On stylized facts of cryptocurrencies returns and their relationship with other assets, with a focus on the impact of COVID-19

This study provides an empirical analysis on the main univariate and multivariate stylized facts iin return series of the two of the largest cryptocurrencies, namely Ethereum and Bitcoin. A Markov-Switching Vector AutoRegression model is considered to further explore the dynamic relationships between cryptocurrencies and other financial assets. We estimate the presence of volatility clustering, a rapid decay of the autocorrelation function, an excess of kurtosis and multivariate little cross-correlation across the series, except for contemporaneous returns. The analysis covers the pandemic period and sheds lights on the behaviour of cryptocurrencies under unexpected extreme events.

Prediction of the SARS-CoV-2 Derived T-Cell Epitopes' Response against COVID Variants

The COVID-19 outbreak began in December 2019 and was declared a global health emergency by the World Health Organization. The four most dominating variants are Beta, Gamma, Delta, and Omicron. After the administration of vaccine doses, an eminent decline in new cases has been observed. The COVID-19 vaccine induces neutralizing antibodies and T-cells in our bodies. However, strong variants like Delta and Omicron tend to escape these neutralizing antibodies elicited by COVID-19 vaccination. Therefore, it is indispensable to study, analyze and most importantly, predict the response of SARS-CoV-2-derived t-cell epitopes against Covid variants in vaccinated and unvaccinated persons. In this regard, machine learning can be effectively utilized for predicting the response of COVID-derived t-cell epitopes. In this study, prediction of T-cells Epitopes' response was conducted for vaccinated and unvaccinated people for Beta, Gamma, Delta, and Omicron variants. The dataset was divided into two classes, i.e., vaccinated and unvaccinated, and the predicted response of T-cell Epitopes was divided into three categories, i.e., Strong, Impaired, and Over-activated. For the aforementioned prediction purposes, a self-proposed Bayesian neural network has been designed by combining variational inference and flow normalization optimizers. Furthermore, the Hidden Markov Model has also been trained on the same dataset to compare the results of the self-proposed Bayesian neural network with this state-of-the-art statistical approach. Extensive experimentation and results demonstrate the efficacy of the proposed network in terms of accurate prediction and reduced error. © 2023 Tech Science Press. All rights reserved.

City-scale model for COVID-19 epidemiology with mobility and social activities represented by a set of hidden Markov models.

BACKGROUND AND OBJECTIVE: SARS-CoV-2 emerged by the end of 2019 and became a global pandemic due to its rapid spread. Various outbreaks of the disease in different parts of the world have been studied, and epidemiological analyses of these outbreaks have been useful for developing models with the aim of tracking and predicting the spread of epidemics. In this paper, an agent-based model that predicts the local daily evolution of the number of people hospitalized in intensive care due to COVID-19 is presented. METHODS: An agent-based model has been developed, taking into consideration the most relevant characteristics of the geography and climate of a mid-size city, its population and pathology statistics, and its social customs and mobility, including the state of public transportation. In addition to these inputs, the different phases of isolation and social distancing are also taken into account. By means of a set of hidden Markov models, the system captures and reproduces virus transmission associated with the stochastic nature of people's mobility and activities in the city. The spread of the virus in the host is also simulated by following the stages of the disease and by considering the existence of comorbidities and the proportion of asymptomatic carriers. RESULTS: As a case study, the model was applied to Paraná city (Entre Ríos, Argentina) in the second half of 2020. The model adequately predicts the daily evolution of people hospitalized in intensive care due to COVID-19. This adequacy is reflected by the fact that the prediction of the model (including its dispersion), as with the data reported in the field, never exceeded 90% of the capacity of beds installed in the city. In addition, other epidemiological variables of interest, with discrimination by age range, were also adequately reproduced, such as the number of deaths, reported cases, and asymptomatic individuals. CONCLUSIONS: The model can be used to predict the most likely evolution of the number of cases and hospital bed occupancy in the short term. By adjusting the model to match the data on hospitalizations in intensive care units and deaths due to COVID-19, it is possible to analyze the impact of isolation and social distancing measures on the disease spread dynamics. In addition, it allows for simulating combinations of characteristics that would lead to a potential collapse in the health system due to lack of infrastructure as well as predicting the impact of social events or increases in people's mobility.

