Artificial Intelligence Deployment Pilot Study at Manufacturing Industry

2020 ◽  
Vol 10 (2) ◽  
pp. 39-46
Author(s):  
Rohit Kenge ◽  
Zafar Khan
Keyword(s):  
Artificial Intelligence ◽  
Pilot Study ◽  
Manufacturing Industry

Artificial Intelligence as a Technological Innovation to Accelerate Economic Development

2019 ◽  
Vol 12 (3) ◽  
pp. 125-133
Author(s):  
S. V. Shchurina ◽  
A. S. Danilov
Keyword(s):  
Artificial Intelligence ◽  
Economic Development ◽  
Technological Innovation ◽  
Manufacturing Industry ◽  
New Technologies ◽  
Retail Trade ◽  
High Tech ◽  
Industrial Complex ◽  
Technological Gap ◽  
The Government

The subject of the research is the introduction of artificial intelligence as a technological innovation into the Russian economic development. The relevance of the problem is due to the fact that the Russian market of artificial intelligence is still in the infancy and the necessity to bridge the current technological gap between Russia and the leading economies of the world is coming to the forefront. The financial sector, the manufacturing industry and the retail trade are the drivers of the artificial intelligence development. However, company managers in Russia are not prepared for the practical application of expensive artificial intelligence technologies. Under these circumstances, the challenge is to develop measures to support high-tech projects of small and medium-sized businesses, given that the technological innovation considered can accelerate the development of the Russian economy in the energy sector fully or partially controlled by the government as well as in the military-industrial complex and the judicial system.The purposes of the research were to examine the current state of technological innovations in the field of artificial intelligence in the leading countries and Russia and develop proposals for improving the AI application in the Russian practices.The paper concludes that the artificial intelligence is a breakthrough technology with a great application potential. Active promotion of the artificial intelligence in companies significantly increases their efficiency, competitiveness, develops industry markets, stimulates introduction of new technologies, improves product quality and scales up manufacturing. In general, the artificial intelligence gives a new impetus to the development of Russia and facilitates its entry into the five largest world’s economies.

Endoscopic prediction of submucosal invasion in Barrett’s cancer with the use of Artificial Intelligence: A pilot Study

Endoscopy ◽  
2020 ◽  
Author(s):  
Alanna Ebigbo ◽  
Robert Mendel ◽  
Tobias Rückert ◽  
Laurin Schuster ◽  
Andreas Probst ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Deep Learning ◽  
Pilot Study ◽  
White Light ◽  
Submucosal Invasion ◽  
Learning System ◽  
Endoscopic Images ◽  
Barrett’S Cancer ◽  
Significant Difference ◽  
Sensitivity Specificity

Background and aims: The accurate differentiation between T1a and T1b Barrett’s cancer has both therapeutic and prognostic implications but is challenging even for experienced physicians. We trained an Artificial Intelligence (AI) system on the basis of deep artificial neural networks (deep learning) to differentiate between T1a and T1b Barrett’s cancer white-light images. Methods: Endoscopic images from three tertiary care centres in Germany were collected retrospectively. A deep learning system was trained and tested using the principles of cross-validation. A total of 230 white-light endoscopic images (108 T1a and 122 T1b) was evaluated with the AI-system. For comparison, the images were also classified by experts specialized in endoscopic diagnosis and treatment of Barrett’s cancer. Results: The sensitivity, specificity, F1 and accuracy of the AI-system in the differentiation between T1a and T1b cancer lesions was 0.77, 0.64, 0.73 and 0.71, respectively. There was no statistically significant difference between the performance of the AI-system and that of human experts with sensitivity, specificity, F1 and accuracy of 0.63, 0.78, 0.67 and 0.70 respectively. Conclusion: This pilot study demonstrates the first multicenter application of an AI-based system in the prediction of submucosal invasion in endoscopic images of Barrett’s cancer. AI scored equal to international experts in the field, but more work is necessary to improve the system and apply it to video sequences and in a real-life setting. Nevertheless, the correct prediction of submucosal invasion in Barret´s cancer remains challenging for both experts and AI.

scholarly journals VR FOR ASSEMBLY TASKS IN THE MANUFACTURING INDUSTRY – INTERACTION AND BEHAVIOUR

2020 ◽  
Vol 1 ◽  
pp. 1697-1706
Author(s):  
Y. Eriksson ◽  
M. Sjölinder ◽  
A. Wallberg ◽  
J. Söderberg
Keyword(s):  
Pilot Study ◽  
Real World ◽  
Manufacturing Industry ◽  
The Real ◽  
Assembly Tasks

AbstractA testbed was developed aiming to contribute to further knowledge on what is required from a VR application in order to be useful for planning of assembly tasks. In a pilot study the testbed was tested on students. The focus of the study was to explore the users’ behaviour, and to gain a better understanding of their experience using VR. The students experienced a gap between the real world and VR, which confirms theories that VR is not a copy or twin of an object or environment.

