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jodhpur, india
It's currently 3:33 PM here
Joined February 23, 2018
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Nilima G.
@nilimagautam94
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jodhpur, india
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Researcher/AI/ML/Computer Vision/IoT/Data Science
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Experience
Research Intern
Jun 2019 - Jun 2020 (1 year)
I have done a project entitled "Emotion detection using Galvanic Skin Response". In this research work, I have used various machine learning algorithms such as DT, KNN, SVM, PCA, ANN, and similar others for emotion (happiness, relax, pain, stress) detection and classification. I have used my own dataset, data collected at a 10Hz sampling rate. I have used these tools MATLAB, Python, Processing IDE, Arduino, GSR sensor for prediction in this research.
Education
Master of Engineering
(3 years)
Bachelor of Engineering
(3 years)
Publications
Discrimination in Sentiments based on Galvanic Skin Response
Weentech Publishers
This research work proposes a system to classify human sentiments with the help of electrodermal activity and discriminate between different emotions viz. happy and relax, pain, etc. using the GSR sensor. The analysis of data was computed in the time domain. The Supervised machine learning model used for classification viz. SVM, Decision Tree, KNN. The predicted accuracy for happy, relax, and pain activity was 91%, 97%, and 98%, respectively, with the help of a KNN model.
A survey on virtualization techniques in Mobile edge computing
Weentech Publishers
Virtualization in MEC can be done by the hypervisor, Virtual machine, Docker Container, or by Kubernetes. Hypervisors and VMs are the technologies used earlier. Docker is the technology we use nowadays, and Kubernetes is the future of Virtualization. This paper, address Docker as new container technology and introduce you to how this technology has solved previous problems in Virtualization, including the creation and deployment of large applications.
Implementation of Docker for Mobile Edge Computing Embedded Platform
Weentech Publishers
This paper will discuss Docker and present how this technology has overcome the earlier problems of virtualization with building and deploying large applications in two ways. We have implemented Docker of YOLO and AQM models on Jetson TX2 and compared both applications on Docker and Host OS.
Galvanic skin response to recognizing human behavior
Weentech Publisher
The main aim is to develop a precise classification model for better accuracy of the emotion recognition system using the GSR (Grove – GSR Sensor V1.2) sensor. Moving average window method was used for data pre-processing. Supervised machine learning models viz., k-nearest neighbors (KNN), support vector machine (SVM), and decision tree (DT) were used for emotion classification. The decision tree model gives the best results with an average accuracy of 97.61%.
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