Fuzzy sets and their variants
Sapan Kumar Das; Indrani Maiti; RAJEEV PRASAD; Surapati Pramanik; Tarni Mandal
Abstract
The prediction of a real-life problem like in industrial sector or health sector the outcome is impossible or sometimes it is difficult. Due to high information uncertainty and complicated influencing factors of industrial sector, the traditional data-driven prediction approaches can hardly reflect the ...
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The prediction of a real-life problem like in industrial sector or health sector the outcome is impossible or sometimes it is difficult. Due to high information uncertainty and complicated influencing factors of industrial sector, the traditional data-driven prediction approaches can hardly reflect the real changes in practical situation. Fuzzy programming is a powerful prediction reasoning and risk assessment model for uncertain environment. This article mainly explores and applies a modified form of fuzzy programming; namely Fuzzy Linear Fractional Programming Problem (FLFPP) having the coefficients of the objectives and constraints as triangular fuzzy numbers (TFNs). The FLFPP is converted into an equivalent crisp multi-objective linear fractional programming problem (MOLFPP) and solved individually to associate an aspiration level to it. Then by applying fuzzy goal programming (FGP) technique the maximum degree of each membership goal is obtained by minimizing the negative deviational variables. We carry out two industrial application simulations in a hypothetical industrial scenario. Our study shows that the proposed model is practical and applicable to the uncertain practical environment to realize the prediction and the obtained results are compared with that of the existing methods.
Hesitant fuzzy sets and their variants
Lubna Shafi; Shilpi Jain; Praveen Agarwal; Pervaiz Iqbal; Aadil Rashid Sheergojri
Abstract
Fuzzy time series forecasting is an approach for dealing with uncertainty in time series data that uses fuzzy logic. The hesitant fuzzy set theory emphasizes the chances of capturing fuzziness and uncertainty due to randomness better than the classic fuzzy set theory. This study aims to improve the previously ...
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Fuzzy time series forecasting is an approach for dealing with uncertainty in time series data that uses fuzzy logic. The hesitant fuzzy set theory emphasizes the chances of capturing fuzziness and uncertainty due to randomness better than the classic fuzzy set theory. This study aims to improve the previously identified hesitant fuzzy time series forecasting models by including various degrees of hesitation to improve forecasting performance. The goal is to deal with the issue of identifying a common membership grade when several fuzzification methods are available to fuzzify time series data.The proposed method utilizes trapezoidal and bell-shaped fuzzy membership functions for constructing hesitant fuzzy sets.Ahesitant fuzzy weighted averaging operator is then applied to the hesitant fuzzy elements to create fuzzy logical relations.The suggested technique is employed to forecast enrollment in the University of Alabama and cancer incidence rates in India. The efficiency of the proposed forecasting approach is determined by rigorously comparing it to various computational fuzzy time series forecasting methods in terms of error measurements like root mean square error, average forecasting error, and mean absolute deviation. The validity of the proposed forecasting model is verified by using correlation coefficients, coefficients of determination, tracking signals, and performance parameters. The significance of improved accuracy in forecasted results is confirmed as well using the two-tailed t-test. The results of the study revealed that the enhanced hesitant fuzzy time series model is more effective and accurate in forecasting the university enrolment of Alabama and the cancer incidence rates of India.
Intuitionistic fuzzy sets and their variants
Poonam Kumar Sharma
Abstract
The investigation of mathematics underlines accuracy, precision, and flawlessness, yet in numerous genuine circumstances, individuals face equivocalness, ambiguity, imprecision, and so forth. Intuitionistic fuzzy set theory, rough set theory, and soft set theory are three noble techniques in mathematics ...
