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• Robust functional form contains true parameters far more often than popular models. • Matches/outperforms widely used regression and Neural Network models. • Finds appropriate balance between Model Fit, Inference and Prediction (MIPs). • Introduces new large-sample DGP test; can use to improve A.I. models. • For MIS field finds Popularity Parameter to be important for predicting citations. This article introduces a versatile functional form for Generalized Linear Models (GLMs) through a simple, yet effective, transformation of the current framework. The models are applied through a new hierarchical bayesian estimation procedure for logistic regression to highly-cited papers in the Management Information Systems (MIS) field. The results are uniformly better, in regards to model fit and inference for in-sample and out-of-sample data, for simulation studies and real-world data applications, requiring very little time to convergence to true population parameters. In simulation studies, I show that the method contains the true parameters nearly three times as often as widely used existing GLMs, and does so while having confidence intervals that are 54.50% smaller, while requiring around two-thirds the number of MCMC iterations as existing bayesian methods. In Scientometric applications the methodology is shown to be highly robust with predictive/classification accuracy, either equaling or exceeding existing methods for identifying highly-cited articles including Artificial Neural Networks (ANN). Thus, the method is shown to be robust to the amount of asymmetry (or symmetry) of the probability of success (or failure) and robust to unbalanced samples and varying Data Generating Processes. Further, the methodology is equivalent to current methods if the data support them and is therefore complementary to existing methods, without loss of interpretability of model parameters. For the MIS field it finds that Popularity Parameter (PP) of an article Keywords can predict whether a paper will be highly-cited (top 25% of highly-cited articles) between two to three years after publication and beyond. Furthermore, given the small number of iterations needed for convergence, the methodology can also be used as a baseline method in Big Data (BD) settings for both Artificial Intelligence (AI) and Machine Learning (ML) contexts as well. [ABSTRACT FROM AUTHOR]

Underwater spectral imaging combined with neural networks provides a practical means for intelligent detection and maintenance of offshore engineering. As an underwater detection technology, the data obtained from underwater hyperspectral imaging can be used for both qualitative analysis and quantitative detection of underwater targets, which is an efficient means of underwater target detection in offshore engineering. In this paper, An underwater hyperspectral imaging system based on PGP spectral structure line scanning imaging has been designed and developed for shallow water detection. It has a spectral range of 400–1000 nm, over 200 channels, a spectral resolution of 2.5 nm, a field of view angle of 35.2°, and a focal length of 25 mm. According to the design results, the system was assembled, calibrated and tested. Aiming at solving the shortcomings of the current underwater hyperspectral system, such as the inability of real-time observation of the scanned image, large data volume and complicated processing, the upper computer software was designed and developed to realize the functions of controlling and adjusting the parameter, real-time displaying the scanned image, reconstructing the image and outputting the data in ENVI standard format to the underwater hyperspectral system. The prototype of the system was used to conduct coral detection experiments in Weizhou Island, and the neural network model was used to classify the experimental data, and the overall classification accuracy was above 98%. The experimental results show that the whole system has good imaging quality and good underwater detection capability, and the developed host computer software greatly reduces the workload of raw data processing, improves the experimental detection efficiency, and provides a feasible technical solution for underwater intelligent detection and analysis. • A line-scanning underwater hyperspectral imaging system has been developed based on the PGP spectroscopic structure. • Development of software for the upper computer, which significantly improves the efficiency of underwater detection. • Combining neural network models to achieve accurate detection and classification of underwater targets. [ABSTRACT FROM AUTHOR]

The rising overheads of data-movement and limitations of general-purpose processing architectures have led to a huge surge in the interest in "processing-in-memory" (PIM) approach and "neural networks" (NN) architectures. Spintronic memories facilitate efficient implementation of PIM approach and NN accelerators, and offer several advantages over conventional memories. In this paper, we present a survey of spintronic-architectures for PIM and NNs. We organize the works based on main attributes to underscore their similarities and differences. This paper will be useful for researchers in the area of artificial intelligence, hardware architecture, chip design and memory system. [ABSTRACT FROM AUTHOR]

This paper mainly deals with the problem of designing delayed state-feedback controller for neural networks with leakage delay. By constructing an appropriate Lyapunov–Krasovskii functional including double integral terms having two different exponential decay rates and utilizing linear matrix inequality (LMI) technique with the help of slack variables, some sufficient conditions for globally exponential stabilization results are obtained by designing of delayed state-feedback controller. The novelty of this paper includes: (i) although many papers dealt with dynamics of neural networks with leakage delay, there is little work on design of feedback controller. Even there is almost no result that deals with the delay-feedback controller for such delay systems, which is the main motivation of this paper; (ii) the derived delay-dependent stability criteria establish the relationship between leakage delay and time delay in the feedback term, which can be easily checked via the MATLAB LMI toolbox; (iii) we consider the general case that the control term is given in the form of B u , where B is an n × m real matrix. Such case is more difficult to handle than the special case that B is unit matrix. It indicates that the controller can be used in all states or in some states by proper selection of the matrix B . The effectiveness of the proposed method in this paper is illustrated via numerical examples. [ABSTRACT FROM AUTHOR]

