Errors in Cyber-Physical Systems present a major problem given the current state of technological complexity. Self-diagnosis can contribute to address it, being the standpoint of the present paper. Hence, an application of exploratory Machine Learning models to assess the functioning of robot software in order to identify anomalies that lead to low performance is proposed. More precisely, Hybrid Unsupervised Exploratory Plots (HUEPs) are extended through density-based clustering techniques, that are applied together with unsupervised exploratory projection models. As a result, intuitive and informative visualizations of software performance are obtained, supporting the monitoring and anomaly detection tasks. The proposed clustering extension of HUEPs is thoroughly validated on a massive and up-to-date open dataset, obtaining promising results.

Soft-computing remains as one of the promising disciplines that successfully address many different real-world problems. In such circumstances, where demanding constraints are present, this set of ...

Abstract Accurate and reliable forecasting of cancellations is important for successful revenue management in the tourism industry. The objective of this study is to develop classification models to predict hotel booking cancellations. The work involves a number of key steps, such as data preprocessing to properly prepare the data; feature engineering to identify relevant attributes to help improve the predictive ability of the models; hyperparameter settings of the models, including choice of optimizers and incorporation of dropout layers to avoid overfitting in the neural networks; potential overfitting is evaluated using K‐fold cross‐validation; and performance is analysed using the confusion matrix and various performance metrics. The algorithms used are Multilayer Perceptron Neural Network, Radial Basis Function Neural Network, Deep Neural Network, Decision Tree Classifier, Random Forest Classifier, Ada Boost Classifier and XgBoost Classifier. Finally, the results of all models are compared, visualizing Deep Neural Network and XgBoost as the most suitable models for predicting hotel reservation cancellations.

As governments access capital, technology, and managerial expertise from private investors, there is an increasing trend of privatizations in infrastructure projects worldwide. Given the large size of these investments, notably in the sector of telecommunications, investors typically create a consortium with other interested firms, an investment vehicle known as private participation project (PPP). Given the critical repercussions on the rest of the economy, and its large financial losses in case of failure, the prediction of the success of PPPs in telecommunications is of utmost importance. The success of PPPs can be predicted by Machine Learning and it is probed in this article. Hence, widely acknowledged classifiers (k-nearest neighbors [k-NNs], support vector machines [SVMs], and random forest [RF]) are applied to PPPs publicly available data from the World Bank. The results on this highly imbalanced dataset are greatly improved by the application of data balancing techniques. It includes some standard ones (random oversampling, random undersampling, and synthetic minority oversampling technique [SMOTE]), together with some other advanced ones (density-based SMOTE and borderline SMOTE). The satisfactory results validate the proposed application of classifiers on the dataset improved by data-balancing techniques.

Novel solutions, based on soft-computing techniques, are proposed in the present issue. All of them target open problems in the environmental and industrial domains. Thanks to the intelligent syste...

