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In the 21st century, as part of the fourth industrial revolution, artificial intelligence (AI), is one of the most important and well-known technology. AI also has made it possible to execute human tasks without the need for humans. However, there is one important concern with consciousness. This is because consciousness is one of the most defining qualities between AI and humans. So, this study presents a literature review and bibliometric analysis of artificial and mind consciousness research around the world from 2000 to 2022 in order to provide researchers and scholars around the world with an overview of the results and trends in artificial and mind consciousness research. A textual query on two databases; Scopus (289 papers), and Web of Science (303 papers) using the term "artificial consciousness" OR "mind consciousness" was performed on 10 June 2022 retrieving 509 scholarly papers from 2000 to 2022 related to artificial and mind consciousness studies for in-depth analysis. Bibliometric analysis were performed using Rstudio software version 4.2.0 and biblioshiniy for bibliometrix to visualize and analyze trends of artificial and mind consciousness research. This bibliometric analysis was analyzed the annual scientific publication growth, the most productive authors, most frequent word has been using, most famous journal name, and which countries has highest collaboration with other country. According to the findings of the analysis, there is significant inconsistency in global trends in annual scientific production, with the number of publications increasing and decreasing. Among all countries, United States (USA) contributed the most publications in the field of artificial and mind consciousness research. According to the findings has show the most relevant authors is Kelley TM (Scopus), and Patel AD (WoS). Moreover, the most relevant journals articles in artificial and mind consciousness studies is Procedia Computer Science (Scopus), and Journal Of Consciousness Studies (WoS). The implication of this study is can help new researchers in this field by providing information on relevant publications and authors to consult when conducting research on this topic. Furthermore, this research helps other researchers understand current trends in this area of study. As a result, the justification for doing this study is to provide the first bibliometric analysis and to fill research gaps in bibliometric studies of artificial consciousness and the mind by providing information in the form of a literature review, overview, and guidelines. [ABSTRACT FROM AUTHOR]

Purpose: To analyze the AI research in the field of nursing, to explore the current situation, hot topics, and prospects of AI research in the field of nursing, and to provide a reference for researchers to carry out related studies. Methods: We used the VOSviewer 1.6.17, SciMAT, and CiteSpace 5.8.R3 to generate visual cooperation network maps for the country, organizations, authors, citations, and keywords and perform burst detection, theme evolution, and so forth. Findings A total of 9318 articles were obtained from the Web of Science Core Collection database. Four hundred and thirty‐one AI research related to the field of nursing was published by 855 institutions from 54 countries. CIN‐Computers Informatics Nursing was the top productive journal. The United States was the dominant country. The transnational cooperation between authors from developed countries was closer than that between authors from developing countries. The main hot topics included nurse rostering, nursing diagnosis, nursing decision support, disease risk factor prediction, nursing big data management, expert system, support vector machine, decision tree, deep learning, natural language processing, and nursing education. Machine learning represented one of the cutting‐edge and most applicable branches of artificial intelligence in the field of nursing, and deep learning was the hottest technology among many machine learning methods in recent years. One of the most cited papers was published by Burke in 2004 and cited 500 times, which critically evaluated AI methods to deal with nurse scheduling problems. Conclusions: Although AI has been paid more and more attention to the field of nursing, there is still a lack of high‐yielding authors who have been engaged in this field for a long time. Most of the high contribution authors and institutions came from developed countries; therefore, more transnational and multi‐disciplinary cooperation is needed to promote the development of AI in the nursing field. This bibliometric analysis not only provided a comprehensive overview to help researchers to understand the important articles, journals, potential collaborators, and institutions in this field but also analyzed the history, hot spots, and future trends of the research topic to provide inspiration for researchers to choose research directions. [ABSTRACT FROM AUTHOR]

The Third International Conference on Data Engineering was held in Los Angeles, California on February 2 to 6, 1987. The conference was designed as an international forum for bringing together researchers, developers, managers, strategic planners and other users with an interest in the research, design and development of data engineering methodologies, strategies and systems. Its scope includes computer science, artificial intelligence, electrical engineering, and computer engineering. It featured papers from all major areas of data engineering, including database design and modeling, performance evaluation, algorithms, integrity, security, fault tolerance, query language, artificial intelligence approaches, knowledge bases, database machines, distributed databases and data engineering applications.