• The DRL employs the Using Graph Convolutional Network (GCN) to extract the network architecture data, the node vector is created by fusing it with traffic-related features. • The deep Q-network uses this node vector to anticipate the best rewards and determine which FDNN task is carried out at the edge nodes. • In light of this choice, the FDNN classifier is deployed to categorize the medical information. • In the end, the simulation findings show that the DRL-LOA significantly enhances optimization, showcasing a 35.7% reduction in power usage and a of 27.9% decrease in latency on 20 edge nodes in cloud-edge systems. • These findings underscore DRL-LOA's efficacy in optimizing medical data categorization and addressing load-balancing challenges in cooperative cloud-edge networks. Addressing the limitations of traffic-centric approaches in cooperative cloud-edge networks, this paper introduces an adaptive deployment strategy for FDNN using LOA. However, the oversight of network structure hampers load-balancing efficiency in medical data categorization. To overcome this, a novel DRL-LOA approach integrates Deep Reinforcement Learning (DRL) with LOA, considering both network structure and traffic-related data. This DRL-LOA considers both network structure and traffic-related data for optimization tasks. The DRL employs the Using Graph Convolutional Network (GCN) to extract the network architecture data, the node vector is created by fusing it with traffic-related features. The deep Q-network uses this node vector to anticipate the best rewards and determine which FDNN task is carried out at the edge nodes. In light of this choice, the FDNN classifier is deployed to categorize the medical information. In the end, the simulation findings show that the DRL-LOA significantly enhances optimization, showcasing a 35.7% reduction in power usage and a of 27.9% % decrease in latency on 20 edge nodes in cloud-edge systems. These findings underscore DRL-LOA's efficacy in optimizing medical data categorization and addressing load-balancing challenges in cooperative cloud-edge networks. [ABSTRACT FROM AUTHOR]

• Low-cost measurement platform for particulate matter monitoring developed. • Artificial intelligence field calibration procedure of low-cost sensor proposed. • Combination of affine scaling and optimized neural network surrogate employed. • Correlation coefficient improved to 0.86 for particulate matter of size 1 μm. • Root-mean-squared error of 3 μm/m3 (1 μm particles) and 4.9 μm/m3 (10 μm particles) Due to detrimental effects of atmospheric particulate matter (PM), its accurate monitoring is of paramount importance, especially in densely populated urban areas. However, precise measurement of PM levels requires expensive and sophisticated equipment. Although low-cost alternatives are gaining popularity, their reliability is questionable, attributed to sensitivity to environmental conditions, inherent instability, and manufacturing imperfections. The objectives of this paper include (i) introduction of an innovative approach to field calibration for low-cost PM sensors using artificial intelligence methods, (ii) implementation of the calibration procedure involving optimized artificial neural network (ANN) and combined multiplicative and additive correction of the low-cost sensor readings, (iii) demonstrating the efficacy of the presented technique using a custom-designed portable PM monitoring platform and reference data acquired from public measurement stations. The results obtained through comprehensive experiments conducted using the aforementioned low-cost sensor and reference data demonstrate remarkable accuracy for the calibrated sensor, with correlation coefficients of 0.86 for PM 1 and PM 2.5 , and 0.76 PM 10 (particles categorized as having diameter equal to or less than 1 μm, 2.5 μm, and 10 μm, respectively), along with low RMSE values of only 3.1, 4.1, and 4.9 µg/m3. [ABSTRACT FROM AUTHOR]

Nonparametric cost estimation in construction projects with the use of artificial networks is presented as suitable mainly for the early estimates. These conceptual estimates are based on the variables – namely cost predictors that characterize the project or a facility. Data gathered on the basis of completed projects are combined together and applied to the current project cost estimation process. The aim of the paper is to discuss the opportunities and challenges of the approach based on the artificial intelligence tools to cost estimation of construction works. The proposed approach is based on the concept of nonparametric cost estimation and application of artificial neural networks. The author's idea and intention is to transfer the mechanisms of nonparametric cost estimating to the level of construction works. Neural networks, due to their general capabilities, seem to be a good tool to aid the proposed approach. The paper contains discussion of the proposed approach and its applicability. [ABSTRACT FROM AUTHOR]

The paper presents the results regarding the identification of the value of the pull-off adhesion between a concrete added layer with a constant thickness and a substrate concrete layer in existing elements. A method of identification, which is based solely on the use of artificial neural networks (ANNs) and two non-destructive acoustic methods: impact-echo and impulse response on the surface of the added layer, was developed. The methodology of identifying the pull-off adhesion between a concrete added layer and a substrate layer in existing elements was developed and presented in the paper and is useful in construction practice. [ABSTRACT FROM AUTHOR]

Currently, there is a need in society to use digital and cybernetic methods of remote diagnostics of a person's mental state. Using a digital or cyberphysical system in the process of remote recognition of the patient's mental state, it is possible to increase the effectiveness of recognizing the actual psycho-emotional state of the patient, both in the consulting mode and in dynamic observation. For remote assessment of the mental state, methods are widely used based on recognition of facial activity, which is reflexively associated with the display of the psycho-emotional state. In this paper, the authors propose a new approach to creating such a methodology. This work was supported by the RFBR grant 18-07-00987. [ABSTRACT FROM AUTHOR]

Due to industrialization and the growth of transportation systems, the number of vehicles continues to increase which causes a significant traffic jam problem especially in big cities. Consequently, the prediction of traffic flows is a key component to an optimal traffic management. As a solution to this issue, the present paper aims at applying the artificial intelligence of neural networks, which offers an interesting approach to modelling in complex, and nonlinear situations. Our resolution method is based on the design of a neural network to predict daily traffic flow. Then, the forecasted traffic flow is compared with a real dataset recorded on a road section and provided by a recognized infrastructure manager in Morocco. Indeed, neural networks have the ability to learn from the past and predict the future. In this study, various neural networks structures are exanimated and simulation results show that the best forecasts are obtained with the use of Multi-Layer Perceptron architecture that has a good generalization capacity with a total Mean Square Error of 0.00927 in the train set and 0.01321 in the test set. [ABSTRACT FROM AUTHOR]

