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Most prior attempts at real estate valuation have focused on the use of metadata such as size and property age, neglecting the fact that the building workmanship in the construction of a house is also a key factor for the estimation of house prices. Building workmanship, such as exterior walls and floor tiling correspond to the visual attributes of a house, and it is difficult to capture and evaluate such attributes efficiently through classical models like regression analysis. Deep learning approach is taken in the valuation process to utilize this visual information.

The authors propose a two-input neural network comprising a multilayer perceptron and a convolutional neural network that can utilize both metadata and the visual information from images of the front view of the house.

The authors applied the two-input neural network to Guri City in Gyeonggi Province, South Korea, as a case study and found that the accuracy of house price estimations can be improved by employing image information along with metadata.

Few studies considered the impact of the building workmanship in the valuation process. The authors revealed that it is useful to use both photographs and metadata for enhancing the accuracy of house price estimation.

Phishing is one of the major threats affecting businesses worldwide in current times. Organizations and customers face the hazards arising out of phishing attacks because of anonymous access to vulnerable details. Such attacks often result in substantial financial losses. Thus, there is a need for effective intrusion detection techniques to identify and possibly nullify the effects of phishing. Classifying phishing and non-phishing web content is a critical task in information security protocols, and full-proof mechanisms have yet to be implemented in practice. The purpose of the current study is to present an ensemble machine learning model for classifying phishing websites.

A publicly available data set comprising 10,068 instances of phishing and legitimate websites was used to build the classifier model. Feature extraction was performed by deploying a group of methods, and relevant features extracted were used for building the model. A twofold ensemble learner was developed by integrating results from random forest (RF) classifier, fed into a feedforward neural network (NN). Performance of the ensemble classifier was validated using k-fold cross-validation. The twofold ensemble learner was implemented as a user-friendly, interactive decision support system for classifying websites as phishing or legitimate ones.

Experimental simulations were performed to access and compare the performance of the ensemble classifiers. The statistical tests estimated that RF_NN model gave superior performance with an accuracy of 93.41 per cent and minimal mean squared error of 0.000026.

The research data set used in this study is publically available and easy to analyze. Comparative analysis with other real-time data sets of recent origin must be performed to ensure generalization of the model against various security breaches. Different variants of phishing threats must be detected rather than focusing particularly toward phishing website detection.

The twofold ensemble model is not applied for classification of phishing websites in any previous studies as per the knowledge of authors.

In this paper, a modification strategy is proposed for the particle swarm optimization (PSO) algorithm. The strategy adds an adaptive scaling term into the algorithm, which aims to increase its convergence rate and thereby to obtain an acceptable solution with a lower number of objective function evaluations. Such an improvement can be useful in many practical engineering optimizations where the evaluation of a candidate solution is a computationally expensive operation and consequently finding the global optimum or a good sub‐optimal solution with the algorithm is too time consuming, or even impossible within the time available. The modified PSO algorithm was empirically studied with a suite of four well‐known benchmark functions, and was further examined with a practical application case, a neural‐network‐based modeling of aerodynamic data. The numerical simulation demonstrates that the modified algorithm statistically outperforms the original one.

The aim of the paper is to find a simple structure of speed controller robust against drive parameters variations. Application of artificial neural network (ANN) in the controller of PI type creates proper non‐linear characteristics, which ensures controller robustness.

The robustness of the controller is based on its non‐linear characteristic introduced by ANN. The paper proposes a novel approach to neural controller synthesis to be performed in two stages. The first stage consists in training the ANN to form the proper shape of the control surface, which represents the non‐linear characteristic of the controller. At the second stage, the PI controller settings are adjusted by means of the random weight change (RWC) procedure, which optimises the control quality index formulated in the paper. The synthesis is performed using simulation techniques and subsequently the behaviour of a laboratory speed control system is validated in the experimental set‐up. 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 ANN guarantees expected robustness.

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

Previous research is mainly devoted to value connotations obtained from the physical environment, rather than the effect on experience value from the perspective of customers' self‐efficacies and involvement. This paper attempts to combine multivariate statistical analysis and nonlinear fuzzy neural network model for data analysis.

Convenience sampling was adopted to investigate the employees from dozens of hi‐tech enterprises in the Hsinchu science‐based Industrial Park and Tainan Science‐based Industrial Park.

Customers' involvement levels have a positive effect on experience value; customers' positive moods have stronger positive effect than negative moods; customers' experience value may vary due to different environmental atmospheres and self‐efficacies.

The investigation for the employees of a hi‐tech industry may deduce different implications due to various parent samples or sampling errors. So, subsequent research may perform a comparison of different regions or industries. Only 179 out of 500 samples are collected in this research. The relatively low‐recovery rate is attributed to the inclusions of numerous items in the questionnaire. This paper discusses transversely the predisposing influential factors of experience value.

The empirical results show that the fuzzy neural network model could measure the relationship of variables more accurately and also eliminate the existing restrictions, making it suitable for social science sectors such as business management.

Previous researches highlight the experience value of playfulness in the tourism industry, but little attention has been paid to the combination of education and playfulness along with self‐efficacy.

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.

To present a neural network‐based approach to the design of electromagnetic devices.

