Deep Learning for Medical Healthcare: Issues, Challenges, and Opportunities

2021 ◽  
pp. 361-392
Author(s):  
Meenu Gupta ◽  
Akash Gupta ◽  
Gaganjot Kaur
Keyword(s):  
Deep Learning ◽  
Challenges And Opportunities ◽  
Medical Healthcare

scholarly journals Towards Scalable Economic Photovoltaic Potential Analysis Using Aerial Images and Deep Learning

Energies ◽  
2021 ◽  
Vol 14 (13) ◽  
pp. 3800
Author(s):  
Sebastian Krapf ◽  
Nils Kemmerzell ◽  
Syed Khawaja Haseeb Khawaja Haseeb Uddin ◽  
Manuel Hack Hack Vázquez ◽  
Fabian Netzler ◽  
...  
Keyword(s):  
Deep Learning ◽  
System Analysis ◽  
State Of The Art ◽  
Critical Role ◽  
Semantic Segmentation ◽  
Energy System ◽  
Aerial Images ◽  
Potential Analysis ◽  
3D Data ◽  
Challenges And Opportunities

Roof-mounted photovoltaic systems play a critical role in the global transition to renewable energy generation. An analysis of roof photovoltaic potential is an important tool for supporting decision-making and for accelerating new installations. State of the art uses 3D data to conduct potential analyses with high spatial resolution, limiting the study area to places with available 3D data. Recent advances in deep learning allow the required roof information from aerial images to be extracted. Furthermore, most publications consider the technical photovoltaic potential, and only a few publications determine the photovoltaic economic potential. Therefore, this paper extends state of the art by proposing and applying a methodology for scalable economic photovoltaic potential analysis using aerial images and deep learning. Two convolutional neural networks are trained for semantic segmentation of roof segments and superstructures and achieve an Intersection over Union values of 0.84 and 0.64, respectively. We calculated the internal rate of return of each roof segment for 71 buildings in a small study area. A comparison of this paper’s methodology with a 3D-based analysis discusses its benefits and disadvantages. The proposed methodology uses only publicly available data and is potentially scalable to the global level. However, this poses a variety of research challenges and opportunities, which are summarized with a focus on the application of deep learning, economic photovoltaic potential analysis, and energy system analysis.

Effects of Deep Learning Technologies on Employment in the Field of Digital Communication Systems

2021 ◽  
Vol 12 (4) ◽  
pp. 35-42
Author(s):  
Thomas Alan Woolman ◽  
Philip Lee
Keyword(s):  
Artificial Intelligence ◽  
Deep Learning ◽  
Communication Systems ◽  
Digital Communication ◽  
The United States ◽  
Learning Technologies ◽  
Technological Unemployment ◽  
Current State ◽  
Challenges And Opportunities ◽  
Digital Communication Systems

There are significant challenges and opportunities facing the economies of the United States in the coming decades of the 21st century that are being driven by elements of technological unemployment. Deep learning systems, an advanced form of machine learning that is often referred to as artificial intelligence, is presently reshaping many aspects of traditional digital communication technology employment, primarily network system administration and network security system design and maintenance. This paper provides an overview of the current state-of-the-art developments associated with deep learning and artificial intelligence and the ongoing revolutions that this technology is having not only on the field of digital communication systems but also related technology fields. This paper will also explore issues and concerns related to past technological unemployment challenges, as well as opportunities that may be present as a result of these ongoing technological upheavals.

scholarly journals Deep Learning in the Era of Edge Computing: Challenges and Opportunities

Fog Computing ◽  
2020 ◽  
pp. 67-78 ◽  
Author(s):  
Mi Zhang ◽  
Faen Zhang ◽  
Nicholas D. Lane ◽  
Yuanchao Shu ◽  
Xiao Zeng ◽  
...  
Keyword(s):  
Deep Learning ◽  
Edge Computing ◽  
Challenges And Opportunities

scholarly journals From Handcrafted to Deep-Learning-Based Cancer Radiomics: Challenges and Opportunities

