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It is important to be able to predict the performance of a company in terms of bankruptcy, however, to date, there is no robust automated model capable of predicting corporate bankruptcy regardless of the sector to which a company belongs. This study has estimated the expected probability of bankruptcy of companies in 3 different sectors (the manufacturing sector for which Z-score had been initially developed as well as wholesale and finance; two of the most important sectors in terms of contribution to the Spain’s GDP). The proposed model has a success rate of over 90% in predicting bankruptcy and analyses fewer company’s attributes than the Altman Z-score model.
María E. Pérez-Pons; Javier Parra; Guillermo Hernández; Jorge González; Juan M. Corchado. Machine Learning and Financial Ratios as an Alternative to Altman’s Z-Score Bankruptcy Model in Spanish Companies. Decision Economics: Minds, Machines, and their Society 2021, 130 -139.
AMA StyleMaría E. Pérez-Pons, Javier Parra, Guillermo Hernández, Jorge González, Juan M. Corchado. Machine Learning and Financial Ratios as an Alternative to Altman’s Z-Score Bankruptcy Model in Spanish Companies. Decision Economics: Minds, Machines, and their Society. 2021; ():130-139.
Chicago/Turabian StyleMaría E. Pérez-Pons; Javier Parra; Guillermo Hernández; Jorge González; Juan M. Corchado. 2021. "Machine Learning and Financial Ratios as an Alternative to Altman’s Z-Score Bankruptcy Model in Spanish Companies." Decision Economics: Minds, Machines, and their Society , no. : 130-139.
Smart cities and artificial intelligence (AI) are among the most popular discourses in urban policy circles. Most attempts at using AI to improve efficiencies in cities have nevertheless either struggled or failed to accomplish the smart city transformation. This is mainly due to short-sighted, technologically determined and reductionist AI approaches being applied to complex urbanization problems. Besides this, as smart cities are underpinned by our ability to engage with our environments, analyze them, and make efficient, sustainable and equitable decisions, the need for a green AI approach is intensified. This perspective paper, reflecting authors’ opinions and interpretations, concentrates on the “green AI” concept as an enabler of the smart city transformation, as it offers the opportunity to move away from purely technocentric efficiency solutions towards efficient, sustainable and equitable solutions capable of realizing the desired urban futures. The aim of this perspective paper is two-fold: first, to highlight the fundamental shortfalls in mainstream AI system conceptualization and practice, and second, to advocate the need for a consolidated AI approach—i.e., green AI—to further support smart city transformation. The methodological approach includes a thorough appraisal of the current AI and smart city literatures, practices, developments, trends and applications. The paper informs authorities and planners on the importance of the adoption and deployment of AI systems that address efficiency, sustainability and equity issues in cities.
Tan Yigitcanlar; Rashid Mehmood; Juan M. Corchado. Green Artificial Intelligence: Towards an Efficient, Sustainable and Equitable Technology for Smart Cities and Futures. Sustainability 2021, 13, 8952 .
AMA StyleTan Yigitcanlar, Rashid Mehmood, Juan M. Corchado. Green Artificial Intelligence: Towards an Efficient, Sustainable and Equitable Technology for Smart Cities and Futures. Sustainability. 2021; 13 (16):8952.
Chicago/Turabian StyleTan Yigitcanlar; Rashid Mehmood; Juan M. Corchado. 2021. "Green Artificial Intelligence: Towards an Efficient, Sustainable and Equitable Technology for Smart Cities and Futures." Sustainability 13, no. 16: 8952.
Yearly population growth will lead to a significant increase in agricultural production in the coming years. Twenty-first century agricultural producers will be facing the challenge of achieving food security and efficiency. This must be achieved while ensuring sustainable agricultural systems and overcoming the problems posed by climate change, depletion of water resources, and the potential for increased erosion and loss of productivity due to extreme weather conditions. Those environmental consequences will directly affect the price setting process. In view of the price oscillations and the lack of transparent information for buyers, a multi-agent system (MAS) is presented in this article. It supports the making of decisions in the purchase of sustainable agricultural products. The proposed MAS consists of a system that supports decision-making when choosing a supplier on the basis of certain preference-based parameters aimed at measuring the sustainability of a supplier and a deep Q-learning agent for agricultural future market price forecast. Therefore, different agri-environmental indicators (AEIs) have been considered, as well as the use of edge computing technologies to reduce costs of data transfer to the cloud. The presented MAS combines price setting optimizations and user preferences in regards to accessing, filtering, and integrating information. The agents filter and fuse information relevant to a user according to supplier attributes and a dynamic environment. The results presented in this paper allow a user to choose the supplier that best suits their preferences as well as to gain insight on agricultural future markets price oscillations through a deep Q-learning agent.
