scholarly journals Classification of Local Seismic Events in the Utah Region: A Comparison of Amplitude Ratio Methods with a Spectrogram‐Based Machine Learning Approach

2019 ◽  
Vol 109 (6) ◽  
pp. 2532-2544 ◽  
Author(s):  
Rigobert Tibi ◽  
Lisa Linville ◽  
Christopher Young ◽  
Ronald Brogan
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Amplitude Ratio ◽  
Anthropogenic Sources ◽  
High Signal ◽  
Seismic Events ◽  
Signal Quality ◽  
Success Rates ◽  
Neural Network Approach ◽  
Low Magnitude

Abstract The capability to discriminate low‐magnitude earthquakes from low‐yield anthropogenic sources, both detectable only at local distances, is of increasing interest to the event monitoring community. We used a dataset of seismic events in Utah recorded during a 14‐day period (1–14 January 2011) by the University of Utah Seismic Stations network to perform a comparative study of event classification at local scale using amplitude ratio (AR) methods and a machine learning (ML) approach. The event catalog consists of 7377 events with magnitudes MC ranging from −2 and lower up to 5.8. Events were subdivided into six populations based on location and source type: tectonic earthquakes (TEs), mining‐induced events (MIEs), and mining blasts from four known mines (WMB, SMB, LMB, and CQB). The AR approach jointly exploits Pg‐to‐Sg phase ARs and Rg‐to‐Sg spectral ARs in multivariate quadratic discriminant functions and was able to classify 370 events with high signal quality from the three groups with sufficient size (TE, MIE, and SMB). For that subset of the events, the method achieved success rates between about 80% and 90%. The ML approach used trained convolutional neural network (CNN) models to classify the populations. The CNN approach was able to classify the subset of events with accuracies between about 91% and 98%. Because the neural network approach does not have a minimum signal quality requirement, we applied it to the entire event catalog, including the abundant extremely low-magnitude events, and achieved accuracies of about 94%–100%. We compare the AR and ML methodologies using a broad set of criteria and conclude that a major advantage to ML methods is their robustness to low signal‐to‐noise ratio data, allowing them to classify significantly smaller events.

scholarly journals A Novel Method for Intrusion Detection Based on SARSA and Radial Bias Feed Forward Network (RBFFN)

2013 ◽  
Vol 7 (3) ◽  
pp. 646-653
Author(s):  
Anshul Chaturvedi ◽  
Prof. Vineet Richharia
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Intrusion Detection ◽  
Rbf Neural Network ◽  
Neural Network Approach ◽  
Feed Forward Network ◽  
Machine Learning Approach ◽  
Network Process ◽  
Reinforced Learning ◽  
Attack Data

The Internet, computer networks and information are vital resources of current information trend and their protection has increased importance in current existence. Any attempt, successful or unsuccessful to finding the middle ground the discretion, truthfulness and accessibility of any information resource or the information itself is measured a security attack or an intrusion. Intrusion compromised a loose of information credential and trust of security concern. The mechanism of intrusion detection faced a problem of new generated schema and pattern of attack data. Various authors and researchers proposed a method for intrusion detection based on machine learning approach and neural network approach all these compromised with new pattern and schema. Now in this paper a new model of intrusion detection based on SARAS reinforced learning scheme and RBF neural network has proposed. SARAS method imposed a state of attack behaviour and RBF neural network process for training pattern for new schema. Our empirical result shows that the proposed model is better in compression of SARSA and other machine learning technique.

scholarly journals An Evaluation Study on Investment Efficiency: A Predictive Machine Learning Approach

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Weiwei Hao ◽  
Hongyan Gao ◽  
Zongqing Liu
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Structural Equation ◽  
Evaluation Index System ◽  
Corporate Debt ◽  
Investment Efficiency ◽  
Neural Network Approach ◽  
Ownership Reform ◽  
Nonlinear Autoregressive ◽  
Autoregressive Neural Network

This paper proposes a nonlinear autoregressive neural network (NARNET) method for the investment performance evaluation of state-owned enterprises (SOE). It is different from the traditional method based on machine learning, such as linear regression, structural equation, clustering, and principal component analysis; this paper uses a regression prediction method to analyze investment efficiency. In this paper, we firstly analyze the relationship between diversified ownership reform, corporate debt leverage, and the investment efficiency of state-owned enterprises (SOE). Secondly, a set of investment efficiency evaluation index system for SOE was constructed, and a nonlinear autoregressive neural network approach was used for verification. The data of A-share state-owned listed companies in Shanghai and Shenzhen stock exchanges from 2009 to 2018 are taken as a sample. The experimental results show that the output value from the NARNET is highly fitted to the actual data. Based on the neural network model regression analysis, this paper conducts a descriptive statistical analysis of the main variables and control variables of the evaluation indicators. It verifies the direct impact of diversified ownership reform on the investment efficiency of SOE and the indirect impact on the investment efficiency of SOE through corporate debt leverage.

