IEEE Access Research Articles

SUSTAINABLE ARCHITECTURE AND THE ROLE OF CYBER-PHYSICAL SYSTEMS: A BIBLIOMETRIC ANALYSIS

Cyber-Physical Systems are next-generation digital systems that focus on the intricate connections and integration between the physical and digital worlds. These systems have highly integrated physical, communication, control, and computing components. The study's purpose is to carry out a bibliometric analysis to explore the research trends, collaboration network, and thematic evaluation of papers on cyber-physical systems. The most prolific countries, authors, sources, highest citations received by authors, and co-occurrences in this research domain were identified. The bibliometric analysis method was employed to empirically analyze the research on CPS in architecture published between 2007 and 2022 in the Web of Science database. All retrieved records were initially filtered by excluding 364 non–non-peer-reviewed publications and another type of languages except English. After that, we excluded 8 records based on the criteria and scope, and again excluded records for duplicates 12 records. Finally, 1336 records met the eligibility criteria. The VOS viewer and biblioshiny software was used to construct the scientific maps. A total of 1336 publications were analyzed. The study results show that the highly cited papers have been published in 297 journals by 4520 authors from 1483 organizations in 83 countries. The most prolific journal was IEEE Access, with 104 publications, 3823 citations, and an h-index of 24. The number of documents progressively increased from 2010 to 2021. Further, 2009 has no publications, and the largest number of articles was published in 2021 (n=272). Furthermore, China led the way with the most publications, active organizations, prolific authors, and international collaborations. This is the first study to look at the global trends in research on CPS in architecture and provides valuable guidelines and motivations for further research. Future studies may also examine the main hazards connected with using cyber-physical systems to manage operations across various industries and sectors.

A quantitative analysis of artificial intelligence research in cervical cancer: a bibliometric approach utilizing CiteSpace and VOSviewer.

Cervical cancer, a severe threat to women's health, is experiencing a global increase in incidence, notably among younger demographics. With artificial intelligence (AI) making strides, its integration into medical research is expanding, particularly in cervical cancer studies. This bibliometric study aims to evaluate AI's role, highlighting research trends and potential future directions in the field. This study systematically retrieved literature from the Web of Science Core Collection (WoSCC), employing VOSviewer and CiteSpace for analysis. This included examining collaborations and keyword co-occurrences, with a focus on the relationship between citing and cited journals and authors. A burst ranking analysis identified research hotspots based on citation frequency. The study analyzed 927 articles from 2008 to 2024 by 5,299 authors across 81 regions. China, the U.S., and India were the top contributors, with key institutions like the Chinese Academy of Sciences and the NIH leading in publications. Schiffman, Mark, featured among the top authors, while Jemal, A, was the most cited. 'Diagnostics' and 'IEEE Access' stood out for publication volume and citation impact, respectively. Keywords such as 'cervical cancer,' 'deep learning,' 'classification,' and 'machine learning' were dominant. The most cited article was by Berner, ES; etal., published in 2008. AI's application in cervical cancer research is expanding, with a growing scholarly community. The study suggests that AI, especially deep learning and machine learning, will remain a key research area, focusing on improving diagnostics and treatment. There is a need for increased international collaboration to maximize AI's potential in advancing cervical cancer research and patient care.

Analysis of international publication trends in artificial intelligence in skin cancer

Bibliometric methods were used to analyze publications on artificial intelligence in skin cancer from 2010 to 2022, aiming to explore current publication trends and future directions. A comprehensive search using four terms - “artificial intelligence,” “machine learning,” “deep learning,” and “skin cancer” was performed in the Web of Science database for original English-language publications on artificial intelligence in skin cancer from 2010 to 2022. We visually analyzed publication, citation, and coupling information, focusing on authors, countries and regions, publishing journals, institutions, and core keywords.The analysis of 989 publications revealed a consistent year-on-year increase in publications from 2010 to 2022 (0.51% vs. 33.57%). The USA, India, and China emerged as the leading contributors. IEEE Access was identified as the most prolific journal in this area. Key journals and influential authors were highlighted. Examination of the top 10 most cited publications highlights the significant potential of AI in oncology. Co-citation network analysis identified four primary categories of classical literature on AI in skin tumors. Keyword analysis indicated that "melanoma," "classification," and "deep learning" were the most prevalent keywords, suggesting that deep learning for melanoma diagnosis and grading is the current research focus. The term “pigmented skin lesions” showed the strongest burst and longest duration, while “texture” was the latest emerging keyword.AI represents a rapidly growing area of research in skin cancer with the potential to significantly improve skin cancer management. Future research is likely to focus on machine learning and deep learning technologies for screening and diagnostic purposes.

