Wavelet networks

An efficient parallel runoff forecasting model for capturing global and local feature information

Artificial intelligence has significantly accelerated the development of hydrological forecasting. However, research on how to efficiently identify the physical characteristics of runoff sequences and develop forecasting models that simultaneously address both global and local features of the sequences is still lacking. To address these issues, this study proposes a new PCPFN (PolyCyclic Parallel Fusion Network) prediction model that leverages the multi-periodic characteristics of runoff sequences and shares global features through a dual-architecture parallel computation approach. Unlike existing models, the PCPFN model can extract both the periodic and trend-based evolution features of runoff sequences. It constructs a multi-feature set in a "sequence-to-sequence" manner and employs a parallel structure of an Encoder and BiGRU (Bidirectional Gated Recurrent Unit) to simultaneously capture changes in both local, adjacent features and global characteristics, ensuring comprehensive attention to the sequence features. When predicting runoff data for three different hydrological conditions, the PCPFN model achieved R2 values of 0.97, 0.98, and 0.97, respectively, with other evaluation indicators significantly outperforming the benchmark models. Additionally, due to the opacity in feature distribution processes of AI models, SHAP (Shapley Additive exPlanations) analysis was used to evaluate the contribution of each feature variable to long-term runoff trends. The proposed PCPFN model, during parallel computation, not only utilizes the intrinsic features of sequences and efficiently handles the distribution of local and global features but also shares predictive information in the output module, achieving accurate runoff forecasting and providing crucial references for timely warning and forecasting.

Multiscale Deep Learning for Detection and Recognition: A Comprehensive Survey

Recently, the multiscale problem in computer vision has gradually attracted people's attention. This article focuses on multiscale representation for object detection and recognition, comprehensively introduces the development of multiscale deep learning, and constructs an easy-to-understand, but powerful knowledge structure. First, we give the definition of scale, explain the multiscale mechanism of human vision, and then lead to the multiscale problem discussed in computer vision. Second, advanced multiscale representation methods are introduced, including pyramid representation, scale-space representation, and multiscale geometric representation. Third, the theory of multiscale deep learning is presented, which mainly discusses the multiscale modeling in convolutional neural networks (CNNs) and Vision Transformers (ViTs). Fourth, we compare the performance of multiple multiscale methods on different tasks, illustrating the effectiveness of different multiscale structural designs. Finally, based on the in-depth understanding of the existing methods, we point out several open issues and future directions for multiscale deep learning.

Support matrix machine: A review

Support vector machine (SVM) is one of the most studied paradigms in the realm of machine learning for classification and regression problems. It relies on vectorized input data. However, a significant portion of the real-world data exists in matrix format, which is given as input to SVM by reshaping the matrices into vectors. The process of reshaping disrupts the spatial correlations inherent in the matrix data. Also, converting matrices into vectors results in input data with a high dimensionality, which introduces significant computational complexity. To overcome these issues in classifying matrix input data, support matrix machine (SMM) is proposed. It represents one of the emerging methodologies tailored for handling matrix input data. SMM preserves the structural information of the matrix data by using the spectral elastic net property which is a combination of the nuclear norm and Frobenius norm. This article provides the first in-depth analysis of the development of the SMM model, which can be used as a thorough summary by both novices and experts. We discuss numerous SMM variants, such as robust, sparse, class-imbalance, and multi-class classification models. We also analyze the applications of the SMM and conclude the article by outlining potential future research avenues and possibilities that may motivate researchers to advance the SMM algorithm.

Advancing epigenetic profiling in cervical cancer: machine learning techniques for classifying DNA methylation patterns

This study investigates the ability to predict DNA methylation patterns in cervical cancer cells using decision-tree-based ensemble approaches and neural network-based models. The research findings suggest that a model based on random forest achieves a significant prediction accuracy of 91.35%. This projection was derived from comprehensive experimentation and a meticulous performance evaluation of the random forest model, employing a range of measures including Accuracy, Sensitivity, Specificity, Matthews Correlation Coefficient, F1-score, Recall, and Precision. The results indicate that the random forest model exhibits superior performance compared to other tree-based models such as the Simple Decision Tree and XGBoost, as well as neural network-based models including Convolutional Neural Networks, Feed Forward Networks, and Wavelet Neural Networks. The findings indicate that using random forest-based techniques has great potential for future study and might be highly valuable in clinical applications, especially in improving diagnostic and treatment strategies based on epigenetic profiles.

