Gain and loss of function CFTR alleles modulate COVID-19 clinical outcome

Background/Objectives: We previously demonstrated that carrying a single pathogenic CFTR allele increases the risk for COVID-19 severity and mortality rate. We now aim to clarify the role of several uncharacterized rare alleles, including complex (cis) alleles, and in trans combinations. Method(s): LASSO logistic regression was used for the association of sets of variants, stratified by MAF, with severity. Immortalized cystic fibrosis bronchial epithelial cell lines and Fischer Rat Thyroid cells were transfected by plasmid carrying specific CFTR mutations. YFP-based assays were used to measure CFTR activity. Result(s): Here we functionally demonstrate that the rare (MAF=0.007) complex G576V/R668C allelemitigates the disease by a gain of function mechanism. Several novel CFTR ultra-rare (MAF <0.001) alleles were proved to have a reduced function;they are associated with disease severity either alone (single or complex alleles) or with another hypomorphic allele in the second chromosome, with a global reduction of CFTR activity between 40 to 72%. Conclusion(s): CFTR is a bidirectional modulator of COVID-19 outcome. At-risk subjects do not have open cystic fibrosis before viral infection and therefore are not easily recognisable in the general population unless a genetic analysis is performed. As the CFTR activity is partially retained, CFTR potentiator drugs could be an option as add-on therapy for at-risk patients.

Enhanced BB84 quantum cryptography protocol for secure communication in wireless body sensor networks for medical applications.

Wireless body sensor network (WBSN) is an interdisciplinary field that could permit continuous health monitoring with constant clinical records updates through the Internet. WBAN is a special category of wireless networks. Coronavirus disease 2019 (COVID-19) pandemic creates the situation to monitor the patient remotely following the social distance. WBSN provides the way to effectively monitor the patient remotely with social distance. The data transmitted in WBSN are vulnerable to attacks and this is necessary to take security procedure like cryptographic protocol to protect the user data from attackers. Several physiological sensors are implanted in the human body that will collect various physiological updates to monitor the patient's healthcare data remotely. The sensed information will be transmitted wirelessly to doctors all over the world. But it has too many security threats like data loss, masquerade attacks, secret key distribution problems, unauthorized access, and data confidentiality loss. When any attackers are attacking the physiological sensor data, there is a possibility of losing the patient's information. The creation, cancellation, and clinical data adjustment will produce a mass effect on the healthcare monitoring system. Present-day cryptographic calculations are highly resistant to attacks, but the only weak point is the insecure movement of keys. In this paper, we look into critical security threats: secure key distribution. While sharing the secret key between communicating parties in the wireless body sensor networks in the conventional method like via phone or email, the attackers will catch the private key. They can decrypt and modify more sensitive medical data. It can cause a significant effect like death also. So need an effective, secure key distribution scheme for transmission of human body health related data to medical professional through wireless links. Moreover, a new enhanced BB84 Quantum cryptography protocol is proposed in this paper for sharing the secret key among communicating parties in a secure manner using quantum theory. Besides, a bitwise operator is combined with quantum concepts to secure the patient's sensed information in the wireless environment. Instead of mail and phone via sharing secret key, quantum theory with the bitwise operator is used here. Therefore, it is not possible to hack the secret key of communication. The body sensor's constrained assets as far as battery life, memory, and computational limit are considered for showing the efficiency of the proposed security framework. Based on experimental results, it is proven that the proposed algorithm EBB84QCP provides high secure key distribution method without direct sharing the secret key and it used the quantum mechanism and bitwise operator for generating and distributing secret key value to communicating parties for sensitive information sharing in the wireless body sensor networks.

VIKOR and TOPSIS framework with a truthful-distance measure for the (t, s)-regulated interval-valued neutrosophic soft set.

