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Artificial neural networks (ANNs), which represent computational models simulating the biological neural systems, have become a dominant paradigm for solving complex analytical problems. ANN applications have been employed in various disciplines such as psychology, computer science, mathematics, engineering, medicine, manufacturing, and business studies. Academic research on ANNs is witnessing considerable publication activity, and there exists a need to track the intellectual structure of the existing research for a better comprehension of the domain. The current study uses a bibliometric approach to ANN business literature extracted from the Web of Science database. The study also performs a chronological review using science mapping and examines the evolution trajectory to determine research areas relevant to future research. The authors suggest that researchers focus on ANN deep learning models as the bibliometric results predict an expeditious growth of the research topic in the upcoming years. The findings reveal that business research on ANNs is flourishing and suggest further work on domains, such as back-propagation neural networks, support vector machines, and predictive modeling. By providing a systematic and dynamic understanding of ANN business research, the current study enhances the readers' understanding of existing reviews and complements the domain knowledge.

Natural flood disasters frequently happen in Malaysia especially during monsoon season and Kuala Kangsar, Perak, is one of the cities with the frequent record of natural flood disasters. Previous flood disaster faced by this city showed the failure in notifying the citizen with sufficient time for preparation and evacuation. The authority in charge of the flood disaster in Kuala Kangsar depends on the real-time monitoring from the hydrological sensor located at several stations along the main river. The real-time information from hydrological sensor failed to provide early notification and warning to the public. Although many hydrological sensors are available at the stations, only water level sensors and rainfall sensors are used by authority for flood monitoring. This study developed a flood prediction model using artificial intelligence to predict the incoming flood in Kuala Kangsar area based on artificial neural network (ANN). The flood prediction model is expected to predict the incoming flood disaster by using information from the variety of hydrological sensors. The study finds that the proposed ANN model based on nonlinear autoregressive network with exogenous inputs (NARX) has better performance than other models with the correlation coefficient that is equal to 0.98930. The NARX model of flood prediction developed in this study can be referred to as the future flood prediction model in Kuala Kangsar, Perak.

Value at risk (VaR) and expected shortfall (ES) are popular market risk measurements. The former is not coherent but robust, whereas the latter is coherent but less interpretable, only conditionally backtestable and less robust. In this chapter, we compare an innovative artificial neural network (ANN) model with a time series model in the context of forecasting VaR and ES of the univariate time series of four asset classes: US large capitalization equity index, European large cap equity index, US bond index, and US dollar versus euro exchange rate price index for the period of January 4, 1999, to December 31, 2018. In general, the ANN model has more favorable backtesting results as compared to the autoregressive moving average, generalized autoregressive conditional heteroscedasticity (ARMA-GARCH) time series model. In terms of forecasting accuracy, the ANN model has much fewer in-sample and out-of-sample exceptions than those of the ARMA-GARCH model.

The consideration of alternative sources of material for construction is imperative to reduce the environmental impacts as two-fifths of the carbon footprint of materials is attributed to the construction industry. One alternative material with improved biodegradable attributes which can contribute to carbon offset is bamboo. The commercialisation of bamboo in modern infrastructures has significant potential to address few of the Sustainable Development Goals (SDGs) itemised by the United Nations, namely SDG 9 about industry, innovation and infrastructure. Other SDGs covering sustainable cities and communities, responsible consumption and production and climate action are also indirectly addressed when utilising sustainable construction materials. Being a natural material however, the full commercialisation of materials such as bamboo is constrained by a lack of durability. Besides fracture mechanisms arising from load-induced cracks and thermal modification, the durability of bamboo material is greatly impaired by biotic and abiotic factors, which equally affect its natural rate of degradation, hence fracture behaviour. In first instance, this chapter outlines the various factors leading to the durability limitations in bamboo material due to load-induced cracks and natural degradation based on recent findings in this field from the author's own work and from past literature. Secondly, part of this chapter is devoted to a new approach of processing the surge of information about the varied aspects of bamboo durability by considering the powerful technique of artificial intelligence (AI), specifically the artificial neural network (ANN) for prediction modelling. Further use of AI-enabled technologies could have an impactful outcome on the life cycle assessment of bamboo-based structures to address the growing challenges outlined by the United Nations.

