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The model integrates artificial intelligence (AI) and big data analytics, utilizing IoMT devices for data acquisition and Hadoop ecosystem for managing big data. The field of medical diagnosis is currently undergoing a remarkable transformation with the emergence of artificial intelligence (AI) techniques, particularly deep learning and big data analytics. By harnessing the power of deep learning and big data analytics, AI-based e-diagnosis has the potential to revolutionize healthcare delivery. [Extracted from the article]

As a main electronic material, X-ray circuits are widely used in various electronic devices, and their quality has an important impact on the overall quality of electronic products. In the process of mass production of circuit boards, due to the large number of layers, tight lines and some harmful external factors, circuit board quality may be problematic. Detecting circuit board defects are important for improving the reliability of electronic products. This paper introduces deep learning and artificial intelligence technology to conduct research on the automatic detection of X-ray circuit board defects. The study used a defect detection system to study X-ray circuit boards as a detection object and obtained the structure, lighting system and composition of the detection system. The working principle of the detection system is explained, and the image is preprocessed. Testing the processing performance of the PCB defect detection system, when the number of pixels is 6526, 7028, 7530 and 8032, the time consumption ratios between the proposed detection system and image processing on a traditional PC are 35.17%, 35.4%, 35% and 35.28%, respectively. The experimental results make a certain contribution to the future artificial intelligence X-ray PCB defect automatic diagnosis algorithm. [ABSTRACT FROM AUTHOR]

Bubble point oil formation volume factor (Bob) and solution gas–oil ratio (Rs) are two crucial PVT parameters used for modeling and volumetric calculations in petroleum industry. They are usually determined in laboratory or estimated using empirical correlations. Experimental methods are time-consuming and expensive where empirical correlations have limitations. Artificial intelligence can be sued overcome these limitations to develop more accurate, robust, and quick predictive tools. In this paper, we used three artificial neural network algorithms to develop intelligent models to predict Bob and Rest using 465 experimental data. Application of the Elman neural network (ENN) for this purpose is being reported for the first time. A variety of input parameters were selected based on a sensitivity analysis which include reservoir temperature (T), oil API gravity (°API), bubble point pressure (Pb), gas-specific gravity (γg), and Rs was used to predict the Bob. T, °API, Pb, γg, and Bob was used to predict the Rs. The ENN model was found superior to the other developed smart models and the empirical correlations with coefficient of determination (R2) of 0.993, root mean square error (RMSE) of 0.0093, and average absolute percent relative error (AAPRE) of 0.93% for the Bob and 0.999, 0.016, and 6.72% for the Rs, respectively. The ENN network has fewer adjustable parameters and provides faster training capabilities using fewer neurons and hidden layers compared to other ANN algorithms. The developed smart predictive tools can be safely used instead of laboratory methods and empirical correlations for a much wider ranges of input parameters and with higher accuracy and confidence. [ABSTRACT FROM AUTHOR]

How to build a football match prediction model and use scientific methods to solve the prediction problem has become a key point in the application of artificial intelligence in the sports industry. In this paper, we choose a BP neural network model that is powerful in processing nonlinear data to perform research. According to the demand, this paper constructs a gray fuzzy prediction model based on neural network, a gray extreme learning machine prediction model, and a gray fuzzy extreme learning machine prediction combination model based on neural network. Moreover, this paper tests the neural network model by comparing actual results with predicted results. In addition, by predicting and analyzing the football league data, this article tests the three models in terms of match result prediction accuracy, data processing speed, data transmission accuracy, match analysis scores, etc., and uses statistical analysis methods to process data, and uses intuitive statistical graphs to obtain the processing results. The research results show that the gray fuzzy extreme learning machine prediction combination model based on neural network constructed in this paper can retain the advantages of a single model and effectively improve the prediction accuracy of the model and the performance of the system. [ABSTRACT FROM AUTHOR]

An artificial neural network (ANN) in information technology is a system of hardware or software modeled after the operation of neurons in the human brain. ANNs, often known as "neural networks," are a form of deep learning technology that falls under the umbrella of Artificial Intelligence (AI). Commercial applications of these technologies typically focus on optimization and solving complex signal processing and pattern recognition problems. Multiple types of optimization techniques are utilized to determine the optimal neural network for a model. These procedures help determine and define the model's accuracy, dependability, functionality, and capacity. The convergence of the neural network helps determine the number of training iterations required to generate the fewest errors. In this paper, we investigate an activation function to help reduce the training time of the analog-to-digital converter (ADC). A new Hopfield ADC model is proposed by using the proteretic activation function property. We supported our research by simulating the new ADC converter and comparing the traditional Hopfield ADC, the hysteretic ADC, and the proteretic ADC. Experiment and simulation demonstrate that the proteretic function provides a faster rate of convergence than other functions, thereby enhancing the performance of the ADC application. [ABSTRACT FROM AUTHOR]

Academic performance, a globally understood metric, is utilized worldwide across disparate teaching and learning environments and is regarded as a quantifiable indicator of learning gain. The ability to reliably estimate student's academic performance is important and can assist academic staff to improve the provision of support. However, it is recognized that academic performance estimation is non-trivial and affected by multiple factors, including a student's engagement with learning activities and their social, geographic, and demographic characteristics. This paper investigates the opportunity to develop reliable models for predicting student performance using Artificial Intelligence. Specifically, we propose two-step academic performance prediction using feature weighted support vector machine and artificial neural network (ANN) learning. A feature weighted SVM, where the importance of different features to the outcome is calculated using information gain ratios, is employed to perform coarse-grained binary classification (pass, P 1 , or fail, P 0 ). Subsequently, detailed score levels are divided from D to A+, and ANN learning is employed for fine-grained, multi-class training of the P 1 and P 0 classes separately. The experiments and our subsequent ablation study, which are conducted on the student datasets from two Portuguese secondary schools, have proved the effectiveness of this hybridized method. [ABSTRACT FROM AUTHOR]

