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Journal articles on the topic 'Ensemble neural noise'
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Timme, Nicholas M., David Linsenbardt, and Christopher C. Lapish. "A Method to Present and Analyze Ensembles of Information Sources." Entropy 22, no. 5 (May 21, 2020): 580. http://dx.doi.org/10.3390/e22050580.
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Information theory is a powerful tool for analyzing complex systems. In many areas of neuroscience, it is now possible to gather data from large ensembles of neural variables (e.g., data from many neurons, genes, or voxels). The individual variables can be analyzed with information theory to provide estimates of information shared between variables (forming a network between variables), or between neural variables and other variables (e.g., behavior or sensory stimuli). However, it can be difficult to (1) evaluate if the ensemble is significantly different from what would be expected in a purely noisy system and (2) determine if two ensembles are different. Herein, we introduce relatively simple methods to address these problems by analyzing ensembles of information sources. We demonstrate how an ensemble built of mutual information connections can be compared to null surrogate data to determine if the ensemble is significantly different from noise. Next, we show how two ensembles can be compared using a randomization process to determine if the sources in one contain more information than the other. All code necessary to carry out these analyses and demonstrations are provided.
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Nanni, Loris, Gianluca Maguolo, Sheryl Brahnam, and Michelangelo Paci. "An Ensemble of Convolutional Neural Networks for Audio Classification." Applied Sciences 11, no. 13 (June 22, 2021): 5796. http://dx.doi.org/10.3390/app11135796.
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Research in sound classification and recognition is rapidly advancing in the field of pattern recognition. One important area in this field is environmental sound recognition, whether it concerns the identification of endangered species in different habitats or the type of interfering noise in urban environments. Since environmental audio datasets are often limited in size, a robust model able to perform well across different datasets is of strong research interest. In this paper, ensembles of classifiers are combined that exploit six data augmentation techniques and four signal representations for retraining five pre-trained convolutional neural networks (CNNs); these ensembles are tested on three freely available environmental audio benchmark datasets: (i) bird calls, (ii) cat sounds, and (iii) the Environmental Sound Classification (ESC-50) database for identifying sources of noise in environments. To the best of our knowledge, this is the most extensive study investigating ensembles of CNNs for audio classification. The best-performing ensembles are compared and shown to either outperform or perform comparatively to the best methods reported in the literature on these datasets, including on the challenging ESC-50 dataset. We obtained a 97% accuracy on the bird dataset, 90.51% on the cat dataset, and 88.65% on ESC-50 using different approaches. In addition, the same ensemble model trained on the three datasets managed to reach the same results on the bird and cat datasets while losing only 0.1% on ESC-50. Thus, we have managed to create an off-the-shelf ensemble that can be trained on different datasets and reach performances competitive with the state of the art.
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Sheng, Chunyang, Haixia Wang, Xiao Lu, Zhiguo Zhang, Wei Cui, and Yuxia Li. "Distributed Gaussian Granular Neural Networks Ensemble for Prediction Intervals Construction." Complexity 2019 (July 3, 2019): 1–17. http://dx.doi.org/10.1155/2019/2379584.
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To overcome the weakness of generic neural networks (NNs) ensemble for prediction intervals (PIs) construction, a novel Map-Reduce framework-based distributed NN ensemble consisting of several local Gaussian granular NN (GGNNs) is proposed in this study. Each local network is weighted according to its contribution to the ensemble model. The weighted coefficient is estimated by evaluating the performance of the constructed PIs from each local network. A new evaluation principle is reported with the consideration of the predicting indices. To estimate the modelling uncertainty and the data noise simultaneously, the Gaussian granular is introduced to the numeric NNs. The constructed PIs can then be calculated by the variance of output distribution of each local NN, i.e., the summation of the model uncertainty variance and the data noise variance. To verify the effectiveness of the proposed model, a series of prediction experiments, including two classical time series with additive noise and two industrial time series, are carried out here. The results indicate that the proposed distributed GGNNs ensemble exhibits a good performance for PIs construction.
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Chaouachi, Aymen, Rashad M. Kamel, and Ken Nagasaka. "Neural Network Ensemble-Based Solar Power Generation Short-Term Forecasting." Journal of Advanced Computational Intelligence and Intelligent Informatics 14, no. 1 (January 20, 2010): 69–75. http://dx.doi.org/10.20965/jaciii.2010.p0069.
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This paper presents the applicability of artificial neural networks for 24 hour ahead solar power generation forecasting of a 20 kW photovoltaic system, the developed forecasting is suitable for a reliable Microgrid energy management. In total four neural networks were proposed, namely: multi-layred perceptron, radial basis function, recurrent and a neural network ensemble consisting in ensemble of bagged networks. Forecasting reliability of the proposed neural networks was carried out in terms forecasting error performance basing on statistical and graphical methods. The experimental results showed that all the proposed networks achieved an acceptable forecasting accuracy. In term of comparison the neural network ensemble gives the highest precision forecasting comparing to the conventional networks. In fact, each network of the ensemble over-fits to some extent and leads to a diversity which enhances the noise tolerance and the forecasting generalization performance comparing to the conventional networks.
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Noh, Kyoungjin, and Joon-Hyuk Chang. "Deep neural network ensemble for reducing artificial noise in bandwidth extension." Digital Signal Processing 102 (July 2020): 102760. http://dx.doi.org/10.1016/j.dsp.2020.102760.
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He, Lei, Xiaohong Shen, Muhang Zhang, and Haiyan Wang. "Discriminative Ensemble Loss for Deep Neural Network on Classification of Ship-Radiated Noise." IEEE Signal Processing Letters 28 (2021): 449–53. http://dx.doi.org/10.1109/lsp.2021.3057539.
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Dai, Feng Yan, Zhao Yao Shi, and Jia Chun Lin. "Research of Defect Detection Method Noise for Bevel Gear." Advanced Materials Research 889-890 (February 2014): 722–25. http://dx.doi.org/10.4028/www.scientific.net/amr.889-890.722.
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Noise signal analysis method is widely available for gearbox bevel gear fault detection. However, the noise from the gearbox is usually concealed by background noise, which leads to poor efficiency analysis. This paper reports an ensemble empirical mode decomposition (EEMD) and neural network method for bevel gear fault detection. To extract useful signal, EEMD algorithm was firstly applied to get rid of the background noise. Characteristics from a group of discriminating defect status were then chosen to build the eigenvector. Finally, the eigenvector was imported into a back propagation (BP) neural network classifier for defect diagnosis automatically. Experimental results show that the proposed approach is capable for signal denoising and providing distinguishing characteristics of founded fault. The developed method is an accurate approach to detect fault for tested bevel gear.
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Et. al., Rajesh Birok,. "ECG Denoising Using Artificial Neural Networks and Complete Ensemble Empirical Mode Decomposition." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12, no. 2 (April 10, 2021): 2382–89. http://dx.doi.org/10.17762/turcomat.v12i2.2033.
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Electrocardiogram (ECG) is a documentation of the electrical activities of the heart. It is used to identify a number of cardiac faults such as arrhythmias, AF etc. Quite often the ECG gets corrupted by various kinds of artifacts, thus in order to gain correct information from them, they must first be denoised. This paper presents a novel approach for the filtering of low frequency artifacts of ECG signals by using Complete Ensemble Empirical Mode Decomposition (CEED) and Neural Networks, which removes most of the constituent noise while assuring no loss of information in terms of the morphology of the ECG signal. The contribution of the method lies in the fact that it combines the advantages of both EEMD and ANN. The use of CEEMD ensures that the Neural Network does not get over fitted. It also significantly helps in building better predictors at individual frequency levels. The proposed method is compared with other state-of-the-art methods in terms of Mean Square Error (MSE), Signal to Noise Ratio (SNR) and Correlation Coefficient. The results show that the proposed method has better performance as compared to other state-of-the-art methods for low frequency artifacts removal from EEG.