This special issue compiles recent applications of Computational Intelligence in the security domain. It is aimed at both researchers and practitioners from academia and industry who are engaged in deploying intelligent solutions for securing both information and systems. Four papers are included in this special issue, covering a wide variety of case studies, ranging from assisted driving to explainable robotics. Additionally, a variety of cutting-edge techniques are proposed, such as blockchain and autoencoders among others. All these works contribute to the scientific progress in this domain, going one step further. In the first contribution by Barreno et al., it is proposed a fuzzy expert system aimed at identifying and classifying conventional two-lane roads based on their geometric characteristics and the expected vehicle performance (passenger cars, trucks, and buses). The road geometric features were measured by sensors in an equipped vehicle, travelling in real-life roads located in the Madrid region (Spain). Fuzzy and neuro-fuzzy systems are applied in order to classify road sections based on their current geometric characteristics. Additionally, a risk identification system is presented. Its main target is to assess whether a vehicle is driving on a two-lane road with an inappropriate speed. In order to do that, an identification model is applied to some variables such as the vehicle type, the road longitudinal gradient, the angle covered by each horizontal curve, and the existence or not of an additional lane of traffic. A fuzzy risk index is proposed to verify relatively unsafe road sections, where more accidents are prone to happen. The next contribution, by Wilusz and Wojtowicz, proposes an architecture for cryptocurrency insurance framework along with a set of corresponding communication protocols and a smart contract template that jointly reduce various risks to which cryptocurrency-based transactions are exposed. Some of the threats are unintentional credential leaks, fraudulent cryptocurrency exchanges and broken private keys among others. Authors analyse such risks and present a solution that can potentially enable new business models based on this technology. In the proposed insurance framework, the holder transfers the risk to the insurer and the insurer applies technological countermeasures to secure the cryptocurrency unit. Funds can be transferred with the bilateral agreement of the cryptocurrency unit holder and the insurer thanks to the use of multi-signatures required by smart contracts. This framework is analysed from the security standpoint, guaranteeing that it can be deployed in several practical scenarios in various domains of applications. Zayas-Gato et al. address the challenging and up-to-date issue of detecting anomalies in industrial systems. In order to automatically detect the anomalies, a hybrid system is proposed. First, data are clustered by the Density-Based Spatial Clustering of Applications With Noise (DBSCAN) algorithm. Then, eight one-class techniques are benchmarked to model the normal operation the target industrial plants, namely: NCBoP, Autoencoders, Gauss, K-Centers, Minimum Spanning Trees, Parzen Density Estimator, Principal Component Analysis, and Support Vector Data Description. Authors' proposal is validated using two real-life domains with data gathered during different operating points of two industrial plants. The first case is controlling the liquid level in a tank while the second one is manufacturing wind generator blades made of carbon fibre material. The validated approach can be used to improve the classification when the target set is scattered in different clusters or groups. As a result, an early detection of anomalous situations is more feasible, increasing the systems optimisation. The last contribution, by Rodríguez-Lera et al., focus on accountability of intelligent robots. This implies that any robot is in charge of logging its activities with verifiable evidence so that all actions are traceable and the events triggering the action are identified. The main contributions of this study are an overview, modelling, and formalization of the three perspectives of accountability for autonomous robots; a GUI tool to graphically illustrate the knowledge supported on Conceptual Graphs; and finally, a proof of concept summarizing the information dumped on each level using a set of innovative debugging tools. The findings of this research support the idea that a verbose logging system drives to serious performance issues on an accountability platform, and the detailed information provided is far from explaining the robot's behaviour. Besides, large volumes of data might not be managed properly in specific situations; also, the access to such information sacrifices the privacy of the human-robot interaction. The target is that the robot could incorporate the mechanisms that help manufacturers, client/user and developers to know the reasons that trigger a certain behaviour. The guest editors wish to thank Prof. Jon G. Hall (Editor-in-Chief of the Wiley-Blackwell Journal Expert Systems: The Journal of Knowledge Engineering) for providing the opportunity to edit this special issue. Additional thanks to Prof. Lucia Rapanotti, for her support in managing the editing process. Finally, guest editors would also like to thank the referees who have thoroughly evaluated the papers and the editorial staff for their support. The authors declare no potential conflict of interest.

Abstract In robotic systems, both software and hardware components are equally important. However, scant attention has been devoted until now in order to detect anomalies/failures affecting the software component of robots while many proposals exist aimed at detecting physical anomalies. To bridge this gap, the present paper focuses on the study of anomalies affecting the software performance of a robot by using a novel visualization tool. Unsupervised visualization methods from the machine learning field are applied in order to upgrade the recently proposed Hybrid Unsupervised Exploratory Plots (HUEPs). Furthermore, Curvilinear Component Analysis and t-distributed stochastic neighbor embedding are added to the original HUEPs formulation and comprehensively compared. Furthermore, all the different combinations of HUEPs are validated in a real-life scenario. Thanks to this intelligent visualization of robot status, interesting conclusions can be obtained to improve anomaly detection in robot performance.

The authors declare no conflicts of interest.

Smart robotics is one of the fields that are been greatly enriched by the development of Cyber-Physical Systems (CPS). Under this frame, the software facet of robots is as important as the physical one. As it is widely known, the ever-increasing complexity of robots usually implies a parallel increase in the number of failures of such systems. Due to this, system monitoring and anomaly detection play a key role in the implementation of smart robotics and Artificial Intelligence (AI) can significantly contribute to this task. Accordingly, some Exploratory Projection Pursuit techniques, mainly implemented through neural networks, are applied an compared in the present paper to monitor the performance of a component-based robotic software. Thanks to the intuitive projections obtained by these techniques, anomaly detection can be also visually carried out. The visualizations are validated on an open and up-to-date dataset containing information about software anomalies that affect the middleware (a crucial component in CPS) of the analysed robot.