An unsupervised wavelet neural network model for approximating the solutions of non-linear nervous stomach model governed by tension, food and medicine

The human stomach is a complex organ. Its role is to degrade food particles by using mechanical forces and chemical reactions in order to release nutrients. All ingested items, including our nutrition, should first pass through the stomach, making it arguably the most crucial segment in the gastrointestinal tract. Computational and mathematical modeling of the stomach is an emerging field of biomechanics where several complex phenomena, such as solid mechanics of the gastric wall, gastric electrophysiology, and fluid mechanics of the digesta need to be addressed. Developing a meshfree comprehensive algorithm for solving the nervous stomach model that enables analysing the relationships between these phenomena remains one of the most significant challenges in biomechanics. This research dedicates to study the dynamics of nervous stomach model governed by a mathematical representation depending on three categories viz. Tension (T), Food (F) and Medicine (M), i.e. TFM model. In this regard, a machine learning paradigm, namely POLYnomial WinOwed with Gaussian (PolyWOG) Wavelet Neural Network (PWNN) model has been implemented for handling the non-linear TFM models. We compared the obtained outcomes of present work with results of a well-known numerical computing paradigm and an existing wavelet neural algorithm. Also, we have done statistical assessment studies at different testing points, which reveal that the proposed architecture is effective and accurate.

Effect of the period of EEG signals on the decoding of motor information

Decoding movement information from electroencephalogram to construct brain-computer interface has promising applications. The EEG data during the entire motor imagery (MI) period or movement execution (ME) period is generally decoded, and calculation of numerous information and massive dataset is time-consuming. In order to improve decoding efficiency, the joint topographic maps of the brain activation state of 15 subjects were studied during different periods. The results showed that the activation intensity of the preparation period in the motor imagery experiment was higher than during the exercise period, while during the exercise period, the activation intensity was higher than in the preparation period in the movement execution experiment. Hence, the wavelet neural network was used to decode the six-class movements including elbow flexion/extension, forearm pronation/supination and hand open/close in periods of MI/ME. The experimental results show that the accuracy obtained in the preparation period is the highest in the motor imagery experiment, which is 80.77%. On the other hand, the highest accuracy obtained in the exercise period of the movement execution experiment is 79.26%. It further proves that the optimized period is a key decoding factor to reduce the cost of calculation, and this new decoding method is effective to build a more intelligent brain-computer interface system.

Bridge Model Updating Based on Wavelet Neural Network and Wind-Driven Optimization

Aging, corrosive environments, and inadequate maintenance may result in performance deterioration of civil infrastructures, and finite element model updating is a commonly employed structural health monitoring procedure in civil engineering to reflect the current situation and to ensure the safety and serviceability of structures. Using the finite element model updating process to obtain the relationship between the structural responses and updating parameters, this paper proposes a method of using the wavelet neural network (WNN) as the surrogate model combined with the wind-driven optimization (WDO) algorithm to update the structural finite element model. The method was applied to finite element model updating of a continuous beam structure of three equal spans to verify its feasibility, the results show that the WNN can reflect the nonlinear relationship between structural responses and the parameters and has an outstanding simulation performance; the WDO has an excellent ability for optimization and can effectively improve the efficiency of model updating. Finally, the method was applied to update a real bridge model, and the results show that the finite element model update based on WDO and WNN is applicable to the updating of a multi-parameter bridge model, which has practical significance in engineering and high efficiency in finite element model updating. The differences between the updated values and measured values are all within the range of 5%, while the maximum difference was reduced from -10.9% to -3.6%. The proposed finite element model updating method is applicable and practical for multi-parameter bridge model updating and has the advantages of high updating efficiency, reliability, and practical significance.

Epicasting: An Ensemble Wavelet Neural Network for forecasting epidemics

Infectious diseases remain among the top contributors to human illness and death worldwide, among which many diseases produce epidemic waves of infection. The lack of specific drugs and ready-to-use vaccines to prevent most of these epidemics worsens the situation. These force public health officials and policymakers to rely on early warning systems generated by accurate and reliable epidemic forecasters. Accurate forecasts of epidemics can assist stakeholders in tailoring countermeasures, such as vaccination campaigns, staff scheduling, and resource allocation, to the situation at hand, which could translate to reductions in the impact of a disease. Unfortunately, most of these past epidemics exhibit nonlinear and non-stationary characteristics due to their spreading fluctuations based on seasonal-dependent variability and the nature of these epidemics. We analyze various epidemic time series datasets using a maximal overlap discrete wavelet transform (MODWT) based autoregressive neural network and call it Ensemble Wavelet Neural Network (EWNet) model. MODWT techniques effectively characterize non-stationary behavior and seasonal dependencies in the epidemic time series and improve the nonlinear forecasting scheme of the autoregressive neural network in the proposed ensemble wavelet network framework. From a nonlinear time series viewpoint, we explore the asymptotic stationarity of the proposed EWNet model to show the asymptotic behavior of the associated Markov Chain. We also theoretically investigate the effect of learning stability and the choice of hidden neurons in the proposal. From a practical perspective, we compare our proposed EWNet framework with twenty-two statistical, machine learning, and deep learning models for fifteen real-world epidemic datasets with three test horizons using four key performance indicators. Experimental results show that the proposed EWNet is highly competitive compared to the state-of-the-art epidemic forecasting methods.