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Jin, Dequan, Jigen Peng, and Bin Li. "A New Clustering Approach on the Basis of Dynamical Neural Field." Neural Computation 23, no. 8 (August 2011): 2032–57. http://dx.doi.org/10.1162/neco_a_00153.
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In this letter, we present a new hierarchical clustering approach based on the evolutionary process of Amari's dynamical neural field model. Dynamical neural field theory provides a theoretical framework macroscopically describing the activity of neuron ensemble. Based on it, our clustering approach is essentially close to the neurophysiological nature of perception. It is also computationally stable, insensitive to noise, flexible, and tractable for data with complex structure. Some examples are given to show the feasibility.
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Chen, Kai, Kai Xie, Chang Wen, and Xin-Gong Tang. "Weak Signal Enhance Based on the Neural Network Assisted Empirical Mode Decomposition." Sensors 20, no. 12 (June 15, 2020): 3373. http://dx.doi.org/10.3390/s20123373.
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In order to enhance weak signals in strong noise background, a weak signal enhancement method based on EMDNN (neural network-assisted empirical mode decomposition) is proposed. This method combines CEEMD (complementary ensemble empirical mode decomposition), GAN (generative adversarial networks) and LSTM (long short-term memory), it enhances the efficiency of selecting effective natural mode components in empirical mode decomposition, thus the SNR (signal-noise ratio) is improved. It can also reconstruct and enhance weak signals. The experimental results show that the SNR of this method is improved from 4.1 to 6.2, and the weak signal is clearly recovered.
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Zahid, Saadia, Fawad Hussain, Muhammad Rashid, Muhammad Haroon Yousaf, and Hafiz Adnan Habib. "Optimized Audio Classification and Segmentation Algorithm by Using Ensemble Methods." Mathematical Problems in Engineering 2015 (2015): 1–11. http://dx.doi.org/10.1155/2015/209814.
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Audio segmentation is a basis for multimedia content analysis which is the most important and widely used application nowadays. An optimized audio classification and segmentation algorithm is presented in this paper that segments a superimposed audio stream on the basis of its content into four main audio types: pure-speech, music, environment sound, and silence. An algorithm is proposed that preserves important audio content and reduces the misclassification rate without using large amount of training data, which handles noise and is suitable for use for real-time applications. Noise in an audio stream is segmented out as environment sound. A hybrid classification approach is used, bagged support vector machines (SVMs) with artificial neural networks (ANNs). Audio stream is classified, firstly, into speech and nonspeech segment by using bagged support vector machines; nonspeech segment is further classified into music and environment sound by using artificial neural networks and lastly, speech segment is classified into silence and pure-speech segments on the basis of rule-based classifier. Minimum data is used for training classifier; ensemble methods are used for minimizing misclassification rate and approximately 98% accurate segments are obtained. A fast and efficient algorithm is designed that can be used with real-time multimedia applications.
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HARTONO, PITOYO, and SHUJI HASHIMOTO. "EXTRACTING THE PRINCIPAL BEHAVIOR OF A PROBABILISTIC SUPERVISOR THROUGH NEURAL NETWORKS ENSEMBLE." International Journal of Neural Systems 12, no. 03n04 (June 2002): 291–301. http://dx.doi.org/10.1142/s0129065702001126.
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In this paper, we propose a model of a neural network ensemble that can be trained with a supervisor having two kinds of input-output functions where the occurrence probability of each function is not even. This condition can be likened to a learning condition, in which the learning data are hampered by noise. In this case, the neural network has the impression that the learning supervisor (object) has a probabilistic behavior in which the supervisor generates correct learning data most of the time but occasionally generates erroneous ones. The objective is to train the neural network to approximate the greatest distributed input-output relation, which can be considered to be the principal nature of the supervisor, so that we can obtain a neural network that is able, to some extent, to suppress the ill effect of erroneous data encountered during the learning process.
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Dey, Subhrajit, Rajdeep Bhattacharya, Friedhelm Schwenker, and Ram Sarkar. "Median Filter Aided CNN Based Image Denoising: An Ensemble Approach." Algorithms 14, no. 4 (March 28, 2021): 109. http://dx.doi.org/10.3390/a14040109.
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Image denoising is a challenging research problem that aims to recover noise-free images from those that are contaminated with noise. In this paper, we focus on the denoising of images that are contaminated with additive white Gaussian noise. For this purpose, we propose an ensemble learning model that uses the output of three image denoising models, namely ADNet, IRCNN, and DnCNN, in the ratio of 2:3:6, respectively. The first model (ADNet) consists of Convolutional Neural Networks with attention along with median filter layers after every convolutional layer and a dilation rate of 8. In the case of the second model, it is a feed forward denoising CNN or DnCNN with median filter layers after half of the convolutional layers. For the third model, which is Deep CNN Denoiser Prior or IRCNN, the model contains dilated convolutional layers and median filter layers up to the dilated convolutional layers with a dilation rate of 6. By quantitative analysis, we note that our model performs significantly well when tested on the BSD500 and Set12 datasets.
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Hu, Sile, Qiaosheng Zhang, Jing Wang, and Zhe Chen. "Real-time particle filtering and smoothing algorithms for detecting abrupt changes in neural ensemble spike activity." Journal of Neurophysiology 119, no. 4 (April 1, 2018): 1394–410. http://dx.doi.org/10.1152/jn.00684.2017.
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Sequential change-point detection from time series data is a common problem in many neuroscience applications, such as seizure detection, anomaly detection, and pain detection. In our previous work (Chen Z, Zhang Q, Tong AP, Manders TR, Wang J. J Neural Eng 14: 036023, 2017), we developed a latent state-space model, known as the Poisson linear dynamical system, for detecting abrupt changes in neuronal ensemble spike activity. In online brain-machine interface (BMI) applications, a recursive filtering algorithm is used to track the changes in the latent variable. However, previous methods have been restricted to Gaussian dynamical noise and have used Gaussian approximation for the Poisson likelihood. To improve the detection speed, we introduce non-Gaussian dynamical noise for modeling a stochastic jump process in the latent state space. To efficiently estimate the state posterior that accommodates non-Gaussian noise and non-Gaussian likelihood, we propose particle filtering and smoothing algorithms for the change-point detection problem. To speed up the computation, we implement the proposed particle filtering algorithms using advanced graphics processing unit computing technology. We validate our algorithms, using both computer simulations and experimental data for acute pain detection. Finally, we discuss several important practical issues in the context of real-time closed-loop BMI applications. NEW & NOTEWORTHY Sequential change-point detection is an important problem in closed-loop neuroscience experiments. This study proposes novel sequential Monte Carlo methods to quickly detect the onset and offset of a stochastic jump process that drives the population spike activity. This new approach is robust with respect to spike sorting noise and varying levels of signal-to-noise ratio. The GPU implementation of the computational algorithm allows for parallel processing in real time.
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Ahn, J., L. J. Kreeger, S. T. Lubejko, D. A. Butts, and K. M. MacLeod. "Heterogeneity of intrinsic biophysical properties among cochlear nucleus neurons improves the population coding of temporal information." Journal of Neurophysiology 111, no. 11 (June 1, 2014): 2320–31. http://dx.doi.org/10.1152/jn.00836.2013.
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Reliable representation of the spectrotemporal features of an acoustic stimulus is critical for sound recognition. However, if all neurons respond with identical firing to the same stimulus, redundancy in the activity patterns would reduce the information capacity of the population. We thus investigated spike reliability and temporal fluctuation coding in an ensemble of neurons recorded in vitro from the avian auditory brain stem. Sequential patch-clamp recordings were made from neurons of the cochlear nucleus angularis while injecting identical filtered Gaussian white noise currents, simulating synaptic drive. The spiking activity in neurons receiving these identically fluctuating stimuli was highly correlated, measured pairwise across neurons and as a pseudo-population. Two distinct uncorrelated noise stimuli could be discriminated using the temporal patterning, but not firing rate, of the spike trains in the neural ensemble, with best discrimination using information at time scales of 5–20 ms. Despite high cross-correlation values, the spike patterns observed in individual neurons were idiosyncratic, with notable heterogeneity across neurons. To investigate how temporal information is being encoded, we used optimal linear reconstruction to produce an estimate of the original current stimulus from the spike trains. Ensembles of trains sampled across the neural population could be used to predict >50% of the stimulus variation using optimal linear decoding, compared with ∼20% using the same number of spike trains recorded from single neurons. We conclude that heterogeneity in the intrinsic biophysical properties of cochlear nucleus neurons reduces firing pattern redundancy while enhancing representation of temporal information.
