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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.

This study aims to investigate the dynamic influences of clustered barriers that hinder electronic procurement technology (EPT) implementation in construction procurement, using the neuro-fuzzy system.

A comprehensive literature review was conducted and 21 barriers to EPT implementation within construction projects were identified. Based on an expert survey, 121 datasets were gathered for this study. Using mean and normalization analysis for the datasets, 15 out of the 21 barriers were deemed to have critical influences in EPT barriers phenomenon. Subsequently, the critical barriers were classified into five groups: human-related; technological risk-related; government-related; industry growth-related; and financial-related. The relationships and influence patterns between the groups of barriers to EPT implementation were analyzed using the neuro-fuzzy system. Furthermore, sensitivity analysis was performed to examine the dynamic influence levels of the barriers within the hindrance level composition.

The results reveal that addressing one barrier group does not reduce the high levels of hindrances experienced in EPT implementation. However, addressing at least two barrier groups mostly tends to reduce the hindrance levels for EPT implementation. Further, this study revealed that addressing some barrier group pairings, such as technological risk-related and government-related barriers, while other barrier groups remained at a high level, still resulted in high levels of hindrances to EPT implementation in construction procurement.

This study provides insights for researchers to help them contribute to the development of theory with contemporary approaches based on the influence patterns of barrier interrelationships.

This study provides a model that would help practitioners and decision makers in construction procurement to understand and effectively determine the complex and dynamic influences of barrier groups to EPT uptake, for the development of suitable mitigation strategies.

This study provides novel insights into the complex influence patterns among grouped barriers concerning EPT adoption in the construction industry. Researchers and practitioners are equipped with knowledge on the influence patterns of barriers. This knowledge aids the development of effective strategies that mitigate the combined groups of barriers, and promote the wider implementation of EPT in the construction industry.

Data-driven marketing is replacing conventional marketing strategies. The modern marketing strategy is based on insights derived from customer behavior information gathered from their facial expressions and neuro-signals. This study explores the potential for face recognition and neuro-marketing in modern-day marketing.

The study conducts an in-depth examination of the extant literature on neuro-marketing and facial recognition marketing. The articles for review are downloaded from the Scopus database, and PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) is then used to screen and choose the relevant papers. The systematic literature review method is applied to conduct the study.

An extensive review of the literature reveals that the domains of neuro-marketing and face recognition marketing remain understudied. The authors’ review of selected papers delivers five neuro-marketing and facial recognition marketing themes that are essential to modern marketing concepts.

Neuro-marketing and facial recognition marketing are artificial intelligence (AI)-enabled marketing techniques that assist in gaining cognitive insights into human behavior. The findings would be of use to managers in designing marketing strategies to enhance their marketing approach and boost conversion rates.

The uniqueness of this study lies in that it provides an updated review on neuro-marketing and face recognition marketing.

The purpose of the paper is to find a simple structure of speed controller robust against drive parameter variations. Application of neuro‐fuzzy technique in the controller of PI type creates proper nonlinear characteristics, which ensures controller robustness.

The robustness of the controller is based on its nonlinear characteristic introduced by neuro‐fuzzy technique. The paper proposes a novel approach to neural controller synthesis to be performed in two stages. The first stage consists in training the neuro‐fuzzy system to form the proper shape of the control surface, which represents the nonlinear characteristic of the controller. At the second stage, the PI controller settings are adjusted by means of the random weight change procedure, which optimises the control quality index formulated in the paper. The synthesis is performed using simulation techniques and subsequently the behavior of a laboratory speed control system is validated in the experimental setup. The control algorithms of the system are performed by a microprocessor floating point DSP control system.

The proposed controller structure with proper control surface created by the neuro‐fuzzy technique guarantees expected robustness.

The proposed controller was tested on a single machine under well defined conditions. Further investigations are required before any industrial applications can be made.

The proposed controller synthesis and its results may be very helpful in the robotic system where changing of system parameters is characteristic for many industrial robots and manipulators.

The original method of robust controller synthesis was proposed and validated by simulation and experimental investigations.