Wavelet-artificial neural network to predict the acetone sensing by indium oxide/iron oxide nanocomposites

This study applies a hybridized wavelet transform-artificial neural network (WT-ANN) model to simulate the acetone detecting ability of the Indium oxide/Iron oxide (In2O3/Fe2O3) nanocomposite sensors. The WT-ANN has been constructed to extract the sensor resistance ratio (SRR) in the air with respect to the acetone from the nanocomposite chemistry, operating temperature, and acetone concentration. The performed sensitivity analyses demonstrate that a single hidden layer WT-ANN with nine nodes is the highest accurate model for automating the acetone-detecting ability of the In2O3/Fe2O3 sensors. Furthermore, the genetic algorithm has fine-tuned the shape-related parameters of the B-spline wavelet transfer function. This model accurately predicts the SRR of the 119 nanocomposite sensors with a mean absolute error of 0.7, absolute average relative deviation of 10.12%, root mean squared error of 1.14, and correlation coefficient of 0.95813. The In2O3-based nanocomposite with a 15 mol percent of Fe2O3 is the best sensor for detecting acetone at wide temperatures and concentration ranges. This type of reliable estimator is a step toward fully automating the gas-detecting ability of In2O3/Fe2O3 nanocomposite sensors.

Tucker network: Expressive power and comparison

Deep neural networks have achieved great success in solving many machine learning and computer vision problems. In this paper, we propose a deep neural network called the Tucker network derived from the Tucker format and analyze its expressive power. The results demonstrate that the Tucker network has exponentially higher expressive power than the shallow network. In other words, a shallow network with an exponential width is required to realize the same score function as that computed by the Tucker network. Moreover, we discuss the expressive power between the hierarchical Tucker tensor network (HT network) and the proposed Tucker network. To generalize the Tucker network into a deep version, we combine the hierarchical Tucker format and Tucker format to propose a deep Tucker tensor decomposition. Its corresponding deep Tucker network is presented. Experiments are conducted on three datasets: MNIST, CIFAR-10 and CIFAR-100. The results experimentally validate the theoretical results and show that the Tucker network and deep Tucker network have better performance than the shallow network and HT network.

DAE-ConvBiLSTM: End-to-end learning single-lead electrocardiogram signal for heart abnormalities detection

BACKGROUND

The electrocardiogram (ECG) is a widely used diagnostic that observes the heart activities of patients to ascertain a heart abnormality diagnosis. The artifacts or noises are primarily associated with the problem of ECG signal processing. Conventional denoising techniques have been proposed in previous literature; however, some lacks, such as the determination of suitable wavelet basis function and threshold, can be a time-consuming process. This paper presents end-to-end learning using a denoising auto-encoder (DAE) for denoising algorithms and convolutional-bidirectional long short-term memory (ConvBiLSTM) for ECG delineation to classify ECG waveforms in terms of the PQRST-wave and isoelectric lines. The denoising reconstruction using unsupervised learning based on the encoder-decoder process can be proposed to improve the drawbacks. First, The ECG signals are reduced to a low-dimensional vector in the encoder. Second, the decoder reconstructed the signals. The last, the reconstructed signals of ECG can be processed to ConvBiLSTM. The proposed architecture of DAE-ConvBiLSTM is the end-to-end diagnosis of heart abnormality detection.

RESULTS

As a result, the performance of DAE-ConvBiLSTM has obtained an average of above 98.59% accuracy, sensitivity, specificity, precision, and F1 score from the existing studies. The DAE-ConvBiLSTM has also experimented with detecting T-wave (due to ventricular repolarisation) morphology abnormalities.

CONCLUSION

The development architecture for detecting heart abnormalities using an unsupervised learning DAE and supervised learning ConvBiLSTM can be proposed for an end-to-end learning algorithm. In the future, the precise accuracy of the ECG main waveform will affect heart abnormalities detection in clinical practice.

SepNet: A neural network for directionally correlated data

Multi-dimensional tensor data appear in diverse settings, including multichannel signals, spectrograms, and hyperspectral data from remote sensing. In many cases, these data are directionally correlated, i.e. the correlation between variables from different dimensions is significantly weaker than the correlation between variables from the same dimension. Convolutional neural networks are readily applicable to directionally correlated data but are often inefficient, as they impose many unnecessary connections between neurons. Here we propose a novel architecture, SepNet, specifically for directionally correlated datasets. SepNet uses directional operators to extract directional features from each dimension separately, followed by a linear operator along the depth to generate higher-level features from the directional features. Experiments on two representative directionally correlated datasets showed that SepNet improved network efficiency up to 100-fold while maintaining high accuracy comparable with state-of-the-art convolutional neural network models. Furthermore, SepNet can be flexibly constructed with minimal restriction on the output shape of each layer. These results reveal the potential of data-specific architecting of neural networks.

