ABSTRACT
In order to move to a stable life rhythm and a satisfactory condition of people, which would ensure the organization of the usual mode of daily activities, it is necessary to achieve a sufficiently complete vaccination of the population in a region. At the same time, significant obstacles to achieving the desired result in Ukraine are the hesitation of a large part of the population regarding the vaccination, fear of a purely medical procedure, and distrust of its effectiveness. Due to the lack of a wide range of scientifically grounded research of this problem, insufficient attention is paid to a deeper analysis of the factors influencing the intensity and effectiveness of vaccination. In view of what has been said in the proposed article, many factors related to the vaccination process have been identified based on the developed ontology. A formalized representation of the connections between factors has been made using the semantic network as an information database, which has become a prerequisite for ranking by weight factors. Using the methodology of hierarchies modelling, the levels of factors preferences are established and a multilevel model of their priority influence on the researched process is synthesized. Alternative options for the vaccination process have been designed and a prognostic assessment of the levels of COVID-19 vaccination intensity has been carried out, which allows the selection of the optimal option for the specific parameters of the initial factors. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
ABSTRACT
The COVID-19 influenza became a curse on the world. It has been around for two years, so no one needs to make a big introduction of it. It has became a significant challenge around the world. Owing to this, we made dynamic networks using an amalgamating of fuzzy logic and neural networks for the prediction of sufferers of COVID-19. These hybrid networks serve for the assessment of the COVID-19 victims and usefully serve for the assessment of the medical resources needed for future victims. This manuscript proposed Sugeno Adaptive Neuro-Fuzzy Inference System (SANFIS) prediction model for COVID-19 prediction in Andhra Pradesh, India. We gathered data on positive COVID-19 sufferers in Andhra Pradesh for this purpose. The data can be separated into three categories: training set, testing set and checking set. We have utilized Root Mean Square Deviation (RMSD) for prediction precision. If the prediction model has a lower RMSD value, it is regarded as the best forecast. In this study, we concluded that the 3 Triangular MFns for each input were excellent with the extreme precision for all of the districts based on our expertise. In the end, we deployed seven SANFIS replicas in Andhra Pradesh, but we discovered that SANFIS6 and SANFIS7 provided excellent COVID-19 prediction results. These findings will assist the government, healthcare agencies, and medical organizations in planning for future COVID-19 victims' medical requirements. These sorts of Sugeno Adaptive Neuro-Fuzzy Inference System (SANFIS) prediction models based on Artificial Intelligence (AI) will be beneficial in overcoming the COVID-19. © 2022 Slovak Academy of Sciences. All rights reserved.
ABSTRACT
The COVID-19 influenza became a curse on the world. It has been around for two years, so no one needs to make a big introduction of it. It has became a significant challenge around the world. Owing to this, we made dy-namic networks using an amalgamating of fuzzy logic and neural networks for the prediction of sufferers of COVID-19. These hybrid networks serve for the assess-ment of the COVID-19 victims and usefully serve for the assessment of the medical resources needed for future victims. This manuscript proposed Sugeno Adaptive Neuro-Fuzzy Inference System (SANFIS) prediction model for COVID-19 predic-tion in Andhra Pradesh, India. We gathered data on positive COVID-19 sufferers in Andhra Pradesh for this purpose. The data can be separated into three categories: training set, testing set and checking set. We have utilized Root Mean Square Devi-ation (RMSD) for prediction precision. If the prediction model has a lower RMSD value, it is regarded as the best forecast. In this study, we concluded that the 3 Tri-angular MFns for each input were excellent with the extreme precision for all of the districts based on our expertise. In the end, we deployed seven SANFIS replicas in Andhra Pradesh, but we discovered that SANFIS6 and SANFIS7 provided excellent COVID-19 prediction results. These findings will assist the government, healthcare agencies, and medical organizations in planning for future COVID-19 victims' med-ical requirements. These sorts of Sugeno Adaptive Neuro-Fuzzy Inference System (SANFIS) prediction models based on Artificial Intelligence (AI) will be beneficial in overcoming the COVID-19.
