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The purpose of the paper is to propose a new optimization algorithm to realize a synchronous optimization of sensor array and classifier, to improve the performance of E-nose in the detection of wound infection. When an electronic nose (E-nose) is used to detect the wound infection, sensor array’s optimization and parameters’ setting of classifier have a strong impact on the classification accuracy.

An enhanced quantum-behaved particle swarm optimization based on genetic algorithm, genetic quantum-behaved particle swarm optimization (G-QPSO), is proposed to realize a synchronous optimization of sensor array and classifier. The importance-factor (I-F) method is used to weight the sensors of E-nose by its degree of importance in classification. Both radical basis function network and support vector machine are used for classification.

The classification accuracy of E-nose is the highest when the weighting coefficients of the I-F method and classifier’s parameters are optimized by G-QPSO. All results make it clear that the proposed method is an ideal optimization method of E-nose in the detection of wound infection.

To make the proposed optimization method more effective, the key point of further research is to enhance the classifier of E-nose.

In this paper, E-nose is used to distinguish the class of wound infection; meanwhile, G-QPSO is used to realize a synchronous optimization of sensor array and classifier of E-nose. These are all important for E-nose to realize its clinical application in wound monitoring.

The innovative concept improves the performance of E-nose in wound monitoring and paves the way for the clinical detection of E-nose.

The purpose of this paper is to produce an intelligent technique for modelling machine tool errors caused by the thermal distortion of Computer Numerical Control (CNC) machine tools. A new metaheuristic method, the cuckoo search (CS) algorithm, based on the life of a bird family is proposed to optimize the GMC(1, N) coefficients. It is then used to predict thermal error on a small vertical milling centre based on selected sensors.

A Grey model with convolution integral GMC(1, N) is used to design a thermal prediction model. To enhance the accuracy of the proposed model, the generation coefficients of GMC(1, N) are optimized using a new metaheuristic method, called the CS algorithm.

The results demonstrate good agreement between the experimental and predicted thermal error. It can therefore be concluded that it is possible to optimize a Grey model using the CS algorithm, which can be used to predict the thermal error of a CNC machine tool.

An attempt has been made for the first time to apply CS algorithm for calibrating the GMC(1, N) model. The proposed CS-based Grey model has been validated and compared with particle swarm optimization (PSO) based Grey model. Simulations and comparison show that the CS algorithm outperforms PSO and can act as an alternative optmization algorithm for Grey models that can be used for thermal error compensation.

The purpose of this study is to discover and model the asymmetry in the price volatility of financial markets, in particular the foreign exchange markets as the first underlying applications.

The volatility of the financial market price is usually defined with the standard deviation or variance of the price or price returns. This standard definition of volatility is split into the upper part and the lower one, which are termed here as Yang volatility and Yin volatility. However, the definition of yin‐yang volatility depends on the scale of the time, thus the notion of scale space of price‐time is also introduced.

It turns out that the duality of yin‐yang volatility expresses not only the asymmetry of price volatility, but also the information about the trend. The yin‐yang volatilities in the scale space of price‐time provide a complete representation of the information about the multi‐level trends and asymmetric volatilities. Such a representation is useful for designing strategies in market risk management and technical trading. A trading robot (a complete automated trading system) was developed using yin‐yang volatility, its performance is shown to be non‐trivial. The notion and model of yin‐yang volatility has opened up new possibilities to rewrite the option pricing formulas, the GARCH models, as well as to develop new comprehensive models for foreign exchange markets.

The asymmetry of price volatility and the magnitude of volatility in the scale space of price‐time has yet to be united in a more coherent model.

The new model of yin‐yang volatility and scale space of price‐time provides a new theoretical structure for financial market risk. It is likely to enable a new generation of core technologies for market risk management and technical trading strategies.

This work is original. The new notion and model of yin‐yang volatility in scale space of price‐time has cracked up the core structure of the financial market risk. It is likely to open up new possibilities such as: a new portfolio theory with a new objective function to minimize the sum of the absolute yin‐volatilities of the asset returns, a new option pricing theory using yin‐yang volatility to replace the symmetric volatility, a new GARCH model aiming to model the dynamics of yin‐yang volatility instead of the symmetric volatility, new technical trading strategies as are shown in the paper.