scholarly journals Recurrence Plot and Machine Learning for Signal Quality Assessment of Photoplethysmogram in Mobile Environment

Sensors ◽  
2021 ◽  
Vol 21 (6) ◽  
pp. 2188
Author(s):  
Donggeun Roh ◽  
Hangsik Shin
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Convolutional Neural Network ◽  
Quality Assessment ◽  
Feature Detection ◽  
Layer Structure ◽  
Recurrence Plot ◽  
Classification Model ◽  
Signal Quality ◽  
Predictive Values

The purpose of this study was to develop a machine learning model that could accurately evaluate the quality of a photoplethysmogram based on the shape of the photoplethysmogram and the phase relevance in a pulsatile waveform without requiring complicated pre-processing. Photoplethysmograms were recorded for 76 participants (5 min for each participant). All recorded photoplethysmograms were segmented for each beat to obtain a total of 49,561 pulsatile segments. These pulsatile segments were manually labeled as ‘good’ and ‘poor’ classes and converted to a two-dimensional phase space trajectory image using a recurrence plot. The classification model was implemented using a convolutional neural network with a two-layer structure. As a result, the proposed model correctly classified 48,827 segments out of 49,561 segments and misclassified 734 segments, showing a balanced accuracy of 0.975. Sensitivity, specificity, and positive predictive values of the developed model for the test dataset with a ‘poor’ class classification were 0.964, 0.987, and 0.848, respectively. The area under the curve was 0.994. The convolutional neural network model with recurrence plot as input proposed in this study can be used for signal quality assessment as a generalized model with high accuracy through data expansion. It has an advantage in that it does not require complicated pre-processing or a feature detection process.

scholarly journals ENN-Ensemble based Neural Network Method for Diabetes Classification

2020 ◽  
Vol 9 (3) ◽  
pp. 574-579
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Selection Process ◽  
Hybrid Approach ◽  
Classification Performance ◽  
Ensemble Classification ◽  
Machine Learning Techniques ◽  
Feature Subset ◽  
Neural Network Approach ◽  
Prediction Of Diabetes

Diabetes is considered as one of the most chronic disease which has serious impact on human health and leading cause of mortality worldwide. The early prediction of diabetes can help clinicians to provide a better diagnosis to the patients. Recently, computed aided diagnosis systems have gained attention due to significant growth in data mining, and machine learning. Several approaches are present based on the machine learning techniques but due to poor classification performance and computational complexity, it becomes difficult to utilize for real-time applications. Ensemble classification approaches have reported a noteworthy improvement in diabetes classification but desired accuracy is still a challenging task. Hence, in this work we introduce a combined hybrid approach called as ENNEnsemble based neural network approach for diabetes classification. In this approach, a feature selection process is presented using neighboring search technique; the selected features are processed through the feature ranking model to generate the efficient feature subset for better classification accuracy. Finally, these features are learned and classified using neural network classifier. The experimental study shows that the proposed approach achieves better accuracy when compared with the existing techniques.

scholarly journals A Neural Network Approach to Identify Glioblastoma Progression Phenotype from Multimodal MRI

Cancers ◽  
2021 ◽  
Vol 13 (9) ◽  
pp. 2006
Author(s):  
Jiun-Lin Yan ◽  
Cheng-Hong Toh ◽  
Li Ko ◽  
Kuo-Chen Wei ◽  
Pin-Yuan Chen
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Tumor Progression ◽  
Diffusion Tensor ◽  
Supervised Machine Learning ◽  
Neural Network Models ◽  
Neural Network Approach ◽  
Clinical Prognosis ◽  
Multimodal Mri ◽  
Progression Patterns

The phenotypes of glioblastoma (GBM) progression after treatment are heterogeneous in both imaging and clinical prognosis. This study aims to apply radiomics and neural network analysis to preoperative multimodal MRI data to characterize tumor progression phenotypes. We retrospectively reviewed 41 patients with newly diagnosed cerebral GBM from 2009–2016 who comprised the machine learning training group, and prospectively included 18 patients from 2017–2018 for data validation. Preoperative MRI examinations included structural MRI, diffusion tensor imaging, and perfusion MRI. Tumor progression patterns were categorized as diffuse or localized. A supervised machine learning model and neural network-based models (VGG16 and ResNet50) were used to establish the prediction model of the pattern of progression. The diffuse progression pattern showed a significantly worse prognosis regarding overall survival (p = 0.032). A total of 153 of the 841 radiomic features were used to classify progression patterns using different machine learning models with an overall accuracy of 81% (range: 77.5–82.5%, AUC = 0.83–0.89). Further application of the pretrained ResNet50 and VGG 16 neural network models demonstrated an overall accuracy of 93.1 and 96.1%. The progression patterns of GBM are an important prognostic factor and can potentially be predicted by combining multimodal MR radiomics with machine learning.