Grammar-Induced Wavelet Network for Human Parsing

Most existing methods of human parsing still face a challenge: how to extract the accurate foreground from similar or cluttered scenes effectively. In this paper, we propose a Grammar-induced Wavelet Network (GWNet), to deal with the challenge. GWNet mainly consists of two modules, including a blended grammar-induced module and a wavelet prediction module. We design the blended grammar-induced module to exploit the relationship of different human parts and the inherent hierarchical structure of a human body by means of grammar rules in both cascaded and paralleled manner. In this way, conspicuous parts, which are easily distinguished from the background, can amend the segmentation of inconspicuous ones, improving the foreground extraction. We also design a Part-aware Convolutional Recurrent Neural Network (PCRNN) to pass messages which are generated by grammar rules. To further improve the performance, we propose a wavelet prediction module to capture the basic structure and the edge details of a person by decomposing the low-frequency and high-frequency components of features. The low-frequency component can represent the smooth structures and the high-frequency components can describe the fine details. We conduct extensive experiments to evaluate GWNet on PASCAL-Person-Part, LIP, and PPSS datasets. GWNet obtains state-of-the-art performance on these human parsing datasets.

Wavelet Probabilistic Neural Networks

In this article, a novel wavelet probabilistic neural network (WPNN), which is a generative-learning wavelet neural network that relies on the wavelet-based estimation of class probability densities, is proposed. In this new neural network approach, the number of basis functions employed is independent of the number of data inputs, and in that sense, it overcomes the well-known drawback of traditional probabilistic neural networks (PNNs). Since the parameters of the proposed network are updated at a low and constant computational cost, it is particularly aimed at data stream classification and anomaly detection in off-line settings and online environments where the length of data is assumed to be unconstrained. Both synthetic and real-world datasets are used to assess the proposed WPNN. Significant performance enhancements are attained compared to state-of-the-art algorithms.

Wavelets as activation functions in Neural Networks

Traditionally, a few activation functions have been considered in neural networks, including bounded functions such as threshold, sigmoidal and hyperbolic-tangent, as well as unbounded ReLU, GELU, and Soft-plus, among other functions for deep learning, but the search for new activation functions still being an open research area. In this paper, wavelets are reconsidered as activation functions in neural networks and the performance of Gaussian family wavelets (first, second and third derivatives) are studied together with other functions available in Keras-Tensorflow. Experimental results show how the combination of these activation functions can improve the performance and supports the idea of extending the list of activation functions to wavelets which can be available in high performance platforms.

Runoff Forecasting Using Machine-Learning Methods: Case Study in the Middle Reaches of Xijiang River

Runoff forecasting is useful for flood early warning and water resource management. In this study, backpropagation (BP) neural network, generalized regression neural network (GRNN), extreme learning machine (ELM), and wavelet neural network (WNN) models were employed, and a high-accuracy runoff forecasting model was developed at Wuzhou station in the middle reaches of Xijiang River. The GRNN model was selected as the optimal runoff forecasting model and was also used to predict the streamflow and water level by considering the flood propagation time. Results show that (1) the GRNN presents the best performance in the 7-day lead time of streamflow; (2) the WNN model shows the highest accuracy in the 7-day lead time of water level; (3) the GRNN model performs well in runoff forecasting by considering flood propagation time, increasing the Qualification Rate (QR) of mean streamflow and water level forecast to 98.36 and 82.74%, respectively, and illustrates scientifically of the peak underestimation in streamflow and water level. This research proposes a high-accuracy runoff forecasting model using machine learning, which would improve the early warning capabilities of floods and droughts, the results also lay an important foundation for the mid-long-term runoff forecasting.

Designing a Mixed Multilayer Wavelet Neural Network for Solving ERI Inversion Problem With Massive Amounts of Data: A Hybrid STGWO-GD Learning Approach

This study aims to develop a novel wavelet neural-network (WNN) model for solving electrical resistivity imaging (ERI) inversion with massive amounts of measured data in control and measurement fields. In the proposed method, we design a mixed multilayer WNN (MMWNN) which uses Morlet and Mexican wavelons as different activation functions in a cascaded hidden layer structure. Meanwhile, a hybrid STGWO-GD learning approach is used to improve the learning ability of the MMWNN, which is a combination of the self-tuning grey wolf optimizer (STGWO) and the gradient descent (GD) algorithm adopting the advantages of each other. Moreover, updating formulas of the GD algorithm are derived, and a Gaussian updating operator with weighted hierarchical hunting, a chaotic oscillation equation, and a nonlinear modulation coefficient are introduced to improve the hierarchical hunting and the control parameter adjustment of the modified STGWO. Five examples are used with the aim of assessing the availability and feasibility of the proposed inversion method. The inversion results are promising and show that the introduced method is superior to other competitors in terms of inversion accuracy and computational efficiency. Furthermore, the effectiveness of the proposed method is demonstrated over a classical benchmark successfully.