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Ahn, Byeongyong, Gu Yong Park, Yoonsik Kim, and Nam Ik Cho. "A Self-ensemble Approach for Noise and Compression Artifacts Removal using Convolutional Neural Network." IEIE Transactions on Smart Processing & Computing 7, no. 4 (August 31, 2018): 296–304. http://dx.doi.org/10.5573/ieiespc.2018.7.4.296.
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Wu, Jianfeng, Yongzhu Hua, Shengying Yang, Hongshuai Qin, and Huibin Qin. "Speech Enhancement Using Generative Adversarial Network by Distilling Knowledge from Statistical Method." Applied Sciences 9, no. 16 (August 18, 2019): 3396. http://dx.doi.org/10.3390/app9163396.
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This paper presents a new deep neural network (DNN)-based speech enhancement algorithm by integrating the distilled knowledge from the traditional statistical-based method. Unlike the other DNN-based methods, which usually train many different models on the same data and then average their predictions, or use a large number of noise types to enlarge the simulated noisy speech, the proposed method does not train a whole ensemble of models and does not require a mass of simulated noisy speech. It first trains a discriminator network and a generator network simultaneously using the adversarial learning method. Then, the discriminator network and generator network are re-trained by distilling knowledge from the statistical method, which is inspired by the knowledge distillation in a neural network. Finally, the generator network is fine-tuned using real noisy speech. Experiments on CHiME4 data sets demonstrate that the proposed method achieves a more robust performance than the compared DNN-based method in terms of perceptual speech quality.
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Mężyk, Miłosz, Michał Chamarczuk, and Michał Malinowski. "Automatic Image-Based Event Detection for Large-N Seismic Arrays Using a Convolutional Neural Network." Remote Sensing 13, no. 3 (January 23, 2021): 389. http://dx.doi.org/10.3390/rs13030389.
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Passive seismic experiments have been proposed as a cost-effective and non-invasive alternative to controlled-source seismology, allowing body–wave reflections based on seismic interferometry principles to be retrieved. However, from the huge volume of the recorded ambient noise, only selected time periods (noise panels) are contributing constructively to the retrieval of reflections. We address the issue of automatic scanning of ambient noise data recorded by a large-N array in search of body–wave energy (body–wave events) utilizing a convolutional neural network (CNN). It consists of computing first both amplitude and frequency attribute values at each receiver station for all divided portions of the recorded signal (noise panels). The created 2-D attribute maps are then converted to images and used to extract spatial and temporal patterns associated with the body–wave energy present in the data to build binary CNN-based classifiers. The ensemble of two multi-headed CNN models trained separately on the frequency and amplitude attribute maps demonstrates better generalization ability than each of its participating networks. We also compare the prediction performance of our deep learning (DL) framework with a conventional machine learning (ML) algorithm called XGBoost. The DL-based solution applied to 240 h of ambient seismic noise data recorded by the Kylylahti array in Finland demonstrates high detection accuracy and the superiority over the ML-based one. The ensemble of CNN-based models managed to find almost three times more verified body–wave events in the full unlabelled dataset than it was provided at the training stage. Moreover, the high-level abstraction features extracted at the deeper convolution layers can be used to perform unsupervised clustering of the classified panels with respect to their visual characteristics.
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Mashhadi, Peyman Sheikholharam, Sławomir Nowaczyk, and Sepideh Pashami. "Stacked Ensemble of Recurrent Neural Networks for Predicting Turbocharger Remaining Useful Life." Applied Sciences 10, no. 1 (December 20, 2019): 69. http://dx.doi.org/10.3390/app10010069.
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Predictive Maintenance (PM) is a proactive maintenance strategy that tries to minimize a system’s downtime by predicting failures before they happen. It uses data from sensors to measure the component’s state of health and make forecasts about its future degradation. However, existing PM methods typically focus on individual measurements. While it is natural to assume that a history of measurements carries more information than a single one. This paper aims at incorporating such information into PM models. In practice, especially in the automotive domain, diagnostic models have low performance, due to a large amount of noise in the data and limited sensing capability. To address this issue, this paper proposes to use a specific type of ensemble learning known as Stacked Ensemble. The idea is to aggregate predictions of multiple models—consisting of Long Short-Term Memory (LSTM) and Convolutional-LSTM—via a meta model, in order to boost performance. Stacked Ensemble model performs well when its base models are as diverse as possible. To this end, each such model is trained using a specific combination of the following three aspects: feature subsets, past dependency horizon, and model architectures. Experimental results demonstrate benefits of the proposed approach on a case study of heavy-duty truck turbochargers.
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Rajaraman, Sivaramakrishnan, Sudhir Sornapudi, Philip O. Alderson, Les R. Folio, and Sameer K. Antani. "Analyzing inter-reader variability affecting deep ensemble learning for COVID-19 detection in chest radiographs." PLOS ONE 15, no. 11 (November 12, 2020): e0242301. http://dx.doi.org/10.1371/journal.pone.0242301.
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Data-driven deep learning (DL) methods using convolutional neural networks (CNNs) demonstrate promising performance in natural image computer vision tasks. However, their use in medical computer vision tasks faces several limitations, viz., (i) adapting to visual characteristics that are unlike natural images; (ii) modeling random noise during training due to stochastic optimization and backpropagation-based learning strategy; (iii) challenges in explaining DL black-box behavior to support clinical decision-making; and (iv) inter-reader variability in the ground truth (GT) annotations affecting learning and evaluation. This study proposes a systematic approach to address these limitations through application to the pandemic-caused need for Coronavirus disease 2019 (COVID-19) detection using chest X-rays (CXRs). Specifically, our contribution highlights significant benefits obtained through (i) pretraining specific to CXRs in transferring and fine-tuning the learned knowledge toward improving COVID-19 detection performance; (ii) using ensembles of the fine-tuned models to further improve performance over individual constituent models; (iii) performing statistical analyses at various learning stages for validating results; (iv) interpreting learned individual and ensemble model behavior through class-selective relevance mapping (CRM)-based region of interest (ROI) localization; and, (v) analyzing inter-reader variability and ensemble localization performance using Simultaneous Truth and Performance Level Estimation (STAPLE) methods. We find that ensemble approaches markedly improved classification and localization performance, and that inter-reader variability and performance level assessment helps guide algorithm design and parameter optimization. To the best of our knowledge, this is the first study to construct ensembles, perform ensemble-based disease ROI localization, and analyze inter-reader variability and algorithm performance for COVID-19 detection in CXRs.
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Kwon, Jihoon, and Nojun Kwak. "Radar Application: Stacking Multiple Classifiers for Human Walking Detection Using Micro-Doppler Signals." Applied Sciences 9, no. 17 (August 28, 2019): 3534. http://dx.doi.org/10.3390/app9173534.
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We propose a stacking method for ensemble learning to distinguish micro-Doppler signals generated by human walking from background noises using radar sensors. We collected micro-Doppler signals caused by four types of background noise (line of sight (LoS), fan, snow and rain) and additionally considered micro-Doppler signals caused by human walking combined with these four types of background noise. We firstly verified the effectiveness of a fully connected deep neural network (DNN) to classify 8 types of signals. The average accuracy was 88.79% for the test set. Then, we propose a stacking method to combine two base classifiers of different structures. The average accuracy of the stacking method on the test set was 91.43%. Lastly, we designed a modified stacking method to reuse feature information stored at the previous stage and the average test accuracy increased to 95.62%. This result shows that the proposed stacking methods can be an effective approach to improve classifier’s accuracy in recognizing human walking using micro-Doppler signals with background noise.
