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Special Issue "Applications of Intelligent Methods for Business Model Innovation and Market Resilience Facing the COVID-19"

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A special issue of Mathematics (ISSN 2227-7390).

Deadline for manuscript submissions: closed (31 January 2023) | Viewed by 5519

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Special Issue Editors

Dr. Ebrahim Navid Sadjadi

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Guest Editor

Department of Informatics, Universidad Carlos III de Madrid, 28903 Madrid, Spain
Interests: smart grids; control theory; fuzzy systems; AI
Special Issues, Collections and Topics in MDPI journals

Dr. Jesús García-Herrero

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Guest Editor

Computer Science Department, Universidad Carlos III de Madrid, Avenida Gregorio Peces-Barba Martínez, 22. 28270 Colmenarejo Madrid, Spain
Interests: data fusion; surveillance systems; contextual information; data mining
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Prof. Dr. Jose Manuel Molina López

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Guest Editor

Computer Science and Engineering Department, Universidad Carlos III de Madrid, Edificio Sabatini, 28911 Leganes, Spain
Interests: data fusion; machine learning; Internet of Things (IoT); ambient intelligent; AAL; privacy
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Dr. Roemi Fernandez

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Guest Editor

CSIC-UPM-Centro de Automatica y Robotica (CAR), Madrid, Spain
Interests: field and service robotic systems; intelligent robotics; multisensory systems; nonlinear actuators and nonlinear controllers
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The objective of this Special Issue is to cover the impact of artificial intelligence and automation in the fight against COVID-19 based on fuzzy systems and data mining. The opportunities of growth in the field will impact the education plans of electrical engineering departments and the expected trajectory in the academic life of students as well as the future goals of researchers toward wellness in the societies.

Hence, the aim of the Special Issue is to identify and study advanced issues related to applying intelligent techniques and information technologies that automate and integrate data analysis systems and optimize the processes of innovation in combatting the COVID-19 pandemic and similar disasters in the future.

This Special Issue is expected to include recent work in theories and practices of information systems and technology management based on fuzzy systems and data mining. In particular, we would like to study the impact of social distance up-taking on the servitization and smartization of industries, engineering education, and medical practices, besides the consequences on the speedy growth of the application of the artificial intelligence, and the widening of automation and industry in societies.

This Special Issue will not only cover selected excellent papers from The 8th International Conference on Fuzzy Systems and Data Mining (FSDM 2022)(November 4-7, 2022, Xiamen, China)(http://www.fsdmconf.org/) and The 3rd International Conference on Modern Management based on Big Data (MMBD2022) (August 15th-18th, 2022, Seoul, South Korea) (http://www.mmbdconf.org/), but also welcome external submissions on related topics.

We assume that the target of this Special Issue will be researchers both from the academia and industry. Hence, we invite business managers and engineers, executives and managers of the universities, plus the graduate students and researchers for participation in the Special Issue.

Topics include but are not limited to the:

Data mining technologies;
Statistical methods and informatics for large data;
Applications of fuzzy systems and neural networks, convolutional neural networks;
Smart data analysis, smart data fusion;
New achievements in dynamic programming, clouding and optimization;
Medical imaging, chest images, CT, etc.;
Innovation in distant education systems;
Smartization of industrial processes;
New platforms for the remote style of life–work balance;
Smart Business management systems;
Infection risk analysis;
Data analysis methods for product development, maintenance, quality improvement;
Automation of industries, internet of things, interconnections of sensors;
Smart methods for internal and external relation improvement of the industries;
Real-time operations;
Data-based advertisement, data-based logistics and delivery;
Smart sales, customer targeting, business optimization, customer needs identification;
Data security, cybersecurity;
Market trend identification;
Robots, unmanned aerial vehicles, service robots, service humanoid;
Remote sensing, virtualization, data decentralization;
Risk management for agile operations;
Data based supply chain, data-based value chain.

