Clustering and Classification of Manufacturing Enterprises Regarding Their Industry 4.0 Reshoring Incentives

The aim of article. The digital industry (Industry 4.0, the fourth-generation industry) is developing - based on the digital transformation of the production sector. Countries must create a workforce ready for future infrastructure. This requires the cooperation of universities, government and industry, including initiatives aimed at training workers for the transforming productive sector. The pandemic has COVID-19 exacerbated the problem of employment. Methodology: it is necessary to study the problem of employment at the systemic level, with an analysis of the structural complexity and development of digital transformations. This article explores this problem for manufacturing enterprises, in particular the automotive industry. The Results and Conclusions present the results of the analysis and make forecasts.

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A Data Mining Technique for Tourist Destination Brand Image Building

International Journal of Recent Technology and Engineering - 2 ◽

10.35940/ijrte.f8329.038620 ◽

2020 ◽

Vol 8 (6) ◽

pp. 4617-4622

Keyword(s):

Data Mining ◽

Clustering Algorithm ◽

Tourism Industry ◽

Destination Image ◽

Data Mining Technique ◽

Tourist Destination ◽

Text Data ◽

Clustering And Classification ◽

Google Search

The destination image branding is the domain of tourism industry where the facts and information is collected and evaluated for finding the credibility of a target tourist destination. Manual collection and processing of collected information accurately is a complicated and time consuming task therefore a data mining model is suggested ,in this presented work that collect and evaluate the destination image accurately and based on evaluation can make the recommendations about visits of tourist. In order to perform this task data mining techniques are applied on text data source. In first the data is extracted from the Google search engine and it is preprocessed for make it impure. In further the data is labeled based on the positive and negative words available in the collected facts. Finally the clustering and classification of text is performed. For clustering of data FCM (fuzzy c means) clustering algorithm and for classification the Bayesian classifier is used. Based on final classification of text data the decision is made for the destination visits.

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K-Means Clustering and Classification of Kinetic Curves on Malignancy in Dynamic Breast MRI

World Congress on Medical Physics and Biomedical Engineering 2006 - IFMBE Proceedings ◽

10.1007/978-3-540-36841-0_639 ◽

2007 ◽

pp. 2536-2539 ◽

Cited By ~ 1

Author(s):

S. H. Lee ◽

Jong Hyo Kim ◽

K. G. Kim ◽

S. J. Park ◽

W. K. Moon

Keyword(s):

Breast Mri ◽

Kinetic Curves ◽

Clustering And Classification

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Challenges and prospects for the Vietnam’s manufacturing industry development in the context of industry 4.0

Transbaikal State University Journal ◽

10.21209/2227-9245-2020-26-9-84-92 ◽

2020 ◽

Vol 26 (9) ◽

pp. 84-92

Author(s):

Lo Thi Hong Van ◽

◽

L. Guzikova ◽

Keyword(s):

Developing Countries ◽

Industry 4.0 ◽

Industrial Development ◽

Manufacturing Industry ◽

Industrial Revolution ◽

Manufacturing Enterprises ◽

Industrial Enterprises ◽

Fourth Industrial Revolution ◽

Leading Role ◽

The One

The purpose of the study is to identify the challenges, prospects and ways for the development of the manufacturing industry in Vietnam in the context of Industry 4.0, after COVID-19. The article examines the development of the manufacturing industry in the context of Industry 4.0. The research methodology includes comparative analysis, elements of positive and normative analysis. The study is based on statistical information available within the period of writing the article (till October 1, 2020). The data of Vietnamese and international research organizations and statistics bodies, in particular, the websites of World Bank and General Statistics Office of were used. The leading role of the manufacturing industry in achieving sustainable economic development in developing countries, including Vietnam, was determined. Based on an assessment of the levels of development of the manufacturing industry in Vietnam from 2011 to 2019 and the state of production of the main manufacturing industries under the influence of the coronavirus pandemic in 2020, urgent problems of the development of the manufacturing industry in the context of Industry 4.0 in Vietnam were identified. The coronavirus pandemic, on the one hand, is seen as the reason for the slowdown in production growth in the manufacturing industry, and on the other hand, as a condition for accelerating digital transformation in industrial manufacturing enterprises. The article identifies the main challenges and prospects for the development of the manufacturing industry in Vietnam in the fourth industrial revolution. The human resource challenge for the development of manufacturing in the context of the fourth industrial revolution was identified in some specific industries such as textiles, food processing, machinery and equipment manufacturing by analyzing the Report of 2019 about Industry 4.0 Readiness of Vietnam’s industrial enterprises. The main priority areas for improving the production of the manufacturing industry in Vietnam to achieve sustainable industrial development are presented. The results of the work can be used in the development of policies for the development of the manufacturing industry not only in Vietnam, but also in other developing countries

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Case-Based Reasoning and Some Typical Applications

Global Trends in Intelligent Computing Research and Development - Advances in Computational Intelligence and Robotics ◽

10.4018/978-1-4666-4936-1.ch009 ◽

2014 ◽

pp. 229-267 ◽

Cited By ~ 1

Author(s):

Durga Prasad Roy ◽

Baisakhi Chakraborty

Keyword(s):

Problem Solving ◽

Machine Intelligence ◽

Case Based Reasoning ◽

Engineering System ◽

Yale University ◽

Control Pattern ◽

Clustering And Classification ◽

How People Learn ◽

Case Based

Case-Based Reasoning (CBR) arose out of research into cognitive science, most prominently that of Roger Schank and his students at Yale University, during the period 1977–1993. CBR may be defined as a model of reasoning that incorporates problem solving, understanding, and learning, and integrates all of them with memory processes. It focuses on the human problem solving approach such as how people learn new skills and generates solutions about new situations based on their past experience. Similar mechanisms to humans who intelligently adapt their experience for learning, CBR replicates the processes by considering experiences as a set of old cases and problems to be solved as new cases. To arrive at the conclusions, it uses four types of processes, which are retrieve, reuse, revise, and retain. These processes involve some basic tasks such as clustering and classification of cases, case selection and generation, case indexing and learning, measuring case similarity, case retrieval and inference, reasoning, rule adaptation, and mining to generate the solutions. This chapter provides the basic idea of case-based reasoning and a few typical applications. The chapter, which is unique in character, will be useful to researchers in computer science, electrical engineering, system science, and information technology. Researchers and practitioners in industry and R&D laboratories working in such fields as system design, control, pattern recognition, data mining, vision, and machine intelligence will benefit.

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