Wyniki wyszukiwania - Biblioteka Nauki

1

Expert systems in assessing the construction process safety taking account of the risk of disturbances

100%

Siemaszko A. , Jakubczyk-Galczynska A. , Grzyl B.

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nr 3(189)

218-227

EN

The objective of the paper is to present the issue of safety management during the construction process. Threats in the form of disturbances may occur in the preparatory phase, during the execution of the construction project and also during its operational use. The article presents the concept of applying the methodology based, among others, on Learning Bayesian Networks, Artificial Neural Networks and Support Vector Machine, which can be used for building a system for diagnostic and decision-making support at each stage of the construction process. The use of expert systems when it comes to making choices related to construction issues can bring many benefits to decision-makers, as it reduces the risk of taking a wrong decision, and, thus, increases the construction process safety.

2

Sztuczna inteligencja w systemach wykrywania intruzów

100%

Lach E.

|

2003

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tom Vol. 24, nr 2A

191-198

PL

W pracy przedstawiono wybrane techniki sztucznej inteligencji wykorzystywane w systemach wykrywania intruzów. Artykuł zawiera opis sieci neuronowych, odkrywania wiedzy, logiki rozmytej, algorytmów genetycznych, programowania genetycznego oraz sztucznych systemów immunologicznych.

EN

The paper survey Artificial Intelligence techniques used in Intrusion Detection systems. Several approaches to intrusion detection were described including: neural networks, data mining, fuzzy logic, genetic algorithms, genetic programming and artificial immune systems.

3

Some Applications of Cellular Automata in Learning Systems Constructions

100%

Borkowski P.

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tom Vol. 10

5--16

EN

Today the von Neumann invention of cellular automata is one of import and tools of artificial intelligence research. Cellular automata show their usage in simultaneous calculations, creating data bases and simulating physical processes. The goal of this article is an introduction to the learning system called CELLS, which marnner of work is directly connected with the cellular automata evolution rules.

4

Expert systems

100%

Pietraszek K.

|

2009

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tom Vol. 2(13)

73--83

EN

Paper presents the key issues relating to the construction of expert systems, their types and uses.

5

Comparison of algorithms for detection of active inflammatory lesions in sacroiliitis

100%

Gawłowski I. , Ożga J. , Raczko A.

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nr 1

188-193

EN

Introduction. Artificial intelligence is increasingly being used in the medicine, particularly in radiological diagnosis of diseases such as an axial spondyloarthritis (axSpA). The aim of this study is to compare the available algorithms designed to detect active sacroiliitis in patients with axSpA. Material and methods. Four algorithms, two semi-automated and two full-automated for the assessment of bone marrow edema (BME) on magnetic resonance imaging (MRI) of the sacroiliac joints (SIJs) were included in the study. They were described and compared in terms of specificity, sensitivity, and correlation of BME detection findings between AI and experts. Analysis of the literature. Among all automated algorithms, the one created by Bressem et al. had the highest number of examinations analyzed in the study, involving 593 MRIs of SIJs. The sensitivity and specificity, as well as the correlation between the AI’s detection of BME versus manual, were not calculated for each algorithm. Rzecki’s algorithm had the greatest sensitivity and specificity for BME detection reaching 0.95 and 0.96, respectively. In addition, its Speraman’s coefficient of correlation between manual and automated measurements was 0.866. Conclusion. Each of described algorithms is certainly useful in assessing BME in the MRI examinations of SIJs.

6

The use of generative models to speed up the discovery of materials

100%

Coto A. G. , Precker C. E. , Andersson T. , Laukkanen A. , Suhonen T. , Rodriguez P. R. , Muíños-Landín S.

