Multi-source information fusion for smart health with artificial intelligence

The rapid developments in Artificial Intelligence present an opportunity for the research community to provide and advance Smart Health for the well-being of our society. By considering the availability of multi-source information and heterogeneous data in the era of Big Data, this Special Issue explores the theories, methodologies and possible breakthroughs that have designed and adopted information fusion for Smart Health powered by recent Artificial Intelligence advances. Specifically, this Special Issue focuses on three questions; How to achieve and realize human-level intelligence in Smart Health, How to achieve and benefit Smart Health from a multi-disciplinary balance, and How to utilize the power of Big Data for Smart Health. The Special Issue is a great success, with a small number of quality studies carefully selected from an overwhelming amount of contributions.     

Artificial intelligence in education: an exploration

The research field of Artificial Intelligence in Education (AIED) embraces a wide diversity of research interests. Psychology, education and cognitive science are strongly represented, alongside computer science and artificial intelligence. A key interest is in modelling, especially of learning processes and cognition. This paper gives a brief outline of the development of AIED, and examples of current issues and projects. The 'AI' in the title may give a misleading picture of a research field that is in fact dynamic and broad, with many links to the classroom.     

Artificial intelligence for visually impaired

The eyes are an essential tool for human observation and perception of the world, helping people to perform their tasks. Visual impairment causes many inconveniences in the lives of visually impaired people. Therefore, it is necessary to focus on the needs of the visually impaired community. Researchers work from different angles to help visually impaired people live normal lives. The advent of the digital age has profoundly changed the lives of the visually impaired community, making life more convenient. Deep learning, as a promising technology, is also expected to improve the lives of visually impaired people. It is increasingly being used in the diagnosis of eye diseases and the development of visual aids. The earlier accurate diagnosis of the eye disease by the doctor, the sooner the patient can receive the appropriate treatment and the better chances of a cure. This paper summarises recent research on the development of artificial intelligence-based eye disease diagnosis and visual aids. The research is divided according to the purpose of the study into deep learning methods applied in diagnosing eye diseases and smart devices to help visually impaired people in their daily lives. Finally, a summary is given of the directions in which artificial intelligence may be able to assist the visually impaired in the future. In addition, this overview provides some knowledge about deep learning for beginners. We hope this paper will inspire future work on the subjects..     

Artificial intelligence and augmented intelligence collaboration: regaining trust and confidence in the financial sector

ABSTRACT

Robots and chatbots are sophisticated. Artificial intelligence (AI) is increasingly popular in the financial industry due to its ability to provide customers with cheap, efficient and personalised services. This article uses doctrinal sources and a case study to show that many banks and FinTech start-ups are investing in AI. Yet, there are a number of challenges arising from the use of AI which could undermine trust and confidence amongst consumers. This article features the issue of bias and discrimination in banking. There is evidence that algorithms discriminate against certain races and gender. Legislative gaps in the Equality Act 2010 and the General Data Protection Regime will be analysed. Ultimately, human beings are still needed to input, train and help machines to learn. Fortunately, the FCA are leading in regulating technology, from the launch of regulatory sandboxes to their co-operative collaboration with FinTech start-ups on regulatory matters. Augmented intelligence collaboration is needed to enable industry players and regulators to provide seamless regulation and financial stability. The future of AI regulation is inter-disciplinary in approach.     

Artificial intelligence and symbols

The introduction of massive parallelism and the renewed interest in neural networks gives a new need to evaluate the relationship of symbolic processing and artificial intelligence. The physical symbol hypothesis has encountered many difficulties coping with human concepts and common sense. Expert systems are showing more promise for the early stages of learning than for real expertise. There is a need to evaluate more fully the inherent limitations of symbol systems and the potential for programming compared with training. This can give more realistic goals for symbolic systems, particularly those based on logical foundations.     

Artificial intelligence: New jobs from old

The age of artificial intelligence (AI) is upon us, and its effect upon society in the coming years will be noteworthy. Artificial intelligence is a field that encompasses such applications as robotics, expert systems, natural language understanding, speech recognition, and computer vision. The effect of these AI systems upon existing and future job occupations will be important. This paper takes a look at artificial intelligence in terms of the creation of new job categories. Also, the introduction of AI into the organization to better familiarize the employees about AI will be discussed.     

Artificial intelligence and natural magic

Robotics with AI is part of a long tradition that has run from ancient times that treated the precursors of robots, the automata, as part of Natural Magic or conjury. Deception is an integral part of AI and robotics; in some ways they form a science of illusion. There are many robot tasks, such as caring for the elderly, minding children, doing domestic chores and being companionable, that involve working closely with humans and so require some illusion of animacy and thought. We discuss how the natural magic of robotics is assisted by the cultural myth of AI together with innate human predispositions such as zoomorphism, the willing suspension of disbelief and a tendency to interpret AI devices as part of the social world. This approach provides a justifiable way of meeting the goals of AI and robotics provided that researchers do not allow themselves to be deceived by their own illusions.     