Don't Be a Victim during a Pandemic! Analysing Security and Privacy Threats in Twitter during COVID-19

There has been a huge spike in the usage of social media platforms during the COVID-19 lockdowns. These lockdown periods have resulted in a set of new cybercrimes, thereby allowing attackers to victimise social media users with a range of threats. This paper performs a large-scale study to investigate the impact of a pandemic and the lockdown periods on the security and privacy of social media users. We analyse 10.6 Million COVID-related tweets from 533 days of data crawling and investigate users' security and privacy behaviour in three different periods (i.e., before, during, and after the lockdown). Our study shows that users unintentionally share more personal identifiable information when writing about the pandemic situation (e.g., sharing nearby coronavirus testing locations) in their tweets. The privacy risk reaches 100% if a user posts three or more sensitive tweets about the pandemic. We investigate the number of suspicious domains shared on social media during different phases of the pandemic. Our analysis reveals an increase in the number of suspicious domains during the lockdown compared to other lockdown phases. We observe that IT, Search Engines, and Businesses are the top three categories that contain suspicious domains. Our analysis reveals that adversaries' strategies to instigate malicious activities change with the country's pandemic situation. © 2013 IEEE.

The Predictive Power of Financial Stress on the Financial Markets Dynamics: Hidden Markov Model

This study investigates the predictive power of the financial stress on the dynamic of the Middle East and North Africa (MENA) financial market returns from 2007 to 2021. Based on a Quantile Regression, we show that financial stress has highest predictive abilities at the lower quantiles when the market is bearish. Then, we propose a Hidden Markov Model (HMM) based on the transition matrix to understand the relationship between financial stress index and the MENA stock market dynamics. We find that the effect of financial stress on stock market return reveals the persistence of regimes: Bullish state exists and persists, and has the longest conditional expected duration for the majority of MENA markets, except Bahrain, Qatar and Jordan. However, the transition probability from the bullish to the calm regime is too low for the financial market of Bahrain, United Arab Emirates and Egypt. Besides, the estimated mean returns for each regime divulge that the bearish and calm states are more attractive destination for both portfolio managers and investors. © 2022, Academy of Economics and Finance.

A Hybrid Deep Fused Learning Approach to Segregate Infectious Diseases

Humankind is facing another deadliest pandemic of all times in history, caused by COVID-19. Apart from this challenging pandemic, World Health Organization (WHO) considers tuberculosis (TB) as a preeminent infectious disease due to its high infection rate. Generally, both TB and COVID-19 severely affect the lungs, thus hardening the job of medical practitioners who can often misidentify these diseases in the current situation. Therefore, the time of need calls for an immediate and meticulous automatic diagnostic tool that can accurately discriminate both diseases. As one of the preliminary smart health systems that examine three clinical states (COVID-19, TB, and normal cases), this study proposes an amalgam of image filtering, data-augmentation technique, transfer learning-based approach, and advanced deep-learning classifiers to effectively segregate these diseases. It first employed a generative adversarial network (GAN) and Crimmins speckle removal filter on X-ray images to overcome the issue of limited data and noise. Each pre-processed image is then converted into red, green, and blue (RGB) and Commission Internationale de l'Elcairage (CIE) color spaces from which deep fused features are formed by extracting relevant features using DenseNet121 and ResNet50. Each feature extractor extracts 1000 most useful features which are then fused and finally fed to two variants of recurrent neural network (RNN) classifiers for precise discrimination of three-clinical states. Comparative analysis showed that the proposed Bi-directional long-short-term-memory (Bi-LSTM) model dominated the long-short-term-memory (LSTM) network by attaining an overall accuracy of 98.22% for the three-class classification task, whereas LSTM hardly achieved 94.22% accuracy on the test dataset. © 2023 Tech Science Press. All rights reserved.

