Machine Learning and Deep Learning Applications for Society

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: closed (10 November 2021) | Viewed by 22149

Special Issue Editors

Department of Communications Engineering, Miguel Hernández University, 03202 Alicante, Spain
Interests: machine learning; deep learning; signal processing; business management and organization; marketing and pricing
Department of Signal Theory and Communications and Telematic Systems and Computation, Rey Juan Carlos University, 28943 Madrid, Spain
Interests: deep learning; machine learning; electronic health records; business; social media
Department of Business, Universidad Rey Juan Carlos, 28943 Madrid, Spain
Interests: deep learning; machine learning; business; social media; sentiment analysis
Departamento de Teoría de la Señal y Comunicaciones, Sistemas Telemáticos y Computación, Universidad Rey Juan Carlos, 28933 Madrid, Spain
Interests: statistical learning theory; digital signal processing; complex system modeling with application to hospitality; valuation; cybersecurity; big data in healthcare and applied to cardiac signals and image processing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Artificial intelligence, machine learning algorithms, and deep learning will play an important role in almost all disciplines in the coming years. These tools have been deeply applied in the past and are bundled to engineering, computing science, and mathematics. With this Special Issue, we provide a space to present multidisciplinary applications that go beyond these disciplines toward social-related activities. Data analytics is becoming crucial for many organizations and companies seeking to characterize and model the hidden patterns present in more complex, everyday environments.

The aim of this Special Issue is to provide the latest developments in machine learning and deep learning applications for addressing several areas of increasing economic and social interest, such as social media, social awareness, sentiment analysis, economic and social risk assessment, asset evaluation, AI for good, humanitarian developments, social trends, pricing mindfulness, marketing, and social communication strategies, among other novel and emerging applications of learning techniques for application to society involving numerous different ways and approaches.

Prof. Dr. Fco. Javier Gimeno-Blanes
Prof. Dr. Cristina Soguero-Ruiz
Prof. Dr. Margarita Rodríguez-Ibáñez
Prof. Dr. José Luis Rojo-Álvarez
Guest Editors

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Keywords

  • social media
  • natural language applications for society
  • financial analysis and business
  • revenue management
  • sentiment analysis
  • machine learning for good
  • marketing
  • hospitality and tourism
  • social awareness and NGOs
  • healthcare management and modeling

Published Papers (6 papers)

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Editorial

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3 pages, 219 KiB  
Editorial
Opening the 21st Century Technologies to Industries: On the Special Issue Machine Learning for Society
by Margarita Rodríguez-Ibáñez, Cristina Soguero-Ruiz, Francisco-Javier Gimeno-Blanes and José-Luis Rojo-Álvarez
Appl. Sci. 2023, 13(13), 7371; https://0-doi-org.brum.beds.ac.uk/10.3390/app13137371 - 21 Jun 2023
Viewed by 527
Abstract
Machine learning techniques, more commonly known today as artificial intelligence, are playing an increasingly important role in all aspects of our lives [...] Full article
(This article belongs to the Special Issue Machine Learning and Deep Learning Applications for Society)