Interactive fiction: Artificial intelligence as a mode of sign production

Interactive media need their own idioms that exploit the characteristics of the computer based sign. The fact that the reader can physically influence the course of events in the system changes the author's role, since he no longer creates a linear text but anarrative space that the reader can use to generate stories. Although stories are not simulations of the real world, they must still contain recognizable parts where everyday constraints of time and space hold. AI-techniques can be used to implement these constraints. In fact, we suggest that AI is probably best seen as an aesthetic phenomenon.     

Towards healthcare business intelligence in long-term care: An explorative case study in the Netherlands

This research contributes to the domain of long-term care by exploring knowledge discovery techniques based on a large dataset and guided by representative information needs to better manage both quality of care and financial spendings, as a next step towards more mature healthcare business intelligence in long-term care. We structure this exploratory research according to the steps of the CRoss Industry Standard Process for Data Mining (CRISP-DM) process. Firstly, we interview 22 experts to determine the information needs in long-term care which we, secondly, translate into 25 data mining goals. Thirdly, we perform a single case study at a Dutch long-term care institution with around 850 clients in five locations. We analyze the institution’s database which contains information from April 2008 to April 2012 to identify patterns in incident information, patterns in risk assessment information, the relationship between risk assessments and incident information, patterns in the average duration of stay, and we identify and predict Care Intensity Package (ZZP) combinations. Fourth and finally, we position all data mining goals in a two-by-two matrix to visualize the relative importance of each goal in relation to both quality of care and financial state of care institutions.     

Artificial intelligence-enabled digital transformation in elderly healthcare field: Scoping review

As the ageing population grows continuously, traditional healthcare providers are experiencing difficulty in keeping up with changing and unpredictable demands as well as rising customer expectations. Artificial intelligence (AI) technology is quickly becoming a potent instrument for accelerating the digital transformation in the aged healthcare sector to deal with the high cost, dynamic nature, and unpredictability of the user environment. In this study, we used a thorough literature analysis to examine the advancements brought about by AI in the field of healthcare for the elderly. The study analyzed AI-enabled elderly healthcare-related articles that were published between 2000 and 2021. In total, 63 articles were extracted from the Web of Science. The review revealed that several elderly healthcare fields have developed and implemented AI-enabled systems and scenarios. It also revealed that AI technology has a substantial positive impact on the elderly healthcare field and leads to significant improvements in this field. The foundation for upcoming studies in the area of aged healthcare is laid forth by this literature review. The findings provide practitioners with crucial references for using artificial intelligence technology in elderly healthcare as well as suggestions for future research topics.     

Artificial intelligence and internet of things in small and medium-sized enterprises: A survey

Internet of things (IoT) and artificial intelligence (AI) are popular topics of Industry 4.0. Many publications regarding these topics have been published, but they are primarily focused on larger enterprises. However, small and medium-sized enterprises (SMEs) are considered the economic backbone of many countries, which is why it is increasingly important that these kinds of companies also have easy access to these technologies and can make them operational. This paper presents a comprehensive survey and investigation of how widespread AI and IoT are among manufacturing SMEs, and discusses the current limitations and opportunities towards enabling predictive analytics. Firstly, an overview of the enablers for AI and IoT is provided along with the four analytics capabilities. Hereafter a comprehensive literature review is conducted and its findings showcased. Finally, emerging topics of research and development, making AI and IoT accessible technologies to SMEs, and the associated future trends and challenges are summarised.     

提速道岔故障的人工智能诊断实现

为提高神经网络诊断在实用工程中的确诊率，采用信息融合技术，提出了一种新型的基于集成神经网络的多方面会诊方法。该方法能快速、有效的并行处理反馈来的不同方面的故障信息，具有训练速度快、扩展性强、鲁棒性强、实时诊断等优点。本文将其应用在提速道岔故障诊断中，实现了故障的人工智能诊断。     

人工智能控制理论现状分析与发展探讨

总结了人工智能控制理论在各个不同阶段的发展状况及理论的突破,介绍了人工智能控制技术在各个时期典型应用的标志成果,分析当前人工智能控制面临要解决的问题,探讨人工智能控制理论今后发展方向。     

Advancing artificial intelligence research and dissemination through conference series: Benchmark,scientific impact and the MICAI experience

This article presents an overview, analysis and benchmark of the best-known artificial intelligence (AI) conferences, including the Mexican International Conference on Artificial Intelligence (MICAI) conference series, and describes how MICAI has contributed to both the growth of artificial intelligence (AI) research in Mexico and the advancement of AI research worldwide. Among the prestigious AI conferences examined are the IJCAI, AAAI, ECAI, IBERAMIA, AAJCAI and PRICAI. Features analyzed include number of papers, acceptance rate and the h index as a measure of the scientific impact. The MICAI has been held in Mexico since 2000, when the National Meeting on AI, held by the Mexican Society for Artificial Intelligence (SMIA) since 1983, and the International Symposium on Artificial Intelligence (ISAI), organized by Tecnológico de Monterrey (ITESM) since 1988, merged into a single conference. Conference trends and future developments are also explained.     

