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 ◽  
...  

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.


Author(s):  
Ismail Nasri ◽  
Mohammed Karrouchi ◽  
Hajar Snoussi ◽  
Abdelhafid Messaoudi ◽  
Kamal Kassmi

2020 ◽  
Vol 35 (03) ◽  
pp. 317-328
Author(s):  
Xunsheng Du ◽  
Yuchen Jin ◽  
Xuqing Wu ◽  
Yu Liu ◽  
Xianping (Sean) Wu ◽  
...  

Sensors ◽  
2019 ◽  
Vol 19 (19) ◽  
pp. 4258 ◽  
Author(s):  
Bo Wei ◽  
Rebeen Ali Hamad ◽  
Longzhi Yang ◽  
Xuan He ◽  
Hao Wang ◽  
...  

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.


Author(s):  
Varalakshmi Perumal ◽  
SureshKumar Murugaiyan ◽  
Pavithran Ravichandran ◽  
R. Venkatesan ◽  
R. Sundar

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