Research

Jump to: Editorial

16 pages, 553 KiB  
Article
Stock Market Crisis Forecasting Using Neural Networks with Input Factor Selection
by Felix Fuchs, Markus Wahl, Rudi Zagst and Xinyi Zheng
Appl. Sci. 2022, 12(4), 1952; https://0-doi-org.brum.beds.ac.uk/10.3390/app12041952 - 13 Feb 2022
Cited by 6 | Viewed by 1765
Abstract
Artificial neural networks have gained increasing importance in many fields, including quantitative finance, due to their ability to identify, learn and regenerate non-linear relationships between targets of investigation. We explore the potential of artificial neural networks in forecasting financial crises with micro-, macroeconomic [...] Read more.
Artificial neural networks have gained increasing importance in many fields, including quantitative finance, due to their ability to identify, learn and regenerate non-linear relationships between targets of investigation. We explore the potential of artificial neural networks in forecasting financial crises with micro-, macroeconomic and financial factors. In this application of neural networks, a huge amount of available input factors, but limited historical data, often leads to over-parameterized and unstable models. Therefore, we develop an input variable reduction method for model selection. With an iterative walk-forward forecasting and testing procedure, we create out-of-sample predictions for crisis periods of the S&P 500 and demonstrate that the model selected with our method outperforms a model with a set of input factors taken from the literature. Full article
(This article belongs to the Special Issue Machine Learning and Deep Learning Applications for Society)
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12 pages, 2451 KiB  
Article
Recurrent Neural Networks and ARIMA Models for Euro/Dollar Exchange Rate Forecasting
by Pedro Escudero, Willian Alcocer and Jenny Paredes
Appl. Sci. 2021, 11(12), 5658; https://0-doi-org.brum.beds.ac.uk/10.3390/app11125658 - 18 Jun 2021
Cited by 22 | Viewed by 4582
Abstract
Analyzing the future behaviors of currency pairs represents a priority for governments, financial institutions, and investors, who use this type of analysis to understand the economic situation of a country and determine when to sell and buy goods or services from a particular [...] Read more.
Analyzing the future behaviors of currency pairs represents a priority for governments, financial institutions, and investors, who use this type of analysis to understand the economic situation of a country and determine when to sell and buy goods or services from a particular location. Several models are used to forecast this type of time series with reasonable accuracy. However, due to the random behavior of these time series, achieving good forecasting performance represents a significant challenge. In this paper, we compare forecasting models to evaluate their accuracy in the short term using data on the EUR/USD exchange rate. For this purpose, we used three methods: Autoregressive Integrated Moving Average (ARIMA), Recurrent Neural Network (RNN) of the Elman type, and Long Short-Term Memory (LSTM). The analyzed period spanned from 2 January 1998, to 31 December 2019, and was divided into training and validation datasets. We performed forecasting calculations to predict windows with six different forecasting horizons. We found that the window of one month with 22 observations better matched the validation dataset in the short term compared to the other windows. Theil’s U coefficients calculated for this window were 0.04743, 0.002625, and 0.001808 for the ARIMA, Elman, and LSTM networks, respectively. LSTM provided the best forecast in the short term, while Elman provided the best forecast in the long term. Full article
(This article belongs to the Special Issue Machine Learning and Deep Learning Applications for Society)
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19 pages, 1736 KiB  
Article
A Big Data Approach to Customer Relationship Management Strategy in Hospitality Using Multiple Correspondence Domain Description
by Lydia González-Serrano, Pilar Talón-Ballestero, Sergio Muñoz-Romero, Cristina Soguero-Ruiz and José Luis Rojo-Álvarez
Appl. Sci. 2021, 11(1), 256; https://0-doi-org.brum.beds.ac.uk/10.3390/app11010256 - 29 Dec 2020
Cited by 13 | Viewed by 4048
Abstract
COVID-19 has hit the hotel sector in a hitherto unknown way. This situation is producing a fundamental change in client behavior that makes crucial an adequate knowledge of their profile to overcome an uncertain environment. Customer Relationship Management (CRM) can provide key strategies [...] Read more.
COVID-19 has hit the hotel sector in a hitherto unknown way. This situation is producing a fundamental change in client behavior that makes crucial an adequate knowledge of their profile to overcome an uncertain environment. Customer Relationship Management (CRM) can provide key strategies in hospitality industry by generating a great amount of valuable information about clients, whereas Big Data tools are providing with unprecedented facilities to conduct massive analysis and to focus the client-to-business relationship. However, few instruments have been proposed to handle categorical features, which are the most usual in CRMs, aiming to adapt the statistical robustness with the best interpretability for the managers. Therefore, our aim was to identify the profiles of clients from an international hotel chain using the overall data in its CRM system. An analysis method was created involving three elements: First, Multiple Correspondence Analysis provides us with a statistical description of the interactions among categories and features. Second, bootstrap resampling techniques give us information about the statistical variability of the feature maps. Third, kernel methods provide easy-to-visualize domain descriptions based on confidence areas in the maps. The proposed methodology can provide an operative and statistically principled way to scrutinize the CRM profiles in hospitality. Full article
