Deep Learning and its Applications: A Real-World Perspective

2021 ◽  
pp. 149-166
Author(s):  
Lakshmi Haritha Medida ◽  
Kasarapu Ramani
Keyword(s):  
Deep Learning ◽  
Real World ◽  
World Perspective

scholarly journals Deep Learning Classification of Canine Behavior Using a Single Collar-Mounted Accelerometer: Real-World Validation

Animals ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. 1549
Author(s):  
Robert D. Chambers ◽  
Nathanael C. Yoder ◽  
Aletha B. Carson ◽  
Christian Junge ◽  
David E. Allen ◽  
...  
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Real World ◽  
Learning Algorithm ◽  
Drinking Behavior ◽  
True Positive Rate ◽  
Training Dataset ◽  
Activity Levels ◽  
Accelerometer Data ◽  
Activity Monitors

Collar-mounted canine activity monitors can use accelerometer data to estimate dog activity levels, step counts, and distance traveled. With recent advances in machine learning and embedded computing, much more nuanced and accurate behavior classification has become possible, giving these affordable consumer devices the potential to improve the efficiency and effectiveness of pet healthcare. Here, we describe a novel deep learning algorithm that classifies dog behavior at sub-second resolution using commercial pet activity monitors. We built machine learning training databases from more than 5000 videos of more than 2500 dogs and ran the algorithms in production on more than 11 million days of device data. We then surveyed project participants representing 10,550 dogs, which provided 163,110 event responses to validate real-world detection of eating and drinking behavior. The resultant algorithm displayed a sensitivity and specificity for detecting drinking behavior (0.949 and 0.999, respectively) and eating behavior (0.988, 0.983). We also demonstrated detection of licking (0.772, 0.990), petting (0.305, 0.991), rubbing (0.729, 0.996), scratching (0.870, 0.997), and sniffing (0.610, 0.968). We show that the devices’ position on the collar had no measurable impact on performance. In production, users reported a true positive rate of 95.3% for eating (among 1514 users), and of 94.9% for drinking (among 1491 users). The study demonstrates the accurate detection of important health-related canine behaviors using a collar-mounted accelerometer. We trained and validated our algorithms on a large and realistic training dataset, and we assessed and confirmed accuracy in production via user validation.

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.

scholarly journals Real world perspective of coronary chronic total occlusion in third world countries: A tertiary care centre study from northern India

2021 ◽  
Vol 73 (2) ◽  
pp. 156-160
Author(s):  
Krishna Santosh Vemuri ◽  
Bhupinder Kumar Sihag ◽  
Yashpaul Sharma ◽  
Krishna prasad Nevali ◽  
Rajesh Vijayvergiya ◽  
...  
Keyword(s):  
Real World ◽  
Chronic Total Occlusion ◽  
Third World ◽  
Tertiary Care ◽  
Care Centre ◽  
Tertiary Care Centre ◽  
Total Occlusion ◽  
Centre Study ◽  
Northern India ◽  
World Perspective

scholarly journals Deep learning-enabled medical computer vision

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Andre Esteva ◽  
Katherine Chou ◽  
Serena Yeung ◽  
Nikhil Naik ◽  
Ali Madani ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Computer Vision ◽  
Deep Learning ◽  
Medical Imaging ◽  
Real World ◽  
Recent Progress ◽  
Medical Applications ◽  
Modern Computer ◽  
Medical Computer

AbstractA decade of unprecedented progress in artificial intelligence (AI) has demonstrated the potential for many fields—including medicine—to benefit from the insights that AI techniques can extract from data. Here we survey recent progress in the development of modern computer vision techniques—powered by deep learning—for medical applications, focusing on medical imaging, medical video, and clinical deployment. We start by briefly summarizing a decade of progress in convolutional neural networks, including the vision tasks they enable, in the context of healthcare. Next, we discuss several example medical imaging applications that stand to benefit—including cardiology, pathology, dermatology, ophthalmology–and propose new avenues for continued work. We then expand into general medical video, highlighting ways in which clinical workflows can integrate computer vision to enhance care. Finally, we discuss the challenges and hurdles required for real-world clinical deployment of these technologies.

A highly accurate delta check method using deep learning for detection of sample mix-up in the clinical laboratory

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Rui Zhou ◽  
Yu-fang Liang ◽  
Hua-Li Cheng ◽  
Wei Wang ◽  
Da-wei Huang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Statistical Methods ◽  
Real World ◽  
Error Detection ◽  
Clinical Laboratory ◽  
Characteristic Curve ◽  
False Positive Rate ◽  
Absolute Difference ◽  
Test Items ◽  
Test Sets

Abstract Objectives Delta check (DC) is widely used for detecting sample mix-up. Owing to the inadequate error detection and high false-positive rate, the implementation of DC in real-world settings is labor-intensive and rarely capable of absolute detection of sample mix-ups. The aim of the study was to develop a highly accurate DC method based on designed deep learning to detect sample mix-up. Methods A total of 22 routine hematology test items were adopted for the study. The hematology test results, collected from two hospital laboratories, were independently divided into training, validation, and test sets. By selecting six mainstream algorithms, the Deep Belief Network (DBN) was able to learn error-free and artificially (intentionally) mixed sample results. The model’s analytical performance was evaluated using training and test sets. The model’s clinical validity was evaluated by comparing it with three well-recognized statistical methods. Results When the accuracy of our model in the training set reached 0.931 at the 22nd epoch, the corresponding accuracy in the validation set was equal to 0.922. The loss values for the training and validation sets showed a similar (change) trend over time. The accuracy in the test set was 0.931 and the area under the receiver operating characteristic curve was 0.977. DBN demonstrated better performance than the three comparator statistical methods. The accuracy of DBN and revised weighted delta check (RwCDI) was 0.931 and 0.909, respectively. DBN performed significantly better than RCV and EDC. Of all test items, the absolute difference of DC yielded higher accuracy than the relative difference for all methods. Conclusions The findings indicate that input of a group of hematology test items provides more comprehensive information for the accurate detection of sample mix-up by machine learning (ML) when compared with a single test item input method. The DC method based on DBN demonstrated highly effective sample mix-up identification performance in real-world clinical settings.

scholarly journals Anti-Imperialism in “Max Havelaar”

K ta Kita ◽  
2019 ◽  
Vol 7 (1) ◽  
pp. 123-129
Author(s):  
Bintang Winarso
Keyword(s):  
Real World ◽  
New Historicism ◽  
Social Culture ◽  
The Real ◽  
World Perspective

This thesis analyzes how Multatuli shows anti-imperialism through the characters of Max Havelaar and Stern in Max Havelaar. The aim of the thesis is to prove that Max Havelaar and Stern embodies anti-imperialism from their author, Multatuli through their actions, ideas, and commentaries. The analysis of the characters uses theory of imperialism and anti-imperialism. The analysis of the characters is then connected to the real-world perspective of social, culture, and economy that happened around 1850s, by using the theory of new historicism. The results of the analysis show that Max Havelaar and Stern proved their anti-imperialism by fighting against economic, cultural, and political imperialism.

scholarly journals "Hello, It's Me": Deep Learning-based Speech Synthesis Attacks in the Real World

2021 ◽  
Author(s):  
Emily Wenger ◽  
Max Bronckers ◽  
Christian Cianfarani ◽  
Jenna Cryan ◽  
Angela Sha ◽  
...  
Keyword(s):  
Deep Learning ◽  
Real World ◽  
Speech Synthesis ◽  
The Real
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