ABSTRACT
Online public opinion warning for emergencies can help people understand the real situation, avoid panic, timely remind people not to go to high-risk areas, and help the government to carry out epidemic work.In this paper, key technologies of network public opinion warning were studied based on improved Stacking algorithm. COVID-19, herpangina, hand, foot and mouth, varicella and several emergency outbreaks were selected as public opinion research objects, and rough set was used to screen indicators and determine the final warning indicators.Finally, the warning model was established by the 50% fold Stacking algorithm, and the training accuracy and prediction accuracy experiments were carried out.According to the empirical study, the prediction accuracy of 50% Stacking is good, and the early warning model is practical and robust.This study has strong practicability in the early warning of the online public opinion of the sudden epidemic. © 2023 SPIE.
ABSTRACT
Since December 2019, the COVID-19 outbreak has been triggering a global crisis. COVID-19 is extremely infectious and spreads quickly across the world, so early detection is essential. Chest imaging has been shown to play an important role in the progression of COVID-19 lung disease. The respiratory system is the part of the human body that is most affected by the COVID-19 virus. Images from a Chest X-ray and a Computed Tomography scan can be used to diagnose COVID-19 quickly and accurately. CT scans are preferred over X-rays to rule out other pulmonary illnesses, assist with venous entry, and pinpoint any new heart problems. Ultrasound may be useful and therapeutic, and Point-Of-Care Ultrasound (POCUS) has been used to aid in the assessment of hospitalized patients. A Novel Tolerance Rough Set Classification approach (NTRSC) is presented in this paper to classify COVID and NON-COVID CT scan images. NTRSC approach uses similarity metrics to compute the similarity between feature values. Then, NTRSC is applied on the test images which is compared with the lower approximation values. The proposed NTRSC approach is applied to predict the COVID and NON-COVID cases based on CT scan images. The outcome of the proposed algorithm produces a higher accuracy of 0.95%, 0.88%, 0.96%, and 0.93% for Gray-Level Co-occurrence Matrix (GLCM 0°, GLCM 45°, GLCM 90°, and GLCM 135°) features, respectively. The proposed classification approach experiment is compared to those of other methods such as Decision Tree classifier, Random Forest Classifier, Naive Bayes Classifier, K-Nearest Neighbor, and Support Vector Machine, to infer that the proposed approach is a less expensive way to predict and make decisions about the disease. The results show that the strength of the proposed NTRSC approach outperforms the other approaches. Using the proposed classification approach, the research indicates an improvement in diagnostic accuracy and minimum error rate. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022.
ABSTRACT
Reports an error in "An approach to MCGDM based on multi-granulation Pythagorean fuzzy rough set over two universes and its application to medical decision problem" by Bingzhen Sun, Sirong Tong, Weimin Ma, Ting Wang and Chao Jiang (Artificial Intelligence Review, 2022[Mar], Vol 55[3], 1887-1913). In the original article, the third and fourth author's affiliation were published incorrectly and the correct affiliations are given in this correction. (The following abstract of the original article appeared in record 2021-74641-001). Exploring efficiency approaches to solve the problems of decision making under uncertainty is a mainstream direction. This article explores the rough approximation of the uncertainty information with Pythagorean fuzzy information on multi-granularity space over two universes combined with grey relational analysis. Based on grey relational analysis, we present a new approach to calculate the relative degree or the attribute weight with Pythagorean fuzzy set and give a new descriptions for membership degree and non-membership. Then, this paper proposes a multi-granulation rough sets combined with Pythagorean fuzzy set, including optimistic multi-granulation Pythagorean fuzzy rough set, pessimistic multi-granulation Pythagorean fuzzy rough set and variable precision Pythagorean fuzzy rough set. Several basic properties for the established models are investigated in detail. Meanwhile, we present an approach to solving the multiple-criteria group decision making problems with fuzzy information based on the proposed model. Eventually, a case study of psychological evaluation of health care workers in COVID-19 show the principle of the established model and is utilized to verify the availability. The main contributions have three aspects. The first contribution of an approach of calculating the attribute weight is presented based on Grey Relational Analysis and gives a new perspective for the Pythagorean fuzzy set. Then, this paper proposes a mutli-granulation rough set model with Pythagorean fuzzy set over two universes. Finally, we apply the proposed model to solving the psychological evaluation problems. (PsycInfo Database Record (c) 2023 APA, all rights reserved)
ABSTRACT
In this paper, we propose a method based on multicriteria classification and a dominancebased rough set approach (DRSA) to support teachers in decision making. The objective is to use teachers' knowledge and preferences to identify ‘atrisk students', i.e. students who are likely to drop out, and ‘leader students', i.e. students who are likely to help their peers, in distance learning. The proposed method is composed of two phases: phase I builds collective decision rules from teachers' preferences, and phase II classifies students into two decision classes: ‘atrisk students' and ‘leader students'. This method was designed, tested, and validated in higher education, with teachers who have acquired rich experience in teaching in online-synchronous mode since the Covid-19 pandemic. © 2023 Informa UK Limited, trading as Taylor & Francis Group.
