Intelligent Services Development Technology Using the IACPaaS Cloud Platform

Грибова, В. В.; Moskalenko, Philip; Timchenko, V. A.; Шалфеева, Елена

doi:10.1007/978-3-031-15882-7_2

Cited by 5 publications

(3 citation statements)

References 14 publications

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“…The results of the research will be accumulated and used in the knowledge portal of technological processes (operations) of powder cladding with the use of laser technological equipment. This knowledge portal was created on the IACPaaS cloud platform [11,12] and is based on the concept of decision support software for laser powder cladding processes, which is briefly presented in the paper. To date, a number of information and software components of the knowledge portal have been developed, and work is continuing to create new components and improve existing ones.…”

Section: Discussionmentioning

confidence: 99%

Features of the Structure of the Material Formed from AK4-1 Powder Using Laser Energy

Kulchin

Никифоров

Pivovarov

et al. 2021

KEM

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The paper presents the results of studying the microstructure of samples obtained with laser processing of AK4-1 aluminum alloy powder. It has been found that increasing the initial temperature of the substrate to 100...150 °C allows to use this powder material in directed energy deposition (DED) additive process to create stiffening elements on the surface of thin-walled aluminum parts. When the specified temperature condition is satisfied, the crystallization rate decreases, allowing to obtain samples with almost no internal cracks. The results of comparing the microstructure and the microhardness of commercial AK4-1 wrought alloy and the samples obtained with DED process are presented. The structure of the samples prepared with laser cladding is more disperse in comparison with wrought alloy. The microhardness of the sample prepared at pre-heated substrate is comparable with wrought alloy. The conceptual architecture of the decision support software for laser powder cladding processes is presented. Its information and software components are briefly described.

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Section: Discussionmentioning

confidence: 99%

Features of the Structure of the Material Formed from AK4-1 Powder Using Laser Energy

Kulchin

Никифоров

Pivovarov

et al. 2021

KEM

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“…The technologies used for this are determined by the HIS being used. For managing knowledge bases, the IACPaaS cloud platform is used [21,22]. The IACPaaS platform allows the creation of formalized knowledge bases, describing prognostic and diagnostic models, their interpretation, clinical recommendations, explanations, etc.…”

Section: Technologiesmentioning

confidence: 99%

“…Contemporary research is characterized by the widespread use of machine learning methods for diagnosing and predicting the development of diseases and their complications [19,21,26]. The use of data-driven machine learning models is a natural extension of CDSS capabilities [30].…”

Section: Introductionmentioning

confidence: 99%

Hybrid clinical decision support for cardiology: architectural foundations for integrations

Shakhgeldyan,

Gribova,

Geltser

et al. 2024

Fifth International Conference on Image, Video Processing, and Artificial Intelligence (IVPAI 2023)

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Cardiovascular diseases (CVD) are the leading cause of death in most countries around the world, making the accurate assessment of risks and the selection of individual preventive strategies a current focus in healthcare. In this article, the authors presented a prototype of a Clinical Decision Support System (CDSS) for predicting and preventing cardiovascular risks based on a hybrid architecture that integrates machine learning models and ontological knowledge bases. A microservice architecture based on the Cloud-Edge approach is proposed for optimizing computational resources when processing tabular data, signals, video, and images, as well as for enhancing the effectiveness of integration with various Healthcare Information Systems (HIS). The CDSS supports the formalization not only of medical history data and results of studies but also the rules for interpreting the results of predictions based on machine learning models and methods of explainable artificial intelligence (XAI). The developed CDSS includes widely used tools in clinical cardiology and cardiothoracic surgery for risk assessment, as well as proprietary machine learning models for predicting in-hospital mortality, and others. These models contribute to making informed medical decisions for the diagnosis, prevention, and treatment of CVD. The prototype was implemented at the Medical Center of the Far Eastern Federal University and integrated with the "1C" HIS. The experience of implementing the prototype demonstrated the high potential of hybrid CDSS based on microservice architecture for use in clinical practice.

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