Demodulation of EM Telemetry Data Using Fuzzy Wavelet Neural Network with Logistic Response

Data telemetry is a critical element of successful unconventional well drilling operations, involving the transmission of information about the well-surrounding geology to the surface in real-time to serve as the basis for geosteering and well planning. However, the data extraction and code recovery (demodulation) process can be a complicated system due to the non-linear and time-varying characteristics of high amplitude surface noise. In this work, a novel model fuzzy wavelet neural network (FWNN) that combines the advantages of the sigmoidal logistic function, fuzzy logic, a neural network, and wavelet transform was established for the prediction of the transmitted signal code from borehole to surface with effluent quality. Moreover, the complete workflow involved the pre-processing of the dataset via an adaptive processing technique before training the network and a logistic response algorithm for acquiring the optimal parameters for the prediction of signal codes. A data reduction and subtractive scheme are employed as a pre-processing technique to better characterize the signals as eight attributes and, ultimately, reduce the computation cost. Furthermore, the frequency-time characteristics of the predicted signal are controlled by selecting an appropriate number of wavelet bases “N” and the pre-selected range for pij3 to be used prior to the training of the FWNN system. The results, leading to the prediction of the BPSK characteristics, indicate that the pre-selection of the N value and pij3 range provides a significantly accurate prediction. We validate its prediction on both synthetic and pseudo-synthetic datasets. The results indicated that the fuzzy wavelet neural network with logistic response had a high operation speed and good quality prediction, and the correspondingly trained model was more advantageous than the traditional backward propagation network in prediction accuracy. The proposed model can be used for analyzing signals with a signal-to-noise ratio lower than 1 dB effectively, which plays an important role in the electromagnetic telemetry system.

Estimation of knee and ankle angles during walking using thigh and shank angles

Estimation of joints' trajectories is commonly used in human gait analysis, and in the development of motion planners and high-level controllers for prosthetics, orthotics, exoskeletons and humanoids. Human locomotion is the result of the cooperation between leg joints and limbs. This suggests the existence of underlying relationships between them which lead to a harmonic gait. In this study we aimed to estimate knee and ankle trajectories using thigh and shank angles. To do so, an estimation approach was developed that continuously mapped the inputs to the outputs, which did not require switching rules, speed estimation, gait percent identification or look-up tables. The estimation algorithm was based on a nonlinear auto-regressive model with exogenous inputs. The method was then combined with wavelets theory, and then the two were used in a neural network. To evaluate the estimation performance, three scenarios were developed which used only one source of inputs (i.e., only shank angles or only thigh angles). First, knee anglesθ_k(outputs) were estimated using thigh anglesθ_th(inputs). Second, ankle anglesθ_a(outputs) were estimated using thigh anglesθ_sh(inputs), and third, the ankle angles were estimated using shank angles (inputs). The proposed approach was investigated for 22 subjects at different walking speeds and the leave-one-subject-out procedure was used for training and testing the estimation algorithm. Average root mean square errors were 3.9°-5.3° and 2.1°-2.3° for knee and ankle angles, respectively. Average mean absolute errors (MAEs) MAEs were 3.2°-4° and 1.7°-1.8°, and average correlation coefficientsρ_ccwere 0.95-0.98 and 0.94-0.96 for knee and ankle angles, respectively. The limitations and strengths of the proposed approach are discussed in detail and the results are compared with several studies.

Predicting Stock Movements: Using Multiresolution Wavelet Reconstruction and Deep Learning in Neural Networks

Stock movement prediction is important in the financial world because investors want to observe trends in stock prices before making investment decisions. However, given the non-linear non-stationary financial time series characteristics of stock prices, this remains an extremely challenging task. A wavelet is a mathematical function used to divide a given function or continuous-time signal into different scale components. Wavelet analysis has good time-frequency local characteristics and good zooming capability for non-stationary random signals. However, the application of the wavelet theory is generally limited to a small scale. The neural networks method is a powerful tool to deal with large-scale problems. Therefore, the combination of neural networks and wavelet analysis becomes more applicable for stock behavior prediction. To rebuild the signals in multiple scales, and filter the measurement noise, a forecasting model based on a stock price time series was provided, employing multiresolution analysis (MRA). Then, the deep learning in the neural network method was used to train and test the empirical data. To explain the fundamental concepts, a conceptual analysis of similar algorithms was performed. The data set for the experiment was chosen to capture a wide range of stock movements from 1 January 2009 to 31 December 2017. Comparison analyses between the algorithms and industries were conducted to show that the method is stable and reliable. This study focused on medium-term stock predictions to predict future stock behavior over 11 days of horizons. Our test results showed a 75% hit rate, on average, for all industries, in terms of US stocks on FORTUNE Global 500. We confirmed the effectiveness of our model and method based on the findings of the empirical research. This study’s primary contribution is to demonstrate the reconstruction model of the stock time series and to perform recurrent neural networks using the deep learning method. Our findings fill an academic research gap, by demonstrating that deep learning can be used to predict stock movement.