The paper considers the application of neural networks to model driver decisions to change lane on a dual carriageway road. The lane changing process is treated as consisting of two decisions, namely motivation and opportunity. Separate backpropagation neural networks are applied to represent each of the two decisions. The trained motivation and opportunity neural network models are linked to produce a layered network which represents the complete lane changing process. Separate models are developed to represent the nearside to offside lane changing decision, and the offside to nearside lane changing decision. This paper describes the development of the model of the nearside to offside lane changing decision.

For model development, data were collected from several subject vehicle drivers. The results are presented and the implications considered. Selected data were applied to train the neural networks and then an independent subset of data were used to assess performance. When the complete nearside lane changing neural network model was presented with the unseen test examples, 93.3% of the examples were correctly predicted as a lane change or no lane change. These results are shown to be a considerable improvement on those obtained previously.

In the last few decades, there has been growing interest in forecasting with computer intelligence, and both fuzzy time series (FTS) and artificial neural networks (ANNs) have gained particular popularity, among others. Rather than the conventional methods (e.g., econometrics), FTS and ANN are usually thought to be immune to fundamental concepts such as stationarity, theoretical causality, post-sample control, among others. On the other hand, a number of studies significantly indicated that these fundamental controls are required in terms of the theory of forecasting, and even application of such essential procedures substantially improves the forecasting accuracy. The aim of this paper is to fill the existing gap on modeling and forecasting in the FTS and ANN methods and figure out the fundamental concepts in a comprehensive work through merits and common failures in the literature. In addition to these merits, this paper may also be a guideline for eliminating unethical empirical settings in the forecasting studies.

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.

Introduction: The insurance industry is one of the lucrative sectors of the economy. However, it is volatile because of the large chunk of data generated by the transactions taking place daily. However, every bit of it is responsible for creating market trends for stock investors to predict the returns. The specialised data mining techniques act as a solution for decision-making, reducing uncertainty in decision-making.

Purpose: There are limited studies that have examined the efficiency and effectiveness of data mining techniques across the companies in the insurance industry to date. To enable the companies to take exact benefit of data mining techniques in insurance, the present study will focus on investigating the efficiency of artificial neural network (ANN) and support vector machine SVM across insurance companies of CNX 500.

Method: For predictive models, various technical indicators were considered independent variables, and change in return, i.e. increase and decrease, was deemed a dependent variable. The indicators were transformed from daily raw data of insurance company’s stock values spanning four years. We formed 90 data sets of varied periods for building the model – specifically six months, one year, two years, and four years for selected six insurance companies.

Findings: The study’s findings revealed that ANN performed best for the ICICIPRULI data model in terms of hit ratio. Whereas the performance of SVM was observed to be the best for the ICICIGI data model. In the case of pairwise comparison among the six selected Indian insurance companies from CNX 500, the extracted data evaluated and concluded that there were eight significantly different pairs based on hit ratio in the case of ANN models and nine significantly different pairs based on hit ratio for SVM models.

This chapter offers an overview of the applications of artificial intelligence (AI) in the textile industry and in particular, the textile colouration and finishing industry. The advent of new technologies such as AI and the Internet of Things (IoT) has changed many businesses and one area AI is seeing growth in is the textile industry. It is estimated that the AI software market shall reach a new high of over US$60 billion by 2022, and the largest increase is projected to be in the area of machine learning (ML). This is the area of AI where machines process and analyse vast amount of data they collect to perform tasks and processes. In the textile manufacturing industry, AI is applied to various areas such as colour matching, colour recipe formulation, pattern recognition, garment manufacture, process optimisation, quality control and supply chain management for enhanced productivity, product quality and competitiveness, reduced environmental impact and overall improved customer experience. The importance and success of AI is set to grow as ML algorithms become more sophisticated and smarter, and computing power increases.