Artificial Intelligence-based Technologies Research Articles

What makes AI ‘intelligent’ and ‘caring’? Exploring affect and relationality across three sites of intelligence and care

This paper scrutinises how AI and robotic technologies are transforming the relationships between people and machines in new affective, embodied and relational ways. Through investigating what it means to exist as human ‘in relation’ to AI across health and care contexts, we aim to make three main contributions. (1) We start by highlighting the complexities of philosophical issues surrounding the concepts of “artificial intelligence” and “ethical machines.” (2) We outline some potential challenges and opportunities that the creation of such technologies may bring in the health and care settings. We focus on AI applications that interface with health and care via examples where AI is explicitly designed as an ‘augmenting’ technology that can overcome human bodily and cognitive as well as socio-economic constraints. We focus on three dimensions of ‘intelligence’ - physical, interpretive, and emotional - using the examples of robotic surgery, digital pathology, and robot caregivers, respectively. Through investigating these areas, we interrogate the social context and implications of human-technology interaction in the interrelational sphere of care practice. (3) We argue, in conclusion, that there is a need for an interdisciplinary mode of theorising ‘intelligence’ as relational and affective in ways that can accommodate the fragmentation of both conceptual and material boundaries between human and AI, and human and machine. Our aim in investigating these sociological, philosophical and ethical questions is primarily to explore the relationship between affect, relationality and ‘intelligence,’ the intersection and integration of ‘human’ and ‘artificial’ intelligence, through an examination of how AI is used across different dimensions of intelligence. This allows us to scrutinise how ‘intelligence’ is ultimately conveyed, understood and (technologically or algorithmically) configured in practice through emerging relationships that go beyond the conceptual divisions between humans and machines, and humans vis-à-vis artificial intelligence-based technologies.

Longevity medicine: upskilling the physicians of tomorrow

Longevity medicine: upskilling the physicians of tomorrow

Artificial intelligence-based network traffic analysis and automatic optimization technology.

Network operation and maintenance (O & M) activities of data centers focus mainly on checking the operating states of devices. O & M engineers determine how services are running and the bearing capacity of a data center by checking the operating states of devices. However, this method cannot reflect the real transmission status of business data; therefore, engineers cannot fully comprehensively perceive the overall running conditions of businesses. In this paper, ERSPAN (Encapsulated Remote Switch Port Analyzer) technology is applied to deliver stream matching rules in the forwarding path of TCP packets and mirror the TCP packets into the network O & M AI collector, which is used to conduct an in-depth analysis on the TCP packets, collect traffic statistics, recapture the forwarding path, carry out delayed computing, and identify applications. This enables O & M engineers to comprehensively perceive the service bearing status in a data center, and form a tightly coupled correlation model between networks and services through end-to-end visualized modeling, providing comprehensive technical support for data center optimization and early warning of network risks.

Application of Artificial Intelligence-Based Technologies in the Healthcare Industry: Opportunities and Challenges.

This study examines the current state of artificial intelligence (AI)-based technology applications and their impact on the healthcare industry. In addition to a thorough review of the literature, this study analyzed several real-world examples of AI applications in healthcare. The results indicate that major hospitals are, at present, using AI-enabled systems to augment medical staff in patient diagnosis and treatment activities for a wide range of diseases. In addition, AI systems are making an impact on improving the efficiency of nursing and managerial activities of hospitals. While AI is being embraced positively by healthcare providers, its applications provide both the utopian perspective (new opportunities) and the dystopian view (challenges to overcome). We discuss the details of those opportunities and challenges to provide a balanced view of the value of AI applications in healthcare. It is clear that rapid advances of AI and related technologies will help care providers create new value for their patients and improve the efficiency of their operational processes. Nevertheless, effective applications of AI will require effective planning and strategies to transform the entire care service and operations to reap the benefits of what technologies offer.