WaveCNet: Wavelet Integrated CNNs to Suppress Aliasing Effect for Noise-Robust Image Classification

Though widely used in image classification, convolutional neural networks (CNNs) are prone to noise interruptions, i.e. the CNN output can be drastically changed by small image noise. To improve the noise robustness, we try to integrate CNNs with wavelet by replacing the common down-sampling (max-pooling, strided-convolution, and average pooling) with discrete wavelet transform (DWT). We firstly propose general DWT and inverse DWT (IDWT) layers applicable to various orthogonal and biorthogonal discrete wavelets like Haar, Daubechies, and Cohen, etc., and then design wavelet integrated CNNs (WaveCNets) by integrating DWT into the commonly used CNNs (VGG, ResNets, and DenseNet). During the down-sampling, WaveCNets apply DWT to decompose the feature maps into the low-frequency and high-frequency components. Containing the main information including the basic object structures, the low-frequency component is transmitted into the following layers to generate robust high-level features. The high-frequency components are dropped to remove most of the data noises. The experimental results show that WaveCNets achieve higher accuracy on ImageNet than various vanilla CNNs. We have also tested the performance of WaveCNets on the noisy version of ImageNet, ImageNet-C and six adversarial attacks, the results suggest that the proposed DWT/IDWT layers could provide better noise-robustness and adversarial robustness. When applying WaveCNets as backbones, the performance of object detectors (i.e., faster R-CNN and RetinaNet) on COCO detection dataset are consistently improved. We believe that suppression of aliasing effect, i.e. separation of low frequency and high frequency information, is the main advantages of our approach. The code of our DWT/IDWT layer and different WaveCNets are available at https://github.com/CVI-SZU/WaveCNet.

ML-based group method of data handling: an improvement on the conventional GMDH

Machine learning (ML) has been recognized as a feasible and reliable technique for the modeling of multi-parametric datasets. In real applications, there are different relationships with various complexities between sets of inputs and their corresponding outputs. As a result, various models have been developed with different levels of complexity in the input–output relationships. The group method of data handling (GMDH) employs a family of inductive algorithms for computer-based mathematical modeling grounded on a combination of quadratic and higher neurons in a certain number of variable layers. In this method, a vector of input features is mapped to the expected response by creating a multistage nonlinear pattern. Usually, each neuron of the GMDH is considered a quadratic partial function. In this paper, the basic structure of the GMDH technique is adapted by changing the partial functions to enhance the complexity modeling ability. To accomplish this, popular ML models that have shown reasonable function approximation performance, such as support vector regression and random forest, are used, and the basic polynomial functions in the GMDH are replaced by these ML models. The regression feasibility and validity of the ML-based GMDH models are confirmed by computer simulation.

Deep Learning-Based Stroke Disease Prediction System Using Real-Time Bio Signals

The emergence of an aging society is inevitable due to the continued increases in life expectancy and decreases in birth rate. These social changes require new smart healthcare services for use in daily life, and COVID-19 has also led to a contactless trend necessitating more non-face-to-face health services. Due to the improvements that have been achieved in healthcare technologies, an increasing number of studies have attempted to predict and analyze certain diseases in advance. Research on stroke diseases is actively underway, particularly with the aging population. Stroke, which is fatal to the elderly, is a disease that requires continuous medical observation and monitoring, as its recurrence rate and mortality rate are very high. Most studies examining stroke disease to date have used MRI or CT images for simple classification. This clinical approach (imaging) is expensive and time-consuming while requiring bulky equipment. Recently, there has been increasing interest in using non-invasive measurable EEGs to compensate for these shortcomings. However, the prediction algorithms and processing procedures are both time-consuming because the raw data needs to be separated before the specific attributes can be obtained. Therefore, in this paper, we propose a new methodology that allows for the immediate application of deep learning models on raw EEG data without using the frequency properties of EEG. This proposed deep learning-based stroke disease prediction model was developed and trained with data collected from real-time EEG sensors. We implemented and compared different deep-learning models (LSTM, Bidirectional LSTM, CNN-LSTM, and CNN-Bidirectional LSTM) that are specialized in time series data classification and prediction. The experimental results confirmed that the raw EEG data, when wielded by the CNN-bidirectional LSTM model, can predict stroke with 94.0% accuracy with low FPR (6.0%) and FNR (5.7%), thus showing high confidence in our system. These experimental results demonstrate the feasibility of non-invasive methods that can easily measure brain waves alone to predict and monitor stroke diseases in real time during daily life. These findings are expected to lead to significant improvements for early stroke detection with reduced cost and discomfort compared to other measuring techniques.

