The Application of Artificial Intelligence Technology in Shipping: A Bibliometric Review

Journal of Marine Science and Engineering, Journal Year: 2024, Volume and Issue: 12(4), P. 624 - 624

Published: April 7, 2024

Artificial intelligence (AI) technologies are increasingly being applied to the shipping industry advance its development. In this study, 476 articles published in Science Citation Index Expanded (SCI-EXPANDED) and Social Sciences (SSCI) of Web Core Collection from 2001 2022 were collected, bibliometric methods conduct a systematic literature field AI technology applications industry. The review commences with an annual publication trend analysis, which shows that research has been growing rapidly recent years. This is followed by statistical analysis journals collaborative network identify most productive journals, countries, institutions, authors. keyword “co-occurrence analysis” then utilized major clusters, as well hot directions field, providing for future field. Finally, based on results co-occurrence content papers years, gaps AIS data applications, ship trajectory, anomaly detection, possible directions, discussed. findings indicate direction mainly reflected behavior repair. Ship trajectory deep learning-based method discussion classification. Anomaly detection application learning improving efficiency detection. These insights offer guidance researchers’ investigations area. addition, we discuss implications both theoretical practical perspectives. Overall, can help researchers understand status development shipping, correctly grasp methodology, promote further

Language: Английский

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Optimizing multi-step wind power forecasting: Integrating advanced deep neural networks with stacking-based probabilistic learning

Applied Energy, Journal Year: 2024, Volume and Issue: 369, P. 123487 - 123487

Published: May 30, 2024

Language: Английский

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Concept-cognitive learning survey: Mining and fusing knowledge from data

Information Fusion, Journal Year: 2024, Volume and Issue: 109, P. 102426 - 102426

Published: April 16, 2024

Language: Английский

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Understanding the impacts of negative advanced driving assistance system warnings on hazardous materials truck drivers’ responses using interpretable machine learning

Engineering Applications of Artificial Intelligence, Journal Year: 2025, Volume and Issue: 146, P. 110308 - 110308

Published: Feb. 20, 2025

Language: Английский

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Sustainable Supply Chain Management in the Age of Machine Intelligence: Addressing Challenges, Capitalizing on Opportunities, and Shaping the Future Landscape

Sustainable Machine Intelligence Journal, Journal Year: 2023, Volume and Issue: 3

Published: June 25, 2023

In today's rapidly evolving business landscape, the convergence of sustainable supply chain management (SSCM) and machine intelligence, encompassing artificial intelligence (AI) learning (ML), represents a dynamic transformative nexus. This comprehensive survey paper navigates intricate terrain practices, delving into its principles, challenges, pressing need for organizations to embrace environmental responsibility, ethical sourcing, social equity. Simultaneously, it explores disruptive potential offering insights underlying vast applications, pivotal role in optimizing operations. Through systematic analysis, this uncovers complex interplay between SSCM starting with foundational principles each discipline. It then scrutinizes challenges encountered integrating sustainability, including data complexities, dilemmas, skilled personnel. Conversely, illuminates myriad opportunities that arise from synergy, enhancing demand forecasting inventory fostering sourcing practices reducing waste. closing, anticipates future landscape chains age highlighting emerging trends, technological innovations, considerations will shape trajectory field. is our hope serves as valuable resource businesses, policymakers, researchers alike, inspiring pursuit environmentally responsible, economically viable, ethically sound an increasingly interconnected world.

Language: Английский

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Fuzzy inference system with interpretable fuzzy rules: Advancing explainable artificial intelligence for disease diagnosis—A comprehensive review

Information Sciences, Journal Year: 2024, Volume and Issue: 662, P. 120212 - 120212

Published: Jan. 26, 2024

Interpretable artificial intelligence (AI), also known as explainable AI, is indispensable in establishing trustable AI for bench-to-bedside translation, with substantial implications human well-being. However, the majority of existing research this area has centered on designing complex and sophisticated methods, regardless their interpretability. Consequently, main prerequisite implementing trustworthy medical domains not been met. Scientists have developed various explanation methods interpretable AI. Among these fuzzy rules embedded a inference system (FIS) emerged novel powerful tool to bridge communication gap between humans advanced machines. there few reviews use FISs diagnosis. In addition, application different kinds multimodal data received insufficient attention, despite potential appropriate methodologies available datasets. This review provides fundamental understanding interpretability rules, conducts comparative analyses other handling three major types (i.e., sequence signals, images, tabular data), offers insights into rule scenarios recommendations future research.

Language: Английский

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A local rough set method for feature selection by variable precision composite measure

Applied Soft Computing, Journal Year: 2024, Volume and Issue: 155, P. 111450 - 111450

Published: March 4, 2024

Language: Английский

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Advanced insights through systematic analysis: Mapping future research directions and opportunities for xAI in deep learning and artificial intelligence used in cybersecurity

Neurocomputing, Journal Year: 2024, Volume and Issue: 590, P. 127759 - 127759

Published: April 25, 2024

This paper engages in a comprehensive investigation concerning the application of Explainable Artificial Intelligence (xAI) within context deep learning and Intelligence, with specific focus on its implications for cybersecurity. Firstly, gives an overview xAI techniques their significance benefits when applied Subsequently, authors methodically delineate systematic mapping study, which serves as investigative tool discerning potential trajectory field. strategic methodological framework lets one identify future research directions opportunities that underlie integration realm Deep Learning, cybersecurity, are described in-depth. Then, brings together all gathered insights from this extensive closes final conclusions.

Language: Английский

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Exploring the Concept of Explainable AI and Developing Information Governance Standards for Enhancing Trust and Transparency in Handling Customer Data

Journal of Engineering Research and Reports, Journal Year: 2024, Volume and Issue: 26(7), P. 244 - 268

Published: June 27, 2024

The increasing integration of Artificial Intelligence (AI) systems in diverse sectors has raised concerns regarding transparency, trust, and ethical data handling. This study investigates the impact Explainable AI (XAI) models robust information governance standards on enhancing use customer data. A mixed-methods approach was employed, combining a comprehensive literature review with survey 342 respondents across various industries. findings reveal that implementation XAI significantly increases user trust compared to black-box models. Additionally, strong positive correlation found between adoption data, highlighting importance transparency frameworks mechanisms. Furthermore, underscores critical role education fostering facilitating informed decision-making interactions. results emphasize need for organizations prioritize techniques, establish frameworks, invest education, foster culture use. These recommendations provide roadmap harness benefits while mitigating potential risks ensuring responsible trustworthy practices.

Language: Английский

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FE-RNN: A fuzzy embedded recurrent neural network for improving interpretability of underlying neural network

Information Sciences, Journal Year: 2024, Volume and Issue: 663, P. 120276 - 120276

Published: Feb. 9, 2024

Deep learning enables effective predictions. But deep structures face some challenges on human interpretability compared to conventional techniques, e.g., fuzzy inference systems. It motivates more research works alleviate the black box nature of with performance maintained. This paper proposes a fuzzy-embedded recurrent neural network (FE-RNN) improve underlying networks. is parallel structure comprising an RNN and Pseudo Outer-Product based Fuzzy Neural Network (POPFNN) that share common set input output linguistic concepts. The processes undertaken are associated by using rules in embedded POPFNN. IF-THEN provide better process hybrid allows realisation data driven implication modelling entailment within networks (FNN) structure. FE-RNN obtains consistent results than other FNN experiment Mackey-Glass dataset. achieves about 99% correlation for forecasting prices market indexes. Its also discussed. then acts as prediction tool financial trading system forecast-assisted technical indicators optimised Genetic Algorithms. outperforms benchmark strategies experiments.

Language: Английский

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