AI,anthropocentrism, and the evolution of ‘intelligence’

Intuitive conceptions guide practice, but practice reciprocally reshapes intuition. The intuitive conception of intelligence in AI was originally highly anthropocentric. However, the internal dynamics of AI research have resulted in a divergence from anthropocentric concerns. In particular, the increasing emphasis on commonsense knowledge and peripheral intelligence (perception and movement) in effect constitutes an incipient reorientation of intuitions about the nature of intelligence in a non-anthropocentric direction. I argue that this conceptual shift undermines Joseph Weizenbaum's claim that the project of artificial intelligence is inherently dehumanizing.     

Information fusion as an integrative cross-cutting enabler to achieve robust,explainable, and trustworthy medical artificial intelligence

Medical artificial intelligence (AI) systems have been remarkably successful, even outperforming human performance at certain tasks. There is no doubt that AI is important to improve human health in many ways and will disrupt various medical workflows in the future. Using AI to solve problems in medicine beyond the lab, in routine environments, we need to do more than to just improve the performance of existing AI methods. Robust AI solutions must be able to cope with imprecision, missing and incorrect information, and explain both the result and the process of how it was obtained to a medical expert. Using conceptual knowledge as a guiding model of reality can help to develop more robust, explainable, and less biased machine learning models that can ideally learn from less data. Achieving these goals will require an orchestrated effort that combines three complementary Frontier Research Areas: (1) Complex Networks and their Inference, (2) Graph causal models and counterfactuals, and (3) Verification and Explainability methods. The goal of this paper is to describe these three areas from a unified view and to motivate how information fusion in a comprehensive and integrative manner can not only help bring these three areas together, but also have a transformative role by bridging the gap between research and practical applications in the context of future trustworthy medical AI. This makes it imperative to include ethical and legal aspects as a cross-cutting discipline, because all future solutions must not only be ethically responsible, but also legally compliant.     

Ambient intelligence: Technologies, applications, and opportunities

Ambient intelligence is an emerging discipline that brings intelligence to our everyday environments and makes those environments sensitive to us. Ambient intelligence (AmI) research builds upon advances in sensors and sensor networks, pervasive computing, and artificial intelligence. Because these contributing fields have experienced tremendous growth in the last few years, AmI research has strengthened and expanded. Because AmI research is maturing, the resulting technologies promise to revolutionarize daily human life by making people’s surroundings flexible and adaptive.In this paper, we provide a survey of the technologies that comprise ambient intelligence and of the applications that are dramatically affected by it. In particular, we specifically focus on the research that makes AmI technologies “intelligent”. We also highlight challenges and opportunities that AmI researchers will face in the coming years.     

Using artificial intelligence and data fusion for environmental monitoring: A review and future perspectives

Analyzing satellite images and remote sensing (RS) data using artificial intelligence (AI) tools and data fusion strategies has recently opened new perspectives for environmental monitoring and assessment. This is mainly due to the advancement of machine learning (ML) and data mining approaches, which facilitate extracting meaningful information at a large scale from geo-referenced and heterogeneous sources. This paper presents the first review of AI-based methodologies and data fusion strategies used for environmental monitoring, to the best of the authors’ knowledge. The first part of the article discusses the main challenges of geographical image analysis. Thereafter, a well-designed taxonomy is introduced to overview the existing frameworks, which have been focused on: (i) detecting different environmental impacts, e.g. land cover land use (LULC) change, gully erosion susceptibility (GES), waterlogging susceptibility (WLS), and land salinity and infertility (LSI); (ii) analyzing AI models deployed for extracting the pertinent features from RS images in addition to data fusion techniques used for combining images and/or features from heterogeneous sources; (iii) describing existing publicly-shared and open-access datasets; (iv) highlighting most frequent evaluation metrics; and (v) describing the most significant applications of ML and data fusion for RS image analysis. This is followed by an overview of existing works and discussions highlighting some of the challenges, limitations and shortcomings. To provide the reader with insight into real-world applications, two case studies illustrate the use of AI for classifying LULC changes and monitoring the environmental impacts due to dams’ construction, where classification accuracies of 98.57% and 97.05% have been reached, respectively. Lastly, recommendations and future directions are drawn.     

基于人工智能的类风湿性关节炎中医辅助诊疗系统

类风湿性关节炎(RA)是一种广泛存在且慢性、难治的全身性免疫风湿病,中医在其治疗中具有副作用较少、价格相对低廉等优势,但是中医师的缺乏限制RA中医诊疗方案的推广.因此,文中提出基于人工智能的RA中医辅助诊疗系统.通过对患者病历文本和关节影像数据的学习实现对RA和RA证型的判断,辅助医生诊断,并根据证型智能推荐中医药方.文中还基于RA中医药典籍知识构建知识图谱,在医生诊疗过程中提供诊疗知识指导.系统可辅助经验不足的临床医师做出诊疗决策,提高RA的治疗水平,有助于对RA治疗的研究和推广.