Maintaining IoT Healthcare Records Using Cloud Storage

Chennam, Krishna Keerthi; Maheshwari, V. Uma; Aluvalu, Rajanikanth

doi:10.1007/978-3-030-82715-1_10

Cited by 16 publications

(4 citation statements)

References 34 publications

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“…The parameter A is given a positive integer value of the form Ak 257, where k can take on any positive integer value between 1 and n. Let's pretend we have an array T with 256 distinct numbers (0-255). Using the linear mapping given by Equation (11), a new array R is constructed from A and T.: 𝑅𝑅(𝑖𝑖) = 𝑖𝑖𝑡𝑡𝑎𝑎((𝐴𝐴 × (𝑇𝑇(𝑖𝑖) + 1)), 257) (11) where i is an integer between 1 and 256. The range of T(i) is from 0 to 255, and any positive integer A fulfils the expression Ak257, where k is an integer bigger than 0.…”

Section: Encryption Processmentioning

confidence: 99%

“…10 While widespread usage of IoT devices has the potential to improve patient care, it's crucial to ensure the safety of sensitive personal data. 11 Attacks on next-generation schemes have increased, making IoT devices vulnerable to both new and old threats. The data that travels from the Internet of Things to the cloud and visualization domains undergo numerous transformations along the way.…”

Section: Introductionmentioning

confidence: 99%

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Securing the patient healthcare data using Deep Inception-ResNet based CPABPP model in Internet of Things

Reddy,

Baswaraju,

Kumari

et al. 2024

JIST

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The IoT is transforming healthcare by enabling extensive connectivity between medical professionals, equipment, staff, and patients, facilitating real-time monitoring. While the network's scale and diversity offer advantages for data exchange, they also pose challenges for privacy and security, particularly with sensitive medical information. To address this, deep learning-based cryptographic and biometric systems are utilized for authentication and anomaly detection in medical systems. However, power constraints on network sensors necessitate efficient security schemes. Thus, the authors propose a novel framework, the deep Inception-ResNetV2 with privacy preservation, to secure data transmission while minimizing encryption and decryption time. Implementing this method reduces the network's burden, saving time and costs in communication. Compared to alternatives like private biometric-based authentication, this model demonstrates superior performance.

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Section: Encryption Processmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Securing the patient healthcare data using Deep Inception-ResNet based CPABPP model in Internet of Things

Reddy,

Baswaraju,

Kumari

et al. 2024

JIST

View full text Add to dashboard Cite

show abstract

“…Reliability, To ensure the reliability and accessibility of HER data, copies are dispersed on more than one node in the Blockchain network to avoid a single node failure, and redundancy and failover procedures are employed to guarantee constant access to health records [25]- [26].…”

Section: Objectivesmentioning

confidence: 99%

Blockchain-based Scalable IoT Cloud for Electronic Health Record using Horizontal and Vertical Mining

K. Lino Fathima Chinna Rani

2024

jes

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The twin technology of Blockchain with IoT(BIoT) deployed in healthcare systems has fetched out the mutative potentiality for accessing and managing Electronic Health Records(EHRs). However, this divergence also has considerable challenges, primarily in terms of the protection of data, scalability, and interoperability. To overcome these challenges, this research paper presents a new approach, leveraging Blockchain technology and mining mechanisms, to launch a secure, scalable, and proficient IoT cloud ecosystem for the administration of Electronic Health Records. This research study includes Blockchain scalable IoT cloud architecture intended to address the scalability problem of storing and managing large amounts of EHR records produced through IoT sensors. The proposed mechanism implements both horizontal and vertical mining concepts for the optimization of data storage as well as the retrieval processes. Horizontal mining concentrates on allocating and distributing Electronic Health Records data to more than one node, which can ultimately sink the overload on each node in the network and increase the scalability. Vertical mining, on the other flip of the coin, prioritizes the transactions, and optimizing the data with each transaction can drastically improve the acceleration speed of retrieving the data access competency. Blockchain technology constructs the core technology of BEHRHVM, assuring data integrity, privacy, and transparency. All the EHR record transactions are authorized as mutable blocks on the Blockchain framework. This mechanism improved patient privacy while accessing the secure distribution of EHRs among authorized health providers. In addition, the proposed system is built to integrate with current healthcare systems and the Internet of Things.

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“…Liver cancer can only be diagnosed through imaging or radiology testing, as a physical examination is not possible. Imagination tests aid to screen cancer at initial phase and assistance to assess the healthiness of post-treatments [ 5 ].…”

Section: Introductionmentioning

confidence: 99%

Detection of Liver Tumour Using Deep Learning Based Segmentation with Coot Extreme Learning Model

et al. 2023

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Systems for medical analytics and decision making that make use of multimodal intelligence are of critical importance in the field of healthcare. Liver cancer is one of the most frequent types of cancer and early identification of it is crucial for effective therapy. Liver tumours share the same brightness and contrast characteristics as their surrounding tissues. Likewise, irregular tumour shapes are a serious concern that varies with cancer stage and tumour kind. There are two main phases of tumour segmentation in the liver: identifying the liver, and then segmenting the tumour itself. Conventional interactive segmentation approaches, however, necessitate a high number of intensity levels, whereas recently projected CNN-based interactive segmentation approaches are constrained by low presentation on liver tumour images. This research provides a unique deep Learning based Segmentation with Coot Extreme Learning Model approach that shows high efficiency in results and also detects tumours from the publicly available data of liver images. Specifically, the study processes the initial segmentation with a small number of additional users clicks to generate an improved segmentation by incorporating inner boundary points through the proposed geodesic distance encoding method. Finally, classification is carried out using an Extreme Learning Model, with the classifier’s parameters having been ideally chosen by means of the Coot Optimization algorithm (COA). On the 3D-IRCADb1 dataset, the research evaluates the segmentation quality metrics DICE and accuracy, finding improvements over approaches in together liver-coloured and tumour separation.

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Maintaining IoT Healthcare Records Using Cloud Storage

Cited by 16 publications

References 34 publications

Securing the patient healthcare data using Deep Inception-ResNet based CPABPP model in Internet of Things

Securing the patient healthcare data using Deep Inception-ResNet based CPABPP model in Internet of Things

Blockchain-based Scalable IoT Cloud for Electronic Health Record using Horizontal and Vertical Mining

Detection of Liver Tumour Using Deep Learning Based Segmentation with Coot Extreme Learning Model

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