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Modeling and Innovative Research Frameworks

Abstract

Developing new technologies pose challenges for modern organizations, including service organizations. The main goal of the article was to present the results of exploration of the passenger car repair process using the Celonis Snap program in the example of an authorized service station. Realization of the main goal was assigned to a partial goal, which was to approximate and present the existing state of knowledge regarding process mining. The following research methods were used in the research procedure: systematic review of the literature on the subject and participant observation. Process mining was performed using the Celonis Snap program. The first section of the article, which is of epistemological character, presents the assumptions of process mining. The next one characterizes the empirical structure of the organization under study and the data generated in it. In the following sections, the course of the exploration process of the repair process, results and directions of further research are presented.

Abstract

Freezing of gait (FOG) consistently reoccurs in the later phases of a patient suffering from Parkinson’s disease (PD). Although it is treated with pharmacological treatment, the impact of the medication fades with increasing duration of the disease and thus diminishing the mobility of a patient. This chapter aims at developing a neural network-based classification model that helps to detect FOG episodes in a patient at early stages so that lethal mishaps can be avoided. In this application example, we build user-independent FOG recognition system that would work along in conjunction with nonpharmacological medications. The structured system of developing a neural network-based classification model can be organized into three different stages. The process starts with extraction of suitable features from the dataset. In the subsequent stage, patients are additionally grouped into two clusters depending on the FOG episodes. In the final stage, two neural network models are developed using feedforward network on the two clusters that were formed. The accuracy of the model is computed using sensitivity and specificity.

Abstract

The present study proposes to determine the fuzzy reliability of systems using different types of conflicting bifuzzy numbers Till now, to evaluate the fuzzy reliability used the failure rates of components of systems follow the same type of fuzzy set or conflicting bifuzzy set. However, in real-life problems, such type of situation rarely occurs. Therefore, in this study, a new method has been introduced to determine the fuzzy reliability of a system having components following different types of conflicting bifuzzy failure rates. Using the introduced method, membership functions and nonmembership functions of fuzzy reliability of a series, parallel, parallel-series, and series-parallel systems are evaluated. Numerical problems are also taken to describe the proposed study.

Abstract

De novo programming, which evaluates the concept of optimality from a different point of view, makes it possible to rearrange the number of resources for constraint functions depending on the budget constraint. While the resource amounts of the constraints are used at full capacity, this arrangement improves the performance level of the objective functions. With the development of fuzzy set theory, mathematical programming models can be examined in a fuzzy environment as in other scientific fields. Mathematical programming models are handled in different ways as fuzzy decision variable, fuzzy goal, and fuzzy parameter. The fuzzy goal and fuzzy parameter models are used in the literature for de novo programming, also known as optimal system design. In this study, a new fuzzy de novo programming approach was proposed by using positive and negative ideal solutions. In the proposed approach, using fuzzy unit prices of resources and fuzzy resource amounts of constraints, the fuzzy budget was created. The solution steps of the developed approach are given step by step on an illustrative example accepted in the literature.

Abstract

This chapter develops a computational algorithm to solve a stochastic two-level programming in a noncooperative game using a fuzzy optimization technique. It also includes all parameters such as fuzzy variables except the constraints on the right side that follow an extreme value distribution. Mellin transformation is considered to convert the fuzzy numbers into the crisp value. Based on stochastic programming, probabilistic constraints are converted into the deterministic form. Furthermore, as the fuzzy programming is used for the degree of acceptance, we present a comparative study of nonlinear and linear membership functions for the acceptance corresponding to LINGO 15.0 iterative scheme and genetic algorithm to see its impact on optimization. A numerical experiment is considered in order to present the applicability and feasibility of the proposed algorithm. Finally, conclusion about the findings and outlook are presented.

Abstract

In this chapter, we introduce the concept of intuitionistic fuzzy sets (IFS). Similarity and distance measures between IFS are explained and also extended these measures to IFSs. Some new trigonometric similarity and distance measures of IFSs are studied, and it is shown that intuitionistic fuzzy distance measures satisfy the required identities of intuitionistic fuzzy similarity measures. Also, basic properties of trigonometric similarity and distance measures of IFS (IFs) are described.

Abstract

This chapter incorporates the different perspective of modeling of activation energies through ingression of an additional parameter of distribution function to increase the flexibility and controlling ability of modeling by the linear mixing. The transmuted functions are adopted to bring flexibility in the numerical solution of multireaction models. A stochastic model is used to represent the activation energy function. Thus, the different activation energies can be represented by a density function to describe the pyrolysis process. The qualitative, as well as quantitative effects of the transmuted family of distribution on biomass pyrolysis, are carefully examined by the proposed mathematical solution. The nonlinear thermal history is implemented to demonstrate the approach.

Abstract

Air pollution is a fundamental problem among the critical challenges of modern society. Acute as well as long-term exposure may pose serious health problems. Real-time air quality predictions are necessary to forecast the pollutant concentrations. This information can be used to issue early air quality warnings that allow government and people to take preventive measures. The authors offer application of soft computing techniques of artificial neural networks (ANN) and genetic programming (GP) for air quality parameter forecasting, a few time steps in advance. Seasonal forecasting models are also useful for proactive measures. Availability of continuous field data sometimes is a challenge. It is time-consuming and expensive. Modeling options are suggested for situations when there are constraints about data availability and the urgent need to understand and broadcast the air quality. A case study of Pune, a metropolitan city in India, demonstrating the strengths of GP is presented. We have evaluated the models by a correlation coefficient, d-statistics, root mean squared error, and mean bias error.