scholarly journals Convolutional Neural Network Technology in Endoscopic Imaging: Artificial Intelligence for Endoscopy

2020 ◽  
Vol 53 (2) ◽  
pp. 117-126 ◽  
Author(s):  
Joonmyeong Choi ◽  
Keewon Shin ◽  
Jinhoon Jung ◽  
Hyun-Jin Bae ◽  
Do Hoon Kim ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Convolutional Neural Network ◽  
Network Technology ◽  
Endoscopic Imaging ◽  
Neural Network Technology

Using intelligent technologies for tourism

2021 ◽  
pp. 96-105
Author(s):  
Nataliia Lytvyn ◽  
Svitlana Panchenko
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Neural Networks ◽  
Knowledge Management ◽  
Research Methodology ◽  
Network Technology ◽  
Inaccurate Information ◽  
Tourism Product ◽  
The Subject ◽  
Neural Network Technology

The purpose of the article is to explore the essence and features of using intelligent technologies in tourism and to develop proposals for their implementation. The subject of research – intelligent technologies in tourism, the technology of forming the “profile” of the tourist. The research methodology consists in the application of methods of analysis, synthesis, comparison, generalization, forecasting, as well as in the use of systematic, activity approaches. The article presents the technology of forming the “profile” of the tourist. It is established that it is necessary to create a world of tourist models, the “profile” of the tourist, as it is a matter of formalizing such poorly structured concepts as “impressions”, “intentions”, etc., it is necessary to use artificial intelligence technologies, in particular neural networks. The scientific novelty is that this article proves the effectiveness of the use of intelligent technologies to create a model of the tourist, his “profile” using neural networks. Conclusions. Effective using of information from various sources in the field of tourism is an important and difficult task. Managers are often forced to make decisions based on partial, incomplete and inaccurate information. The article considers knowledge management in a rapidly changing environment for the task of promoting a tourism product. Neural network technology allows for the effective formation of the “tourist profile” and use all the information in available databases. Key words: tourism, intelligent technologies for tourism, neural networks, tourist profile, tourist product.

scholarly journals The effect of artificial intelligence technologies on audit evidence

Accounting ◽  
2021 ◽  
pp. 281-288 ◽  
Author(s):  
Saleh Mohammed Al-Sayyed ◽  
Shaher Falah Al-Aroud ◽  
Lena Mustafa Zayed
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Information Technology ◽  
Point Of View ◽  
Network Technology ◽  
Descriptive Research ◽  
Audit Evidence ◽  
Effectiveness And Efficiency ◽  
Neural Network Technology ◽  
Structured Questionnaire

Technologies of Artificial Intelligence (AI) are critical for future of the auditing profession. These technologies are actually vital tools that provide the auditing professionals with the means necessary for increasing the effectiveness and efficiency of their jobs. The aim of this study was to examine the effect of artificial intelligence technologies on audit evidence, from the point of view of certified auditors in information technology (IT) companies in Jordan. Descriptive research design was adopted in the study among 314 auditors. Structured questionnaire was used to obtain the information needed for the study. The findings of the study showed that expert system had a significant effect on the audit evidence. Neural network technology did not provide any significant effect on the audit evidence. The study recommended increased interest in artificial intelligence technologies by audit offices operating in Jordan because of its scientific importance in improving the collection of audit evidence.

scholarly journals Convolutional Neural Network-Based Artificial Intelligence for Classification of Protein Localization Patterns

Biomolecules ◽  
2021 ◽  
Vol 11 (2) ◽  
pp. 264
Author(s):  
Kaisa Liimatainen ◽  
Riku Huttunen ◽  
Leena Latonen ◽  
Pekka Ruusuvuori
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Convolutional Neural Network ◽  
High Throughput ◽  
Protein Function ◽  
Protein Localization ◽  
Convolutional Network ◽  
High Throughput Analysis ◽  
Fully Convolutional Network

Identifying localization of proteins and their specific subpopulations associated with certain cellular compartments is crucial for understanding protein function and interactions with other macromolecules. Fluorescence microscopy is a powerful method to assess protein localizations, with increasing demand of automated high throughput analysis methods to supplement the technical advancements in high throughput imaging. Here, we study the applicability of deep neural network-based artificial intelligence in classification of protein localization in 13 cellular subcompartments. We use deep learning-based on convolutional neural network and fully convolutional network with similar architectures for the classification task, aiming at achieving accurate classification, but importantly, also comparison of the networks. Our results show that both types of convolutional neural networks perform well in protein localization classification tasks for major cellular organelles. Yet, in this study, the fully convolutional network outperforms the convolutional neural network in classification of images with multiple simultaneous protein localizations. We find that the fully convolutional network, using output visualizing the identified localizations, is a very useful tool for systematic protein localization assessment.

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