This page has only limited features, please log in for full access.

Dr. Álvaro Herrero
GICAP Research Group, University of Burgos, Burgos, Spain

Basic Info


Research Keywords & Expertise

0 Case-Based Reasoning
0 Machine Learning
0 Neural Networks
0 multiagent systems
0 Unsupervised exploratory projection

Fingerprints

Neural Networks
Case-Based Reasoning
Machine Learning
multiagent systems

Honors and Awards

The user has no records in this section


Career Timeline

The user has no records in this section.


Short Biography

The user biography is not available.
Following
Followers
Co Authors
The list of users this user is following is empty.
Following: 0 users

Feed

Book
Published: 16 July 2021 in Proceedings of the 15th International Conference on Industrial Engineering and Industrial Management and XXV Congreso de Ingeniería de Organización
Reads 0
Downloads 0

This book contains the extended abstracts of the papers presented at the 15th International Conference on Industrial Engineering and Industrial Management (ICIEIM) – XXV Congreso de Ingeniería de Organización (CIO2021). This conference was promoted by ADINGOR (Asociación para el Desarrollo de la Ingeniería de Organización) and AIM (European Academy for Industrial Management). The conference was organized by the Universidad de Burgos. The conference was a forum to disseminate, to all branches of academy and industry, information on the most recent and relevant research, theories, and practices in Industrial Engineering and Operations Management. The conference motto was: “Industry 4.0: The Power of Data”. The main objective was to promote links between researchers and practitioners from different branches, to enhance an interdisciplinary perspective of industrial engineering and management.

ACS Style

José Manuel Galán; Silvia Díaz-De la Fuente; Carlos Alonso De Armiño Pérez; Roberto Alcalde Delgado; Juan José Lavios Villahoz; Álvaro Herrero Cosío; Miguel Ángel Manzanedo del Campo; Ricardo Del Olmo Martínez. Proceedings of the 15th International Conference on Industrial Engineering and Industrial Management and XXV Congreso de Ingeniería de Organización. Proceedings of the 15th International Conference on Industrial Engineering and Industrial Management and XXV Congreso de Ingeniería de Organización 2021, 1 .

AMA Style

José Manuel Galán, Silvia Díaz-De la Fuente, Carlos Alonso De Armiño Pérez, Roberto Alcalde Delgado, Juan José Lavios Villahoz, Álvaro Herrero Cosío, Miguel Ángel Manzanedo del Campo, Ricardo Del Olmo Martínez. Proceedings of the 15th International Conference on Industrial Engineering and Industrial Management and XXV Congreso de Ingeniería de Organización. Proceedings of the 15th International Conference on Industrial Engineering and Industrial Management and XXV Congreso de Ingeniería de Organización. 2021; ():1.

Chicago/Turabian Style

José Manuel Galán; Silvia Díaz-De la Fuente; Carlos Alonso De Armiño Pérez; Roberto Alcalde Delgado; Juan José Lavios Villahoz; Álvaro Herrero Cosío; Miguel Ángel Manzanedo del Campo; Ricardo Del Olmo Martínez. 2021. "Proceedings of the 15th International Conference on Industrial Engineering and Industrial Management and XXV Congreso de Ingeniería de Organización." Proceedings of the 15th International Conference on Industrial Engineering and Industrial Management and XXV Congreso de Ingeniería de Organización , no. : 1.

Book
Published: 16 July 2021 in Proceedings of the 15th International Conference on Industrial Engineering and Industrial Management and XXV Congreso de Ingeniería de Organización
Reads 0
Downloads 0

This book contains the extended abstracts of the papers presented at the 15th International Conference on Industrial Engineering and Industrial Management (ICIEIM) – XXV Congreso de Ingeniería de Organización (CIO2021). This conference was promoted by ADINGOR (Asociación para el Desarrollo de la Ingeniería de Organización) and AIM (European Academy for Industrial Management). The conference was organized by the Universidad de Burgos. The conference was a forum to disseminate, to all branches of academy and industry, information on the most recent and relevant research, theories, and practices in Industrial Engineering and Operations Management. The conference motto was: “Industry 4.0: The Power of Data”. The main objective was to promote links between researchers and practitioners from different branches, to enhance an interdisciplinary perspective of industrial engineering and management.

ACS Style

José Manuel Galán; Silvia Díaz-De la Fuente; Carlos Alonso De Armiño Pérez; Roberto Alcalde Delgado; Juan José Lavios Villahoz; Álvaro Herrero Cosío; Miguel Ángel Manzanedo del Campo; Ricardo Del Olmo Martínez. Proceedings of the 15th International Conference on Industrial Engineering and Industrial Management and XXV Congreso de Ingeniería de Organización. Proceedings of the 15th International Conference on Industrial Engineering and Industrial Management and XXV Congreso de Ingeniería de Organización 2021, 1 .

