Three-way investment decisions during the epidemic with Choquet-based bi-projection method

The outbreak of epidemic has had a big impact on the investment market of China. Facing the turbulence in the investment market, many enterprises find it difficult to judge the development prospects of investment projects and make the right investment decisions. The three-way decisions offer a novel study perspective to solve this problem. Then the developed model is applied to select the investment projects. Firstly, some relevant attributes of the project are described with the double hierarchy hesitant fuzzy linguistic term sets. And a double hierarchy hesitant fuzzy linguistic information system is constructed for each project. Secondly, the weights of attributes are determined with the Choquet integral method. And the closeness degree calculated by Choquet-based bi-projection method is taken as the conditional probability that the project will be profitable. Next, considering the influence of the bounded rationality of decision makers, the threshold parameters are calculated based on prospect theory. Finally, the decision results about investment projects during four stages are deduced based on the principle of maximum-utility, which demonstrates the practicability and effectiveness of the proposed model.

Reliability Analysis using Bayesian Network for Medical Test of Covid-19

Covid-19 is a corona virus pandemic disease affected by a new corona virus. Maximum people infected by covid-19 will experience symptoms namely mild to moderate respiratory illness and recover without requiring any special treatment. However elderly people and those having underlying medical diseases such as diabetes, cardiovascular diseases, cancer and chronic respiratory disease are more prone to develop serious illness. Reliability analysis for medical test for covid-19 is performed using a Bayesian network. A Bayesian network (BN) is a probabilistic graphical model that represents knowledge about an uncertain domain where each node corresponds to a random variable and each edge represents the corresponding conditional probability. The BN is used to prioritize the factors that influence virus symptoms of covid-19. The BN model is constructed based on a list of general symptoms of covid-19. The marginal probabilities for all states are computed. The comparison of prior and conditional probabilities is determined. Using BN the reliability of medical test for covid-19 is obtained. © 2022 American Institute of Physics Inc.. All rights reserved.

A novel technique for the detection of Covid-19 patients with the applications of three-way decisions using variance-based criterion.

Everyone is making constant efforts to establish an effective diagnostic approach, therapy and control of the spread of the pandemic. Due to a flexible formulation, the parameters prior to the normal distributions and explicitly formulate assumptions on the transition probabilities between these categories over time. The spread of the COVID-19 pandemic represents a serious threat for scientists and academics, health professionals and even governments today. The Hospital wards are classified into Intensive Care Unit (ICU), Regular Wards (RW) with Recovered (R) and Deceased (D).. The formulation may be truncated to include particular hypotheses with an epidemiological interpretation. The principles of Three-Way Decision Theory could be used to anticipate and diagnose COVID-19 patients were classified into one of three zones based on their symptoms: Positive, Negative, or Boundary, and treatment are recommended if necessary. The thresholds that distinguish the three zones are determined using a variance-based criterion. Examine the impact of nonpharmaceutical interventions and the findings from data gathered during the second wave of the pandemic in Trivandrum, India.The Three-Way Decision Theory model has a good fit and gives good predictive performance, especially for RW and ICU patients, according to suitable discrepancy metrics that were created to assess and compare models. 95 percent accuracy increased and calculated values for 10 days to demonstrate the temporal aspects of the expected daily reproduction number R.

Pre-service mathematics teachers' understanding of conditional probability in the context of the COVID-19 pandemic

During the last two years, the COVID-19 pandemic had a secondary effect of increased media content loaded with mathematical, often probabilistic information (and misinformation). Our exploratory study investigates the probabilistic intuitions, misconceptions, biases, and fallacies in conditional probability reasoning of mathematics teacher candidates in the context of the pandemic. The pre-service mathematics teachers who participated in our study were given a questionnaire with five contextual conditional probability problems, all formulated similarly to media statements often encountered when discussing the COVID-19 pandemic. Our findings confirm the previous findings on biases and fallacies related to conditional probability problems with a social context. They were also indicative of several types of errors (both numerical and logical) as more common than expected. Our results also reveal that pre-service mathematics teachers apparently separate the content learned in the classroom from the application of the knowledge in critical examination of the information to which they are daily exposed by the media. © 2023 by authors;licensee EJSME by Bastas.