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Since January 2020, the corona epidemic has created havoc worldwide. Although this virus has been mutated many times, the recent variant is more fatal for humans. Increasing active and death cases in the globe as well as in our country affect the psychological well-being of the people. India has experienced all variants including Alpha variant (B.1.1.7), Delta variant (B.1.617.2), and Omicron variant (B.1.1.529). All variants have some common symptoms along with extended symptoms. In this paper, we propose a rule base to classify and predict the variants of COVID-19 using a rough set approach. Our approach works for the elimination of redundant symptoms to create effective reduct, core, and selection of important symptoms to maintain the accuracy in a rule base. Our rules are validated to computer-generated data with 90% accuracy. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
ABSTRACT
From the last two years due to emergence of COVID-19, a first pandemic of the century, caused hard time to continuing normal lifestyle in all aspects including the campus lifestyle of students. All the academic activities such as classes, examinations, evaluations and placement are going as usual in online mode like earlier. In this regard, we have conducted a Web-based survey on students about their mental condition concerning corona anxiety, coping with stress, worry, and fear. In our survey, 620 students participated from different discipline and states to rejoin the campus either online or offline mode. 372 (60%) students want to attend offline classes while 248 (40%) students want online classes. Additionally, generating the rules using a rough set approach to identify corona anxiety in students. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
ABSTRACT
Rough Set is a machine learning algorithm that analyses and determines important attributes based on an uncertain data set. The purpose of this study is to classify public interest in the Covid-19 vaccine. Vaccination is one of the solutions from the government that is considered the most appropriate to reduce the number of Covid-19 cases. Data collection was taken through a questionnaire distributed to the village community in Air Manik Village, Padang-West Sumatra, randomly as many as 100 respondents. The assessment attributes in this study are Vaccine Understanding (1), Environment (2), Community Education (3), Vaccine Confidence (4), and Cost (5), while the target attribute is the result that contains the community's interest or not to participate in vaccination. The analysis process is assisted using the Rosetta application. This study resulted in 3 reductions with 58 rules based on 100 respondents. This study concludes that the Rough Set algorithm can be used to classify public interest in the Covid-19 vaccine. Based on this research, it is hoped that it can provide information and input for local governments to be more aggressive in urging and encouraging the public to be vaccinated. © Published under licence by IOP Publishing Ltd.