The objective of this work is to compare the efficiency of three computational intelligence techniques: Artificial Neural Networks (ANNs), Support Vector Machines (SVMs) and Adaptive Neuro-Fuzzy Inference System (ANFIS) to model the dynamic adsorption of organic pollutants on activated carbon. A comparison study was enhanced using five models: ANN with conventional transfer functions, ANN with new transfer function called "SPOCU", SVM, SVM hybrid with Dragonfly optimisation algorithm (SVM-DA) and ANFIS. A set of data points, collected from scientific papers containing the dynamic adsorption kinetics of adsorption on activated carbon, was used in the modelling process. The studied parameters were molar mass, initial concentration, flow rate, bed height, BET surface area, time and concentration of non-dimensional effluents. Overall, the developed model was able to accurately estimate 11,763 experimental data points gathered from the literature. The performance of the optimised models has been evaluated using different metrics between the experimental and the predicted data. Results show that SVM-DA model can estimate accurately the dynamic adsorption of organic pollutants on activated carbon against the other tested models. Also a graphical user interface is developed in this paper in order to keep the traceability of the estimated results. [ABSTRACT FROM AUTHOR]

The proposed paper presents the analysis, design, implementation and evaluation of an ultra-short-term frequency trading system for the foreign exchange (FOREX) market, which features all stages of the trading process (Pretrade Analysis, Trend Forecasting, Transaction Execution) substantially exploiting artificial intelligence techniques. Our goal is to simulate the judgment and decision making of the human expert (technical analyst or broker) with a system that responds in a timely manner to changes in market conditions, thus allowing the optimization of ultra-short-term transactions. We designed and implemented a series of technical indicator simulators, which are fed to a novel artificial neural network architecture, to eventually generate the trend forecasting signal. We also designed and implemented a series of customizable ultra-short-term automated trading machines, which receive as inputs the generated forecasting signals and perform real-time virtual transactions. A comparative analysis of the results of both automated trading machines and each machine is carried out for a comprehensive variety of trend forecasting sources. [ABSTRACT FROM AUTHOR]

Permeability is an important parameter for oil and gas reservoir characterization. Permeability can be traditionally determined by well testing and core analysis. These conventional methods are very expensive and time-consuming. Permeability estimation in heterogeneous carbonate reservoirs is a challenge task to be handled accurately. Many researches tried to relate permeability and reservoir properties using complex mathematical equations which resulted in inaccurate estimation of the formation permeability values. Permeability prediction based on well logs using artificial intelligent techniques was presented by many authors. They used several wire-line logs such as gamma ray, neutron porosity, bulk density, resistivity, sonic, spontaneous potential, hole size, depths, and other logs. The objective of this paper is to develop an artificial neural network (ANN) model that can be used to predict the permeability of heterogeneous reservoir based on three logs only, namely resistivity, bulk density, and neutron porosity. In addition to the ANN model, in this paper and for the first time a mathematical equation from the ANN model will be extracted that can be used for permeability prediction for any data set without the need for the ANN model. Also, in this study and for the first time we introduced a new term which is the mobility index that can be used effectively in the permeability prediction. Mobility index term is derived from the mobile oil saturation that occurred due to the drilling fluid filtrate invasion. The obtained results showed that ANN model gave a comparable results with support vector machine and adaptive neuro-fuzzy inference system model. The developed mathematical equation from ANN model can be used to estimate the permeability for heterogamous carbonate reservoir based only on three parameters: bulk density, neutron porosity, and mobility index. Actual core data points (1223 points) with the three logs were used to train (857 data points, 70% of the data) and test the model for unseen data (366 data points, 30% of the data). The correlation coefficient for training and testing was 0.95, and the root-mean-square error was 0.28. The developed mathematical equation will help the engineers to save time and predict the permeability with a high accuracy using inexpensive technique. Introducing the new parameter, mobility index, in the prediction process greatly improved the permeability prediction from the log data compared to the actual measured data. [ABSTRACT FROM AUTHOR]

Manual calculation of the compressive strength of concrete (CSC) is an expensive and time-consuming process. Soft computing methods outperform the statistical methods used to resolve these problems. Nonetheless, complicated prediction models are still incomplete and require more exploration. Artificial neural networks (ANNs) provide a better and faster technique featuring solitary hidden layers and have improved the generalization capacity. The present paper presents three ANN-based (shuffled complex evolution, evaporation rate based water cycle algorithm (ERWCA), and Cuckoo optimization algorithm) prediction models to anticipate the compressive strength of concrete efficiently. An available database from the UCI repository is employed to develop and access the model performance. A comparison is made between the prediction accuracies of the above three techniques. Using all models, a comparative investigation has been conducted to predict the compressive strength of concrete at the curing ages of 91, 56, and 28 days. The experimental findings obtained from the ERWCA-MLP method indicate its capability of robust CSC prediction. On average, this method achieves the minimum RMSE of 0.55314 and 0.43329 and R2 of 0.99803 and 0.99824. The statistical significance test and the comparative analysis of simulation results indicate the superiority of ERWCA-MLP in predicting the compressive strength of concrete. [ABSTRACT FROM AUTHOR]