scholarly journals Low-Order Spherical Harmonic HRTF Restoration Using a Neural Network Approach

2020 ◽  
Vol 10 (17) ◽  
pp. 5764
Author(s):  
Benjamin Tsui ◽  
William A. P. Smith ◽  
Gavin Kearney
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Spherical Harmonic ◽  
High Frequency ◽  
Prediction Models ◽  
Machine Learning Algorithms ◽  
Network Approach ◽  
Spectral Difference ◽  
Neural Network Approach ◽  
Head Related Transfer Function

Spherical harmonic (SH) interpolation is a commonly used method to spatially up-sample sparse head related transfer function (HRTF) datasets to denser HRTF datasets. However, depending on the number of sparse HRTF measurements and SH order, this process can introduce distortions into high frequency representations of the HRTFs. This paper investigates whether it is possible to restore some of the distorted high frequency HRTF components using machine learning algorithms. A combination of convolutional auto-encoder (CAE) and denoising auto-encoder (DAE) models is proposed to restore the high frequency distortion in SH-interpolated HRTFs. Results were evaluated using both perceptual spectral difference (PSD) and localisation prediction models, both of which demonstrated significant improvement after the restoration process.

Jitter Decomposition Meets Machine Learning: 1D-Convolutional Neural Network Approach

2021 ◽  
pp. 1-1
Author(s):  
Nan Ren ◽  
Zaiming Fu ◽  
Dandan Zhou ◽  
Dexuan Kong ◽  
Hanglin Liu ◽  
...  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Convolutional Neural Network ◽  
Network Approach ◽  
Neural Network Approach

scholarly journals Neural network approach to the prediction of seismic events based on low-frequency signal monitoring of the Kuril-Kamchatka and Japanese regions

2013 ◽  
Vol 56 (3) ◽  
Author(s):  
Irina Popova ◽  
Alexander Rozhnoi ◽  
Maria Solovieva ◽  
Boris Levin ◽  
Masashi Hayakawa ◽  
...  
Keyword(s):  
Neural Network ◽  
Seismic Event ◽  
Back Propagation ◽  
Low Frequency ◽  
Signal Amplitude ◽  
Seismic Events ◽  
Network Approach ◽  
Neural Network Approach ◽  
Propagation Technique ◽  
Japanese Regions

<p>Very-low-frequency/ low-frequency (VLF/LF) sub-ionospheric radiowave monitoring has been widely used in recent years to analyze earthquake preparatory processes. The connection between earthquakes with M ≥5.5 and nighttime disturbances of signal amplitude and phase has been established. Thus, it is possible to use nighttime anomalies of VLF/LF signals as earthquake precursors. Here, we propose a method for estimation of the VLF/LF signal sensitivity to seismic processes using a neural network approach. We apply the error back-propagation technique based on a three-level perceptron to predict a seismic event. The back-propagation technique involves two main stages to solve the problem; namely, network training, and recognition (the prediction itself). To train a neural network, we first create a so-called ‘training set’. The ‘teacher’ specifies the correspondence between the chosen input and the output data. In the present case, a representative database includes both the LF data received over three years of monitoring at the station in Petropavlovsk-Kamchatsky (2005-2007), and the seismicity parameters of the Kuril-Kamchatka and Japanese regions. At the first stage, the neural network established the relationship between the characteristic features of the LF signal (the mean and dispersion of a phase and an amplitude at nighttime for a few days before a seismic event) and the corresponding level of correlation with a seismic event, or the absence of a seismic event. For the second stage, the trained neural network was applied to predict seismic events from the LF data using twelve time intervals in 2004, 2005, 2006 and 2007. The results of the prediction are discussed.</p>

scholarly journals A Neural Network Approach for Birds of a Feather Solvability Prediction

2019 ◽  
Vol 33 ◽  
pp. 9706-9712
Author(s):  
Benjamin Sang ◽  
Sejong Yoon
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Deep Neural Network ◽  
Machine Learning Method ◽  
Card Game ◽  
Learning Method ◽  
Solution Path ◽  
Network Approach ◽  
Neural Network Approach ◽  
Search Spaces

Birds of a Feather is a single player, perfect information card game. The game can have multiple board sizes with larger boards introducing larger search spaces that grow exponentially. In this paper, we investigate the solvability of the game, aiming at building a machine learning method to automatically classify whether a given board state has a solution path or not. We propose a method based on image-based features of the board state and deep neural network. Experimental results show that the proposed method can make reasonable predictions of the solvability of a game at an arbitrary stage of the game.

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