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Scheuerer, Michael, Matthew B. Switanek, Rochelle P. Worsnop, and Thomas M. Hamill. "Using Artificial Neural Networks for Generating Probabilistic Subseasonal Precipitation Forecasts over California." Monthly Weather Review 148, no. 8 (July 31, 2020): 3489–506. http://dx.doi.org/10.1175/mwr-d-20-0096.1.
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Abstract Forecast skill of numerical weather prediction (NWP) models for precipitation accumulations over California is rather limited at subseasonal time scales, and the low signal-to-noise ratio makes it challenging to extract information that provides reliable probabilistic forecasts. A statistical postprocessing framework is proposed that uses an artificial neural network (ANN) to establish relationships between NWP ensemble forecast and gridded observed 7-day precipitation accumulations, and to model the increase or decrease of the probabilities for different precipitation categories relative to their climatological frequencies. Adding predictors with geographic information and location-specific normalization of forecast information permits the use of a single ANN for the entire forecast domain and thus reduces the risk of overfitting. In addition, a convolutional neural network (CNN) framework is proposed that extends the basic ANN and takes images of large-scale predictors as inputs that inform local increase or decrease of precipitation probabilities relative to climatology. Both methods are demonstrated with ECMWF ensemble reforecasts over California for lead times up to 4 weeks. They compare favorably with a state-of-the-art postprocessing technique developed for medium-range ensemble precipitation forecasts, and their forecast skill relative to climatology is positive everywhere within the domain. The magnitude of skill, however, is low for week-3 and week-4, and suggests that additional sources of predictability need to be explored.
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Fallahian, Milad, Faramarz Khoshnoudian, and Viviana Meruane. "Ensemble classification method for structural damage assessment under varying temperature." Structural Health Monitoring 17, no. 4 (July 7, 2017): 747–62. http://dx.doi.org/10.1177/1475921717717311.
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Vibration-based damage assessment approaches use modal parameters, such as frequency response functions, mode shapes, and natural frequencies, as indicators of structural damage. Nevertheless, these parameters are sensitive not only to damage but also to temperature variations. Most civil engineering structures are exposed to varying environmental conditions, thus hindering vibration-based damage assessment. Therefore, in this article, a new damage assessment algorithm based on pattern recognition is proposed to scrutinize the healthy state of a structure in the presence of uncertainties such as noise and temperature. The algorithm adopts a combination of couple sparse coding and deep neural network as an ensemble system to assess damage. The proposed method is validated using a numerical model of a truss bridge and experimental data of the I-40 bridge. The results demonstrate its efficiency in the localization and quantification of damages under varying temperature conditions.
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Hou, Sizu, and Wei Guo. "Faulty Line Selection Based on Modified CEEMDAN Optimal Denoising Smooth Model and Duffing Oscillator for Un-Effectively Grounded System." Mathematical Problems in Engineering 2020 (April 6, 2020): 1–21. http://dx.doi.org/10.1155/2020/5761642.
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As the un-effectively grounded system fails, the zero-sequence current contains strong noise and nonstationary features. This paper proposes a novel faulty line selection method based on modified complete ensemble empirical mode decomposition with adaptive noise (MCEEMDAN) and Duffing oscillator. Here, based on multiscale permutation entropy, fuzzy c-means clustering, and general regression neural network for abnormal signal detection, the MCEEMDAN is proposed. The endpoint mirror method is used to suppress the endpoint effect problem in the decomposition stage. The proposed algorithm is able to decompose the original signal into a series of intrinsic mode functions, which can complete the first filtering. The research shows that it can efficiently suppress the mode confusing phenomenon of empirical mode decomposition (EMD) and is also more complete and orthogonal than ensemble empirical mode decomposition (EEMD) and complementary ensemble empirical mode decomposition (CEEMD). The optimal denoising smooth model is established for choosing optimal intrinsic mode functions to complete the second filtering. It can ensure that the reconstructed filtered signal has better smoothness and similarity. The optimal denoising smooth model of MCEEMDAN can not only keep useful details of the original signal but also reduce the noise and smooth signal. The bifurcation characteristic of the chaotic oscillator is applied in weak signal detection. The zero-sequence current’s denoising result is extracted as the input signal of the Duffing system. The faulty line could be selected by observing the phase diagram of the system. The research results verify the usability and effectiveness of the proposed method.
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Fishman, Yonatan I., and Mitchell Steinschneider. "Spectral Resolution of Monkey Primary Auditory Cortex (A1) Revealed With Two-Noise Masking." Journal of Neurophysiology 96, no. 3 (September 2006): 1105–15. http://dx.doi.org/10.1152/jn.00124.2006.
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An important function of the auditory nervous system is to analyze the frequency content of environmental sounds. The neural structures involved in determining psychophysical frequency resolution remain unclear. Using a two-noise masking paradigm, the present study investigates the spectral resolution of neural populations in primary auditory cortex (A1) of awake macaques and the degree to which it matches psychophysical frequency resolution. Neural ensemble responses (auditory evoked potentials, multiunit activity, and current source density) evoked by a pulsed 60-dB SPL pure-tone signal fixed at the best frequency (BF) of the recorded neural populations were examined as a function of the frequency separation (ΔF) between the tone and two symmetrically flanking continuous 80-dB SPL, 50-Hz-wide bands of noise. ΔFs ranged from 0 to 50% of the BF, encompassing the range typically examined in psychoacoustic experiments. Responses to the signal were minimal for ΔF = 0% and progressively increased with ΔF, reaching a maximum at ΔF = 50%. Rounded exponential functions, used to model auditory filter shapes in psychoacoustic studies of frequency resolution, provided excellent fits to neural masking functions. Goodness-of-fit was greatest for response components in lamina 4 and lower lamina 3 and least for components recorded in more superficial cortical laminae. Physiological equivalent rectangular bandwidths (ERBs) increased with BF, measuring nearly 15% of the BF. These findings parallel results of psychoacoustic studies in both monkeys and humans, and thus indicate that a representation of perceptual frequency resolution is available at the level of A1.
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Kim, Sungil, Baehyun Min, Seoyoon Kwon, and Min-gon Chu. "History Matching of a Channelized Reservoir Using a Serial Denoising Autoencoder Integrated with ES-MDA." Geofluids 2019 (April 16, 2019): 1–22. http://dx.doi.org/10.1155/2019/3280961.
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For an ensemble-based history matching of a channelized reservoir, loss of geological plausibility is challenging because of pixel-based manipulation of channel shape and connectivity despite sufficient conditioning to dynamic observations. Regarding the loss as artificial noise, this study designs a serial denoising autoencoder (SDAE) composed of two neural network filters, utilizes this machine learning algorithm for relieving noise effects in the process of ensemble smoother with multiple data assimilation (ES-MDA), and improves the overall history matching performance. As a training dataset of the SDAE, the static reservoir models are realized based on multipoint geostatistics and contaminated with two types of noise: salt and pepper noise and Gaussian noise. The SDAE learns how to eliminate the noise and restore the clean reservoir models. It does this through encoding and decoding processes using the noise realizations as inputs and the original realizations as outputs of the SDAE. The trained SDAE is embedded in the ES-MDA. The posterior reservoir models updated using Kalman gain are imported to the SDAE which then exports the purified prior models of the next assimilation. In this manner, a clear contrast among rock facies parameters during multiple data assimilations is maintained. A case study at a gas reservoir indicates that ES-MDA coupled with the noise remover outperforms a conventional ES-MDA. Improvement in the history matching performance resulting from denoising is also observed for ES-MDA algorithms combined with dimension reduction approaches such as discrete cosine transform, K-singular vector decomposition, and a stacked autoencoder. The results of this study imply that a well-trained SDAE has the potential to be a reliable auxiliary method for enhancing the performance of data assimilation algorithms if the computational cost required for machine learning is affordable.