Learning-based systems for assessing hazard places of contagious diseases and diagnosing patient possibility

To manage the propagation of infectious diseases, particularly fast-spreading pandemics, it is necessary to provide information about possible infected places and individuals, however, it needs diagnostic tests and is time-consuming and expensive. To smooth these issues, and motivated by the current Coronavirus disease (COVID-19) pandemic, in this paper, we propose a learning-based system and a hidden Markov model (i) to assess hazardous places of a contagious disease, and (ii) to predict the probability of individuals' infection. To this end, we track the trajectories of individuals in an environment. For evaluating the models and the approaches, we use the Covid-19 outbreak in an urban environment as a case study. Individuals in a closed population are explicitly represented by their movement trajectories over a period of time. The simulation results demonstrate that by adjusting the communicable disease parameters, the detector system and the predictor system are able to correctly assess the hazardous places and determine the infection possibility of individuals and cluster them accurately with high probability, i.e., on average more than 96%. In general, the proposed approaches to assessing hazardous places and predicting the infection possibility of individuals can be applied to contagious diseases by tailoring them to the influential features of the disease. • Utilizing the movement trajectories of individuals in a city to manage infection disease. • Proposing a learning-based system to assess hazardous places of a contagious disease. • Proposing a hidden Markov model to predict the probability of individuals infection. • Applying the Covid-19 outbreak in an urban environment as a case study. [Display omitted] [ FROM AUTHOR]

Bidirectional Data-Driven Trajectory Prediction for Intelligent Maritime Traffic

Intelligent maritime transportation is one of the most promising enabling technologies for promoting trade efficiency and releasing the physical labor force. The trajectory prediction method is the foundation to guarantee collision avoidance and route optimization for ship transportation. This article proposes a bidirectional data-driven trajectory prediction method based on Automatic Identification System (AIS) spatio-temporal data to improve the accuracy of ship trajectory prediction and reduce the risk of accidents. Our study constructs an encoder-decoder network driven by a forward and reverse comprehensive historical trajectory and then fuses the characteristics of the sub-network to predict the ship trajectory. The AIS historical trajectory data of US West Coast ships are employed to investigate the feasibility of the proposed method. Compared with the current methods, the proposed approach lessens the prediction error by studying the comprehensive historical trajectory, and 60.28% has reduced the average prediction error. The ocean and port trajectory data are analyzed in maritime transportation before and after COVID-19. The prediction error in the port area is reduced by 95.17% than the data before the epidemic. Our work helps the prediction of maritime ship trajectory, provides valuable services for maritime safety, and performs detailed insights for the analysis of trade conditions in different sea areas before and after the epidemic.

TrajSGAN: A Semantic-Guiding Adversarial Network for Urban Trajectory Generation

Simulating human mobility contributes to city behavior discovery and decision-making. Although the sequence-based and image-based approaches have made impressive achievements, they still suffer from respective deficiencies such as omitting the depiction of spatial properties or ordinal dependency in trajectory. In this article, we take advantage of the above two paradigms and propose a semantic-guiding adversarial network (TrajSGAN) for generating human trajectories. Specifically, we first devise an attention-based generator to yield trajectory locations in a sequence-to-sequence manner. The encoded historical visits are queried with semantic knowledge (e.g., travel modes and trip purposes) and their important features are enhanced by the multihead attention mechanism. Then, we designate a rollout module to complete the unfinished trajectory sequence and transform it into an image that can depict its spatial structure. Finally, a convolutional neural network (CNN)-based discriminator signifies how “real”the trajectory image looks, and its output is regarded as a reward signal to update the generator by the policy gradient. Experimental results show that the proposed TrajSGAN model significantly outperforms the benchmarks under the MTL-Trajet mobility dataset, with the divergence of spatial-related metrics such as radius of gyration and travel distance reduced by 10%–27%. Furthermore, we apply the real and synthetic trajectories, respectively, to simulate the COVID-19 epidemic spreading under three preventive actions. The coefficient of determination metric between real and synthetic results achieves 91%–98%, indicating that the synthesized data from TrajSGAN can be leveraged to study the epidemic diffusion with an acceptable difference. All of these results verify the superiority and utility of our proposed method. IEEE

