A Scientometric Analysis Of Drone Technology Publications

This study focus on the growth and development of drone technology research in forms of publications reflected in Web of Science database, during the span of 1998-2017. A total 3433 publications were found and the highest 1040 (30.29%) publications published in 2017. The average number of 343.3 publications were published per year in the study field and there was a variation in Annual Growth, because there is no constant growth of publications every year in the area of study. Out of total publications, 3123 (90.97%) contributed by collaboration of multiple authors and 310 (9.03%) by single authors. Authors from United States of America (USA) published the highest number of publications with a total of 774 (22.55%), followed by China and South Korea with 618 (18.00%) and 238 (6.93%) publications were produced respectively. It exposed that the most prolific author is Kim Y secured first place by contributing 31 (0.90%) publications, followed by Zarco 21(0.61%), and Zhang 17 (0.50%) publications were published in drone technology. The collaborative index range from 3.43 (2008) to 4.19 (2012) with an average of 3.88 and 3.79 (2013) to 4.45 (2017) with an average of 4.16 per joint authored paper. For a total of 3123 multiple authored publications has 4.13 of an average per joint authors. It implies the research team falls between 3 and 4 authorship pattern in field of drone technology. It is identified the domination of Chinese institutions by contributing 23.77% (816) of a total research output in drone technology. In respect to, 7.31% (251) form Chines Academy of Science, 6.26% (215) from Beihang University, 5.33% (183) from Nanjing University of Aeronautics and Astronautics

A Scientometric Analysis of Drone Technology Publications

This study focus on the growth and development of drone technology research in forms of publications reflected in Web of Science database, during the span of 1998-2017. A total 3433 publications were found and the highest 1040 (30.29%) publications published in 2017. The average number of 343.3 publications were published per year in the study field and there was a variation in Annual Growth, because there is no constant growth of publications every year in the area of study. Out of total publications, 3123 (90.97%) contributed by collaboration of multiple authors and 310 (9.03%) by single authors. Authors from United States of America (USA) published the highest number of publications with a total of 774 (22.55%), followed by China and South Korea with 618 (18.00%) and 238 (6.93%) publications were produced respectively. It exposed that the most prolific author is Kim Y secured first place by contributing 31 (0.90%) publications, followed by Zarco 21(0.61%), and Zhang 17 (0.50%) publications were published in drone technology. The collaborative index range from 3.43 (2008) to 4.19 (2012) with an average of 3.88 and 3.79 (2013) to 4.45 (2017) with an average of 4.16 per joint authored paper. For a total of 3123 multiple authored publications has 4.13 of an average per joint authors. It implies the research team falls between 3 and 4 authorship pattern in field of drone technology. It is identified the domination of Chinese institutions by contributing 23.77% (816) of a total research output in drone technology. In respect to, 7.31% (251) form Chines Academy of Science, 6.26% (215) from Beihang University, 5.33% (183) from Nanjing University of Aeronautics and Astronautics

Trends and Patterns in Artificial Intelligence Research for Oil and Gas Industry: A Bibliometric Review

Purpose: This paper aims to outline a broad-spectrum perspective of the structure of research in artificial intelligence (AI), in the oil and gas industry (OGI) based on bibliometric and distance-based visualisation of similarities (VOS) analysis.   Theoretical framework: The OGI has been one of the major contributors to the world economy. With the increasing energy demand, it has become necessary for the industry to adopt the latest technologies to enhance efficiency, reduce costs, and improve safety. One such technology is AI, which has the potential to revolutionise OGI.   Design/methodology/approach: The paper uses the data from Scopus online database as of April 2023. Based on “key-terms” search results, 251 valid documents were obtained for further analysis using VOS viewer software and Harzing’s Publish or Perish for citation metrics and analysis.   Findings: The finding shows that the Journal of Petroleum Science and Engineering is the field's most relevant journal, with 14 (5.58) published Articles. The People's Republic of China is the most productive country regarding AI research in the OGI. El-Sebakhy's (2009) article is the most cited article, with 113 citations and an average of 8.07 citations per year.   Research, Practical & Social implications: AI could transform OGI. Thus, adopting AI technologies can increase efficiency, reduce costs, and improve safety, also may increase productivity and economic benefits in AI research-intensive countries.   Originality/value: This study provides a comprehensive analysis of the existing AI research in the OGI, utilising bibliometric data and graphical networks