Wavelet-Prototypical Network Based on Fusion of Time and Frequency Domain for Fault Diagnosis

Neural networks for fault diagnosis need enough samples for training, but in practical applications, there are often insufficient samples. In order to solve this problem, we propose a wavelet-prototypical network based on fusion of time and frequency domain (WPNF). The time domain and frequency domain information of the vibration signal can be sent to the model simultaneously to expand the characteristics of the data, a parallel two-channel convolutional structure is proposed to process the information of the signal. After that, a wavelet layer is designed to further extract features. Finally, a prototypical layer is applied to train this network. Experimental results show that the proposed method can accurately identify new classes that have never been used during the training phase when the number of samples in each class is very small, and it is far better than other traditional machine learning models in few-shot scenarios.

Available Transfer Capability Enhancement by FACTS Devices Using Metaheuristic Evolutionary Particle Swarm Optimization (MEEPSO) Technique

Energy power flows are an important factor to be calculated and, thus, are needed to be enhanced in an electrical generation system. It is very necessary to optimally locate the Flexible Alternating Current Transmission Systems (FACTS) devices and improve the Available Transfer Capability (ATC) of the power transmission lines. It relieves the congestion of the system and increases the flow of power. This research study has been accomplished in two stages: optimization of location of FACTS device by the novel Sensitivity and Power loss-based Congestion Reduction (SPCR) method and the calculation of ATC using the proposed Metaheuristic Evolutionary Particle Swarm Optimization (MEEPSO) technique. The Thyristor Controlled Series Capacitor (TCSC) is used as a FACTS device to control the reactance of power transmission line. The effectiveness of the proposed methods is validated, utilizing the six bus as well as 30 bus system. The acquired outcomes are contrasted with conventional ACPTDF and DCPTDF procedures. These values are determined with the assistance of MATLAB version 2017 on the Intel Core i5 framework by taking two-sided exchanges and they are contrasted and values determined with the assistance of Power World Simulator (PWS) programming.

A Hybrid Dynamic Wavelet-Based Modeling Method for Blood Glucose Concentration Prediction in Type 1 Diabetes

Background:

Diabetes mellitus (DM) is a chronic disease that affects public health. The prediction of blood glucose concentration (BGC) is essential to improve the therapy of type 1 DM (T1DM).

Methods:

Having considered the risk of hyper- and hypo-glycemia, we provide a new hybrid modeling approach for BGC prediction based on a dynamic wavelet neural network (WNN) model, including a heuristic input selection. The proposed models include a hybrid dynamic WNN (HDWNN) and a hybrid dynamic fuzzy WNN (HDFWNN). These wavelet-based networks are designed based on dominant wavelets selected by the genetic algorithm-orthogonal least square method. Furthermore, the HDFWNN model structure is improved using fuzzy rule induction, an important innovation in the fuzzy wavelet modeling. The proposed networks are tested on real data from 12 T1DM patients and also simulated data from 33 virtual patients with an UVa/ Padova simulator, an approved simulator by the US Food and Drug Administration.

Results:

A comparison study is performed in terms of new glucose-based assessment metrics, such as gFIT, glucose-weighted form of ESOD_n (gESOD_n), and glucose-weighted R² (gR²). For real patients’ data, the values of the mentioned indices are accomplished as gFIT = 0.97 ± 0.01, gESOD_n = 1.18 ± 0.38, and gR² = 0.88 ± 0.07. HDFWNN, HDWNN and jump NN method showed the prediction error (root mean square error [RMSE]) of 11.23 ± 2.77 mg/dl, 10.79 ± 3.86 mg/dl and 16.45 ± 4.33 mg/dl, respectively.

Conclusion:

Furthermore, the generalized estimating equation and post hoc tests show that proposed models perform better compared with other proposed methods.

Fuzzy wavelet neural networks applied as inferential sensors of neonatal incubator dynamics

Newborns with health complications have great difficulty in regulating the body temperature due to distinct factors, which include the high metabolism rate and low weight. In this context, neonatal incubators help maintaining good health conditions because they provide a thermally-neutral environment, which is adequate to ensure the least energy expenditure by the newborn. In the last decades, artificial neural networks (ANNs) have been established as one of the main tools for the identification of nonlinear systems. Among the various approaches used in the identification process, the fuzzy wavelet neural network (FWNN) can be regarded as a prominent technique, consisting of the combination of wavelet neural network (WNN) and adaptive network-based fuzzy inference system (ANFIS). This work proposes the use of FWNN to infer the temperature and humidity values inside the incubator in order to certify the equipment operation. Results obtained with the analyzed neural system have shown the generalization and inference capacities of FWNNs, thus allowing their application to practical tasks aiming to increase the efficiency of incubators.

