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With the limitation of air traffic and the rapid increase in the number of flights, flight delay is becoming more frequent. Flight delay leads to financial and time losses for passengers and increases operating costs for airlines. Therefore, the establishment of an accurate prediction model for flight delay becomes vital to build an efficient airline transportation system. The air transportation system has a huge amount of data and complex operation modes, which is suitable for analysis by using machine learning methods. This paper discusses the factors that may affect the flight delay, and presents a new flight delay prediction model. The five warning levels are defined based on flight delay database by using K-means clustering algorithm. After extracting the key factors related to flight operation by the grey relational analysis (GRA) algorithm, an improved machine learning algorithm called GRA — Genetic algorithm (GA) — back propagation neural network, GRA-GA-BP, is introduced, which is optimized by GA. The calculation results show that, compared with models before optimization and other two algorithms in previous papers, the proposed prediction model based on GRA-GA-BP algorithm shows a higher prediction accuracy and more stability. In terms of operation efficiency and memory consumption, it also has good performance. The analysis presented in this paper indicates that this model can provide effective early warnings for flight delay, and can help airlines to intervene in flights with abnormal trend in advance. [ABSTRACT FROM AUTHOR]

Vocal fold, a significant body structure, is accountable for phonation, which regulates air motion within and out of the lungs. The disorders in the vocal fold influence the quality of life. Thus, diagnosis of vocal fold disorders has a significant need, and CT of the neck is employed for an effective imaging scheme. Accordingly, this paper proposes an advanced multi-resolution algorithm (MRA) that optimally identifies and classifies pathologies. The vocal regions are acquired using the genetic k-means algorithm. The pathology features are generated using the local directional pattern (LDP) fed to pathology classification using moth search-rider optimization-based deep convolutional neural networks (MRA-based DCNN). The hybrid optimization (MRA), integrates the standard rider optimization algorithm (ROA) and moth search algorithm (MS) that trains deep learning classifier (DCNN). The analysis using the real databases regarding the performance metrics divulge that the proposed pathology detection module obtained the accuracy, specificity, and sensitivity of 97.020%, 91.698%, and 96.624%. [ABSTRACT FROM AUTHOR]

Siamese network-based object tracking algorithms have recently gained popularity due to continuous improvement in tracking accuracy and robustness. However, these trackers are often limited by inefficient initialization and unstable online update. The model drift accumulates as more frames are processed. To address the above issues, this paper introduced a method that combines both deep learning and conventional regression algorithms (SVR, Support Vector Regression). Specifically, we propose a feature projection algorithm, which can effectively reduce the dimension of the features extracted from deep neural networks and improve the distinguishability of them at the same time. To make the features more robust for SVR training, we further propose two feature aggregation methods at both the channel level and the spatial level. We update the SVR model online instead of the deep neural network to make the tracking process more robust. Extensive experiments on four challenging benchmarks indicate that our proposed tracker is superior to baseline methods both qualitatively and quantitatively. [ABSTRACT FROM AUTHOR]

With the increasing demand for security in many fastest growing applications, biometric recognition is the most prominent authentication system. User authentication through speech and face recognition is the important biometric technique to enhance the security. This paper proposes a speech and facial feature-based multi-modal biometric recognition technique to improve the authentication of any system. Mel Frequency Cepstral Coefficients (MFCC) is extracted from audio as speech features. In visual recognition, this paper proposes cascade hybrid facial (visual) feature extraction method based on static, dynamic and key-point salient features of the face and it proves that the proposed feature extraction method is more efficient than the existing method. In this proposed method, Viola–Jones algorithm is used to detect static and dynamic features of eye, nose, lip, Scale Invariant Feature Transform (SIFT) algorithm is used to detect some stable key-point features of face. In this paper, a research on the audio-visual integration method using AND logic is also made. Furthermore, all the experiments are carried out using Artificial Neural Network (ANN) and Support Vector Machine (SVM). An accuracy of 94.90% is achieved using proposed feature extraction method. The main objective of this work is to improve the authenticity of any application using multi-modal biometric features. Adding facial features to the speech recognition improve system security because biometric features are unique and combining evidence from two modalities increases the authenticity as well as integrity of the system. [ABSTRACT FROM AUTHOR]

In this paper, we show how the quasinormal modes (QNMs) arise from the perturbations of massive scalar fields propagating in the curved background by using the artificial neural networks. To this end, we architect a special algorithm for the feedforward neural network method (FNNM) to compute the QNMs complying with the certain types of boundary conditions. To test the reliability of the method, we consider two black hole spacetimes whose QNMs are well known: 4 D pure de Sitter (dS) and five-dimensional Schwarzschild anti-de Sitter (AdS) black holes. Using the FNNM, the QNMs of are computed numerically. It is shown that the obtained QNMs via the FNNM are in good agreement with their former QNM results resulting from the other methods. Therefore, our method of finding the QNMs can be used for other curved spacetimes that obey the same boundary conditions. [ABSTRACT FROM AUTHOR]