Abstract: Fault location is a critical task when a severe disturbance is caused by insulation failure on a transmission line. In order to avoid further economical and social costs because of load interruptions, the fault diagnosis has to be concluded as soon as possible. Intelligent systems have been successful in dealing with fault diagnosis problems. This paper proposes the application of complex-domain neural networks for mapping the relationship between electrical signals and fault locations on transmission lines. Complex-domain neural networks allow voltage/current representation without arbitrarily decoupling amplitude and phase. Furthermore, several voltage and current representation schemes, based on electromagnetic transient and steady-state information, are analyzed in this paper. For comparison purpose, these input representations are also tested with real-domain neural networks. The tests consider realistic operating/fault conditions and assume that fault classification has already been handled. [Copyright &y& Elsevier]

Abstract: ICT-based decision aids are currently making waves in the modern business world simultaneously with increased pressure on auditors to play a more effective role in the governance and control of corporate entities. This paper aims to review the main research efforts and current debates on auditors’ use of artificial intelligent systems, with a view to predicting future directions of research and software development in the area. The paper maps the development process of artificial intelligent systems in auditing in the light of their identified benefits and drawbacks. It also reviews previous research efforts on the use of expert systems and neural networks in auditing and the implications thereof. The synthesis of these previous studies revealed certain research vacuum which future studies in the area could fill. Such areas include matching the benefits of adopting these intelligent agents with their costs, assessing the impact of artificial intelligence on internal control systems’ design and monitoring as well as audit committees’ effectiveness, and implications of using such systems for small and medium audit firms’ operations and survival, audit education, public sector organisations’ audit, auditor independence and audit expectations-performance gap. [Copyright &y& Elsevier]

Abstract Functional networks (FNs) are extensions of neural networks (NNs). Unlike NNs, FNs considers general functional models instead of sigmoid-like models. Additionally, in FNs, there are no weights associated with the links that connect neurons. In this paper, we review the research progress and applications of FNs models in recent years. First, we introduce FNs architecture, three typical functional models and the learning process, and we explain the differences between NNs and FNs. Second, we discuss recent applications of FNs that have been introduced in many fields, such as time series prediction, differential and functional equations, pattern classification, detection and prediction, approximation computation, complex system modeling, computer-aided design (CAD), and linear and nonlinear regression. Finally, we present some remarks on future research directions for FNs. [ABSTRACT FROM AUTHOR]

Due to industrialization and the growth of transportation systems, the number of vehicles continues to increase which causes a significant traffic jam problem especially in big cities. Consequently, the prediction of traffic flows is a key component to an optimal traffic management. As a solution to this issue, the present paper aims at applying the artificial intelligence of neural networks, which offers an interesting approach to modelling in complex, and nonlinear situations. Our resolution method is based on the design of a neural network to predict daily traffic flow. Then, the forecasted traffic flow is compared with a real dataset recorded on a road section and provided by a recognized infrastructure manager in Morocco. Indeed, neural networks have the ability to learn from the past and predict the future. In this study, various neural networks structures are exanimated and simulation results show that the best forecasts are obtained with the use of Multi-Layer Perceptron architecture that has a good generalization capacity with a total Mean Square Error of 0.00927 in the train set and 0.01321 in the test set. [ABSTRACT FROM AUTHOR]

Abstract: Stock price predictions have been a field of study from several points of view including, among others, artificial intelligence and expert systems. For short-term predictions, the technical indicator relative strength indicator (RSI) has been published in many papers and used worldwide. CAST is presented in this paper. CAST can be seen as a set of solutions for calculating the RSI using artificial intelligence techniques. The improvement is based on the use of feedforward neural networks to calculate the RSI in a more accurate way, which we call the iRSI. This new tool will be used in two scenarios. In the first, it will predict a market – in our case, the Spanish IBEX 35 stock market. In the second, it will predict single-company values pertaining to the IBEX 35. The results are very encouraging and reveal that the CAST can predict the given market as a whole along with individual stock pertaining to the IBEX 35 index. [Copyright &y& Elsevier]

Abstract: This paper reports the results of an advanced algorithm for the optimization of electron beam parameters in Free Electron Laser (FEL) Linacs. In the novel approach presented in this paper, the system uses state of the art developments in video games to mimic an operator''s decisions to perform an optimization task when no prior knowledge, other than constraints on the actuators is available. The system was tested for the simultaneous optimization of the energy spread and the transmission of the Australian Synchrotron Linac. The proposed system successfully increased the transmission of the machine from 90% to 97% and decreased the energy spread of the beam from 1.04% to 0.91%. Results of a control experiment performed at the new FERMI@Elettra FEL is also reported, suggesting the adaptability of the scheme for beam-based control. [Copyright &y& Elsevier]

Abstract Using tools from artificial intelligence, mainly artificial neural networks, this paper presents an walking-engine for humanoid robots. This engine uses dynamic neural networks with feedback for gait generation, a modified Zhang neural network for a singularity-robust inverse kinematics solver, and feedforward neural networks for neuro-adaptive control. The Atlas humanoid robot model in simulation is used to test and verify the capabilities of the neuro-dynamic walking engine. Results show that the engine is capable of generating conventional ZMP stable walking gaits and executing them using the Atlas robot in simulation. Highlights • Develop, implement, and test the feasibility of a neuro-dynamic walking-engine. • Presents abstraction of a neural-process based walking engine. • Dynamic-model-free humanoid that is robust and can adapt to dynamic uncertainties. • Develops a novel feedback Dynamic Neural Network for gait generation. • Modified Zhang Neural Network for singularity robust Inverse Kinematics. [ABSTRACT FROM AUTHOR]