Water temperature forecasting based on modified artificial neural network methods: Two cases of the Yangtze River

Water temperature is a controlling indicator of river habitat since many physical, chemical and biological processes in rivers are temperature dependent. Highly precise and reliable predictions of water temperature are important for river ecological management. In this study, a hybrid model named BP_PSO3, based on the BPNN (back propagation neural network) optimized by the PSO (particle swarm optimization) algorithm, is proposed for water temperature prediction using air temperature (Ta), discharge (Q) and day of year (DOY) as input variables. The performance of the BP_PSO3 model was compared with that of the BP_PSO1 (with Ta as the input) and BP_PSO2 (with Ta and Q as the inputs) models to evaluate the importance of the inputs. In addition, a comparison among the BPNN, RBFNN (radial basis function neural network), WNN (wavelet neural network), GRNN (general regression neural network), ELMNN (Elman neural network), and BP_PSO-based models was carried out based on the MAE, RMSE, NSE and R². The eight artificial intelligence models were examined to predict the water temperature at the Cuntan and Datong stations in the Yangtze River. The results indicated that the hybrid BPNN-PSO3 model had a stronger ability to forecast water temperature under both normal and extreme drought conditions. Optimization by the PSO algorithm and the inclusion of Q and DOY could help capture river thermal dynamics more accurately. The findings of this study could provide scientific references for river water temperature forecasting and river ecosystem protection.

A comparative analysis of artificial neural networks and wavelet hybrid approaches to long-term toxic heavy metal prediction

The occurrence of toxic metals in the aquatic environment is as caused by a variety of contaminations which makes difficulty in the concentration prediction. In this study, conventional methods of back-propagation neural network (BPNN) and nonlinear autoregressive network with exogenous inputs (NARX) were applied as benchmark models. Explanatory variables of Fe, pH, electrical conductivity, water temperature, river flow, nitrate nitrogen, and dissolved oxygen were used as different input combinations to forecast the long-term concentrations of As, Pb, and Zn. The wavelet transformation was applied to decompose the time series data, and then was integrated with conventional methods (as WNN and WNARX). The modelling performances of the hybrid models of WNN and WNARX were compared with the conventional models. All the given models were trained, validated, and tested by an 18-year data set and demonstrated based on the simulation results of a 2-year data set. Results revealed that the given models showed general good performances for the long-term prediction of the toxic metals of As, Pb, and Zn. The wavelet transform could enhance the long-term concentration predictions. However, it is not necessarily useful for each metal prediction. Therefore, different models with different inputs should be used for different metals predictions to achieve the best predictions.

Climatic Characteristics and Modeling Evaluation of Pan Evapotranspiration over Henan Province, China

Pan evapotranspiration (E) is an important physical parameter in agricultural water resources research. Many climatic factors affect E, and one of the essential challenges is to model or predict E utilizing limited climatic parameters. In this study, the performance of four different artificial neural network (ANN) algorithms i.e., multiple hidden layer back propagation (MBP), generalized regression neural network (GRNN), probabilistic neural networks (PNN), and wavelet neural network (WNN) and one empirical model namely Stephens–Stewart (SS) were employed to predict monthly E. Long-term climatic data (i.e., 1961–2013) was used for the validation of the proposed model in the Henan province of China. It was found that different models had diverse prediction accuracies in various geographical locations, MBP model outperformed other models over almost all stations (maximum R2 = 0.96), and the WNN model was the best over two sites, the accuracies of the five models ranked as MBP, WNN, GRNN, PNN, and SS. The performances of WNN and GRNN were almost the same, five-input ANN models provided better accuracy than the two-input (solar radiation (Ro) and air temperature (T)) SS empirical model (R2 = 0.80). Similarly. the two-input ANN models (maximum R2 = 0.83) also generally performed better than the two-input (Ro and T) SS empirical model. The study could reveal that the above ANN models can be used to predict E successfully in hydrological modeling over Henan Province.

Bearing Fault Diagnosis Using a Particle Swarm Optimization-Least Squares Wavelet Support Vector Machine Classifier

Bearing is one of the key components of a rotating machine. Hence, monitoring health condition of the bearing is of paramount importace. This paper develops a novel particle swarm optimization (PSO)-least squares wavelet support vector machine (PSO-LSWSVM) classifier, which is designed based on a combination between a PSO, a least squares procedure, and a new wavelet kernel function-based support vector machine (SVM), for bearing fault diagnosis. In this work, bearing fault classification is transformed into a pattern recognition problem, which consists of three stages of data processing. Firstly, a rich information dataset is built by extracting the features from the signals, which are decomposed by the nonlocal means (NLM) and empirical mode decomposition (EMD). Secondly, a minimum-redundancy maximum-relevance (mRMR) method is employed to determine a subset of feature that can provide an optimal performance. Thirdly, a novel classifier, namely LSWSVM, is proposed with the aid of a PSO, to provide higher classification accuracy. The key innovative science of this work is to propropose a new classifier with the aid of an new wavelet kernel type to increase the classification precision of bearing fault diagnosis. The merit features of the proposed approach are demonstrated based on a benchmark bearing dataset and a comprehensive comparison procedure.