ABSTRACT
In order to move to a stable life rhythm and a satisfactory condition of people, which would ensure the organization of the usual mode of daily activities, it is necessary to achieve a sufficiently complete vaccination of the population in a region. At the same time, significant obstacles to achieving the desired result in Ukraine are the hesitation of a large part of the population regarding the vaccination, fear of a purely medical procedure, and distrust of its effectiveness. Due to the lack of a wide range of scientifically grounded research of this problem, insufficient attention is paid to a deeper analysis of the factors influencing the intensity and effectiveness of vaccination. In view of what has been said in the proposed article, many factors related to the vaccination process have been identified based on the developed ontology. A formalized representation of the connections between factors has been made using the semantic network as an information database, which has become a prerequisite for ranking by weight factors. Using the methodology of hierarchies modelling, the levels of factors preferences are established and a multilevel model of their priority influence on the researched process is synthesized. Alternative options for the vaccination process have been designed and a prognostic assessment of the levels of COVID-19 vaccination intensity has been carried out, which allows the selection of the optimal option for the specific parameters of the initial factors. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
ABSTRACT
COVID-19 affects the banking sector to its maximum and moreover the repayments of the loans have become very dicey. Financial Industries like SBI are one of the major elements of the economic development of India. In the pandemic the Government has formulated various monetary and fiscal policies to deal with crisis for commercial banks under the supervision of Reserve Bank of India. To pursue these policies forward ensuring economic, industrial, socio-political and methodical development, they need proper funds to support lending to various corporate and individual customers. If any of the loan facilities granted become bad debt or doubtful debts then the goal of the policies is not fulfilled and it will mount the record of bad debt in the books of commercial banking especially after demonetization and pandemic. The paper deals with the factors like Loan Portfolio Management, Term Loan, Monetary Policies, and Change in Tax Rates and Loan performances. It represents and compares the input variables and its relations to predict the upcoming low performer of the credits. It determines to what extent the factors are affecting an individual and a corporate customer of the State Bank of India. A model through fuzzy logic and neural network is being developed to predict the low performing creditors. The factors are evaluated on the platform of python and the results are satisfactory. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
ABSTRACT
We propose a dynamic fare pricing model based on demand prediction to mitigate peak hours’ congestion in public transportation. To deal with demand uncertainties, we propose the Kumaraswamy membership function (KMF) as a flexible membership function. The proposed KMF is applied to construct the new KMF-TSK fuzzy logic system (FLS) for passenger demand prediction and the new fuzzy bi-level programming model (KMF-FBP) for fare price determination. We also introduce a new fare structure based on smooth function considering passenger demand and travel distances. The fare structure is a combination of peak hours charging and off-peak hours discounting. We consider passengers' heterogeneity according to their income levels to improve equity in pricing and to increase the acceptance rate of pricing policies. Applying the proposed model, passengers could be informed about the ticket prices for the upcoming week, which helps to mitigate peak congestion. Data for Tehran subway system is utilized as a case study to verify our proposed fare pricing model. The experimental results demonstrate the superiority of the proposed KMF-FBP model over both conventional bi-level and Triangular fuzzy bi-level programming models. Physical distancing is also taken into account in a simulation experiment to assess the effects of the COVID-19 situation.
ABSTRACT
The entire education system has undergone numerous changes to stand unhindered during the current COVID-19 pandemic. All over the world, the educational system has changed its teaching and learning methods. One of its important aspects, evaluating the students' overall performance has become a complex task with these changing patterns. The traditional approach of evaluation may not be a best fit anymore since multiple factors are required to make an all-inclusive, multifaceted decision to keep up with the upgrades in evaluation schemes and patterns. Also, Universities and educational institutes understood the importance of skill based learning and major changes are being made in the curriculum, which in turn need cognitive approach to evaluate the students' performance. Hence, we have proposed, designed and implemented a solution, a fuzzy logic-based model. This model, while showing the difference between the traditional approach and the inference system, will enable the educational institutes not only to evaluate a students' performance but also to understand the students in a comprehensive manner. © 2021 IEEE.
