A Study on Deep Learning Methods in the Concept of Digital Industry 4.0

2021 ◽  
pp. 318-339
Author(s):  
Mehmet Ali Şimşek ◽  
Zeynep Orman
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Deep Learning ◽  
Industry 4.0 ◽  
Production Efficiency ◽  
Learning Technologies ◽  
High Potential ◽  
Data Sets ◽  
Learning Methods ◽  
Intelligence Studies

Nowadays, the main features of Industry 4.0 are interpreted to the ability of machines to communicate with each other and with a system, increasing the production efficiency and development of the decision-making mechanisms of robots. In these cases, new analytical algorithms of Industry 4.0 are needed. By using deep learning technologies, various industrial challenging problems in Industry 4.0 can be solved. Deep learning provides algorithms that can give better results on datasets owing to hidden layers. In this chapter, deep learning methods used in Industry 4.0 are examined and explained. In addition, data sets, metrics, methods, and tools used in the previous studies are explained. This study can lead to artificial intelligence studies with high potential to accelerate the implementation of Industry 4.0. Therefore, the authors believe that it will be very useful for researchers and practitioners who want to do research on this topic.

scholarly journals Frontiers of Computer Vision Technologies on Real Estate Property Photographs and Floorplans

2021 ◽  
pp. 325-337
Author(s):  
Yoji Kiyota
Keyword(s):  
Artificial Intelligence ◽  
Computer Vision ◽  
Deep Learning ◽  
Real Estate ◽  
Image Data ◽  
Learning Technologies ◽  
Data Sets ◽  
Academic Communities ◽  
Estate Property ◽  
Extract Information

AbstractThis article describes frontier efforts to apply deep learning technologies, which is the greatest innovation of research on artificial intelligence and computer vision, to image data such as real estate property photographs and floorplans. Specifically, attempts to detect property photographs that violate regulations or were misclassified, or to extract information that can be used as new recommendation features from property photographs, were mentioned. Besides, this article introduces an innovation created by providing data sets for academic communities.

Artificial Intelligence and Human Rights: Four Realms of Discussion: Summary of Remarks

2020 ◽  
Vol 114 ◽  
pp. 242-245
Author(s):  
Jootaek Lee
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Decision Making ◽  
Deep Learning ◽  
Language Processing ◽  
Human Reasoning ◽  
The Past ◽  
Making Choices ◽  
Data Capturing ◽  
Intensive Procedures

The term, Artificial Intelligence (AI), has changed since it was first coined by John MacCarthy in 1956. AI, believed to have been created with Kurt Gödel's unprovable computational statements in 1931, is now called deep learning or machine learning. AI is defined as a computer machine with the ability to make predictions about the future and solve complex tasks, using algorithms. The AI algorithms are enhanced and become effective with big data capturing the present and the past while still necessarily reflecting human biases into models and equations. AI is also capable of making choices like humans, mirroring human reasoning. AI can help robots to efficiently repeat the same labor intensive procedures in factories and can analyze historic and present data efficiently through deep learning, natural language processing, and anomaly detection. Thus, AI covers a spectrum of augmented intelligence relating to prediction, autonomous intelligence relating to decision making, automated intelligence for labor robots, and assisted intelligence for data analysis.

scholarly journals The Application and Development of Deep Learning in Radiotherapy: A Systematic Review

2021 ◽  
Vol 20 ◽  
pp. 153303382110163
Author(s):  
Danju Huang ◽  
Han Bai ◽  
Li Wang ◽  
Yu Hou ◽  
Lan Li ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Systematic Review ◽  
Neural Networks ◽  
Decision Making ◽  
Deep Learning ◽  
Radiation Oncology ◽  
Clinical Work ◽  
Radiation Oncologists ◽  
Basic Concepts ◽  
Further Development

With the massive use of computers, the growth and explosion of data has greatly promoted the development of artificial intelligence (AI). The rise of deep learning (DL) algorithms, such as convolutional neural networks (CNN), has provided radiation oncologists with many promising tools that can simplify the complex radiotherapy process in the clinical work of radiation oncology, improve the accuracy and objectivity of diagnosis, and reduce the workload, thus enabling clinicians to spend more time on advanced decision-making tasks. As the development of DL gets closer to clinical practice, radiation oncologists will need to be more familiar with its principles to properly evaluate and use this powerful tool. In this paper, we explain the development and basic concepts of AI and discuss its application in radiation oncology based on different task categories of DL algorithms. This work clarifies the possibility of further development of DL in radiation oncology.

ProBot – A Procedure Chatbot for Digital Procedural Adherence

2020 ◽  
Vol 64 (1) ◽  
pp. 224-228
Author(s):  
Nilesh Ade ◽  
Noor Quddus ◽  
Trent Parker ◽  
S.Camille Peres
Keyword(s):  
Artificial Intelligence ◽  
Deep Learning ◽  
Natural Language ◽  
Industry 4.0 ◽  
Natural Language Generation ◽  
Conversational Agent ◽  
Safe Distance ◽  
Process Industries ◽  
Language Generation ◽  
Plastic Pellets

One of the major implications of Industry 4.0 will be the application of digital procedures in process industries. Digital procedures are procedures that are accessed through a smart gadget such as a tablet or a phone. However, like paper-based procedures their usability is limited by their access. The issue of accessibility is magnified in tasks such as loading a hopper car with plastic pellets wherein the operators typically place the procedure at a safe distance from the worksite. This drawback can be tackled in the case of digital procedures using artificial intelligence-based voice enabled conversational agent (chatbot). As a part of this study, we have developed a chatbot for assisting digital procedure adherence. The chatbot is trained using the possible set of queries from the operator and text from the digital procedures through deep learning and provides responses using natural language generation. The testing of the chatbot is performed using a simulated conversation with an operator performing the task of loading a hopper car.

