ABSTRACT
The concepts of relations and information measures have importance whenever we deal with medical diagnosis problems. The aim of this paper is to investigate the global pandemic COVID-19 scenario using relations and information measures in an interval-valued T-spherical fuzzy (IVTSF) environment. An IVTSF set (IVTSFS) allows describing four aspects of human opinions i.e., membership, abstinence, non-membership, and refusal grade that process information in a significant way and reduce information loss. We propose similarity measures and relations in the IVTSF environment and investigate their properties. Both information measures and relations are applied in a medical diagnosis problem keeping in view the global pandemic COVID-19. How to determine the diagnosis based on symptoms of a patient using similarity measures and relations is discussed. Finally, the advantages of dealing with such problems using the IVTSF framework are demonstrated with examples.
ABSTRACT
The notion of a complex hesitant fuzzy set (CHFS) is one of the better tools in order to deal with complex information. Since distance plays a crucial role in order to differentiate between two things or sets, in this paper, we first develop a priority degree for the comparison between complex hesitant fuzzy elements (HFEs). Then a variety of distance measures are developed, namely, Complex hesitant normalized Hamming-Hausdorff distance (CHNHHD), Complex hesitant normalized Euclidean-Hausdorff distance (CHNEHD), Generalized complex hesitant normalized Hausdorff distance (GCHNHD), Complex hesitant hybrid normalized Hamming distance (CHHNHD), Complex hesitant hybrid normalized Euclidean distance (CHHNED), Generalized complex hesitant hybrid normalized distance (GCHHND) and their weighted forms. Moreover, the continuous form of the proposed distances is also developed. Further, the proposed distances are applied to medical diagnosis problems for their effectiveness and application. Furthermore, a multi-criteria decision making (MCDM) approach is developed based on the TOPSIS method and proposed distances. Finally, a practical example related to the effectiveness of COVID-19 tests is presented for the application and validity of the proposed method. A comparison study was also done with the method that was already in place to see how well the new method worked.
ABSTRACT
The expansion of the online food delivery apps (OFDAs) around the globe has accelerated because of the sudden growing cases of the COVID-19 pandemic. OFDAs are quickly expanding in India, providing a huge number of chances for different OFDA platforms and creating a competitive market. There are several criteria and dimensions for OFDAs businesses to explore to keep with the frequently changing competitive market and achieve long-term success. A Pythagorean fuzzy set (PFS) is a powerful tool for dealing with uncertainty. Distance measure of PFS is a hot research topic and has real-life applications in many areas, such as decision making, medical diagnosis, patterns analysis, clustering, etc. The article aims to examine the results of the novel Pythagorean fuzzy distance measure strategy to select the best online app using TOPSIS method to select the best OFDAs. Firstly, all the axioms related to distance measures are proved for the proposed measures. The proposed work uses five distinct alternatives/options and four attributes/criteria in a fuzzy environment to deal with imprecise and conflicting information. The findings indicate that the proposed methodology is a more realistic way to choose the best OFDAs among others. Finally, a sensitivity analysis is used to determine whether the chosen alternative was the best option among the other components and to ensure that the TOPSIS technique results were accurate.
ABSTRACT
The Personalized Job Recommender System is a subset of the custom recommendation system that provides a solution to the problem of information overload and is widely applied in numerous domains to solve a plethora of problems, such as unemployment and employment churn that we have seen emerging at higher rates in the COVID era. Furthermore, different jobs require divergent skill sets from their candidates to get hired. In this paper, we analyze the similarity techniques for Job Recommendation Systems based on the research done in the field of Job Recommendations. In our implementation, we have used three similarity measures: Tanimoto, Cosine (Orchini), and City Block similarity metrics. These techniques have been tested on a new Job Recommendation Systems Dataset taken from Kaggle. We have also analyzed the performance of similar techniques involving other distance measures, such as Euclidean distance. The performance of these similarity score-based techniques for generating the highest score-based recommendations is assessed using different evaluation metrics such as Accuracy, Precision, Recall, and F1-score respectively. © 2023 IEEE.
