EEG-based emotion recognition using LSTM-RNN machine learning algorithm

2019 ◽  
Author(s):  
Reddy Koya Jeevan ◽  
Venu Madhava Rao S.P. ◽  
Pothunoori Shiva Kumar ◽  
Malyala Srivikas
Keyword(s):  
Machine Learning ◽  
Emotion Recognition ◽  
Learning Algorithm ◽  
Machine Learning Algorithm

An Analysis of Machine Learning Algorithm for the Classification of Emotion Recognition

2021 ◽  
pp. 399-408
Author(s):  
Aditi Sakalle ◽  
Pradeep Tomar ◽  
Harshit Bhardwaj ◽  
Divya Acharya ◽  
Arpit Bhardwaj
Keyword(s):  
Machine Learning ◽  
Emotion Recognition ◽  
Learning Algorithm ◽  
Machine Learning Algorithm

scholarly journals Emotion Recognizing Virtual Assistant

2021 ◽  
Vol 9 (VI) ◽  
pp. 1554-1559
Author(s):  
Prof. Y. D. Choudhari
Keyword(s):  
Machine Learning ◽  
Emotion Recognition ◽  
Learning Algorithm ◽  
Task Completion ◽  
Machine Learning Algorithm ◽  
Python Language ◽  
Effective Product

Virtual Assistants are the most effective product of AI which makes people’s life easier. They are used in many machines. With AI many other technologies are also born like emotion recognition. This paper presents the AI with the feature of emotion recognition. This AI will complete the task by considering the emotion of user. As so as it takes the command it will analyze the task but before it perform it will recognize the emotion of user and then according to it, it will proceed for task completion. We have used Python language with machine learning algorithm. It is very effective to detect the emotions and avoid any problems. It will provide closer interaction with user like friend.

Development of Real-Time Emotion Recognition System Based on Machine Learning Algorithm

2021 ◽  
pp. 101-114
Author(s):  
Mohd Amzar Azizan ◽  
Muhammad Ismail Al Fatih ◽  
Alya Nabila ◽  
Nurhakimah Norhashim ◽  
Mohd Nadzeri Omar
Keyword(s):  
Machine Learning ◽  
Emotion Recognition ◽  
Real Time ◽  
Learning Algorithm ◽  
Recognition System ◽  
Machine Learning Algorithm

scholarly journals Gabor Filter Selection and Computational Processing for Emotion Recognition

2021 ◽  
Author(s):  
Erhan Coşkun ◽  
Torran Elson ◽  
Sean Lim ◽  
James Mathews ◽  
Gruff Morris ◽  
...  
Keyword(s):  
Machine Learning ◽  
Emotion Recognition ◽  
Processing Speed ◽  
Cost Efficiency ◽  
Learning Algorithm ◽  
Gabor Filter ◽  
Image Filtering ◽  
Local Binary Patterns ◽  
Gabor Filters ◽  
Machine Learning Algorithm

CrowdEmotion produce software to measure a person's emotions based on analysis of microfacial expressions using a machine learning algorithm to recognize which features correspond with which emotions. The features are derived by applying a bank of Gabor filters to a set of frames. CrowdEmotion needed to improve the accuracy, processing speed and cost-efficiency of the tool. In particular they wanted to know if a subset of the bank of Gabor filters was sufficient, and whether the image filtering stage could be implemented on a GPU. A framework for choosing the optimum set of Gabor filters was established and ways of reducing the dimensionality of this were interrogated. Taking a subset of Local Binary Patterns was found to be fully justified. Meanwhile choosing a gridding pattern is open to interpretation; some suggestions were made about how this choice might be improved.

Machine Learning Algorithm to Predict Early Complications after Brain Tumor Surgery

2018 ◽  
Author(s):  
C.H.B. van Niftrik ◽  
F. van der Wouden ◽  
V. Staartjes ◽  
J. Fierstra ◽  
M. Stienen ◽  
...  
Keyword(s):  
Machine Learning ◽  
Brain Tumor ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Tumor Surgery ◽  
Early Complications ◽  
Brain Tumor Surgery

Feature extraction and prediction of Dengue Outbreaks

2020 ◽  
pp. 216-222
Author(s):  
Kunal Parikh ◽  
Tanvi Makadia ◽  
Harshil Patel
Keyword(s):  
Public Health ◽  
Machine Learning ◽  
Developing Countries ◽  
Feature Extraction ◽  
Predictive Analytics ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Health Concerns ◽  
The World ◽  
Dengue Outbreaks

Dengue is unquestionably one of the biggest health concerns in India and for many other developing countries. Unfortunately, many people have lost their lives because of it. Every year, approximately 390 million dengue infections occur around the world among which 500,000 people are seriously infected and 25,000 people have died annually. Many factors could cause dengue such as temperature, humidity, precipitation, inadequate public health, and many others. In this paper, we are proposing a method to perform predictive analytics on dengue’s dataset using KNN: a machine-learning algorithm. This analysis would help in the prediction of future cases and we could save the lives of many.

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