PMRSS: Privacy-Preserving Medical Record Searching Scheme for Intelligent Diagnosis in IoT Healthcare

The containers play a crucial role in the cloud environment during application deployment as it shares same OS kernel. It reduces resource requirements and start-up time for deploying applications by an individual organizations or users. Even though containers provide light-weight virtualization, it generates a security bottleneck for the number of dedicated resources, libraries, and applications since the container isolation is comparatively weak to the legacy VMs. In the general architec ture of container, attackers can perform privilege escalation by exploiting the kernel vulnerabilities to gain the root privilege and leaks the critical information of a system. To address the present security concerns in the container, a better security based solution is essential. In this work, an extensive analysis is performed to predict the various existing access control mechanisms used for security purposes and the challenges encountered during the architecture modeling. Some use cases are considered to ensure the fulfilment of security requirements such as container protection, inter-container protection, and host protection, and it needs to provide both software and hardware solutions. This work also includes the research problems, research gaps, and further research extensions to provide security to the containers.

Section: Discussionmentioning

confidence: 99%

Container Security: An Extensive Roadmap

Subramanian¹,

Honnavalli²,

Shylaja³

2021

“…Sachin Shetty and Y. S. Rao proposed an SVMbased machine learning approach to identify Parkinson's disease based on gait analysis [25]. The Support vector machine (SVM) classifier built on a Gaussian radial basis function kernel achieves an overall precision of 83.33 %, a strong identification rate for Parkinson's disease of 74.99 %, and poor false positive findings of 16.66 % [36][37][38][39][40].…”

Section: Techniques For Machine Learning In Parkinson's Diseasementioning

confidence: 99%

A Study on Brain Tumor and Parkinson’s Disease Diagnosis and Detection using Deep Learning

Warjurkar¹,

Ridhorkar²

2021

Consider the possibility that we live in an area far from a doctor, or that we may not have enough resources to pay the hospital cost, or that we may not have enough time to take off work. The use of advanced computers to diagnose diseases will be lifesaving in such situations. Scientists have developed a number of artificially intelligent diagnostic algorithms for illnesses such as cancer, lung disease and Parkinson's disease. Deep learning employs massive artificial neural network layers of interlinked nodes that can reorganize themselves in response to updated data. This approach enables machines to self-learn without the need for assistance from humans. The emphasis of this article is on current developments in machine learning that have had major effects on identification for the detection of a variety of illnesses, such as brain tumor segmentation. Human-assisted manual categorization may lead to erroneous prediction and diagnosis, thus one of the most important and a useful technique is brain tumor segmentation tasks in medical image processing that are difficult. Furthermore, it is a difficult challenge because there is a vast volume of data to assist. Since brain tumors have such a wide range of appearances and since tumor and normal tissues are so close, extracting tumor regions from photographs becomes difficult. The advancement of clinical decision systems of support necessitates the identification and recognition of the appropriate biomarkers in relation to specific health problems. It has been established that handwriting deficiency is proportionate to the severity of the situation of individuals' Parkinson's disease (PD).

“…Similarly, the DDR-ECS receiver comprises the Edge stream receiver, Decoder and a control Finite state machine. With the comparison of standard ECS, the data rate of DDR-ECS is doubled which doesn't cause any form of effect on power and area constraints [37][38][39][40].…”

Section: Dynamic Edge-coded Signalling (Ecs)mentioning

confidence: 99%

A Study of Communication Protocols for Internet of Things (IoT) Devices: Review

Jamuna¹,

Prakash²

2021

Wired and Wireless communication technology for IoT devices play an important role in various applications like transportation, healthcare systems, logistics, personal, social gaming robot, smart environment and city information. The design of low power architecture and development of the protocols is a challenging task for wireless and wired communication in IoT devices. Many communication technologies were used to improve the data rate for IoT communication but the error rate was increased, which reduces the reliability of the system. This paper focuses on various communication protocols for IoT devices. In addition, a comparison is done between different IoT communication protocols with respect to different metrics such as frequency bands, networks, topology, power consumption, data rate etc. The goal of this comparison is to present the guidelines for the researchers which help them to select the right protocol for various IoT applications.