Artificial intelligence to detect unknown stimulants from scientific literature and media reports

BACKGROUND Research and Development (R&D) of Artificial Intelligence (AI) in medicine involve clinical, technical and economic aspects. Better understanding the relationship between these dimensions seems necessary to coordinate efforts of R&D among stakeholders. OBJECTIVE To assess systematically existing literature on the cost-effectiveness of Artificial Intelligence (AI) from a clinical, technical and economic perspective. METHODS A systematic literature review was conducted to study the cost-effectiveness of AI solutions and summarised within a scoping framework of health policy analysis developed to study clinical, technical and economic dimensions. RESULTS Of the 4820 eligible studies, 13 met the inclusion criteria. Internal medicine and emergency medicine were the most studied clinical disciplines. Technical R&D aspects have not been uniformly disclosed in the studies we analysed. Monetisation aspects such as payment models assumed have not been reported in the majority of cases. CONCLUSIONS Existing scientific literature on the cost-effectiveness of AI currently does not allow to draw conclusive recommendations. Further research and improved reporting on technical and economic aspects seem necessary to assess potential use-cases of this technology, as well as to secure reproducibility of results. CLINICALTRIAL Not applicable

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A Review of Further Directions for Artificial Intelligence, Machine Learning, and Deep Learning in Smart Logistics

Sustainability ◽

10.3390/su12093760 ◽

2020 ◽

Vol 12 (9) ◽

pp. 3760 ◽

Cited By ~ 1

Author(s):

Manuel Woschank ◽

Erwin Rauch ◽

Helmut Zsifkovits

Keyword(s):

Artificial Intelligence ◽

Machine Learning ◽

Deep Learning ◽

Scientific Literature ◽

Future Research ◽

Industrial Enterprises ◽

Smart Logistics ◽

Research Findings ◽

Ongoing Development ◽

Research Initiatives

Industry 4.0 concepts and technologies ensure the ongoing development of micro- and macro-economic entities by focusing on the principles of interconnectivity, digitalization, and automation. In this context, artificial intelligence is seen as one of the major enablers for Smart Logistics and Smart Production initiatives. This paper systematically analyzes the scientific literature on artificial intelligence, machine learning, and deep learning in the context of Smart Logistics management in industrial enterprises. Furthermore, based on the results of the systematic literature review, the authors present a conceptual framework, which provides fruitful implications based on recent research findings and insights to be used for directing and starting future research initiatives in the field of artificial intelligence (AI), machine learning (ML), and deep learning (DL) in Smart Logistics.

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Methodological Proposals for the Development of Services in a Smart City: A Literature Review

Sustainability ◽

10.3390/su122410249 ◽

2020 ◽

Vol 12 (24) ◽

pp. 10249

Author(s):

Joel Serey ◽

Luis Quezada ◽

Miguel Alfaro ◽

Guillermo Fuertes ◽

Rodrigo Ternero ◽

...

Keyword(s):

Artificial Intelligence ◽

Literature Review ◽

Scientific Literature ◽

Smart Cities ◽

The Internet ◽

Urban Settlement ◽

Urban Settlements ◽

Analytical Frameworks ◽

The Internet Of Things ◽

Conceptual Research

This literature review analyzes and classifies methodological contributions that answer the different challenges faced by smart cities. This study identifies city services that require the use of artificial intelligence (AI); which they refer to as AI application areas. These areas are classified and evaluated, taking into account the five proposed domains (government, environment, urban settlements, social assistance, and economy). In this review, 168 relevant studies were identified that make methodological contributions to the development of smart cities and 66 AI application areas, along with the main challenges associated with their implementation. The review methodology was content analysis of scientific literature published between 2013 and 2020. The basic terminology of this study corresponds to AI, the internet of things, and smart cities. In total, 196 references were used. Finally, the methodologies that propose optimization frameworks and analytical frameworks, the type of conceptual research, the literature published in 2018, the urban settlement macro-categories, and the group city monitoring–smart electric grid, make the greater contributions.

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ВИКОРИСТАННЯ СИСТЕМ ШТУЧНОГО ІНТЕЛЕКТУ У ЗАДАЧАХ ПРОГНОЗУВАННЯ ФІНАНСОВИХ ІНДЕКСІВ: ОГЛЯД НАУКОВИХ ДЖЕРЕЛ

RADIOELECTRONIC AND COMPUTER SYSTEMS ◽

10.32620/reks.2020.3.11 ◽

2020 ◽

pp. 108-117

Author(s):

Николай Ярославович Кушнир ◽

Катерина Токарева

Keyword(s):