This article introduces the structure of the (t,s)-regulated interval-valued neutrosophic soft set (abbr. (t,s)-INSS). The structure of (t,s)-INSS is shown to be capable of handling the sheer heterogeneity and complexity of real-life situations, i.e. multiple inputs with various natures (hence neutrosophic), uncertainties over the input strength (hence interval-valued), the existence of different opinions (hence soft), and the perception at different strictness levels (hence (t,s)-regulated). Besides, a novel distance measure for the (t,s)-INSS model is proposed, which is truthful to the nature of each of the three membership (truth, indeterminacy, falsity) values present in a neutrosophic system. Finally, a Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) and a Viekriterijumsko Kompromisno Rangiranje (VIKOR) algorithm that works on the (t,s)-INSS are introduced. The design of the proposed algorithms consists of TOPSIS and VIKOR frameworks that deploy a novel distance measure truthful to its intuitive meaning. The conventional method of TOPSIS and VIKOR will be generalized for the structure of (t,s)-INSS. The parameters t and s in the (t,s)-INSS model take the role of strictness in accepting a collection of data subject to the amount of mutually contradicting information present in that collection of data. The proposed algorithm will then be subjected to rigorous testing to justify its consistency with human intuition, using numerous examples which are specifically made to tally with the various human intuitions. Both the proposed algorithms are shown to be consistent with human intuitions through all the tests that were conducted. In comparison, all other works in the previous literature failed to comply with all the tests for consistency with human intuition. The (t,s)-INSS model is designed to be a conclusive generalization of Pythagorean fuzzy sets, interval neutrosophic sets, and fuzzy soft sets. This combines the advantages of all the three previously established structures, as well as having user-customizable parameters t and s, thereby enabling the (t,s)-INSS model to handle data of an unprecedentedly heterogeneous nature. The distance measure is a significant improvement over the current disputable distance measures, which handles the three types of membership values in a neutrosophic system as independent components, as if from a Euclidean vector. Lastly, the proposed algorithms were applied to data relevant to the ongoing COVID-19 pandemic which proves indispensable for the practical implementation of artificial intelligence.

Comparative Analysis of Mathematical Model of Covid-Sars Using Atangana-Baleanu and Yang-Abdel-Cattani Fractional Derivative Operators

Today's world is suffering from a disease known as the Corona Virus (COVID-19). Since this virus has turned into a pandemic at a global level, it is required to investigate the virus and its related attributes to anticipate future outbreaks and also to make strategies for its control through mathematical models. In this article, we perform a comparative analysis of the model using the Atangana-Baleanu and Yang-Abdel-Cattani fractional derivative operators with the help of Sumudu transform. We also compute the numerical results with graphical representation to show the behavior of the operators.

