QoS routing in cluster OLSR by using the artificial intelligence model MSSP in the big data environnment

The most complex problems, in data science and more specifically in artificial intelligence, can be modeled as cases of the maximum stable set problem (MSSP). this article describes a new approach to solve the MSSP problem by proposing the continuous hopfield network (CHN) to build optimized link state protocol routing (OLSR) protocol cluster. our approach consists in proposing in two stages: the first acts at the level of the choice of the OLSR master cluster in order to quickly make a local minimum using the CHN, by modeling the MSSP problem. As for the second step, the objective is the improvement of the precision making a solution of efficient at the first rank of neighborhood as a linear constraint, and at the end, to find the resolution of the model using the CHN. We will show that this model determines a good solution of the MSSP problem. To test the theoretical results, we propose a comparison with a classic OLSR.

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About digital transformation of the transport industry in Russia

Scientific notes of the Russian academy of entrepreneurship ◽

10.24182/2073-6258-2020-19-3-95-105 ◽

2020 ◽

Vol 19 (3) ◽

pp. 95-105

Author(s):

Natalia V. Vysotskaya ◽

T. V. Kyrbatskaya

Keyword(s):

Artificial Intelligence ◽

Business Model ◽

Data Science ◽

Digital Transformation ◽

Digital Culture ◽

Blockchain Technology ◽

Community Approach ◽

Non Linear ◽

Transport Industry ◽

Artificial Intelligence Systems

The article is devoted to the consideration of the main directions of digital transformation of the transport industry in Russia. It is proposed in the process of digital transformation to integrate the community approach into the company's business model using blockchain technology and methods and results of data science; complement the new digital culture with a digital team and new communities that help management solve business problems; focus the attention of the company's management on its employees and develop those competencies in them that robots and artificial intelligence systems cannot implement: develop algorithmic, computable and non-linear thinking in all employees of the company.

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Data and research in the era of artificial intelligence and advanced data science (NSF)

Federal Grants & Contracts ◽

10.1002/fgc.31450 ◽

2020 ◽

Vol 45 (2) ◽

pp. 3-3

Keyword(s):

Artificial Intelligence ◽

Data Science

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Polyhedral approximations of the semidefinite cone and their application

Computational Optimization and Applications ◽

10.1007/s10589-020-00255-2 ◽

2021 ◽

Author(s):

Yuzhu Wang ◽

Akihiro Tanaka ◽

Akiko Yoshise

Keyword(s):

Initial Approximation ◽

Semidefinite Relaxation ◽

Stable Set ◽

Cutting Plane Methods ◽

Diagonally Dominant Matrices ◽

Maximum Stable Set ◽

Simple Expansion ◽

Diagonally Dominant ◽

Polyhedral Approximations ◽

Semidefinite Cone

AbstractWe develop techniques to construct a series of sparse polyhedral approximations of the semidefinite cone. Motivated by the semidefinite (SD) bases proposed by Tanaka and Yoshise (Ann Oper Res 265:155–182, 2018), we propose a simple expansion of SD bases so as to keep the sparsity of the matrices composing it. We prove that the polyhedral approximation using our expanded SD bases contains the set of all diagonally dominant matrices and is contained in the set of all scaled diagonally dominant matrices. We also prove that the set of all scaled diagonally dominant matrices can be expressed using an infinite number of expanded SD bases. We use our approximations as the initial approximation in cutting plane methods for solving a semidefinite relaxation of the maximum stable set problem. It is found that the proposed methods with expanded SD bases are significantly more efficient than methods using other existing approximations or solving semidefinite relaxation problems directly.

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Medical artificial intelligence readiness scale for medical students (MAIRS-MS) – development, validity and reliability study

BMC Medical Education ◽

10.1186/s12909-021-02546-6 ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Ozan Karaca ◽

S. Ayhan Çalışkan ◽

Kadir Demir

Keyword(s):

Artificial Intelligence ◽

Factor Analysis ◽

Medical Students ◽

Data Science ◽

Factor Model ◽

Health Science ◽

Measurement Tool ◽

Validity And Reliability ◽

Mathematical Concepts ◽

Evaluation And Monitoring

Abstract Background It is unlikely that applications of artificial intelligence (AI) will completely replace physicians. However, it is very likely that AI applications will acquire many of their roles and generate new tasks in medical care. To be ready for new roles and tasks, medical students and physicians will need to understand the fundamentals of AI and data science, mathematical concepts, and related ethical and medico-legal issues in addition with the standard medical principles. Nevertheless, there is no valid and reliable instrument available in the literature to measure medical AI readiness. In this study, we have described the development of a valid and reliable psychometric measurement tool for the assessment of the perceived readiness of medical students on AI technologies and its applications in medicine. Methods To define medical students’ required competencies on AI, a diverse set of experts’ opinions were obtained by a qualitative method and were used as a theoretical framework, while creating the item pool of the scale. Exploratory Factor Analysis (EFA) and Confirmatory Factor Analysis (CFA) were applied. Results A total of 568 medical students during the EFA phase and 329 medical students during the CFA phase, enrolled in two different public universities in Turkey participated in this study. The initial 27-items finalized with a 22-items scale in a four-factor structure (cognition, ability, vision, and ethics), which explains 50.9% cumulative variance that resulted from the EFA. Cronbach’s alpha reliability coefficient was 0.87. CFA indicated appropriate fit of the four-factor model (χ2/df = 3.81, RMSEA = 0.094, SRMR = 0.057, CFI = 0.938, and NNFI (TLI) = 0.928). These values showed that the four-factor model has construct validity. Conclusions The newly developed Medical Artificial Intelligence Readiness Scale for Medical Students (MAIRS-MS) was found to be valid and reliable tool for evaluation and monitoring of perceived readiness levels of medical students on AI technologies and applications. Medical schools may follow ‘a physician training perspective that is compatible with AI in medicine’ to their curricula by using MAIRS-MS. This scale could be benefitted by medical and health science education institutions as a valuable curriculum development tool with its learner needs assessment and participants’ end-course perceived readiness opportunities.

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Perceiving at a distance: enaction, exteriority and possibility – a tribute to John Stewart

Adaptive Behavior ◽

10.1177/10597123211031016 ◽

2021 ◽

pp. 105971232110310

Author(s):

Charles Lenay

Keyword(s):

Experimental Study ◽

Perceptual Experience ◽

Spatial Perception ◽

Distant Object ◽

New Approach ◽

Second Stage ◽

The Moment ◽

Two Stages

The aim of this article is to offer a new approach of perception regarding the position of a distant object. It is also a tribute to John Stewart who accompanied the first stages of this research. Having already examined the difficulties surrounding questions of the perception of exteriority within the framework of enactive approaches, we will proceed in two stages. The first stage will consist of an attempt to explain distal perception in terms of individual sensorimotor invariants. This poses the problem but fails to solve it. The second stage will propose a new pathway to account for spatial perception; a pathway that does not deny the initial intuitions of the autopoietic enactive approaches, but one which radically changes the conception of cognition by considering, from the perceptual stage, the need to take into account interindividual interactions. The protocol of an original experimental study will characterize this new approach considering the perceptual experience of objects at a distance, in exteriority, in a space of possibilities without parting from the domain of interaction. To do this, we have to work at the limits of the perceptual crossing, that is, at the moment when the perceptual reciprocity between different subjects begins to disappear.

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