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 Digital Education, Work and Artificial Intelligence: Health and Law

2020 ◽  
pp. 277-288
Author(s):  
Abílio Azevedo ◽  
Patricia Anjos Azevedo
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Qualitative Methodology ◽  
Learning Methods ◽  
Digital Education ◽  
Ethical Concerns ◽  
Application Potential ◽  
New Challenges ◽  
Formal Application ◽  
Daily Application

The use and possibilities of artificial intelligence (AI) have been assuming great importance in recent years. This fact led to a greater attention on the topic in various fields, especially in health and law, both in its daily application potential and in learning methods. The aim of this article was to present a brief perspective of the challenges and effects of the AI use in teaching and application on health and law domains. Therefore, to better define the theme it was performed a qualitative methodology of bibliographic review. The applications of artificial intelligence have a great potential in clinical and legal use, facilitating the tasks of those involved by helping to reduce workflow, to avoid errors and in decision-making. However, despite these benefits and new opportunities, there are still obstacles regarding regulation and ethical concerns, as well as some reluctance from professionals in their adoption and formal application. In addition, there also the need to proper implement these technologies in learning to keep up the change and the new challenges currently posed, so there is a path that still needs to be followed.

scholarly journals Advanced Deep Learning Model

2021 ◽  
Author(s):  
Yew Kee Wong
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Decision Making ◽  
Deep Learning ◽  
Learning Algorithms ◽  
Learning Model ◽  
Intelligence Analysis ◽  
Basic Parameters ◽  
Deep Learning Model ◽  
Learning Sets

Deep learning is a type of machine learning that trains a computer to perform human-like tasks, such as recognizing speech, identifying images or making predictions. Instead of organizing data to run through predefined equations, deep learning sets up basic parameters about the data and trains the computer to learn on its own by recognizing patterns using many layers of processing. This paper aims to illustrate some of the different deep learning algorithms and methods which can be applied to artificial intelligence analysis, as well as the opportunities provided by the application in various decision making domains.

AB1161 ARTIFICIAL INTELLIGENCE FOR RHEUMATOLOGY

2020 ◽  
Vol 79 (Suppl 1) ◽  
pp. 1871-1872
Author(s):  
A. C. Genç ◽  
F. Turkoglu Genc ◽  
A. B. Kara ◽  
L. Genc Kaya ◽  
Z. Ozturk ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Deep Learning ◽  
Magnetic Resonance ◽  
Sacroiliac Joint ◽  
Axial Skeleton ◽  
Observer Agreement ◽  
Mri Findings ◽  
Learning Methods ◽  
Expert Radiologist ◽  
Active Sacroiliitis

Background:Magnetic resonance imaging (MRI) of sacroiliac (SI) joints is used to detect early sacroiliitis(1). There can be an interobserver disagreement in MRI findings of SI joints of spondyloarthropathy patients between a rheumatologist, a local radiologist, and an expert radiologist(2). Artificial Intelligence and deep learning methods to detect abnormalities have become popular in radiology and other medical fields in recent years(3). Search for “artificial intelligence” and “radiology” in Pubmed for the last five years returned around 1500 clinical studies yet no results were retrieved for “artificial intelligence” and “rheumatology”.Objectives:Artificial Intelligence (AI) can help to detect the pathological area like sacroiliitis or not and also allows us to characterize it as quantitatively rather than qualitatively in the SI-MRI.Methods:Between the years of 2015 and 2019, 8100 sacroiliac MRIs were taken at our center. The MRIs of 1150 patients who were reported as active or chronic sacroiliitis from these sacroiliac MRIs or whose MRIs were considered by the primary physician in favor of sacroiliitis was included in the study. 1441 MRI coronal STIR sequence of 1150 patients were tagged as ‘’active sacroiliitis’’ and trained to detect and localize active sacroiliitis and provide prediction performance. This model is available for various operating systems. (Image1)Results:Precision score, the percentage of sacroiliac images of the trained model, is 87.1%. Recall, the percentage of the total sacroiliac MRIs correctly classified by the model, is 82.1% and the mean average precision (mAP) of the model is 89%.Conclusion:There are gray areas in medicine like sacroiliitis. Inter-observer variability can be reduced by AI and deep learning methods. The efficiency and reliability of health services can be increased in this way.References:[1]Jans L, Egund N, Eshed I, Sudoł-Szopińska I, Jurik AG. Sacroiliitis in Axial Spondyloarthritis: Assessing Morphology and Activity. Semin Musculoskelet Radiol. 2018;22: 180–188.[2]B. Arnbak, T. S. Jensen, C. Manniche, A. Zejden, N. Egund, and A. G. Jurik, “Spondyloarthritis-related and degenerative MRI changes in the axial skeleton—an inter- and intra-observer agreement study,”BMC Musculoskeletal Disorders, vol. 14, article 274, 2013.[3]Rueda, Juan C et al. “Interobserver Agreement in Magnetic Resonance of the Sacroiliac Joints in Patients with Spondyloarthritis.”International journal of rheumatology(2017).Image1.Bilateral active sacroiliitis detected automatically by AI model (in right sacroiliac joint 75.6%> (50%), in left sacroiliac joint 65% (>50%))Disclosure of Interests:None declared

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