IEEE Access? Harness the publishing power of IEEE Access

IEEE Access? Harness the publishing power of IEEE Access

Study of an Adaptive Waiting Control Algorithm for Channel Access in IEEE 8012.11be Networks

Study of an Adaptive Waiting Control Algorithm for Channel Access in IEEE 8012.11be Networks

Bibliometric analysis of the application of deep learning in cancer from 2015 to 2023

BackgroundRecently, the application of deep learning (DL) has made great progress in various fields, especially in cancer research. However, to date, the bibliometric analysis of the application of DL in cancer is scarce. Therefore, this study aimed to explore the research status and hotspots of the application of DL in cancer.MethodsWe retrieved all articles on the application of DL in cancer from the Web of Science database Core Collection database. Biblioshiny, VOSviewer and CiteSpace were used to perform the bibliometric analysis through analyzing the numbers, citations, countries, institutions, authors, journals, references, and keywords.ResultsWe found 6,016 original articles on the application of DL in cancer. The number of annual publications and total citations were uptrend in general. China published the greatest number of articles, USA had the highest total citations, and Saudi Arabia had the highest centrality. Chinese Academy of Sciences was the most productive institution. Tian, Jie published the greatest number of articles, while He Kaiming was the most co-cited author. IEEE Access was the most popular journal. The analysis of references and keywords showed that DL was mainly used for the prediction, detection, classification and diagnosis of breast cancer, lung cancer, and skin cancer.ConclusionsOverall, the number of articles on the application of DL in cancer is gradually increasing. In the future, further expanding and improving the application scope and accuracy of DL applications, and integrating DL with protein prediction, genomics and cancer research may be the research trends.

An examination of immersive journalism by bibliometric analysis from 1999 to 2023

Immersive journalism is an innovative storytelling approach that aims to enable the audience to experience the event or situation in the news using virtual reality, unlike traditional news narration. In this study, the literature related to the subject was searched using the keywords Immersive Journalism, 360-Degree Video, Narrative journalism, Newsgame, VR Storytelling through the Web of Science database and a data set was created from 955 publications between 1999 and 2023. No filter was applied to the studies in the data set of the study and articles, books, and early access publications as well as book chapters, editorial materials or conference proceedings in the Web of Science database were included in the study. Bibliometric analysis techniques were applied to the obtained data set and the collaboration status and citation maps of authors and institutions were revealed together with various parameters including authors, institutions, keywords used, number of citations and distribution of publications according to countries.As a result of the analyses, while the number of studies related to the subject has increased since 2017, the highest number of studies was conducted in 2021. The most articles on the subject of the study are in the “Communication” category. While the majority of academic studies on the subject were carried out in the United States, studies originating from this country were cited the most. While “Virtual Reality” is the most commonly used word in the studies on the subject, “IEEE Access” stands out as the journal with the most publications on the subject. On the other hand, the study revealed important authors, studies and themes in the literature on immersive journalism, emphasised the interactions between authors and disciplines, and provided a road map for future research. The study has limitations in terms of the publication languages of some of the studies in the dataset, the time period of the dataset and the singularity of the preferred database.