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Tian, Juan, and Yingxiang Li. "Convolutional Neural Networks for Steganalysis via Transfer Learning." International Journal of Pattern Recognition and Artificial Intelligence 33, no. 02 (October 24, 2018): 1959006. http://dx.doi.org/10.1142/s0218001419590067.
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Recently, a large number of studies have shown that Convolutional Neural Networks are effective for learning features automatically for steganalysis. This paper uses the transfer learning method to help the training of CNNs for steganalysis. First, a Gaussian high-pass filter is designed for pretreatment of the images, that can enhance the weak stego noise in the stegos. Then, the classical Inception-V3 model is improved, and the improved network is used for steganalysis through the method of transfer learning. In order to test the effectiveness of the developed model, two spatial domain content-adaptive steganographic algorithms WOW and S-UNIWARD are used. The results imply that the proposed CNN achieves a better performance at low embedding rates compared with the SRM with ensemble classifiers and the SPAM implemented with a Gaussian SVM on BOSSbase. Finally, a steganalysis system based on the trained model was designed. Through experiments, the generalization ability of the system was tested and discussed.
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Jiang, Zhencun, Zhengxin Dong, Lingyang Wang, and Wenping Jiang. "Method for Diagnosis of Acute Lymphoblastic Leukemia Based on ViT-CNN Ensemble Model." Computational Intelligence and Neuroscience 2021 (August 21, 2021): 1–12. http://dx.doi.org/10.1155/2021/7529893.
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Acute lymphocytic leukemia (ALL) is a deadly cancer that not only affects adults but also accounts for about 25% of childhood cancers. Timely and accurate diagnosis of the cancer is an important premise for effective treatment to improve survival rate. Since the image of leukemic B-lymphoblast cells (cancer cells) under the microscope is very similar in morphology to that of normal B-lymphoid precursors (normal cells), it is difficult to distinguish between cancer cells and normal cells. Therefore, we propose the ViT-CNN ensemble model to classify cancer cells images and normal cells images to assist in the diagnosis of acute lymphoblastic leukemia. The ViT-CNN ensemble model is an ensemble model that combines the vision transformer model and convolutional neural network (CNN) model. The vision transformer model is an image classification model based entirely on the transformer structure, which has completely different feature extraction method from the CNN model. The ViT-CNN ensemble model can extract the features of cells images in two completely different ways to achieve better classification results. In addition, the data set used in this article is an unbalanced data set and has a certain amount of noise, and we propose a difference enhancement-random sampling (DERS) data enhancement method, create a new balanced data set, and use the symmetric cross-entropy loss function to reduce the impact of noise in the data set. The classification accuracy of the ViT-CNN ensemble model on the test set has reached 99.03%, and it is proved through experimental comparison that the effect is better than other models. The proposed method can accurately distinguish between cancer cells and normal cells and can be used as an effective method for computer-aided diagnosis of acute lymphoblastic leukemia.
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Knuth, Kevin H., Ankoor S. Shah, Wilson A. Truccolo, Mingzhou Ding, Steven L. Bressler, and Charles E. Schroeder. "Differentially Variable Component Analysis: Identifying Multiple Evoked Components Using Trial-to-Trial Variability." Journal of Neurophysiology 95, no. 5 (May 2006): 3257–76. http://dx.doi.org/10.1152/jn.00663.2005.
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Electric potentials and magnetic fields generated by ensembles of synchronously active neurons, either spontaneously or in response to external stimuli, provide information essential to understanding the processes underlying cognitive and sensorimotor activity. Interpreting recordings of these potentials and fields is difficult because detectors record signals simultaneously generated by various regions throughout the brain. We introduce a novel approach to this problem, the differentially variable component analysis (dVCA) algorithm, which relies on trial-to-trial variability in response amplitude and latency to identify multiple components. Using simulations we demonstrate the importance of response variability to component identification, the robustness of dVCA to noise, and its ability to characterize single-trial data. We then compare the source-separation capabilities of dVCA with those of principal component analysis and independent component analysis. Finally, we apply dVCA to neural ensemble activity recorded from an awake, behaving macaque—demonstrating that dVCA is an important tool for identifying and characterizing multiple components in the single trial.
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Ma, Jianpeng, Zhenghui Li, Chengwei Li, Liwei Zhan, and Guang-Zhu Zhang. "Rolling Bearing Fault Diagnosis Based on Refined Composite Multi-Scale Approximate Entropy and Optimized Probabilistic Neural Network." Entropy 23, no. 2 (February 23, 2021): 259. http://dx.doi.org/10.3390/e23020259.
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A rolling bearing early fault diagnosis method is proposed in this paper, which is derived from a refined composite multi-scale approximate entropy (RCMAE) and improved coyote optimization algorithm based probabilistic neural network (ICOA-PNN) algorithm. Rolling bearing early fault diagnosis is a time-sensitive task, which is significant to ensure the reliability and safety of mechanical fault system. At the same time, the early fault features are masked by strong background noise, which also brings difficulties to fault diagnosis. So, we firstly utilize the composite ensemble intrinsic time-scale decomposition with adaptive noise method (CEITDAN) to decompose the signal at different scales, and then the refined composite multi-scale approximate entropy of the first signal component is calculated to analyze the complexity of describing the vibration signal. Afterwards, in order to obtain higher recognition accuracy, the improved coyote optimization algorithm based probabilistic neural network classifiers is employed for pattern recognition. Finally, the feasibility and effectiveness of this method are verified by rolling bearing early fault diagnosis experiment.
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Xie, Yingchun, Yucheng Xiao, Xuyan Liu, Guijie Liu, Weixiong Jiang, and Jin Qin. "Time-Frequency Distribution Map-Based Convolutional Neural Network (CNN) Model for Underwater Pipeline Leakage Detection Using Acoustic Signals." Sensors 20, no. 18 (September 4, 2020): 5040. http://dx.doi.org/10.3390/s20185040.
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Detection technology of underwater pipeline leakage plays an important role in the subsea production system. In this paper, a new method based on the acoustic leak signal collected by a hydrophone is proposed to detect pipeline leakage in the subsea production system. Through the pipeline leakage test, it is found that the radiation noise is a continuous spectrum of the medium and high-frequency noise. Both the increase in pipe pressure and the diameter of the leak hole will narrow the spectral structure and shift the spectrum center towards the low frequencies. Under the same condition, the pipe pressure has a greater impact on the noise; every 0.05 MPa increase in the pressure, the radiation sound pressure level increases by 6-7 dB. The time-frequency images were obtained by processing the acoustic signals using the Ensemble Empirical Mode Decomposition (EEMD) and Hilbert–Huang transform (HHT), and fed into a two-layer Convolutional Neural Network (CNN) for leakage detection. The results show that CNN can correctly identify the degree of pipeline leakage. Hence, the proposed method provides a new approach for the detection of pipeline leakage in underwater engineering applications.
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Antoniades, Andreas, Loukianos Spyrou, David Martin-Lopez, Antonio Valentin, Gonzalo Alarcon, Saeid Sanei, and Clive Cheong Took. "Deep Neural Architectures for Mapping Scalp to Intracranial EEG." International Journal of Neural Systems 28, no. 08 (August 26, 2018): 1850009. http://dx.doi.org/10.1142/s0129065718500090.
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Data is often plagued by noise which encumbers machine learning of clinically useful biomarkers and electroencephalogram (EEG) data is no exemption. Intracranial EEG (iEEG) data enhances the training of deep learning models of the human brain, yet is often prohibitive due to the invasive recording process. A more convenient alternative is to record brain activity using scalp electrodes. However, the inherent noise associated with scalp EEG data often impedes the learning process of neural models, achieving substandard performance. Here, an ensemble deep learning architecture for nonlinearly mapping scalp to iEEG data is proposed. The proposed architecture exploits the information from a limited number of joint scalp-intracranial recording to establish a novel methodology for detecting the epileptic discharges from the sEEG of a general population of subjects. Statistical tests and qualitative analysis have revealed that the generated pseudo-intracranial data are highly correlated with the true intracranial data. This facilitated the detection of IEDs from the scalp recordings where such waveforms are not often visible. As a real-world clinical application, these pseudo-iEEGs are then used by a convolutional neural network for the automated classification of intracranial epileptic discharges (IEDs) and non-IED of trials in the context of epilepsy analysis. Although the aim of this work was to circumvent the unavailability of iEEG and the limitations of sEEG, we have achieved a classification accuracy of 68% an increase of 6% over the previously proposed linear regression mapping.
