Feature Adaptation Predictive Coding for Quantized Block Compressive Sensing of COVID-19 X-Ray Images

With the development of remote X-ray detection for Corona Virus Disease 2019 (COVID-19), the quantized block compressive sensing technology plays an important role when remotely acquiring the chest X-ray images of COVID-19 infected people and significantly promoting the portable telemedicine imaging applications. In order to improve the encoding performance of quantized block compressive sensing, a feature adaptation predictive coding (FAPC) method is proposed for the remote transmission of COVID-19 X-ray images. The proposed FAPC method can adaptively calculate the block-wise prediction coefficients according to the main features of COVID-19 X-ray images, and thus provide the optimal prediction candidate from the feature-guided candidate set. The proposed method can implement the high-efficiency encoding of X-ray images, and then swiftly transmit the telemedicine-oriented chest images. The experimental results show that compared with the state-of-the-art predictive coding methods, both rate-distortion and complexity performance of our FAPC method have enough competitive advantages. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Early Detection of Covid-19 Through Cough Sound Recognition using LPC and K-NN algorithm

COVID-19 (Coronavirus Disease 2019) is an infectious disease caused by the SARS-CoV-2 virus. This disease has spread worldwide since the beginning of 2020. Patients with this highly contagious disease generally experience only mild to moderate respiratory problems such as sore throat, cough, runny nose, fever, shortness of breath, and fatigue. However, some will become seriously ill and may cause severe respiratory distress or in severe cases multiple organ failure. Therefore, early identification of COVID-19 patients is very important. In this study, a disease detection system was created using an open dataset from COUGHVID which were contained the coughing sound of the Covid-19 disease. The implementation of the cough voice recognition system uses the K-Nearest Neighbor (K-NN) machine learning method and the Linear Predictive Coding (LPC) as method of extracting features from voice. The system was built using the Raspberry Pi 3 b+ microcontroller with microphone voice input and connected to a 3.5-inch LCD touchscreen display as the interface of the system device. The test uses a coughing sound as input through a microphone and processed by LPC feature extraction. At each running process, about 399 MB of memory is used from a total of 1 GB of memory. Meanwhile, the prediction of coughing sounds with the K-NN classification algorithm using 5 neighbors produces accuracy of 62% to predict disease. © 2022 ACM.

Spatiotemporal disease case prediction using contrastive predictive coding

Time series prediction models have played a vital role in guiding effective policymaking and response during the COVID-19 pandemic by predicting future cases and deaths at the country, state, and county levels. However, for emerging diseases, there is not sufficient historic data to fit traditional supervised prediction models. In addition, such models do not consider human mobility between regions. To mitigate the need for supervised models and to include human mobility data in the prediction, we propose Spatial Probabilistic Contrastive Predictive Coding (SP-CPC) which leverages Contrastive Predictive Coding (CPC), an unsupervised time-series representation learning approach. We augment CPC to incorporate a covariate mobility matrix into the loss function, representing the relative number of individuals traveling between each county on a given day. The proposal distribution learned by the algorithm is then sampled by the Metropolis-Hastings algorithm to give a final prediction of the number of COVID-19 cases. We find that the model applied to COVID-19 data can make accurate short-term predictions, more accurate than ARIMA and simple time-series extrapolation methods, one day into the future. However, for longer-term prediction windows of seven or more days into the future, we find that our predictions are not as competitive and require future research. © 2022 ACM.

Identification and classification of coronavirus genomic signals based on linear predictive coding and machine learning methods.

Corona disease has become one of the problems and challenges of humankind over the past two years. One of the problems that existed from the first days of this epidemic was clinical symptoms similar to other infectious viruses such as colds and influenza. Therefore, diagnosis of this disease and its coping and treatment approaches is also been difficult. In this study, Attempts has been made to investigate the origin of this disease and the genetic structure of the virus leading to it. For this purpose, signal processing and linear predictive coding approaches were used which are widely used in data compression. A pattern recognition model was presented for the detection and separation of covid samples from the influenza virus case studies. This model, which was based on support vector machine classifier, was tested successfully on several datasets collected from different countries. The obtained results performed on all datasets by more than 98% accuracy. The proposed model, in addition to its good performance accuracy, can be a step forward in quantifying and digitizing medical big data information.

How China's Cognitive Warfare Works: A Frontline Perspective of Taiwan's Anti-Disinformation Wars

Cognitive warfare-controlling others' mental states and behaviors by manipulating environmental stimuli-is a significant and ever-evolving issue in global conflict and security, especially during the COVID-19 crisis. In this article, we aim to contribute to the field by proposing a two-dimensional framework to evaluate China's cognitive warfare and explore promising ways of counteracting it. We first define the problem by clarifying relevant concepts and then present a case study of China's attack on Taiwan. Next, based on predictive coding theory from the cognitive sciences, we offer a framework to explain how China's cognitive warfare works and to what extent it succeeds. We argue that this framework helps identify vulnerable targets and better explains some of the conflicting data in the literature. Finally, based on the framework, we predict China's strategy and discuss Taiwan's options in terms of cognitive and structural interventions.

[Coronavirus: Predictive brain and terror management]. / Coronavirus : cerveau prédictif et gestion de la terreur.

Emerging infectious diseases like Covid-19 cause a major threat to global health. When confronted with new pathogens, individuals generate several beliefs about the epidemic phenomenon. Many studies have shown that individual protective behaviors largely depend on these beliefs. Due to the absence of treatment and vaccine against these emerging pathogens, the relation between these beliefs and these behaviors represents a crucial issue for public health policies. In the premises of the Covid-19 pandemic, several preliminary studies have highlighted a delay in the perception of risk by individuals, which potentially holds back the implementing of the necessary precautionary measures: people underestimated the risks associated with the virus, and therefore also the importance of complying with sanitary guidelines. During the peak of the pandemic, the salience of the threat and of the risk of mortality could then have transformed the way people generate their beliefs. This potentially leads to upheavals in the way they understand the world. Here, we propose to explore the evolution of beliefs and behaviors during the Covid-19 crisis, using the theory of predictive coding and the theory of terror management, two influential frameworks in cognitive science and in social psychology.