Digital Technology Increases the Sustainability of Cross-Border Agro-Food Supply Chains: A Review

The increasing prominence of climate change, geopolitical crises, and global economic slowdown highlights the challenges and structural deficiencies of traditional cross-border agro-food supply chains. As a result, there has been a growing consensus on the need to leverage digital technology to rebuild and innovate a safe, stable, and sustainable global food system. This study assessed the knowledge progress and development trends in the sustainable development of cross-border agro-food supply chains enabled by digital technology. A total of 352 authoritative papers from the core Web of Science database were selected for analysis. The Citespace tool was utilized to visually examine research elements. The findings reveal that research outcomes in this territory experienced a significant period of rapid growth, particularly after 2020. Sustainability and IEEE Access are the journals with the highest and second-highest number of publications. China and the France National Institute are the countries and research institutions with the largest number of publications in this field. The research hotspots are mainly the application of digital technologies, food safety, and supply chain system model innovation. In the past ten years, the research in this territory has gone through three stages: precise timeliness orientation, intelligent strategic decision-making orientation, and model predictability orientation. We further construct the ‘antecedent–practice–performance’ conceptual framework of the sustainability of the digital technology-enabled cross-border agro-food supply chain. Finally, this paper presents the potential research directions in this territory, focusing on four aspects: research method, research mechanism, research topic, and research frontier.

Optimized method of thermoelectric cooler-based thermal management for improving the luminous efficacy of light-emitting diodes

We experimentally performed a method of active cooling for white LED that can reduce the temperature of the LED by using TEC for reducing the temperature of the LED board. The relation of LED board and input power for TEC was investigated to find out the optimized curve of LED board temperature and input power for TEC. Also, the TEC based cooling system was applied for RGBW LEDs. The obtained result is meaningful in LED thermal management, improving the lighting quality and increases the lifespan for both middle and high-power LED types. Full Text: PDF References E.F. Schubert, J.K. Kim, "Solid-State Light Sources Getting Smart", Science, 308, 1274 ¬(2005). CrossRef Q.K. Nguyen, "Emission spectrum modeling of white LEDs light source with using Gaussian function", Photonics Lett. Pol.,15(4), 54 (2023). CrossRef C.C. Sun, S.H. Ma, Q.K. Nguyen, "Advanced LED Solid-State Lighting Optics", Crystals, 10, 758 (2020). CrossRef S.P. Ying, H.K. Fu, W.F. Tang, R.C. Hong, "The Study of Thermal Resistance Deviation of High-Power LEDs", IEEE Trans. Electron Devices, 61, 2843 (2014). CrossRef K. Górecki, P. Ptak, "Compact Modelling of Electrical, Optical and Thermal Properties of Multi-Colour Power LEDs Operating on a Common PCB", Energies, 14, 1286 (2021). CrossRef C.C. Sun, Q.K. Nguyen, T.X. Lee, S.K. Lin, C.S. Wu, T.H. Yang, Y.W.Yu. "Active thermal-fuse for stopping blue light leakage of white light-emitting diodes driven by constant current", Sci. Rep. 12, 12433 (2022). CrossRef Q.K. Nguyen, B. Glorieux, G. Sebe , T.H. Yang, Y.W. Yu , C.C. Sun, "Passive anti-leakage of blue light for phosphor-converted white LEDs with crystal nanocellulose materials", Sci Rep. 13, 13039 (2023). CrossRef N. Bădălan (Drăghici), P. Svasta, C. Marghescu, "A Method for Improving an Active Cooling Solution for LEDs ", IEEE 24th International Symposium for Design and Technology in Electronic Packaging (SIITME), Iași, Romania (2018). CrossRef A. Fan, R. Bonner, S. Sharratt, Y.S. Ju, "An innovative passive cooling method for high performance light-emitting diodes", 28th Annual IEEE Semiconductor Thermal Measurement and Management Symposium (SEMI-THERM), 319-324 (2012). CrossRef D.W. Kim, E. Rahim, A. Bar-Cohen, B. Han, "Direct Submount Cooling of High-Power LEDs", Compon. Packag. Technol., 33, 698 (2010). CrossRef N. Guan, N. Amador-Mendez, A. Kunti, A. Babichev, S. Das, A. Kapoor, N. Gogneau, J. Eymery, F.H. Julien, C. Durand, M. Tchernycheva, "Heat Dissipation in Flexible Nitride Nanowire Light-Emitting Diodes", Nanomater, 10, 2271 (2020). CrossRef S. Li, J.L. Liu, L. Ding, J.X. Liu, J. Xu, Y. Peng, M.X. Chen, "Active Thermal Management of High-Power LED Through Chip on Thermoelectric Cooler", IEEE Trans. Electron Devices, 68, 1753 (2021). CrossRef G.R. Sui, J.J. Chen, H. Ni, Y.Y. Ma, X.M. Gao, N. Wang, "Improvement of LED Performance With an Integrated Thermoelectric Cooling Package", IEEE Access, 8, 116535 (2020). CrossRef