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tom Vol. 23, No. 1

13--26

EN

Material Science is a key factor in the evolution of many industrial sectors. Fields such as the aeronautics, automotive, construction, and biotechnology industries have experienced tremendous development with the introduction of advanced, high-performance materials. Such materials not only provide new functionalities to products, but also significant consequences in terms of economic and environmental sustainability of the products and processes triggered by the more efficient use of energy that they provide. Under this scenario, materials that provide such high performance, such as high entropy alloys (HEAs) or polymer derived ceramics (PDCs), have captured the attention of both industry and researchers in recent years. However, the remarkable number of resources required to develop such materials, from its design phase to its synthesis and characterization, means that the discovery of new high-performance materials is moving at a relatively low pace. This fact places emergent strategies based on artificial intelligence (AI) for the design of materials in a good position to be used to accelerate the whole process, providing an impulse in the initial phases of materials design. The enormous number of combinations of elements and the complexity of synthesizability conditions of HEAs and PDCs respectively, paves the way to the deployment of AI techniques such as Generative Models addressed in this work to create synthetic HEAs and PDCs for highly intensive industrial processes. A specific conditional tabular generative adversarial network (CTGAN) was developed to be used on tabular data to generate novel synthetic compounds for each kind of material. The generated synthetic data was based on the conventional parametric design parameters used for HEAs and PDCs, with specific datasets created for them. The real and generated data are compared, calculation of phase diagrams (CALPHAD) simulations are provided to evaluate the performance of the generated samples and a verification of the novel generated compositions is done in open materials databases available in the literature.

7

Expert systems in assessing the construction process safety taking account of the risk of disturbances

100%

Siemaszko A. , Jakubczyk-Galczynska A. , Grzyl B.

|

tom Vol. 50, No. 3(189)

218--227

EN

The objective of the paper is to present the issue of safety management during the construction process. Threats in the form of disturbances may occur in the preparatory phase, during the execution of the construction project and also during its operational use. The article presents the concept of applying the methodology based, among others, on Learning Bayesian Networks, Artificial Neural Networks and Support Vector Machine, which can be used for building a system for diagnostic and decision-making support at each stage of the construction process. The use of expert systems when it comes to making choices related to construction issues can bring many benefits to decision-makers, as it reduces the risk of taking a wrong decision, and, thus, increases the construction process safety.

8

Artificial intelligence methods in diagnostics of analog systems

100%

Bilski P. , Wojciechowski J.

|

nr 2

271-282

EN

The paper presents the state of the art and advancement of artificial intelligence methods in analog systems diagnostics. Firstly, the diagnostic domain is introduced and its problems explained. Then, computational intelligence approaches usable for fault detection and identification are reviewed. Particular groups of methods are presented in detail, explaining their usefulness and drawbacks. Examples, such as the induction motor or the electronic filter, are provided to show the applicability of the presented approaches for monitoring the state of analog objects from engineering domains. The discussion section reviews the presented approaches, their future prospects and problems to be solved.

9

Logistyczne systemy informatyczne wykorzystujące sztuczną inteligencję w branży motoryzacyjnej

100%

Stawiarska E.

|

tom nr 4

101--119

PL

Artykuł podzielono na rozdziały zawierające opisy działania inteligentnych systemów wspomagających/zastępujących pracę logistyka w systemach zaopatrzenia, produkcji, dystrybucji oraz w integrujących się metasystemach. Do każdego z rozdziałów przygotowano podstawowe definicje oraz przykłady firm, które wykorzystują sztuczną inteligencję. W rozdziale pierwszym opisano firmę TRW Automotive – firma zarządza procesami produkcyjnymi z zastosowaniem AI (Artificial Intelligenss) opartej na koncepcji Six Sigma. W rozdziale drugim opisano możliwość pobierania nieustrukturalizowanych danych do zintegrowanych systemów zarządzania klasy ERP (z wykorzystaniem techniki ETL) w firmie General Motors. Również w rozdziale drugim zaprezentowano funkcje bota ALICE wspomagającego zarządzanie łańcuchami logistycznymi. Rozdział trzeci poświęcony jest AI wykorzystywanej w motoryzacyjnych sieciach dystrybucji marki BMW. Omówiono koncepcję Internetu rzeczy i działanie becon-ów. Czwarty rozdział poświęcony jest sztucznej inteligencji wspomagającej powoływanie i koordynowanie dynamicznych sieci dostaw, wykorzystującej oprogramowanie stworzone na podstawie teorii CAS (Złożonych Systemów Adaptacyjnych – Complex Adaptive Systems). Przewodnim celem artykułu było pokazanie trendów w rozwoju AI na potrzeby zarządzania logistyką w organizacji i sieci współpracujących przedsiębiorstw.