A neural model is created which reproduces the relationship between the design parameters of the device and the performance parameters, typically field values.

The neural model is a single hidden layer MLP network, trained by using a set of cases calculated, for example, by means of a finite element analysis. The design problem can be solved by fixing the performance values at the output of the network and by calculating the corresponding input values. The relationship between the input and the output of the neural network is represented by three equations systems. By means of these three systems, we can forward the domain of the input, and we can back propagate the desired output throughout the network layers. In such a way, both the domain of the design parameters and the domain of the desired performances values can be projected in the same space. Whatever point inside the intersection between the two projected domains corresponds to a solution of the design problem.

Presents a procedure which is able to find a point belonging to such an intersection.

New approaches for non‐classical neural‐based computing are introduced. The developed approaches utilize new concepts in three‐dimensionality, invertibility and reversibility to perform the required neural computing. The various implementations of the new neural circuits using the introduced paradigms and architectures are presented, several applications are shown, and the extension for the utilization in neural‐systolic computing is also introduced.

The new neural paradigms utilize new findings in computational intelligence and advanced logic synthesis to perform the functionality of the basic neural network (NN). This includes the techniques of three‐dimensionality, invertibility and reversibility. The extension of implementation to neural‐systolic computing using the introduced reversible neural‐systolic architecture is also presented.

Novel NN paradigms are introduced in this paper. New 3D paradigm of NL circuits called three‐dimensional inverted neural logic (3DINL) circuits is introduced. The new 3D architecture inverts the inputs and weights in the standard neural architecture: inputs become bases on internal interconnects, and weights become leaves of the network. New reversible neural network (RevNN) architecture is also introduced, and a RevNN paradigm using supervised learning is presented. The applications of RevNN to multiple‐output feedforward discrete plant control and to reversible neural‐systolic computing are also shown. Reversible neural paradigm that includes reversible neural architecture utilizing the extended mapping technique with an application to the reversible solution of the maze problem using the reversible counterpropagation NN is introduced, and new neural paradigm of reversibility in both architecture and training using reversibility in independent component analysis is also presented.

Since the new 3D NNs can be useful as a possible optimal design choice for compacting a learning (trainable) circuit in 3D space, and because reversibility is essential in the minimal‐power computing as the reduction of power consumption is a main requirement for the circuit synthesis of several emerging technologies, the introduced methods for non‐classical neural computation are new and interesting for the design of several future technologies that require optimal design specifications such as three‐dimensionality, regularity, super‐high speed, minimum power consumption and minimum size such as in low‐power control, adiabatic signal processing, quantum computing, and nanotechnology.

Widely used in micro‐position devices and vibration control, the piezoelectric actuator exhibits strong hysteresis effects, which can cause inaccuracy and oscillations, even lead to instability. If the hysteretic effects can be predicted, a controller can be designed to correct for these effects. This paper aims to present a neural network hysteresis model with an improved Preisach model to predict the output of piezoelectric actuator.

The improved Preisach model is given: A wiping‐out memory sequence is defined that is along a single axis only and at the same time the ascending and the descending extreme points are separated. The extended area variable is calculated according to wiping‐out memory sequence. The relationship between the two inputs (the extended area variable and variable rate of input signal) and the hysteresis output is implemented with a neural network to approximate the hysteresis model for the piezoelectric actuators.

Some experiments are carried out with a piezoelectric ceramic (PST150/7/40 VS12) and the results show the neural network hysteresis model can reliably predict the hysteretic behaviours in piezoelectric actuators.

The improved Preisach model is a simple model that is implemented by a neural network to reliably predict the hysteretic output in piezoelectric actuators.

This study proposes a predictive neural network model reference decoupling control method for the coupling problem between the leg joints of hydraulic quadruped robots, and verifies its decoupling effect..

The machine–hydraulic cross-linking coupling is studied as the coupling behavior of the hydraulically driven quadruped robot, and the mechanical dynamics coupling force of the robot system is controlled as the disturbance force of the hydraulic system through the Jacobian matrix transformation. According to the principle of multivariable decoupling, a prediction-based neural network model reference decoupling control method is proposed; each module of the control algorithm is designed one by one, and the stability of the system is analyzed by the Lyapunov stability theorem.

The simulation and experimental research on the robot joint decoupling control method is carried out, and the prediction-based neural network model reference decoupling control method is compared with the decoupling control method without any decoupling control method. The results show that taking the coupling effect experiment between the hip joint and knee joint as an example, after using the predictive neural network model reference decoupling control method, the phase lag of the hip joint response line was reduced from 20.3° to 14.8°, the amplitude attenuation was reduced from 1.82% to 0.21%, the maximum error of the knee joint coupling line was reduced from 0.67 mm to 0.16 mm and the coupling effect between the hip joint and knee joint was reduced from 1.9% to 0.48%, achieving good decoupling.

The prediction-based neural network model reference decoupling control method proposed in this paper can use the neural network model to predict the next output of the system according to the input and output. Finally, the weights of the neural network are corrected online according to the predicted output and the given reference output, so that the optimization index of the neural network decoupling controller is extremely small, and the purpose of decoupling control is achieved.