In this paper we present a method for the recognition of hand-written digits and a practical implementation of this method for real-time recognition. A theoretical framework for the neural networks used to classify the handwritten digits is also presented. The classification task is performed using a Convolutional Neural Network (CNN). CNN is a special type of multi-layer neural network, being trained with an optimized version of the back-propagation learning algorithm. CNN is designed to recognize visual patterns directly from pixel images with minimal preprocessing, being capable to recognize patterns with extreme variability (such as handwritten characters), and with robustness to distortions and simple geometric transformations. The main contributions of this paper are related to the original methods for increasing the efficiency of the learning algorithm by preprocessing the images before the learning process and a method for increasing the precision and performance for real-time applications, by removing the non useful information from the background. By combining these strategies we have obtained an accuracy of 96.76%, using as training set the NIST (National Institute of Standards and Technology) database. [ABSTRACT FROM AUTHOR]

The generalization error bounds found by current error models using the number of effective parameters of a classifier and the number of training samples are usually very loose. These bounds are intended for the entire input space. However, support vector machine (SVM), radial basis function neural network (RBFNN), and multilayer perceptron neural network (MLPNN) are local learning machines for solving problems and treat unseen samples near the training samples to be more important. In this paper, we propose a localized generalization error model which bounds from above the generalization error within a neighborhood of the training samples using stochastic sensitivity measure. It is then used to develop an architecture selection technique for a classifier with maximal coverage of unseen samples by specifying a generalization error threshold. Experiments using 17 University of California at Irvine (UCI) data sets show that, in comparison with cross validation (CV), sequential learning, and two other ad hoc methods, our technique consistently yields the best testing classification accuracy with fewer hidden neurons and less training time. [ABSTRACT FROM AUTHOR]

A comparison between the development of cognitive science in France and the USA enables us to analyze some national differences linked to specific connections between the scientific, military, economic and political worlds. The influence of new practices and tools developed during World War II and the Cold War appears to be of crucial importance in understanding the development of this new field, as well as that of cybernetics, computer science, artificial intelligence and molecular biology. This paper can be considered as a study in how the differing contexts in France and the USA shaped the history of the construction of cognitive science in each of these two countries. In spite of various differences, some common aspects may be pointed out: in both cases, computer experts and psychologists using a computational modelling approach were those first engaged in the construction of cognitive science. If in France neuroscience-oriented cognitive science research was stronger than in the USA, it seems that the artificial intelligence orientation is also of growing importance in France. [ABSTRACT FROM AUTHOR]

The Irvine School refers to an intellectual perspective on information technology in complex organizational settings that emerged at the University of California in Irvine, California (UCI), over the last three decades of the 20th century. In many ways, the rise of the Irvine School was synonymous with the rise of researcher Rob Kling's influence on the international community of scholars who would eventually form what Kling called social informatics. This article reflects on Kling's role in the creation of the Irvine School. Kenneth Kraemer, an architect and city planner by training, had joined the UCI Graduate School of Administration in 1967. He was one of the first scholars to begin careful empirical study of the effect of computerization in government agencies, and soon started a research program on information technology and local government at UCI's Public Policy Research Organization. In the early 1970s, researchers Jim Danziger and Rob Kling joined him in this endeavor. Danziger had been trained in political science and public administration. Kling had been trained in electrical engineering and computer science, and had done important work at the intersection of fuzzy logic and planning systems in the area of symbolic artificial intelligence.

This is a study of a well-known controversy in computer science, between artificial intelligence and neural networks researchers. It examines claims made by participants in the field that the controversy was triggered, shaped and finally resolved in connection to activities of state research agencies, particularly the US Department of Defense Advanced Research Projects Agency (ARPA). In contrast to these claims, this account concludes that, although ARPA's relations to the controversy among researchers were important, they were indirect. This study thus brings the analytical resources of studies of scientific controversy to bear on a domain which has so far gone largely unexamined in studies of 20th-century controversies— namely, state research policy and management. The account is based on contemporary documents, historical literature, and interviews with researchers and ARPA officials. [ABSTRACT FROM AUTHOR]

This article discusses the relationship in the U.S. between current symbolic interactionism and computer sciences-specifically, distributed artificial intelligence (DAI). The genera! thesis is twofold. First, current interactionist approaches to organization, science, and technology show a special affinity to goals and problems of DAI research, and in research style, methods, and theoretical concepts, symbolic interactionism can provide useful suggestions in the design of DAI systems. Second, a good way to analyze the relationship between computer sciences and symbolic interactionism is reflexive of theoretical concepts provided by interactionist approaches. In this sense, DAI is a "going concern" which extends across various fields and intersecting social worlds connected through a set of conceptual "boundary objects." It is concluded that the interaction between technology and sociological thought must go beyond a mere exchange of ideas. What is required is continual, hands-on, transdisciplinary collaboration. [ABSTRACT FROM AUTHOR]