IoT Security Challenges in Cloud Environment

Bonkra, Anupam; Dhiman, Pummy

doi:10.1109/iccmst54943.2021.00018

Cited by 14 publications

(7 citation statements)

References 40 publications

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“…There were 28 papers with undefined authors, which we omitted. Apart from English, there were articles in other languages including Korean (3), Chinese (2), Russian (1), and Turkish (1). For this study, we limited our documents to those in the English language.…”

Section: Search Strategymentioning

confidence: 99%

“…New applications for artificial intelligence (AI) have been generated by recent developments in machine learning (ML), the Internet of Things (IoT) [1], big data, and assisted fog and edge networks, which offer several benefits to many different sectors. However, many of these systems struggle to justify their own decisions and actions to those who are not computers.…”

Section: Introductionmentioning

confidence: 99%

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Healthcare Trust Evolution with Explainable Artificial Intelligence: Bibliometric Analysis

Dhiman,

Bonkra,

Kaur

et al. 2023

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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.

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Section: Search Strategymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Healthcare Trust Evolution with Explainable Artificial Intelligence: Bibliometric Analysis

Dhiman,

Bonkra,

Kaur

et al. 2023

Information

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“…The given data show that, among all nations, China has the highest rate of production, followed by the United States, Poland, India, Italy, and France. There are numerous strategies used for apple leaf disease detection by using AI features such as ML/DL [ 29 ], data preprocessing [ 30 ], picture segmentation [ 31 ], image classification [ 32 ], and feature extraction [ 33 ] in order to save crops and sustain the global economy, and all information will be shared and collected by IoT devices collaborating with the cloud so that future work will be time-saving [ 33 , 34 , 35 ]. Since more than 20 years ago, there has been intense and ongoing research into apple integrated pest management (IPM) and the development of apple IPM programmes through the transfer technology activities of Extension and Advisory personnel and crop consultants [ 36 ].…”

Section: Introductionmentioning

confidence: 99%

Apple Leave Disease Detection Using Collaborative ML/DL and Artificial Intelligence Methods: Scientometric Analysis

Bonkra

Bhatt

Rosak-Szyrocka

et al. 2023

IJERPH

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Infection in apple leaves is typically brought on by unanticipated weather conditions such as rain, hailstorms, draughts, and fog. As a direct consequence of this, the farmers suffer a significant loss of productivity. It is essential to be able to identify apple leaf diseases in advance in order to prevent the occurrence of this disease and minimise losses to productivity caused by it. The research offers a bibliometric analysis of the effectiveness of artificial intelligence in diagnosing diseases affecting apple leaves. The study provides a bibliometric evaluation of apple leaf disease detection using artificial intelligence. Through an analysis of broad current developments, publication and citation structures, ownership and cooperation patterns, bibliographic coupling, productivity patterns, and other characteristics, this scientometric study seeks to discover apple diseases. Nevertheless, numerous exploratory, conceptual, and empirical studies have concentrated on the identification of apple illnesses. However, given that disease detection is not confined to a single field of study, there have been very few attempts to create an extensive science map of transdisciplinary studies. In bibliometric assessments, it is important to take into account the growing amount of research on this subject. The study synthesises knowledge structures to determine the trend in the research topic. A scientometric analysis was performed on a sample of 214 documents in the subject of identifying apple leaf disease using a scientific search technique on the Scopus database for the years 2011–2022. In order to conduct the study, the Bibliometrix suite’s VOSviewer and the web-based Biblioshiny software were also utilised. Important journals, authors, nations, articles, and subjects were chosen using the automated workflow of the software. Furthermore, citation and co-citation checks were performed along with social network analysis. In addition to the intellectual and social organisation of the meadow, this investigation reveals the conceptual structure of the area. It contributes to the body of literature by giving academics and practitioners a strong conceptual framework on which to base their search for solutions and by making perceptive recommendations for potential future research areas.

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“…To address the issues of production failure and quality degradation caused by apple diseases, an automated system that can quickly and accurately detect illnesses is necessary. Farmers that use IoT devices and cloud-based collaboration will also be better able to assess their operations since they will have access to real-time data and analytics that might help them optimize their procedures and react quickly to changing situations [3][4] [5]. By utilizing fewer resources, including water and fertilizer, and by permitting more precise application of these inputs based on actual crop demands, this strategy can also assist in lessening the impact of agriculture on the environment.…”

Section: Introductionmentioning

confidence: 99%

Apple Leaf Disease Recognition with Segmentation Research Trend: A Systematic Analysis Using VoSViewer

Bonkra

Bhatt

Kaur

et al. 2023

Preprint

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The study’s goals were to examine the effects of unforeseen weather circumstances on agricultural productivity and to test artificial intelligence-based strategies for feature extraction and segmentation for plant leaf disease identification. A bibliometric analysis was used in the study to look into current trends, publications, citation structures, patterns of ownership and collaboration, production patterns, and other aspects connected to the early detection of apple diseases. While there have been many studies on recognising apple illnesses, there have been few attempts to develop a thorough multidisciplinary science map because disease detection is not exclusive to any one field of study. The study’s objectives were to combine knowledge frameworks and identify the current state of the research field. A scientometric examination of 200 papers from the Scopus database between 2017 and 2022 was done by the researchers in order to accomplish this goal. VOSviewer was utilised for the research, and its automated workflow was used to pick significant journals, authors, nations, articles, and themes. To find patterns and trends, social network studies, citation and co-citation checks, and citation counts were all carried out. The study revealed changes in the cognitive and social structure of the area and concluded that the interdisciplinary field of sickness detection employing segmentation has expanded over time. The study also revealed areas that need additional investigation and clarified the intellectual underpinnings of the topic.

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IoT Security Challenges in Cloud Environment

Cited by 14 publications

References 40 publications

Healthcare Trust Evolution with Explainable Artificial Intelligence: Bibliometric Analysis

Healthcare Trust Evolution with Explainable Artificial Intelligence: Bibliometric Analysis

Apple Leave Disease Detection Using Collaborative ML/DL and Artificial Intelligence Methods: Scientometric Analysis

Apple Leaf Disease Recognition with Segmentation Research Trend: A Systematic Analysis Using VoSViewer

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