In this paper, calculations of the (n , t) reaction cross-sections at 14.5 MeV have been presented by utilizing artificial neural network algorithms (ANNs). The systematics are based on the account for the non-equilibrium reaction mechanism and the corresponding analytical formulas of the pre-equilibrium exciton model. Experimental results, obtained from the EXFOR database, have been used to train the ANN with the Levenberg–Marquardt (LM) algorithm which is a feed-forward algorithm and is considered one of the well-known and most effective methods in neural networks. The Regression (R) values for the ANN estimation have been determined as 0.9998, 0.9927 and 0.9895 for training, testing and for all process. The (n , t) reaction cross-sections have been reproduced with the TALYS 1.95 and the EMPIRE 3.2 codes. In summary, it has been demonstrated that the ANN algorithms can be used to calculate the (n , t) reaction cross-section with the semi-empirical systematics. [ABSTRACT FROM AUTHOR]

Time series change point detection can identify the locations of abrupt points in many dynamic processes. It can help us to find anomaly data in an early stage. At the same time, detecting change points for long, periodic, and multiple input series data has received a lot of attention recently, and is universally applicable in many fields including power, environment, finance, and medicine. However, the performance of classical methods typically scales poorly for such time series. In this paper, we propose CPMAN, a novel prediction-based change point detection approach via multi-stage attention networks. Our model consists of two key modules. First, in the time series prediction module, we employ the multi-stage attention-based networks and integrate the multi-series fusion mechanism. This module can adaptively extract features from the relevant input series and capture the long-term temporal dependencies. Secondly, in the change point detection module, we use the wavelet analysis-based algorithm to detect change points efficiently and identify the change points and outliers. Extensive experiments are conducted on various real-world datasets and synthetic datasets, proving the superiority and effectiveness of CPMAN. Our approach outperforms the state-of-the-art methods by up to 12.1% on the F1 Score. [ABSTRACT FROM AUTHOR]

The 3D measurement system based on line-structured light uses a camera to capture laser stripes due to changing in the shape of an object, and uses the acquired pixel coordinates for 3D reconstruction. System calibration is an important step in 3D measurement. The current camera calibration algorithm research mainly focuses on improving the algorithm itself, and there is less research on the influence of external factors. This paper proposes a coplanar hybrid calibration algorithm based on the error screening model by combining the error screening model, mathematical model and neural network model. It is mainly divided into two steps. The first step is to use the radial array constraint calibration algorithm based on the error screening model to solve the camera's internal and external parameters. The second step uses the camera internal and external parameters obtained in the first step to convert the pixel coordinates into real three-dimensional coordinates, and compares the calculated three-dimensional coordinates with the actual coordinates. Using machine learning to establish a compensation network, get a compensation function, and use the resulting 3D world coordinates to perform point cloud stitching. Experiments show that compared with the traditional calibration algorithm, the calibration algorithm has a small error and reduces the calibration error by about 6.5%. [ABSTRACT FROM AUTHOR]

Recently, photoplethysmography (PPG)-based techniques have been extensively used for cuff-less, automated estimation of blood pressure because of their inexpensive and effortless acquisition technology compared to other conventional approaches. However, most of the reported PPG-based, generalized BP estimation methods often lack the desired accuracy due to pathophysiological diversity. Moreover, some methods rely on several correction factors, which are not globalized yet and require further investigation. In this paper, a simple and automated systolic (SBP) and diastolic (DBP) blood pressure estimation method is proposed based on patient-specific neural network (NN) modeling. Initially, 15 time-plane PPG features are extracted and after feature selection, only four selected features are used in the NN model for beat-to-beat estimation of SBP and DBP, respectively. The proposed technique also presents reasonable accuracy while used for generalized estimation of BP. Performance of the algorithm is evaluated on 670 records of 50 intensive care unit (ICU) patients taken from MIMIC, MIMIC II and MIMIC Challenge databases. The proposed algorithm exhibits high average accuracy with (mean ± SD) of the estimated SBP as (0. 4 6 1 ± 2. 6 2 6) mmHg and DBP as (0. 1 5 0 ± 1. 4 8 2) mmHg. Compared to the other generalized models, the use of patient-specific approach eliminates the necessity of individual correction factors, thus increasing the robustness, accuracy and potential of the method to be implemented in personal healthcare applications. [ABSTRACT FROM AUTHOR]

Because of interdependence between different network layers, interdependent networks are more fragile than single-layer networks, and large-scale iterative paralysis occurs easily. How to seek nodes whose removal can effectively dismantle networks has attracted great research attention. In this paper, a novel optimal dismantling strategy Maximum Entropy Centrality (EC) and overlapping betweenness (OB) based on residual theory (ECOB) is proposed. In the ECOB, the residual theory is used to detect the highest influence nodes according to the quality of the residual networks. In addition, to make sorting more accurate, EC and OB parameters are both considered in the node selection mechanism. Simulation shows that the ECOB strategy performs much better than existing methods both in artificial interdependent networks and real-world interdependent networks. This is thanks to the introduced ECOB node selection algorithm with proper parameter criterions. [ABSTRACT FROM AUTHOR]