A review on epidemic models in sight of fractional calculus

Biomathematics has become one of the most significant areas of research as a result of interdisciplinary study. Chronic diseases sometimes referred to as non-communicable and communicable diseases, are conditions that develop over an extended period as a result of different factors like genetics, lifestyle, and environment. The most important common types of disease are cardiovascular, alcohol, cancer, and diabetes. More than three-quarters of the world's (31.4 million) deaths occur in low- and middle-income nations, which are disproportionately affected by different infections. Fractional Calculus is a prominent topic for research within the discipline of Applied Mathematics due to its usefulness in solving problems in many different branches of science, engineering, and medicine. Recent researchers have identified the importance of mathematical tools in various disease models as being very useful to study the dynamics with the help of fractional and integer calculus modeling. Due to the complexity of the underlying connections, both deterministic and stochastic epidemiological models are founded on an inadequate understanding of the infectious network. Over the past several years, the use of different fractional operators to model the problem has grown, and it is now a common way to study how epidemics spread. Recently, researchers have actively considered fractional calculus to study different diseases like COVID-19, cancer, TB, HIV, dengue fever, diabetes, cholera, pine welts, smoking and heart attacks, etc. With the help of fractional operator, we modified a mathematical model for the dynamical transmission, analysis, treatment, vaccination, and precaution leveling necessary to mitigate the negative impact of illness on society in the long run, overcoming the memory effect without defining or considering others parameters. In this review paper, we considered all the recent studies based on the fractional modeling of infectious and non-infectious diseases with different fractional operators such as Caputo, Caputo Fabrizio, ABC, and constant proportional with Caputo, etc. This review paper aims to bring all the information together by considering different fractional operators and their uses in the field of infectious disease modeling. The steps taken to accomplish the goal were developing a mathematical model, identifying the equilibrium point, figuring out the minimal reproductive number, and assessing the stability around the equilibrium point. For future direction, we consider the cancer model to study the growth cells of cancer and the impact of therapy to control infections. An equilibrium solution and an analysis of the behavior dynamics of the cell spread with treatment in the form of chemotherapy were obtained. The simulation shows that the population of cancer cells is influenced by the pace of cancer cell growth with the Caputo fractional derivative. The acquired results show how effective and precise the suggested approach is in helping to better understand how chemotherapy works. Chemotherapy medications have been found to increase immunity against particular cancer by reducing the number of tumor cells. Further, we suggested some future work directions with the help of the new hybrid fractional operator. Our innovative methodology might have significant effects on global stakeholders, policymakers, and national health systems. The current strategies for controlling outbreaks and the vaccination and prevention policies that have been implemented would benefit from a more accurate representation of the dynamics of contagious diseases, which necessitates the development of highly complex mathematical models. Microorganisms, interactions between individuals or groups, and environmental, social, economic, and demographic factors on a broader scale are all examples.

Asymptotic and transient dynamics of SEIR epidemic models on weighted networks

We study the effect of population mobility on the transmission dynamics of infectious diseases by considering a susceptible-exposed-infectious-recovered (SEIR) epidemic model with graph Laplacian diffusion, that is, on a weighted network. First, we establish the existence and uniqueness of solutions to the SEIR model defined on a weighed graph. Then by constructing Liapunov functions, we show that the disease-free equilibrium is globally asymptotically stable if the basic reproduction number is less than unity and the endemic equilibrium is globally asymptotically stable if the basic reproduction number is greater than unity. Finally, we apply our generalized weighed graph to Watts–Strogatz network and carry out numerical simulations, which demonstrate that degrees of nodes determine peak numbers of the infectious population as well as the time to reach these peaks. It also indicates that the network has an impact on the transient dynamical behaviour of the epidemic transmission.

A Modified Bottom Up Approach to Estimate Expected Demand Reduction During a Rare Event

Indian power system witnessed its largest very short-time demand ramping during light off event conducted to express solidarity with COVID-19 volunteers. 32 GW demand ramping was observed within 25 minutes and recorded as the highest ramping event across the globe. System operator has taken precautions and successfully handled the event with the help of hydro generation. However, system experienced severe frequency and voltage deviations due to unexpected consumer behaviour. A systematic study and an in-depth analysis of such a severe event would help system operators and planners to prepare for similar events. This paper presents a critical analysis of the activity and conducted a survey to understand consumer response during that event. It also proposes a modified Bottom-Up Approach to estimate Expected Demand Reduction (EDR) for such critical events. Proposed model is validated using data collected from the conducted survey. Proposed EDR estimation model offers better results than the Top Down and Bottom-up approach models used by system operator. © 2022 IEEE.

A numerical study of COVID-19 epidemic model with vaccination and diffusion.

The coronavirus infectious disease (or COVID-19) is a severe respiratory illness. Although the infection incidence decreased significantly, still it remains a major panic for human health and the global economy. The spatial movement of the population from one region to another remains one of the major causes of the spread of the infection. In the literature, most of the COVID-19 models have been constructed with only temporal effects. In this paper, a vaccinated spatio-temporal COVID-19 mathematical model is developed to study the impact of vaccines and other interventions on the disease dynamics in a spatially heterogeneous environment. Initially, some of the basic mathematical properties including existence, uniqueness, positivity, and boundedness of the diffusive vaccinated models are analyzed. The model equilibria and the basic reproductive number are presented. Further, based upon the uniform and non-uniform initial conditions, the spatio-temporal COVID-19 mathematical model is solved numerically using finite difference operator-splitting scheme. Furthermore, detailed simulation results are presented in order to visualize the impact of vaccination and other model key parameters with and without diffusion on the pandemic incidence. The obtained results reveal that the suggested intervention with diffusion has a significant impact on the disease dynamics and its control.