AMA Style

José Manuel Galán, Silvia Díaz-De la Fuente, Carlos Alonso De Armiño Pérez, Roberto Alcalde Delgado, Juan José Lavios Villahoz, Álvaro Herrero Cosío, Miguel Ángel Manzanedo del Campo, Ricardo Del Olmo Martínez. Proceedings of the 15th International Conference on Industrial Engineering and Industrial Management and XXV Congreso de Ingeniería de Organización. Proceedings of the 15th International Conference on Industrial Engineering and Industrial Management and XXV Congreso de Ingeniería de Organización. 2021; ():1.

Chicago/Turabian Style

José Manuel Galán; Silvia Díaz-De la Fuente; Carlos Alonso De Armiño Pérez; Roberto Alcalde Delgado; Juan José Lavios Villahoz; Álvaro Herrero Cosío; Miguel Ángel Manzanedo del Campo; Ricardo Del Olmo Martínez. 2021. "Proceedings of the 15th International Conference on Industrial Engineering and Industrial Management and XXV Congreso de Ingeniería de Organización." Proceedings of the 15th International Conference on Industrial Engineering and Industrial Management and XXV Congreso de Ingeniería de Organización , no. : 1.

Journal article
Published: 25 March 2021 in PeerJ Computer Science
Reads 0
Downloads 0

Firms face an increasingly complex economic and financial environment in which the access to international networks and markets is crucial. To be successful, companies need to understand the role of internationalization determinants such as bilateral psychic distance, experience, etc. Cutting-edge feature selection methods are applied in the present paper and compared to previous results to gain deep knowledge about strategies for Foreign Direct Investment. More precisely, evolutionary feature selection, addressed from the wrapper approach, is applied with two different classifiers as the fitness function: Bagged Trees and Extreme Learning Machines. The proposed intelligent system is validated when applied to real-life data from Spanish Multinational Enterprises (MNEs). These data were extracted from databases belonging to the Spanish Ministry of Industry, Tourism, and Trade. As a result, interesting conclusions are derived about the key features driving to the internationalization of the companies under study. This is the first time that such outcomes are obtained by an intelligent system on internationalization data.

ACS Style

Álvaro Herrero; Alfredo Jiménez; Roberto Alcalde. Advanced feature selection to study the internationalization strategy of enterprises. PeerJ Computer Science 2021, 7, e403 .

AMA Style

Álvaro Herrero, Alfredo Jiménez, Roberto Alcalde. Advanced feature selection to study the internationalization strategy of enterprises. PeerJ Computer Science. 2021; 7 ():e403.

Chicago/Turabian Style

Álvaro Herrero; Alfredo Jiménez; Roberto Alcalde. 2021. "Advanced feature selection to study the internationalization strategy of enterprises." PeerJ Computer Science 7, no. : e403.

Journal article
Published: 30 December 2020 in Applied Soft Computing
Reads 0
Downloads 0

Fake news has now grown into a big problem for societies and also a major challenge for people fighting disinformation. This phenomenon plagues democratic elections, reputations of individual persons or organizations, and has negatively impacted citizens, (e.g., during the COVID-19 pandemic in the US or Brazil). Hence, developing effective tools to fight this phenomenon by employing advanced Machine Learning (ML) methods poses a significant challenge. The following paper displays the present body of knowledge on the application of such intelligent tools in the fight against disinformation. It starts by showing the historical perspective and the current role of fake news in the information war. Proposed solutions based solely on the work of experts are analysed and the most important directions of the application of intelligent systems in the detection of misinformation sources are pointed out. Additionally, the paper presents some useful resources (mainly datasets useful when assessing ML solutions for fake news detection) and provides a short overview of the most important R&D projects related to this subject. The main purpose of this work is to analyse the current state of knowledge in detecting fake news; on the one hand to show possible solutions, and on the other hand to identify the main challenges and methodological gaps to motivate future research.

ACS Style

Michał Choraś; Konstantinos Demestichas; Agata Giełczyk; Álvaro Herrero; Paweł Ksieniewicz; Konstantina Remoundou; Daniel Urda; Michał Woźniak. Advanced Machine Learning techniques for fake news (online disinformation) detection: A systematic mapping study. Applied Soft Computing 2020, 101, 107050 .