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Wang, Jing, Guigen Nie, Shengjun Gao, Shuguang Wu, Haiyang Li, and Xiaobing Ren. "Landslide Deformation Prediction Based on a GNSS Time Series Analysis and Recurrent Neural Network Model." Remote Sensing 13, no. 6 (March 10, 2021): 1055. http://dx.doi.org/10.3390/rs13061055.
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The prediction of landslide displacement is a challenging and essential task. It is thus very important to choose a suitable displacement prediction model. This paper develops a novel Attention Mechanism with Long Short Time Memory Neural Network (AMLSTM NN) model based on Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) landslide displacement prediction. The CEEMDAN method is implemented to ingest landslide Global Navigation Satellite System (GNSS) time series. The AMLSTM algorithm is then used to realize prediction work, jointly with multiple impact factors. The Baishuihe landslide is adopted to illustrate the capabilities of the model. The results show that the CEEMDAN-AMLSTM model achieves competitive accuracy and has significant potential for landslide displacement prediction.
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Altuve, Miguel, Paula Lizarazo, and Javier Villamizar. "Human activity recognition using improved complete ensemble EMD with adaptive noise and long short-term memory neural networks." Biocybernetics and Biomedical Engineering 40, no. 3 (July 2020): 901–9. http://dx.doi.org/10.1016/j.bbe.2020.04.007.
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Cao, Yang, Xiaokang Zhou, and Ke Yan. "Deep Learning Neural Network Model for Tunnel Ground Surface Settlement Prediction Based on Sensor Data." Mathematical Problems in Engineering 2021 (August 27, 2021): 1–14. http://dx.doi.org/10.1155/2021/9488892.
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Monitoring and prediction of ground settlement during tunnel construction are of great significance to ensure the safe and reliable operation of urban tunnel systems. Data-driven techniques combining artificial intelligence (AI) and sensor networks are popular methods in the field, which have several advantages, including high prediction accuracy, efficiency, and low cost. Deep learning, as one of the advanced techniques in AI, is demanded for the tunnel settlement forecasting problem. However, deep neural networks often require a large amount of training data. Due to the tunnel construction, the available training data samples are limited, and the data are univariate (i.e., containing only the settlement data). In response to the above problems, this research proposes a deep learning model that only requires limited number of training data for short-period prediction of the tunnel surface settlement. In the proposed complete ensemble empirical mode decomposition with adaptive noise long short term memory (CEEMDAN-LSTM model), single-dimensional data is divided into multidimensional data by CEEMDAN through the complete ensemble empirical mode decomposition. Each component is then predicted by a LSTM neural network and superimposed for obtaining the final prediction result. Experimental results show that, compared with existing machine learning techniques and algorithms, this deep learning method has higher prediction accuracy and acceptable computational efficiency. In the case of small samples, this method can significantly improve the accuracy of time series forecasting.
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Akpudo, Ugochukwu Ejike, and Jang-Wook Hur. "A CEEMDAN-Assisted Deep Learning Model for the RUL Estimation of Solenoid Pumps." Electronics 10, no. 17 (August 25, 2021): 2054. http://dx.doi.org/10.3390/electronics10172054.
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This paper develops a data-driven remaining useful life prediction model for solenoid pumps. The model extracts high-level features using stacked autoencoders from decomposed pressure signals (using complementary ensemble empirical mode decomposition with adaptive noise (CEEMDAN) algorithm). These high-level features are then received by a recurrent neural network-gated recurrent units (GRUs) for the RUL estimation. The case study presented demonstrates the robustness of the proposed RUL estimation model with extensive empirical validations. Results support the validity of using the CEEMDAN for non-stationary signal decomposition and the accuracy, ease-of-use, and superiority of the proposed DL-based model for solenoid pump failure prognostics.
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Rudd, Michael E., and Lawrence G. Brown. "Noise Adaptation in Integrate-and-Fire Neurons." Neural Computation 9, no. 5 (July 1, 1997): 1047–69. http://dx.doi.org/10.1162/neco.1997.9.5.1047.
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The statistical spiking response of an ensemble of identically prepared stochastic integrate-and-fire neurons to a rectangular input current plus gaussian white noise is analyzed. It is shown that, on average, integrate-and-fire neurons adapt to the root-mean-square noise level of their input. This phenomenon is referred to as noise adaptation. Noise adaptation is characterized by a decrease in the average neural firing rate and an accompanying decrease in the average value of the generator potential, both of which can be attributed to noise-induced resets of the generator potential mediated by the integrate-and-fire mechanism. A quantitative theory of noise adaptation in stochastic integrate-and-fire neurons is developed. It is shown that integrate-and-fire neurons, on average, produce transient spiking activity whenever there is an increase in the level of their input noise. This transient noise response is either reduced or eliminated over time, depending on the parameters of the model neuron. Analytical methods are used to prove that nonleaky integrate-and-fire neurons totally adapt to any constant input noise level, in the sense that their asymptotic spiking rates are independent of the magnitude of their input noise. For leaky integrate-and-fire neurons, the long-run noise adaptation is not total, but the response to noise is partially eliminated. Expressions for the probability density function of the generator potential and the first two moments of the potential distribution are derived for the particular case of a nonleaky neuron driven by gaussian white noise of mean zero and constant variance. The functional significance of noise adaptation for the performance of networks comprising integrate-and-fire neurons is discussed.
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Wu, Jiang, Feng Miu, and Taiyong Li. "Daily Crude Oil Price Forecasting Based on Improved CEEMDAN, SCA, and RVFL: A Case Study in WTI Oil Market." Energies 13, no. 7 (April 10, 2020): 1852. http://dx.doi.org/10.3390/en13071852.
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Crude oil is one of the strategic energies and plays an increasingly critical role effecting on the world economic development. The fluctuations of crude oil prices are caused by various extrinsic and intrinsic factors and usually demonstrate complex characteristics. Therefore, it is a great challenge for accurately forecasting crude oil prices. In this study, a self-optimizing ensemble learning model incorporating the improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN), sine cosine algorithm (SCA), and random vector functional link (RVFL) neural network, namely ICEEMDAN-SCA-RVFL, is proposed to forecast crude oil prices. Firstly, we employ ICEEMDAN to decompose the raw series of crude oil prices into a group of relatively simple subseries. Secondly, RVFL is used to forecast the target values for each decomposed subseries individually. Due to the complex parameter settings of ICEEMDAN and RVFL, SCA is introduced to optimize the parameters for ICEEMDAN and RVFL in the above decomposition and prediction stages simultaneously. Finally, we assemble the predicted values of all individual subseries as the final predicted values of crude oil prices. Our proposed ICEEMDAN-SCA-RVFL significantly outperforms the single and ensemble benchmark models, as demonstrated by a case study conducted using the time series of West Texas Intermediate (WTI) daily crude oil spot prices.
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Nti, Isaac Kofi, Adebayo Felix Adekoya, and Benjamin Asubam Weyori. "Efficient Stock-Market Prediction Using Ensemble Support Vector Machine." Open Computer Science 10, no. 1 (July 4, 2020): 153–63. http://dx.doi.org/10.1515/comp-2020-0199.