The complementary contributions of academia and industry to AI research

Artificial intelligence (AI) has seen tremendous development in industry and academia. However, striking recent advances by industry have stunned the world, inviting a fresh perspective on the role of academic research in this field. Here, we characterize the impact and type of AI produced by both environments over the last 25 years and establish several patterns. We find that articles published by teams consisting exclusively of industry researchers tend to get greater attention, with a higher chance of being highly cited and citation-disruptive, and several times more likely to produce state-of-the-art models. In contrast, we find that exclusively academic teams publish the bulk of AI research and tend to produce higher novelty work, with single papers having several times higher likelihood of being unconventional and atypical. The respective impact-novelty advantages of industry and academia are robust to controls for subfield, team size, seniority, and prestige. We find that academic-industry collaborations struggle to replicate the novelty of academic teams and tend to look similar to industry teams. Together, our findings identify the unique and nearly irreplaceable contributions that both academia and industry make toward the healthy progress of AI.Comment: 28 pages, 7 figure

Bayesian inference of spatial and temporal relations in AI patents for EU countries

In the paper, we propose two models of Artificial Intelligence (AI) patents in European Union (EU) countries addressing spatial and temporal behaviour. In particular, the models can quantitatively describe the interaction between countries or explain the rapidly growing trends in AI patents. For spatial analysis Poisson regression is used to explain collaboration between a pair of countries measured by the number of common patents. Through Bayesian inference, we estimated the strengths of interactions between countries in the EU and the rest of the world. In particular, a significant lack of cooperation has been identified for some pairs of countries. Alternatively, an inhomogeneous Poisson process combined with the logistic curve growth accurately models the temporal behaviour by an accurate trend line. Bayesian analysis in the time domain revealed an upcoming slowdown in patenting intensity.The research was supported in part by PL-Grid Infrastructure, POWER 2014–2020 program and the Polish Ministry of Science and Higher Education with the subvention funds of the Faculty of Computer Science, Electronics and Telecommunications of AGH University.Peer ReviewedPostprint (published version

Organizational Learning in the Rise of Machine Learning

Organizational learning (OL) is associated with experience and knowledge in an organization. Information Technology (IT) enables the creation, dissemination, and use of knowledge, and as such, plays an important role in an organization’s learning process. This role has inspired a large body of literature studying the link between OL and IT and the relation between IT and knowledge exploration and exploitation. The recent rise of Machine Learning (ML) with its Deep Learning (DL) capabilities has nevertheless brought about new ways of creating, retaining, and transferring knowledge. I argue that the learning occurring within the machine plays a role in the learning occurring within the organization, calling for revisiting OL in light of this disruptive IT. In this paper, I focus on three different ways in which the machine achieves its learning, namely supervised, unsupervised, and reinforcement learning, and advance propositions on how each impacts OL differently

Um Estudo Bibliométrico sobre a pesquisa em Inteligência Artificial no Brasil

The production of data sources has increased in recent years and making their treatment, retrieval and use operational becomes a challenge and a strategic differential for the nations that have this domain. Knowledge about Artificial Intelligence (AI) has been crucial for data processing and use, being considered decisive for economic and social development. This work aimed to survey research on AI in Brazil. A comprehensive search expression was created, and related articles were obtained for the period 2011-2020 in the Web of Science database. Using bibliometric methods, the obtained records were analyzed using the software VantagePoint, VOSViewer, and Excel. An analysis was carried out regarding the participation of Brazilian research in relation to the world, the main AI research institutions in the country, the main research topics, and the most active authors. Keyword co-occurrence networks, the collaboration between institutions and between authors were built. It was found that Brazil has a peripheral but increasing participation in relation to publications and that public institutions have a fundamental role in this production despite regional discrepancies, which could help in the development of public policies for technological inclusion.A produção de fontes de dados tem aumentado nos últimos anos e operacionalizar o seu tratamento, recuperação e uso torna-se um desafio e um diferencial estratégico para os países que detém este domínio. O conhecimento sobre a Inteligência Artificial (IA) tem sido crucial para o processamento e uso dos dados, sendo considerado decisivo para o desenvolvimento econômico e social. Este trabalho teve como objetivo fazer um levantamento sobre a pesquisa em IA no Brasil. Foi elaborada uma expressão de busca abrangente e foram obtidos artigos relacionados no período 2011-2020 na base Web of Science. A partir de métodos bibliométricos, os registros encontrados foram analisados nos softwares VantagePoint, VOSViewer e Excel. Foi realizada uma análise a respeito da participação da pesquisa brasileira em relação ao mundo, das principais instituições de pesquisa em IA no país, dos principais temas de pesquisa e dos autores mais atuantes. Foram construídas redes de coocorrência de palavras-chave, de colaboração entre instituições e entre autores. Constatou-se que o Brasil possui uma participação periférica em relação a publicações, mas em crescimento e que as instituições públicas possuem um papel fundamental nesta produção apesar de discrepâncias regionais, o que poderia auxiliar no desenvolvimento de políticas públicas de inclusão tecnológica