The accurate assessment of tool life of any given tool is a great significance in any manufacturing industry. The purpose of this paper is to predict the life of a cutting tool, in order to help decision making of the next scheduled replacement of tool and improve productivity.

This paper reports the use of two soft computing techniques, namely, neuro-fuzzy logic and support vector regression (SVR) techniques for the assessment of cutting tools. In this work, experiments are conducted based on Taguchi approach and tool life values are obtained.

The analysis is carried out using the two soft computing techniques. Tool life values are predicted using aforesaid techniques and these values are compared.

The proposed approaches are relatively simple and can be implemented easily by using software like MATLAB and Weka.

The proposed methodology compares neuro – fuzzy logic and SVR techniques.

This research, based on the idea of contingency fit, attempts to construct a strategic selection system to select the best fit competency of channel strategic selection by using neuro‐fuzzy technique.

Neuro‐fuzzy technique is used.

In addition to, providing a concrete bilateral solution to select the right and better fit competency of channel strategy and giving an alternative way to explain fit, this strategic selection system can also be used as a competency diagnosis system for channel members in the supply chain organization, providing clues for further channel performance promotion and channel modernization.

Through the literature and construction of strategy channel satisfaction model (SCSM) concepts and ideas, an empirical study of the Asian Commerce Community meet distribution channel relationships will pose an Asian developed channel modernization perspective of SCSM to discuss the critical marketing strategy selection for the traditional orient “commercial channel”.

The paper aims to advance methodologies to optimize fuzzy logic controller parameters via neural network and use the neuro‐fuzzy scheme to control two cooperating robots.

The paper presents a special neural network architecture that can be converted to fuzzy logic controller. Concepts of model predictive control (MPC) have been used to generate optimal signal to be used to train the neural network via backpropagation. Subsequently, a trained neural network is used to obtain fuzzy logic controller parameters.

The proposed neuro‐fuzzy scheme is able to precisely learn the control relation between input‐output training data generated by the learning algorithm. From the experiments performed on the industrial grade robots at Robotics and Manufacturing Automation (RAMA) Laboratory, it was found that the neuro‐fuzzy controller was able to learn fuzzy logic rules and parameters accurately.

The backpropagation method, used in this research, is extremely dependent on initial choice of parameters, and offers no mechanism to restrict the parameters within specified range during training. Use of alternative learning mechanisms, such as reinforcement learning, needs to be investigated.

The neuro‐fuzzy scheme presented can be used to develop controller for plants for which it is difficult to obtain analytical model or sufficient information about input‐output heuristic relation is not available.

The paper presents the neural network architecture and introduces a learning mechanism to train this architecture online.

This paper aims to propose a novel computational framework called EvoPOL (EVOlving POLicies) to support governmental policy analysis in restricting recruitment of smokers. EvoPOL is a fuzzy inference‐based decision support system that uses an evolutionary algorithm (EA) to optimize the if‐then rules and its parameters. The performance of the proposed method is compared with a fuzzy inference method adapted using neural network learning technique (neuro‐fuzzy).

EA is a population‐based adaptive method, which may be used to solve optimization problems, based on the genetic processes of biological organisms. The Takagi‐Sugeno fuzzy decision support system was developed based on three sub‐systems: fuzzification, fuzzy knowledge base (if‐then rules) and defuzzification. The fine‐tuning of the fuzzy rule base and membership function parameters is achieved by using an EA.

The proposed EvoPOL technique is simple and efficient when compared to the neuro‐fuzzy approach. However, EvoPOL attracts extra computational cost due to the population‐based hierarchical search process. When compared to neuro‐fuzzy model the error values on the test sets have improved considerably. Hence, when policy makers require more accuracy EvoPOL seems to be a good solution.

When policy makers require more accuracy EvoPOL seems to be a good solution. For complicated decision support systems involving more input variables, EvoPOL would be an excellent candidate for framing if‐then rules with precise decision scores that could help the government representatives as to what extent to concentrate on available social regulation measures in restricting the recruitment of smokers.