María Pérez-Pons; Ricardo Alonso; Oscar García; Goreti Marreiros; Juan Corchado. Deep Q-Learning and Preference Based Multi-Agent System for Sustainable Agricultural Market. Sensors 2021, 21, 5276 .
AMA StyleMaría Pérez-Pons, Ricardo Alonso, Oscar García, Goreti Marreiros, Juan Corchado. Deep Q-Learning and Preference Based Multi-Agent System for Sustainable Agricultural Market. Sensors. 2021; 21 (16):5276.
Chicago/Turabian StyleMaría Pérez-Pons; Ricardo Alonso; Oscar García; Goreti Marreiros; Juan Corchado. 2021. "Deep Q-Learning and Preference Based Multi-Agent System for Sustainable Agricultural Market." Sensors 21, no. 16: 5276.
The agricultural industry must adapt to todays market by using resources efficiently and respecting the environment. This paper presents the analysis of data and the application of the Internet of Things (IoT) and advanced computing technologies in a real-world scenario. The proposed model monitors environmental conditions on a farm through a series of deployed sensors and the most outstanding feature of this model is the robust data transmission it offers. The analysis of information collected by the sensors is measured using state-of-the-art computing technology that helps reduce data traffic between the IoT layers and the cloud. The designed methodology integrates sensors and a state-of-the-art computing platform for data mining. This small study forms the basis for a future test with several operations at the same time.
María E. Pérez-Pons; Ricardo S. Alonso; Javier Parra-Domínguez; Marta Plaza-Hernández; Saber Trabelsi. An Edge-IoT Architecture and Regression Techniques Applied to an Agriculture Industry Scenario. Sustainable Smart Cities and Territories 2021, 92 -102.
AMA StyleMaría E. Pérez-Pons, Ricardo S. Alonso, Javier Parra-Domínguez, Marta Plaza-Hernández, Saber Trabelsi. An Edge-IoT Architecture and Regression Techniques Applied to an Agriculture Industry Scenario. Sustainable Smart Cities and Territories. 2021; ():92-102.
Chicago/Turabian StyleMaría E. Pérez-Pons; Ricardo S. Alonso; Javier Parra-Domínguez; Marta Plaza-Hernández; Saber Trabelsi. 2021. "An Edge-IoT Architecture and Regression Techniques Applied to an Agriculture Industry Scenario." Sustainable Smart Cities and Territories , no. : 92-102.
It is estimated that we spend one-third of our lives at work. It is therefore vital to adapt traditional equipment and systems used in the working environment to the new technological paradigm so that the industry is connected and, at the same time, workers are as safe and protected as possible. Thanks to Smart Personal Protective Equipment (PPE) and wearable technologies, information about the workers and their environment can be extracted to reduce the rate of accidents and occupational illness, leading to a significant improvement. This article proposes an architecture that employs three pieces of PPE: a helmet, a bracelet and a belt, which process the collected information using artificial intelligence (AI) techniques through edge computing. The proposed system guarantees the workers’ safety and integrity through the early prediction and notification of anomalies detected in their environment. Models such as convolutional neural networks, long short-term memory, Gaussian Models were joined by interpreting the information with a graph, where different heuristics were used to weight the outputs as a whole, where finally a support vector machine weighted the votes of the models with an area under the curve of 0.81.
Sergio Márquez-Sánchez; Israel Campero-Jurado; Jorge Herrera-Santos; Sara Rodríguez; Juan Corchado. Intelligent Platform Based on Smart PPE for Safety in Workplaces. Sensors 2021, 21, 4652 .
AMA StyleSergio Márquez-Sánchez, Israel Campero-Jurado, Jorge Herrera-Santos, Sara Rodríguez, Juan Corchado. Intelligent Platform Based on Smart PPE for Safety in Workplaces. Sensors. 2021; 21 (14):4652.
Chicago/Turabian StyleSergio Márquez-Sánchez; Israel Campero-Jurado; Jorge Herrera-Santos; Sara Rodríguez; Juan Corchado. 2021. "Intelligent Platform Based on Smart PPE for Safety in Workplaces." Sensors 21, no. 14: 4652.
The participation of household prosumers in wholesale electricity markets is very limited, considering the minimum participation limit imposed by most market participation rules. The generation capacity of households has been increasing since the installation of distributed generation from renewable sources in their facilities brings advantages for themselves and the system. Due to the growth of self-consumption, network operators have been putting aside the purchase of electricity from households, and there has been a reduction in the price of these transactions. This paper proposes an innovative model that uses the aggregation of households to reach the minimum limits of electricity volume needed to participate in the wholesale market. In this way, the Aggregator represents the community of households in market sales and purchases. An electricity transactions portfolio optimization model is proposed to enable the Aggregator reaching the decisions on which markets to participate to maximize the market negotiation outcomes, considering the day-ahead market, intra-day market, and retail market. A case study is presented, considering the Iberian wholesale electricity market and the Portuguese retail market. A community of 50 prosumers equipped with photovoltaic generators and individual storage systems is used to carry out the experiments. A cost reduction of 6–11% is achieved when the community of households buys and sells electricity in the wholesale market through the Aggregator.