Abstract: This paper presents a fast and reliable approach for the localization of an iris image using neural networks by comparing some of the existing localization methods. Normally any biometric recognition system is used for identification of an individual and verified with the available database to check whether the person is authorized or not. Nowadays, iris recognition systems are considered the best authentication method compared to other biometric systems due to the unique characteristic feature of the human iris. In iris recognition systems, the important and difficult step is to locate or segment the iris from the input eye image which is responsible for success rate of the iris recognition. Hence this paper suggests a efficient approach to locate the iris using neural networks in order to improve the efficiency of recognition systems. Further, the paper compares the existing iris localization methods such as Daugman algorithm, Hough transform and Canny edge detector algorithm. The best localization algorithm is chosen for training a network and simulating for efficient and fast segmentation of irises with good success rate. [Copyright &y& Elsevier]

Abstract: This paper presents a fully automatic system intended to detect leaks of dielectric fluid in underground high-pressure, fluid-filled (HPFF) cables. The system combines a number of artificial intelligence (AI) and data processing techniques to achieve high detection capabilities for various rates of leaks, including leaks as small as 15 l per hour. The system achieves this level of precision mainly thanks to a novel auto-tuning procedure, enabling learning of the Bayesian network – the decision-making component of the system – using simulated leaks of various rates. Significant new developments extending the capabilities of the original leak detection system described in and form the basis of this paper. Tests conducted on the real-life HPFF cable system in New York City are also discussed. [ABSTRACT FROM AUTHOR]

Abstract: The discrete delayed Hopfield neural networks is an extension of the discrete Hopfield neural networks. In this paper, the convergence of discrete delayed Hopfield neural networks is mainly studied, and some results on the convergence are obtained by using Lyapunov function. Several new sufficient conditions for the delayed networks converging towards a limit cycle with period at most 2 are proved in parallel updating mode. Also, some conditions for the delayed networks converging towards a limit cycle with 2-period are investigated in parallel updating mode. All results established in this paper extend the previous results on the convergence of both the discrete Hopfield neural networks, and the discrete delayed Hopfield neural networks in parallel updating mode. [Copyright &y& Elsevier]

Abstract: This paper discusses an experimental study of the Japanese candlestick method as used in hybrid stock market forecasting models. Two models are presented in this paper. Model 1 is a committee machine with simple generalized regression neural networks (GRNN) experts. This model also has a simple gating network. Model 2 has a similar committee machine along with a hybrid type gating network that contains fuzzy logic. Model 1 was developed to introduce the candlestick method and examine whether using the candlestick method improves performance. Model 2 is developed to determine whether the application of fuzzy logic could improve the former model. This model uses standard IF-THEN rules based fuzzy logic. In the experiment, a few simple Japanese candlestick patterns are integrated into the models. Both models use the same simple candlestick patterns to provide a basis for comparison. The Japanese candlestick method is implemented in the gating network. Model 1 uses features of candlestick patterns in the gating network. Model 2 uses candlestick patterns for recognizing the strength of market conditions. To investigate the performance of these models, the daily stock quotes of Hewlett-Packard, Bank of America, Ford, DuPont, and Yahoo are used as input data sets. The performance of the models was satisfactory based on the mean squared error. [Copyright &y& Elsevier]

Abstract: The present paper surveys the application of soft computing (SC) techniques in engineering design. Within this context, fuzzy logic (FL), genetic algorithms (GA) and artificial neural networks (ANN), as well as their fusion are reviewed in order to examine the capability of soft computing methods and techniques to effectively address various hard-to-solve design tasks and issues. Both these tasks and issues are studied in the first part of the paper accompanied by references to some results extracted from a survey performed for in some industrial enterprises. The second part of the paper makes an extensive review of the literature regarding the application of soft computing (SC) techniques in engineering design. Although this review cannot be collectively exhaustive, it may be considered as a valuable guide for researchers who are interested in the domain of engineering design and wish to explore the opportunities offered by fuzzy logic, artificial neural networks and genetic algorithms for further improvement of both the design outcome and the design process itself. An arithmetic method is used in order to evaluate the review results, to locate the research areas where SC has already given considerable results and to reveal new research opportunities. [Copyright &y& Elsevier]

Abstract: In this paper, a class of generalized bi-directional associative memory (BAM) neural networks with recent history distributed delays and impulses are considered. By using Lyapunov functional method and fixed point theorem, we derive several sufficient conditions for the existence and globally exponential stability of a unique periodic solution of the networks. The results of this paper are new and different from previously known results. [Copyright &y& Elsevier]

Abstract: Feature selection methods can be classified broadly into filter and wrapper approaches. Filter-based methods filter out features which are irrelevant to the target concept by ranking each feature according to some discrimination measure and then select features with high ranking value. In this paper, a filter-based feature selection method based on correlation fractal dimension (CFD) discrimination measure is proposed. One of the subgoals of this paper is to outline some issues that arise while calculating fractal dimension for higher dimensional ‘empirical’ data sets. It is well known that the calculation of fractal dimension for empirical data sets is meaningful only for an appropriate range of scales. We demonstrate through experimentation on data sets of various sizes that fractal dimension-based algorithms cannot be applied routinely to higher dimensional data sets as the calculation of fractal dimension is inherently sensitive to parameters like range of scales and the size of the data sets. Based on the empirical analysis, we propose a new feature selection technique using CFD that avoids the above issues. We successfully applied the proposed algorithm on a challenging classification problem in bioinformatics, namely, Promoter Recognition. [Copyright &y& Elsevier]