Using artificial intelligence technologies for sustainable development

Artificial intelligence as a simulator of human behavior and thinking emerged as a result of machine learning. Through AI, they recognize and interpret data, on the basis of which programs of various types of activities are subsequently built. The rapid introduction of artificial intelligence-based technologies into the economic and social spheres of the international community has not been left out of the United Nations’ view from the point of view of using the capabilities of digital computers to solve problems at the level of intelligent beings in order to achieve the goals of sustainable development. The article discusses the specific aspects of I, the application of which will make the process of achieving the SDGs more effective and of high-quality.

Variational level set and fuzzy clustering for enhanced thermal image segmentation and damage assessment

One of fundamental steps in the infrared thermographic process is the accurate segmentation of defects displayed on thermal images. State-of-the art segmentation algorithms are still inefficient to background noise, which may cause poor damage detection. In this study, an infrared image segmentation algorithm combined with artificial intelligence-based technology such as the variational level set and fuzzy clustering algorithm is proposed to enhance the quality of thermal images for damage assessment. Local Shannon entropy and fuzzy membership functions are introduced into the external clustering energy in order to make the algorithm robust to the clustering segmentation of noisy images. A higher-order derivative edge detection operator is used in the regularization energy to solve the singularity in the evolution of the level set function. An internal penalty energy is also introduced into the energy functional to avoid the re-initialization of the level set function and reduce the computational time. Experimental results on thermographic data are shown to demonstrate the efficiency and robustness of the proposed methodology.

Artificial Intelligence and the GDPR: Inevitable Nemeses?

Abstract The rapid development of computer technology over the past decades has brought about countless benefits across industries and social benefits as well—constant interpersonal connectivity is facilitated through numerous communication channels and social media outlets, energy-producing enterprises employ complex machinery management systems for increased efficiency, ease of access and safety, hedge funds make use of high-frequency trading algorithms to engage in trades happening at a fraction of a second, while medical professionals use predictive technologies to diagnose diseases and forecast viral outbreaks. Widespread adoption of technology necessitated the creation of regulatory frameworks that would ensure the safeguarding of rights and regulatory and judicial supervision over the exploitation of high technology. one such framework is the Gdpr, created due to the need for a comprehensive, contemporary legal regime governing the processing of personal data in a time when such data has become a commodity that is traded and sold in return for services or financial gain. However, in the authors’ view, the Gdpr suffers in terms of efficacy in the context of artificial intelligence-based technologies, and full compliance of data controllers and processors employing such technologies is unlikely to be achieved, particularly in regards to the right to information, the general principle of transparency and the right to erasure. The article provides an overview of these issues, including a discussion on the movement towards a regime of data ownership, and proposes legislative amendments as an effective method of mitigating these drawbacks.

Five-Year Cost-Effectiveness Modeling of Primary Care-Based, Nonmydriatic Automated Retinal Image Analysis Screening Among Low-Income Patients With Diabetes.

Artificial intelligence-based technology systems offer an alternative solution for diabetic retinopathy (DR) screening compared with standard, in-office dilated eye examinations. We performed a cost-effectiveness analysis of Automated Retinal Image Analysis System (ARIAS)-based DR screening in a primary care medicine clinic that serves a low-income patient population. A model-based, cost-effectiveness analysis of two DR screening systems was created utilizing data from a recent study comparing adherence rates to follow-up eye care among adults ages 18 or older with a clinical diagnosis of diabetes. In the study, the patients were prescreened with an ARIAS-based, nonmydriatic (undilated), point-of-care tool in the primary care setting and were compared with patients with diabetes who were referred for dilated retinal screening without prescreening, as is the current standard of care. Using a Markov model with microsimulation resulting in a total of 600 000 simulated patient experiences, we calculated the incremental cost-utility ratio (ICUR) of the two screening approaches, with regard to five-year cost-effectiveness of DR screening and treatment of vision-threatening DR. At five years, ARIAS-based screening showed similar utility as the standard of care screening systems. However, ARIAS reduced costs by 23.3%, with an ICUR of $258 721.81 comparing the current practice to ARIAS. Primary care-based ARIAS DR screening is cost-effective when compared with standard of care screening methods.