Ricardo Faia; Tiago Pinto; Zita Vale; Juan Corchado. Prosumer Community Portfolio Optimization via Aggregator: The Case of the Iberian Electricity Market and Portuguese Retail Market. Energies 2021, 14, 3747 .
AMA StyleRicardo Faia, Tiago Pinto, Zita Vale, Juan Corchado. Prosumer Community Portfolio Optimization via Aggregator: The Case of the Iberian Electricity Market and Portuguese Retail Market. Energies. 2021; 14 (13):3747.
Chicago/Turabian StyleRicardo Faia; Tiago Pinto; Zita Vale; Juan Corchado. 2021. "Prosumer Community Portfolio Optimization via Aggregator: The Case of the Iberian Electricity Market and Portuguese Retail Market." Energies 14, no. 13: 3747.
The need for studies connecting machine explainability with human behavior is essential, especially for a detailed understanding of a human’s perspective, thoughts, and sensations according to a context. A novel system called RYEL was developed based on Subject-Matter Experts (SME) to investigate new techniques for acquiring higher-order thinking, the perception, the use of new computational explanatory techniques, support decision-making, and the judge’s cognition and behavior. Thus, a new spectrum is covered and promises to be a new area of study called Interpretation-Assessment/Assessment-Interpretation (IA-AI), consisting of explaining machine inferences and the interpretation and assessment from a human. It allows expressing a semantic, ontological, and hermeneutical meaning related to the psyche of a human (judge). The system has an interpretative and explanatory nature, and in the future, could be used in other domains of discourse. More than 33 experts in Law and Artificial Intelligence validated the functional design. More than 26 judges, most of them specializing in psychology and criminology from Colombia, Ecuador, Panama, Spain, Argentina, and Costa Rica, participated in the experiments. The results of the experimentation have been very positive. As a challenge, this research represents a paradigm shift in legal data processing.
Luis Rodríguez Oconitrillo; Juan Vargas; Arturo Camacho; Álvaro Burgos; Juan Corchado. RYEL: An Experimental Study in the Behavioral Response of Judges Using a Novel Technique for Acquiring Higher-Order Thinking Based on Explainable Artificial Intelligence and Case-Based Reasoning. Electronics 2021, 10, 1500 .
AMA StyleLuis Rodríguez Oconitrillo, Juan Vargas, Arturo Camacho, Álvaro Burgos, Juan Corchado. RYEL: An Experimental Study in the Behavioral Response of Judges Using a Novel Technique for Acquiring Higher-Order Thinking Based on Explainable Artificial Intelligence and Case-Based Reasoning. Electronics. 2021; 10 (12):1500.
Chicago/Turabian StyleLuis Rodríguez Oconitrillo; Juan Vargas; Arturo Camacho; Álvaro Burgos; Juan Corchado. 2021. "RYEL: An Experimental Study in the Behavioral Response of Judges Using a Novel Technique for Acquiring Higher-Order Thinking Based on Explainable Artificial Intelligence and Case-Based Reasoning." Electronics 10, no. 12: 1500.
Following data ethics and respecting the clients’ privacy, the banking environment can use the client data that is available to them to offer personalized services to its clients. Intelligent recommender systems can support this attempt through specialized technological architectures. This article proposes the inclusion of CEBRA (CasE-Based Reasoning Application), a case-based reasoning system oriented to commercial banking, in a Fog Computing architecture coordinated by virtual agents. Throughout this article, the model of this architecture is presented and its life cycle is described, and improvements are proposed through the incorporation of several techniques in the retrieve and reuse phases, including the extraction of interests expressed by users on their social network profiles and collaborative filtering systems. A comprehensive case study has been carried out and a dataset of 60,000 cases has been generated to evaluate CEBRA. As a result, the Recommender System is presented, by including, the recommendation algorithm and a REST interface for its use. The recommendations are based on the user’s profile, previous ratings and/or additional knowledge such as the user’s contextual information. The proposal takes advantage of contextual information to support the promotion of banking and financial products, improving user satisfaction.
Elena Hernández-Nieves; Guillermo Hernández; Ana B. Gil-González; Sara Rodríguez-González; Juan M. Corchado. CEBRA: A CasE-Based Reasoning Application to recommend banking products. Engineering Applications of Artificial Intelligence 2021, 104, 104327 .