Diagnostic Performance of Human Readers and Artificial Intelligence Model in Classification Task of Computed Tomography Images of Different Types of Viral Pneumonia

Purposes: Artificial Intelligence (AI) models are constantly developing to help clinicians in challenging tasks such as classification of images in radiological practice. The aim of this work was to compare the diagnostic performance of an AI classifier model developed in our hospital with the results obtained from the radiologists reading the CT images in discriminating different types of viral pneumonia. Material(s) and Method(s): Chest CT images of 1028 patients with positive swab for SARS-CoV-2 (n=646) and other respiratory viruses (n=382) were segmented automatically for lung extraction and Radiomic Features (RF) of first (n=18) and second (n=120) order were extracted using PyRadiomics tools. RF, together with patient age and sex, were used to develop a Multi-Layer Perceptron classifier to discriminate images of patients with COVID-19 and non-COVID-19 viral pneumonia. The model was trained with 808 CT images performing a LASSO regression (Least Absolute Shrinkage and Selection Operator), a hyper-parameter tuning and a final 4-fold cross validation. The remaining 220 CT images (n=151 COVID-19, n=69 non-COVID-19) were used as independent validation (IV) dataset. Four readers (three radiologists with >10 years of experience and one radiology resident with 3 years of experience) were recruited to blindly evaluate the IV dataset using the 5-points scale CO-RADS score. CT images with CO-RADS >=3 were considered "COVID-19". The same images were classified as "COVID-19" or "non-COVID-19" by applying the AI model with a threshold on the predicted values of 0.5. Diagnostic accuracy, specificity, sensibility and F1 score were calculated for human readers and AI model. Result(s): The AI model was trained using 24 relevant features while the Area under ROC curve values after 4-fold cross validation and its application to the IV dataset were, respectively, 0.89 and 0.85. Interreader agreement in assigning CO-RADS class, analyzed with Fleiss' kappa with ordinal weighting, was good (k=0.68;IC95% 0.63-0.72) and diagnostic performance were then averaged among readers. Diagnostic accuracy, specificity, sensibility and F1 score resulted 78.6%, 78.3%, 78.8% and 78.5% for AI model and 77.7%, 65.6%, 83.3% and 72.0% for human readers. The difference between specificity and sensitivity observed in human readers could be related to the higher rate of false positive due to the higher incidence of COVID-19 patients in comparison with other types of viral pneumonitis during the last 2 years. Conclusion(s): A model based on RF and artificial intelligence provides comparable results with human readers in terms of diagnostic performance in a classification task.Copyright © 2023 Southern Society for Clinical Investigation.

Numerical Simulation for a Hybrid Variable-Order Multi-Vaccination COVID-19 Mathematical Model

In this paper, a hybrid variable-order mathematical model for multi-vaccination COVID-19 is analyzed. The hybrid variable-order derivative is defined as a linear combination of the variable-order integral of Riemann–Liouville and the variable-order Caputo derivative. A symmetry parameter σ is presented in order to be consistent with the physical model problem. The existence, uniqueness, boundedness and positivity of the proposed model are given. Moreover, the stability of the proposed model is discussed. The theta finite difference method with the discretization of the hybrid variable-order operator is developed for solving numerically the model problem. This method can be explicit or fully implicit with a large stability region depending on values of the factor Θ. The convergence and stability analysis of the proposed method are proved. Moreover, the fourth order generalized Runge–Kutta method is also used to study the proposed model. Comparative studies and numerical examples are presented. We found that the proposed model is also more general than the model in the previous study;the results obtained by the proposed method are more stable than previous research in this area.

