ABSTRACT
This paper aims to investigate an innovative framework to handle emergency response scheme selection (ERSS) issues by integrating TODIM and TPZSG (two-person zero-sum game) methods under novel T-spherical hesitant probabilistic fuzzy set (T-SHPFS) environments. First, T-SHPFS is defined as an extension of the existing tools, which can depict the complex assessment information including several possible values of the various membership functions' degrees and the associated statistical uncertainty information. Concomitantly, T-SHPFS's normalization method, comparison laws, operation rules, cross-entropy measure and Hausdorff distance are explored. Then, an objective attribute weight determining model is constructed, considering the credibility of T-SHPF evaluations and the divergence degrees between attribute assessments simultaneously. Next, an integrated TODIM-TPZSG decision-making approach is developed to select the most desirable emergency response scheme. Finally, an illustrative example concerning the selection of the best medical waste disposal method during the COVID-19 epidemic is conducted to verify the effectiveness of the proposed TODIM-TPZSG method. Sensitivity analysis and comparisons between the TODIM-TPZSG and other representative methods are also provided to demonstrate the superiorities of the proposed method. The results reveal that the developed T-SHPFSs give DMs more assessment freedom;the proposed TODIM-TPZSG approach considers the decision makers' psychological behaviors;the ranking results of the proposed method can reflect the specific divergence degrees among the alternatives;and the needed computation burden and computational complexity are low and less affected by the number of alternatives and criteria than most current ERSS methods. © 2023
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
One of the sectors affected by rapid developments in technology and epidemics is the food and beverage sector. Especially with the Covid-19 pandemic, the use of robots in this sector has become important and has become increasingly widespread in order to keep human contact at the minimum level. Businesses such as restaurants, hotels and catering companies, which are the pioneers of the food and beverage service industry, have started to use robots more effectively to fulfill the tasks performed by their staff. The dishwashing room, which is one of the units where robots are used in the service sector, is important in terms of both reducing the interaction of people with dishes as much as possible in terms of health and time saving by washing the dishes quickly and classifying them according to their types. Therefore, in this study, classification and separation process of tea, dessert, dinner and salad plates with known width and depth dimensions of the surface according to their types was carried out by using the triangular and gaussian membership functions with Mamdani fuzzy interference metod. © 2022 IEEE.
ABSTRACT
Genetics is a highly relevant field of science. During the time of COVID-19 pandemic, it has gained additional importance. In this paper, a novel approach to genetic research using fuzzy sets is presented. Such a synergy of two so far rarely interacting scientific disciplines opens new avenues of research. The proposed approach shows only a sample of the possibilities offered by interdisciplinary research. In this study, a new approach using fuzzy set-based techniques to analyze the phenomena of homozygosity of microsatellite markers is presented. The analyses carried out using one of the most intuitive types of membership functions allowed us to achieve results that shed new light on the examined data. Moreover, the analysis of the distributions of individual markers using fuzzy sets allowed for a more in-depth study of the problem under consideration. © 2022 IEEE.
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
Time-series forecasting is a vital concern for any data having temporal variations. Comparing with the other conventional time-series methodologies, the fuzzy time-series (FTS) proved its superiority. Substantial research using time-series forecasting to predict the stock index data has been found in the earlier works. The fuzzy sets approach alone cannot explain the data thoroughly. In this article, we have proposed three different methods of time-series forecasting. The first method is based on a rough set of FTS, a rule induction-based method;the second method is based on intuitionistic FTS. The last method is the extension of the second method using differential evolution. In the first model, a fuzzy algorithm based on rules is used to derive prediction rules from the time-series data and adopt an adaptive expectation model that replaces the fuzzy logical relationships or groups. In the second method, to split the universe of discourse into a non-uniform interval, a clustering algorithm-based intuitionistic fuzzy approach is used, taking care of the membership and non-membership function. Finally, the last method has been tuned for a better outcome using differential evolution. To examine the results, contrast analyses on the Taiwan stock exchange data and daily cases of COVID-19 pandemic prediction have been carried out. The outcome of the proposed approaches validates that the first and second techniques, showing promising results. However, the third method outperforms the other methods and the present techniques concerning the root-mean-square error metric. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
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
Unfortunately, an abrupt corona-virus disease (COVID-19) outbreak brought a drastic change in human lives. Almost every sector of human-beings and their related activities are severely infected and affected by this COVID-19 pandemic. As days are passing, the impact of the COVID-19 epidemic is going to be more severe. The fundamental needs for personal protective equipment (PPEs) are rising drastically all over the world. In India, many non-pharmaceutical companies or organizations such as automobile companies are engaged in producing the PPEs at a very marginal rate. Thus this paper proposes a modeling and optimization framework for sustainable production and waste management (SPWM) decision-making model for COVID-19 medical equipment under uncertainty. To quantify the uncertainties among parameter values, we have taken advantage of the intuitionistic fuzzy set theory. A robust ranking function is presented to obtain a crisp version of it. Furthermore, a novel interactive intuitionistic fuzzy programming approach is developed to solve the proposed SPWM model. An ample opportunity to generate the desired solution sets are also depicted. The performance analysis based on multiple criteria such as savings from baseline, co-efficient of variations, and desirability degrees is also introduced. Practical managerial implications are also discussed based on the significant findings after applying to the real case study data-set. Finally, conclusive remarks and the future research direction are also addressed on behalf of the current contributing study.