Multimedia IoT (M-IoT) is an emerging type of Internet of things (IoT) relaying multimedia data (images, videos, audio and speech, etc.). The rapid growth of M-IoT devices enables the creation of a massive volume of multimedia data with different characteristics and requirements. With the development of artificial intelligence (AI), AI-based multimedia IoT systems have been recently designed and deployed for various video-based services for contemporary daily life, like video surveillance with high definition (HD) and ultra-high definition (UHD) and mobile multimedia streaming. These new services need higher video quality in order to meet the quality of experience (QoE) required by the users. Versatile video coding (VVC) is the new video coding standard that achieves significant coding efficiency over its predecessor high-efficiency video coding (HEVC). Moreover, VVC can achieve up to 30% BD rate savings compared to HEVC. Inspired by the rapid advancements in deep learning, we propose in this paper a wide-activated squeeze-and-excitation deep convolutional neural network (WSE-DCNN) technique-based video quality enhancement for VVC. Therefore, we replace the conventional in-loop filtering in VVC by the proposed WSE-DCNN model that eliminates the compression artifacts in order to improve visual quality and hence increase the end user QoE. The obtained results prove that the proposed in-loop filtering technique achieves - 2.85 %, - 8.89 %, and - 10.05 % BD rate reduction for luma and both chroma components under random access configuration. Compared to the traditional CNN-based filtering approaches, the proposed WSE-DCNN-based in-loop filtering framework achieves efficient performance in terms of RD cost. [ABSTRACT FROM AUTHOR]

Automated continuous condition monitoring of industrial electrical machines to identify internal faults has become one of the critical research areas for the past decade. Among various defects, early-stage identification of insulation failure in stator winding is of notable demand as it often occurs and accounts for 37% of the overall motor failures. Identifying the current signature at its embryonic stage will effectively improve industrial machinery's downtime and repair costs. Recent advances in computational performance and sensor technology concede advanced systems for achieving these goals. The design of an AI-based fault signature analyzer (FSA) has been developed in this paper. FSA uses real-time stator current data in the time and frequency domain from healthy and faulty induction motors to train the various AI-based machine learning classifiers to identify health conditions using wavelets. Comparing machine learning algorithms such as artificial neural network, random forest, fuzzy logic, neuro-fuzzy logic, K-nearest neighbors is performed, and various performance attributes are quantified. A reliable, automatic fault signature from a motor current is thus analyzed using the fusion of a wavelet-based feature extraction technique and a capable knowledge-based efficient artificial intelligence (AI) approach. [ABSTRACT FROM AUTHOR]

This paper is concerned with the mixed H∞ and dissipativity performance for Markovian jump neural networks with time delay in the leakage term and randomly occurring uncertainties. The randomly occurring uncertainties are assumed to be mutually uncorrelated Bernoulli-distributed white noise sequences. By introducing a triple-integrable term in the Lyapunov functional, the Wirtinger-based double-integral inequality is utilized to bound the derivative of the triple-integral term and then a sufficient condition is derived to ensure that the considered neural networks to be strict (Q,S,R)-γ-dissipative and passive. These conditions are presented in terms of linear matrix inequalities, which can be easily solved by using standard numerical software. Finally, numerical examples are given to show the effectiveness and the potential significance of the proposed results. [ABSTRACT FROM AUTHOR]

Liao et al. (Neurocomputing 128:81-87, 2014) proposed a meta-learning approach to extreme learning machine (Meta-ELM), which can obtain good generalization performance by training multiple ELMs. However, one of its open problems is overfitting when minimizing training error. In this paper, we propose an improved meta-learning model of ELM (improved Meta-ELM) to handle the problem. The improved Meta-ELM architecture is composed of some base ELMs which are error feedback incremental extreme learning machine (EFI-ELM) and the top ELM. The improved Meta-ELM includes two stages. First, each base ELM with EFI-ELM is trained on a subset of training data. Then, the top ELM learns with the base ELMs as hidden nodes. Simulation results on some artificial and benchmark datasets show that the proposed improved Meta-ELM model is more feasible and effective than Meta-ELM. [ABSTRACT FROM AUTHOR]

This paper investigates a class of non-autonomous cellular neural networks with mixed delays. Based on the basic theory of the weighted pseudo-almost periodic functions, several sufficient conditions are established to ensure that every solution of the addressed model exponentially tends to a weighted pseudo-almost periodic solution as t→+∞, which generalize some existing ones. In particular, some numerical examples are also given. [ABSTRACT FROM AUTHOR]

This paper proposes a pursuit system that utilizes the artificial life concept where autonomous mobile agents emulate the social behavior of animals and insects and realize their group behavior. Each agent contains sensors to perceive other agents in several directions, and decides its behavior based on the information obtained by these sensors. In this paper, a neural network is used for behavior decision controlling. The input of the neural network is decided by the existence of other agents, and the distance to the other agents. The output determines the directions in which the agent moves. The connection weight values of this neural network are encoded as genes, and the fitness individuals are determined using a genetic algorithm. Here, the fitness values imply how much group behavior adequately fit the goal and can express group behavior. The validity of the system is verified through simulation. Also in this paper, we have observed the agents’ emergent behavior during simulation. [ABSTRACT FROM AUTHOR]