scholarly journals Research on machine learning Algorithm optimization based on 0-1 coding

2021 ◽  
Vol 2083 (4) ◽  
pp. 042086
Author(s):  
Yuqi Qin
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Manufacturing Industry ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Algorithm Optimization ◽  
The Core ◽  
Coding Method ◽  
Discrete Manufacturing

Abstract Machine learning algorithm is the core of artificial intelligence, is the fundamental way to make computer intelligent, its application in all fields of artificial intelligence. Aiming at the problems of the existing algorithms in the discrete manufacturing industry, this paper proposes a new 0-1 coding method to optimize the learning algorithm, and finally proposes a learning algorithm of “IG type learning only from the best”.

scholarly journals Perancangan Aplikasi Penjadwalan Traveling Salesman Problem dengan Algoritma Genetika

2012 ◽  
Vol 3 (2) ◽  
pp. 977
Author(s):  
Hendy Tannady ◽  
Andrew Verrayo Limas
Keyword(s):  
Artificial Intelligence ◽  
Manufacturing Industry ◽  
Travelling Salesman Problem ◽  
Traveling Salesman ◽  
Operational Cost ◽  
Travelling Salesman ◽  
Optimal Sequence ◽  
Chain Management ◽  
Efficiency And Effectiveness ◽  
A Company

Supply chain management plays an important role in enhancing the efficiency and effectiveness of manufacturing industry business process. In this research, the problem is taken from a sales division in a company in determining the optimal sequence when delivering goods into nine cities. This problem is oftenreferred as travelling salesman problem. This problem is considered important since the optimal sequence can cut off operational cost. Creating an artificial intelligence for the company in determining the location and the optimal sequence of delivering goods is the main objective of this research. A genetic algorithm is utilized to determine the location and the optimal sequence. While for processing the data and concluding the result, researcher designed a Java-based application that provides the capability of automatic computing. The result of this computation is a sequence of locations with a fitness number for each. The best fitness number for the sequence location will be used for the final result and the conclusion to answer the company’s problem.

Time to Incorporate Artificial Intelligence into High-Fidelity Patient Simulators for Nursing Education: A Secondary Analysis of a Pilot Study

2021 ◽  
pp. 227-236
Author(s):  
Angelo Dante ◽  
Carmen La Cerra ◽  
Luca Bertocchi ◽  
Vittorio Masotta ◽  
Alessia Marcotullio ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Nursing Education ◽  
Pilot Study ◽  
Secondary Analysis ◽  
High Fidelity ◽  
Patient Simulators

The prioritization of technologies and public R&D roles between the manufacturing and service industries in the fourth industrial revolution

foresight ◽  
2019 ◽  
Vol 21 (6) ◽  
pp. 680-694 ◽  
Author(s):  
Jinwon Kang ◽  
Jong-Seok Kim ◽  
Seonmi Seol
Keyword(s):  
Artificial Intelligence ◽  
Manufacturing Industry ◽  
Market Failure ◽  
Industrial Revolution ◽  
Service Industry ◽  
Service Industries ◽  
Delphi Survey ◽  
Smart Factory ◽  
Content Type ◽  
Fourth Industrial Revolution

Purpose The purpose of this study is to reveal the similarities and differences between the manufacturing and service industries in their prioritization of technologies and public research and development (R&D) roles, along with the complementation of properties of technology and public R&D role in the context of Fourth Industrial Revolution. Design/methodology/approach Two rounds of Delphi surveys were designed to meet the purpose of this study, which used rigorous triangulation techniques. The Delphi method was combined with the brainstorming method in the first-round Delphi survey, while the second-round Delphi survey focused on experts’ judgments. Finally, language network analysis was performed on the properties of technology and public R&D roles to complement the data analyses regarding prioritization. Findings This study identifies different prioritizations of five similar key technologies in each industry, so that it can note different technological impacts to the two industries in the Fourth Industrial Revolution. Smart factory technology is the first priority in the manufacturing industry, whereas artificial intelligence is the first priority in the service industry. The properties of the three common technologies: artificial intelligence, big data and Internet of things in both industries are summarized in hyper-intelligence on hyper-connectivity. Moreover, it is found that different technological priorities in the service and manufacturing industries require different approaches to public R&D roles, while public R&D roles cover market failure, system failure and government failure. The highest priority public R&D role for the service industry is the emphasis of non-R&D roles. Public R&D role to solve dy-functions, focus basic technologies and support challenging areas of R&D is prioritized at the highest for the manufacturing industry. Originality/value This study of the different prioritizations of technologies in the manufacturing and service industries offers practical lessons for executive officers, managers and policy-makers. They, by noting the different technological impacts in the manufacturing and service industries, can prepare for current actions and establish the priority of technology for R&D influencing the future paths of their industries in the context of the Fourth Industrial Revolution. While managers in the service industry should pay greater attention to the technological content of hyper-intelligence and hyper-connectivity, managers in the manufacturing industry should consider smart factory and robot technology.

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