As far as the treatment of most complex issues in the design is concerned, approaches based on classical artificial intelligence are inferior compared to the ones based on computational intelligence, particularly this involves dealing with vagueness, multi-objectivity and good amount of possible solutions. In practical applications, computational techniques have given best results and the research in this field is continuously growing. The purpose of this paper is to search for a general and effective intelligent tool for prediction of patient survival after surgery. The present study involves the construction of such intelligent computational models using different configurations, including data partitioning techniques that have been experimentally evaluated by applying them over realistic medical data set for the prediction of survival in pancreatic cancer patients.

On the basis of the experiments and research performed over the data belonging to various fields using different intelligent tools, the authors infer that combining or integrating the qualification aspects of fuzzy inference system and quantification aspects of artificial neural network can prove an efficient and better model for prediction. The authors have constructed three soft computing-based adaptive neuro-fuzzy inference system (ANFIS) models with different configurations and data partitioning techniques with an aim to search capable predictive tools that could deal with nonlinear and complex data. After evaluating the models over three shuffles of data (training set, test set and full set), the performances were compared in order to find the best design for prediction of patient survival after surgery. The construction and implementation of models have been performed using MATLAB simulator.

On applying the hybrid intelligent neuro-fuzzy models with different configurations, the authors were able to find its advantage in predicting the survival of patients with pancreatic cancer. Experimental results and comparison between the constructed models conclude that ANFIS with Fuzzy C-means (FCM) partitioning model provides better accuracy in predicting the class with lowest mean square error (MSE) value. Apart from MSE value, other evaluation measure values for FCM partitioning prove to be better than the rest of the models. Therefore, the results demonstrate that the model can be applied to other biomedicine and engineering fields dealing with different complex issues related to imprecision and uncertainty.

The originality of paper includes framework showing two-way flow for fuzzy system construction which is further used by the authors in designing the three simulation models with different configurations, including the partitioning methods for prediction of patient survival after surgery. Several experiments were carried out using different shuffles of data to validate the parameters of the model. The performances of the models were compared using various evaluation measures such as MSE.

The problem of cyclic variation has been an interesting area of research and has been investigated by many researchers. It is more severe in the case of two‐stroke engines compared with four‐stroke engines. One of the reasons for these cycle‐to‐cycle variations is the variations in the air‐fuel ratios of individual cycles and, if these values of individual cycle air‐fuel ratios are available by some means, they can be used for controlling the cyclic variations. The purpose of this paper is to find a technique to predict the air‐fuel ratio of the individual cycles and use the same for reducing cyclic variations.

In this work, a neuro‐fuzzy model was developed using MATLAB software to compute the air‐fuel ratio of the individual cycles based on the relationship between the air‐fuel ratio and the combustion parameters such as those indicating mean effective pressure (IMEP), crank angle occurrence of peak pressure, and angles of different percentages of heat releases. In‐cylinder pressure traces of 1,000 continuous cycles were measured using a Personal Computer (PC)‐based data acquisition system and an investigation was carried out. The readings were taken for two modes of operations, namely gasoline carburetion and electronic gasoline injection. The engine was loaded by an eddy current dynamometer. The air‐fuel ratio was varied from rich to lean by adjusting the fuel quantity in the carburetion mode and adjusting the pulse width (measure of quantity of fuel to be injected) in the injection mode, at constant throttle. The cyclic variation was identified by the variations in the peak pressures and IMEPs of the individual cycles. The stored data were given as input to the developed neuro‐fuzzy model and, using SIMULINK, the air‐fuel ratios of individual cycles were obtained. These predicted values are fed to the electronic control module (ECM) (meant for injecting the fuel) for refining the pulse width to get cyclic variations reduced.

Results show that cyclic variation increases when the mixture becomes lean. It was also found that cyclic variation in an injected engine was less in comparison with the carbureted engine, as the precise control of air‐fuel mixture was possible in the case of the injected engine.

The technique used in this work may be modified to give more precise pulse width by incorporating various other parameters like exhaust temperature, etc. Future research may be focused to incorporate this system in a moving vehicle to get more fuel efficiency and fewer emissions.

The design of vehicle and engine should be slightly modified to incorporate the ECM and various sensors.

The originality in this paper is that a new technique was developed to find the air‐fuel ratio of individual cycles. This will be useful for the engine manufacturers and for those researchers doing research on the engine side.