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.

"Recent Advances on the Application of Soft Computing to Industrial and Environmental Enterprises." Cybernetics and Systems, 51(7), pp. 647–648

Internationalization is nowadays common for many large enterprises and as a result Foreign Direct Investment (FDI) flows are increasing. Bilateral psychic distance stimuli between home and host countries and vicarious experience are analyzed, together with other firm- and country-level FDI determinants, by means of a feature selection model to gain deep knowledge about the internationalization strategy of Spanish large firms. Wrapper feature selection based on a genetic algorithm is applied to identify the most important features leading to internationalization decision. Additionally, obtained results are compared to those obtained by logistic regressions (Standard, Rare Event, and Conditional).

Artificial Intelligence (AI) has proved to be a promising field of study that can greatly contribute to the development of computational systems that successfully address an increasing number of challenges.From the very beginning, when the term was coined in 1956 during the Dartmouth Summer Research Project, researchers from this field have shared a vision that computers can be made to perform intelligent tasks (Moor, 2006).A vast array of different techniques has been proposed so far to implement such intelligent systems, ranging from expert or knowledge-based systems (some of the earlier products of AI) to cutting-edge proposals such us deep and machine learning.Among all the branches of the AI tree, in last decades, there has been significant progress in soft computing (Karray & De Silva, 2004).As pointed out by Zadeh (1994), while hard computing focuses on precision, certainty, and rigour, soft computing requires that computation, reasoning, and decision making exploit a tolerance for imprecision and uncertainty wherever possible (Zadeh, 1994).Soft computing can be seen as a

A feedback solution for approximate optimal scheduling of switched systems with autonomous subsystems and continuous-time dynamics is presented. The proposed solution is based on policy iteration algorithm which provides the optimal switching schedule. Algorithms for offline, online, and concurrent implementation of the proposed solution are presented. For online and concurrent training, gradient descent training laws are used and the performance of the training laws is analyzed. The effectiveness of the presented algorithms is verified through numerical simulations.

Current advances in the development of mobile and smart devices have generated a growing demand for natural human-machine interaction and favored the intelligent assistant metaphor, in which a single interface gives access to a wide range of functionalities and services. Conversational systems constitute an important enabling technology in this paradigm. However, they are usually defined to interact in semantic-restricted domains in which users are offered a limited number of options and functionalities. The design of multi-domain systems implies that a single conversational system is able to assist the user in a variety of tasks. In this paper we propose an architecture for the development of multi-domain conversational systems that allows: (1) integrating available multi and single domain speech recognition and understanding modules, (2) combining available system in the different domains implied so that it is not necessary to generate new expensive resources for the multi-domain system, (3) achieving better domain recognition rates to select the appropriate interaction management strategies. We have evaluated our proposal combining three systems in different domains to show that the proposed architecture can satisfactory deal with multi-domain dialogs.

Accurate intrusion detection is still an open challenge. The present work aims at being one step toward that purpose by studying the combination of clustering and visualization techniques. To do that, the mobile visualization connectionist agent-based intrusion detection system (MOVICAB-IDS), previously proposed as a hybrid intelligent IDS based on visualization techniques, is upgraded by adding automatic response thanks to clustering methods. To check the validity of the proposed clustering extension, it has been applied to the identification of different anomalous situations related to the simple network management network protocol by using real-life data sets. Different ways of applying neural projection and clustering techniques are studied in the present article. Through the experimental validation it is shown that the proposed techniques could be compatible and consequently applied to a continuous network flow for intrusion detection.

Neural intelligent systems can provide a visualization of the network traffic for security staff, in order to reduce the widely known high false-positive rate associated with misuse-based Intrusion Detection Systems (IDSs). Unlike previous work, this study proposes an unsupervised neural models that generate an intuitive visualization of the captured traffic, rather than network statistics. These snapshots of network events are immensely useful for security personnel that monitor network behavior. The system is based on the use of different neural projection and unsupervised methods for the visual inspection of honeypot data, and may be seen as a complementary network security tool that sheds light on internal data structures through visual inspection of the traffic itself. Furthermore, it is intended to facilitate verification and assessment of Snort performance (a well-known and widely-used misuse-based IDS), through the visualization of attack patterns. Empirical verification and comparison of the proposed projection methods are performed in a real domain, where two different case studies are defined and analyzed.