Policy Approaches to Artificial Intelligence Based Technologies in China, European Union and the United States

This paper provides a summary survey of the policy approaches to artificial intelligence-based technologies in China, the European Union, and the United States. China has the most aggressive approach, launching major initiatives since 2015 such as ‘Made in China 2025’, the Internet Plus Plan, the New Generation Artificial Intelligence Development Plan, and the Artificial Intelligence Standardization White Paper. In 2018, the EU finalized both the European AI Strategy and ‘Made in Europe’ or the Coordinated Plan on the Development and Use of Artificial Intelligence. Despite a traditional reticence to adopt national strategies and perhaps pushed by growing concerns about China, the US Government announced the American AI Initiative and a National AI R&D Strategic Plan in 2019. The AI approaches in these three economies reflect their relative strengths—state control in China, citizen voice in Europe, and business practices in America. Unencumbered by privacy concerns, China’s strategy is geared to exploit the abundance of domestic data and to develop AI talent through central schemes and massive injections of money. The European Union’s regulations and spending priorities are guided by the objective of building citizen trust in AI-based technologies by safeguarding privacy and ameliorating disruptions in national labor markets. The mainstay of the US approach is to strengthen the linkages between business and AI-related research, and find ways to fund basic R&D. Despite efforts to indigenize AI innovations, all three economies face challenges. China’s AI strategy continues to rely disproportionately on just three tech giants: Baidu, Tencent and Alibaba, which have investments in more than a 100 AI-involved companies. Europe’s AI resources are unbalanced geographically—a quarter of Europe’s AI talent is in the UK and another quarter in Germany and France—and Brexit poses a serious risk. More than half of the AI talent in the US is foreign born, so immigration policies will inevitably be a central component of a national AI strategy.

A Review on Robust Computational Approaches Based Identification and Authentication of Herbal Raw Drugs

Background: Over the last decade, there has been a sudden rise in the demand for herbal as well as Information and Technology (IT) industry around the world. Identification of plant species has become useful and relevant to all the members of the society including farmers, traders, hikers, etc. Conventional authentication techniques such as morphological characterization, histological methods, and optical microscopy require multiple skills which are tedious, timeconsuming and difficult to learn for non-experts. This creates a hurdle for individuals interested in acquiring knowledge of species. Relying on rapid, economical and computerized approaches to identify and authenticate medicinal plants has become a recent development. Objective: The purpose of this review is to summarize artificial intelligence-based technologies for wider dissemination of common plant-based knowledge such as identification and authentication to common people earlier limited to only experts. Methods: A robust plant identification design enabling automated plant-organ and feature-based identification utilizing pattern recognition and image processing techniques resulting in image retrieval and recognition has been highlighted in this review for all the concerned stakeholders. Attempts have been made to compare conventional authentication methods with advanced computerized techniques to emphasize the advantages and future applications of an automated identification system in countering adulteration and providing fair trade opportunities to farmers. Results: Major findings suggested that microscopical features such as shape and size of calcium oxalate crystals, trichomes, scleriods, stone cells, fibers, etc. are the essential descriptors for identification and authentication of herbal raw drugs using computational approaches. Conclusion: This computational design can be successfully employed to address quality issues of medicinal plants. Therefore, computational techniques proved as a milestone in the growth of agriculture and medicinal plant industries.

Policy strategies for personalising medicine “in the data moment”

Objective‘Data’ is relevant in evolving value-based healthcare that involves machine learning and artificial intelligence-based technologies which are increasingly changing the landscape of personalised medicine (PM). However, a lack in adequate data for decision making may lead to new forms of health inequalities spite of the advancement in the technological front. There exists a dearth in alignment between incentive structures for innovation and policy measures for collective action and data transparency. Against this background, this article identifies the legal challenges for a data-driven PM in Europe. Transparency, data protection and Intellectual property rights (IP) are major legal challenges for data-oriented personalised research in Europe. Consequently, there is a need for restructuring public policy strategies in the interest of patients. MethodLegal dogmatic analysis of structural inefficiencies within interrelated legal paradigm over data that limits a functional development of a data-driven PM. ResultEngaging in the data movement in personalised health care calls for special attention to strategic tools such as co-regulation and self-regulation that bridge the gap in law and practice.