2019 ◽  
Vol 36 (4) ◽  
pp. 132-160 ◽  
Author(s):  
Parnian Afshar ◽  
Arash Mohammadi ◽  
Konstantinos N. Plataniotis ◽  
Anastasia Oikonomou ◽  
Habib Benali
Keyword(s):  
Deep Learning ◽  
Challenges And Opportunities

scholarly journals Robustness of convolutional neural networks to physiological electrocardiogram noise

2021 ◽  
Vol 379 (2212) ◽  
Author(s):  
J. Venton ◽  
P. M. Harris ◽  
A. Sundar ◽  
N. A. S. Smith ◽  
P. J. Aston
Keyword(s):  
Deep Learning ◽  
Careful Consideration ◽  
Training Data ◽  
Learning Methods ◽  
Ecg Signals ◽  
Open Questions ◽  
Image Transforms ◽  
Challenges And Opportunities ◽  
Electrocardiogram Ecg ◽  
Image Transformations

The electrocardiogram (ECG) is a widespread diagnostic tool in healthcare and supports the diagnosis of cardiovascular disorders. Deep learning methods are a successful and popular technique to detect indications of disorders from an ECG signal. However, there are open questions around the robustness of these methods to various factors, including physiological ECG noise. In this study, we generate clean and noisy versions of an ECG dataset before applying symmetric projection attractor reconstruction (SPAR) and scalogram image transformations. A convolutional neural network is used to classify these image transforms. For the clean ECG dataset, F1 scores for SPAR attractor and scalogram transforms were 0.70 and 0.79, respectively. Scores decreased by less than 0.05 for the noisy ECG datasets. Notably, when the network trained on clean data was used to classify the noisy datasets, performance decreases of up to 0.18 in F1 scores were seen. However, when the network trained on the noisy data was used to classify the clean dataset, the decrease was less than 0.05. We conclude that physiological ECG noise impacts classification using deep learning methods and careful consideration should be given to the inclusion of noisy ECG signals in the training data when developing supervised networks for ECG classification. This article is part of the theme issue ‘Advanced computation in cardiovascular physiology: new challenges and opportunities’.

Glacier evolution modelling with deep learning: challenges and opportunities

2020 ◽  
Author(s):  
Jordi Bolibar ◽  
Antoine Rabatel ◽  
Isabelle Gouttevin ◽  
Clovis Galiez ◽  
Thomas Condom ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Deep Learning ◽  
Mass Balance ◽  
Open Source ◽  
Data Science ◽  
Data Availability ◽  
Alternative Methods ◽  
Surface Mass Balance ◽  
Surface Mass ◽  
Challenges And Opportunities

<div>Glacier surface mass balance (SMB) and glacier evolution modelling have traditionally been tackled with physical/empirical methods, and despite some statistical studies very few efforts have been made towards machine learning approaches. With the end of this past decade, we have witnessed an impressive increase in the available amount of data, mostly coming from remote sensing products and reanalyses, as well as an extensive list of open-source tools and libraries for data science. Here we introduce a first effort to use deep learning (i.e. a deep artificial neural network) to simulate glacier-wide surface mass balance at a regional scale, based on direct and remote sensing SMB data, climate reanalysis and multitemporal glacier inventories. Coupled with a parameterized glacier-specific ice dynamics function, this allows us to simulate the evolution of glaciers for a whole region. This has been developed as the ALpine Parameterized Glacier Model (ALPGM), an open-source Python glacier evolution model. To illustrate this data science approach, we present the results of a glacier-wide surface mass balance reconstruction of all the glaciers in the French Alps from 1967-2015. These results were analysed and compared with all the available observations in the region as well as another physical/empirical SMB reconstruction study. We observe some interesting differences between the two SMB reconstructions, which further highlight the interest of using alternative methods in glacier modelling. Due to (relatively) recent advances in data availability and open tools (e.g. Tensorflow, Keras, Pangeo) this research field is ripe for progress, with many interesting challenges and opportunities lying ahead. To conclude, some perspectives on data science glacier modelling are discussed, based on the limitations of our current approach and on upcoming tools and methods, such as convolutional and physics-informed neural networks. </div>

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