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Due to the increasing size and complexity of construction projects, construction engineering and management involves the coordination of many complex and dynamic processes and relies on the analysis of uncertain, imprecise and incomplete information, including subjective and linguistically expressed information. Various modelling and computing techniques have been used by construction researchers and applied to practical construction problems in order to overcome these challenges, including fuzzy hybrid techniques. Fuzzy hybrid techniques combine the human-like reasoning capabilities of fuzzy logic with the capabilities of other techniques, such as optimization, machine learning, multi-criteria decision-making (MCDM) and simulation, to capitalise on their strengths and overcome their limitations. Based on a review of construction literature, this chapter identifies the most common types of fuzzy hybrid techniques applied to construction problems and reviews selected papers in each category of fuzzy hybrid technique to illustrate their capabilities for addressing construction challenges. Finally, this chapter discusses areas for future development of fuzzy hybrid techniques that will increase their capabilities for solving construction-related problems. The contributions of this chapter are threefold: (1) the limitations of some standard techniques for solving construction problems are discussed, as are the ways that fuzzy methods have been hybridized with these techniques in order to address their limitations; (2) a review of existing applications of fuzzy hybrid techniques in construction is provided in order to illustrate the capabilities of these techniques for solving a variety of construction problems and (3) potential improvements in each category of fuzzy hybrid technique in construction are provided, as areas for future research.

Several different simulation techniques, such as discrete event simulation (DES), system dynamics (SD) and agent-based modelling (ABM), have been used to model complex construction systems such as construction processes and project management practices; however, these techniques do not take into account the subjective uncertainties that exist in many construction systems. Integrating fuzzy logic with simulation techniques enhances the capabilities of those simulation techniques, and the resultant fuzzy simulation models are then capable of handling subjective uncertainties in complex construction systems. The objectives of this chapter are to show how to integrate fuzzy logic and simulation techniques in construction modelling and to provide methodologies for the development of fuzzy simulation models in construction. In this chapter, an overview of simulation techniques that are used in construction is presented. Next, the advancements that have been made by integrating fuzzy logic and simulation techniques are introduced. Methodologies for developing fuzzy simulation models are then proposed. Finally, the process of selecting a suitable simulation technique for each particular aspect of construction modelling is discussed.

In today's advanced economy, there is a broader presence of information revolution, such as artificial intelligence (AI). AI primarily drives modern banking, leading to innovative banking channels, services and solutions disruptions. Thus, this chapter intends to determine AI's place in contemporary banking and stock market trading.

Stock market forecasting is hampered by the inherently noisy environments and significant volatility surrounding market trends. There needs to be more research on the mantle of AI in revolutionising banking and stock market trading. Attempting to bridge this gap, the present research study looks at the function of AI in banking and stock market trading.

The researchers have synthesised the literature pool. They undertook a systematic review and meta-synthesis method by identifying the major themes and a systematic literature review aided in the critical analysis, synthesis and mapping of the body of existing material.

The study's conclusions demonstrated the efficacy of AI, which has played a robust role in banking and finance by reducing risk and operational costs, enabling better customer experience, improving regulatory complaints and fraud detection and improving credit and loan decisions. AI has revolutionised stock market trading by forecasting future prices or trends in financial assets, optimising financial portfolios and analysing news or social media comments on the assets or firms.

AI's debut in banking and finance has brought sea changes in banking and stock market trading. AI in the banking industry and capital market can provide timely and apt information to its customers and customise the products as per their requirements.