Experience shows that cooperating and communicating computing systems, comprising segregated single processors, have severe performance limitations, which cannot be explained using von Neumann's classic computing paradigm. In his classic "First Draft," he warned that using a "too fast processor" vitiates his simple "procedure" (but not his computing model!); furthermore, that using the classic computing paradigm for imitating neuronal operations is unsound. Amdahl added that large machines, comprising many processors, have an inherent disadvantage. Given that artificial neural network's (ANN's) components are heavily communicating with each other, they are built from a large number of components designed/fabricated for use in conventional computing, furthermore they attempt to mimic biological operation using improper technological solutions, and their achievable payload computing performance is conceptually modest. The type of workload that artificial intelligence-based systems generate leads to an exceptionally low payload computational performance, and their design/technology limits their size to just above the "toy" level systems: The scaling of processor-based ANN systems is strongly nonlinear. Given the proliferation and growing size of ANN systems, we suggest ideas to estimate in advance the efficiency of the device or application. The wealth of ANN implementations and the proprietary technical data do not enable more. Through analyzing published measurements, we provide evidence that the role of data transfer time drastically influences both ANNs performance and feasibility. It is discussed how some major theoretical limiting factors, ANN's layer structure and their methods of technical implementation of communication affect their efficiency. The paper starts from von Neumann's original model, without neglecting the transfer time apart from processing time, and derives an appropriate interpretation and handling for Amdahl's law. It shows that, in that interpretation, Amdahl's law correctly describes ANNs. [ABSTRACT FROM AUTHOR]

As a consequence of increasing wind power penetration level, it will be a big challenge to control and operate the power system because of the inherent uncertainty of the wind energy. One of the ways to deal with the wind power variability is to predict it accurately and reliably. The traditional point forecasting-based technique cannot notably solve the uncertainty in power system operation. In order to compute the probabilistic forecasting, which yields information on the uncertainty of wind power, a novel hybrid intelligent method that incorporates the wavelet transform, neural network (NN), and improved krill herd optimization algorithm (IKHOA), is used in this paper. Also, the extreme learning machine is exerted to train NN and calculates point forecasts, and IKHOA is applied to forecast the noise variance. The robust method called bootstrap is regarded to create prediction intervals and calculate the model uncertainty. The efficiency of proposed forecasting engine is evaluated by usage of wind power data from the Alberta, Canada. [ABSTRACT FROM AUTHOR]

This paper investigates the ability of four artificial intelligence techniques, including artificial neural network (ANN), radial basis neural network (RBNN), adaptive neuro-fuzzy inference system (ANFIS) with grid partitioning, and ANFIS with fuzzy c-means clustering, to predict the peak and residual conditions of actively confined concrete. A large experimental test database that consists of 377 axial compression test results of actively confined concrete specimens was assembled from the published literature, and it was used to train, test, and validate the four models proposed in this paper using the mentioned artificial intelligence techniques. The results show that all of the neural network and ANFIS models fit well with the experimental results, and they outperform the conventional models. Among the artificial intelligence models investigated, RBNN model is found to be the most accurate to predict the peak and residual conditions of actively confined concrete. The predictions of each proposed model are subsequently used to study the interdependence of critical parameters and their influence on the behavior of actively confined concrete. [ABSTRACT FROM AUTHOR]

A constrained optimization method based on back-propagation (BP) neural network is proposed in this paper. Taking the maximization of output for example, using unipolar sigmoid function as transfer function, the method presents a general mathematical expression of BP neural network constrained optimization and derives the partial derivative of output with respect to input. On this basis, the fundamental idea, algorithms and related models are given in this article. When BP neural network is on the basis of fitting, this method can adjust the input values of BP neural network to make the output values maximal or minimal. Therefore, with this method the application of BP neural network is expanded by combining BP network's fitting with optimization. At the same time, the article also provides a new method to study the black-box problem. The experiments show that the constrained optimization method is effective. [ABSTRACT FROM AUTHOR]

The analysis on economic data based on time series takes an important position in the field of analysis on time-series data and is also an important task of the field of big data and artificial intelligence. Traditional time-series analysis method is of relatively weak competence in dealing with multi-series analysis. In this research, based on the problem associated with the analysis on time-series economic data, efficient handling method and model are put forward in the face of multi-series analysis task. Also, combined with the association rules, trend correlation and self-trend correlation among multiple series, a trend and correlation deep neural network model (TC-DNM) is established and then tested and verified by using three kinds of economic datasets with representativeness based on the trend analysis task handed by multi-series analysis. The results show that the model proposed in this research is effective than a number of baseline models, can be employed to achieve precision–recall balance and also possesses strong reusability. The two correlation models and joint models in this paper are of peculiarity and innovativeness. [ABSTRACT FROM AUTHOR]

Wind energy is getting more and more integrated into power grids, giving rise to some challenges because of its inherent intermittent and irregular nature. Wind speed forecasting plays a fundamental role in overcoming such challenging issues and, thus, assisting the power utility manager in optimizing the supply–demand balancing through wind energy generation. This paper suggests a new hybrid scheme WNN, based on discrete wavelet transform (DWT) combined with artificial neural network (ANN), for wind speed forecasting. More specifically, this work aims at designing the most appropriate discrete wavelet filters, best adapted to a one day ahead wind speed forecasting. The optimized DWT filters are intended to effectively preprocess the wind speed time series data in order to enhance the prediction accuracy. Using wind speed data collected from three different locations in the Magherbian region, the obtained simulation results indicate that the proposed approach outperforms other conventional wavelet-based forecasting structures regarding the wind speed prediction precision. Moreover, compared to the standard wavelet 'db4' based approach, the optimized wavelet filter-based structure leads to a forecasting accuracy improvement, in terms of RMSE and MAPE index errors, that amounts to nearly 13% and 19%, respectively. [ABSTRACT FROM AUTHOR]