Abstract: In this paper we present a complex hybrid model in the water management field based on a synergetic combination of deterministic and machine learning model components. The objective of a Water Supply System (WSS) is to convey treated water to consumers through a pressurized network of pipes. A number of meters and gauges are used to take continuous or periodic measurements that are sent via a telemetry system to the control and operation center and used to monitor the network. Using this typically limited number of measures together with demand predictions the state of the system must be assessed. Suitable state estimation is of paramount importance in diagnosing leaks and other faults and anomalies in WSS. But this task can be really cumbersome, if not unachievable, for human operators. The aim of this paper is to explore the possibility for a technique borrowed from machine learning, specifically a neuro-fuzzy approach, to perform such a task. For one thing, state estimation of a network is performed by using optimization techniques that minimize the discrepancies between the measures taken by telemetry and the values produced by the mathematical model of the network, which tries to reconcile all the available information. But, for another, although the model can be completely accurate, the estimation is based on data containing non-negligible levels of uncertainty, which definitely influences the precision of the estimated states. The quantification of the uncertainty of the input data (telemetry measures and demand predictions) can be achieved by means of robust estate estimation. By making use of the mathematical model of the network, estimated states together with uncertainty levels, that is to say, fuzzy estimated states, for different anomalous states of the network can be obtained. These two steps rely on a theory-driven model. The final aim is to train a neural network (using the fuzzy estimated states together with a description of the associated anomaly) capable of assessing WSS anomalies associated with particular sets of measurements received by telemetry and demand predictions. This is the data-driven counterpart of the hybrid model. [Copyright &y& Elsevier]

This paper presents a methodology for fast time-domain simulation of analog systems with nonlinear parameters. Specifically, the paper focuses on analog-to-digital converters (ADC). The method creates compiled-code simulators based on symbolic analysis. Code is optimized using loop invariant elimination and constant folding, well-known compiler optimization methods. Circuits are described as structural macromodels. Nonlinear parameters are expressed using piecewise linear (PWL) models. The paper presents a technique for automatically creating PWL models through model extraction from trained neural networks. As compared to existing behavioral simulation methods for ADC, this technique is more systematic and accurate. In our experiments, compiled-code simulation was significantly faster than numerical simulation. Hence, the methodology is very useful in analog and mixed-signal system synthesis, which is known to require a large number of simulation steps. [Copyright &y& Elsevier]

Abstract: In this paper, we consider the periodic neural networks with variable coefficients and time-varying delays, and investigate the existence of periodic solution and its global exponential stability. Various sufficient conditions ensuring the existence of periodic and its global exponential stability are given. The results obtained in this paper extend and generalize those given in previous literature. [Copyright &y& Elsevier]

Abstract: This paper aims to provide, on one hand, an introduction to the theoretical principles of artificial neural networks (ANN) and on the other, a step-by-step methodology for designing a neural network for tourism time series forecasting. The time series corresponding to tourism expenditure in the Balearic Islands (Spain), one of the world''s major tourist destinations, has been used as data to illustrate this process. A number of practical rules and discussion points between authors, comprehensible to both academic researchers and practitioners, have been included throughout the paper so that ANN can be applied successfully. Lastly, the results obtained in this study provide information for researchers interested in applying ANN to tourism data forecasting. [Copyright &y& Elsevier]

Abstract: In this paper, a new classification method is proposed based on the radial basis function (RBF) neural network architecture. The method is particularly useful for manufacturing processes, in cases where on-line sensors for classifying the product quality are not available. More specifically, the fuzzy means algorithm is employed on a set of training data, where the input data refer to variables that are measured on-line and the output data correspond to quality variables that are classified by human experts. The produced neural network model acts as an artificial sensor that is able to classify the product quality in real time. The proposed method is illustrated through an application to real data collected from a paper machine. The method produces successful results and outperforms a number of classifiers, which are based on the feedforward neural network (FNN) architecture. [Copyright &y& Elsevier]

Abstract: In this paper, we theoretically prove the existence of periodic solutions for a nonautonomous discrete-time neural networks by using the topological degree theory. Sufficient conditions are also obtained for the existence of an asymptotically stable periodic solution. As a special case, we obtain the existence of a fixed point to the corresponding autonomous discrete-time neural networks which corrects the error in [W.R. Zhao, W. Lin, R.S. Liu, J. Ruan, Asymptotical stability in discrete-time neural networks, IEEE Trans. Circuits Syst. I 49 (2002) 1516–1520]. Numerical simulations are given at the end of the paper. [Copyright &y& Elsevier]

Abstract: The paper presents a concept of model-based detection of coded signals on the example of 13-element Barker code signal, embedded in additive chaos. The process of signal detection consists of two stages: approximation of chaotic dynamics and decision making. Dynamic models of chaotic signals, considered in this paper, were created in the form of linear autoregressive models as well as in the form of non-linear feedforward neural networks (of several types). The accuracy of models in one step ahead prediction of chaotic signals is satisfactory, even for chaotic signals with fast changes of their values. The error between an observed signal and its model is passed as the input to the decision-making (detection) module. When the signal received is a composite of Barker code and chaos, its dynamic properties change rapidly in the periods of Barker code appearance. Thus the error between the signal and its model becomes significant, and that allows for successful detection of Barker code. In this paper the detection module is based on a neural network; various architectures of neural net-based detectors have been proposed and tested in numerical experiments. Numerical simulations presented in this paper show good performance of detection of Barker code embedded in chaos. Robustness of such a detection scheme was also examined: the neural detectors, trained for a specific energy ratio between Barker code and chaos (SNR ratio), turned out capable detecting Barker code in a relatively wide range of SNR ratios. Also the comparison between neural detection and a detection structure, using matched filter, has been presented. Experiments have shown superiority of neural detection over detection with a matched filter, especially for low SNR ratios. It should be also noted that very simple neural network architectures were proposed as the models of signal dynamics and for the detection module. [Copyright &y& Elsevier]