Transformation of the Educational Services Market in the Russian Federation

The modern market for educational services is undergoing major changes. The rapidly evolving digitalization of public life and economic relations, as well as the manifestation of global financial, environmental and, more recently, biological threats, have contributed to the global, large-scale and widespread introduction of remote educational technologies at all levels of the traditional education system. This process has led to the beginning of the transformation of the educational services market in the Russian Federation as well. The emergence of digital remote education at the state level, in fact, changed the classification of the educational services market at the fundamental level, highlighting, besides the market of on-campus and off-campus education, the market of remote educational services. Ideological problems of perception of possible negative results of education, economy and society digitalization by the public arose. The introduction of artificial intelligence-based technologies into the society management system has posed a threat of digital totalitarianism. The prospect of digital design of society in future generations based on mass truncated remote educational services should be a central topic of scientific research and public discussion.

Enhancing polymer electrolyte membrane fuel cell system diagnostics through semantic modelling

Polymer electrolyte membrane fuel cells (PEMFC) are a promising technology for economic and environmentally friendly energy production. However, they haven’t reached their full potential in the market yet as only few reliable PEMFC systems have successfully passed the prototyping face. A drawback of the current diagnostic tools is that only a select few are of high genericity, reliability and can perform efficiently on-line at the same time. Furthermore, there is only limited research identifying both PEMFC stack faults and ancillary system faults simultaneously. While none of the existing tools can be interrogated by the end-user. In this research, we develop novel artificial intelligence-based technologies to overcome these existing barriers, i.e., (i) a semantically enriched integrating schema (ontology) of the overall operation and structure of the PEMFC that allows automatic inference engines to automatically deduce fault detection; (ii) a knowledge-based, light-weight, on-line fuel cell system diagnosis (FuCSyDi) platform. FuCSyDi detects and provides the location of failures by considering only the data from the reliable sensors. Additionally, it provides the reasons underpinning any forthcoming failures and enables the end-user to interrogate the platform for further information regarding its operation and structure. Our platform is validated by performing tests against common automotive stress conditions. This innovative approach enhances the reliability of the fuel cell system diagnosis and, hence, its lifetime performance.

Orchestrating Artificial Intelligence in Exit Strategies from Lockdowns

The novel human coronavirus is highly contagious and can lead to significant morbidity and mortality. This has triggered major and cascading economic and societal consequences that have contributed considerably to global impact and suffering. It is obvious that there is no immediate solution and one has to learn to live with the virus until either herd immunity has been achieved or a vaccine has been found. As countries exit lockdowns and economies try to revive themselves, it is important to practice social distancing, use masks, avoid fomites and adopt safe practices. Artificial intelligence-based technologies hold the key to success as we emerge back to work and try to revive economies. At the same time, one must also acknowledge its downsides. This article reviews the various artificial intelligence technologies employed as part of exit strategies from lockdowns.

SVM kernel based on particle swarm optimized vector and Bayesian optimized SVM in atmospheric particulate matter forecasting

The application of Artificial Intelligence (AI) has been upgraded in many scientific fields the last years, with the development of new artificial intelligence-based technologies and techniques. Considering that in the literature there is a very limited number of studies proposing and testing new SVM kernels in regression problems, this research introduces a novel SVM Kernel by incorporating a transformed particle swarm optimized ANN weight vector in a Bayesian optimized SVM kernel in a time series problem for predicting the atmospheric pollutant factor Particulate Matter 10 (PM10). The proposed model introduces a new SVM kernel that illustrates an increased forecasting accuracy compared to the conventional optimized ANN and SVM models according to the experimental results. The findings of the proposed methodology illustrate that the new proposed SVM Kernel can be utilized as an improved forecasting technique.

