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With the recent advances in the field of artificial intelligence, an increasing number of decision-making tasks are delegated to software systems. A key requirement for the success and adoption of such systems is that users must trust system choices or even fully automated decisions. To achieve this, explanation facilities have been widely investigated as a means of establishing trust in these systems since the early years of expert systems. With today’s increasingly sophisticated machine learning algorithms, new challenges in the context of explanations, accountability, and trust towards such systems constantly arise. In this work, we systematically review the literature on explanations in advice-giving systems. This is a family of systems that includes recommender systems, which is one of the most successful classes of advice-giving software in practice. We investigate the purposes of explanations as well as how they are generated, presented to users, and evaluated. As a result, we derive a novel comprehensive taxonomy of aspects to be considered when designing explanation facilities for current and future decision support systems. The taxonomy includes a variety of different facets, such as explanation objective, responsiveness, content and presentation. Moreover, we identified several challenges that remain unaddressed so far, for example related to fine-grained issues associated with the presentation of explanations and how explanation facilities are evaluated.

Artificial intelligence (AI) techniques have grown rapidly in recent years in the context of computing with smart mobile phones that typically allows the devices to function in an intelligent manner. Popular AI techniques include machine learning and deep learning methods, natural language processing, as well as knowledge representation and expert systems, can be used to make the target mobile applications intelligent and more effective. In this paper, we present a comprehensive view on “mobile data science and intelligent apps” in terms of concepts and AI-based modeling that can be used to design and develop intelligent mobile applications for the betterment of human life in their diverse day-to-day situation. This study also includes the concepts and insights of various AI-powered intelligent apps in several application domains, ranging from personalized recommendation to healthcare services, including COVID-19 pandemic management in recent days. Finally, we highlight several research issues and future directions relevant to our analysis in the area of mobile data science and intelligent apps. Overall, this paper aims to serve as a reference point and guidelines for the mobile application developers as well as the researchers in this domain, particularly from the technical point of view.

PurposeThis paper reviews literature on the application of intelligent systems in the libraries with a special issue on the ES/AI and Robot. Also, it introduces the potential of libraries to use intelligent systems, especially ES/AI and robots.Design/methodology/approachDescriptive and content review methods are applied, and the researchers critically reviewed the articles related to library ESs and robots from Web of Science as a general database and Emerald as a specific database in library and information science from 2007–2017. Four scopes considered to classify the articles as technology, service, user and resource. It is found that published researches on the intelligent systems have contributed to many librarian purposes like library technical services like the organization of information resources, storage and retrieval of information resources, library public services as reference services, information desk and other purposes.FindingsA review of the previous studies shows that ESs are a useable intelligent system in library and information science that mimic librarian expert’s behaviors to support decision making and management. Also, it is shown that the current information systems have a high potential to be improved by integration with AI technologies. In this researches, librarian robots mostly designed for detection and replacing books on the shelf. Improving the technology of gripping, localizing and human-robot interaction are the main concern in recent librarian robot research. Our conclusion is that we need to develop research in the area of smart resources.Originality/valueThis study has a new approach to the literature review in this area. We compared the published papers in the field of ES/AI and robot and library from two databases, general and specific.

Coverage is accessible to practitioners and academic readers alike. * Features end-of-chapter problems with answers provided in an appendix. * Includes discussions of rule-based systems not available in any other book. * Includes problem sets and tutorial programs available on the Wiley ftp site.

Intelligent scheduling covers various tools and techniques for successfully and efficiently solving the scheduling problems. In this paper, we provide a survey of intelligent scheduling systems by categorizing them into five major techniques containing fuzzy logic, expert systems, machine learning, stochastic local search optimization algorithms and constraint programming. We also review the application case studies of these techniques.

In early December 2019, a new virus named “2019 novel coronavirus (2019-nCoV)” appeared in Wuhan, China. The disease quickly spread worldwide, resulting in the COVID-19 pandemic. In the current work, we will propose a novel fuzzy soft modal (i.e., fuzzy-soft expert system) for early detection of COVID-19. The main construction of the fuzzy-soft expert system consists of five portions. The exploratory study includes sixty patients (i.e., forty males and twenty females) with symptoms similar to COVID-19 in (Nanjing Chest Hospital, Department of Respiratory, China). The proposed fuzzy-soft expert system depended on five symptoms of COVID-19 (i.e., shortness of breath, sore throat, cough, fever, and age). We will use the algorithm proposed by Kong et al. to detect these patients who may suffer from COVID-19. In this way, the present system is beneficial to help the physician decide if there is any patient who has COVID-19 or not. Finally, we present the comparison between the present system and the fuzzy expert system.

This paper develops a new fault-tolerant control (FTC) of wireless sensor network in vehicle that aims to solve three problems in engineering practice: lack of data in sensor failure state, high system complexity and multiple sensors concurrent failure. In the new FTC framework, a new belief rule base expert system for concurrent events is developed to aggregate the data and knowledge and handle the concurrent events. In the FTC framework, fault detection and diagnosis (FDD) part is firstly conducted, and then, the output of the failure sensors is reconstructed based on the output reconstruction strategy by the observation information from the available sensors. In the FDD model, the fault diagnosis strategy of proximity classification based on distance is applied in the FDD model. To further improve the performance of the FTC framework, a new optimization model is proposed. A case study is conducted to illustrate the effectiveness of the proposed framework.

Computer vision is a branch of computer science that studies how computers can ‘see’. It is a field that provides significant value for advancements in academia and artificial intelligence by processing images captured with a camera. In other words, the purpose of computer vision is to impart computers with the functions of human eyes and realise ‘vision’ among computers. Deep learning is a method of realising computer vision using image recognition and object detection technologies. Since its emergence, computer vision has evolved rapidly with the development of deep learning and has significantly improved image recognition accuracy. Moreover, an expert system can imitate and reproduce the flow of reasoning and decision making executed in human experts’ brains to derive optimal solutions. Machine learning, including deep learning, has made it possible to ‘acquire the tacit knowledge of experts’, which was not previously achievable with conventional expert systems. Machine learning ‘systematises tacit knowledge’ based on big data and measures phenomena from multiple angles and in large quantities. In this review, we discuss some knowledge-based computer vision techniques that employ deep learning.

Granular computing focuses on formalizing information granules and unifying them to create a coherent methodological and developmental environment for intelligent system design and analysis. This innovative book presents the unified principles of granular computing along with its comprehensive algorithmic framework and design practices. It explores key concepts and formalisms as well as applications. It also emphasizes the need to consider information granularity as an important design asset that helps in the construction of more realistic models of real-world systems and in facilitating collaborative pursuits of system modeling.

Purpose Nowadays, the agri-food sector is facing several challenges due to a rapid technological change which calls for knowledge sharing (KS) practices to enhance businesses’ performance. This has spurred a collaborative approach and the creation of networks. Since there still is a paucity of research on the quality degree of KS, the purpose of this study is to offer an empirical research on the quality degree of KS by exploring outcome expectations and social exchange dimensions. Theoretically, it is examined by a double lens of social capital and social cognitive theory. Design/methodology/approach This study offers an empirical analysis of 313 directors of 11 “consortia” in the agri-food sector in Italy by using the fuzzy expert system. The model allows to aggregate multi criteria dimensions of KS and rates its quality. Findings As resulted, the quality degree of KS is influenced by outcome expectations – personal and community expectations – and three forms of dimensions of social exchange: structural, relational and cognitive. The paper ends with a discussion of research findings, its limitations and implications. Originality/value As there is still a paucity of research on the determinants of quality degree of KS, the research adopts a double lens of social capital and social cognitive theories to explore what are these determinants.