AMA Style

Michał Choraś, Konstantinos Demestichas, Agata Giełczyk, Álvaro Herrero, Paweł Ksieniewicz, Konstantina Remoundou, Daniel Urda, Michał Woźniak. Advanced Machine Learning techniques for fake news (online disinformation) detection: A systematic mapping study. Applied Soft Computing. 2020; 101 ():107050.

Chicago/Turabian Style

Michał Choraś; Konstantinos Demestichas; Agata Giełczyk; Álvaro Herrero; Paweł Ksieniewicz; Konstantina Remoundou; Daniel Urda; Michał Woźniak. 2020. "Advanced Machine Learning techniques for fake news (online disinformation) detection: A systematic mapping study." Applied Soft Computing 101, no. : 107050.

Editorial
Published: 09 November 2020 in Expert Systems
Reads 0
Downloads 0
ACS Style

Álvaro Herrero; Alfredo Jiménez; Secil Bayraktar; Ángel Arroyo. Novel applications of soft computing techniques for industrial and environmental enterprises. Expert Systems 2020, 38, 1 .

AMA Style

Álvaro Herrero, Alfredo Jiménez, Secil Bayraktar, Ángel Arroyo. Novel applications of soft computing techniques for industrial and environmental enterprises. Expert Systems. 2020; 38 (1):1.

Chicago/Turabian Style

Álvaro Herrero; Alfredo Jiménez; Secil Bayraktar; Ángel Arroyo. 2020. "Novel applications of soft computing techniques for industrial and environmental enterprises." Expert Systems 38, no. 1: 1.

Conference paper
Published: 04 November 2020 in Transactions on Petri Nets and Other Models of Concurrency XV
Reads 0
Downloads 0

It is widely claimed that a major challenge in Robotics is to get reliable systems while both response and down times are minimized. In keeping with this idea, present paper proposes the application of a Hybrid Artificial Intelligence System (HAIS) to preprocess data with the aim of improving the detection of performance anomalies. One of the main problems when analyzing real-life data is the presence of missing values. It is usually solved by removing incomplete data, what causes a loss of information that may be critical in some domains. As an alternative, present paper proposes the application of regression models to impute those missing values. Prediction is optimized by generating personalized models on previously clustered data. Experiments are run on a public and up-to-date dataset that contains information about anomalies affecting the component-based software of a robot. The obtained results validate the proposed HAIS, as it successfully imputes missing values from the different features in the original dataset.

ACS Style

Ángel Arroyo; Nuño Basurto; Carlos Cambra; Álvaro Herrero. Clustering and Regression to Impute Missing Values of Robot Performance. Transactions on Petri Nets and Other Models of Concurrency XV 2020, 86 -94.

AMA Style

Ángel Arroyo, Nuño Basurto, Carlos Cambra, Álvaro Herrero. Clustering and Regression to Impute Missing Values of Robot Performance. Transactions on Petri Nets and Other Models of Concurrency XV. 2020; ():86-94.

Chicago/Turabian Style

Ángel Arroyo; Nuño Basurto; Carlos Cambra; Álvaro Herrero. 2020. "Clustering and Regression to Impute Missing Values of Robot Performance." Transactions on Petri Nets and Other Models of Concurrency XV , no. : 86-94.

Conference paper
Published: 27 October 2020 in Transactions on Petri Nets and Other Models of Concurrency XV
Reads 0
Downloads 0

The detection of anomalies (affecting hardware or software) is an open challenge for cyber-physical systems in general and robots in particular. Physical anomalies related to the hardware components of such systems have been widely researched. However, scant attention has been devoted so far to study the anomalies affecting the software components. In order to bridge this gap, the present paper proposes the application of different classifiers to a robot performance dataset for the first time. The applied supervised models are targeted at detecting synthetically-induced software anomalies, having a detrimental impact on the performance of a collaborative robot. Obtained results demonstrate that the applied Machine Learning models can successfully address the target problem, with acceptable detection rates.

ACS Style

Héctor Quintián; Esteban Jove; José Luis Calvo-Rolle; Nuño Basurto; Carlos Cambra; Álvaro Herrero; Emilio Corchado. Detecting Performance Anomalies in the Multi-component Software a Collaborative Robot. Transactions on Petri Nets and Other Models of Concurrency XV 2020, 533 -540.

AMA Style

Héctor Quintián, Esteban Jove, José Luis Calvo-Rolle, Nuño Basurto, Carlos Cambra, Álvaro Herrero, Emilio Corchado. Detecting Performance Anomalies in the Multi-component Software a Collaborative Robot. Transactions on Petri Nets and Other Models of Concurrency XV. 2020; ():533-540.