AMA StyleElena Hernández-Nieves, Guillermo Hernández, Ana B. Gil-González, Sara Rodríguez-González, Juan M. Corchado. CEBRA: A CasE-Based Reasoning Application to recommend banking products. Engineering Applications of Artificial Intelligence. 2021; 104 ():104327.
Chicago/Turabian StyleElena Hernández-Nieves; Guillermo Hernández; Ana B. Gil-González; Sara Rodríguez-González; Juan M. Corchado. 2021. "CEBRA: A CasE-Based Reasoning Application to recommend banking products." Engineering Applications of Artificial Intelligence 104, no. : 104327.
New mapping and location applications focus on offering improved usability and services based on multi-modal door to door passenger experiences. This helps citizens develop greater confidence in and adherence to multi-modal transport services. These applications adapt to the needs of the user during their journey through the data, statistics and trends extracted from their previous uses of the application. The My-Trac application is dedicated to the research and development of these user-centered services to improve the multi-modal experience using various techniques. Among these techniques are preference extraction systems, which extract user information from social networks, such as Twitter. In this article, we present a system that allows to develop a profile of the preferences of each user, on the basis of the tweets published on their Twitter account. The system extracts the tweets from the profile and analyzes them using the proposed algorithms and returns the result in a document containing the categories and the degree of affinity that the user has with each category. In this way, the My-Trac application includes a recommender system where the user receives preference-based suggestions about activities or services on the route to be taken.
Alberto Rivas; Alfonso González-Briones; Juan Cea-Morán; Arnau Prat-Pérez; Juan Corchado. My-Trac: System for Recommendation of Points of Interest on the Basis of Twitter Profiles. Electronics 2021, 10, 1263 .
AMA StyleAlberto Rivas, Alfonso González-Briones, Juan Cea-Morán, Arnau Prat-Pérez, Juan Corchado. My-Trac: System for Recommendation of Points of Interest on the Basis of Twitter Profiles. Electronics. 2021; 10 (11):1263.
Chicago/Turabian StyleAlberto Rivas; Alfonso González-Briones; Juan Cea-Morán; Arnau Prat-Pérez; Juan Corchado. 2021. "My-Trac: System for Recommendation of Points of Interest on the Basis of Twitter Profiles." Electronics 10, no. 11: 1263.
Wearable technologies are becoming a profitable means of monitoring a person’s health state, such as heart rate and physical activity. The use of the smartwatch is becoming consolidated, not only as a novelty but also as a very useful tool for daily use. In addition, other devices, such as helmets or belts, are beneficial for monitoring workers and the early detection of any anomaly. They can provide valuable information, especially in work environments, where they help reduce the rate of accidents and occupational diseases, which makes them powerful Personal Protective Equipment (PPE). The constant monitoring of the worker’s health can be done in real-time, through temperature, falls, noise, impacts, or heart rate meters, activating an audible and vibrating alarm when an anomaly is detected. The gathered information is transmitted to a server in charge of collecting and processing it. In the first place, this paper provides an exhaustive review of the state of the art on works related to electronics for human activity behavior. After that, a smart multisensory bracelet, combined with other devices, developed a control platform that can improve operators’ security in the working environment. Artificial Intelligence and the Internet of Things (AIoT) bring together the information to improve safety on construction sites, power stations, power lines, etc. Real-time and historic data is used to monitor operators’ health and a hybrid system between Gaussian Mixture Model and Human Activity Classification. That is, our contribution is also founded on the use of two machine learning models, one based on unsupervised learning and the other one supervised. Where the GMM gave us a performance of 80%, 85%, 70%, and 80% for the 4 classes classified in real time, the LSTM obtained a result under the confusion matrix of 0.769, 0.892, and 0.921 for the carrying-displacing, falls, and walking-standing activities, respectively. This information was sent in real time through the platform that has been used to analyze and process the data in an alarm system.
Sergio Márquez-Sánchez; Israel Campero-Jurado; Daniel Robles-Camarillo; Sara Rodríguez; Juan Corchado-Rodríguez. BeSafe B2.0 Smart Multisensory Platform for Safety in Workplaces. Sensors 2021, 21, 3372 .
AMA StyleSergio Márquez-Sánchez, Israel Campero-Jurado, Daniel Robles-Camarillo, Sara Rodríguez, Juan Corchado-Rodríguez. BeSafe B2.0 Smart Multisensory Platform for Safety in Workplaces. Sensors. 2021; 21 (10):3372.
Chicago/Turabian StyleSergio Márquez-Sánchez; Israel Campero-Jurado; Daniel Robles-Camarillo; Sara Rodríguez; Juan Corchado-Rodríguez. 2021. "BeSafe B2.0 Smart Multisensory Platform for Safety in Workplaces." Sensors 21, no. 10: 3372.