The use of artificial neural networks as the objective function of optimization algorithms is proposed in the recent past. In this paper, the use of artificial neural networks ensemble as objective function in place of single artificial neural networks is proposed. An ensemble hybrid algorithm is developed by using artificial neural networks and bacterial foraging optimization algorithm technique for designing a fractal antenna of a rectenna system working at 2.45 GHz. The closed form expressions are not available for the fractal antennas, so the use of artificial intelligence techniques for their design is appropriate. As the size reduction in rectenna systems used in wireless devices is a significant research domain to meet the demand for reduced size handheld devices, so the geometry of the antenna is selected to achieve this objective and a size reduction of 34.39 % is attained. The bandwidth enhancement of the proposed antenna is also achieved so that it can be used over wide band. The performance of the proposed optimized fractal antenna is verified using simulation and experimental results. [ABSTRACT FROM AUTHOR]

In this paper, an adaptive robust finite-time neural control scheme is proposed for uncertain permanent magnet synchronous motor servo system with nonlinear dead-zone input. According to the differential mean value theorem, the dead zone is represented as a linear time-varying system, and the model uncertainty including the dead zone is approximated by using a simple neural network. Then, an adaptive finite-time controller is designed based on a fast terminal sliding mode control principle, and the singularity problem in the initial TSMC is circumvented by modifying the terminal sliding manifold. Comparative experiments are conducted to validate the effectiveness and superior performance of the proposed method. [ABSTRACT FROM AUTHOR]

This paper addresses the globally stable tracking control problem of a class of uncertain multiple-input-multiple-output nonlinear systems. By employing the radial basis function neural networks to compensate for the system uncertainties, a novel switching controller is developed. The key features of the proposed control scheme are presented as follows. First, to design the desired adaptive neural controller successfully, an nth-order smoothly switching function is constructed originally. Second, the number of the neural networks and the adaptive parameters is reduced by adopting the direct adaptive approach, so a simplified controller is designed and it is easy to implement in practice. By utilizing the special properties of the affine terms of the considered systems, the singularity problem of the controller is completely avoided. Finally, the overall controller guarantees that all the signals in the closed-loop system are globally uniformly ultimately bounded and the system output converges to a small neighborhood of the reference trajectory by appropriately choosing the design parameters. A simulation example is given to illustrate the effectiveness of the proposed control scheme. [ABSTRACT FROM AUTHOR]

In this paper, the event-triggered H state estimation problem is investigated for a class of neural networks with mixed time delays and sensor saturations. The mixed time delays consist of discrete and distributed delays. The measurement outputs are subject to the sensor saturations due to the physical constraints. Through the available measurement outputs, the main purpose of the addressed problem is to design a state estimator to estimate the actual neural states. In order to improve the efficiency in resource utilization, an event-triggered mechanism is employed to decide whether the received measurement output is transmitted to the state estimator. Different from the existing event-triggering strategies, the triggering condition is given for each sensor, and the measurement output from each sensor is sent according to their separate triggering conditions. By using the Lyapunov functional approach, sufficient conditions are derived to guarantee that the estimation error dynamics is exponentially stable and the H performance requirement is satisfied. Then, the desired H state estimator is designed in terms of the solution to a linear matrix inequality that can be easily solved by the MATLAB toolboxes. Finally, one simulation example is provided to show the effectiveness of the proposed event-triggered estimation scheme. [ABSTRACT FROM AUTHOR]

Early-warning models provide means for ex ante identification of elevated risks that may lead to a financial crisis. This paper taps into the early-warning literature by introducing biologically inspired models for predicting systemic financial crises. We create three models: a conventional statistical model, a back-propagation neural network (NN) and a neuro-genetic (NG) model that uses a genetic algorithm for choosing the optimal NN configuration. The models are calibrated and evaluated in terms of usefulness for policymakers that incorporates preferences between type I and type II errors. Generally, model evaluations show that biologically inspired models outperform the statistical model. NG models are, however, shown not only to provide largest usefulness for policymakers as an early-warning model, but also in form of decreased expertise and labor needed for, and uncertainty caused by, manual calibration of an NN. For better generalization of data-driven models, we also advocate adopting to the early-warning literature a training scheme that includes validation data. [ABSTRACT FROM AUTHOR]

Recorded time series of relative humidity (RH) are modeled by using genetic expression programming (GEP) and artificial neural networks (ANNs) models. The data are noisy and contain missing datapoints. RH is modeled as a function of three meteorological variables: temperature, wind speed, and pressure. Various model structures of both of these models are investigated with the aim of testing the robustness of the predicted values in the presence of noise and missing data. Due to the presence of noise, a sophisticated treatment of missing data was not justifiable, and therefore, the strategy adopted was just to carry the datapoints backward, although this may induce bias in the time dimension and contaminate the predicted results. The results of this study indicate that through a careful selection of model structures both GEP and ANN can produce adequately reliable prediction of RH values 1 year into the future. The paper provides evidence that this model structure is feasible when the dependent variables include both the present and past values. [ABSTRACT FROM AUTHOR]