Chicago/Turabian Style

Héctor Quintián; Esteban Jove; José Luis Calvo-Rolle; Nuño Basurto; Carlos Cambra; Álvaro Herrero; Emilio Corchado. 2020. "Detecting Performance Anomalies in the Multi-component Software a Collaborative Robot." Transactions on Petri Nets and Other Models of Concurrency XV , no. : 533-540.

Editorial
Published: 20 October 2020 in Neurocomputing
Reads 0
Downloads 0
ACS Style

Manuel Graña; José Manuel López-Guede; José Antonio Sáez Muñoz; Álvaro Herrero Cosio; Héctor Quintián; Emilio Corchado. Special issue SOCO-CISIS 2018: New trends in soft computing and computational intelligence in security and its application in industrial and environmental problems. Neurocomputing 2020, 452, 414 -415.

AMA Style

Manuel Graña, José Manuel López-Guede, José Antonio Sáez Muñoz, Álvaro Herrero Cosio, Héctor Quintián, Emilio Corchado. Special issue SOCO-CISIS 2018: New trends in soft computing and computational intelligence in security and its application in industrial and environmental problems. Neurocomputing. 2020; 452 ():414-415.

Chicago/Turabian Style

Manuel Graña; José Manuel López-Guede; José Antonio Sáez Muñoz; Álvaro Herrero Cosio; Héctor Quintián; Emilio Corchado. 2020. "Special issue SOCO-CISIS 2018: New trends in soft computing and computational intelligence in security and its application in industrial and environmental problems." Neurocomputing 452, no. : 414-415.

Conference paper
Published: 29 August 2020 in Advances in Intelligent Systems and Computing
Reads 0
Downloads 0

Anomaly detection has been a challenging topic for decades and it still is open to new contributions nowadays. More specifically, the detection of anomalies (not only hardware ones but also those affecting the software) suffers from many problems when monitoring cyber-physical systems. One such usual problem is the much fewer data samples of anomalies than those available for the normal functioning of systems. This class-imbalance problem is addressed in the present paper and a novel strategy for oversampling the minority class is applied to an open dataset containing information about the performance of a component-based robot. The proposed strategy mainly consists on selecting the instances to be oversampled according to different criteria instead of randomly oversampling. Obtained results demonstrate that the proposed strategy improves predictive performance, especially when the SVM (Support Vector Machine) is used as classifier.

ACS Style

Nuño Basurto; Michał Woźniak; Carlos Cambra; Álvaro Herrero. Advanced Oversampling for Improved Detection of Software Anomalies in a Robot. Advances in Intelligent Systems and Computing 2020, 3 -12.

AMA Style

Nuño Basurto, Michał Woźniak, Carlos Cambra, Álvaro Herrero. Advanced Oversampling for Improved Detection of Software Anomalies in a Robot. Advances in Intelligent Systems and Computing. 2020; ():3-12.

Chicago/Turabian Style

Nuño Basurto; Michał Woźniak; Carlos Cambra; Álvaro Herrero. 2020. "Advanced Oversampling for Improved Detection of Software Anomalies in a Robot." Advances in Intelligent Systems and Computing , no. : 3-12.

Conference paper
Published: 29 August 2020 in Advances in Intelligent Systems and Computing
Reads 0
Downloads 0

Investments in the telecom industry are often conducted through private participation projects, allowing a group of investors to build and/or operate large infrastructure projects in the host country. As governments progressively removed the barriers to foreign ownership in this sector, these investment consortia have become increasingly international. Obviously, an accurate and early prediction of the success of such projects is very useful. Softcomputing can certainly contribute to address such challenge. However, the error rate obtained by classifiers when trying to forecast the project success is high due to the class imbalance (success vs. fail). To overcome such problem, present paper proposes the application of classifiers (Support Vector Machines and Random Forest) to data improved by means of data balancing techniques (both oversampling and undersampling). Results have been obtained on a real-life and publicly-available dataset from the World Bank.

ACS Style

Nuño Basurto; Alfredo Jiménez; Secil Bayraktar; Álvaro Herrero. Data Balancing to Improve Prediction of Project Success in the Telecom Sector. Advances in Intelligent Systems and Computing 2020, 366 -373.

AMA Style

Nuño Basurto, Alfredo Jiménez, Secil Bayraktar, Álvaro Herrero. Data Balancing to Improve Prediction of Project Success in the Telecom Sector. Advances in Intelligent Systems and Computing. 2020; ():366-373.

Chicago/Turabian Style

Nuño Basurto; Alfredo Jiménez; Secil Bayraktar; Álvaro Herrero. 2020. "Data Balancing to Improve Prediction of Project Success in the Telecom Sector." Advances in Intelligent Systems and Computing , no. : 366-373.

