Uncertainty is the difference between actual information and obtained information which can exists in two different forms: one is randomness, and another is impreciseness. The fuzzy soft sets are widely used in a variety of applications in the fields of medical science, automation industry, engineering, economics, share market, social science, game theory, operational research, and so on. Chemical engineering, electrical engineering, mechanical engineering, computer science engineering, and telecommunication engineering are prone to a variety of uncertainty in terms of system uncertainty and parameter uncertainty. Hence, with the application of fuzzy soft set, the uncertainty in the mentioned engineering types can be handled very well. In this chapter, fuzzy soft set is applied for handling the uncertainty in the mentioned engineering domains. The performance achieved by the fuzzy soft set-enabled computer science and engineering and biotechnology is found to be good towards the performance metrics like response time, throughput, resource utilization rate, and error rate.
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The works carried out in literature for handling the uncertainties in engineering domains is discussed and following gaps are identified.
The uncertainty in computer science computing domain is addressed by making use of conventional fuzzy logic technique or probability theory (Tchernykh, Schwiegelsohn, Alexandrov, and Talbi 2015). But the conventional fuzzy logic works by forming rules that are static in nature which handles the uncertainty only up to some degree of uncertainty. The time consumed to formulate the knowledge database by forming the rules is too high.
Chemical engineering uncertainty is handled using finite discrete models, stochastic d design, transformation methods, and cubature rules (Mc Cabe, Smith, and Harriott, 2018). The degree up to which uncertainty is measured and handled is acute as the approaches followed are deterministic in nature and fails to solve the large state problems with high accuracy and speed of operation.
Mechanical engineering is prone several forms of uncertainty in terms of design, components, safety, operation, and maintenance (Vu, and Le 2019). The approaches followed to handle uncertainty in mechanical engineering are quasi Monte Carlo, intrinsic variables, nonlinear dynamic variables, linguistic variables and many more. But these approaches have inherent drawbacks in terms of poor degree of approximation, black box approach, poor knowledge formation, high probability of mechanical failures and so on.
The uncertainty in electrical engineering arises from several factors which include operator measurement errors, poor structure modelling, optimization process is slow, too much of power consumption and so on (Xu, Zhang, Jiang, Wang, Chen, Hu, and Chu 2019). The existing methods for handling uncertainty are approximate reasoning, contingency ranking, fuzzy logic, multi valued probability theory, and so on. The drawbacks observed are does not consider hidden uncertainty causing parameters, poor structuring of data, lesser accuracy is achieved in reasoning, accuracy gets affected due to distortion, and so on.