In this paper, an intelligent operation system, which consists of an intelligent diagnostic subsystem (with a neural network) and an intelligent maintenance subsystem (with an expert system), was presented and discussed. The artificial neural network and the expert system, which use the information developed in the neural network, perform a special function in this system. The functional combination of the artificial neural network and the expert system together created a new solution in the form of an intelligent system, which was referred to as an intelligent maintenance system. This article also covers decision-making methods that are used in an expert maintenance system and whose purpose is an organization and control of the process of the prevention of technical objects. For this purpose, the method was described of taking decisions by an expert for complex parametric type hypotheses and for simple finished type hypotheses in the set of possible decisions’ hypotheses. A considerable part of this paper covers the presentation of the method to transform diagnostic information into the required form of maintenance information. For this purpose, an algorithm of the work of maintenance system was performed and descried. In the creation process of the maintenance knowledge base, the specialist knowledge of a human specialist was also used. Hence, a skilful and proper taking of decisions by an expert to create this set of information is essential. Two inference methods were characterized and described in this paper. The theoretical results obtained were verified in the examination of the influence of each of these decision-making inference methods on the final results of the process of the prevention treatment of an object. [ABSTRACT FROM AUTHOR]

The key to the coordination of photovoltaic power generation and conventional energy power load lies in the accurate prediction of photovoltaic power generation. At present, prediction models have problems with accuracy and system operation stability. Based on the neural network algorithm, this research carries the prediction of energy photovoltaic power generation and establishes a BP neural network prediction model and a wavelet neural network prediction model. Moreover, this research studies the influence of various factors on the prediction t of photovoltaic power generation, and analyzes the relationship between the various factors. In addition, in this study, a comparative test is constructed to analyze the model performance, and a statistical graph is drawn to take a visual comparison of performance. The research shows that the model proposed in this paper has certain effects and has certain advantages in the prediction of photovoltaic power generation. [ABSTRACT FROM AUTHOR]

The automatic narration of a natural scene is an important trait in artificial intelligence that unites computer vision and natural language processing. Caption generation is a challenging task in scene understanding. Most of the state-of-the-art methods are using deep convolutional neural network models to extract visual features of the entire image, based on which the parallel structures between images and sentences are exploited using recurrent neural networks for image captioning. However, in such models, only visual features are exploited for caption generation. This work investigated that fusion of text available in an image can give more fined-grained captioning of a scene. In this paper, we have proposed a model which incorporates a deep convolutional neural network and long short-term memory to boost the accuracy of image captioning by fusing text feature available in an image with the visual features extracted in state-of-the-art methods. We have validated the effectiveness of the proposed model on the benchmark datasets (Flickr8k and Flickr30k). The experimental outcomes illustrate that the proposed model outperformed the state-of-the-art methods for image captioning. [ABSTRACT FROM AUTHOR]

Supplier evaluation and selection is a complicated process which deals with conflicting attributes such as quality, cost. To mitigate the computational complexity, intelligent-based techniques have gained much popularity. But the main shortcoming of the existing models in this regard is to be a black box system. In this paper, we aim to combine analytical hierarchy process with multi-expression programming to both introduce a new evolutionary approach in the field of supplier evaluation and selection and cope with the earlier problem. To show the validity of the model, statistical test was carried out. The finding showed that the proposed model is accurate and acceptable for using in the evaluation process. [ABSTRACT FROM AUTHOR]

This paper focuses on the problem of delay-dependent robust stability analysis for a class of uncertain stochastic neural networks with time-varying delay by employing improved free-weighting matrix method. Taking the relationship among the time-varying delay, its upper bound and their difference into account and using $$\hbox{It}\hat{o}\hbox{'s}$$ differential formula, some improved LMI-based delay-dependent stability criteria for stochastic neural networks are obtained without ignoring any terms, which guarantee systems globally robustly stochastically stable in the mean square. Finally, three numerical examples are given to demonstrate the effectiveness and the benefits of the proposed method. [ABSTRACT FROM AUTHOR]

The paper presents a system for the diagnosis of repairable technical objects with the use of an artificial neural network of a radial basis function (RBF) type. The structure and the algorithm of the work of an RBF type neural network are described. This paper presents a method to control an operation process of a complex technical object with the use of trivalent diagnostic information. Also, a general diagram of the complex technical object was presented, and its internal structure was described. A diagnostic analysis was conducted, as a result of which the sets of the functional elements of the object and its diagnostic signals were determined. Also, the methodology of the diagnostic examination of the technical system was presented. The result was a functional and diagnostic model, which constituted the basis for initial diagnostic information which is provided by the sets of information concerning the elements of the basic modules and their output signals. The final results obtained for the computations conducted by the DIAG programme were presented in the table of the states of the object. [ABSTRACT FROM AUTHOR]

A new growing and pruning algorithm is proposed for radial basis function (RBF) neural network structure design in this paper, which is named as self-organizing RBF (SORBF). The structure of the RBF neural network is introduced in this paper first, and then the growing and pruning algorithm is used to design the structure of the RBF neural network automatically. The growing and pruning approach is based on the radius of the receptive field of the RBF nodes. Meanwhile, the parameters adjusting algorithms are proposed for the whole RBF neural network. The performance of the proposed method is evaluated through functions approximation and dynamic system identification. Then, the method is used to capture the biochemical oxygen demand (BOD) concentration in a wastewater treatment system. Experimental results show that the proposed method is efficient for network structure optimization, and it achieves better performance than some of the existing algorithms. [ABSTRACT FROM AUTHOR]

Architecture design is a very important issue in neural network research. One popular way to find proper size of a network is to prune an oversize trained network to a smaller one while keeping established performance. This paper presents a sensitivity-based approach to prune hidden Adalines from a Madaline with causing as little as possible performance loss and thus easy compensating for the loss. The approach is novel in setting up a relevance measure, by means of an Adalines’ sensitivity measure, to locate the least relevant Adaline in a Madaline. The sensitivity measure is the probability of an Adaline’s output inversions due to input variation with respect to overall input patterns, and the relevance measure is defined as the multiplication of the Adaline’s sensitivity value by the summation of the absolute value of the Adaline’s outgoing weights. Based on the relevance measure, a pruning algorithm can be simply programmed, which iteratively prunes an Adaline with the least relevance value from hidden layer of a given Madaline and then conducts some compensations until no more Adalines can be removed under a given performance requirement. The effectiveness of the pruning approach is verified by some experimental results. [ABSTRACT FROM AUTHOR]