EN

The article consists of four chapters: Intelligent systems in production logistics; Intelligent transport logistics in supply chain management; Intelligent logistics and transportation management supporting distribution system; Intelligent systems supporting organizing and coordinating the work of the dynamic supply network. In each of the chapters there is the theory part and examples of companies that use artificial intelligence. In the first chapter the TRW Automotive Company was described, which manages production processes using Artificial Intelligence (AI) based on the concept of Six Sigma. In the second chapter the ability to retrieve unstructured data to integrated management systems e.g. ERP (using techniques ETL), was described based on the practice at General Motors. Also in the second chapter the ALICE bot functions supporting the management of logistics chains was presented. The third chapter is devoted to AI used in the automotive distribution networks of the BMW brand. As well as the concept of the Internet of Things and operation of the beacon technology was discussed. The fourth chapter is devoted to artificial intelligence supporting the establishment and coordination of the dynamic supply network, using software developed based on the theory of CAS (Complex Adaptive Systems). The aim of this article was to show trends in the development of AI to manage logistics in the organization and a network of cooperating companies.

10

A Survey on Nature-Inspired Medical Image Analysis : A Step Further in Biomedical Data Integration

100%

Rundo L. , Militello C. , Vitabile S. , Russo G. , Sala E. , Gilardi M. C.

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tom Vol. 171, nr 1-4

345--365

EN

Natural phenomena and mechanisms have always intrigued humans, inspiring the design of effective solutions for real-world problems. Indeed, fascinating processes occur in nature, giving rise to an ever-increasing scientific interest. In everyday life, the amount of heterogeneous biomedical data is increasing more and more thanks to the advances in image acquisition modalities and high-throughput technologies. The automated analysis of these large-scale datasets creates new compelling challenges for data-driven and model-based computational methods. The application of intelligent algorithms, which mimic natural phenomena, is emerging as an effective paradigm for tackling complex problems, by considering the unique challenges and opportunities pertaining to biomedical images. Therefore, the principal contribution of computer science research in life sciences concerns the proper combination of diverse and heterogeneous datasets — i.e., medical imaging modalities (considering also radiomics approaches), Electronic Health Record engines, multi-omics studies, and real-time monitoring — to provide a comprehensive clinical knowledge. In this paper, the state-of-the-art of nature-inspired medical image analysis methods is surveyed, aiming at establishing a common platform for beneficial exchanges among computer scientists and clinicians. In particular, this review focuses on the main nature-inspired computational techniques applied to medical image analysis tasks, namely: physical processes, bio-inspired mathematical models, Evolutionary Computation, Swarm Intelligence, and neural computation. These frameworks, tightly coupled with Clinical Decision Support Systems, can be suitably applied to every phase of the clinical workflow. We show that the proper combination of quantitative imaging and healthcare informatics enables an in-depth understanding of molecular processes that can guide towards personalised patient care.

11

A Novel Digital Service Taxation & Sustainability Legal Framework Utilizing Artificial Intelligence Analysis of Subsea Cable Data Transmissions

100%

Katterbauer K.

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nr 1

146-157

EN

The digital economy has led to massive changes in the economy and international trading, where user data have become the cornerstone of new business models. Digital services have become transformational and led to significant revenue generation for these corporations. However, there is a growing perception amongst individuals and governments that these digital services are not taxed fairly, given the ability of companies to shift profits between different countries. Digital service taxes have recently become very attractive and implemented in a variety of countries, but significant challenges remain. Artificial intelligence has become an attractive way of determining patterns across data and has been increasingly utilized in legal environments. I will outline a new legal framework for the integration of artificial intelligence for the determination of digital service taxes and outline the integration of subsea cable communication data into the framework. Furthermore, I will address the legal environmental challenges, specifically related to the South China Sea, and how cost associated with can be incorporated into the digital service tax environment.

12

Computability and human symbolic output

100%

Megill J. , Melvin T.

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nr 4

391–401

EN

This paper concerns “human symbolic output,” or strings of characters produced by humans in our various symbolic systems; e.g., sentences in a natural language, mathematical propositions, and so on. One can form a set that consists of all of the strings of characters that have been produced by at least one human up to any given moment in human history. We argue that at any particular moment in human history, even at moments in the distant future, this set is finite. But then, given fundamental results in recursion theory, the set will also be recursive, recursively enumerable, axiomatizable, and could be the output of a Turing machine. We then argue that it is impossible to produce a string of symbols that humans could possibly produce but no Turing machine could. Moreover, we show that any given string of symbols that we could produce could also be the output of a Turing machine. Our arguments have implications for Hilbert’s sixth problem and the possibility of axiomatizing particular sciences, they undermine at least two distinct arguments against the possibility of Artificial Intelligence, and they entail that expert systems that are the equals of human experts are possible, and so at least one of the goals of Artificial Intelligence can be realized, at least in principle.