Research on Diversification Strategies of Terminal Operators—Evidence from Polish Seaports

In response to changes taking place in the global environment, seaport terminal operators constantly search for lines of development in their operations, choosing i.a. a strategy of diversification or specialisation. So far, the issue of applying a diversification strategy in business models used by operators of multipurpose terminals has not been sufficiently addressed in the literature on the subject. In view of the above, the purpose of this paper is to identify and hierarchize the motivations for diversification and to specify the areas of diversification strategies and corresponding measures taken by operators of multipurpose terminals. The multi-case-study method was applied to conduct the research, along with the research technique of semi-structured in-depth interviews held with representatives of five terminal operators that had been running their business activity in Polish seaports and applying a diversification strategy. As a result of the completed research study, it was possible to specify the motivations for implementing a diversification strategy, and to hierarchize them. The main motives in selecting a diversification strategy as the main business strategy among the interviewed terminal operators were safeguarding against seasonal or sporadic business cycle fluctuations, and changes taking place in maritime trade and transport. Moreover, four areas of diversification strategies pursued by the terminal operators were identified: cargo diversification, contract diversification, services diversification, and cargo flow direction diversification. The diversification measures taken by the terminal operators in the specified areas were analysed in detail. The most important areas of the diversification measures in the studied entities were cargo diversification and services diversification. A heat map was developed to present the dependencies between the motivations for diversification and the areas of diversification strategies implemented by the terminal operators. The identified specific measures taken by the terminal operators as part of the indicated diversification areas included technical and organisational measures. The diversification strategy developed by terminal operators proved to be an effective strategy in coping with the effects of economic slowdown and disruptions ensuing from the COVID-19 pandemic and war in Ukraine. The results of the considerations may be of interest to seaports, transshipment terminals or other entities interested in implementation of a business activity diversification strategy. © 2023 by the authors.

Fractional-type integral operators and their applications to trend estimation of COVID-19

In this paper, we construct a novel family of fractional-type integral operators of a function (Formula presented.) by replacing sample values (Formula presented.) with the fractional mean values of that function. We give some explicit formulas for higher order moments of the proposed operators and investigate some approximation properties. We also define the fractional variants of Mirakyan–Favard–Szász and Baskakov-type operators and calculate the higher order moments of these operators. We give an explicit formula for fractional derivatives of proposed operators with the help of the Caputo-type fractional derivative Furthermore, several graphical and numerical results are presented in detail to demonstrate the accuracy, applicability, and validity of the proposed operators. Finally, an illustrative real-world example associated with the recent trend of Covid-19 has been investigated to demonstrate the modeling capabilities of fractional-type integral operators. © 2023 John Wiley & Sons, Ltd.

OWA Operators in Pensions

The public pension system crisis, arising mainly from the changing demographic, has hit different countries worldwide. For governments and citizens, it is very important to have reliable information regarding pensions in order to make decisions with a maximum degree of effectiveness and to ensure a decent income in retirement. This study presents a new method for optimizing forecasts of the average pension by using the ordered weighted averaging (OWA) operator, the induced ordered weighted averaging (IOWA) operator, the generalized ordered weighted averaging (GOWA) operator, the induced generalized ordered weighted averaging (IGOWA) operator, and particular forms of the probabilistic ordered weighted averaging (POWA) operator and the quasi-arithmetic ordered weighted averaging (Quasi-OWA) operator. It also accounts for inflation or deflation, providing a more realistic assessment of the average pension. The main advantage of this approach is the possibility to include the attitudinal character of experts or decision-makers into the calculation. The study also presents an illustrative example of how to forecast the real average pension for all autonomous communities of Spain by using this new approach. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Extended Multicriteria Group Decision Making with a Novel Aggregation Operator for Emergency Material Supplier Selection.