OCR for TIFF Compressed Document Images Directly in Compressed Domain Using Text segmentation and Hidden Markov Model

In today's technological era, document images play an important and integral part in our day to day life, and specifically with the surge of Covid-19, digitally scanned documents have become key source of communication, thus avoiding any sort of infection through physical contact. Storage and transmission of scanned document images is a very memory intensive task, hence compression techniques are being used to reduce the image size before archival and transmission. To extract information or to operate on the compressed images, we have two ways of doing it. The first way is to decompress the image and operate on it and subsequently compress it again for the efficiency of storage and transmission. The other way is to use the characteristics of the underlying compression algorithm to directly process the images in their compressed form without involving decompression and re-compression. In this paper, we propose a novel idea of developing an OCR for CCITT (The International Telegraph and Telephone Consultative Committee) compressed machine printed TIFF document images directly in the compressed domain. After segmenting text regions into lines and words, HMM is applied for recognition using three coding modes of CCITT-horizontal, vertical and the pass mode. Experimental results show that OCR on pass modes give a promising results. © 2022 IEEE.

COVID-19 Spread Detection and Controlling with Fog-based Infection Probability Evaluation Model

COVID-19 has created a pandemic worldwide, paused the path of building the future, and is still ongoing without any long-term solution. The time taken in vaccine distribution is too slow compared to the spread of COVID-19. Hence, it is important to be aware and take precautions on time without delaying and waiting for long-duration after getting infected with the virus. Technology nowadays is more advanced than ever before. Almost everyone has access to at least one mobile device with internet connection. Therefore, we propose a Fog Server (FS) based system that helps create awareness about the spread of COVID-19 within the surroundings of an individual, utilizing the concept of Hidden Markov Model (HMM) and Bluetooth contact tracing in polynomial computational time complexity. Moreover, we evaluate the effectiveness of the proposed model through real-world data analysis on different simulation settings. © 2023 ACM.

Summarizing Global SARS-CoV-2 Geographical Spread by Phylogenetic Multitype Branching Models

Using available phylogeographical data of 3585 SARS–CoV–2 genomes we attempt at providing a global picture of the virus's dynamics in terms of directly interpretable parameters. To this end we fit a hidden state multistate speciation and extinction model to a pre-estimated phylogenetic tree with information on the place of sampling of each strain. We find that even with such coarse–grained data the dominating transition rates exhibit weak similarities with the most popular, continent–level aggregated, airline passenger flight routes. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Bidirectional Data-Driven Trajectory Prediction for Intelligent Maritime Traffic

Intelligent maritime transportation is one of the most promising enabling technologies for promoting trade efficiency and releasing the physical labor force. The trajectory prediction method is the foundation to guarantee collision avoidance and route optimization for ship transportation. This article proposes a bidirectional data-driven trajectory prediction method based on Automatic Identification System (AIS) spatio-temporal data to improve the accuracy of ship trajectory prediction and reduce the risk of accidents. Our study constructs an encoder-decoder network driven by a forward and reverse comprehensive historical trajectory and then fuses the characteristics of the sub-network to predict the ship trajectory. The AIS historical trajectory data of US West Coast ships are employed to investigate the feasibility of the proposed method. Compared with the current methods, the proposed approach lessens the prediction error by studying the comprehensive historical trajectory, and 60.28% has reduced the average prediction error. The ocean and port trajectory data are analyzed in maritime transportation before and after COVID-19. The prediction error in the port area is reduced by 95.17% than the data before the epidemic. Our work helps the prediction of maritime ship trajectory, provides valuable services for maritime safety, and performs detailed insights for the analysis of trade conditions in different sea areas before and after the epidemic.