Artificial Intelligence ◽

Time Series ◽

Large Scale ◽

Scientific Literature ◽

Financial Time Series ◽

Technical Analysis ◽

Soft Data ◽

Scientific Publications ◽

Financial Time ◽

Stock Indices

The paper investigates methods of artificial intelligence in the prognostication and analysis of financial data time series. The usage of well-known methods of artificial intelligence in forecasting and analysis of time series is investigated. Financial time series are inherently highly dispersed, complex, dynamic, nonlinear, nonparametric, and chaotic nature, so large-scale and soft data mining techniques should be used to predict future values. As the scientific literature superficially describes the numerous artificial intelligence algorithms to be used in forecasting financial time series, a detailed analysis of the relevant scientific literature was conducted in scientometric databases Scopus, Science Direct, Google Scholar, IEEExplore, and Springer. It is revealed that the existing scientific publications do not contain a comprehensive analysis of literature sources devoted to the use of artificial intelligence methods in forecasting stock indices. Besides, the analyzed works, which are related in detail to the object of our study, have a limited scope because they focus on only one family of artificial intelligence algorithms, namely artificial neural networks. It was found that the analysis of the use of artificial intelligence systems should be based on two well-known approaches to predicting the behavior of financial markets: fundamental and technical analysis. The first approach is based on the study of economic factors that have a possible impact on market dynamics and more common in long-term planning. Representatives of technical analysis, on the other hand, argue that the price already contains all the fundamental factors that affect it. In this regard, technical analysis involves forecasting the dynamics of price changes based on the analysis of their change in the past, ie time series. Although today there are many developed models for forecasting stock indices using artificial intelligence algorithms, in the scientific literature there is no established methodology that defines the main elements and stages of the algorithm for forecasting financial time series. Therefore, this study has improved the methodology for forecasting financial time series.

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How Artificial Intelligence Can Augment the Collection of Scientific Literature

European Journal of Formal Sciences and Engineering ◽

10.26417/772udo89i ◽

2020 ◽

Vol 3 (2) ◽

pp. 77

Author(s):

Mokeddem Allal

Keyword(s):

Artificial Intelligence ◽

Deep Learning ◽

Conceptual Framework ◽

Scientific Literature ◽

Collection Process ◽

Learning Techniques

This article describes the contribution of artificial intelligence (AI) to the literature collection process, which has become more efficient and more homogeneous. In this context, the researcher will receive his literature not only according to his field. Moreover, the literature is strongly linked to scientific and academic ambitions. AI through its deep learning techniques offers the possibility of speeding up the process of collecting augmented literature via an approach based on the annotation of scientific names and none-scientific names related to the field. AI provides original or reproduced research avenues with reliable and precise results. In this article, we have highlighted how to develop conceptual framework based on scientific and none-scientific names related to the area of expertise, all ensuring the reproducibility, reliability and accuracy of the study.

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Prospects for the Development of Artificial Intelligence in the Current Agenda of Political and Social Risks of Global Transformations

Journal of Political Research ◽

10.12737/2587-6295-2020-10-22 ◽

2020 ◽

Vol 4 (2) ◽

pp. 10-22

Author(s):

V. Bagdasaryan ◽

P. Baldin

Keyword(s):

Artificial Intelligence ◽

Social Structure ◽

National Security ◽

Political Science ◽

Public Discourse ◽

Scientific Literature ◽

Political Risks ◽

Social Risks ◽

The World ◽

Modern System

The purpose of this research was to identify political and social risks for humanity and Russia in connection with the development of artificial intelligence technologies. Methodologically, the research correlates with the direction of political scientific futurology. When identifying political risks of the development of artificial intelligence, the method of scenario forecasting is used. Based on the study of scientific literature and public discourse, the main positions in understanding the threats to the development of artificial intelligence for humanity are identified. In the course of the study, eleven possible groups of political and social risks were identified based on the analysis of various futurological models. The conclusion is made about the production of risks by the modern system of the world social structure, its contradictions and conflicts. It is emphasized that the need for developments in the field of artificial intelligence is due to the threats of falling behind potential opponents and competitors, which may mean the loss of Russia's sovereign status. The results of the research can be used as a basis for practical developments on the modernization of the national security system of Russia in connection with the actualization of the risks of the development of artificial intelligence technologies. In theoretical and methodological meaning the presented research can be used for further understanding of new technological realities and prospects through the prism of political science analysis.

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A Look at the Past, Present and Future Research Trends of Artificial Intelligence in Agriculture

Agronomy ◽

10.3390/agronomy10111839 ◽

2020 ◽

Vol 10 (11) ◽

pp. 1839

Author(s):

José Luis Ruiz-Real ◽

Juan Uribe-Toril ◽

José Antonio Torres Arriaza ◽

Jaime de Pablo Valenciano

Keyword(s):

Artificial Intelligence ◽

Web Of Science ◽

Scientific Literature ◽

Green Economy ◽

Future Research ◽

Agriculture Sector ◽

The Past ◽

Use Of Resources ◽

Research Organizations ◽

Prolific Authors

Technification in agriculture has resulted in the inclusion of more efficient companies that have evolved into a more complex sector focused on production and quality. Artificial intelligence, one of the relevant areas of technology, is transforming the agriculture sector by reducing the consumption and use of resources. This research uses a bibliometric methodology and a fractional counting method of clustering to analyze the scientific literature on the topic, reviewing 2629 related documents recorded on the Web of Science and Scopus databases. The study found significant results regarding the most relevant and prolific authors (Hoogenboom), supporting research organizations (National Natural Science Foundation of China) and countries (U.S., China, India, or Iran). The identification of leaders in this field gives researchers new possibilities for new lines of research based on previous studies. An in-depth examination of authors’ keywords identified different clusters and trends linking Artificial Intelligence and green economy, sustainable development, climate change, and the environment.