How to ensure the normal production of industries in an uncertain emergency environment has aroused a lot of concern in society. Selecting the best emergency material suppliers using the multicriteria group decision making (MCGDM) method will ensure the normal production of industries in this environment. However, there are few studies in emergency environments that consider the impact of the decision order of decision makers (DMs) on the decision results. Therefore, in order to fill the research gap, we propose an extended MCGDM method, whose main steps include the following: Firstly, the DMs give their assessment of all alternatives. Secondly, we take the AHP method and entropy weight method to weight the criteria and the DMs. Thirdly, we take the intuitionistic fuzzy hybrid priority weight average (IFHPWA) operator we proposed to aggregate evaluation information and take the TOPSIS method to rank all the alternatives. Finally, the proposed method is applied in a case to prove its practicability and effectiveness. The proposed method considers the influence of the decision order of the DMs on the decision results, which improves the accuracy and efficiency of decision-making results.

CIDC-Net: Chest-X Ray Image based Disease Classification Network using Deep Learning

Recently, COVID-19 is spreading rapidly and fast detection of COVID-19 can save millions of lives. Further, the COVID-19 can be detected easily from chest x ray (CXR) images using artificial intelligence methods. However, the performance of these application and methods are reduced due to noises presented in the CXR images, which degrading the performance of overall systems. Therefore, this article is focused on implementation of an innovative method for quickly processing CXR images of low quality, which enhances the contrast using fuzzy logic. This method makes use of tuned fuzzy intensification operators and is intended to speed up the processing time. Therefore, this work is focused on implementation of CXR image-based disease classification network (CIDC-Net) for identification of COVID-19 and pneumonia related 21 diseases. The CIDC-Net utilizes the deep learning convolutional neural network (CNN) model for training and testing. Finally, the simulations revealed that the proposed CIDC-Net resulted in superior performance as compared to existing models. © 2022 IEEE.

RBTM: Prediction of COVID-19 from Random Blood Test with Machine Learning Algorithms

Recently, the ongoing global pandemic of novel coronavirus infection had a devastating impact worldwide. We develop an efficient classification model that effectively produces the predictive values of infected patients with suspicious symptoms and epidemiological history to defeat this. The research aims to use the Traditional technique to compare clinical blood tests of positive and negative cases. The diagnostic Machine Learning model incorporates 551random blood samples with the following parameters of the patient's demographic features, Platelet, Hemoglobin, Lymphocyte, Neutrophil, Leukocyte (WBC), Turbidimetric, Troponin-I of COVID positive and negative cases. The prediction model can achieve the classification report of Accuracy, Precision, Recall, and F1 score values. In this analysis, considering seven different algorithms for the prediction and the observation's estimation, the data is 5-fold cross-validated. Finally, investigational outcomes attain accurate predictions. Logistic Regression predicted 0.83% of accuracy. The Receiver Operator Characteristic (ROC) metrics for Logistic Regression, the Precision was 0.78%, Recall was 0.85%, and F1-score was 0.82%, Specificity was 0.58%, and Sensitivity was 0.41%. © 2022 IEEE.

Dombi Bonferroni Mean Operators under Bipolar Complex Fuzzy Environment and Their Applications in Internet World

Internet is almost a necessary facility and tool to solve daily life problems in every field life. Whether at the individual level or national and international level sale purchase of any kind of object has always been of much importance, especially after Corona Pandemic, when online business is at its peak. Because of the enhancement of online sales and purchases, various businessmen are looking for suitable internet websites for their businesses, and the selection of the most suitable internet websites is one of the multi-attribute decision-making (MADM) dilemmas. Thus, in this script, we take benefits of three various concepts that are Bonferroni mean (BM) operator which is a significant technique to catch the interrelatedness among any number of inputs, Dombi operations which are based on Dombi t-norm and t-conorm and the ability to create an aggregation procedure more flexible because of the parameter, bipolar complex fuzzy set (BCFS) which is an outstanding model for tackling two-dimensional information with negative aspect and interpret bipolar complex fuzzy (BCF) Dombi Bonferroni mean (BCFDBM), BCF weighted Dombi Bonferroni mean (BCFWDBM), BCF Dombi geometric Bonferroni mean (BCFDGBM), and BCF weighted Dombi geometric Bonferroni mean (BCFWDGBM) operators. After ward, in this script, for tackling MADM dilemmas in the setting of BCFS, we investigate a MADM procedure based on the investigated operators and solve a MADM dilemma (selection of a suitable internet website for businessmen). Further, to display the superiority and efficiency of our work, we compare our approach and operators with a few current approaches and operators. Author