Conference paper
Published: 29 August 2020 in Advances in Intelligent Systems and Computing
Reads 0
Downloads 0

Reducing water consumption is an important target required for a sustainable farming. In order to do that, the actual water needs of different crops must be known and irrigation scheduling must be adjusted to satisfy them. This is a complex task as the phenology of plants and its water demand vary with soil properties and weather conditions. To address such problem, present paper proposes the application of time-series neural networks in order to predict the soil water content in a potato field crop, in which a soil humidity probe was installed. More precisely, Non-linear Input-Output, Non-linear Autoregressive and Non-linear Autoregressive with Exogenous Input models are applied. They are benchmarked, together with different interpolation methods in order to find the best combination for accurately predicting water needs. Promising results have been obtained, supporting the proposed models and their viability when predicting the real humidity level in the soil.

ACS Style

Mercedes Yartu; Carlos Cambra; Milagros Navarro; Carlos Rad; Ángel Arroyo; Álvaro Herrero. Neural Models to Predict Irrigation Needs of a Potato Plantation. Advances in Intelligent Systems and Computing 2020, 600 -613.

AMA Style

Mercedes Yartu, Carlos Cambra, Milagros Navarro, Carlos Rad, Ángel Arroyo, Álvaro Herrero. Neural Models to Predict Irrigation Needs of a Potato Plantation. Advances in Intelligent Systems and Computing. 2020; ():600-613.

Chicago/Turabian Style

Mercedes Yartu; Carlos Cambra; Milagros Navarro; Carlos Rad; Ángel Arroyo; Álvaro Herrero. 2020. "Neural Models to Predict Irrigation Needs of a Potato Plantation." Advances in Intelligent Systems and Computing , no. : 600-613.

Conference paper
Published: 28 August 2020 in Proceedings of the 2nd International Conference on Data Engineering and Communication Technology
Reads 0
Downloads 0

In order to store information in a decentralized context and without the presence of a guarantor authority, it is necessary to replicate the information on multiple nodes. This is the underlying idea of the blockchain, that is generating increasing interest nowadays as one of the most-promising disruptive technologies. However, the ledger is accessible to all participants and if adequate precautions are not taken, this may lead to serious privacy issues. Present paper retraces the history of blockchain with particular attention to the evolution of privacy and anonymity concerns, starting from bitcoin. Furthermore, this work presents the most popular solutions to ensure privacy in the blockchain, as well as the main cryptocurrencies that have been proposed after bitcoin to overcome this problem. A critical survey is presented classifying the approaches in mixing protocols and knowledge limitation protocols. Open challenges and future directions of research in this field are proposed.

ACS Style

Sergio Marciante; Álvaro Herrero. The Evolution of Privacy in the Blockchain: A Historical Survey. Proceedings of the 2nd International Conference on Data Engineering and Communication Technology 2020, 23 -34.

AMA Style

Sergio Marciante, Álvaro Herrero. The Evolution of Privacy in the Blockchain: A Historical Survey. Proceedings of the 2nd International Conference on Data Engineering and Communication Technology. 2020; ():23-34.

Chicago/Turabian Style

Sergio Marciante; Álvaro Herrero. 2020. "The Evolution of Privacy in the Blockchain: A Historical Survey." Proceedings of the 2nd International Conference on Data Engineering and Communication Technology , no. : 23-34.

Conference paper
Published: 28 August 2020 in Proceedings of the 2nd International Conference on Data Engineering and Communication Technology
Reads 0
Downloads 0

The present research work focuses on Intrusion Detection (ID), identifying “anomalous” patterns that may be related to an attack to a system or a network. In order to detect such anomalies, this present paper proposes the visualization of network flows for ID by applying a novel neural method called Beta Hebbian Learning (BHL). Four real-life traffic segments from the University of Twente datasets have been analysed by means of the BHL. Such datasets were gathered from a honeypot directly connected to the Internet so it is guaranteed that it contains real-attack data. Results obtained by BHL provide clear evidence of the ID System clearly separating the different types of attacks present in each dataset and outperforming other well-known projection algorithms.

ACS Style

Héctor Quintián; Esteban Jove; José-Luis Casteleiro-Roca; Daniel Urda; Ángel Arroyo; Jose Luis Calvo-Rolle; Álvaro Herrero; Emilio Corchado. Beta-Hebbian Learning for Visualizing Intrusions in Flows. Proceedings of the 2nd International Conference on Data Engineering and Communication Technology 2020, 446 -459.