ABSTRACT
• AI promotes the sustainability development in higher education. • A soft-computing technique extracts key factors from large amounts of data. • DEMATEL analysis accounts for dependence and feedback among factors. • A framework of AI-enabled Higher Education was proposed. • "Intelligent instructional systems" is the most important criterion. The application of AI in higher education has greatly increased globally in the dynamic digital age. The adoption of developmentally appropriate practices using AI-enabled techniques for facilitating the performance of teaching and learning in the higher education domain is thus a necessary task, especially in the COVID 19 pandemic era. The development and implementation of such techniques involve many factors and are related to the classical multiple criteria decision-making (MCDM) issue;however, these factors surrounding supervisors will confuse them and may result in misjudgment. To clarify the relevant issues and illustrate the cause-and-effect relationships among factors, a hybrid soft-computing technique (i.e., the fuzzy rough set theory (FRST) with ant colony optimization (ACO)) and a DEMATEL approach was proposed in this study, which can help decision makers capture the best model necessary for achieving aspiration-level in a higher education management strategy. In the results submitted, the improvement priority for dimensions is based on the measurement of the influences, running in order of tutors for learners (A), skills and competences (B), interaction data to support learning (C), and universal access to global classrooms (D), and which can serve as a reference for the plan of AI-enabled teaching/learning for higher education. [ FROM AUTHOR]
ABSTRACT
This article focuses on the relationship between mathematical morphology operations and rough sets, mainly based on the context of image retrieval and the basic image correspondence problem. Mathematical morphological procedures and set approximations in rough set theory have some clear parallels. Numerous initiatives have been made to connect rough sets with mathematical morphology. Numerous significant publications have been written in this field. Others attempt to show a direct connection between mathematical morphology and rough sets through relations, a pair of dual operations, and neighborhood systems. Rough sets are used to suggest a strategy to approximatemathematicalmorphology within the general paradigm of soft computing. A single framework is defined using a different technique that incorporates the key ideas of both rough sets and mathematical morphology. This paper examines rough set theory from the viewpoint of mathematical morphology to derive rough forms of themorphological structures of dilation, erosion, opening, and closing. These newly defined structures are applied to develop algorithm for the differential analysis of chest X-ray images from a COVID-19 patient with acute pneumonia and a health subject. The algorithm and rough morphological operations show promise for the delineation of lung occlusion in COVID-19 patients from chest X-rays. The foundations of mathematical morphology are covered in this article. After that, rough set theory ideas are taken into account, and their connections are examined. Finally, a suggested image retrieval application of the concepts from these two fields is provided. © 2023 Tech Science Press. All rights reserved.
ABSTRACT
Since January 2020, the corona epidemic has created havoc worldwide. Although this virus has been mutated many times, the recent variant is more fatal for humans. Increasing active and death cases in the globe as well as in our country affect the psychological well-being of the people. India has experienced all variants including Alpha variant (B.1.1.7), Delta variant (B.1.617.2), and Omicron variant (B.1.1.529). All variants have some common symptoms along with extended symptoms. In this paper, we propose a rule base to classify and predict the variants of COVID-19 using a rough set approach. Our approach works for the elimination of redundant symptoms to create effective reduct, core, and selection of important symptoms to maintain the accuracy in a rule base. Our rules are validated to computer-generated data with 90% accuracy. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
ABSTRACT
From the last two years due to emergence of COVID-19, a first pandemic of the century, caused hard time to continuing normal lifestyle in all aspects including the campus lifestyle of students. All the academic activities such as classes, examinations, evaluations and placement are going as usual in online mode like earlier. In this regard, we have conducted a Web-based survey on students about their mental condition concerning corona anxiety, coping with stress, worry, and fear. In our survey, 620 students participated from different discipline and states to rejoin the campus either online or offline mode. 372 (60%) students want to attend offline classes while 248 (40%) students want online classes. Additionally, generating the rules using a rough set approach to identify corona anxiety in students. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
ABSTRACT
The spread of COVID-19 has led many people to turn to O2O platforms to buy daily supplies leading to a boom in the O2Oe-commerce industry. How to improve the service quality of O2O platforms to attract more customers has become an important concern for service providers. This study differs from previous statistical analysis studies in that it applies the data mining methodology to extract the key factors that affect the service quality of O2O e-commerce platforms. A hybrid multi-criteria decision-making method is then utilized to obtain the influence relationships and weights of the dimensions and criteria. The results suggest that privacy security, and reliability have a positive impact on social interaction, recommendation quality, efficiency and empathy. Empathy, social interaction and recommendation quality are the three most important factors for evaluating the service quality of O2Oe-commerce platforms. Finally, the theoretical implications and management implications based on the findings are discussed. © 2022 IEEE.
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COVID pandemic and the subsequent recent emergence of its different variants have posed significant challenges for continuing everyday lifestyle, including any educational institute's campus life. In contrast, educational institutes conduct classes, exams, placement, and other co-curricular activities online, offline, and hybrid modes. Because of this, we have achieved a web-based survey on students about their mental health and other related issues such as anxiety, worry, disturbance, fear of infection, and mental anguish caused by COVID-19 in university undergraduates. 1100 pupils completed a digital survey in this crosssectional study. All these are college graduates from various universities in Bhubaneswar, India, and other universities in Odisha. COVID-19 awareness, nervousness, tension, panic, and mental illness in the past were used to screen the psychological distress. This paper reviews the current scenario of COVID-19 concerning psychological distress and related issues. Students' mental health can be affected by using the development of RST (rough set theory) principles. © 2022 Nova Science Publishers, Inc. All rights reserved.