This article describes the development of a recommender system to obtain buy/sell signals from the results of technical analyses and of forecasts performed for companies operating in the Spanish continuous market. It has a modular design to facilitate the scalability of the model and the improvement of functionalities. The modules are: analysis and data mining, the forecasting system, the technical analysis module, the recommender system, and the visualization platform. The specification of each module is presented, as well as the dependencies and communication between them. Moreover, the proposal includes a visualization platform for high-level interaction between the user and the recommender system. This platform presents the conclusions that were abstracted from the resulting values.
Elena Hernández-Nieves; Javier Parra-Domínguez; Pablo Chamoso; Sara Rodríguez-González; Juan Corchado. A Data Mining and Analysis Platform for Investment Recommendations. Electronics 2021, 10, 859 .
AMA StyleElena Hernández-Nieves, Javier Parra-Domínguez, Pablo Chamoso, Sara Rodríguez-González, Juan Corchado. A Data Mining and Analysis Platform for Investment Recommendations. Electronics. 2021; 10 (7):859.
Chicago/Turabian StyleElena Hernández-Nieves; Javier Parra-Domínguez; Pablo Chamoso; Sara Rodríguez-González; Juan Corchado. 2021. "A Data Mining and Analysis Platform for Investment Recommendations." Electronics 10, no. 7: 859.
Predictive maintenance often relies on the continuous monitorization of equipment behavior, generally provided by sensors or by the very equipment. Additional data from management software, including which materials are being used and what processes are executed on the equipment can be used to enrich the data streams and ontologies can be used to bridge the gap between these different domains, while also facilitating the comprehension of the results obtained by the analytic methods applied to the data. Existing ontologies model these problems independently, and a holistic view that takes in consideration the temporal requirements of predictive maintenance is not yet available. This paper analysis existing ontologies and proposes a number of extensions that bridge the gaps between them, while meeting the time-sensitive requirements of the problem.
Alda Canito; Juan Corchado; Goreti Marreiros. Bridging the Gap Between Domain Ontologies for Predictive Maintenance with Machine Learning. Advances in Intelligent Systems and Computing 2021, 533 -543.
AMA StyleAlda Canito, Juan Corchado, Goreti Marreiros. Bridging the Gap Between Domain Ontologies for Predictive Maintenance with Machine Learning. Advances in Intelligent Systems and Computing. 2021; ():533-543.
Chicago/Turabian StyleAlda Canito; Juan Corchado; Goreti Marreiros. 2021. "Bridging the Gap Between Domain Ontologies for Predictive Maintenance with Machine Learning." Advances in Intelligent Systems and Computing , no. : 533-543.
COVID-19 is caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and has a case-fatality rate of 2–3%, with higher rates among elderly patients and patients with comorbidities. Radiologically, COVID-19 is characterised by multifocal ground-glass opacities, even for patients with mild disease. Clinically, patients with COVID-19 present respiratory symptoms, which are very similar to other respiratory virus infections. Our knowledge regarding the SARS-CoV-2 virus is still very limited. These facts make it vitally important to establish mechanisms that allow to model and predict the evolution of the virus and to analyze the spread of cases under different circumstances. The objective of this article is to present a model developed for the evolution of COVID in the city of Manizales, capital of the Department of Caldas, Colombia, focusing on the methodology used to allow its application to other cases, as well as on the monitoring tools developed for this purpose. This methodology is based on a hybrid model which combines the population dynamics of the SIR model of differential equations with extrapolations based on recurrent neural networks. This combination provides self-explanatory results in terms of a coefficient that fluctuates with the restraint measures, which may be further refined by expert rules that capture the expected changes in such measures.
Luis Castillo Ossa; Pablo Chamoso; Jeferson Arango-López; Francisco Pinto-Santos; Gustavo Isaza; Cristina Santa-Cruz-González; Alejandro Ceballos-Marquez; Guillermo Hernández; Juan Corchado. A Hybrid Model for COVID-19 Monitoring and Prediction. Electronics 2021, 10, 799 .
AMA StyleLuis Castillo Ossa, Pablo Chamoso, Jeferson Arango-López, Francisco Pinto-Santos, Gustavo Isaza, Cristina Santa-Cruz-González, Alejandro Ceballos-Marquez, Guillermo Hernández, Juan Corchado. A Hybrid Model for COVID-19 Monitoring and Prediction. Electronics. 2021; 10 (7):799.
Chicago/Turabian StyleLuis Castillo Ossa; Pablo Chamoso; Jeferson Arango-López; Francisco Pinto-Santos; Gustavo Isaza; Cristina Santa-Cruz-González; Alejandro Ceballos-Marquez; Guillermo Hernández; Juan Corchado. 2021. "A Hybrid Model for COVID-19 Monitoring and Prediction." Electronics 10, no. 7: 799.