ABSTRACT
The investment of time and resources for developing better strategies is key to dealing with future pandemics. In this work, we recreated the situation of COVID‐19 across the year 2020, when the pandemic started spreading worldwide. We conducted experiments to predict the coronavirus cases for the 50 countries with the most cases during 2020. We compared the performance of state‐of‐the‐art machine learning algorithms, such as long‐short‐term memory networks, against that of online incremental machine learning algorithms. To find the best strategy, we performed experiments to test three different approaches. In the first approach (single‐country), we trained each model using data only from the country we were predicting. In the second one (multiple‐country), we trained a model using the data from the 50 countries, and we used that model to predict each of the 50 countries. In the third experiment, we first applied clustering to calculate the nine most similar countries to the country that we were predicting. We consider two countries to be similar if the differences between the curve that represents the COVID‐19 time series are small. To do so, we used time series similarity measures (TSSM) such as Euclidean Distance (ED) and Dynamic Time Warping (DTW). TSSM return a real value that represents the distance between the points in two time series which can be interpreted as how similar they are. Then, we trained the models with the data from the nine more similar countries to the one that was predicted and the predicted one. We used the model ARIMA as a baseline for our results. Results show that the idea of using TSSM is a very effective approach. By using it with the ED, the obtained RMSE in the single‐country and multiple‐country approaches was reduced by 74.21% and 74.70%, respectively. And by using the DTW, the RMSE was reduced by 74.89% and 75.36%. The main advantage of our methodology is that it is very simple and fast to apply since it is only based on time series data, as opposed to more complex methodologies that require a deep and thorough study to consider the number of parameters involved in the spread of the virus and their corresponding values. We made our code public to allow other researchers to explore our proposed methodology. [ FROM AUTHOR]
ABSTRACT
T-spherical fuzzy set is an effective tool to deal with vagueness and uncertainty in real-life problems, especially in a situation when there are more than two circumstances, like in casting a ballot. The correlation coefficient of T-spherical fuzzy sets is a tool to calculate the association of two T-spherical fuzzy sets. It has several applications in various disciplines like science, management, and engineering. The noticeable applications incorporate pattern analysis, decision-making, medical diagnosis, and clustering. The aim of this article is to introduce some correlation coefficients for T-spherical fuzzy sets with their applications in pattern recognition and decision-making. The under discussion correlation coefficients are far more advantageous than the existing and many other tools used for T-spherical fuzzy sets, because they are used completely and demonstrate the nature as well as the limit of association between two T-spherical fuzzy sets. Further, an application of proposed correlation coefficients in pattern analysis is discussed here. In addition to it, the proposed correlation coefficients are applied to a multi-attribute decision-making problem, in which the selection of a suitable COVID-19 mask is presented. A comparative analysis has also been made to check the effectiveness of the proposed work with the existing correlation coefficients.
ABSTRACT
Ontology is the kernel technique of the Semantic Web (SW), which models the domain knowledge in a formal and machine-understandable way. To ensure different ontologies’ communications, the cutting-edge technology is to determine the heterogeneous entity mappings through the ontology matching process. During this procedure, it is of utmost importance to integrate different similarity measures to distinguish heterogeneous entity correspondence. The way to find the most appropriate aggregating weights to enhance the ontology alignment’s quality is called ontology meta-matching problem, and recently, Evolutionary Algorithm (EA) has become a great methodology of addressing it. Classic EA-based meta-matching technique evaluates each individual through traversing the reference alignment, which increases the computational complexity and the algorithm’s running time. For overcoming this drawback, an Interpolation Model assisted EA (EA-IM) is proposed, which introduces the IM to predict the fitness value of each newly generated individual. In particular, we first divide the feasible region into several uniform sub-regions using lattice design method, and then precisely evaluate the Interpolating Individuals (INIDs). On this basis, an IM is constructed for each new individual to forecast its fitness value, with the help of its neighborhood. For testing EA-IM’s performance, we use the Ontology Alignment Evaluation Initiative (OAEI) Benchmark in the experiment and the final results show that EA-IM is capable of improving EA’s searching efficiency without sacrificing the solution’s quality, and the alignment’s f-measure values of EA-IM are better than OAEI’s participants.