Spiking neural network (SNN) trained by spike-timing-dependent plasticity (STDP) is a promising computing paradigm for energy-efficient artificial intelligence systems. During the learning procedure of SNN trained by STDP, another two bio-inspired mechanisms of lateral inhibition and homeostasis are usually implemented to achieve competitive learning. However, the previous methods to implement lateral inhibition and homeostasis are not designed with hardware in mind, resulting in solutions that are not efficient for deployment on neuromorphic hardware. For example, the existing lateral inhibition methods induce a great number of connections that are proportional to the square of the number of learning neurons. The classical homeostasis methods depend on the fine-tuned membrane threshold with no hardware solution provided. In this paper, we propose two hardware-friendly and scalable methods to achieve lateral inhibition and homeostasis. Using only one inhibitory neuron for one learning layer, our proposed lateral inhibition method can reduce inhibitory connection number from N 2 to 2N and hardware overhead by sharing refractory control circuits. Utilizing the adaptive resistance of memristor, we propose a novel homeostasis method through adapting the leaky current of spiking neurons. In addition, the learning efficiency of different homeostasis methods are studied for the first time by simulating on the cognitive task of digital recognition of MNIST dataset. Simulation results show that our proposed homeostasis method can improve the learning efficiency by 30–50% while maintaining the state-of-the-art performance. [ABSTRACT FROM AUTHOR]

In the field of artificial intelligence, the development of many technologies requires technical support for relational classification. Recently, deep learning has been applied more and more to text-based entity relationship classification tasks, but most of the previous methods need to use syntax or dependency structure feature. However, due to the time and space complexity of syntactic parsing, the structural features are inconvenient to use directly in the pre-processing stage. In addition, structural features may have serious domain dependence problems. This paper studies the current recommendation algorithm, analyzes the current research status of the recommendation system, and deeply analyzes the research of deep learning in the field of recommendation systems, based on BPSO algorithm, the context complex segmentation method is applied, and then the deep convolutional neural network is applied for feature extraction. The extracted feature set is sent to WordEmbedding, and using the technology to generates the word vector, the input layer of the CBOW is used to represent the size of the training window. The experimental results show that the model has obvious advantages over the methods proposed in other literature. It can adapt to multi-category context semantic analysis, more accurate related recommendations, and obtain a better user experience. [ABSTRACT FROM AUTHOR]

Editorial. Introduces a series of articles on neural computing.

Scene analysis is an important area of research with the aim of identifying objects and their relationships in natural scenes. MINERVA benchmark has been recently introduced in this area for testing different image processing and classification schemes. In this paper we present results on the classification of eight natural objects in the complete set of 448 natural images using neural networks. An exhaustive set of experiments with this benchmark has been conducted using four different segmentation methods and five texture-based feature extraction methods. The results in this paper show the performance of a neural network classifier on a tenfold cross-validation task. On the basis of the results produced, we are able to rank how well different image segmentation algorithms are suited to the task of region of interest identification in these images, and we also see how well texture extraction algorithms rank on the basis of classification results. [ABSTRACT FROM AUTHOR]

The problem of obtaining the error at the output of a neuro sliding mode controller is analyzed in this paper. The controller operates in discrete time and the method presented describes an error measure that can be used if the task to be achieved is to drive the system under control to a predefined sliding regime. Once the task-specific output error is calculated, the neurocontroller parameters can be tuned so that the task is achieved. The paper postulates the strategy for discrete time representation of uncertain nonlinear systems belonging to a particular class. The performance of the proposed technique has been clarified on a third order nonlinear system, and the parameters of the controller are adjusted by using the error backpropagation algorithm. It is observed that the prescribed behavior can be achieved with a simple network configuration. [ABSTRACT FROM AUTHOR]

This paper proposes using neural networks (NN) to implement a real coded genetic algorithm (GA) with the center of gravity crossover (CGX) and the minimal generation gap (MGG) model. With all genetic operations of GA including selection, crossover, mutation and evaluation implemented with NN modules, this approach can realize in parallel genetic operations on the whole chromosome to achieve the maximum parallel realization potential of the MGG model of the GA. At the same time expensive hardware for field programmable gate arrays (FPGA) and the high speed memory of hardware for GA can be avoided. The performance of our solution is validated with a suite of benchmark test functions. This paper suggests that implementing GA with NN is a promising research direction for greatly reducing the running time of GA. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the method of solving partial differential equations (PDEs) using artificial neural network (ANN) structures, which have been actively applied in artificial intelligence models. The ANN model for solving PDEs offers the advantage of providing explicit and continuous solutions. However, the ANN model for solving PDEs cannot construct a conventionally solvable linear system with known matrix solvers; thus, computational speed could be a significant concern. We study the implementation of the multigrid method, developing a general concept for a coarse-grid correction method to be integrated into the ANN-PDE architecture, with the goal of enhancing computational efficiency. By developing a reduced form of the multigrid method for ANN, we demonstrate that it can be interpreted as an equivalent representation of the classic multigrid expansion. We validated the applicability of the proposed method through rigorous experiments, which included analyzing loss decay and the number of iterations along with improvements in terms of accuracy, speed, and complexity. We accomplished this by employing the gradient descent method and the Broyden–Fletcher–Goldfarb–Shanno (BFGS) method to update the gradients while solving the given ANN systems of PDEs. [ABSTRACT FROM AUTHOR]