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The pandemic outbreak of severe acute respiratory syndrome caused by the Coronavirus 2 disease in 2019, also known as SARS-COV-2 and COVID-19, has claimed over 5.6 million lives till now. The highly infectious nature of the Covid-19 virus has resulted into multiple massive upsurges in counts of new infections termed as 'waves.' These waves consist of numerous rising and falling counts of Covid-19 infection cases with changing dates that confuse analysts and researchers. Due to this confusion, the detection of emergence or drop of Covid waves is currently a subject of intensive research. Hence, we propose an algorithmic framework to forecast the upcoming details of Covid-19 infection waves for a region. The framework consists of a displaced double moving average ( δ DMA) algorithm for forecasting the start, rise, fall, and end of a Covid-19 wave. The forecast is generated by detection of potential dates with specific counts called 'markers.' This detection of markers is guided by decision rules generated through rough set theory. We also propose a novel 'corrected moving average' ( χ SMA) technique to forecast the upcoming count of new infections in a region. We implement our proposed framework on a database of Covid-19 infection specifics fetched from 12 countries, namely: Argentina, Colombia, New Zealand, Australia, Cuba, Jamaica, Belgium, Croatia, Libya, Kenya, Iran, and Myanmar. The database consists of day-wise time series of new and total infection counts from the date of first case till 31st January 2022 in each of the countries mentioned above. The δ DMA algorithm outperforms other baseline techniques in forecasting the rise and fall of Covid-19 waves with a forecast precision of 94.08%. The χ SMA algorithm also surpasses its counterparts in predicting the counts of new Covid-19 infections for the next day with the least mean absolute percentage error (MAPE) of 36.65%. Our proposed framework can be deployed to forecast the upcoming trends and counts of new Covid-19 infection cases under a minimum observation window of 7 days with high accuracy. With no perceptible impact of countermeasures on the pandemic until now, these forecasts will prove supportive to the administration and medical bodies in scaling and allotment of medical infrastructure and healthcare facilities.
ABSTRACT
Environmental deterioration, the COVID-19 pandemic and the Russian-Ukrainian conflict had brought chronic and dramatic impacts on agricultural supply chain around the world, resulting in high inflation rates and unavoidable costs. In order to reduce the adverse impacts and achieve sustainability in agricultural supply chain, it's necessary to scientifically explore composite indicators interlinked with agricultural sustainable supply chain management (ASSCM). The current study developed an integrated rough-fuzzy WINGS-ISM method to reveal the hierarchal and causal structure of indicators. It is found that environmental legislation, regulation, licensing, and government subsidies are the main drivers of ASSCM. Specifically, the government can guide the sustainable development of ASSCM by regulating the business environment. The financial support needs to be enlarged to optimize the structure in science and technology of ASSCM. Moreover, corporates and organizations are highly motivated by the increasing awareness of social responsibility and sustainability consciousness to improve the economic performance and achieve the ASSCM goals. A comparative analysis is proposed to illustrate the practicality and reliability of the results obtained from the proposed method, which can be utilized as a reference in ASSCM.
ABSTRACT
The application of AI in higher education has greatly increased globally in the dynamic digital age. The adoption of developmentally appropriate practices using AI-enabled techniques for facilitating the performance of teaching and learning in the higher education domain is thus a necessary task, especially in the COVID 19 pandemic era. The development and implementation of such techniques involve many factors and are related to the classical multiple criteria decision-making (MCDM) issue;however, these factors surrounding supervisors will confuse them and may result in misjudgment. To clarify the relevant issues and illustrate the cause-and-effect relationships among factors, a hybrid soft-computing technique (i.e., the fuzzy rough set theory (FRST) with ant colony optimization (ACO)) and a DEMATEL approach was proposed in this study, which can help decision makers capture the best model necessary for achieving aspiration-level in a higher education management strategy. In the results submitted, the improvement priority for dimensions is based on the measurement of the influences, running in order of tutors for learners (A), skills and competences (B), interaction data to support learning (C), and universal access to global classrooms (D), and which can serve as a reference for the plan of AI-enabled teaching/learning for higher education.