Abstract: This paper presents a new approach to analyze or to select the most influential inputs for locating the reason for poor education among rural people, with the use of bidirectional associative memories (BAM). Quantifying the reason for poor education in rural areas can improve the education of rural people. This paper develops a extended BAM (EBAM) , using BAM and it tries to test this method for analyzing the reason for poor eduction in the rural areas. This analysis can be used to determine the method of education for rural people, and it can help to improve the quality or level of education of rural people. So, this paper uses the EBAM with different neurons for the problem of poor education in rural area. [Copyright &y& Elsevier]

Abstract: Fracture of ceramic components is governed by the multiaxial Weibull theory. Due to the inherent scatter in fracture behaviour of ceramics, strength and lifetime predictions are prone to statistical uncertainty. In this paper we present a strategy for the assessment of uncertainty in the estimation of the failure probability of ceramic components due to the scatter of material data. Confidence intervals for the failure probability are obtained by means of stochastic resampling methods. A considerable computational effort can be saved when using neural networks for part of the simulation. The strategy presented in this paper may be applied for different purposes: estimation of the reliability of a ceramic component, setup of parameters for an experimental investigation, and interpretation of experimental results. [Copyright &y& Elsevier]

This paper examines the simultaneous use of graph based evolutionary algorithms (GBEAs) and a real-time estimate of the final fitness for evolutionary optimization of systems modeled using computational fluid dynamics (CFDs). GBEAs are used to control the rate at which information travels, enabling the diversity of the population to be tuned to match the solution space. During each fitness evaluation, the CFD solver iteratively solves the fluid flow and heat transfer characteristics of the proposed design. In this paper, an artificial neural network is used to develop a real-time estimate of the final fitness and error bounds at each iteration of the solver. Using these estimates, the evolutionary algorithm can determine when the fitness of the design is known with sufficient accuracy for the evolutionary process. This significantly reduces the overall compute time. These techniques are demonstrated by optimizing the spatial temperature profile of the cooking surface of a biomass cookstove. In this cookstove, hot gases from biomass combustion flow under the cooking surface. Within this flow area, a set of baffles direct the flow of hot gases and establish the spatial temperature profile of the stove''s cooking surface. The location and size of a series of baffles within the hot gas flow area are determined by the optimization routine. In this design problem, it is found that the two techniques are compatible; both the number of fitness evaluations and the time required for each CFD fitness evaluation are reduced while utilizing GBEAs to preserve the diversity of the population. [Copyright &y& Elsevier]

In this paper, a fault detection scheme for application to large-scale data acquisition systems is presented. The detection scheme has to process up to several hundreds of different measurements at a time and check them for consistency. The fault detection scheme works in three steps: First, principal component analysis of training data is used to determine non-sparse areas of the measurement space. Fault detection is accomplished by checking whether a new data record lies in a cluster of training data or not. Therefore, in a second step the distribution function of the available data is estimated using kernel regression techniques. In order to reduce the degrees of freedom and to determine clusters of data efficiently, in a third step the distribution function is approximated by a neural network. In order to use as few basis functions as possible a new training algorithm for ellipsoidal basis function networks is presented: New neurons are placed such that they approximate the distribution function in the vicinity of their centers up to the second order. This is accomplished by adapting the spread parameters using Taylor''s theorem. Thus, the amount of necessary parameters and the computational effort for online supervision can be reduced dramatically. An important requirement for the fault detection scheme is that it is able to automatically adapt itself to new data. The present paper also addresses this feature. It is demonstrated how a gradient optimization with algebraic constraints can be applied to adapt a pre-existing network to new data points. Numerical examples with real data show that the proposed method produces excellent results. [Copyright &y& Elsevier]

This paper describes a feasibility study of “multi-agent oriented” techniques on a 2-D and 3-D object recognition system. The main aim of the project is to develop an inspection supporting tool that understands objects in both 2-D and 3-D in a unified system. 2-D and 3-D worlds are mapped to each other via agent-like entities each of which holds a conceptualized representation, allowing for a robust inference ability. Agents, each of which has symbolic representation of a part of an object, are hierarchically organized to represent a complete representation of an object. In this paper, object recognition is carried out with two matching methods: (1) the matching between an object model (agent''s knowledge) and observed data, and (2) a constraint propagation-like method to achieve overall consistency among agents. The first is carried out with a symbolic Hopfield-type neural network and the second via a hierarchical Winner-Takes-All algorithm. [Copyright &y& Elsevier]

This paper is concerned with leaderless synchronization of coupled delayed neural networks. A distributed event-triggered control strategy under the periodic sampling scheme is introduced to reduce control updates. By introducing a weighted average state as a virtual leader, the leaderless synchronization problem can be transformed to the stability problem of the error system, which is defined as the distance between each node and the virtual leader. A leaderless synchronization criterion under the periodic event-triggered scheme in strongly connected networks is first derived based on Finsler’s lemma. The results are then extended to the network containing a directed spanning tree. A corollary with the lower dimension in the form of LMIs is further presented. Two examples are provided to validate the effectiveness of the theoretical results. [ABSTRACT FROM AUTHOR]