The formation of manufacturing cells forms the backbone of designing a cellular manufacturing system. In this paper, we present a novel intelligent particle swarm optimization algorithm for the cell formation problem. The proposed solution method benefits from the advantages of particle swarm optimization algorithm (PSO) and self-organization map neural networks by combining artificial individual intelligence and swarm intelligence. Numerical examples demonstrate that the proposed intelligent particle swarm optimization algorithm significantly outperforms PSO and yields better solutions than the best solutions existed in the literature of cell formation. The application of the proposed approach is examined in a case problem where real data is utilized for cell reconfiguration of an actual company involved in agricultural manufacturing sector. [ABSTRACT FROM AUTHOR]

This paper deals with the problem of global stability of stochastic reaction-diffusion neural networks with impulses. The influence of diffusions, noises, delays, impulses, and Levy jumps upon the stability of the concerned system is discussed. A sufficient condition is obtained to ensure the existence, uniqueness, and exponential p-stability of the equilibrium point of the addressed stochastic reaction-diffusion neural networks with impulses by using M-matrix theory and stochastic analysis. The proposed results extend those in the earlier literature and are easier to verify. [ABSTRACT FROM AUTHOR]

A numeric quantity that characterizes the whole structure of a network is called a topological index. In the studies of quantitative structure-activity relationship and quantitative structure-property relationship, the topological indices are utilized to guess the physical features related to the bioactivities and chemical reactivities in certain networks. A neural network is a computer system modeled on the nerve tissue and nervous system. The neural networks are not only studied in Neurochemistry. There are many applications of these networks in different areas of studies such as intrusion detection system, image processing, artificial intelligence, localization, medicine, chemical, and environmental sciences. In this paper, we compute the degree-based topological indices of the probabilistic neural network for the first time. At the end, a numerical comparison between all the indices is also shown with the help of the Cartesian coordinate system. [ABSTRACT FROM AUTHOR]

System identification problems are generally inverse vibration problems. Sometimes it is difficult to handle the inverse problems by traditional methods and classical artificial neural network. As such, the objective of this paper is to identify structural parameters by developing a novel functional link neural network (FLNN) model. FLNN model is more efficient than multi-layer neural network (MNN) as computation is less because hidden layer is not required. Here, single-layer neural network with multi-input and multi-output with feed-forward neural network model and principle of error back propagation has been used to identify structural parameters. The hidden layer is excluded by enlarging the input patterns with the help of Legendre and Hermite polynomials. Comparison of results among MNN, Legendre neural network, Hermite neural network and desired is considered and it is found that FLNN models are more effective than MNN. [ABSTRACT FROM AUTHOR]

Automatic network clustering is an important method for mining the meaningful communities of complex networks. Uncovered communities help to understand the potential system structure and functionality. Many algorithms that use multiple optimization criteria and optimize a population of solutions are difficult to apply to real systems because they suffer a long optimization process. In this paper, in order to accelerate the optimization process and to uncover multiple significant community structures more effectively, a multi-objective evolutionary algorithm is proposed and evaluated using problem-specific genetic mutation and group crossover, and problem-specific initialization. Since crossover operators mainly contribute to performance of genetic algorithms, more problem-specific group crossover operators are introduced and evaluated for intelligent evolution of population. The experiments on both artificial and real-world networks demonstrate that the proposed evolutionary algorithm with problem-specific genetic operations has effective performance on discovering the community structure of networks. [ABSTRACT FROM AUTHOR]

This paper presents an adaptive technique for obtaining centers of the hidden layer neurons of radial basis function neural network (RBFNN) for face recognition. The proposed technique uses firefly algorithm to obtain natural sub-clusters of training face images formed due to variations in pose, illumination, expression and occlusion, etc. Movement of fireflies in a hyper-dimensional input space is controlled by tuning the parameter gamma (γ) of firefly algorithm which plays an important role in maintaining the trade-off between effective search space exploration, firefly convergence, overall computational time and the recognition accuracy. The proposed technique is novel as it combines the advantages of evolutionary firefly algorithm and RBFNN in adaptive evolution of number and centers of hidden neurons. The strength of the proposed technique lies in its fast convergence, improved face recognition performance, reduced feature selection overhead and algorithm stability. The proposed technique is validated using benchmark face databases, namely ORL, Yale, AR and LFW. The average face recognition accuracies achieved using proposed algorithm for the above face databases outperform some of the existing techniques in face recognition. [ABSTRACT FROM AUTHOR]

Computational intelligent systems are becoming an increasingly attractive solution for power amplifier (PA) behavioural modelling, due to their excellent approximation capability. This paper utilizes an adaptive fuzzy logic system (AFLS) for the modelling of the highly nonlinear MIMIX CFH2162-P3 PA. Moreover, PA’s inverse model based also on AFLS has been developed in order to act as a pre-distorter unit. Driving an LTE 1.4 MHz 64 QAM signal at 880 MHz as centre frequency at PA’s input, very good modelling performance was achieved, for both PA’s forward and inverse dynamics. A comparative study of AFLS and neural networks (NN) has been carried out to establish AFLS as an effective, robust and easy-to-implement baseband model, which is suitable for inverse modelling of PAs and capable to be used as an effective digital pre-distorter. Pre-distortion system based on AFLS achieved distortion suppression of 84.2%, compared to the 48.4% gained using the NN-based equivalent scheme. [ABSTRACT FROM AUTHOR]