Human Health Risk Assessment under Uncertainty Via Possibilistic Approaches | Book Publisher International

Humans are significantly impacted by environmental pollutants that come from many artificial or man-made sources. The largest percentage of the radiation exposure to humans comes from the natural environment. Another cause of environmental contamination is the development of industry. Human exposure mostly occurs through air inhalation, water intake, food consumption, etc.

Therefore, it is necessary to conduct an assessment to ascertain the degree to which hazardous compounds' discharge into the environment would have an adverse effect on human health. In order to quantify the level of risk posed to people by environmentally dangerous chemicals, health risk assessments are performed. The Environmental Protection Agency, USA's health risk assessment models (EPA-USA, 2010) are used for this purpose. Models for assessing health risk depend on variables that are often of an unclear nature. To cope with the uncertain model parameters, a variety of fuzzy number types are taken into consideration. Additionally, a variety of arithmetic techniques are used to combine the fuzzy input values. Risk analyses for human health will now be carried out.

The Dempster-Shafer theory of evidence (DST) is next examined, and for that purpose, we first provide three methods for determining BPA when just the parameter's lowest, maximum, and most probable values are known. The arithmetic combination of interval focal elements is described in a similar manner. We talk about fuzzy focal elements and explain how to combine fuzzy focal elements using Dempster's rule. For fuzzy focal elements, we expanded the concept of the arithmetic combination of interval focal elements. The proposed arithmetic combination of focal elements (PACF) and Yager arithmetic combination of focal elements (YACF) are used to estimate risk for interval focal elements, and results are examined. Finally, fuzzy focus elements are used to do risk evaluation.

Few parameters in health risk assessment models are represented probabilistically, with mean and standard deviation (variance) provided as interval or fuzzy values. Some additional factors, however, are thought to be possibilistic. Therefore, the hybrid framework is also used for risk assessment.

Additionally, risk evaluation is carried out employing Probability-Possibility transformations. Similarly, utilising three separate situations and a probability-possibility transformation that satisfies consistency requirements, we have offered a comparative case study of the propagation of uncertainty.

Author(s) Details:

Palash Dutta,

Department of Mathematics, Dibrugarh University, Dibrugarh: Assam, India.

View Book: https://stm.bookpi.org/HHRAUVPA/article/view/7405

Determination of Environmental Humidity and Temperature Prediction in Agriculture Using Mamdani Inference Systems| Chapter 1 | Innovations in Science and Technology Vol. 6

The findings of a humidity and temperature prediction model in the environment for agriculture, which utilised diffuse sets and optimised their parameters using heuristic approaches such as genetic algorithms and exact methods such as Quasi-Newton, are presented in this chapter. It has been discovered that non-specialized users may have difficulty comprehending system dynamics and varied behaviour over time. The purpose of this research is to create models for predicting temperature and humidity values in the environment that are both interpretable and accurate. Because fuzzy logic has a high interpretability rating, using it to present a solution has various advantages. Non-specialized users can have a better grasp of the system dynamics by categorising the obtained values for environment humidity and temperature as high, medium, or low. The humidity and temperature are forecasted using two optimization algorithms used to two separate diffuse sets. Mamdani fuzzy inference systems have been effectively employed for crop prediction, such as crop recommendation based on temperature, humidity, and rain or weather level, using microcontroller-based devices. A Mamdani fuzzy inference system optimised with the Quasi-Newton algorithm, which uses a set of initial values generated during a previous optimization phase using a genetic algorithm, is the best implementation.

Author(s) Details:

Julio Baron Velandia,
Faculty of Engineering, Universidad Distrital “Francisco José de Cáldas”, Colombia.


Jonathan Steven Capera Quintana,
Faculty of Engineering, Universidad Distrital “Francisco José de Cáldas”, Colombia.


Sebastian Camilo Vanegas Ayala,
Faculty of Engineering, Universidad Distrital “Francisco José de Cáldas”, Colombia.

Please see the link here: https://stm.bookpi.org/IST-V6/article/view/6030

Theory and Application of Average fs-Aggregate Algorithm | Chapter 8 | Theory and Practice of Mathematics and Computer Science Vol. 10

 In our daily lives, we frequently encounter situations in which determining the best decision is challenging. Wherever there is complication and uncertainty, such as in economics, social science, medical science, and the environment. In order to solve such situations, traditional mathematical methods are ineffective. To answer this problem, we must first identify the many parameters that are relevant to the problem and then apply them to obtain an accurate solution. As a result, the greatest mathematical instrument for handling such issues utilising various strategies is fuzzy soft set theory. We define fuzzy soft sets in this chapter and use the average fuzzy soft set aggregation approach to tackle decision-making problems. We used a real-life multi-criteria problem to find a suitable city that meets the best conditions given the inputs. We used the average fs-aggregation approach for multi-criteria parameters to arrive at the answer.

Author(s) Details

Rajesh Kumar Pal
Department of Mathematics, DAV (PG) College, Dehradun (U.K.), Pin-248001, India.

View Book :- https://stm.bookpi.org/TPMCS-V10/article/view/1078

On Applications of Fuzzy Soft and Intuitionistic Fuzzy Soft Sets in Dimension Reduction and Medical Diagnosis | Chapter 7 | New Ideas Concerning Science and Technology Vol. 8

 In our everyday lives, we are often confronted with a variety of issues relating to data with a high dimensionality. Irrelevant and superfluous data, as well as valuable data, are present in such problems. As a result, dimensionality reduction has a broad range of applications in data processing and analysis. In recent years, the problem of dimensionality reduction in a fuzzy situation has gotten a lot of attention from researchers. They have developed different methods and hypotheses to solve these types of problems. Some of these approaches are based on a probabilistic approach, while others are not. Fuzzy soft set theory is gaining popularity for seeking coherent and logical solutions to a variety of real-world problems involving complexity, imprecision, and vagueness. A theoretical analysis of intuitionistic fuzzy soft has been developed recently. When ambiguity due to vagueness is more complicated, the combination of intuitionistic fuzzy set and intuitionistic fuzzy soft set is more useful from an application standpoint. The definition of fuzzy soft set is described in this chapter as a hybridization of fuzzy set and soft set theory. A new technique for converting a soft set table into a fuzzy soft set table is proposed, which can be used in big data dimension reduction. As a hybridization of fuzzy set and soft set theory, fuzzy soft set and intuitionistic fuzzy soft set are described. Following Sanchez's approach, new methods of applying fuzzy soft and intuitionistic soft sets are also defined in Medical Diagnosis.

Author (s) Details

Prof. D. S. Hooda
Former PVC, Kurukshetra University, Department of Mathematics, G. J. University of Science and Technology, Hisar- 125001, India.

View Book :- https://stm.bookpi.org/NICST-V8/issue/view/45