SMA-Net: Sobel Operator Combined with Multi-attention Networks for COVID-19 Lesion Segmentation

Coronavirus disease 2019 (COVID-19) has been spreading since late 2019, leading the world into a serious health crisis. To control the spread rate of infection, identifying patients accurately and quickly is the most crucial step. Computed tomography (CT) images of the chest are an important basis for diagnosing COVID-19. They also allow doctors to understand the details of the lung infection. However, manual segmentation of infected areas in CT images is time-consuming and laborious. With its excellent feature extraction capabilities, deep learning-based method has been widely used for automatic lesion segmentation of COVID-19 CT images. But, the segmentation accuracy of these methods is still limited. To effectively quantify the severity of lung infections, we propose a Sobel operator combined with Multi-Attention networks for COVID-19 lesion segmentation (SMA-Net). In our SMA-Net, an edge feature fusion module uses Sobel operator to add edge detail information to the input image. To guide the network to focus on key regions, the SMA-Net introduces a self-attentive channel attention mechanism and a spatial linear attention mechanism. In addition, Tversky loss function is adopted for the segmentation network for small size of lesions. Comparative experiments on COVID-19 public datasets show that the average Dice similarity coefficient (DSC) and joint intersection over Union (IOU) of proposed SMA-Net are 86.1% and 77.8%, respectively, which are better than most existing neural networks used for COVID-19 lesion segmentation. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Optimal Knowledge Component Extracting Model for Knowledge-Concept Graph Completion in Education

As people have become accustomed to non-face-to-face education because of the COVID-19 pandemic, adaptive and personalized learning is being emphasized in the field of education. Learning paths suitable for each student may differ from those normally provided by teachers. To support coaching based on the concept of adaptive learning, the first step is to discover the relationships among the concepts in the curriculum provided in the form of a knowledge graph. In this study, feature reduction for the target knowledge-concept was first performed using Elastic Net and Random Forest algorithms, which are known to have the best performance in machine learning. Deep knowledge tracing (DKT) in the form of a dual-net, which is more efficient because of the already slimmer data, was then applied to increase the accuracy of feature selection. The new approach, termed the optimal knowledge component extracting (OKCE) model, was proven to be superior to a feature reduction approach using only Elastic Net and Random Forest using both open and commercial datasets. Finally, the OKCE model showed a meaningful knowledge-concept graph that could help teachers in adaptive and personalized learning. © 2013 IEEE.

On T-Spherical Fuzzy Hypersoft Sets and Their Aggregation Operators with Application in Soft Computing

In the decision sciences problems, systematic evaluation of information containing incompleteness and impreciseness having the feature of parametrization is one of the substantial features. In the present communication, a new notion of T-spherical fuzzy hypersoft set (TSFHSS) has been introduced which contains an additional capacity of accommodating the components of neutral membership (abstain) and refusal compared to intuitionistic fuzzy hypersoft set under the sub-parametrization in an exponential way. Some of the basic operations on T-spherical fuzzy hypersoft set and some important aggregation operators have been presented and studied in detail. Further, in order to exhibit an application in the field of soft computing, the selection problem of COVID-19 mask has been numerically illustrated with some advantageous and concluding remarks. © 2022 IEEE.