ABSTRACT
The concepts of relations and information measures have importance whenever we deal with medical diagnosis problems. The aim of this paper is to investigate the global pandemic COVID-19 scenario using relations and information measures in an interval-valued T-spherical fuzzy (IVTSF) environment. An IVTSF set (IVTSFS) allows describing four aspects of human opinions i.e., membership, abstinence, non-membership, and refusal grade that process information in a significant way and reduce information loss. We propose similarity measures and relations in the IVTSF environment and investigate their properties. Both information measures and relations are applied in a medical diagnosis problem keeping in view the global pandemic COVID-19. How to determine the diagnosis based on symptoms of a patient using similarity measures and relations is discussed. Finally, the advantages of dealing with such problems using the IVTSF framework are demonstrated with examples.
ABSTRACT
Face-to-face learning has been replaced by E-learning due to the closing of academic institutions in the world during the covid-19 pandemic. Educational institutions faced many challenges in the online platforms and the most important of which was assessing students' performance, which resulted in the general problem of cheating detection in the online exams. E-learning has grown significantly every day over the last decade with the growth of the internet and technology. Therefore, an online examination can be beneficial for people to take the exam, but cheating in tests is a common phenomenon around the world. As a consequence, the prevention of cheating can no longer be completely effective. Many researchers discussed online examination cheating without addressing an important point, which is analyzing students' answers to find similar responses between them. This paper proposed a recommendation system for evaluating students' answers and detecting cheating during an online exam utilizing statistical methods, similarity measures, and clustering algorithms by presenting a set of features derived from an online exam based on the Moodle platform. The results showed that the suggested online examination system effectively reduces cheating and provides a reliable online exam. In conclusion, presenting an effective and fair system that maintains academic integrity, which is the most important aspect of education.
ABSTRACT
The idea of composition relations on Fermatean fuzzy sets based on the maximum-extreme values approach has been investigated and applied in decision making problems. However, from the perspective of the measure of central tendency, this approach is not reliable because of the information loss occasioned by the use of extreme values. Based on this limitation, we introduce an enhanced Fermatean fuzzy composition relation with a better performance rating based on the maximum-average approach. An easy-to-follow algorithm based on this approach is presented with numerical computations. An application of Fermatean fuzzy composition relations is discussed in diagnostic analysis where diseases and patients are mirrored as Fermatean fuzzy pairs characterized with some related symptoms. To ascertain the veracity of the novel Fermatean fuzzy composition relation, a comparative analysis is presented to showcase the edge of this novel Fermatean fuzzy composition relation over the existing Fermatean fuzzy composition relation.
ABSTRACT
The selection of proper healthcare device suppliers in sustainable organ transplantation networks has become an essential topic of increasing life expectancy. Assessment of sustainable healthcare device suppliers can be regarded as a complex multi-criteria decision-making (MCDM) problem that consists of multiple alternative solutions with sustainable criteria. For this reason, this paper proposes a new integrated MCDM model based on combining an extended vlsekriterijuska optimizacija i komoromisno resenje (E-VIKOR) and measurement alternatives and ranking according to the compromise solution (MARCOS) approaches under interval-valued intuitionistic fuzzy sets (IVIFSs). The aggregating technique of the E-VIKOR method is a strong point of this method compared to the original approach. The IVIFS is taken to cope with the uncertain situation of real-world applications. In this regard, an IVIF-similarity measure is introduced to compute weights of the decision-makers (DMs). The IVIF-Shannon entropy method is utilized to calculate the criteria weights, and a new hybrid proposed model is developed by presenting the IVIF-E-VIKOR method and IVIF-MARCOS, to calculate the ranking of sustainable supplier alternatives in organ transplantation networks to supply the surgery devices. Afterward, an illustrative example is introduced to evaluate the performance of the proposed model, and a comparative analysis is presented to confirm and validate the proposed approach. Moreover, sensitivity analysis for essential parameters of the proposed model is performed to assess their effects on outcomes.