(This article belongs to the Special Issue Machine Learning and Deep Learning Applications for Society)
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21 pages, 1790 KiB  
Article
On the Differential Analysis of Enterprise Valuation Methods as a Guideline for Unlisted Companies Assessment (I): Empowering Discounted Cash Flow Valuation
by Germania Vayas-Ortega, Cristina Soguero-Ruiz, José-Luis Rojo-Álvarez and Francisco-Javier Gimeno-Blanes
Appl. Sci. 2020, 10(17), 5875; https://0-doi-org.brum.beds.ac.uk/10.3390/app10175875 - 25 Aug 2020
Cited by 9 | Viewed by 6017
Abstract
The Discounted Cash Flow (DCF) method is probably the most extended approach used in company valuation, its main drawbacks being probably the known extreme sensitivity to key variables such as Weighted Average Cost of Capital (WACC) and Free Cash Flow (FCF) estimations not [...] Read more.
The Discounted Cash Flow (DCF) method is probably the most extended approach used in company valuation, its main drawbacks being probably the known extreme sensitivity to key variables such as Weighted Average Cost of Capital (WACC) and Free Cash Flow (FCF) estimations not unquestionably obtained. In this paper we propose an unbiased and systematic DCF method which allows us to value private equity by leveraging on stock markets evidences, based on a twofold approach: First, the use of the inverse method assesses the existence of a coherent WACC that positively compares with market observations; second, different FCF forecasting methods are benchmarked and shown to correspond with actual valuations. We use financial historical data including 42 companies in five sectors, extracted from Eikon-Reuters. Our results show that WACC and FCF forecasting are not coherent with market expectations along time, with sectors, or with market regions, when only historical and endogenous variables are taken into account. The best estimates are found when exogenous variables, operational normalization of input space, and data-driven linear techniques are considered (Root Mean Square Error of 6.51). Our method suggests that FCFs and their positive alignment with Market Capitalization and the subordinate enterprise value are the most influencing variables. The fine-tuning of the methods presented here, along with an exhaustive analysis using nonlinear machine-learning techniques, are developed and discussed in the companion paper. Full article
(This article belongs to the Special Issue Machine Learning and Deep Learning Applications for Society)
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17 pages, 1839 KiB  
Article
On the Differential Analysis of Enterprise Valuation Methods as a Guideline for Unlisted Companies Assessment (II): Applying Machine-Learning Techniques for Unbiased Enterprise Value Assessment
by Germania Vayas-Ortega, Cristina Soguero-Ruiz, Margarita Rodríguez-Ibáñez, José-Luis Rojo-Álvarez and Francisco-Javier Gimeno-Blanes
Appl. Sci. 2020, 10(15), 5334; https://0-doi-org.brum.beds.ac.uk/10.3390/app10155334 - 02 Aug 2020
Cited by 3 | Viewed by 3570
Abstract
The search for an unbiased company valuation method to reduce uncertainty, whether or not it is automatic, has been a relevant topic in social sciences and business development for decades. Many methods have been described in the literature, but consensus has not been [...] Read more.
The search for an unbiased company valuation method to reduce uncertainty, whether or not it is automatic, has been a relevant topic in social sciences and business development for decades. Many methods have been described in the literature, but consensus has not been reached. In the companion paper we aimed to review the assessment capabilities of traditional company valuation model, based on company’s intrinsic value using the Discounted Cash Flow (DCF). In this paper, we capitalized on the potential of exogenous information combined with Machine Learning (ML) techniques. To do so, we performed an extensive analysis to evaluate the predictive capabilities with up to 18 different ML techniques. Endogenous variables (features) related to value creation (DCF) were proved to be crucial elements for the models, while the incorporation of exogenous, industry/country specific ones, incrementally improves the ML performance. Bagging Trees, Supported Vector Machine Regression, Gaussian Process Regression methods consistently provided the best results. We concluded that an unbiased model can be created based on endogenous and exogenous information to build a reference framework, to price and benchmark Enterprise Value for valuation and credit risk assessment. Full article
(This article belongs to the Special Issue Machine Learning and Deep Learning Applications for Society)
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