【标题】Exploring the Future Development of Artificial Intelligence (AI) Applications in Chatbots: A Bibliometric Analysis
【参考中译】人工智能在聊天机器人中应用的文献计量学分析
【类型】 期刊
【关键词】 Chatbot; Artificial intelligence; Bibliometric analysis; Co-citation analysis
【参考中译】 聊天机器人；人工智能；文献计量分析；共引分析
【作者】 Liu Li; Duffy Vincent G.
【摘要】 Chatbots are fast becoming a key instrument in the research community and enterprise, while few researchers have discussed the focus of existing research and future research directions from the perspective of bibliometrics. This study aimed to explore the evolution tracks of AI applications in chatbots through strict and systematic bibliometric analysis. The analysis began with data retrieval in Web of Science using defined search terms associated with AI and chatbots. Bibliometric tools, including BibExcel and CiteSpace, were employed to conduct performance analysis and co-citation network analysis. Results showed that 1320 documents have been identified until 2022 and research on chatbots exploded after 2016. The USA contributed the most publications and the leading journal mainly focused on engineering and science. The co-citation analysis revealed that the development of AI-based chatbots and the related topics can be summarized as user-centered design, implementation techniques, performance requirements, and media type and future directions of AI in chatbots can mainly focus on improvement of technological means, increased demand, and expansion of applications. This study can help researchers have a broader and deeper understanding of AI-based chatbots and provide an insight into aggregate performance in the AI-based chatbot field.
【参考中译】 聊天机器人正迅速成为研究界和企业界的关键工具，而很少有研究人员从文献计量学的角度讨论现有研究的重点和未来的研究方向。本研究旨在通过严格系统的文献计量学分析，探索人工智能在聊天机器人中的应用演变轨迹。分析始于在Web of Science中使用与人工智能和聊天机器人相关的定义搜索词进行数据检索。使用文献计量学工具BibExcel和CiteSpace进行绩效分析和共引网络分析。结果显示，截至2022年，已识别出1320份文档，2016年后，对聊天机器人的研究呈爆炸式增长。美国贡献了最多的出版物，主要期刊主要集中在工程和科学上。共引分析显示，基于AI的聊天机器人及其相关主题的发展可以概括为以用户为中心的设计、实现技术、性能要求和媒体类型，聊天机器人AI的未来方向主要集中在改进技术手段、增加需求和扩大应用上。这项研究可以帮助研究人员对基于AI的聊天机器人有更广泛和更深入的了解，并为基于AI的聊天机器人领域的聚合性能提供洞察。
【来源】 International Journal of Social Robotics 2023, vol.15, no.5
【入库时间】 2023/12/27
【标题】Artificial intelligence based tool condition monitoring for digital twins and industry 4.0 applications
【参考中译】用于数字孪生和工业4.0应用的基于人工智能的刀具状态监控
【关键词】 Condition monitoring; Smart factory; Intelligent cutting tools; Digital twins; Sensors; Automation; Industry 4; 0; Artificial Intelligence; SUPPORT VECTOR MACHINE; HIDDEN MARKOV-MODELS; ACOUSTIC-EMISSION; NEURAL-NETWORK; FLANK WEAR; TURNING OPERATIONS; SURFACE-ROUGHNESS; STATISTICAL-ANALYSIS; VIBRATION; PREDICTION
【参考中译】 状态监测；智能工厂；智能刀具；数字孪生；传感器；自动化；工业4；0；人工智能；支持向量机；隐马尔可夫模型；声发射；神经网络；后刀面磨损；车削操作；表面粗糙度；统计分析；振动；预测
【作者】 Muthuswamy, Padmakumar; Shunmugesh, K.
【摘要】 The high demand for machining process automation has placed real-time tool condition monitoring as one of the top priorities of academic and industrial scholars in the past decade. But the presence of numerous known and unknown machining variables and challenging operating conditions such as high temperature and pressure makes it a daunting task. However, recent advancements in sensor and digital technologies have enabled in-process condition monitoring and real-time process optimization a highly accurate, robust, and effective process. Hence, the objective of the article is to provide a summary of the factors influencing the performance of cutting tools, critical machining variables to be monitored, techniques applied to monitor tool conditions, and artificial intelligence algorithms used to predict tool performance by analyzing and reviewing the literature. The future direction of intelligent cutting tools and how they would help in building the foundation for advanced smart factory ecosystems such as digital twins and Industry 4.0 are also discussed.
