scholarly journals Facial Emotion Based Music Recommendation System using computer vision and machine learning techiniques

2021 ◽  
Vol 12 (2) ◽  
pp. 912-917
Author(s):  
ShanthaShalini. K, Et. al.
Keyword(s):  
Machine Learning ◽  
Computer Vision ◽  
Recommendation System ◽  
Input Image ◽  
Machine Learning Techniques ◽  
Music Recommendation ◽  
Emotion Detection ◽  
Learning Techniques ◽  
Human Emotions ◽  
Music Recommendation System

The face is an important aspect in predicting human emotions and mood. Usually the human emotions are extracted with the use of camera. There are many applications getting developed based on detection of human emotions. Few applications of emotion detection are business notification recommendation, e-learning, mental disorder and depression detection, criminal behaviour detection etc. In this proposed system, we develop a prototype in recommendation of dynamic music recommendation system based on human emotions. Based on each human listening pattern, the songs for each emotions are trained. Integration of feature extraction and machine learning techniques, from the real face the emotion are detected and once the mood is derived from the input image, respective songs for the specific mood would be played to hold the users. In this approach, the application gets connected with human feelings thus giving a personal touch to the users. Therefore our projected system concentrate on identifying the human feelings for developing emotion based music player using computer vision and machine learning techniques. For experimental results, we use openCV for emotion detection and music recommendation.

scholarly journals Computer Vision-Enabled Character Recognition of Hand Gestures for Patients with Hearing and Speaking Disability

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Sapna Juneja ◽  
Abhinav Juneja ◽  
Gaurav Dhiman ◽  
Shashank Jain ◽  
Anu Dhankhar ◽  
...  
Keyword(s):  
Machine Learning ◽  
Computer Vision ◽  
Sign Language ◽  
Character Recognition ◽  
Recommendation System ◽  
Machine Learning Techniques ◽  
Deaf People ◽  
Layer By Layer ◽  
Learning Techniques ◽  
Reliable Solution

Hand gesture recognition is one of the most sought technologies in the field of machine learning and computer vision. There has been an unprecedented demand for applications through which one can detect the hand signs for deaf people and people who use sign language to communicate, thereby detecting hand signs and correspondingly predicting the next word or recommending the word that may be most appropriate, followed by producing the word that the deaf people and people who use sign language to communicate want to say. This article presents an approach to develop such a system by that we can determine the most appropriate character from the sign that is being shown by the user or the person to the system. To enable pattern recognition, various machine learning techniques have been explored and we have used the CNN networks as a reliable solution in our context. The creation of such a system involves several convolution layers through which features have been captured layer by layer. The gathered features from the image are further used for training the model. The trained model efficiently predicts the most appropriate character in response to the sign exposed to the model. Thereafter, the predicted character is used to predict further words from it according to the recommendation system used in this case. The proposed system attains a prediction accuracy of 91.07%.

Statistical Analysis of Human Emotions to Suggest Suitable Music as per Individual's Mood

2021 ◽  
Vol 11 (3) ◽  
pp. 34-67
Author(s):  
Rohit Rastogi ◽  
Prabhat Yadav ◽  
Jayash Raj Singh Yadav
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Statistical Analysis ◽  
Leisure Time ◽  
Recommendation System ◽  
Music Industry ◽  
Music Recommendation ◽  
Emotion Detection ◽  
Current Mood ◽  
Music Recommendation System

There is music recommendation software and music providers that are well explored and commonly used, but those are generally based on simple similarity calculations and manually tagged parameters. This project proposes a music recommendation system based on emotion detection of users, automatic computing, and classification. Music is recommended based on the emotion expressed and temper of the user. Like artists and genre, emotion of the user can also be a crucial recommendation point for music listeners. The different mооds in whiсh the system will сlаssify the imаges аre hаррy, neutrаl, аnd sаd. The system will рre-sоrt the songs according to their genre in the above-mentioned categories. This research project gives us advancement in the music industry with the help of machine learning and artificial intelligence and will reduce the hassle of selecting songs in our leisure time and will automatically play songs by detecting the emotion of the user. This data can be used to play the songs that match the current mood detected from the provided input by the user.

scholarly journals A Mining Frame Work of CO-PO Attainment using Deep Learning Techniques

2020 ◽  
Vol 9 (4S2) ◽  
pp. 60-63
Keyword(s):  
Machine Learning ◽  
Student Performance ◽  
Answer Sheet ◽  
Input Image ◽  
Machine Learning Techniques ◽  
Optimal Model ◽  
Program Outcome ◽  
Learning Techniques ◽  
Frame Work ◽  
Machine Learning Models

student performance measured in CO-PO (Course Outcome and Program Outcome) attainment for OMR based answer sheet automation playing very curtail role in pupil concert analysis in this approach. In the proposed work, marks evaluation sheet is consider as input image, then apply frame cropping technique to extract the marks filled table by subdividing into cells as individual images by frame cropping technique. In order to recognition of hand written digit in each frame, various machine learning models are adopted, trained. Experimental results from proposed work show that convolutional neural network excels higher in identification digits from frames. The outputs are then converted to CSV version, which is used to evaluate CO-PO attainment for each learner. The experiments have been conducted and tested in proposed work on various machine learning techniques and compared the results to pick the optimal model

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