Intelligent and Interactive Healthcare System ((IHS)-H-2) Using Machine Learning

There has been a gigantic stir in the world's healthcare sector for the past couple of years with the advent of the Covid-19 pandemic. The healthcare system has suffered a major setback and, with the lack of doctors, nurses, and healthcare facilities the need for an intelligent healthcare system has come to the fore more than ever before. Smart healthcare technologies and AI/ML algorithms provide encouraging and favorable solutions to the healthcare sector's challenges. An Intelligent Human-Machine Interactive system is the need of the hour. This paper proposes a novel architecture for an Intelligent and Interactive Healthcare System that incorporates edge/fog/cloud computing techniques and focuses on Speech Recognition and its extensive application in an interactive system. The focal reason for using speech in the healthcare sector is that it is easily available and can easily predict any physical or psychological discomfort. Simply put, human speech is the most natural form of communication. The Hidden Markov Model is applied to process the proposed approach as using the probabilistic approach is more realistic for prediction purposes. Ongoing projects and directions for future work along with challenges/issues are also addressed.

Learning Large Graph-Based MDPs With Historical Data

We consider learning the dynamics and measurement model parameters of a graph-based Markov decision process (GMDP) given a history of measurements. Graph-based models have been used in modeling many data-based applications, such as recognition tasks, disease epidemics, forest wildfires, freeway traffic, and social networks. We leverage the expectation-maximization framework and develop an algorithm that optimizes the measurement likelihood and has favorable complexity for large models. In contrast to prior work, we directly consider GMDPs with significantly large discrete state spaces, arbitrary coupling structure, and long measurement sequences. We also consider a special structural property called Anonymous Influence, which we use to test hypotheses and gain insights into the data. We demonstrate the effectiveness of our learning algorithm by considering two real-world data sets on the 2020 Novel Coronavirus (COVID-19) pandemic in California and on user interactions on Twitter. Our results show that the learned GMDP models better explain the data compared to an uncoupled model assumption.

Chinese Named Entity Recognition of Epidemiological Investigation of Information on COVID-19 Based on BERT

The named entity recognition based on the epidemiological investigation of information on COVID-19 can help analyze the source and route of transmission of the epidemic to control the spread of the epidemic better. Therefore, this paper proposes a Chinese named entity recognition model BERT-BiLSTM-IDCNN-ELU-CRF (BBIEC) based on the epidemiological investigation of information on COVID-19 of the BERT pre-training model. The model first processes the unlabeled epidemiological investigation of information on COVID-19 into the character-level corpus and annotates it with artificial entities according to the BIOES character-level labeling system and then uses the BERT pre-training model to obtain the word vector with position information;then, through the bidirectional long-short term memory neural network (BiLSTM) and the improved iterated dilated convolutional neural network (IDCNN) extract global context and local features from the generated word vectors and concatenate them serially;output all possible label sequences to the conditional random field (CRF);finally pass the condition random The airport decodes and generates the entity tag sequence. The experimental results show that the model is better than other traditional models in recognizing the entity of the epidemiological investigation of information on COVID-19.