13

The Prediction of Optimized Metalloid Content in Fe-Si-B-P Amorphous Alloys Using Artificial Intelligence Algorithm

88%

Lee M. W. , Choi Y.-S. , Kwon D.-H. , Cha E.-J. , Kang H.-B. , Jeong J.-I. , Lee S.-J. , Kim H.-J.

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tom Vol. 67, iss. 4

1539--1542

EN

Artificial intelligence operated with machine learning was performed to optimize the amount of metalloid elements (Si, B, and P) subjected to be added to a Fe-based amorphous alloy for enhancement of soft magnetic properties. The effect of metalloid elements on magnetic properties was investigated through correlation analysis. Si and P were investigated as elements that affect saturation magnetization while B was investigated as an element that affect coercivity. The coefficient of determination R2 (coefficient of determination) obtained from regression analysis by learning with the Random Forest Algorithm (RFR) was 0.95 In particular, the R2 value measured after including phase information of the Fe-Si-B-P ribbon increased to 0.98. The optimal range of metalloid addition was predicted through correlation analysis method and machine learning.

14

Stakeholder-accountability model for artificial intelligence projects

88%

Miller G. J.

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tom 44

EN

Aim/purpose – This research presents a conceptual stakeholder accountability model for mapping the project actors to the conduct for which they should be held accountable in artificial intelligence (AI) projects. AI projects differ from other projects in important ways, including in their capacity to inflict harm and impact human and civil rights on a global scale. The in-project decisions are high stakes, and it is critical who decides the system’s features. Even well-designed AI systems can be deployed in ways that harm individuals, local communities, and society. Design/methodology/approach – The present study uses a systematic literature review, accountability theory, and AI success factors to elaborate on the relationships between AI project actors and stakeholders. The literature review follows the preferred reporting items for systematic reviews and meta-analyses (PRISMA) statement process. Bovens’ accountability model and AI success factors are employed as a basis for the coding framework in the thematic analysis. The study uses a web-based survey to collect data from respondents in the United States and Germany employing statistical analysis to assess public opinion on AI fairness, sustainability, and accountability. Findings – The AI stakeholder accountability model specifies the complex relationships between 16 actors and 22 stakeholder forums using 78 AI success factors to define the conduct and the obligations and consequences that characterize those relationships. The survey analysis suggests that more than 80% of the public thinks AI development should be fair and sustainable, and it sees the government and development organizations as most accountable in this regard. There are some differences between the United States and Germany regarding fairness, sustainability, and accountability. Research implications/limitations – The results should benefit project managers and project sponsors in stakeholder identification and resource assignment. The definitions offer policy advisors insights for updating AI governance practices. The model presented here is conceptual and has not been validated using real-world projects. Originality/value/contribution – The study adds context-specific information on AI to the project management literature. It defines project actors as moral agents and provides a model for mapping the accountability of project actors to stakeholder expectations and system impacts.

15

Sztuczna inteligencja a inteligencja moralna. Zagadnienia wstępne cybernetyki

88%

Szulczewski G.

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nr 3

19-31

EN

We live in a time when artificial intelligence is being used in more and more areas of our lives. Its use in the sphere of management has become a big problem. Should we comply with the judgments of the cold rationality of artificial intelligence or, by contrast, are we able to realize the limits of its application? The question should be asked: why can good management not be limited to solutions presented by artificial intelligence? In order to answer this question, one should show what the difference is between artificial intelligence and moral intelligence, and what the use of moral intelligence in management is. The role of ethics in the decision-making process will be shown. The final conclusion is that artificial intelligence will never replace man in management.

16

The use of artificial intelligence in assessing a bank customer’s debt capacity

88%

Chłopecka A.

|

tom 101

73-87

EN

The purpose of this article is to discuss the issue of financial institutions, and especially banks, using artificial intelligence algorithms to assess the debt capacity of their potential borrowers. The author presents the view that the regulations currently in place are insufficient. In particular, there are no provisions in place to sufficiently protect the interests of bank customers. Additionally, the author considers what claims bank customers could have in the event that an algorithm made an incorrect assessment of their creditworthiness.

