scholarly journals Advanced Ultrasound and Photoacoustic Imaging in Cardiology

Sensors ◽  
2021 ◽  
Vol 21 (23) ◽  
pp. 7947
Author(s):  
Min Wu ◽  
Navchetan Awasthi ◽  
Nastaran Mohammadian Rad ◽  
Josien P. W. Pluim ◽  
Richard G. P. Lopata
Keyword(s):  
Deep Learning ◽  
Imaging Technique ◽  
Photoacoustic Imaging ◽  
Accurate Diagnosis ◽  
Effective Management ◽  
Clinical Imaging ◽  
Imaging Methods ◽  
Learning Techniques ◽  
Technological Developments ◽  
Absorption Contrast

Cardiovascular diseases (CVDs) remain the leading cause of death worldwide. An effective management and treatment of CVDs highly relies on accurate diagnosis of the disease. As the most common imaging technique for clinical diagnosis of the CVDs, US imaging has been intensively explored. Especially with the introduction of deep learning (DL) techniques, US imaging has advanced tremendously in recent years. Photoacoustic imaging (PAI) is one of the most promising new imaging methods in addition to the existing clinical imaging methods. It can characterize different tissue compositions based on optical absorption contrast and thus can assess the functionality of the tissue. This paper reviews some major technological developments in both US (combined with deep learning techniques) and PA imaging in the application of diagnosis of CVDs.

scholarly journals AndroAnalyzer: android malicious software detection based on deep learning

2021 ◽  
Vol 7 ◽  
pp. e533
Author(s):  
Recep Sinan Arslan
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Learning System ◽  
Machine Learning Techniques ◽  
Detection Methods ◽  
Smart Devices ◽  
Android Malware ◽  
Original Dataset ◽  
Learning Techniques ◽  
Technological Developments

Background Technological developments have a significant effect on the development of smart devices. The use of smart devices has become widespread due to their extensive capabilities. The Android operating system is preferred in smart devices due to its open-source structure. This is the reason for its being the target of malware. The advancements in Android malware hiding and detection avoidance methods have overridden traditional malware detection methods. Methods In this study, a model employing AndroAnalyzer that uses static analysis and deep learning system is proposed. Tests were carried out with an original dataset consisting of 7,622 applications. Additional tests were conducted with machine learning techniques to compare it with the deep learning method using the obtained feature vector. Results Accuracy of 98.16% was achieved by presenting a better performance compared to traditional machine learning techniques. Values of recall, precision, and F-measure were 98.78, 99.24 and 98.90, respectively. The results showed that deep learning models using trace-based feature vectors outperform current cutting-edge technology approaches.

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.

Deep Learning Techniques for Koala Activity Detection

2018 ◽  
Author(s):  
Ivan Himawan ◽  
Michael Towsey ◽  
Bradley Law ◽  
Paul Roe
Keyword(s):  
Deep Learning ◽  
Activity Detection ◽  
Learning Techniques

scholarly journals Deep Learning-Based Growth Prediction System: A Use Case of China Agriculture

Agronomy ◽  
2021 ◽  
Vol 11 (8) ◽  
pp. 1551
Author(s):  
Tamoor Khan ◽  
Jiangtao Qiu ◽  
Hafiz Husnain Raza Sherazi ◽  
Mubashir Ali ◽  
Sukumar Letchmunan ◽  
...  
Keyword(s):  
Deep Learning ◽  
Crop Production ◽  
Agricultural Development ◽  
Production Efficiency ◽  
Fruit Production ◽  
National Bureau ◽  
System A ◽  
Future Production ◽  
Learning Techniques ◽  
Agricultural Yields

Agricultural advancements have significantly impacted people’s lives and their surroundings in recent years. The insufficient knowledge of the whole agricultural production system and conventional ways of irrigation have limited agricultural yields in the past. The remote sensing innovations recently implemented in agriculture have dramatically revolutionized production efficiency by offering unparalleled opportunities for convenient, versatile, and quick collection of land images to collect critical details on the crop’s conditions. These innovations have enabled automated data collection, simulation, and interpretation based on crop analytics facilitated by deep learning techniques. This paper aims to reveal the transformative patterns of old Chinese agrarian development and fruit production by focusing on the major crop production (from 1980 to 2050) taking into account various forms of data from fruit production (e.g., apples, bananas, citrus fruits, pears, and grapes). In this study, we used production data for different fruits grown in China to predict the future production of these fruits. The study employs deep neural networks to project future fruit production based on the statistics issued by China’s National Bureau of Statistics on the total fruit growth output for this period. The proposed method exhibits encouraging results with an accuracy of 95.56% calculating by accuracy formula based on fruit production variation. Authors further provide recommendations on the AGR-DL (agricultural deep learning) method being helpful for developing countries. The results suggest that the agricultural development in China is acceptable but demands more improvement and government needs to prioritize expanding the fruit production by establishing new strategies for cultivators to boost their performance.

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