scholarly journals Emotion Based Smart Music Player

2021 ◽  
pp. 08-13
Author(s):  
Chavi Ralhan ◽  
Kodamanchili Mohan ◽  
Kalleda Vinay Raj ◽  
Pendli Anirudh Reddy ◽  
Pannamaneni Saiprasad
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Significant Role ◽  
Facial Features ◽  
Learning Techniques ◽  
Static Pictures ◽  
And Performance ◽  
Music Player ◽  
And Behavior ◽  
Individual Human

Every individual human might have completely different faces; however, their expressions tell us the same story and it notably plays a significant role in extraction of an individual’s emotions and behavior. Music is the purest form of art and a medium of expression, which is known to have a greater connection with a person’s emotions. It has a novel ability to lift one’s mood. This project system focuses on building an efficient music player which works on emotion of user using facial recognition techniques. The facial features extracted will generate a system thereby reducing the effort and time involved in doing it manually. Facial data is captured by employing a camera. The emotion module makes use of deep learning techniques to spot the exact mood relative to that expression. The accuracy of mood detection module in the system for real time footage is above 80%; while for static pictures it is 95 to one hundred percent. Therefore, it brings out higher accuracy relating to time and performance.

Real Time Gender and Age Prediction using Deep Learning Techniques

2020 ◽  
Vol 9 (6) ◽  
pp. 797-801
Keyword(s):  
Deep Learning ◽  
Face Recognition ◽  
Real Time ◽  
Vital Role ◽  
Learning Techniques ◽  
Gender And Age ◽  
The Face ◽  
And Gender ◽  
Gender Detection ◽  
Age Prediction

Face recognition plays a vital role in security purpose. In recent years, the researchers have focused on the pose illumination, face recognition, etc,. The traditional methods of face recognition focus on Open CV’s fisher faces which results in analyzing the face expressions and attributes. Deep learning method used in this proposed system is Convolutional Neural Network (CNN). Proposed work includes the following modules: [1] Face Detection [2] Gender Recognition [3] Age Prediction. Thus the results obtained from this work prove that real time age and gender detection using CNN provides better accuracy results compared to other existing approaches.

MaskNet: CNN for Real-Time Face Mask Detection Based on Deep Learning Techniques

2021 ◽  
pp. 87-97
Author(s):  
Ismail Nasri ◽  
Mohammed Karrouchi ◽  
Hajar Snoussi ◽  
Abdelhafid Messaoudi ◽  
Kamal Kassmi
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Face Mask ◽  
Learning Techniques

Classifying Cutting Volume at Shale Shakers in Real-Time Via Video Streaming Using Deep-Learning Techniques

2020 ◽  
Vol 35 (03) ◽  
pp. 317-328
Author(s):  
Xunsheng Du ◽  
Yuchen Jin ◽  
Xuqing Wu ◽  
Yu Liu ◽  
Xianping (Sean) Wu ◽  
...  
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Video Streaming ◽  
Learning Techniques ◽  
Cutting Volume

scholarly journals A Framework to Estimate the Nutritional Value of Food in Real Time Using Deep Learning Techniques

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 2643-2652 ◽  
Author(s):  
Raza Yunus ◽  
Omar Arif ◽  
Hammad Afzal ◽  
Muhammad Faisal Amjad ◽  
Haider Abbas ◽  
...  
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Nutritional Value ◽  
Learning Techniques

scholarly journals A Deep-Learning-Driven Light-Weight Phishing Detection Sensor

Sensors ◽  
2019 ◽  
Vol 19 (19) ◽  
pp. 4258 ◽  
Author(s):  
Bo Wei ◽  
Rebeen Ali Hamad ◽  
Longzhi Yang ◽  
Xuan He ◽  
Hao Wang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Energy Saving ◽  
Real Time ◽  
Learning Algorithm ◽  
Rapid Development ◽  
Social Engineering ◽  
Machine Learning Techniques ◽  
Light Weight ◽  
Learning Techniques ◽  
Phishing Detection

This paper designs an accurate and low-cost phishing detection sensor by exploring deep learning techniques. Phishing is a very common social engineering technique. The attackers try to deceive online users by mimicking a uniform resource locator (URL) and a webpage. Traditionally, phishing detection is largely based on manual reports from users. Machine learning techniques have recently been introduced for phishing detection. With the recent rapid development of deep learning techniques, many deep-learning-based recognition methods have also been explored to improve classification performance. This paper proposes a light-weight deep learning algorithm to detect the malicious URLs and enable a real-time and energy-saving phishing detection sensor. Experimental tests and comparisons have been conducted to verify the efficacy of the proposed method. According to the experiments, the true detection rate has been improved. This paper has also verified that the proposed method can run in an energy-saving embedded single board computer in real-time.

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