ABSTRACT
The undergoing research aims to address the problem of COVID-19 which has turned out to be a global pandemic. Despite developing some successful vaccines, the pace has not overcome so far. Several studies have been proposed in the literature in this regard, the present study is unique in terms of its dynamic nature to adapt the rules by reconfigurable fuzzy membership function. Based on patient’s symptoms (fever, dry cough etc.) and history related to travelling, diseases/medications and interactions with confirmed patients, the proposed dynamic fuzzy rule-based system (FRBS) identifies the presence/absence of the disease. This can greatly help the healthcare professionals as well as laymen in terms of disease identification. The main motivation of this paper is to reduce the pressure on the health services due to frequent test assessment requests, in which patients can do the test anytime without the need to make reservations. The main findings are that there is a relationship between the disease and the symptoms in which some symptoms can indicate the probability of the presence of the disease such as high difficulty of breathing, cough, sore throat, and so many more. By knowing the common symptoms, we developed membership functions for these symptoms, and a model generated to distinguish between infected and non-infected people with the help of survey data collected. The model gave an accuracy of 88.78%, precision of 72.22%, sensitivity of 68.42%, specificity of 93.67%, and an f1-score of 69.28%. © 2021. All Rights Reserved.
ABSTRACT
The apparatus of fuzzy sets membership functions is a powerful tool widely used to express expert preferences when solving problems in various subject areas. Algorithmic ways to modify membership functions without additional involvement of experts are common modern means of obtaining new functions on the basis of the existing ones. This paper investigates methods of possible resolution of ambiguity in determining the position of the middle point of expert confidence concentration of triangular membership functions in cases of their modification with the application of algorithms that use the reference functions. The obtained membership functions of fuzzy sets can be used in modeling the behavior of agents under conditions of substantial uncertainty caused by COVID–19. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.
ABSTRACT
In addition to the increasing population and rapid urbanization, the amount and variety of medical waste are rapidly increasing due to the coronavirus disease (COVID-19) pandemic affecting the whole world. COVID-19 does not only increase the amount of medical waste produced, medical wastes generated in the care of COVID-19 carries a high risk of transmission as well. In this regard, the safe and effective management of medical wastes has become a serious health and safety issue. This research aims to determine the safest and shortest transportation routes for medical waste vehicles. The safety scores used in this study were obtained in our previous study. The resulting safety scores were used in a multi-objective traveling salesman problem for deriving two objective functions, which are based on safety scores and total transportation distance. A conciliating solution was obtained by solving this linear programming model. The proposed model faced by health institutions in Istanbul has been applied for a specific district. According to the obtained results, suggestions for the direction of medical waste vehicles have been proposed.
ABSTRACT
COVID-19 originally known as Corona VIrus Disease of 2019, has been declared as a pandemic by World Health Organization (WHO) on 11th March 2020. Unprecedented pressures have mounted on each country to make compelling requisites for controlling the population by assessing the cases and properly utilizing available resources. The rapid number of exponential cases globally has become the apprehension of panic, fear and anxiety among people. The mental and physical health of the global population is found to be directly proportional to this pandemic disease. The current situation has reported more than twenty four million people being tested positive worldwide as of 27th August, 2020. Therefore, it is the need of the hour to implement different measures to safeguard the countries by demystifying the pertinent facts and information. This paper aims to bring out the fact that tweets containing all handles related to COVID-19 and WHO have been unsuccessful in guiding people around this pandemic outbreak appositely. This study analyzes two types of tweets gathered during the pandemic times. In one case, around twenty three thousand most re-tweeted tweets within the time span from 1st Jan 2019 to 23rd March 2020 have been analyzed and observation says that the maximum number of the tweets portrays neutral or negative sentiments. On the other hand, a dataset containing 226,668 tweets collected within the time span between December 2019 and May 2020 have been analyzed which contrastingly show that there were a maximum number of positive and neutral tweets tweeted by netizens. The research demonstrates that though people have tweeted mostly positive regarding COVID-19, yet netizens were busy engrossed in re-tweeting the negative tweets and that no useful words could be found in WordCloud or computations using word frequency in tweets. The claims have been validated through a proposed model using deep learning classifiers with admissible accuracy up to 81%. Apart from these the authors have proposed the implementation of a Gaussian membership function based fuzzy rule base to correctly identify sentiments from tweets. The accuracy for the said model yields up to a permissible rate of 79%.