Electronic voting systems must find solutions to various issues with authentication, data privacy and integrity, transparency, and verifiability. On the other hand, Blockchain technology offers an innovative solution to many of these problems. The scalability of Blockchain has arisen as a fundamental barrier to realizing the promise of this technology, especially in electronic voting. This study seeks to highlight the solutions regarding scalable Blockchain-based electronic voting systems and the issues linked with them while also attempting to foresee future developments. A systematic literature review (SLR) was used to complete the task, leading to the selection of 76 articles in the English language from 1 January 2017 to 31 March 2022 from the famous databases. This SLR was conducted to identify well-known proposals, their implementations, verification methods, various cryptographic solutions in previous research to evaluate cost and time. It also identifies performance parameters, the primary advantages and obstacles presented by different systems, and the most common approaches for Blockchain scalability. In addition, it outlines several possible research avenues for developing a scalable electronic voting system based on Blockchain technology. This research helps future research before proposing or developing any solutions to keep in mind all the voting requirements, merits, and demerits of the proposed solutions and provides further guidelines for scalable voting solutions.
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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9572428 | PMC |
http://dx.doi.org/10.3390/s22197585 | DOI Listing |
Sci Rep
January 2025
School of Information and Electronic Engineering and Zhejiang Key Laboratory of Biomedical Intelligent Computing Technology, Zhejiang University of Science and Technology, No. 318, Hangzhou, Zhejiang, China.
Skin cancer is common and deadly, hence a correct diagnosis at an early age is essential. Effective therapy depends on precise classification of the several skin cancer forms, each with special traits. Because dermoscopy and other sophisticated imaging methods produce detailed lesion images, early detection has been enhanced.
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December 2024
College of Science and Engineering, Hamad Bin Khalifa University, Doha 34110, Qatar.
Lung Cancer
December 2024
Università Vita-Salute San Raffaele, Milan, Italy; Department of Medical Oncology, IRCCS Ospedale San Raffaele, Milan, Italy.
Background: Artificial intelligence (AI) models are emerging as promising tools to identify predictive features among data coming from health records. Their application in clinical routine is still challenging, due to technical limits and to explainability issues in this specific setting. Response to standard first-line immunotherapy (ICI) in metastatic Non-Small-Cell Lung Cancer (NSCLC) is an interesting population for machine learning (ML), since up to 30% of patients do not benefit.
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December 2024
Institut d'Intelligence Artificielle en Santé, CHU de Reims, Université de Reims Champagne- Ardenne, Reims, F-51100, France.
A medication error is an inadvertent failure in the drug therapy process that can cause serious harm to patients by increasing morbidity and mortality and are associated with significant economic costs to the healthcare system. Medication reconciliation is the most cost-effective intervention and can result in a 66% reduction in medication errors. To improve patient safety, we developed a machine learning-based tool that prioritizes patients at risk of medication errors upon admission to the hospital to ensure that they undergo medication reconciliation by clinical pharmacists.
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December 2024
Department of Biology and Neuroscience Program, Hamline University, St Paul, MN, United States. Electronic address:
Connecting socially relevant topics with biological content can boost student engagement and comprehension. Autism Spectrum Disorder (ASD) is an increasingly prevalent diagnosis with a number of intersecting topic areas between developmental biology and social justice. Here I describe two exercises that I developed to engage students in learning opportunities that link scientific process learning goals with real-world applications.
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