ABSTRACT
Pharmaceutical logistics are primarily concerned with handling transportation and supply chain management of numerous complex goods most of which need particular requirements for their logistical care. To find the high level of specialization, suppliers of pharmaceutical logistics must be selected under a mathematical model that can treat vague and uncertain real-life circumstances. The notion of bipolarity is a key factor to address such uncertainties. A bipolar fuzzy soft set (BFSS) is a strong mathematical tool to cope with uncertainty and unreliability in various real-life problems including logistics and supply chain management. In this paper, we introduce new similarity measures (SMs) based on certain properties of bipolar fuzzy soft sets (BFSSs). The proposed SMs are the extensions of Frobenius inner product, cosine similarity measure, and weighted similarity measure for BFSSs. The proposed SMs are also illustrated with respective numerical examples. An innovative multi-attribute decision-making algorithm (MADM) and its flow chart are being developed for pharmaceutical logistics and supply chain management in COVID-19. Furthermore, the application of the suggested MADM method is presented for the selection of the best pharmaceutical logistic company and a comparative analysis of the suggested SMs with some of the existing SMs is also demonstrated.
ABSTRACT
Evaluation of E-Learning resources plays a significant role in the context of pedagogic systems. Resource evaluation is important in both conventional 'talk-and-chalk' teaching and in blended learning. In on-line (e-learning) teaching [an enforced feature of pedagogic systems in tertiary education during the Covid-19 pandemic] the effective evaluation of teaching resources has obtained importance given the lack of 'face-to-face' student-teached interaction. Moreover, the enforced use of e-learning has demonstrated the effectiveness of on-line pedagogic systems, which has been argued in blended learning pedagogic systems. Additionally, in e-learning, the lack of 'face-to-face' meetings [between teaching staff and students and in staff meetings] makes feedback (positive and negative) important for all actors in the pedagogic system. In this paper we present a novel approach to enable effective evaluation of teaching resources, which provides effective group decision-support designed to evaluate e-learning resources, enhancing students' satisfaction. The proposed approach employs Picture Fuzzy Sets to quantify survey responses from actors, including: agree, disagree, neutral, and refuse to answer. In our approach, the system can manage the evaluation of e-learning resources based on both explicit and tacit knowledge using a picture fuzzy rule-based approach in which linguistic semantic terms are used to express rules and preferences. The proposed system has been tested using e-learning case studies with the goal of enhancing the learning experience and increasing students' satisfaction. Experimental results demonstrate that our proposed approach achieves a significant improvement in performance in the evaluation of e-learning resources.
ABSTRACT
The contemporary situation of the world is very pathetic due to the spread of COVID-19. In this article, we have prepared a decision making model on COVID-19 pandemic patients with the help of the neutrosophic similarity measures. The model is to predict the COVID-19 patents for testing positive and testing negative. The decision making is based on the testing result of the COVID-19 cases. We have used the neutrosophic similarity measure theory and the distance function. We have used the C-programming for finding the result of the suspected patients.
ABSTRACT
The COVID-19 pandemic has taken more than 1.78 million of lives across the globe. Identifying the underlying evolutive patterns between different countries would help us single out the mutated paths and behavior of this virus. I devise an orthonormal basis which would serve as the features to relate the evolution of one country's cases and deaths to others another's via coefficients from the inner product. Then I rank the coefficients measured by the inner product via the featured frequencies. The distances between these ranked vectors are evaluated by Manhattan metric. Afterwards, I associate each country with its nearest neighbor which shares the evolutive pattern via the distance matrix. Our research shows such patterns is are not random at all, i.e., the underlying pattern could be contributed to by some factors. In the end, I perform the typical cosine similarity on the time-series data. The comparison shows our mechanism differs from the typical one, but is also related to each it in some way. These findings reveal the underlying interaction between countries with respect to cases and deaths of COVID-19.