【参考中译】 在过去的十年里，对加工过程自动化的高要求使得刀具状态的实时监测成为学术界和工业界学者的首要任务之一。但是，大量已知和未知的加工变量以及具有挑战性的操作条件(如高温和压力)的存在使其成为一项艰巨的任务。然而，传感器和数字技术的最新进步使过程中的状态监控和实时过程优化成为一个高度准确、稳健和有效的过程。因此，本文的目的是通过分析和回顾文献，总结影响刀具性能的因素、需要监测的关键加工变量、应用于监测刀具状况的技术以及用于预测刀具性能的人工智能算法。还讨论了智能刀具的未来发展方向，以及它们将如何帮助为数字孪生和工业4.0等先进的智能工厂生态系统奠定基础。
【来源】 IJIDeM: International Journal on Interactive Design and Manufacturing 2023, vol.17, no.3
【标题】Artificial intelligence for template-free protein structure prediction: a comprehensive review
【参考中译】人工智能无模板蛋白质结构预测综述
【关键词】 Bioinformatics; Protein structure prediction; Machine learning; Deep learning; Search-based optimisation
【参考中译】 生物信息学；蛋白质结构预测；机器学习；深度学习；基于搜索的优化
【作者】 Mufassirin M. M. Mohamed; Newton M. A. Hakim; Sattar Abdul
【摘要】 Protein structure prediction (PSP) is a grand challenge in bioinformatics, drug discovery, and related fields. PSP is computationally challenging because of an astronomically large conformational space to be searched and an unknown very complex energy function to be minimised. To obtain a given protein’s structure, template-based PSP approaches adopt a similar protein’s known structure, while template-free PSP approaches work when no similar protein’s structure is known. Currently, proteins with known structures are greatly outnumbered by proteins with unknown structures. Template-free PSP has obtained significant progress recently via machine learning and search-based optimisation approaches. However, very accurate structures for complex proteins are yet to be achieved at a level suitable for effective drug design. Moreover, ab initio prediction of a protein’s structure only from its amino acid sequence remains unsolved. Furthermore, the number of protein sequences with unknown structures is growing rapidly. Hence, to make further progress in PSP, more sophisticated and advanced artificial intelligence (AI) approaches are needed. However, getting involved in PSP research is difficult for AI researchers because of the lack of a comprehensive understanding of the whole problem, along with the background and the literature of all related sub-problems. Unfortunately, existing PSP review papers cover PSP research at a very high level and only some parts of PSP and only from a particular singular viewpoint. Using a systematic approach, this review paper provides a comprehensive survey of the state-of-the-art template-free PSP research to fill this knowledge gap. Moreover, covering required PSP preliminaries and computational formulations, this paper presents PSP research from AI perspectives, discusses the challenges, provides our commentaries, and outlines future research directions.
【参考中译】 蛋白质结构预测(PSP)是生物信息学、药物开发及相关领域的一大挑战。PSP在计算上具有挑战性，因为要搜索的构象空间非常大，并且需要最小化未知的非常复杂的能量函数。为了获得给定蛋白质的结构，基于模板的PSP方法采用相似蛋白质的已知结构，而无模板PSP方法在没有相似蛋白质结构的情况下工作。目前，结构已知的蛋白质远远多于结构未知的蛋白质。通过机器学习和基于搜索的优化方法，无模板PSP最近取得了重大进展。然而，复杂蛋白质的非常精确的结构还没有达到适合有效药物设计的水平。此外，仅根据蛋白质的氨基酸序列从头算预测蛋白质的结构仍然没有解决。此外，具有未知结构的蛋白质序列的数量正在迅速增长。因此，为了在PSP方面取得进一步的进展，需要更复杂和更先进的人工智能(AI)方法。然而，对于人工智能研究人员来说，参与PSP研究是困难的，因为缺乏对整个问题的全面了解，以及所有相关子问题的背景和文献。不幸的是，现有的PSP综述论文涵盖了PSP研究的非常高的水平，并且只涉及PSP的一些部分，并且仅从特定的单一观点。本文采用系统的方法，对无模板PSP研究的最新进展进行了全面综述，以填补这一知识空白。此外，本文从人工智能的角度介绍了PSP的研究，讨论了挑战，提供了我们的评论，并概述了未来的研究方向。
【来源】 Artificial Intelligence Review: An International Science and Engineering Journal 2023, vol.56, no.8