ABSTRACT
Due to the high necessity of medical face masks and face shields during the COVID-19 pandemic, healthcare centers dealing with infected patients have faced serious challenges due to the high consumption rate face masks and face shields. In this regard, the supply chain of healthcare centers should put all of their efforts into avoiding any shortages of masks and shields as these products are considered as primary ways to prevent the spread of the virus. Since, any shortages in these products would lead to irrecoverable and costly consequences in terms of the mortality rate of patients and medical staff. Therefore, healthcare centers should decide on best supplier to supply required products, considering technical, and sustainability measures. Dynamicity and uncertainty of the pandemic are other factors that add up to the complexity of the supplier selection problem. Therefore, this paper develops a novel decision-making approach using Measuring attractiveness through a categorical-based evaluation technique (MACBETH) and a new combinative distance-based assessment method to address the supplier selection problem during the COVID-19 pandemic. Due to high uncertainty, vague and incomplete information for decision-making problems during the COVID-19 pandemic, the developed decision-making approach is implemented under fuzzy rough numbers as a superior uncertainty set of the traditional fuzzy set and rough numbers. Extensive sensitivity analysis tests are performed based on parameters of the decision-making approach, impacts of weight coefficients, and consistency of results in comparison to other MCDM methods. A real-life case study is investigated for a hospital in Istanbul, Turkey to show the applicability of the developed approach. Based on the results of MACBETH method, job creation and occupational health and safety systems are two top criteria. Results of the case study for five suppliers indicate that supplier (A1) is the best supplier with a distance score of 3.308.
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In this paper, a hybrid intelligent structure called “Double Bounded Rough Neutrosophic Sets” is defined, which is a combination of Neutrosophic sets theory and Rough sets theory. Further, the Attribute based Double Bounded Rough Neutrosophic Sets was implemented using this hybrid intelligent structure for Facial Expression Detection on real time data. Facial expression detection is becoming increasingly important to understand one's emotion automatically and efficiently and is rich in applications. This paper implements some of these applications of facial expression such as: differentiating between Genuine and Fake smiles, prediction of Depression, determining the Degree of Closeness to a particular Attribute/Expression and detection of fake expression during an examination. With the onset of COVID - 19 pandemic, majority of people are choosing to wear masks. A suitable method to detect Facial Expression with and without mask is also implemented. Double Bounded Rough Neutrosophic Sets proposed in this paper is found to yield better results as compared to that of individual structures (Neutrosophic sets theory or Rough sets theory) © 2022. All Rights Reserved.
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Covid-19 is one of the biggest pandemics in the history of mankind. It has kept the modern world hostage for more than one and a half years now. Strict lockdown is creating more havoc in the minds of everybody. The decision of exiting from a lockdown and deciding the kind of strictness needed as per the scenario is not an easy task for any administration. This paper provides a new approach to estimate the seriousness of the situation to strategize the exit from lockdown. There are many mathematical models to handle uncertainty such as fuzzy set, rough set, soft set, generalizations of these models, and their hybrid models. In this paper, a decision-making application using the notion of a fuzzy soft set is provided to assess the seriousness of the corona situation, which helps to decide on lockdown relaxations. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
ABSTRACT
Topology is a beneficial structure to study the approximation operators in the rough set theory. In this work, we first introduce six new types of neighborhoods with respect to finite binary relations. We study their main properties and show under what conditions they are equivalent. Then we applied these types of neighborhoods to initiate some topological spaces that are utilized to define new types of rough set models. We compare these models and prove that the best accuracy measures are obtained in the cases of i and i. Also, we illustrate that our approaches are better than those defined under one arbitrary relation. To improve rough sets’ accuracy, we define some topological spaces using the idea of ideals. With the help of examples, we demonstrate that our methods are better than some methods studied in some published literature. Finally, we give a real-life application showing the merits of the approaches followed in this manuscript.