AMA Style

Héctor Quintián, Esteban Jove, José-Luis Casteleiro-Roca, Daniel Urda, Ángel Arroyo, Jose Luis Calvo-Rolle, Álvaro Herrero, Emilio Corchado. Beta-Hebbian Learning for Visualizing Intrusions in Flows. Proceedings of the 2nd International Conference on Data Engineering and Communication Technology. 2020; ():446-459.

Chicago/Turabian Style

Héctor Quintián; Esteban Jove; José-Luis Casteleiro-Roca; Daniel Urda; Ángel Arroyo; Jose Luis Calvo-Rolle; Álvaro Herrero; Emilio Corchado. 2020. "Beta-Hebbian Learning for Visualizing Intrusions in Flows." Proceedings of the 2nd International Conference on Data Engineering and Communication Technology , no. : 446-459.

Conference paper
Published: 15 August 2020 in Advances in Intelligent Systems and Computing
Reads 0
Downloads 0

As in many other fields, mobility of people is a fact in higher education. Although attention has been devoted to this topic, new challenges emerge at the present time. Globalization is an increasing reality and investigating the recent trends associated with it is required also in the educational context. In keeping with this idea, the present paper analyzes student mobility in both European and Non-European contexts under the frame of the Computer Science Bachelor at the University of Burgos. The findings are useful for students and lecturers together with university managers and policy makers in order to improve student mobility programs.

ACS Style

Ángel Arroyo; Secil Bayraktar; Carlos Cambra; Daniel Urda; Álvaro Herrero. Trends and Patterns of International Student Mobility: The Case of Bachelor’s Degrees in Computer Science at the University of Burgos. Advances in Intelligent Systems and Computing 2020, 142 -153.

AMA Style

Ángel Arroyo, Secil Bayraktar, Carlos Cambra, Daniel Urda, Álvaro Herrero. Trends and Patterns of International Student Mobility: The Case of Bachelor’s Degrees in Computer Science at the University of Burgos. Advances in Intelligent Systems and Computing. 2020; ():142-153.

Chicago/Turabian Style

Ángel Arroyo; Secil Bayraktar; Carlos Cambra; Daniel Urda; Álvaro Herrero. 2020. "Trends and Patterns of International Student Mobility: The Case of Bachelor’s Degrees in Computer Science at the University of Burgos." Advances in Intelligent Systems and Computing , no. : 142-153.

Research article
Published: 28 July 2020 in Cybernetics and Systems
Reads 0
Downloads 0

Governments are increasingly relying on private participation projects and foreign ownership to access technology and capital in infrastructure projects. As a result, the ubiquity of these projects in all regions of the world is a reality that has caught the attention of both managers and scholars. Predicting the final status (success/failure) of these projects in advance is a key element to be taken into account when deciding about participation. To support this kind of decision, the present paper proposes a multidimensional study where a set of heterogeneous classifiers have been applied to forecast the final success of private participation projects. They are applied to a real-life dataset, comprising information from the World Bank about projects all over the world and within four sectors (Energy, Telecommunication, Transport, and Water Sewerage). Classification results are compared under the scope of the sector and host region of the projects. Results show that the predictability of the success of private participation projects depends on the specific industry and region on which the project operates, with projects in the Telecommunication sector and Sub-Saharan Africa exhibiting the highest rates.

ACS Style

Álvaro Herrero; Secil Bayraktar; Alfredo Jiménez. Machine Learning to Forecast the Success of Infrastructure Projects Worldwide. Cybernetics and Systems 2020, 51, 714 -731.

AMA Style

Álvaro Herrero, Secil Bayraktar, Alfredo Jiménez. Machine Learning to Forecast the Success of Infrastructure Projects Worldwide. Cybernetics and Systems. 2020; 51 (7):714-731.

Chicago/Turabian Style

Álvaro Herrero; Secil Bayraktar; Alfredo Jiménez. 2020. "Machine Learning to Forecast the Success of Infrastructure Projects Worldwide." Cybernetics and Systems 51, no. 7: 714-731.

Journal article
Published: 18 July 2020 in Computers & Electrical Engineering
Reads 0
Downloads 0

Intelligent robots are foreseen as a technology that would be soon present in most public and private environments. In order to increase the trust of humans, robotic systems must be reliable while both response and down times are minimized. In keeping with this idea, present paper proposes the application of machine learning (regression models more precisely) to preprocess data in order to improve the detection of failures. Such failures deeply affect the performance of the software components embedded in human-interacting robots. To address one of the most common problems of real-life datasets (missing values), some traditional (such as linear regression) as well as innovative (decision tree and neural network) models are applied. The aim is to impute missing values with minimum error in order to improve the quality of data and consequently maximize the failure-detection rate. Experiments are run on a public and up-to-date dataset and the obtained results support the viability of the proposed models.