Analyzing Emerging Trends in Wireless Implantable Medical Devices (IMDs): A Bibliometric Study

In the present era of advanced medical technology, there has been a significant increase in the utilization of wireless-based implantable medical devices (IMDs) in various biomedical applications, including health diagnosis, monitoring, recording, treatment, and other clinical concerns. It is essential to closely examine the research trends in wireless IMD topics to understand their growth trajectory. This can be achieved through the utilization of bibliometric reviews. While there are numerous experimental studies and survey papers on the topic, there is a dearth of review papers that utilize a bibliometric approach. Therefore, this study aims to conduct a bibliometric analysis to identify emerging research trends in wireless IMDs for biomedical applications for the first time. An analysis was conducted on 1,650 articles retrieved from the Scopus database from its inception until September 2023. The study used tools such as VOS Viewer to measure performance and science map parameters, and Tableau Cloud to visually enhance the analysis process. This research endeavor allowed us to analyze historical trends, influential papers, top journals, institutions, countries, and authors within this field. The top three influential journals in this field are IEEE Transactions on Biomedical Circuits and Systems, IEEE Transactions on Antenna and Propagation, and IEEE access. The National University of Singapore is the most productive institution in terms of overall publications. The United States, China, and India make significant contributions to overall publications. Finally, we also examined the most significant keywords and current research areas within this field.

Mapping the Landscape of Misinformation Detection: A Bibliometric Approach

The proliferation of misinformation presents a significant challenge in today’s information landscape, impacting various aspects of society. While misinformation is often confused with terms like disinformation and fake news, it is crucial to distinguish that misinformation involves, in mostcases, inaccurate information without the intent to cause harm. In some instances, individuals unwittingly share misinformation, driven by a desire to assist others without thorough research. However, there are also situations where misinformation involves negligence, or even intentional manipulation, with the aim of shaping the opinions and decisions of the target audience. Another key factor contributing to misinformation is its alignment with individual beliefs and emotions. This alignment magnifies the impact and influence of misinformation, as people tend to seek information that reinforces their existing beliefs. As a starting point, some 56 papers containing ‘misinformation detection’ in the title, abstract, or keywords, marked as “articles”, written in English, published between 2016 and 2022, were extracted from the Web of Science platform and further analyzed using Biblioshiny. This bibliometric study aims to offer a comprehensive perspective on the field of misinformation detection by examining its evolution and identifying emerging trends, influential authors, collaborative networks, highly cited articles, key terms, institutional affiliations, themes, and other relevant factors. Additionally, the study reviews the most cited papers and provides an overview of all selected papers in the dataset, shedding light on methods employed to counter misinformation and the primary research areas where misinformation detection has been explored, including sources such as online social networks, communities, and news platforms. Recent events related to health issues stemming from the COVID-19 pandemic have heightened interest within the research community regarding misinformation detection, a statistic which is also supported by the fact that half of the papers included in top 10 papers based on number of citations have addressed this subject. The insights derived from this analysis contribute valuable knowledge to address the issue, enhancing our understanding of the field’s dynamics and aiding in the development of effective strategies to detect and mitigate the impact of misinformation. The results spotlight that IEEE Access occupies the first position in the current analysis based on the number of published papers, the King Saud University is listed as the top contributor for the misinformation detection, while in terms of countries, the top-5 list based on the highest contribution to this area is made by the USA, India, China, Spain, and the UK. Moreover, the study supports the promotion of verified and reliable sources of data, fostering a more informed and trustworthy information environment.

