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Security and Privacy in Smart Healthcare Applications

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: closed (30 November 2022) | Viewed by 3878

Special Issue Editor


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Guest Editor
Department of Computer Information Engineering, Dongseo University, Busan, 47011, Korea
Interests: Cloud Computing; Ubiquitous Computing; Wireless Sensor Network; Internet of Things; Distributed System; Security in Cloud; Middleware; Security in Wireless Sensor Network; Cloud Integration
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

With the development of technology and new healthcare solutions, implementing them has become a new trend among various research group as well as among general users. These research groups mainly focus on the use of the latest information and communication technology to meet the needs of those who are associated with the healthcare industry: healthcare professionals, patients, and policymakers. Along with the emergence of smart homes, smart cities, and all things smart, healthcare has become an area of unbelievable influence and development. In short, a combination of high-speed internet, low latency, and high bandwidth will improve digital health care. However, technology for the transmission and access to medical information poses serious security and privacy issues that need to be addressed urgently. The greater connectivity to existing IT networks has in fact exposed administrations to new IT security vulnerabilities, as healthcare is an extremely interesting target for cybercrime. The authenticity of medical information and images is therefore very worrying, as it forms the basis for diagnostic results.

The main purpose of this Special Issue is to focus on all aspects related to this particular area and the direction of future research. It will target all of the state-of-the-art research in academia and industry with a focus on current developments and will provide future guidance for the security and privacy of medical data and applications. The list of possible topics includes but is not limited to:

  • Privacy preserving methods;
  • Cryptographic techniques in healthcare;
  • Secure and trustworthy information sharing;
  • Threat detection and analysis;
  • Guidelines for the protection of personal health data;
  • Security risk assessment in healthcare;
  • Ethical and patient safety implications;
  • Secure sharing of healthcare data.

Dr. Mangal Sain
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • smart computing
  • IoT
  • AI and ML
  • blockchain
  • cybersecurity
  • cloud computing
  • wearables

Published Papers (3 papers)

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Research

10 pages, 2178 KiB  
Article
Hybrid Precoder Using Stiefel Manifold Optimization for Mm-Wave Massive MIMO System
by Divya Singh, Aasheesh Shukla, Kueh Lee Hui and Mangal Sain
Appl. Sci. 2022, 12(23), 12282; https://doi.org/10.3390/app122312282 - 30 Nov 2022
Cited by 1 | Viewed by 1023
Abstract
Due to the increasing demand for fast data rates and large spectra, millimeter-wave technology plays a vital role in the advancement of 5G communication. The idea behind Mm-Wave communications is to take advantage of the huge and unexploited bandwidth to cope with future [...] Read more.
Due to the increasing demand for fast data rates and large spectra, millimeter-wave technology plays a vital role in the advancement of 5G communication. The idea behind Mm-Wave communications is to take advantage of the huge and unexploited bandwidth to cope with future multigigabit-per-second mobile data rates, imaging, and multimedia applications. In Mm-Wave systems, digital precoding provides optimal performance at the cost of complexity and power consumption. Therefore, hybrid precoding, i.e., analog–digital precoding, has received significant consideration as a favorable alternative to digital precoding. The conventional methods related to hybrid precoding suffer from low spectral efficiency and large processing time due to nested loops and the number of iterations. A manifold optimization-based algorithm using the gradient method is proposed to increase the spectral efficiency to be near optimal and to speed up the processing speed. A comparison of performances is shown using the simulation outcomes of the proposed work and those of the existing techniques. Full article
(This article belongs to the Special Issue Security and Privacy in Smart Healthcare Applications)
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17 pages, 2934 KiB  
Article
Modified Uncertainty Error Aware Estimation Model for Tracking the Path of Unmanned Aerial Vehicles
by Rui Fu, Mohammed Abdulhakim Al-Absi, Young-Sil Lee, Ahmed Abdulhakim Al-Absi and Hoon Jae Lee
Appl. Sci. 2022, 12(22), 11313; https://doi.org/10.3390/app122211313 - 8 Nov 2022
Cited by 2 | Viewed by 1364
Abstract
Recently, with the advancement of technology, unmanned aerial vehicles (UAVs) have had a significant impact on our daily lives. UAVs have gained critical importance due to their potential threat. In this study, the problem of UAV tracks were investigated. The first study deals [...] Read more.
Recently, with the advancement of technology, unmanned aerial vehicles (UAVs) have had a significant impact on our daily lives. UAVs have gained critical importance due to their potential threat. In this study, the problem of UAV tracks were investigated. The first study deals with a particle filter (PF) and a diffusion map with a Kalman filter (DMK). From the experimental analysis, it is found that both PF and DMK are very suitable for drone tracking because the trajectories of drones are highly uncertain in highly dynamic and noisy environments. To address this problem, we introduce a Kalman filter (KFUEA) for drone tracking based on uncertainty and error. The KFUEA uses regularized least squares (RLS) to minimize measurement errors and provides an appropriate balance between confidence in previous estimates and future measurements. The experiment was conducted to evaluate the performance of KFUEA compared to PF and DMK, taking into account the high uncertainty and noisy UAV tracking environment. The KFUEA algorithm achieved an excellent result in the root mean square error (RMSE) compared to the non-parametric filtering algorithms PF and DMK. Full article
(This article belongs to the Special Issue Security and Privacy in Smart Healthcare Applications)
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19 pages, 4022 KiB  
Article
An Optimized Neuro_Fuzzy Based Regression Trees for Disease Prediction Framework
by Ankit Verma, Gaurav Agarwal, Amit Kumar Gupta and Mangal Sain
Appl. Sci. 2022, 12(17), 8487; https://doi.org/10.3390/app12178487 - 25 Aug 2022
Cited by 1 | Viewed by 1176
Abstract
Nowadays, all the applications have been moved to the intelligent world for easy usage and advancements. Hence, the sensed data have been utilized in the smart medical field to analyze the disease based on the symptom and to suggest controlling the disease severity [...] Read more.
Nowadays, all the applications have been moved to the intelligent world for easy usage and advancements. Hence, the sensed data have been utilized in the smart medical field to analyze the disease based on the symptom and to suggest controlling the disease severity rate. However, predicting the disease severity range based on the sensed disease symptom is more complicated because of the complex and vast data. So, the present work has introduced a novel Generalized approximate Reasoning base Intelligence Control (GARIC) with Ant Lion Optimization (ALO) algorithm to forecast the disease type and measure the severity range. Here, the presence of the Ant lion fitness has afforded the finest disease classification and severity analysis results. Finally, the parameters were measured and compared with other conventional models and have recorded the finest disease prediction score and severity range. This verified the success rate of the designed model in estimating the disease severity range. In addition, the presented system helps to notify the people of medical advice by message, email, or other application. Full article
(This article belongs to the Special Issue Security and Privacy in Smart Healthcare Applications)
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