In recent years, many solutions have been developed for the Nurse Rostering Problem. When a new problem of this kind arrives, it is not so easy to choose the proper solution from previous work. This study presents an application of a bio-inspired intelligent system to analyse and select previous nurse rostering solutions. This applied research presents a multidisciplinary study based on the application of unsupervised neural projection models in order to identify the similar solutions to the mentioned problem. The system has been tested under a real data set gathered from the current state of the art, achieving promising results.

From an enterprise point of view, Knowledge Management (KM) enables organizations to capture, share, and apply the collective experience and know-how (knowledge) of their staff. Up to now, little effort has been devoted to apply Artificial Intelligent techniques to KM systems. This paper proposes the application of case-based reasoning, in combination with a neural model, to develop a KM system. This combined approach profiles the KM status of the whole organization and automatically generates proposals, aimed at improving the KM situation of organization units. The system is fed with KM data collected at the organization and unit contexts. The outcome consists of customized solutions for different areas of expertise related to the organization units, once a lack of knowledge in any of those has been identified.

SQL Injection attacks on web applications have become one of the most important information security concerns over the past few years. This paper presents a hybrid approach based on the Adaptive Intelligent Intrusion Detector Agent (AIIDA-SQL) for the detection of those attacks. The AIIDA-SQL agent incorporates a Case-Based Reasoning (CBR) engine which is equipped with learning and adaptation capabilities for the classification of SQL queries and detection of malicious user requests. To carry out the tasks of attack classification and detection, the agent incorporates advanced algorithms in the reasoning cycle stages. Concretely, an innovative classification model based on a mixture of an Artificial Neuronal Network together with a Support Vector Machine is applied in the reuse stage of the CBR cycle. This strategy enables to classify the received SQL queries in a reliable way. Finally, a projection neural technique is incorporated, which notably eases the revision stage carried out by human experts in the case of suspicious queries. The experimental results obtained on a real-traffic case study show that AIIDA-SQL performs remarkably well in practice.

This study presents a novel, multidisciplinary research project entitled DIPKIP (data acquisition, intelligent processing, knowledge identification and proposal), which is a Knowledge Management (KM) system that profiles the KM status of a company. Qualitative data is fed into the system that allows it not only to assess the KM situation in the company in a straightforward and intuitive manner, but also to propose corrective actions to improve that situation. DIPKIP is based on four separate steps. An initial “Data Acquisition” step, in which key data is captured, is followed by an “Intelligent Processing” step, using neural projection architectures. Subsequently, the “Knowledge Identification” step catalogues the company into three categories, which define a set of possible theoretical strategic knowledge situations: knowledge deficit, partial knowledge deficit, and no knowledge deficit. Finally, a “Proposal” step is performed, in which the “knowledge processes”—creation/acquisition, transference/distribution, and putting into practice/updating—are appraised to arrive at a coherent recommendation. The knowledge updating process (increasing the knowledge held and removing obsolete knowledge) is in itself a novel contribution. DIPKIP may be applied as a decision support system, which, under the supervision of a KM expert, can provide useful and practical proposals to senior management for the improvement of KM, leading to flexibility, cost savings, and greater competitiveness. The research also analyses the future for powerful neural projection models in the emerging field of KM by reviewing a variety of robust unsupervised projection architectures, all of which are used to visualize the intrinsic structure of high‐dimensional data sets. The main projection architecture in this research, known as Cooperative Maximum‐Likelihood Hebbian Learning (CMLHL), manages to capture a degree of KM topological ordering based on the application of cooperative lateral connections. The results of two real‐life case studies in very different industrial sectors corroborated the relevance and viability of the DIPKIP system and the concepts upon which it is founded.

MOVICAB-IDS enables the more interesting projections of a massive traffic data set to be analysed, thereby providing an overview of any possible anomalous situations taking place on a computer network. This IDS responds to the challenges presented by traffic volume and diversity. It is a connectionist agent-based model extended by means of a functional and mobile visualization interface. The IDS is designed to be more flexible, accessible and portable by running on a great variety of applications, including small mobile ones such as PDA’s, mobile phones or embedded devices. Furthermore, its effectiveness has been demonstrated in different tests.