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Personalized Clinical Phenotyping through Systems Medicine and Artificial Intelligence

Journal of Personalized Medicine ◽

10.3390/jpm11040265 ◽

2021 ◽

Vol 11 (4) ◽

pp. 265

Author(s):

Alfredo Cesario ◽

Marika D’Oria ◽

Francesco Bove ◽

Giuseppe Privitera ◽

Ivo Boškoski ◽

...

Keyword(s):

Artificial Intelligence ◽

Personalized Medicine ◽

Scientific Literature ◽

Genomic Medicine ◽

Brain Disorders ◽

Systems Medicine ◽

National Leader ◽

Biomedical Technologies ◽

Practical Experiences ◽

Clinical Phenotyping

Personalized Medicine (PM) has shifted the traditional top-down approach to medicine based on the identification of single etiological factors to explain diseases, which was not suitable for explaining complex conditions. The concept of PM assumes several interpretations in the literature, with particular regards to Genetic and Genomic Medicine. Despite the fact that some disease-modifying genes affect disease expression and progression, many complex conditions cannot be understood through only this lens, especially when other lifestyle factors can play a crucial role (such as the environment, emotions, nutrition, etc.). Personalizing clinical phenotyping becomes a challenge when different pathophysiological mechanisms underlie the same manifestation. Brain disorders, cardiovascular and gastroenterological diseases can be paradigmatic examples. Experiences on the field of Fondazione Policlinico Gemelli in Rome (a research hospital recognized by the Italian Ministry of Health as national leader in “Personalized Medicine” and “Innovative Biomedical Technologies”) could help understanding which techniques and tools are the most performing to develop potential clinical phenotypes personalization. The connection between practical experiences and scientific literature highlights how this potential can be reached towards Systems Medicine using Artificial Intelligence tools.

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Research of Toponyms of the Irkutsk Region Using the Method of Artificial Intelligence

Известия Байкальского государственного университета ◽

10.17150/2500-2759.2021.31(3).382-390 ◽

2021 ◽

Vol 31 (3) ◽

pp. 382-390

Author(s):

Andrei Borovsky ◽

Elena Rakovskaya

Keyword(s):

Artificial Intelligence ◽

Data Analysis ◽

Information Technologies ◽

Scientific Literature ◽

Semantic Relations ◽

Vector Representation ◽

Text Data ◽

Modern Language ◽

Irkutsk Region ◽

Local Topography

Essential issues of toponymy presuppose studying separate words to reconstruct the denotative meaning of geographical names that were lost in the modern language and to find out how the peculiarities of the local topography, the inhabitants’ activities, etc. are reflected in them. It is possible to solve this kind of problems using intellectual methods of data analysis on the basis of information technologies. However, in scientific literature on toponymy, such methods are practically ignored. The article is devoted to the study of the origin and semantic meanings of geographical names based on finding semantic associates and calculating the semantic similarity of words using the embedding model. According to the proposed method, the origin of some toponyms of the Irkutsk region was determined, their semantic relations were revealed. The dichotomy method was used for toponyms that have two roots in their structure. This made it possible to improve the operation of the model by clarifying the morphemic composition of the original word. The method of word transformation was used to determine the etymology of the toponym «Moscow». We have received new versions of the origin of the toponym. It is shown that the application of the methods based on distributive semantics and vector representation of words, obtained on the basis of large arrays of text data, significantly expands the possibilities of research in the field of determining the origin of toponyms and clarifying their meaning.

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Artificial Intelligence and Its Impact on Labour Relations in Estonia

10.34135/sjps.200204 ◽

2020 ◽

pp. 255-277

Author(s):

Kristi Joamets ◽

Archil Chochia

Keyword(s):

Artificial Intelligence ◽

Labour Market ◽

New Technologies ◽

Scientific Literature ◽

Labour Force ◽

Policy Documents ◽

Labour Relations ◽

Eu Policy ◽

The Impact ◽

The Eu

The third industrial revolution, the digital revolution, affected economy and thus labour relations, too. Now the so-called fourth revolution, the artificial intelligence (AI) revolution, will cause further massive changes in the labour market. This is not just about the caution that robots will replace all employees, but this also raises a question about new skills the labour market requires the employees to have. Scientific literature and the EU policy documents do not cover the AI – labour market issues in a unified approach, however welcoming the development of new technologies on the one hand, with concerns about weakening the labour force by jobs loses, on the other hand. The article elucidates the AI revolution and analyses the AI influence on labour market, specifically identifying the new skills required, based on relevant scientific literature and the EU policy documents. Considering the AI impact on labour relations, continuous alteration of skills and knowledge offered should be of special concern– it is not only about a labour relation per se, new models emerge all the time in the labour market. The authors also investigate the impact of AI on the Estonian labour market, i.e. whether the AI´s effects appear as disastrous as expected or simply a welcome development for the welfare of the state. The article discusses how AI impacts labour relations and which professions fall in a greater risk of disappearing and, more specifically, the AI´s influence on the Estonian labour market.

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