It is possible to see effective use of Artificial Intelligence-based systems in many fields because it easily outperforms traditional solutions or provides solutions for the problems not previously solved. Prediction applications are a widely used mechanism in research because they allow for forecasting of future states. Logical inference mechanisms in the field of Artificial Intelligence allow for faster and more accurate and powerful computation. Machine Learning, which is a sub-field of Artificial Intelligence, has been used as a tool for creating effective solutions for prediction problems.

In this chapter the authors will focus on employing Machine Learning techniques for predicting data for future states of economic using techniques which include Artificial Neural Networks, Adaptive Neuro-Fuzzy Inference System, Dynamic Boltzmann Machine, Support Vector Machine, Hidden Markov Model, Bayesian Learning on Gaussian process model, Autoregressive Integrated Moving Average, Autoregressive Model (Poggi, Muselli, Notton, Cristofari, & Louche, 2003), and K-Nearest Neighbor Algorithm. Findings revealed positive results in terms of predicting economic data.

Certain elements of Hayek’s work are prominent precursors to the modern field of complex adaptive systems, including his ideas on spontaneous order, his focus on market processes, his contrast between designing and gardening, and his own framing of complex systems. Conceptually, he was well ahead of his time, prescient in his formulation of novel ways to think about economies and societies. Technically, the fact that he did not mathematically formalize most of the notions he developed makes his insights hard to incorporate unambiguously into models. However, because so much of his work is divorced from the simplistic models proffered by early mathematical economics, it stands as fertile ground for complex systems researchers today. I suggest that Austrian economists can create a progressive research program by building models of these Hayekian ideas, and thereby gain traction within the economics profession. Instead of mathematical models the suite of techniques and tools known as agent-based computing seems particularly well-suited to addressing traditional Austrian topics like money, business cycles, coordination, market processes, and so on, while staying faithful to the methodological individualism and bottom-up perspective that underpin the entire school of thought.

Examines the fourteenth published year of the ITCRR. Runs the whole gamut of textile innovation, research and testing, some of which investigates hitherto untouched aspects. Subjects discussed include cotton fabric processing, asbestos substitutes, textile adjuncts to cardiovascular surgery, wet textile processes, hand evaluation, nanotechnology, thermoplastic composites, robotic ironing, protective clothing (agricultural and industrial), ecological aspects of fibre properties – to name but a few! There would appear to be no limit to the future potential for textile applications.

Meta-heuristic algorithms are efficient in achieving the optimal solution for engineering problems. Hybridization of different algorithms may enhance the quality of the solutions and improve the efficiency of the algorithms. The purpose of this paper is to propose a novel, robust hybrid meta-heuristic optimization approach by adding differential evolution (DE) mutation operator to the accelerated particle swarm optimization (APSO) algorithm to solve numerical optimization problems.

The improvement includes the addition of DE mutation operator to the APSO updating equations so as to speed up convergence.

A new optimization method is proposed by introducing DE-type mutation into APSO, and the hybrid algorithm is called differential evolution accelerated particle swarm optimization (DPSO). The difference between DPSO and APSO is that the mutation operator is employed to fine-tune the newly generated solution for each particle, rather than random walks used in APSO.

A novel hybrid method is proposed and used to optimize 51 functions. It is compared with other methods to show its effectiveness. The effect of the DPSO parameters on convergence and performance is also studied and analyzed by detailed parameter sensitivity studies.

Online ratings (review valence) have been found to exert a strong influence on hotel room prices. This study aims to systematically synthesize research estimating the impact of online ratings on room rates using a meta-analytical method.

From major academic databases, a total of 163 estimates of the effects of online ratings on room rates were coded from 22 studies across different countries through a systematic review of relevant literature. All estimates were converted into elasticity-type effect sizes, and a hierarchical linear meta-regression was used to investigate factors explaining variations in the effect sizes.

The median elasticity of online ratings on hotel room rates was estimated to be 0.851. Meta-regression results highlighted four categories of factors moderating the size of this elasticity: data characteristics, research settings, variable measures and publication outlet. Among sub-ratings, results revealed value rating and room rating to exert the largest impact on room rates, whereas staff and cleanliness ratings demonstrated non-significant impacts.

This study provides practical implications on the relative importance of different types of online ratings for online reputation and revenue management.

This study represents the first research effort to understand factors moderating the effects of online ratings on hotel room rates based on a quantitative review of the literature. Moreover, this study provides beneficial insights into the specification of empirical hedonic pricing models and data-collection strategies, such as the selection of price variables and choices of model functional forms.