Due to the energy crisis and the awareness of sustainable development, the research on energy-efficient buildings has increasingly attracted attention. To achieve this objective, one important factor is to capture occupancy properties for building control systems, which refers to occupancy modeling in buildings. Due to the complexity of building occupancy, previous works try to simplify the modeling with some specific assumptions which may not always hold. In this paper, we propose a Generative Adversarial Network (GAN) framework for building occupancy modeling without any prior assumptions. The GAN approach contains two key components, i.e. a generative network and a discriminative network, which are designed as two powerful neural networks. Owing to the strong generalization capacity of neural networks and the adversarial mechanism in the GAN approach, it is able to accurately model building occupancy. We perform real experiments to verify the effectiveness of the proposed GAN approach and compare it with two state-of-the-art approaches for building occupancy modeling. To quantify the performance of all the models, we define five variables with two evaluation criteria. Results show that our proposed GAN approach can achieve a superior performance. [ABSTRACT FROM AUTHOR]

We present artificial neural network (ANN) based software for rapidly predicting the recovery effectiveness (REN) of aquifer storage and recovery (ASR) wells in fresh water aquifers. The REN performance criterion equals the amount of the water injected in an ASR well that is recoverable via the same well during a specified extraction period. The software circumvents the need to prepare and run computationally intensive simulations, by invoking ANN models as surrogate simulators. The paper presents the development of the ANN models and a graphical user interface (GUI) to facilitate using the ANN models and to perform sensitivity analysis. ANN inputs include all factors significantly affecting REN in freshwater aquifers. The software can evaluate REN in any ASR system in confined aquifers whose parameters are within the range of parameters used for training the ANNs in this study. [ABSTRACT FROM AUTHOR]

Feature extraction is a key algorithm to solve the dimensionality problem. Most feature extraction algorithms use a batch mode, which requires all data available at the same time to calculate new features. Recently, with more available data and advancements of transmission technology, the need for incremental algorithms has increased. In this paper, we propose a gradient descent DBFE method (GDDBFE) that shows a substantial improvement in processing time. Based on this GDDBFE, we then propose an incremental gradient descent decision boundary feature extraction method (IGDDBFE). The proposed IGDDBFE method consists of two steps: updating the decision boundaries and adding discriminately informative features with newly added samples and then updating the feature vectors by incremental eigenvector updates. Experiments with real-world databases show that the proposed method shows improved performance compared to some existing methods. [ABSTRACT FROM AUTHOR]

In this paper, we are concerned with the anti-synchronization of master–slave neural networks with time delays. By dividing the whole anti-synchronization process into two procedures: the absolute value of error state e i ( t ) flowing from the initial state to 1, then from 1 to 0, i = 1 , … , n , it gets a new viewpoint and a clear illustration on how controller works to the systems. And combining the H o ¨ lder inequality and other techniques, rigorous analysis gives that, each component of error state e ( t ) ( t  ≥ 0) would flow to 1 in finite time, and continue to flow to 0 in fixed time. A numerical example is presented to illustrate the efficiency and effectiveness of our obtained results. [ABSTRACT FROM AUTHOR]

Artificial intelligence, including neural networks, deep learning and machine learning, has made numerous progress and offered new opportunity for academic research and applications in many fields, especially for business activities and firm development. This paper summarizes different applications of artificial intelligence technologies in several domains of business administration. Finance, retail industry, manufacturing industry, and enterprise management are all included. In spite of all the existing challenges, we conclude that the rapid development of artificial intelligence will show its great impact on more fields. [ABSTRACT FROM AUTHOR]

This paper focuses on the parameter estimation problem of fractional-order neural network. By combining the adaptive control and parameter update law, we generalize the synchronization-based identification method that has been reported in several literatures on identifying unknown parameters of integer-order systems. With this method, parameter identification and synchronization can be achieved simultaneously. Finally, a numerical example is given to illustrate the effectiveness of the theoretical results. [ABSTRACT FROM AUTHOR]

This paper demonstrates how different scientific disciplines can be successfully merged for efficient real life applications. We propose a novel approach to a social science application using artificial intelligence, by suggesting a neural network designto anticipate or predict people’s perceptions regarding the Cyprus conflict and the peace mediation process.Our novel model is based on associating people’s views and thoughts on critical issues related to the conflict together with their anticipation for securing a peace treaty by year 2020. Our database is our own and has been collected through completed surveys by the international relations department students at the Near East University. The obtained digitally-coded values of the entire database as well as the proposed associative neural predicting model can be further used in training and testing the neural model, which certainly waives the way for further research in this rare application area for artificial intelligence. [ABSTRACT FROM AUTHOR]

The fate of many construction projects is determined using preliminary project cost estimates. These estimates play a key role during the conceptual phase of projects; as in many cases they are the primary element used to decide their viability. The lack of information and the high levels of uncertainty during the conceptual phase, make it infeasible to have reliable building information models that could be used to generate quantity takeoffs for preliminary cost estimates (known in the industry as 5D BIM), in line with a Level 2 BIM maturity. This paper presents a way to combine artificial intelligence (case-based reasoning and neural networks), with traditional techniques (regression analysis), to develop accurate estimates of the resources needed in a project (e.g., construction material quantities). These estimates of resources can then be coupled with unit cost information to make preliminary resource-based cost estimates. The clear division between the technical and financial components of such an estimate gives improved decision support to project managers and decision makers. This enhances the tracking and control mechanisms which could be used to check the estimates prepared in subsequent project phases. The combination of case-based reasoning with regression analysis and the use of neural networks has shown an improved performance in the estimation of the amount construction material quantities. The proposed combination was used to estimate the amount of concrete, reinforcement, and structural steel required for the construction of tall-frame structures. The results show lower errors (overall mean absolute percentage error-MAPE) for the combined models (2.55%) when compared to the regression models (12.01%), neural network models (5.84%), and case-based reasoning models (9.30%). [ABSTRACT FROM AUTHOR]

