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Pandemic Equation and COVID-19 Evolution
The Pandemic Equation describes multiple pandemic waves and has been applied to describe the COVID-19 pandemic. Using the generalized approaches of solid-state physics, we derive the Pandemic Equation, which accounts for the effects of pandemic mitigation measures and multiple pandemic waves. The Pandemic Equation uses slow and fast time scales for “curve flattening” and describing vaccination and mitigation measures and the Scaled Fermi–Dirac distribution functions for describing transitions between pandemic waves. The Pandemic Equation parameters extracted from the pandemic curves can be used for comparing different scenarios of the pandemic evolution and for extrapolating the pandemic evolution curves for the periods of time on the order of the instantaneous Pandemic Equation characteristic time constant. The parameter extraction for multiple locations could also allow for uncertainty quantification for such pandemic evolution predictions.
  • 606
  • 19 Apr 2024
Topic Review
Deep Learning-Based Methods for Crop Disease Estimation
Deep learning methods such as U-Net, SegNet, YOLO, Faster R-CNN, VGG and ResNet have been used extensively for crop disease estimation using Unmanned Aerial Vehicle (UAV)  imagery. The basic building block of the deep learning architecture is basically the success of convolutional neural networks (CNN). The deep learning models implemented for crop disease estimation using UAV imagery can be categorized into classification-based, segmentation-based and detection-based approaches. Segmentation-based models attempt to classify each pixel in an image into different categories such as healthy vs. diseased pixels, whereas classification-based models look into overall images and classify the image into pre-defined disease classes.
  • 606
  • 16 May 2023
Topic Review
Image-Based Malware Detection
Image conversion of malicious binaries, or binary visualisation, is a relevant approach in the security community. It has exceeded the role of a single-file malware analysis tool and has become a part of Intrusion Detection Systems (IDSs) thanks to the adoption of Convolutional Neural Networks (CNNs).
  • 605
  • 16 Nov 2023
Topic Review
Emission Quantification via Passive Infrared Optical Gas Imaging
Passive infrared optical gas imaging (IOGI) is sensitive to toxic or greenhouse gases of interest, offers non-invasive remote sensing, and provides the capability for spatially resolved measurements. It has been broadly applied to emission detection, localization, and visualization.
  • 605
  • 08 Jul 2022
Topic Review
Evaluating the Presence of Software-as-a-Medical-Device
SaMD is a growing trend within medical device innovation. In this work, we provide the first empirical analysis of SaMD. Within Australia, which relies heavily on importation of medical devices, SaMD shows a greater domestic production than other types of medical devices.
  • 605
  • 31 Aug 2021
Topic Review
Pseudonymity
Pseudonymity, a word derived from pseudonym, meaning 'false name', is a state of disguised identity. The pseudonym identifies a holder, that is, one or more human beings who possess but do not disclose their true names (that is, legal identities). Most pseudonym holders use pseudonyms because they wish to remain anonymous, but anonymity is difficult to achieve and is often fraught with legal issues. True anonymity requires unlinkability, such that an attacker's examination of the pseudonym holder's message provides no new information about the holder's true name.
  • 605
  • 14 Nov 2022
Topic Review
Smart Healthcare Using ML and Cognitive Radio Technologies
The rapid technological advancements in the modern world bring the attention of researchers to fast and real-time healthcare and monitoring systems. Smart healthcare is one of the best choices for this purpose, in which different on-body and off-body sensors and devices monitor and share patient data with healthcare personnel and hospitals for quick and real-time decisions about patients’ health. Cognitive radio (CR) can be very useful for effective and smart healthcare systems to send and receive patient’s health data by exploiting the primary user’s (PU) spectrum.
  • 605
  • 21 Sep 2023
Topic Review
Modified Generalized Feistel Network Block Cipher
With the advent of the Internet-of-Things (IoT) technologies, millions of low-resource devices are constantly used at the network’s edge. As a result, the large amount of private and sensitive data generated by these devices must be securely transported, stored, and processed, posing a challenge because these resource-constrained IoT devices cannot meet the criteria of conventional encryption ciphers. Feistel networks are further divided into two types: classical Feistel networks and generalized Feistel networks (GFN). While classical Feistel ciphers divide a message into two sub-blocks, GFN divides a message into k sub-blocks for some k > 2 called the partition number.
  • 604
  • 30 Oct 2023
Topic Review
Background of Machine/Deep Learning Approaches on Mental Health
Mental health can be seen as a person’s emotional, psychological, and social well-being. It can be harmed by various mental health conditions, which negatively influence a person’s intellectual capacity, emotions, and social relationships. Machine learning (ML) is a subfield of artificial intelligence (AI) that deals with three problems: classification, regression, and clustering. It utilizes data and algorithms to mimic how people learn while progressively improving accuracy in various tasks.
  • 604
  • 08 Feb 2023
Topic Review
Post-Stroke Movement with Motion Capture and Musculoskeletal Modeling
Research of post-stroke locomotion via musculoskeletal (MSK) modeling has offered an unprecedented insight into pathological muscle function and its interplay with skeletal geometry and external stimuli. Advances in solving the dynamical system of post-stroke effort and the generic MSK models used have triggered noticeable improvements in simulating muscle activation dynamics of stroke populations.
  • 604
  • 09 Dec 2022
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