Assessing supply chain resilience to the outbreak of COVID-19 in Indian manufacturing firms

As the world has seen the impact of COVID-19, development of resilient supply chain strategies has emerged as top priority. The inconsistent demands, product consumption and the shorter lifecycle of products during the pandemic needs appropriate planning and designing to make the supply chain more resilient. In this study, an analytical model is proposed to assess the resilience of supply chain to overcome the effect of the disruption impacts. The supply chain risks will depend on the nature of the business and therefore, besides literature review on supply chain resilience the inputs from experts were required. The interdependency among the indicators was analysed by employing Interpretive Structural Modelling (ISM) and demonstrated with the help of a framework. The strength of the interdependence is assessed using Bayesian Network approach. BN transformed the qualitative expert inputs to quantitative assessment by utilising the principles of conditional probability. Three cases from Indian manufacturing industries were used to demonstrate and assess the critical supply chain resilience indicators using integrated ISM-BN approach. The cases showed that the proposed approach can assist decision makers in identifying the critical indicators to be focused towards improving the supply chain resilience to overcome the outbreak of Covid-19 pandemic. A comparative analysis of the supply chain risk indicators has also been performed, thereby extending the practical implication of supply chain resilience.

Robust multifocus deep neural network for progression prediction on patient trajectory data

Purpose>Patient treatment trajectory data are used to predict the outcome of the treatment to particular disease that has been carried out in the research. In order to determine the evolving disease on the patient and changes in the health due to treatment has not considered existing methodologies. Hence deep learning models to trajectory data mining can be employed to identify disease prediction with high accuracy and less computation cost.Design/methodology/approach>Multifocus deep neural network classifiers has been utilized to detect the novel disease class and comorbidity class to the changes in the genome pattern of the patient trajectory data can be identified on the layers of the architecture. Classifier is employed to learn extracted feature set with activation and weight function and then merged on many aspects to classify the undetermined sequence of diseases as a new variant. The performance of disease progression learning progress utilizes the precision of the constituent classifiers, which usually has larger generalization benefits than those optimized classifiers.Findings>Deep learning architecture uses weight function, bias function on input layers and max pooling. Outcome of the input layer has applied to hidden layer to generate the multifocus characteristics of the disease, and multifocus characterized disease is processed in activation function using ReLu function along hyper parameter tuning which produces the effective outcome in the output layer of a fully connected network. Experimental results have proved using cross validation that proposed model outperforms methodologies in terms of computation time and accuracy.Originality/value>Proposed evolving classifier represented as a robust architecture on using objective function to map the data sequence into a class distribution of the evolving disease class to the patient trajectory. Then, the generative output layer of the proposed model produces the progression outcome of the disease of the particular patient trajectory. The model tries to produce the accurate prognosis outcomes by employing data conditional probability function. The originality of the work defines 70% and comparisons of the previous methods the method of values are accurate and increased analysis of the predictions.

Analysis of the Complex Network of the Urban Function under the Lockdown of COVID-19: Evidence from Shenzhen in China

In this paper, the complex network of the urban functions in Shenzhen of China under the lockdown of the corona virus disease 2019 (COVID-19) is studied. The location quotient is used to obtain the dominant urban functions of the districts in Shenzhen before and under the lockdown of COVID-19. By using the conditional probability, the interdependencies between the urban functions are proposed to obtain the complex networks of urban functions and their clusters. The relationships between the urban functions, and the overall and cluster characteristics of the urban functions before and under the lockdown of COVID-19 are analyzed based on the complex networks. The mean degree and mean weighted degree of the primary categories of the urban functions are obtained to discuss the classification characteristics of the urban functions before and under the lockdown of COVID-19. Then, the differences and changes of the urban functions before and under the lockdown of COVID-19 are compared, and the corresponding policy implications under the lockdown of COVID-19 are presented. The results show that under the lockdown of COVID-19, the correlation of the urban functions is stronger than that before the lockdown;the common urban functions are more useful and essential, and finance, fine food and medical treatment are important;public service and government departments have the most positive relationship with other urban functions, and finance service has the highest spatial agglomeration distribution trend;and the cluster characteristics of urban functions are more related to people's livelihood, and the urban functions show incomplete and cannot be operated for long term. [ FROM AUTHOR] Copyright of Mathematics (2227-7390) is the property of MDPI and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

Quantitative Ground Risk Assessment for Urban Logistical Unmanned Aerial Vehicle (UAV) Based on Bayesian Network