A smart city is an environment that uses innovative technologies to make networks and services more flexible, effective, and sustainable with the use of information, digital, and telecommunication technologies, improving the city’s operations for the benefit of its citizens. Most cities incorporate data acquisition elements from their own systems or those managed by subcontracted companies that can be used to optimise their resources: energy consumption, smart meters, lighting, irrigation water consumption, traffic data, camera images, waste collection, security systems, pollution meters, climate data, etc. The city-as-a-platform concept is becoming popular and it is increasingly evident that cities must have efficient management systems capable of deploying, for instance, IoT platforms, open data, etc., and of using artificial intelligence intensively. For many cities, data collection is not a problem, but managing and analysing data with the aim of optimising resources and improving the lives of citizens is. This article presents deepint.net, a platform for capturing, integrating, analysing, and creating dashboards, alert systems, optimisation models, etc. This article shows how deepint.net has been used to estimate pedestrian traffic on the streets of Melbourne (Australia) using the XGBoost algorithm. Given the current situation, it is advisable not to transit urban roads when overcrowded, thus, the model proposed in this paper (and implemented with deepint.net) facilitates the identification of areas with less pedestrian traffic. This use case is an example of an efficient crowd management system, implemented and operated via a platform that offers many possibilities for the management of the data collected in smart territories and cities.
David Garcia-Retuerta; Pablo Chamoso; Guillermo Hernández; Agustín Guzmán; Tan Yigitcanlar; Juan Corchado. An Efficient Management Platform for Developing Smart Cities: Solution for Real-Time and Future Crowd Detection. Electronics 2021, 10, 765 .
AMA StyleDavid Garcia-Retuerta, Pablo Chamoso, Guillermo Hernández, Agustín Guzmán, Tan Yigitcanlar, Juan Corchado. An Efficient Management Platform for Developing Smart Cities: Solution for Real-Time and Future Crowd Detection. Electronics. 2021; 10 (7):765.
Chicago/Turabian StyleDavid Garcia-Retuerta; Pablo Chamoso; Guillermo Hernández; Agustín Guzmán; Tan Yigitcanlar; Juan Corchado. 2021. "An Efficient Management Platform for Developing Smart Cities: Solution for Real-Time and Future Crowd Detection." Electronics 10, no. 7: 765.
The urbanization problems we face may be alleviated using innovative digital technology. However, employing these technologies entails the risk of creating new urban problems and/or intensifying the old ones instead of alleviating them. Hence, in a world with immense technological opportunities and at the same time enormous urbanization challenges, it is critical to adopt the principles of responsible urban innovation. These principles assure the delivery of the desired urban outcomes and futures. We contribute to the existing responsible urban innovation discourse by focusing on local government artificial intelligence (AI) systems, providing a literature and practice overview, and a conceptual framework. In this perspective paper, we advocate for the need for balancing the costs, benefits, risks and impacts of developing, adopting, deploying and managing local government AI systems in order to achieve responsible urban innovation. The statements made in this perspective paper are based on a thorough review of the literature, research, developments, trends and applications carefully selected and analyzed by an expert team of investigators. This study provides new insights, develops a conceptual framework and identifies prospective research questions by placing local government AI systems under the microscope through the lens of responsible urban innovation. The presented overview and framework, along with the identified issues and research agenda, offer scholars prospective lines of research and development; where the outcomes of these future studies will help urban policymakers, managers and planners to better understand the crucial role played by local government AI systems in ensuring the achievement of responsible outcomes.
Tan Yigitcanlar; Juan Corchado; Rashid Mehmood; Rita Li; Karen Mossberger; Kevin Desouza. Responsible Urban Innovation with Local Government Artificial Intelligence (AI): A Conceptual Framework and Research Agenda. Journal of Open Innovation: Technology, Market, and Complexity 2021, 7, 71 .
AMA StyleTan Yigitcanlar, Juan Corchado, Rashid Mehmood, Rita Li, Karen Mossberger, Kevin Desouza. Responsible Urban Innovation with Local Government Artificial Intelligence (AI): A Conceptual Framework and Research Agenda. Journal of Open Innovation: Technology, Market, and Complexity. 2021; 7 (1):71.
Chicago/Turabian StyleTan Yigitcanlar; Juan Corchado; Rashid Mehmood; Rita Li; Karen Mossberger; Kevin Desouza. 2021. "Responsible Urban Innovation with Local Government Artificial Intelligence (AI): A Conceptual Framework and Research Agenda." Journal of Open Innovation: Technology, Market, and Complexity 7, no. 1: 71.
