Deep learning for health informatics: Recent trends and future directions

2017 ◽  
Author(s):  
Siddharth Srivastava ◽  
Sumit Soman ◽  
Astha Rai ◽  
Praveen K Srivastava
Keyword(s):  
Deep Learning ◽  
Health Informatics ◽  
Future Directions ◽  
Recent Trends

scholarly journals Deep Learning for Radio-based Human Sensing: Recent Advances and Future Directions

2021 ◽  
pp. 1-1
Author(s):  
Isura Nirmal ◽  
Abdelwahed Khamis ◽  
Mahbub Hassan ◽  
Wen Hu ◽  
Xiaoqing Zhu
Keyword(s):  
Deep Learning ◽  
Future Directions ◽  
Recent Advances ◽  
Human Sensing

scholarly journals Deep Learning for Safe Autonomous Driving: Current Challenges and Future Directions

2020 ◽  
pp. 1-21
Author(s):  
Khan Muhammad ◽  
Amin Ullah ◽  
Jaime Lloret ◽  
Javier Del Ser ◽  
Victor Hugo C. de Albuquerque
Keyword(s):  
Deep Learning ◽  
Autonomous Driving ◽  
Future Directions ◽  
Driving Current

scholarly journals A Survey on Bias and Fairness in Machine Learning

2021 ◽  
Vol 54 (6) ◽  
pp. 1-35
Author(s):  
Ninareh Mehrabi ◽  
Fred Morstatter ◽  
Nripsuta Saxena ◽  
Kristina Lerman ◽  
Aram Galstyan
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Deep Learning ◽  
Real World ◽  
State Of The Art ◽  
Future Directions ◽  
Discriminatory Behavior ◽  
Real World Applications ◽  
Near Future ◽  
Different Sources

With the widespread use of artificial intelligence (AI) systems and applications in our everyday lives, accounting for fairness has gained significant importance in designing and engineering of such systems. AI systems can be used in many sensitive environments to make important and life-changing decisions; thus, it is crucial to ensure that these decisions do not reflect discriminatory behavior toward certain groups or populations. More recently some work has been developed in traditional machine learning and deep learning that address such challenges in different subdomains. With the commercialization of these systems, researchers are becoming more aware of the biases that these applications can contain and are attempting to address them. In this survey, we investigated different real-world applications that have shown biases in various ways, and we listed different sources of biases that can affect AI applications. We then created a taxonomy for fairness definitions that machine learning researchers have defined to avoid the existing bias in AI systems. In addition to that, we examined different domains and subdomains in AI showing what researchers have observed with regard to unfair outcomes in the state-of-the-art methods and ways they have tried to address them. There are still many future directions and solutions that can be taken to mitigate the problem of bias in AI systems. We are hoping that this survey will motivate researchers to tackle these issues in the near future by observing existing work in their respective fields.

Forum: Dagmar Herzog's Sexuality in Europe

2013 ◽  
Vol 22 (2) ◽  
pp. 275-275
Keyword(s):  
Twentieth Century ◽  
History Of Sexuality ◽  
Future Directions ◽  
University Of Vienna ◽  
History Of ◽  
Recent Trends ◽  
East And West ◽  
Twentieth Century Europe

This discussion of Dagmar Herzog's Sexuality in Europe (2011) continues our new series of book fora. Herzog's new overview of changing European sexual mores and behaviour offers a jumping-off point for our panellists to discuss recent trends and future directions in the history of sexuality in twentieth-century Europe, East and West. Jeffrey Weeks (London South Bank University), Franz Eder (University of Vienna), Daniel Healey (University of Reading) and Victoria Harris (University of Birmingham) give their responses, and Herzog replies.

Improving the Stress Tolerance of Probiotic Cultures: Recent Trends and Future Directions

2011 ◽  
pp. 395-438 ◽  
Author(s):  
Aditya Upadrasta ◽  
Catherine Stanton ◽  
Colin Hill ◽  
Gerald F. Fitzgerald ◽  
R. Paul Ross
Keyword(s):  
Stress Tolerance ◽  
Future Directions ◽  
Recent Trends

Compute-in-Memory Chips for Deep Learning: Recent Trends and Prospects

2021 ◽  
Vol 21 (3) ◽  
pp. 31-56
Author(s):  
Shimeng Yu ◽  
Hongwu Jiang ◽  
Shanshi Huang ◽  
Xiaochen Peng ◽  
Anni Lu
Keyword(s):  
Deep Learning ◽  
Recent Trends

A Review of Recent Deep Learning Models in COVID-19 Diagnosis

2021 ◽  
Vol 6 (5) ◽  
pp. 10-15
Author(s):  
Ela Bhattacharya ◽  
D. Bhattacharya
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Deep Learning ◽  
Deep Neural Networks ◽  
Test Results ◽  
Learning Models ◽  
Future Directions ◽  
Human Contact ◽  
The World ◽  
Short Span

COVID-19 has emerged as the latest worrisome pandemic, which is reported to have its outbreak in Wuhan, China. The infection spreads by means of human contact, as a result, it has caused massive infections across 200 countries around the world. Artificial intelligence has likewise contributed to managing the COVID-19 pandemic in various aspects within a short span of time. Deep Neural Networks that are explored in this paper have contributed to the detection of COVID-19 from imaging sources. The datasets, pre-processing, segmentation, feature extraction, classification and test results which can be useful for discovering future directions in the domain of automatic diagnosis of the disease, utilizing artificial intelligence-based frameworks, have been investigated in this paper.

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