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AbstractPredicting stock-price remains an important subject of discussion among financial analysts and researchers. However, the advancement in technologies such as artificial intelligence and machine learning techniques has paved the way for better and accurate prediction of stock-price in recent years. Of late, Support Vector Machines (SVM) have earned popularity among Machine Learning (ML) algorithms used for predicting stock price. However, a high percentage of studies in algorithmic investments based on SVM overlooked the overfitting nature of SVM when the input dataset is of high-noise and high-dimension. Therefore, this study proposes a novel homogeneous ensemble classifier called GASVM based on support vector machine enhanced with Genetic Algorithm (GA) for feature-selection and SVM kernel parameter optimisation for predicting the stock market. The GA was introduced in this study to achieve a simultaneous optimal of the diverse design factors of the SVM. Experiments carried out with over eleven (11) years’ stock data from the Ghana Stock Exchange (GSE) yielded compelling results. The outcome shows that the proposed model (named GASVM) outperformed other classical ML algorithms (Decision Tree (DT), Random Forest (RF) and Neural Network (NN)) in predicting a 10-day-ahead stock price movement. The proposed (GASVM) showed a better prediction accuracy of 93.7% compared with 82.3% (RF), 75.3% (DT), and 80.1% (NN). It can, therefore, be deduced from the fallouts that the proposed (GASVM) technique puts-up a practical approach feature-selection and parameter optimisation of the different design features of the SVM and thus remove the need for the labour-intensive parameter optimisation.
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Mousavi, Asma Alsadat, Chunwei Zhang, Sami F. Masri, and Gholamreza Gholipour. "Structural Damage Localization and Quantification Based on a CEEMDAN Hilbert Transform Neural Network Approach: A Model Steel Truss Bridge Case Study." Sensors 20, no. 5 (February 26, 2020): 1271. http://dx.doi.org/10.3390/s20051271.
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Vibrations of complex structures such as bridges mostly present nonlinear and non-stationary behaviors. Recently, one of the most common techniques to analyze the nonlinear and non-stationary structural response is Hilbert–Huang Transform (HHT). This paper aims to evaluate the performance of HHT based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) technique using an Artificial Neural Network (ANN) as a proposed damage detection methodology. The performance of the proposed method is investigated for damage detection of a scaled steel-truss bridge model which was experimentally established as the case study subjected to white noise excitations. To this end, four key features of the intrinsic mode function (IMF), including energy, instantaneous amplitude (IA), unwrapped phase, and instantaneous frequency (IF), are extracted to assess the presence, severity, and location of the damage. By analyzing the experimental results through different damage indices defined based on the extracted features, the capabilities of the CEEMDAN-HT-ANN model in detecting, addressing the location and classifying the severity of damage are efficiently concluded. In addition, the energy-based damage index demonstrates a more effective approach in detecting the damage compared to those based on IA and unwrapped phase parameters.
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Georges, Hassana Maigary, Dong Wang, and Zhu Xiao. "GNSS/Low-Cost MEMS-INS Integration Using Variational Bayesian Adaptive Cubature Kalman Smoother and Ensemble Regularized ELM." Mathematical Problems in Engineering 2015 (2015): 1–13. http://dx.doi.org/10.1155/2015/682907.
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Among the inertial navigation system (INS) devices used in land vehicle navigation (LVN), low-cost microelectromechanical systems (MEMS) inertial sensors have received more interest for bridging global navigation satellites systems (GNSS) signal failures because of their price and portability. Kalman filter (KF) based GNSS/INS integration has been widely used to provide a robust solution to the navigation. However, its prediction model cannot give satisfactory results in the presence of colored and variational noise. In order to achieve reliable and accurate positional solution for LVN in urban areas surrounded by skyscrapers or under dense foliage and tunnels, a novel model combining variational Bayesian adaptive Kalman smoother (VB-ACKS) as an alternative of KF and ensemble regularized extreme learning machine (ERELM) for bridging global positioning systems outages is proposed. The ERELM is applied to reduce the fluctuating performance of GNSS during an outage. We show that a well-organized collection of predictors using ensemble learning yields a more accurate positional result when compared with conventional artificial neural network (ANN) predictors. Experimental results show that the performance of VB-ACKS is more robust compared with KF solution, and the prediction of ERELM contains the smallest error compared with other ANN solutions.
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Wu, Jiang, Tengfei Zhou, and Taiyong Li. "A Hybrid Approach Integrating Multiple ICEEMDANs, WOA, and RVFL Networks for Economic and Financial Time Series Forecasting." Complexity 2020 (October 22, 2020): 1–17. http://dx.doi.org/10.1155/2020/9318308.
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The fluctuations of economic and financial time series are influenced by various kinds of factors and usually demonstrate strong nonstationary and high complexity. Therefore, accurately forecasting economic and financial time series is always a challenging research topic. In this study, a novel multidecomposition and self-optimizing hybrid approach integrating multiple improved complete ensemble empirical mode decompositions with adaptive noise (ICEEMDANs), whale optimization algorithm (WOA), and random vector functional link (RVFL) neural networks, namely, MICEEMDAN-WOA-RVFL, is developed to predict economic and financial time series. First, we employ ICEEMDAN with random parameters to separate the original time series into a group of comparatively simple subseries multiple times. Second, we construct RVFL networks to individually forecast each subseries. Considering the complex parameter settings of RVFL networks, we utilize WOA to search the optimal parameters for RVFL networks simultaneously. Then, we aggregate the prediction results of individual decomposed subseries as the prediction results of each decomposition, respectively, and finally integrate these prediction results of all the decompositions as the final ensemble prediction results. The proposed MICEEMDAN-WOA-RVFL remarkably outperforms the compared single and ensemble benchmark models in terms of forecasting accuracy and stability, as demonstrated by the experiments conducted using various economic and financial time series, including West Texas Intermediate (WTI) crude oil prices, US dollar/Euro foreign exchange rate (USD/EUR), US industrial production (IP), and Shanghai stock exchange composite index (SSEC).
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Krasnopolsky, Vladimir, Sudhir Nadiga, Avichal Mehra, Eric Bayler, and David Behringer. "Neural Networks Technique for Filling Gaps in Satellite Measurements: Application to Ocean Color Observations." Computational Intelligence and Neuroscience 2016 (2016): 1–9. http://dx.doi.org/10.1155/2016/6156513.
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A neural network (NN) technique to fill gaps in satellite data is introduced, linking satellite-derived fields of interest with other satellites andin situphysical observations. Satellite-derived “ocean color” (OC) data are used in this study because OC variability is primarily driven by biological processes related and correlated in complex, nonlinear relationships with the physical processes of the upper ocean. Specifically, ocean color chlorophyll-a fields from NOAA’s operational Visible Imaging Infrared Radiometer Suite (VIIRS) are used, as well as NOAA and NASA ocean surface and upper-ocean observations employed—signatures of upper-ocean dynamics. An NN transfer function is trained, using global data for two years (2012 and 2013), and tested on independent data for 2014. To reduce the impact of noise in the data and to calculate a stable NN Jacobian for sensitivity studies, an ensemble of NNs with different weights is constructed and compared with a single NN. The impact of the NN training period on the NN’s generalization ability is evaluated. The NN technique provides an accurate and computationally cheap method for filling in gaps in satellite ocean color observation fields and time series.
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Opitz, D., and R. Maclin. "Popular Ensemble Methods: An Empirical Study." Journal of Artificial Intelligence Research 11 (August 1, 1999): 169–98. http://dx.doi.org/10.1613/jair.614.
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An ensemble consists of a set of individually trained classifiers (such as neural networks or decision trees) whose predictions are combined when classifying novel instances. Previous research has shown that an ensemble is often more accurate than any of the single classifiers in the ensemble. Bagging (Breiman, 1996c) and Boosting (Freund & Shapire, 1996; Shapire, 1990) are two relatively new but popular methods for producing ensembles. In this paper we evaluate these methods on 23 data sets using both neural networks and decision trees as our classification algorithm. Our results clearly indicate a number of conclusions. First, while Bagging is almost always more accurate than a single classifier, it is sometimes much less accurate than Boosting. On the other hand, Boosting can create ensembles that are less accurate than a single classifier -- especially when using neural networks. Analysis indicates that the performance of the Boosting methods is dependent on the characteristics of the data set being examined. In fact, further results show that Boosting ensembles may overfit noisy data sets, thus decreasing its performance. Finally, consistent with previous studies, our work suggests that most of the gain in an ensemble's performance comes in the first few classifiers combined; however, relatively large gains can be seen up to 25 classifiers when Boosting decision trees.