Emerging trends and performance measurement of cryptocurrency research during 2013–2022: a systematic review based on computational mapping

Purpose The emergence of cryptocurrencies has tremendously changed the way of financial transactions around the world which has led to form distinct discussions in the field regarding its reliability. This paper aims to evaluate the published literatures on cryptocurrency identifying its growth, citation, prolific authors, journals, countries, active funding agencies, collaboration pattern and emerging research hotspots in the area. Design/methodology/approach Scientometrics and Altmetrics parameters have been incorporated in the study. Literatures covered from the Scopus database searching within “Article Title, Abstract, Keywords” with keywords “cryptocurrency” OR “digital currency” OR “bitcoin” OR “Ethereum” by limiting the time range of 2013–2022, English language and journal articles only. Total 6,107 documents have been identified. The further analysis and visualisation is performed using MSExcel, VOSviewer, Biblioshiny and Tableau. Another tool, Dimension.ai is used to identify the Altmetric Attention Score. Findings The findings reveal that the growth of research and citation rate hiked from the year 2017 till now. Elie Bouri is the top contributor, IEEE Access is the most prolific journal, China being the prolific country. Topics like Blockchain, Bitcoin, Ethereum, smart contracts, financial markets are emerging researched hotspots. The reliability of crypto market is still not clear because of its high volatility. The findings of the study will be more useful in the academia, subject specialists, research institutions, funding agencies, publishing agencies in decision-making. Originality/value To the best of the authors’ knowledge, there is no such study found considering both Scientometrics and Altmetrics approaches on cryptocurrency research with the selected time bound.

UAV Head-On Situation Maneuver Generation Using Transfer-Learning-Based Deep Reinforcement Learning

AbstractRecently, the demand for unmanned aerial vehicle technology has increased. In particular, AI pilots through reinforcement learning (RL) are more flexible than those using rule-based methods. Further, AI pilots with RL are expected to replace human pilots in the future. In a recent study, rather than completely replacing humans, studies on AI pilots are conducted toward the collaboration between man and unmanned aircraft. AI pilots have several advantages over humans. For example, on the one hand, human pilots avoid head-on situations due to collision. On the other hand, AI pilots may prefer head-on situations to finish the episode quickly. This study proposes a two-circle-based transfer learning method to demonstrate excellent performance in head-on situations. Based on the experimental results, the proposed two-circle-based multi-task transfer learning model outperforms the model without transfer learning-based RL. A study on transfer-learning-based learning technique has been conducted. However, it had a one-circle-based learning technique was specialized only for tail-chasing, limiting its application (Bae et al. in IEEE Access 11:26427–26440, 2023). Practically, the proposed two-circle-based learning technique outperforms the one-circle-based transfer learning technique in head-on situations.

Decision-making in Cybersecurity: A Bibliometric analysis

This bibliometric analysis explores research trends and patterns in the intersection of decision-making and cybersecurity. Using Scopus data, we conducted a systematic search and identified 4,637 relevant documents published between 2018-2024. Quantitative analysis reveals rising annual publications with a peak in 2023, the predominance of journal articles, and robust international collaboration networks. China and the USA lead global scientific production. Key topics include risk assessment, network security, decision support systems, and emerging technologies like machine learning and artificial intelligence. Core journals with high citation impact such as IEEE Access and Expert Systems with Applications highlight significant sources of literature. The study provides a holistic overview of the landscape, evolution, contributors, and themes within decision-making and cybersecurity research.

(Invited) Effect of Electrochemical Treatment and pH on the Kinetics of VII-VIII Reactions on Glassy Carbon Electrodes