Homology Continuity is a fundamental property of the nature, but few of the traditional pattern recognition algorithms were aware of it. Firstly, this paper gives a brief description to the Principle of Homology Continuity (PHC), and tries to mathematically redefine it. Then, we introduce a PHC-based pattern learning method — Geometrical Covering Learning (GCL), following the Hyper sausage neural network as an instance of GCL. Lastly, we propose a GCL solution to the “two-spirals” pattern recognition problem. The final experimental results show that the new method is feasible and efficient. [ABSTRACT FROM AUTHOR]

Deep learning based on structured deep neural networks has provided powerful applications in various fields. The structures imposed on the deep neural networks are crucial, which makes deep learning essentially different from classical schemes based on fully connected neural networks. One of the commonly used deep neural network structures is generated by convolutions. The produced deep learning algorithms form the family of deep convolutional neural networks. Despite of their power in some practical domains, little is known about the mathematical foundation of deep convolutional neural networks such as universality of approximation. In this paper, we propose a family of new structured deep neural networks: deep distributed convolutional neural networks. We show that these deep neural networks have the same order of computational complexity as the deep convolutional neural networks, and we prove their universality of approximation. Some ideas of our analysis are from ridge approximation, wavelets, and learning theory. [ABSTRACT FROM AUTHOR]

This paper intends to propose an integrated hybrid algorithm for training radial basis function neural network (RBFNN) learning. The proposed integrated of particle swarm and genetic algorithm based optimization (IPGO) algorithm is composed of two approaches based on particle swarm optimization (PSO) and genetic algorithm (GA) for gathering both their virtues to improve the learning performance of RBFNN. The diversity of individuals results in higher chance to search in the direction of global optimal instead of being confined to local optimal particularly in problem with higher complexity. The IPGO algorithm with PSO-based and GA-based approaches has shown promising results in some benchmark problems with three continuous test functions. After proposing the algorithm for these problems with result providing its outperforming performance, this paper supplements a practical application case for the papaya milk sales forecasting to expound the superiority of the IPGO algorithm. In addition, model evaluation results of the case have showed that the IPGO algorithm outperforms other algorithms and auto-regressive moving average (ARMA) models in terms of forecasting accuracy and execution time. [ABSTRACT FROM AUTHOR]

One of the greatest challenges in data mining is related to processing and analysis of massive data streams. Contrary to traditional static data mining problems, data streams require that each element is processed only once, the amount of allocated memory is constant and the models incorporate changes of investigated streams. A vast majority of available methods have been developed for data stream classification and only a few of them attempted to solve regression problems, using various heuristic approaches. In this paper, we develop mathematically justified regression models working in a time-varying environment. More specifically, we study incremental versions of generalized regression neural networks, called IGRNNs, and we prove their tracking properties - weak (in probability) and strong (with probability one) convergence assuming various concept drift scenarios. First, we present the IGRNNs, based on the Parzen kernels, for modeling stationary systems under nonstationary noise. Next, we extend our approach to modeling time-varying systems under nonstationary noise. We present several types of concept drifts to be handled by our approach in such a way that weak and strong convergence holds under certain conditions. Finally, in the series of simulations, we compare our method with commonly used heuristic approaches, based on forgetting mechanism or sliding windows, to deal with concept drift. Finally, we apply our concept in a real life scenario solving the problem of currency exchange rates prediction. [ABSTRACT FROM AUTHOR]

Differing from the conventional back-propagation (BP) neural networks, a novel multi-input Euler polynomial neural network, in short, MIEPNN (specifically, 4-input Euler polynomial neural network, 4IEPNN) is established and investigated in this paper. In order to achieve satisfactory performance of the established MIEPNN, a weights and structure determination (WASD) algorithm with pruning-while-growing (PWG) and twice-pruning (TP) techniques is built up for the established MIEPNN. By employing the weights direct determination (WDD) method, the WASD algorithm not only determines the optimal connecting weights between hidden layer and output layer directly, but also obtains the optimal number of hidden-layer neurons. Specifically, a sub-optimal structure is obtained via the PWG technique, then the redundant hidden-layer neurons are further pruned via the TP technique. Consequently, the optimal structure of the MIEPNN is obtained. To provide a reasonable choice in practice, several different MATLAB computing routines related to the WDD method are studied. Comparative numerical-experiment results of the 4IEPNN using these different MATLAB computing routines and the standard multi-layer perceptron (MLP) neural network further verify the superior performance and efficacy of the proposed MIEPNN equipped with the WASD algorithm including PWG and TP techniques in terms of training, testing and predicting. [ABSTRACT FROM AUTHOR]