Learning-based systems for assessing hazard places of contagious diseases and diagnosing patient possibility

To manage the propagation of infectious diseases, particularly fast-spreading pandemics, it is necessary to provide information about possible infected places and individuals, however, it needs diagnostic tests and is time-consuming and expensive. To smooth these issues, and motivated by the current Coronavirus disease (COVID-19) pandemic, in this paper, we propose a learning-based system and a hidden Markov model (i) to assess hazardous places of a contagious disease, and (ii) to predict the probability of individuals’ infection. To this end, we track the trajectories of individuals in an environment. For evaluating the models and the approaches, we use the Covid-19 outbreak in an urban environment as a case study. Individuals in a closed population are explicitly represented by their movement trajectories over a period of time. The simulation results demonstrate that by adjusting the communicable disease parameters, the detector system and the predictor system are able to correctly assess the hazardous places and determine the infection possibility of individuals and cluster them accurately with high probability, i.e., on average more than 96%. In general, the proposed approaches to assessing hazardous places and predicting the infection possibility of individuals can be applied to contagious diseases by tailoring them to the influential features of the disease.

On stylized facts of cryptocurrencies returns and their relationship with other assets, with a focus on the impact of COVID-19

This study provides an empirical analysis on the main univariate and multivariate stylized facts iin return series of the two of the largest cryptocurrencies, namely Ethereum and Bitcoin. A Markov-Switching Vector AutoRegression model is considered to further explore the dynamic relationships between cryptocurrencies and other financial assets. We estimate the presence of volatility clustering, a rapid decay of the autocorrelation function, an excess of kurtosis and multivariate little cross-correlation across the series, except for contemporaneous returns. The analysis covers the pandemic period and sheds lights on the behaviour of cryptocurrencies under unexpected extreme events. © 2022 Informa UK Limited, trading as Taylor & Francis Group.

A Generalized Entropy Approach to Portfolio Selection under a Hidden Markov Model

This paper develops a dynamic portfolio selection model incorporating economic uncertainty for business cycles. It is assumed that the financial market at each point in time is defined by a hidden Markov model, which is characterized by the overall equity market returns and volatility. The risk associated with investment decisions is measured by the exponential Rényi entropy criterion, which summarizes the uncertainty in portfolio returns. Assuming asset returns are projected by a regime-switching regression model on the two market risk factors, we develop an entropy-based dynamic portfolio selection model constrained with the wealth surplus being greater than or equal to the shortfall over a target and the probability of shortfall being less than or equal to a specified level. In the empirical analysis, we use the select sector ETFs to test the asset pricing model and examine the portfolio performance. Weekly financial data from 31 December 1998 to 30 December 2018 is employed for the estimation of the hidden Markov model including the asset return parameters, while the out-of-sample period from 3 January 2019 to 30 April 2022 is used for portfolio performance testing. It is found that, under both the empirical Sharpe and return to entropy ratios, the dynamic portfolio under the proposed strategy is much improved in contrast with mean variance models.

An Estimation Approach to Optimize Energy Consumption in Wireless Sensor Network: A Health-Care Application

Wireless Sensor Network (WSN) is gaining popularity day by day in a large area of applications. However, the operation of WSN is facing a multitude of challenges, mainly in terms of energy consumption since WSN nodes operate with battery power and changing the batteries is a complicated task, as networks may include hundreds to thousands of nodes. In this context, it is very crucial to know the remaining energy value in the battery of the sensor node to take required actions before losing sensor's function. Sending these measurements is very expensive in terms of energy and reduces the battery lifetime of the sensor and thus of the entire network. In this paper, we are interested in defining a probabilistic approach which aims to estimate these monitoring energy values and optimize energy consumption in WSN. Our approach is based on hidden Markov chains and includes two phases namely a learning phase and a prediction phase. Our approach is implemented as a web service. We illustrate our approach with a sensor-based health-care monitoring case study for COVID-19 patients. To evaluate our approach, we carry out experimentations based on the AvroraZ(a) simulator with a test for different types of applications and for different energy models: mu AMPS-specific model, Mica2-specific model, and Mica2-specific model with actual measurements. These experimentations demonstrate the accuracy and efficiency of our approach. Our results show that periodic WSN applications i.e. applications which send monitoring data periodically, tested with the mu AMPS-specific model perform an accuracy of 98.65%. In addition, our approach can perform a gain up to 75% of the battery charge of the sensor with an estimation of three-quarters of the remaining energy values.