ACS Style

Nuño Basurto; Ángel Arroyo; Carlos Cambra; Álvaro Herrero. Imputation of Missing Values Affecting the Software Performance of Component-based Robots. Computers & Electrical Engineering 2020, 87, 106766 .

AMA Style

Nuño Basurto, Ángel Arroyo, Carlos Cambra, Álvaro Herrero. Imputation of Missing Values Affecting the Software Performance of Component-based Robots. Computers & Electrical Engineering. 2020; 87 ():106766.

Chicago/Turabian Style

Nuño Basurto; Ángel Arroyo; Carlos Cambra; Álvaro Herrero. 2020. "Imputation of Missing Values Affecting the Software Performance of Component-based Robots." Computers & Electrical Engineering 87, no. : 106766.

Journal article
Published: 01 July 2020 in International Journal of Environmental Research and Public Health
Reads 0
Downloads 0

As entrepreneurial interest is believed to represent a causal factor increasing entrepreneurship, research has begun to explore how family systems affect youth entrepreneurial interests. In the present study, we attempt to identify different types of family influence on the entrepreneurial interests of young people. A questionnaire was used to obtain data from 1633 Spanish adolescents (15 to 18 years old) and another questionnaire was used to obtain data from 839 parents. Principal component analysis identified unique family types and revealed that they have differential associations to entrepreneurial interest among youth. These findings reaffirm the influence of family on the entrepreneurial ecosystem and the promotion of an entrepreneurial family culture. This study further suggests that early attention should focus on the detection of entrepreneurial interest among youth so that actions can be implemented in the families to incentivize an entrepreneurial family culture.

ACS Style

María-Isabel Luis-Rico; María-Camino Escolar-Llamazares; Tamara De La Torre-Cruz; Álvaro Herrero; Alfredo Jiménez; Pablo Arranz Val; Carmen Palmero-Cámara; Alfredo Jiménez-Eguizábal. The Association of Parental Interest in Entrepreneurship with the Entrepreneurial Interest of Spanish Youth. International Journal of Environmental Research and Public Health 2020, 17, 4744 .

AMA Style

María-Isabel Luis-Rico, María-Camino Escolar-Llamazares, Tamara De La Torre-Cruz, Álvaro Herrero, Alfredo Jiménez, Pablo Arranz Val, Carmen Palmero-Cámara, Alfredo Jiménez-Eguizábal. The Association of Parental Interest in Entrepreneurship with the Entrepreneurial Interest of Spanish Youth. International Journal of Environmental Research and Public Health. 2020; 17 (13):4744.

Chicago/Turabian Style

María-Isabel Luis-Rico; María-Camino Escolar-Llamazares; Tamara De La Torre-Cruz; Álvaro Herrero; Alfredo Jiménez; Pablo Arranz Val; Carmen Palmero-Cámara; Alfredo Jiménez-Eguizábal. 2020. "The Association of Parental Interest in Entrepreneurship with the Entrepreneurial Interest of Spanish Youth." International Journal of Environmental Research and Public Health 17, no. 13: 4744.

Journal article
Published: 25 June 2020 in Applied Sciences
Reads 0
Downloads 0

In recent years, the digital transformation has been advancing in industrial companies, supported by the Key Enabling Technologies (Big Data, IoT, etc.) of Industry 4.0. As a consequence, companies have large volumes of data and information that must be analyzed to give them competitive advantages. This is of the utmost importance in fields such as Failure Detection (FD) and Predictive Maintenance (PdM). Finding patterns in such data is not easy, but cutting-edge technologies, such as Machine Learning (ML), can make great contributions. As a solution, this study extends Hybrid Unsupervised Exploratory Plots (HUEPs), as a visualization technique that combines Exploratory Projection Pursuit (EPP) and Clustering methods. An extended formulation of HUEPs is proposed, adding for the first time the following EPP methods: Classical Multidimensional Scaling, Sammon Mapping and Factor Analysis. Extended HUEPs are validated in a case study associated with a multinational company in the automotive industry sector. Two real-life datasets containing data gathered from a Waterjet Cutting tool are visualized in an intuitive and informative way. The obtained results show that HUEPs is a technique that supports the continuous monitoring of machines in order to anticipate failures. This contribution to visual data analytics can help companies in decision-making, regarding FD and PdM projects.

ACS Style

Raquel Redondo; Álvaro Herrero; Emilio Corchado; Javier Sedano. A Decision-Making Tool Based on Exploratory Visualization for the Automotive Industry. Applied Sciences 2020, 10, 4355 .