Vanadium flow batteries (VFBs) are known for their long cycle life, high efficiency, and scalability, which make them suitable for various energy storage applications, including grid-scale energy storage, renewable energy integration, and remote power systems. There are several areas of active research on VFBs. These include electrolyte chemistry, electrode materials, cell design, system integration, modeling and simulation, performance optimization, scale-up and commercialization1.Carbon felts are often used as electrodes in VFBs due to their high electrical conductivity, chemical stability, and availability2. However, the surface properties of carbon materials can be influenced by the surrounding environment, leading to changes in their surface chemistry and reactivity. These changes in the environment can alter the kinetics of the vanadium reactions at the carbon surfaces, which in turn can impact the performance and efficiency of the battery system. Various electrochemical, chemical, and thermal treatments have been reported to have a significant impact on the carbon electrode kinetics3. It is thus important to investigate electrode kinetics to improve the performance of VFBs.Previously our group has reported that electrochemical treatments of carbon electrodes at different potentials significantly affect the electrode kinetics of VIV-VV and VII-VIII redox reactions4,5. The cathodic treatment of carbon electrode enhances the kinetics of VIV-VV redox reaction but inhibits the kinetics of VII-VIII redox reaction, while anodic treatment inhibits the kinetics of the VIV-VV redox reaction but enhances the kinetics of the VII-VIII redox reaction6-9. We have further investigated the effect of electrochemical treatment on the VIV-VV kinetics at glassy carbon electrodes following treatment in electrolytes of various pH. For all pHs investigated (pH 0 – pH 6), VIV-VV kinetics are enhanced by cathodic treatment of the electrode and inhibited by anodic treatment. The activating effect of cathodization increased markedly with increasing pH, whereas the deactivating effect of anodization appears approximately the same for all pHs. The observed effects are attributed to changes to electrode surface states by electrochemical polarization reactions, the rates of which appear to be pH dependent10.We have extended our investigation into the VII-VIII reaction kinetics. We will present results on the effect of electrochemical treatment and pH on the kinetics of VII-VIII reactions on glassy carbon electrodes. The kinetic rates for the VII-VIII couple on these electrodes are measured and compared using cyclic voltammetry (CV) and electrochemical impedance spectroscopy (EIS). Acknowledgements Varsha Sasikumar S P would like to thank the Irish Research Council (IRC) for a postgraduate scholarship to perform this research and acknowledge facilitation of this research by University of Limerick (UL), Ireland and South East Technological University (SETU), Ireland. References Aluko and A. Knight, IEEE Access, 11, 13773-13793 (2023).Kim et al., Scientific Reports, 4, 6906 (2014).K. Sankaralingam et al., Journal of Energy Storage, 41, 102857 (2021).N. Buckley et al., ECS Transactions, 98(9), 223-239 (2020).N. Buckley et al., Electrodes for Vanadium Flow Batteries (VFBs) In Flow Batteries: From Fundamentals to Applications, Chapter 24, Edited by. C. Roth et al., Wiley-VCH, ISBN 978-3-527-34922-7 (2023).Bourke et al., Journal of Electrochemical Society, 163, A5097 (2016).Bourke et al., Journal of Electrochemical Society, 162, A1547 (2015).A. Miller. et al., Journal of Electrochemical Society, 163, A2095-A2102, (2016).Bourke et al., Journal of Electrochemical Society, 170, 030504 (2023).Sasikumar et al., ECS Transactions, 109, 107 (2022).

Bibliometric Analysis of IoT Lightweight Cryptography

In the rapidly developing world of the Internet of Things (IoT), data security has become increasingly important since massive personal data are collected. IoT devices have resource constraints, which makes traditional cryptographic algorithms ineffective for securing IoT devices. To overcome resource limitations, lightweight cryptographic algorithms are needed. To identify research trends and patterns in IoT security, it is crucial to analyze existing works, keywords, authors, journals, and citations. We conducted a bibliometric analysis using performance mapping, science mapping, and enrichment techniques to collect the necessary information. Our analysis included 979 Scopus articles, 214 WOS articles, and 144 IEEE Xplore articles published during 2015–2023, and duplicates were removed. We analyzed and visualized the bibliometric data using R version 4.3.1, VOSviewer version 1.6.19, and the bibliometrix library. We discovered that India is the leading country for this type of research. Archarya and Bansod are the most relevant authors; lightweight cryptography and cryptography are the most relevant terms; and IEEE Access is the most significant journal. Research on lightweight cryptographic algorithms for IoT devices (Raspberry Pi) has been identified as an important area for future research.