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The investigation of mathematics underlines accuracy, precision, and flawlessness, yet in numerous genuine circumstances, individuals face equivocalness, ambiguity, imprecision, and so forth. Intuitionistic fuzzy set theory, rough set theory, and soft set theory are three noble techniques in mathematics that are utilized for decision-making in vague and uncertain information systems. Intuitionistic fuzzy algebra-based math plays a huge part in the current era of mathematical research, and it deals with the algebraic concepts and models of intuitionistic fuzzy sets. The investigation of different ordered algebraic structures, like lattice-ordered groups, Riesz spaces, etc., is of great importance in algebra. The theory of lattice-ordered G-modules is very useful in the study of lattice-ordered groups and similar algebraic structures. In this article, the theories of intuitionistic fuzzy sets and lattice-ordered G-modules are synchronised in a reasonable way to develop a novel concept in mathematics, i.e., intuitionistic fuzzy lattice-ordered G-modules, which would pave the way for new researchers in intuitionistic fuzzy mathematics to explore much more in this field.
Artificial Intelligence with uncertainty
Lincy Jacquline M; Sudha N
Abstract
Problem Statement: Chronic nephritic sickness is another name for chronic kidney disease (CKD). Numerous complications, such as elevated blood levels, anemia, weak bones, and nerve damage, constitute a problem. It is usually possible to prevent chronic uropathy from getting worse by early identification ...
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Problem Statement: Chronic nephritic sickness is another name for chronic kidney disease (CKD). Numerous complications, such as elevated blood levels, anemia, weak bones, and nerve damage, constitute a problem. It is usually possible to prevent chronic uropathy from getting worse by early identification and treatment. Methodology: To circumvent these problems, current research has presented the fruit fly optimization algorithm (FFOA) and effective multi-kernel support vector machine (MKSVM) for illness classification. Finding best features from a collection is usually done using FFOA. Main findings/Contributions: MKSVM categorizes medical data using chosen dataset criteria. The accuracy of classifier will be impacted by any range variations in data obtained for this study. MKSVM continues to yield more incorrectly classified findings. To resolve those problems introduces a pre-processing step based on min max normalization to normalize scale of input CKD data values. Then significant features will be selected utilizing Improved FFOA (IFFOA). The selected features will be clustered using Weighted Fuzzy C means clustering (WFCM) to predict the class label of the data sample to reduce the misclassification results. Finally, CKD classification will be performed using the Enhanced Adaptive Neuro Fuzzy Inference System (EANFIS) as normal or abnormal. Conclusions: The suggested strategy efficacy is demonstrated by findings in fields of recall, accuracy, precision, and f-measure.
Artificial Intelligence with uncertainty
Yinghao Li; Jawis M N
Abstract
In an effort to fulfil the requirements of China's quality education policy, several Chinese institutions and colleges have recently included badminton as an optional sport. Examining current issues in the field, the paper argues that badminton instruction in Chinese higher education needs improvement. ...
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In an effort to fulfil the requirements of China's quality education policy, several Chinese institutions and colleges have recently included badminton as an optional sport. Examining current issues in the field, the paper argues that badminton instruction in Chinese higher education needs improvement. It then proposes new approaches to teaching the sport in the classroom, including ideas for lesson plans, instructional strategies, and pedagogical techniques. Concerning badminton education in higher education, it outlines a strategy to address the issues. In order to overcome the obstacles of fuzzy assisted virtual reality badminton instruction, teachers should think about their own lesson planning and delivery processes, as well as their Badminton Students’ perceptions of physical education programmers. To teach badminton in universities, the article proposes VR-ITM, or fuzzy assisted virtual reality assisted interactive teaching techniques using neural network. This study aims to examine the advantages and disadvantages of utilizing fuzzy assisted virtual reality (VR) to teach badminton in PE classes, as well as the difficulties and solutions that teachers have found for these issues through the use of a neural network. This study investigates the potential benefits of incorporating virtual reality technology into the physical education curriculum and uses VR-ITM, which stands for virtual reality based interactive teaching methods, to teach badminton at college locations. Incorporating badminton into university curricula as a means of encouraging students to lead healthier lifestyles is the primary focus of this study. In addition to fostering Badminton Students’ professional skills, universities should emphasise the importance of Badminton Students’ physical well-being in comparison to the conventional approaches used by the control group to teach badminton, the neural network-based intelligent teaching system performs better.
