Unveiling the Potential of Natural Language Processing in Collaborative Robots (Cobots): A Comprehensive Survey

Siwach, Gautam; Li, Cheryl

doi:10.1109/icce59016.2024.10444393

Cited by 3 publications

References 38 publications

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Impact of artificial intelligence and knowledge management on proactive green innovation: the moderating role of trust and sustainability

Abdulmuhsin,

Hussein,

AL-Abrrow

et al. 2024

APJBA

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PurposeIn this research, we seek to understand the effects of artificial intelligence (AI) and knowledge management (KM) processes in enhancing proactive green innovation (PGI) within oil and gas organizations. It also aims to investigate the moderator role of trust and sustainability in these relationships.Design/methodology/approachThis paper employs a quantitative analysis. Surveys have been gathered from the middle-line managers of twenty-four oil and gas government organizations to evaluate the perceptions of the managers towards AI, KM processes, trust, sustainability measures and proactive measures toward green innovation. Analytical and statistical tools that were employed in this study, including structural equation modeling with SmartPLSv3.9, have been used to analyze the data and to examine the measurement and structural models of this study.FindingsThe study results reveal a significant and positive impact of AI utilization, KM processes and PGI within oil and gas organizations. Furthermore, trust and sustainability turn out to be viable moderators affecting, and influencing the strength and direction of AI, KM and PGI relationships. In particular, higher levels of trust and more substantial sustainability commitments enhance the positive impact of AI and KM on green innovation outcomes.Practical implicationsUnderstanding the impact of AI, KM, trust and sustainability offers valuable insights for organizational leaders and policymakers seeking to promote proactive green innovation within the oil and gas industry. Thus, organizations can increase the efficiency of sustainable product development, process improvement and environmental management by using robust AI technologies and effective KM systems. Furthermore, fostering trust among stakeholders and embedding sustainability principles into organizational culture can amplify the effectiveness of AI and KM initiatives in driving green innovation outcomes.Originality/valueThis study extends the current knowledge by assessing the effect of AI and KM on proactive green innovation while accounting for trust and sustainability as moderators. Utilizing quantitative methods offers a nuanced understanding of the complex interactions between these variables, thereby advancing theoretical knowledge in the fields of innovation management, sustainability and organizational behavior. Additionally, the identification of specific mechanisms and contextual factors enriches practical insights for organizational practitioners striving for a practical understanding of the dynamics of the complexities of sustainable innovation in an AI-driven era.

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Impact of artificial intelligence and knowledge management on proactive green innovation: the moderating role of trust and sustainability

Abdulmuhsin,

Hussein,

AL-Abrrow

et al. 2024

APJBA

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Enhancing Autonomous System Security With AI and Secure Computation Technologies

Singh,

Kumar,

Singh

et al. 2025

Advances in Computational Intelligence and Robotics

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Exploring the intersection of transparency and security in autonomous systems, this chapter examines the dynamic landscape of industrial robots and intelligent drones. Advanced technologies such as machine learning, AI, robotics, and deep learning shape this intricate domain. As autonomous systems gain prominence across sectors, a focus lies on understanding decision-making frameworks. Methodologies for achieving algorithmic transparency and strengthening security protocols are outlined, emphasizing the fusion of technological innovation with ethical considerations. Real-world case studies offer practical insights and best practices. Ethical responsibilities in AI and robotics integration are emphasized, alongside a forward-looking view on emerging trends and technologies, providing a tailored roadmap for researchers, practitioners, and enthusiasts navigating the evolving realm of autonomous systems. This chapter provides a thorough analysis of transparency and security challenges and opportunities in autonomous systems, benefiting policymakers and industry stakeholders.

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Human–robot interaction: predicting research agenda by long short-term memory

Borregan-Alvarado,

Alvarez-Meaza,

Cilleruelo-Carrasco

et al. 2024

PeerJ Computer Science

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The article addresses the identification and prediction of research topics in human–robot interaction (HRI), fundamental in Industry 4.0 (I4.0) and future Industry 5.0 (I5.0). In the absence of research agendas in the scientific literature, the study proposes a multilayered model to create a precise agenda to guide the scientific community in new developments in collaborative robotics and HRI technologies. The methodology is divided into four stages, which make up the three layers of the model. In the first two stages, scientific articles on HRI for the period 2020–2021 were collected and analyzed using data mining techniques together with VantagePoint and Gephi software to identify keywords and their relationships. These initial stages form layer 1 of the model, where the main scientific themes are recognized. In the third stage, article titles and abstracts are cleaned and processed using natural language processing (NLP) techniques, generating word embeddings models that highlight relevant HRI-related terms, forming layer 2. The fourth and final stage uses Recurrent Neural Networks (RNN) with long short-term memory (LSTM) architecture to predict future topics, consolidating the previously identified terms and forming layer 3 of the model. The results show that in layer 1 HRI has intensive application in various sectors through advanced computational algorithms, with trust as a key feature. In layer 2, terms such as vision, sensors, communication, collaboration and anthropomorphic aspects are fundamental, while layer 3 anticipates future topics such as design, performance, method and controllers, essential to improve robot interaction. The study concludes that the methodology is effective in defining a robust and relevant research agenda. By identifying future trends and needs, this work fills a gap in the scientific literature, providing a valuable tool for the research community in the field of HRI.

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Unveiling the Potential of Natural Language Processing in Collaborative Robots (Cobots): A Comprehensive Survey

Cited by 3 publications

References 38 publications

Impact of artificial intelligence and knowledge management on proactive green innovation: the moderating role of trust and sustainability

Impact of artificial intelligence and knowledge management on proactive green innovation: the moderating role of trust and sustainability

Enhancing Autonomous System Security With AI and Secure Computation Technologies

Human–robot interaction: predicting research agenda by long short-term memory

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