AMA Style

Raquel Redondo, Álvaro Herrero, Emilio Corchado, Javier Sedano. A Decision-Making Tool Based on Exploratory Visualization for the Automotive Industry. Applied Sciences. 2020; 10 (12):4355.

Chicago/Turabian Style

Raquel Redondo; Álvaro Herrero; Emilio Corchado; Javier Sedano. 2020. "A Decision-Making Tool Based on Exploratory Visualization for the Automotive Industry." Applied Sciences 10, no. 12: 4355.

Journal article
Published: 27 March 2020 in Applied Sciences
Reads 0
Downloads 0

The present research work focuses on overcoming cybersecurity problems in the Smart Grid. Smart Grids must have feasible data capture and communications infrastructure to be able to manage the huge amounts of data coming from sensors. To ensure the proper operation of next-generation electricity grids, the captured data must be reliable and protected against vulnerabilities and possible attacks. The contribution of this paper to the state of the art lies in the identification of cyberattacks that produce anomalous behaviour in network management protocols. A novel neural projectionist technique (Beta Hebbian Learning, BHL) has been employed to get a general visual representation of the traffic of a network, making it possible to identify any abnormal behaviours and patterns, indicative of a cyberattack. This novel approach has been validated on 3 different datasets, demonstrating the ability of BHL to detect different types of attacks, more effectively than other state-of-the-art methods.

ACS Style

Rafael Alejandro Vega Vega; Pablo Chamoso-Santos; Alfonso González Briones; José-Luis Casteleiro-Roca; Esteban Jove; María Del Carmen Meizoso-López; Benigno Antonio Rodríguez-Gómez; Héctor Quintián; Álvaro Herrero; Kenji Matsui; Emilio Corchado; José Luis Calvo-Rolle. Intrusion Detection with Unsupervised Techniques for Network Management Protocols over Smart Grids. Applied Sciences 2020, 10, 2276 .

AMA Style

Rafael Alejandro Vega Vega, Pablo Chamoso-Santos, Alfonso González Briones, José-Luis Casteleiro-Roca, Esteban Jove, María Del Carmen Meizoso-López, Benigno Antonio Rodríguez-Gómez, Héctor Quintián, Álvaro Herrero, Kenji Matsui, Emilio Corchado, José Luis Calvo-Rolle. Intrusion Detection with Unsupervised Techniques for Network Management Protocols over Smart Grids. Applied Sciences. 2020; 10 (7):2276.

Chicago/Turabian Style

Rafael Alejandro Vega Vega; Pablo Chamoso-Santos; Alfonso González Briones; José-Luis Casteleiro-Roca; Esteban Jove; María Del Carmen Meizoso-López; Benigno Antonio Rodríguez-Gómez; Héctor Quintián; Álvaro Herrero; Kenji Matsui; Emilio Corchado; José Luis Calvo-Rolle. 2020. "Intrusion Detection with Unsupervised Techniques for Network Management Protocols over Smart Grids." Applied Sciences 10, no. 7: 2276.

Research article
Published: 10 March 2020 in Canadian Journal of Administrative Sciences / Revue Canadienne des Sciences de l'Administration
Reads 0
Downloads 0

This paper investigates the influence of vicarious experience and national animosity on the relationship between corruption and the performance of private participation infrastructure projects. Our analysis of 27,264 projects in 114 countries from 1997 to 2013 shows that higher levels of corruption are associated with higher risk of project failure. We also find that this effect is weakened by the presence of other firms from the same industry, as firms may learn from other companies how to deal with corruption. In contrast, we find the effect is strengthened by the presence of other firms from different industries. This result is due to a lower applicability of knowledge and to an increase in national animosity and discrimination from local stakeholders.

ACS Style

Alfredo Jiménez; Secil Bayraktar; Julio César Puche‐Regaliza; Alvaro Herrero. Corruption and private participation infrastructure projects: The influence of vicarious experience and national animosity. Canadian Journal of Administrative Sciences / Revue Canadienne des Sciences de l'Administration 2020, 37, 513 -527.

AMA Style

Alfredo Jiménez, Secil Bayraktar, Julio César Puche‐Regaliza, Alvaro Herrero. Corruption and private participation infrastructure projects: The influence of vicarious experience and national animosity. Canadian Journal of Administrative Sciences / Revue Canadienne des Sciences de l'Administration. 2020; 37 (4):513-527.

Chicago/Turabian Style

Alfredo Jiménez; Secil Bayraktar; Julio César Puche‐Regaliza; Alvaro Herrero. 2020. "Corruption and private participation infrastructure projects: The influence of vicarious experience and national animosity." Canadian Journal of Administrative Sciences / Revue Canadienne des Sciences de l'Administration 37, no. 4: 513-527.