Dr. Ebrahim Navid Sadjadi
Dr. Jesús García-Herrero
Prof. Dr. Jose Manuel Molina López
Dr. Roemi Fernandez
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Mathematics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

data mining technologies
data based supply chain, data-based value chain
data security, cybersecurity
real-time operations
data-based advertisement, data-based logistics and delivery

Published Papers (3 papers)

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Research

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Open AccessArticle

Prior Knowledge-Based Causal Inference Algorithms and Their Applications for China COVID-19 Analysis

Haifeng Li

Mo Hai

and

Wenxun Tang

Mathematics 2022, 10(19), 3568; https://doi.org/10.3390/math10193568 - 30 Sep 2022

Viewed by 1297

Abstract

Causal inference has become an important research direction in the field of computing. Traditional methods have mainly used Bayesian networks to discover the causal effects between variables. These methods have limitations, namely, on the one hand, the computing cost is expensive if one wants to achieve accurate results, i.e., exponential growth along with the number of variables. On the other hand, the accuracy is not good enough if one tries to reduce the computing cost. In this study, we use prior knowledge iteration or time series trend fitting between causal variables to resolve the limitations and discover bidirectional causal edges between the variables. Subsequently, we obtain real causal graphs, thus establishing a more accurate causal model for the evaluation and calculation of causal effects. We present two new algorithms, namely, the PC+ algorithm and the DCM algorithm. The PC+ algorithm is used to address the problem of the traditional PC algorithm, which needs to enumerate all Markov equivalence classes at a high computational cost or with immediate output of non-directional causal edges. In the PC+ algorithm, the causal tendency among some variables was analyzed via partial exhaustive analysis. By fixing the relatively certain causality as prior knowledge, a causal graph of higher accuracy is the final output at a low running cost. The DCM algorithm uses the d-separation strategy to improve the traditional CCM algorithm, which can only handle the pairwise fitting of variables, and thus identify the indirect causality as the direct one. By using the d-separation strategy, our DCM algorithm achieves higher accuracy while following the basic criteria of Bayesian networks. In this study, we evaluate the proposed algorithms based on the COVID-19 pandemic with experimental and theoretical analysis. The experimental results show that our improved algorithms are effective and efficient. Compared to the exponential cost of the PC algorithm, the time complexity of the PC+ algorithm is reduced to a linear level. Moreover, the accuracies of the PC+ algorithm and DCM algorithm are improved to different degrees; specifically, the accuracy of the PC+ algorithm reaches 91%, much higher than the 33% of the PC algorithm. Full article

(This article belongs to the Special Issue Applications of Intelligent Methods for Business Model Innovation and Market Resilience Facing the COVID-19)

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Open AccessArticle

Optimal Investment Portfolios for Internet Money Funds Based on LSTM and La-VaR: Evidence from China

Hanxiao Wang

and

Huizi Ma

Mathematics 2022, 10(16), 2864; https://doi.org/10.3390/math10162864 - 11 Aug 2022

Cited by 1 | Viewed by 863

Abstract

The rapid development of Internet finance has impacted traditional investment patterns, and Internet money funds (IMFs) are involved extensively in finance. This research constructed a long short-term memory (LSTM) neural network model to predict the return rates of IMFs and utilized the value-at-risk (VaR) and liquidity-adjusted VaR (La-VaR) methods to measure the IMFs’ risk. Then, an objective programming model based on prediction and risk assessment was established to design optimal portfolios. The results indicate the following: (1) The LSTM model results show that the forecast curves are consistent with the actual curves, and the root-mean-squared error (RMSE) result is mere 0.009, indicating that the model is suitable for forecasting data with reliable time-periodic characteristics. (2) With unit liquidity cost, the La-VaR results match the actuality better than the VaR as they demonstrate that the fund-based IMFs (FUND) have the most significant risk, the bank-based IMFs (BANK) rank 2nd, and the third-party-based IMFs (THIRD) rank 3rd. (3) The programming model based on LSTM and the La-VaR can meet different investors’ preferences by adjusting the objectives and constraints. It shows that the designed models have more practical significance than the traditional investment strategies. Full article

(This article belongs to the Special Issue Applications of Intelligent Methods for Business Model Innovation and Market Resilience Facing the COVID-19)

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Review

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Open AccessReview

Challenges and Opportunities for Education Systems with the Current Movement toward Digitalization at the Time of COVID-19

Ebrahim Navid Sadjadi

Mathematics 2023, 11(2), 259; https://doi.org/10.3390/math11020259 - 04 Jan 2023

Cited by 5 | Viewed by 2620

Abstract

The spread of coronavirus has caused the shutdown of businesses and classroom participation to enable social distancing. It has led to the promotion of digitalization in societies and online activities. This manuscript presents an overview of the measures education systems could take to present appropriate courses in accordance with the present movement toward digitalization, and other requirements of societies in the (post) crisis period. Full article

(This article belongs to the Special Issue Applications of Intelligent Methods for Business Model Innovation and Market Resilience Facing the COVID-19)

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