17

ChatGPT in hospital infection prevention and control – assessing knowledge of an AI model based on a validated questionnaire

88%

Jawanpuria A. , Behera A. R. , Dash C. , Hifz Ur Rahman M.

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nr 2

347-352

EN

Introduction and aim. An AI model like ChatGPT is a good source of knowledge. We can explore the potential of AI models to complement the expertise of healthcare professionals by providing real-time, evidence-based information in infection prevention and control (IPC). Material and methods. This study involved 110 queries related to IPC, validated by subject experts in IPC. The responses from ChatGPT were evaluated using Bloom’s taxonomy by experienced microbiologists. The scores were divided as <3 as being a poor response, 3–4 as an average response, and >4 as a good response. Statistical analysis was done by correlation coefficient and Cohen’s Kappa. Results. The overall score was 4.33 (95% CI, q1 3.65–q3 4.64) indicating ChatGPT’s substantial IPC knowledge. A good response (i.e.>4 score) was found in 70 (63.6%) questions, while in 10 (9%) questions, it showed a poor response. The poor response was seen in needle stick injury and personal protective equipment (PPE) doffing-related questions. The overall correlations were found to be significant. Cohen’s Kappa confirmed moderate to substantial agreement between evaluators. Conclusion. ChatGPT demonstrated a commendable understanding of IPC principles in various domains and the study identifies specific instances where the model may require further refinement especially in critical scenarios such as needlestick injuries and PPE doffing.

18

Implementation in the exploitation domain

88%

Pająk M. , Woropay M.

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tom Vol. 13, No. 4

35-39

EN

The optimization issues are very often met in the area of industrial exploitation systems management. The solution space of the optimization process expands because of the production systems complexity. Frequently, there is no possibility to find the accurate solution of the problem. Sometimes, in case of real industrial systems it is not necessary to find the optimal solution. Very often the suboptimal one is sufficient. In the paper the genetic algorithms are proposed as a tool for optimization process of the exploitation issues. Thanks to it, the computerised expert systems could be created. The implementation of the systems in the area of exploitation management could increase the quality of the process. Optimization characteristic of exploitation processes, the possibility of optimisation implementation, genetic algorithms characteristic, service graph critical path searching, optimisation of power units maintenance scheduling process are presented in the paper. Within the framework of work a real industrial issue of the service processes optimisation the genetic algorithms have been proposed.

19

Estimation of operational potential consumption on base of exploitation parameters values

88%

Pająk M. , Woropay M.

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tom Vol. 13, No. 3

259-262

EN

The using processes are carried out on a machine during its exploitation phase. It decreases an operational potential value. Because of this it is necessary to carry out the maintenance processes. When the maintenance processes are executed as an exchange of elements, not exploited operational potential increases the costs of the exploitation process of a machine. In case of real industrial objects there is defined value of availability, which should be kept. Then the highest value of the exploited operational potential assures an implementation of a maintenance strategy according to the machine operational condition. In the paper, the idea of an implementation of the mentioned above strategy is presented. The implementation is based on measured values of the exploitation parameters. An estimation of an operational potential consumption and a fuzzy estimation of the exploitation potential consumption are an object of the paper. Implementation of the fuzzy logic theory could be good solution of the problems, which can be meet during the estimation process of the operational potential consumption on base of the exploitation parameters course of the machine.

20

Zastosowanie sztucznej inteligencji w diagnostyce procesów przetwórczych

88%

Langman J.

|

tom R. 10, nr 13(88)

279-285

PL

W artykule przedstawiono podstawowe schematy diagnostyczne złożonych procesów przetwórczych produktów rolniczych. Z uwagi na silnie nieliniowe związki pomiędzy wejściem i wyjściem danego procesu przetwórczego, do opracowania modułu wnioskowania diagnostycznego procesu można wykorzystać metody sztucznej inteligencji.

EN

The article presents basic diagnostic diagrams of complex farm produce processing. Due to heavily non-linear relations between the input and output of the particular process, for developing a process diagnostic inferencing module methods of artificial intelligence can be used.