Secure Internet of Things (IoT) using a novel Brooks Iyengar quantum Byzantine Agreement-centered blockchain Networking (BIQBA-BCN) model in smart healthcare

Zhao, Zhenwei; Li, Xiaoming; Luan, Bing; Jiang, Weining; Gao, Weidong; Neelakandan, S.

doi:10.1016/j.ins.2023.01.020

Cited by 40 publications

(4 citation statements)

References 41 publications

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“…The results derived from the application of a CNN-based architecture for heart disease detection using wearable device data and the analysis of respiratory sounds for respiratory disease identification underscore the potential of these technologies in enhancing diagnostic accuracy and patient care. The model's performance, indicated by high accuracy, precision, recall, and F-score metrics, demonstrates the efficacy of machine learning algorithms in interpreting complex physiological data [48]. The segmentation of stroke lesions using the UNet model [49] further illustrates the capability of deep learning methods in medical imaging analysis, providing clear delineation of affected areas for accurate diagnosis [50].…”

Section: A Interpretation Of Resultsmentioning

confidence: 99%

Framework for Organization of Medical Processes in Medical Institutions Based on Big Data Technologies

Zhussipbek,

Turymbetov,

Ibragimova

et al. 2024

IJACSA

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This research paper delves into the burgeoning field of Big Data analytics in healthcare, proposing an innovative framework aimed at refining the organization and management of medical processes within healthcare institutions. Through the lens of detailed case studies, including stroke diagnosis leveraging the UNet model, and the identification of heart and respiratory diseases via machine learning algorithms applied to data from wearable devices, the study illuminates the profound capabilities of Big Data technologies in enhancing the precision of diagnostics, tailoring patient treatment, and elevating the overall efficiency of healthcare services. It meticulously interprets the outcomes of these applications, discusses the practical implications for healthcare professionals and institutions, confronts the challenges inherent in the integration of sophisticated analytics in clinical settings, and outlines potential directions for future research. Among the pivotal challenges highlighted are issues related to data privacy, security, the need for advanced infrastructure, and the imperative for ongoing training and interdisciplinary cooperation to navigate the complexities of Big Data in healthcare. The paper underscores the transformative promise of Big Data analytics, suggesting that comprehensive adoption and adept implementation could revolutionize healthcare delivery, making it more personalized, efficient, and cost-effective. Through this exploration, the paper contributes to the ongoing discourse on the integration of technology in healthcare, offering insights into how Big Data analytics can serve as a cornerstone for the next generation of medical diagnostics and patient care management, thereby enhancing health outcomes on a global scale.

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Section: A Interpretation Of Resultsmentioning

confidence: 99%

Framework for Organization of Medical Processes in Medical Institutions Based on Big Data Technologies

Zhussipbek,

Turymbetov,

Ibragimova

et al. 2024

IJACSA

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“…Blockchain dapat digunakan untuk menyimpan dan mengelola informasi asuransi kesehatan dengan aman, meningkatkan efisiensi dan akurasi pemrosesan klaim dan mengurangi penipuan [21].…”

Section: Health Insurance (Hi)unclassified

Blockchain Untuk Aplikasi Iot Healthcare: Studi Literatur

Cahyono,

Hadikurniawati

2023

dinamik

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Teknologi blockchain telah mengalami perkembangan pesat dalam beberapa tahun terakhir dan telah menyentuh hampir setiap aspek informasi dan bidang teknologi komunikasi. Potensi blockchain dapat diterapkan di berbagai industri dan bidang, termasuk logistik, rantai suplai pasokan, IoT, dan layanan kesehatan. Dalam industri kesehatan, blockchain memiliki dampak yang signifikan. Namun penilaian kritis terhadap aplikasi blockchain dalam pelayanan kesehatan masih belum tersedia secara komprehensif. Penelitian ini bertujuan untuk memberikan landasan teknis tentang berbagai aplikasi kesehatan berbasis blockchain, dengan fokus pada perkembangan terkini di bidang ini. Studi ini akan membahas teknologi blockchain kontemporer yang telah diterapkan dalam industri kesehatan, termasuk berbagi data medis, manajemen rantai suplai medis, rekam medis elektronik, klaim asuransi kesehatan, farmasi industri, dan pemantauan pasien jarak jauh. Penelitian ini memberikan wawasan mendalam tentang penggunaan blockchain dalam aplikasi pelayanan kesehatan berbasis IoT dan menyediakan analisis SWOT yang komprehensif untuk membantu pemangku kepentingan dalam memahami manfaat, tantangan, peluang, dan ancaman yang terkait dengan teknologi ini. Diharapkan bahwa hasil survei literatur ini akan memberikan landasan yang kuat untuk pengembangan lebih lanjut dan penerapan blockchain dalam pelayanan kesehatan IoT yang aman, efisien, dan terpercaya.

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“…RNNs are a subclass of Artificial Neural Networks that, unlike Feed-forward Neural Networks, have a hidden internal state () t r that is engaged in the computation at the stage 1 t + (FNN). RNN is trained to process sequences of varying lengths [35].…”

Section: Recurrent Neural Networkmentioning

confidence: 99%

Robust Deep Learning Based Framework for Detecting Cyber Attacks from Abnormal Network Traffic

Swathi,

Narsimha

2023

IJRITCC

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The internet's recent rapid growth and expansion have raised concerns about cyberattacks, which are constantly evolving and changing. As a result, a robust intrusion detection system was needed to safeguard data. One of the most effective ways to meet this problem was by creating the artificial intelligence subfields of machine learning and deep learning models. Network integration is frequently used to enable remote management, monitoring, and reporting for cyber-physical systems (CPS). This work addresses the primary assault categories such as Denial of Services(DoS), Probe, User to Root(U2R) and Root to Local(R2L) attacks. As a result, we provide a novel Recurrent Neural Networks (RNN) cyberattack detection framework that combines AI and ML techniques. To evaluate the developed system, we employed the Network Security Laboratory-Knowledge Discovery Databases (NSL-KDD), which covered all critical threats. We used normalisation to eliminate mistakes and duplicated data before pre-processing the data. Linear Discriminant Analysis(LDA) is used to extract the characteristics. The fundamental rationale for choosing RNN-LDA for this study is that it is particularly efficient at tackling sequence issues, time series prediction, text generation, machine translation, picture descriptions, handwriting recognition, and other tasks. The proposed model RNN-LDA is used to learn time-ordered sequences of network flow traffic and assess its performance in detecting abnormal behaviour. According to the results of the experiments, the framework is more effective than traditional tactics at ensuring high levels of privacy. Additionally, the framework beats current detection techniques in terms of detection rate, false positive rate, and processing time.

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Secure Internet of Things (IoT) using a novel Brooks Iyengar quantum Byzantine Agreement-centered blockchain Networking (BIQBA-BCN) model in smart healthcare

Cited by 40 publications

References 41 publications

Framework for Organization of Medical Processes in Medical Institutions Based on Big Data Technologies

Framework for Organization of Medical Processes in Medical Institutions Based on Big Data Technologies

Blockchain Untuk Aplikasi Iot Healthcare: Studi Literatur

Robust Deep Learning Based Framework for Detecting Cyber Attacks from Abnormal Network Traffic

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