University

About: Christ University is a education organization based out in Bengaluru, India. It is known for research contribution in the topics: Computer science & Convection. The organization has 2267 authors who have published 2715 publications receiving 14575 citations. The organization is also known as: Christ College & Christ University.

Street Girls and Their Fight for Survival Across Four Developing Countries

Abstract: Utilizing both quantitative and qualitative approaches this study explored the lives of 115 girls on the streets, the data were gathered from the cities of New Delhi (India), Jakarta (Indonesia), Manila (Philippines), and Pretoria (South Africa). The average age of the sample was 15.17 years and the sample comprised of family based, street based, and shelter based children. The results indicated that the girls who were street based were at maximum risk of being involved in antisocial activities with peers, being low on problem solving, and high on depression and mental health related problems. However, the sample was also high on community engagement, religiosity, and individual attributes of self esteem, self efficacy, and resilience. The results were further substantiated via protocols from the participants. Given the vulnerable position of the girl child on the streets, programs that directly address the well-being and health of the girl child, especially those who are street based, are important to be examined.

Sentimental Analysis on voice using AWS Comprehend

Abstract: Sentimental analysis plays an important role in these days because many start-ups have started with user-driven content [1]. Sentiment analysis is an important research area in natural language processing. Natural language processing has a wide range of applications like voice recognition, machine translation, product review, aspect-oriented product analysis, sentiment analysis and text classification etc [2]. This process will improve the business by analyse the emotions of the conversation. In this project author going to perform sentimental analysis using Amazon Comprehend. Amazon Comprehend is a natural language processing (NLP) service that uses machine learning to extract the content of the document. By using this service can extract the unstructured data like images, voice etc. Thus, will identify the emotions of the conversation and give the output whether the conversation is Positive, Negative, Neutral, or Mixed. To perform this author going to use some services from Aws like s3 which is used for the data store, Transcribe which is used for converting the audio to text, Aws Glue is used to generate the metadata from the comprehend file, Aws Comprehend is used to generate the sentiment file from the audio, Lambda is used to trigger from the data store s3, Aws Athena is used to convert text into structured data and finally there is quick sight where he can visualize the data from the given file.

Mental Workload Estimation Using EEG

Abstract: Mental workload contributes considerably to the outcome or the performance of any task. The concern of human workload increases during a human-machine collaboration task or in a multitasking environment. This paper presents a comparative study of machine learning algorithms used to estimate workload using Electroencephalography (EEG) data. An open-access EEG dataset acquired during a “simultaneous capacity (SIMKAP) experiment” and “no task” is used to create and validate models for binary classification of workload as present and absent respectively. The paper presents an implementation of various classification models that use EEG data to predict the workload. In this paper, implementation for KNN classifier (57.3%), Random Forest classifier (57.19%), MLP network classifier (58.2%), CNN+ LSTM network classifier (58.68%), and LSTM network classifier (61.08%) has been reported. The paper can be further extended to study operator workload in real-time using a brain-computer interface paradigm for any kind of task in a real-world application. The workload classification can be further used in human-machine tasks to decide task allocation between the system to achieve optimal performance in a complex critical system.