A forty years scientometric investigation of artificial intelligence for fluid-flow and heat-transfer (AIFH) during 1982 and 2022

A scientometric approach is utilized to investigate the dynamic maps of relationships among researchers, institutes, and countries in the field of Artificial Intelligence for Fluid-flow and Heat-transfer (AIFH). The Web of Science database was searched for related publications during the last 40 years (1982 and 2022). A total of 6151 articles were discovered, which were analyzed in detail. Using a bibliometric analysis, the most relevant and most cited sources of publications were identified. The most active researchers, institutions, and countries leading AIFH were reported. Then, the worldwide dynamic collaboration maps and coupling maps of relationships were reported. The Islamic Azad University (1893 T.C.), the Chinese Academy of Sciences (1374 T.C.), and Beihang University (1266 T.C.) were the most influential institutes in AIFH. The most influential countries were China, the USA, and Iran. The dynamic map of collaborations shows a good worldwide collaboration distribution. The USA and China established the most connection with the rest of the world. ANNs are the most studied topic (19.5% of publications), followed by Machine Learning (17.9%) and Neural Networks (15.4%). Support Vector Machines lag behind at 1.4%. ANNs boast the highest total citations (17,064) and H-index (63). Most ANIF papers were published by Medical Physics (119 T.P.). Half of the articles in AIFH were published by five journals of Medical Physics, Neurocomputing, International Journal of Heat and Mass Transfer, International Journal of Radiation Oncology Biology Physics, and IEEE Access. The International Journal of Heat and Mass Transfer received the most citations in AIFH.

Collaborative Landscape and Knowledge Dynamics in IoT and Blockchain Research: A Bibliometric Analysis

This research article presents a bibliometric analysis of the intersection between Internet of Things (IoT) and Blockchain, utilizing data sourced from the Web of Science database. The analysis employs the VOSviewer tool to investigate cocitation, co-occurrence, and bibliometric correlations among the publications. The findings indicate a surge in publications, with the highest number recorded in 2022, reflecting the growing interest and significance of this research area. Moreover, the analysis reveals China as the leading country in terms of research output, underscoring its active involvement in IoT and Blockchain research. Additionally, the study identifies IEEE Access as the leading publisher, indicating its prominence in disseminating relevant research. Notably, the analysis of keywords reveals "blockchain" as the most frequently occurring term, highlighting its central role in the field. These findings contribute to a better understanding of the research landscape in IoT and Blockchain, providing insights into collaboration patterns, influential countries, leading publishers, and the prominence of blockchain as a research topic.

Healthcare Trust Evolution with Explainable Artificial Intelligence: Bibliometric Analysis

Recent developments in IoT, big data, fog and edge networks, and AI technologies have had a profound impact on a number of industries, including medical. The use of AI for therapeutic purposes has been hampered by its inexplicability. Explainable Artificial Intelligence (XAI), a revolutionary movement, has arisen to solve this constraint. By using decision-making and prediction outputs, XAI seeks to improve the explicability of standard AI models. In this study, we examined global developments in empirical XAI research in the medical field. The bibliometric analysis tools VOSviewer and Biblioshiny were used to examine 171 open access publications from the Scopus database (2019–2022). Our findings point to several prospects for growth in this area, notably in areas of medicine like diagnostic imaging. With 109 research articles using XAI for healthcare classification, prediction, and diagnosis, the USA leads the world in research output. With 88 citations, IEEE Access has the greatest number of publications of all the journals. Our extensive survey covers a range of XAI applications in healthcare, such as diagnosis, therapy, prevention, and palliation, and offers helpful insights for researchers who are interested in this field. This report provides a direction for future healthcare industry research endeavors.

Introducing CNN-LSTM network adaptations to improve remaining useful life prediction of complex systems

AbstractPrognostics and Health Management (PHM) models aim to estimate remaining useful life (RUL) of complex systems, enabling lower maintenance costs and increased availability. A substantial body of work considers the development and testing of new models using the NASA C-MAPSS dataset as a benchmark. In recent work, the use of ensemble methods has been prevalent. This paper proposes two adaptations to one of the best-performing ensemble methods, namely the Convolutional Neural Network – Long Short-Term Memory (CNN-LSTM) network developed by Li et al. (IEEE Access, 2019, 7, pp 75464–75475)). The first adaptation (adaptable time window, or ATW) increases accuracy of RUL estimates, with performance surpassing that of the state of the art, whereas the second (sub-network learning) does not improve performance. The results give greater insight into further development of innovative methods for prognostics, with future work focusing on translating the ATW approach to real-life industrial datasets and leveraging findings towards practical uptake for industrial applications.