Rockfall risk management has traditionally been built upon scenarios based on statistical analysis of historic frequency-magnitude rockfall patterns which allows for an estimation of the return period of rockfalls within given ranges of magnitude. Τhe availability of rockfall databases is thereby of crucial importance to quantify rockfall activity. In recent years, engineering geologists have been able to leverage light detection and ranging (LiDAR) technology with automated change detection and clustering workflows, for spatially accurate rockfall monitoring and inventorization. This wealth of digital data generates new research prospects for the development of an artificial intelligence (AI) system able to analyze change detection sequences and map spatio-temporal rockfall patterns by precisely capturing the rock slope evolution in near-real-time. However, prerequisite to such an advancement is the need to efficiently automate the process of classifying surface changes corresponding to rockfall. This task typically requires intensive human effort and is prone to multiple sources of human error like subjectivity, expertise, and experience that may differ from person to person involved in a particular project. This paper investigates the potential of integrating sophisticated deep learning architectures into dynamic rockfall database population processes, with the goal of relieving experts from the daunting task of manually classifying rockfall events within large loads of change detection data. Deep neural networks based on the pioneering PointNet and PointNet++ architectures for 3D point cloud learning are developed for this purpose based on a 5-year change detection database consisting of >8000 rock slope clusters of identified change for training, with scanning intervals ranging from 5 days to 6 months. The models are tested on the 536 clusters from the two last data acquisitions to simulate the real monitoring situation and subsequently on the most frequent of the campaigns to increase the probability of working with single-event clusters. The best-performing model achieves an accuracy of about 89% and 84% on the last and shortest campaign, respectively. The optimized deep learning models are further evaluated on a geologically different rockfall database achieving almost 93% accuracy in a location where discrete geomorphologic features such as steep rock outcrops and erosion channels are present. The study shows that although it is challenging to achieve generalization in rockfall detection, site-specific training of the proposed deep learning architecture can lead to high-level performance and support further advancements in rockfall risk management. • LiDAR monitoring provides detailed 3D point cloud models of rock slope deformations. • High-level feature learning from point clouds is achieved with modern deep learning. • Rockfall detection can be approached as a binary point cloud classification problem. • Global shape feature learning is suggested compared to local texture features. • The prediction confidence increases with the cluster volume. [ABSTRACT FROM AUTHOR]

Image/Video Quality Assessment (IQA/VQA) plays a significant role in image and video processing, as it can directly predict the impact of distortions on the video in the quality of experience (QoE) of the user. For this propose, in this paper, it is presented a new method for objective video quality assessment using an artificial neural network to predict the subjective evaluation of the video as if it were observed by a human user. The network was trained using degradation indicators extracted from the VQEG Phase I video database, which describe the level of distortion suffered by the original video under spatial and temporal scopes. The proposed method obtained an excellent correlation with the subjective scores over this same database. [ABSTRACT FROM AUTHOR]

A three-phase intelligent technique has been constructed to improve the data-hiding algorithm in colour images with imperceptibility. The first phase of the learning system (LS) has been applied in advance, whereas the other phases have been applied after the hiding process. The first phase has been constructed to estimate the number of bits to be hidden at each pixel (NBH); this phase is based on adaptive neural networks with an adaptive genetic algorithm using upwind adaptive relaxation (LS ANN _ AGAUpAR1 ). The LS of the second phase (LS ANN _ AGAUpAR2 ) has been introduced as a detector to check the performance of the proposed steganographic algorithm by creating a rich images model from available cover and stego images. The LS of the last phase (LS CANN _ AGAUpAR3 ) has been implemented through three steps, and it is based on a concurrent approach to improve the stego image and defend against attacks. The adaptive image filtering and adaptive image segmentation algorithms have been introduced to randomly hide a compressed and encrypted secret message into a cover image. The NBH for each pixel has been estimated cautiously using 32 principle situations (PS) with their 6 branch situations (BS). These situations have been worked through seven layers of security to augment protection from attacks. In this paper, hiding algorithms have been produced to fight three types of attacks: visual, structural, and statistical attacks. The simulation results have been discussed and compared with new literature using data hiding algorithms for colour images. The results of the proposed algorithm can efficiently embed a large quantity of data, up to 12 bpp (bits per pixel), with better image quality. [ABSTRACT FROM AUTHOR]

In this paper we present the modeling of a neuro-fuzzy system to be applied in the mix of paint from color model cyan, magenta and yellow (CMY). We aim to research an efficient method to get a specific hue color that is located within color wheel. The modeling is focused in to generate a wide gamma of colors to paint crafts like Panama Hats only with mentioned colors. Variables of the system are given by the frame and shape of selection of color. We use a fuzzy controller to generate the magnitude of the CMY percentages and a neural network to interpret the components to dosage the assigned color. Finally proceed data are used to develop an embedded controller under open source, the resulting color from the system is verified using different samples to determine the accuracy with selected color. [ABSTRACT FROM AUTHOR]

Object grasping by robot hands is challenging due to the hand and object modeling uncertainties, unknown contact type and object stiffness properties. To overcome these challenges, the essential purpose is to achieve the mathematical model of the robot hand, model the object and the contact between the object and the hand. In this paper, an intelligent hand-object contact model is developed for a coupled system assuming that the object properties are known. The control is simulated in the Matlab Simulink/SimMechanics, Neural Network Toolbox and Computer Vision System Toolbox. [ABSTRACT FROM AUTHOR]