Artificial Intelligence in Modern Medicine - The Evolving Necessity of the Present and Role in Transforming the Future of Medical Care.

The dexterity of computer systems to resemble and mimic human intelligence is artificial intelligence. Artificial intelligence has reformed the diagnostic and therapeutic precision and competence in various fields of medicine. Artificial intelligence appears to play a bright role in medical diagnosis. Computer systems using artificial intelligence help in the assessment of medical images and enormous data. This research aims to identify how artificial intelligence-based technology is reforming the art of medicine. Artificial intelligence empowers providers in improving efficiency and overall healthcare. Newer machine learning techniques lead the automatic diagnostic systems. Areas of medicine such as medical imaging, automated clinical decision-making support have made significant advances with respect to artificial intelligence technology. With improved diagnosis and prognosis, artificial intelligence possesses the capability to revolutionize various fields of medicine. Artificial intelligence has its own limitations and cannot replace a bedside clinician. In the evolving modern medical digital world, physicians need to support artificial intelligence rather than fear it replacing trained physicians for improved healthcare.

Cardiac MRI-Update 2020.

To review emerging techniques in cardiac magnetic resonance imaging (CMR) and their clinical applications with aspecial emphasis on new technologies, recent trials, and updated guidelines. The utility of CMR has expanded with the development of new MR sequences, postprocessing techniques, and artificial intelligence-based technologies, which have substantially increased the spectrum, quality, and reliability of information that can be obtained by CMR. The CMR modality has become an irreplaceable tool for diagnosis, treatment guidance and follow-up of patients with ischemic heart disease, myocarditis, and cardiomyopathies. Its role has been further strengthened by recent trials and guidelines. Quantitative mapping techniques are increasingly used for tissue characterization and detection of diffuse myocardial changes including myocardial storage diseases. With state-of-the-art CMR sequences, postprocessing techniques and understanding of their interpretation, CMR makes invaluable contributions to provide state-of-the-art diagnostics and care for cardiac patients in amultidisciplinary team.

Surveys in artificial intelligence-based technologies

Surveys in artificial intelligence-based technologies

Software Architecture System to Automate the Admission Process in the Higher Education Institutions

The admission process in the higher education system is one of the most tedious methods for parents and their wards. The common issues faced by them include confusion in the choice of the curriculum, equal care of individuals and the opportunities before them. The development of simple, scalable, and economical methods for admission process is the first step to realize their potential applications. Rational design and scalable approaches will enable an integrated system with multiple functionalities and broader applications. This paper proposes a software architecture model with several productivity features that uses the latest technologies to reduce inconveniences faced by applicants and their parents during the admission process. The application of this alternate model, adopting artificial intelligence-based technologies and its proper implementation can also minimize workforce and accelerate the admission process

Online tourism-related text: a perspective article

PurposeThis paper aims to outline the development of online tourism-related text with particular focus on the role of technology in transforming this text. It also offers a vision of this ongoing development for the next 75 years, followed by a discussion of its implications for tourism research.Design/methodology/approachThis paper offers a critical analysis and synthesis of the literature and brings a number of conceptual and theoretical frameworks to present a rich, in-depth discussion of the subject.FindingsThere have been important transformations in online tourism-related text because of the development of information technology. Particularly, the internet, along with more recent social media and the smartphone, has produced vast amount of text in a variety of formats and modalities. Computer programs are increasingly smarter in collecting, processing, understanding and even generating textual content in the context of tourism. Future communications will likely be mediated and dominated by artificial intelligence-based technologies, emerging interfaces and a mix of text and visual contents.Research limitations/implicationsTourism research entails a paradigm shift that recognizes the changing characteristics of online tourism-related text and the importance of new conceptual and methodological tools for understanding the new language of tourism.Originality/valueThis paper offers a unique view that connects the socio-linguistic basis of tourism with the development of information technology. It may serve as the conceptual foundation for tourism research that uses text as data in understanding a range of marketing and managerial problems in tourism.