Evaluating web traffic on a web server is highly critical for web service providers since, without a proper demand forecast, customers could have lengthy waiting times and abandon that website. However, this is a challenging task since it requires making reliable predictions based on the arbitrary nature of human behavior. We introduce an architecture that collects source data and in a supervised way performs the forecasting of the time series of the page views. Based on the Wikipedia page views dataset proposed in a competition by Kaggle in 2017, we created an updated version of it for the years 2018–2020. This dataset is processed and the features and hidden patterns in data are obtained for later designing an advanced version of a recurrent neural network called Long Short-Term Memory. This AI model is distributed training, according to the paradigm called data parallelism and using the Downpour training strategy. Predictions made for the seven dominant languages in the dataset are accurate with loss function and measurement error in reasonable ranges. Despite the fact that the analyzed time series have fairly bad patterns of seasonality and trend, the predictions have been quite good, evidencing that an analysis of the hidden patterns and the features extraction before the design of the AI model enhances the model accuracy. In addition, the improvement of the accuracy of the model with the distributed training is remarkable. Since the task of predicting web traffic in as precise quantities as possible requires large datasets, we designed a forecasting system to be accurate despite having limited data in the dataset. We tested the proposed model on the new Wikipedia page views dataset we created and obtained a highly accurate prediction; actually, the mean absolute error of predictions regarding the original one on average is below 30. This represents a significant step forward in the field of time series prediction for web traffic forecasting.
Roberto Casado-Vara; Angel Martin del Rey; Daniel Pérez-Palau; Luis De-La-Fuente-Valentín; Juan Corchado. Web Traffic Time Series Forecasting Using LSTM Neural Networks with Distributed Asynchronous Training. Mathematics 2021, 9, 421 .
AMA StyleRoberto Casado-Vara, Angel Martin del Rey, Daniel Pérez-Palau, Luis De-La-Fuente-Valentín, Juan Corchado. Web Traffic Time Series Forecasting Using LSTM Neural Networks with Distributed Asynchronous Training. Mathematics. 2021; 9 (4):421.
Chicago/Turabian StyleRoberto Casado-Vara; Angel Martin del Rey; Daniel Pérez-Palau; Luis De-La-Fuente-Valentín; Juan Corchado. 2021. "Web Traffic Time Series Forecasting Using LSTM Neural Networks with Distributed Asynchronous Training." Mathematics 9, no. 4: 421.
The current energy strategy of the European Union puts the end-user as a key participant in electricity markets. The creation of energy communities has been encouraged by the European Union to increase the penetration of renewable energy and reduce the overall cost of the energy chain. Energy communities are mostly composed of prosumers, which may be households with small-size energy production equipment such as rooftop photovoltaic panels. The local electricity market is an emerging concept that enables the active participation of end-user in the electricity markets and is especially interesting when energy communities are in place. This paper proposes an optimization model to schedule peer-to-peer transactions via local electricity market, grid transactions in retail market, and battery management considering the photovoltaic production of households. Prosumers have the possibility of transacting energy with the retailer or with other consumers in their community. The problem is modeled using mixed-integer linear programming, containing binary and continuous variables. Four scenarios are studied, and the impact of battery storage systems and peer-to-peer transactions is analyzed. The proposed model execution time according to the number of prosumers involved (3, 5, 10, 15, or 20) in the optimization is analyzed. The results suggest that using a battery storage system in the energy community can lead to energy savings of 11-13%. Besides, combining the use of peer-to-peer transactions and energy storage systems can potentially provide energy savings of up to 25% in the overall costs of the community members.
Ricardo Faia; Joao Soares; Tiago Pinto; Fernando Lezama; Zita Vale; Juan Manuel Corchado. Optimal Model for Local Energy Community Scheduling Considering Peer to Peer Electricity Transactions. IEEE Access 2021, 9, 12420 -12430.
AMA StyleRicardo Faia, Joao Soares, Tiago Pinto, Fernando Lezama, Zita Vale, Juan Manuel Corchado. Optimal Model for Local Energy Community Scheduling Considering Peer to Peer Electricity Transactions. IEEE Access. 2021; 9 (99):12420-12430.
Chicago/Turabian StyleRicardo Faia; Joao Soares; Tiago Pinto; Fernando Lezama; Zita Vale; Juan Manuel Corchado. 2021. "Optimal Model for Local Energy Community Scheduling Considering Peer to Peer Electricity Transactions." IEEE Access 9, no. 99: 12420-12430.