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Han, Te, Dongxiang Jiang, Qi Zhao, Lei Wang, and Kai Yin. "Comparison of random forest, artificial neural networks and support vector machine for intelligent diagnosis of rotating machinery." Transactions of the Institute of Measurement and Control 40, no. 8 (June 1, 2017): 2681–93. http://dx.doi.org/10.1177/0142331217708242.
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Nowadays, the data-driven diagnosis method, exploiting pattern recognition method to diagnose the fault patterns automatically, achieves much success for rotating machinery. Some popular classification algorithms such as artificial neural networks and support vector machine have been extensively studied and tested with many application cases, while the random forest, one of the present state-of-the-art classifiers based on ensemble learning strategy, is relatively unknown in this field. In this paper, the behavior of random forest for the intelligent diagnosis of rotating machinery is investigated with various features on two datasets. A framework for the comparison of different methods, that is, random forest, extreme learning machine, probabilistic neural network and support vector machine, is presented to find the most efficient one. Random forest has been proven to outperform the comparative classifiers in terms of recognition accuracy, stability and robustness to features, especially with a small training set. Additionally, compared with traditional methods, random forest is not easily influenced by environmental noise. Furthermore, the user-friendly parameters in random forest offer great convenience for practical engineering. These results suggest that random forest is a promising pattern recognition method for the intelligent diagnosis of rotating machinery.
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Saghi, Faramarz, and Mustafa Jahangoshai Rezaee. "An ensemble approach based on transformation functions for natural gas price forecasting considering optimal time delays." PeerJ Computer Science 7 (April 7, 2021): e409. http://dx.doi.org/10.7717/peerj-cs.409.
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Natural gas, known as the cleanest fossil fuel, plays a vital role in the economies of producing and consuming countries. Understanding and tracking the drivers of natural gas prices are of significant interest to the many economic sectors. Hence, accurately forecasting the price is very important not only for providing an effective factor for implementing energy policy but also for playing an extremely significant role in government strategic planning. The purpose of this study is to provide an approach to forecast the natural gas price. First, optimal time delays are identified by a new approach based on the Euclidean Distance between input and target vectors. Then, wavelet decomposition has been implemented to reduce noise. Moreover, fuzzy transform with different membership functions has been used for modeling uncertainty in time series. The wavelet decomposition and fuzzy transform have been integrated into the preprocessing stage. An ensemble method is used for integrating the outputs of various neural networks. The results depict that the proposed preprocessing methods used in this paper cause to improve the accuracy of natural gas price forecasting and consider uncertainty in time series.
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Sarmiento, Carlos, and Jesus Savage. "Comparison of Two Objects Classification Techniques using Hidden Markov Models and Convolutional Neural Networks." Informatics and Automation 19, no. 6 (December 11, 2020): 1222–54. http://dx.doi.org/10.15622/ia.2020.19.6.4.
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This paper presents a comparison between discrete Hidden Markov Models and Convolutional Neural Networks for the image classification task. By fragmenting an image into sections, it is feasible to obtain vectors that represent visual features locally, but if a spatial sequence is established in a fixed way, it is possible to represent an image as a sequence of vectors. Using clustering techniques, we obtain an alphabet from said vectors and then symbol sequences are constructed to obtain a statistical model that represents a class of images. Hidden Markov Models, combined with quantization methods, can treat noise and distortions in observations for computer vision problems such as the classification of images with lighting and perspective changes.We have tested architectures based on three, six and nine hidden states favoring the detection speed and low memory usage. Also, two types of ensemble models were tested. We evaluated the precision of the proposed methods using a public domain data set, obtaining competitive results with respect to fine-tuned Convolutional Neural Networks, but using significantly less computing resources. This is of interest in the development of mobile robots with computers with limited battery life, but requiring the ability to detect and add new objects to their classification systems.
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Deweese, Michael R., and Anthony M. Zador. "Shared and Private Variability in the Auditory Cortex." Journal of Neurophysiology 92, no. 3 (September 2004): 1840–55. http://dx.doi.org/10.1152/jn.00197.2004.
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The high variability of cortical sensory responses is often assumed to impose a major constraint on efficient computation. In the auditory cortex, however, response variability can be very low. We have used in vivo whole cell patch-clamp methods to study the trial-to-trial variability of the subthreshold fluctuations in membrane potential underlying tone-evoked responses in the auditory cortex of anesthetized rats. Using methods adapted from classical quantal analysis, we partitioned this subthreshold variability into a private component (which includes synaptic, thermal, and other sources local to the recorded cell) and a shared component arising from network interactions. Here we report that this private component is remarkably small, usually about 1–3 mV, as quantified by the variance divided by the mean of the ensemble of tone-evoked response heights. The shared component can be much larger, and shows more heterogeneity across the population, ranging from about 0 to 10 mV. The remarkable fact that, at least 5 synapses from the auditory periphery, this variability remains so small raises the possibility that the intervening neural circuitry is organized so as to prevent private noise from accumulating as neural signals propagate to the cortex.
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Жарикова, Е. П., Я. Ю. Григорьев, and А. Л. Григорьева. "Application of neural networks for water area analysis." MORSKIE INTELLEKTUAL`NYE TEHNOLOGII), no. 2(52) (June 20, 2021): 129–33. http://dx.doi.org/10.37220/mit.2021.52.2.063.
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Современные задачи, связанные с эксплуатацией морских судов, транспортировкой нефтепродуктов различными способами в морских акваториях связаны с необходимостью контроля мониторинга возможности загрязнения нефтепродуктами вод мирового океана. В статье предлагается подход к решению задач оценки состояния акваторий на основе методов искусственного интеллекта. В исследовании рассматривается модель анализа состояния водной поверхности, основанная на расчете коэффициентов, определяемых отношением значений спектральных каналов. Применение метода обладает рядом недостатков, состоящих в необходимости постоянной экспертной оценки, а результаты имеют значительные погрешности в виду слабой устойчивости к шумам. В качестве альтернативы предлагаются решения вышеуказанной проблемы посредством применения моделей искусственного интеллекта: полносвязные многослойные нейронные сети и ансамблевые методы. Для анализа используется спектральные снимки с видимыми загрязнениями. Сравнение полученных результатов производится общеприменимыми метриками. Modern tasks associated with the operation of sea vessels, transportation of oil products in various ways in sea areas are associated with the need to monitor the monitoring of the possibility of oil pollution in the waters of the oceans. The article proposes an approach to solving the problems of assessing the state of water areas based on artificial intelligence methods. The study considers a model for analyzing the state of the water surface, based on the calculation of the coefficients determined by the ratio of the values of the spectral channels. The application of the method has a number of drawbacks, consisting in the need for constant expert assessment, and the results have significant errors due to the weak resistance to noise. As an alternative, solutions to the above problem are proposed through the use of artificial intelligence models: fully connected multilayer perceptrons and ensemble methods. For the analysis, spectral images of fresh and sea water with visible pollution are used. Comparison of the results obtained is made using generally applicable metrics.
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Redlich, A. Norman. "Redundancy Reduction as a Strategy for Unsupervised Learning." Neural Computation 5, no. 2 (March 1993): 289–304. http://dx.doi.org/10.1162/neco.1993.5.2.289.
Full textAbstract:
A redundancy reduction strategy, which can be applied in stages, is proposed as a way to learn as efficiently as possible the statistical properties of an ensemble of sensory messages. The method works best for inputs consisting of strongly correlated groups, that is features, with weaker statistical dependence between different features. This is the case for localized objects in an image or for words in a text. A local feature measure determining how much a single feature reduces the total redundancy is derived which turns out to depend only on the probability of the feature and of its components, but not on the statistical properties of any other features. The locality of this measure makes it ideal as the basis for a "neural" implementation of redundancy reduction, and an example of a very simple non-Hebbian algorithm is given. The effect of noise on learning redundancy is also discussed.