The Unmanned Aerial Vehicle (UAV) has been used for the delivery of medical supplies in urban logistical distribution, due to its ability to reduce human contact during the global fight against COVID-19. However, due to the reliability of the UAV system and the complex and changeable operation scene and population distribution in the urban environment, a few ground-impact accidents have occurred and generated enormous risks to ground personnel. In order to reduce the risk of UAV ground-impact accidents in the urban logistical scene, failure causal factors, and failure modes were classified and summarized in the process of UAV operation based on the accumulated operation data of more than 20,000 flight hours. The risk assessment model based on the Bayesian network was built. According to the established network and the probability of failure causal factors, the probabilities of ground impact accidents and intermediate events under different working conditions were calculated, respectively. The posterior probability was carried out based on the network topology to deduce the main failure inducement of the accidents. Mitigation measures were established to achieve the equivalent safety level of manned aviation, aiming at the main causes of accidents. The results show that the safety risk of the UAV was reduced to 3.84 × 10−8 under the action of risk-mitigation measures.

Modeling and Reasoning of Contexts in Smart Spaces Based on Stochastic Analysis of Sensor Data

In the last decade, smart spaces and automatic systems have gained significant popularity and importance. Moreover, as the COVID-19 pandemic continues, the world is seeking remote intervention applications with autonomous and intelligent capabilities. Context-aware computing (CAC) is a key paradigm that can satisfy this need. A CAC-enabled system recognizes humans’ status and situation and provides proper services without requiring manual participation or extra control by humans. However, CAC is insufficient to achieve full automaticity since it needs manual modeling and configuration of context. To achieve full automation, a method is needed to automate the modeling and reasoning of contexts in smart spaces. In this paper, we propose a method that consists of two phases: the first is to instantiate and generate a context model based on data that were previously observed in the smart space, and the second is to discern a present context and predict the next context based on dynamic changes (e.g., user behavior and interaction with the smart space). In our previous work, we defined “context” as a meaningful and descriptive state of a smart space, in which relevant activities and movements of human residents are consecutively performed. The methods proposed in this paper, which is based on stochastic analysis, utilize the same definition, and enable us to infer context from sensor datasets collected from a smart space. By utilizing three statistical techniques, including a conditional probability table (CPT), K-means clustering, and principal component analysis (PCA), we are able to automatically infer the sequence of context transitions that matches the space–state changes (the dynamic changes) in the smart space. Once the contexts are obtained, they are used as references when the present context needs to discover the next context. This will provide the piece missing in traditional CAC, which will enable the creation of fully automated smart-space applications. To this end, we developed a method to reason the current state space by applying Euclidean distance and cosine similarity. In this paper, we first reconsolidate our context models, and then we introduce the proposed modeling and reasoning methods. Through experimental validation in a real-world smart space, we show how consistently the approach can correctly reason contexts.

Statistical Inferences: Based on Exponentiated Exponential Model to Assess Novel Corona Virus (COVID-19) Kerala Patient Data

In this article, we use exponentiated exponential distribution as a suitable statistical lifetime model for novel corona virus (covid-19) Kerala patient data. The suitability of the model has been followed by different statistical tools like the value of logarithm of likelihood, Kolmogorov–Smirnov distance, Akaike information criterion, Bayesian information criterion. Moreover, likelihood ratio test and empirical posterior probability analysis are performed to show its suitability. The maximum-likelihood and asymptotic confidence intervals for the parameters are derived from Fisher information matrix. We use the Markov Chain Monte Carlo technique to generate samples from the posterior density function. Based on generated samples, we can compute the Bayes estimates of the unknown parameters and can also construct highest posterior density credible intervals. Further we discuss the Bayesian prediction for future observation based on the observed sample. The Gibbs sampling technique has been used for estimating the posterior predictive density and also for constructing predictive intervals of the order statistics from the future sample.

The Impact of COVID-19 on China's Capital Market and Major Industry Sectors.

This paper studied the impact of COVID-19 on China's capital market and major industry sectors via an improved ICSS algorithm, a time series model with the exogenous variable and a non-parametric conditional probability estimation. Through the empirical analysis, it is found that the epidemic has no significant impact on the return of the stock and bond markets, but it has increased the market volatility and the impact on the stock market volatility is gradual and more obvious. There are significant differences in the significance, direction and duration of the epidemic on different sectors. In addition, the impact of COVID-19 has been gradual in some industries and rapid in others. Different industries show different sensitivities in their response to COVID-19. Based on the analysis of the impact, this paper put forward the corresponding suggestions for investment strategies and macro-control decisions.