Statistical Machine Learning in Terms of Industry 4.0 and Investigation of the Impact of Big Data on the Competitiveness of Firms

AbstractThe explosive development of artificial intelligence, machine learning and big data methods in the last 10 years has been felt in the financial-banking field which has subjected to profound changes aimed at determining an unprecedented increase in the efficiency and profitability of the businesses they carry out. The tendencies of applying the concepts coming from AI, together with the continuous increase of the volume, complexity and variety of the data that the banks collect, store and process have acquired the generic names of FinTech, respectively BigTech. Five main areas exist where Fintechs and Bigtechs can provide improvements in business models for the banks: introducing specialized platforms, covering neglected customer segments, improving customer selection, reduction of the operating costs of the banks, and optimization of the business processes of the banks. We will present some of these improvements, and then we will show how the business models of the banks dramatically transform under the influence of these changes.

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THE IMPLEMENTATION OF BIG DATA ANALYSIS IN REGULATING ONLINE SHORT-TERM RENTAL BUSINESS: A CASE OF AIRBNB IN BEIJING

ISPRS Annals of Photogrammetry Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-annals-iv-4-w9-79-2019 ◽

2019 ◽

Vol IV-4/W9 ◽

pp. 79-86

Author(s):

J. Li ◽

F. Biljecki

Keyword(s):

Machine Learning ◽

Big Data ◽

Data Analysis ◽

Housing Prices ◽

Big Data Analysis ◽

Price Sensitivity ◽

Short Term ◽

New Business ◽

The Impact ◽

The Relationship

Abstract. With the fast expansion and controversial impacts of short-term rental platforms such as Airbnb, many cities have called for regulating this new business model. This research aims to establish an approach to understand the impact of Airbnb (and similar services) through big data analysis and provide insights potentially useful for its regulation. The paper reveals how Airbnb is influencing Beijing’s neighbourhood housing prices through machine learning and GIS. Machine learning models are developed to analyse the relationship between Airbnb activities in a neighbourhood and prevailing housing prices. The model of the best fit is then used to analyse the neighbourhood price sensitivity in view of increasing Airbnb activities. The results show that the sensitivity is variable: there are neighbourhoods that are likely to be more price sensitive to Airbnb activities, but also neighbourhoods that are likely to be price robust. Finally, the paper gives policy recommendations for regulating short-term rental businesses based on neighbourhood’s price sensitivity.

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Statistical Machine Learning in Model Predictive Control of Nonlinear Processes

Mathematics ◽

10.3390/math9161912 ◽

2021 ◽

Vol 9 (16) ◽

pp. 1912

Author(s):

Zhe Wu ◽

David Rincon ◽

Quanquan Gu ◽

Panagiotis D. Christofides

Keyword(s):

Machine Learning ◽

Neural Networks ◽

Model Predictive Control ◽

Predictive Control ◽

Closed Loop ◽

Series Data ◽

Generalization Error ◽

Statistical Machine Learning ◽

The Impact ◽

Loop Stability

Recurrent neural networks (RNNs) have been widely used to model nonlinear dynamic systems using time-series data. While the training error of neural networks can be rendered sufficiently small in many cases, there is a lack of a general framework to guide construction and determine the generalization accuracy of RNN models to be used in model predictive control systems. In this work, we employ statistical machine learning theory to develop a methodological framework of generalization error bounds for RNNs. The RNN models are then utilized to predict state evolution in model predictive controllers (MPC), under which closed-loop stability is established in a probabilistic manner. A nonlinear chemical process example is used to investigate the impact of training sample size, RNN depth, width, and input time length on the generalization error, along with the analyses of probabilistic closed-loop stability through the closed-loop simulations under Lyapunov-based MPC.

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The impact of machine learning on processing big data in high-performance computing

Smart Computing ◽

10.1201/9781003167488-40 ◽

2021 ◽

pp. 344-353

Author(s):

Rohit ◽

B. Gupta ◽

K. K. Gola

Keyword(s):

Machine Learning ◽

Big Data ◽

High Performance Computing ◽

High Performance ◽

The Impact ◽

Performance Computing

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Big Data Technology To Improve The Reliability Of Solutions When Choosing A Contractor

Promyshlennoe i Grazhdanskoe Stroitel stvo ◽

10.33622/0869-7019.2019.10.65-70 ◽

2019 ◽

pp. 65-70

Keyword(s):

Machine Learning ◽

Big Data ◽

Big Data Analytics ◽

Machine Learning Algorithms ◽

Prognostic Models ◽

Modeling And Analysis ◽

Current Economic Situation ◽

Contract Failure ◽

Contract Implementation ◽

The Impact

Considering current economic situation, the level of competition among different companies is great. In order to gain a higher position in the ratings, to attract more new customers, to predict the demand for products, and finally to protect themselves from wrong decisions, companies are increasingly turning to big data analytics. In the sphere of construction an opportunity to foresee the probability of contract implementation before its conclusion is always relevant. The higher the probability, the more attractive the contractor and lower the risks of the customer. Developing the topic of applicability of machine learning methods to the problem of determining the probability of successful completion of the contract, the authors are experimenting with a set of analyzed indicators assessing the impact of each of them on the decision on the possibility of contract failure. The article considers in detail the stages of data preparation for modeling, direct modeling and analysis of the results obtained. The authors tested the adequacy of the models on actual data and set the metrics by which it is possible to customize and improve the models for the needs of a particular organization. The prognostic models with a predictive power, based on machine learning algorithms, such as logistic regression, decision tree, random forest, developed by the authors, have the potential for practical use in construction organizations at the stage of contract conclusion.

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