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

Skin cancer, which is one of the most common types of cancer in the world, is a malignant growth seen on the skin due to various reasons. There was an increase in the number of the cases of skin cancer nearly 200% between 2004-2009. Since the ozone layer is depleting, harmful rays reflected from the sun cannot be filtered. In this case, the likelihood of skin cancer will increase over the years and pose more risks for human beings. Early diagnosis is very significant as in all types of cancers. In this study, a mobile application is developed in order to detect whether the skin spots photographed by using the machine learning technique for early diagnosis have a suspicion of skin cancer. Thus, an auxiliary decision support system is developed that can be used both by the clinicians and individuals. For cases that are predicted to have a risk higher than a certain rate by the machine learning algorithm, early diagnosis could be initiated for the patients by consulting a physician when the case is considered to have a higher risk by machine learning algorithm.

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

10.1007/978-981-16-2712-5_33 ◽

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

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Feature Extraction for Emotion Recognition in Speech with Machine Learning Algorithm

International Journal of Advanced Trends in Computer Science and Engineering ◽

10.30534/ijatcse/2020/116942020 ◽

2020 ◽

Vol 9 (4) ◽

pp. 4998-5002

Author(s):

Aishwarya R.

Keyword(s):

Machine Learning ◽

Feature Extraction ◽

Emotion Recognition ◽

Learning Algorithm ◽

Machine Learning Algorithm

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Emotion Recognizing Virtual Assistant

International Journal for Research in Applied Science and Engineering Technology ◽

10.22214/ijraset.2021.35151 ◽

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.

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Machine learning algorithms can predict tail biting outbreaks in pigs using feeding behaviour records

10.1101/2021.05.11.443554 ◽

2021 ◽

Author(s):

Catherine Ollagnier ◽

Claudia Kasper ◽

Anna Wallenbeck ◽

Linda Keeling ◽

Siavash A Bigdeli

Keyword(s):

Machine Learning ◽

Random Forest ◽

Real Time ◽

Feeding Behaviour ◽

Learning Algorithm ◽

Learning Algorithms ◽

Machine Learning Algorithms ◽

Machine Learning Algorithm ◽

Tail Biting ◽

Testing Set

Tail biting is a detrimental behaviour that impacts the welfare and health of pigs. Early detection of tail biting precursor signs allows for preventive measures to be taken, thus avoiding the occurrence of the tail biting event. This study aimed to build a machine-learning algorithm for real time detection of upcoming tail biting outbreaks, using feeding behaviour data recorded by an electronic feeder. Prediction capacities of seven machine learning algorithms (e.g., random forest, neural networks) were evaluated from daily feeding data collected from 65 pens originating from 2 herds of grower-finisher pigs (25-100kg), in which 27 tail biting events occurred. Data were divided into training and testing data, either by randomly splitting data into 75% (training set) and 25% (testing set), or by randomly selecting pens to constitute the testing set. The random forest algorithm was able to predict 70% of the upcoming events with an accuracy of 94%, when predicting events in pens for which it had previous data. The detection of events for unknown pens was less sensitive, and the neural network model was able to detect 14% of the upcoming events with an accuracy of 63%. A machine-learning algorithm based on ongoing data collection should be considered for implementation into automatic feeder systems for real time prediction of tail biting events.

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