Electric vehicles have emerged as one of the most promising technologies, and their mass introduction may pose threats to the electricity grid. Several solutions have been proposed in an attempt to overcome this challenge in order to ease the integration of electric vehicles. A promising concept that can contribute to the proliferation of electric vehicles is the local electricity market. In this way, consumers and prosumers may transact electricity between peers at the local community level, reducing congestion, energy costs and the necessity of intermediary players such as retailers. Thus, this paper proposes an optimization model that simulates an electric energy market between prosumers and electric vehicles. An energy community with different types of prosumers is considered (household, commercial and industrial), and each of them is equipped with a photovoltaic panel and a battery system. This market is considered local because it takes place within a distribution grid and a local energy community. A mixed-integer linear programming model is proposed to solve the local energy transaction problem. The results suggest that our approach can provide a reduction between 1.6% to 3.5% in community energy costs.
Ricardo Faia; João Soares; Zita Vale; Juan Manuel Corchado. An Optimization Model for Energy Community Costs Minimization Considering a Local Electricity Market between Prosumers and Electric Vehicles. Electronics 2021, 10, 129 .
AMA StyleRicardo Faia, João Soares, Zita Vale, Juan Manuel Corchado. An Optimization Model for Energy Community Costs Minimization Considering a Local Electricity Market between Prosumers and Electric Vehicles. Electronics. 2021; 10 (2):129.
Chicago/Turabian StyleRicardo Faia; João Soares; Zita Vale; Juan Manuel Corchado. 2021. "An Optimization Model for Energy Community Costs Minimization Considering a Local Electricity Market between Prosumers and Electric Vehicles." Electronics 10, no. 2: 129.
Internet of Things (IoT) should not be seen only as a cost reduction mechanism for manufacturing companies; instead, it should be seen as the basis for transition to a new business model that monetizes the data from an intelligent ecosystem. In this regard, deciphering the operation of the value creation system and finding the balance between the digital strategy and the deployment of technological platforms, are the main motivations behind this research. To achieve the proposed objectives, systems theory has been adopted in the conceptualization stage, later, fuzzy logic has been used to structure a subsystem for the evaluation of input parameters. Subsequently, system dynamics have been used to build a computational representation and later, through dynamic simulation, the model has been adjusted according to iterations and the identified limits of the system. Finally, with the obtained set of results, different value creation and capture behaviors have been identified. The simulation model, based on the conceptualization of the system and the mathematical representation of the value function, allows to establish a frame of reference for the evaluation of the behaviour of IoT ecosystems in the context of the connected home.
Carlos Alberto Lopez; Luis Fernando Castillo; Juan M. Corchado. Discovering the Value Creation System in IoT Ecosystems. Sensors 2021, 21, 328 .
AMA StyleCarlos Alberto Lopez, Luis Fernando Castillo, Juan M. Corchado. Discovering the Value Creation System in IoT Ecosystems. Sensors. 2021; 21 (2):328.
Chicago/Turabian StyleCarlos Alberto Lopez; Luis Fernando Castillo; Juan M. Corchado. 2021. "Discovering the Value Creation System in IoT Ecosystems." Sensors 21, no. 2: 328.
Predictive maintenance is a field of research that has emerged from the need to improve the systems in place. This research focuses on controlling the degradation of photovoltaic (PV) modules in outdoor solar panels, which are exposed to a variety of climatic loads. Improved reliability, operation, and performance can be achieved through monitoring. In this study, a system capable of predicting the output power of a solar module was implemented. It monitors different parameters and uses automatic learning techniques for prediction. Its use improved reliability, operation, and performance. On the other hand, automatic learning algorithms were evaluated with different metrics in order to optimize and find the best configuration that provides an optimal solution to the problem. With the aim of increasing the share of renewable energy penetration, an architectural proposal based on Edge Computing was included to implement the proposed model into a system. The proposed model is designated for outdoor predictions and offers many advantages, such as monitoring of individual panels, optimization of system response, and speed of communication with the Cloud. The final objective of the work was to contribute to the smart Energy system concept, providing solutions for planning the entire energy system together with the identification of suitable energy infrastructure designs and operational strategies.
Jorge Vicente-Gabriel; Ana-Belén Gil-González; Ana Luis-Reboredo; Pablo Chamoso; Juan M. Corchado. LSTM Networks for Overcoming the Challenges Associated with Photovoltaic Module Maintenance in Smart Cities. Electronics 2021, 10, 78 .
AMA StyleJorge Vicente-Gabriel, Ana-Belén Gil-González, Ana Luis-Reboredo, Pablo Chamoso, Juan M. Corchado. LSTM Networks for Overcoming the Challenges Associated with Photovoltaic Module Maintenance in Smart Cities. Electronics. 2021; 10 (1):78.
Chicago/Turabian StyleJorge Vicente-Gabriel; Ana-Belén Gil-González; Ana Luis-Reboredo; Pablo Chamoso; Juan M. Corchado. 2021. "LSTM Networks for Overcoming the Challenges Associated with Photovoltaic Module Maintenance in Smart Cities." Electronics 10, no. 1: 78.