STUDY ON THE PARAMETERIZATION RESPONSE OF PROBABILISTIC NEURAL NETWORKS FOR SEISMIC FACIES CLASSIFICATION IN THE GULF OF MEXICO

2021 ◽  
pp. 1-105
Author(s):  
Diana Salazar Florez ◽  
Heather Bedle
Keyword(s):  
Neural Networks ◽  
Seismic Facies ◽  
Supervised Machine Learning ◽  
Machine Learning Techniques ◽  
Small Scale ◽  
Probabilistic Neural Networks ◽  
Computational Costs ◽  
Facies Classification ◽  
Learning Techniques ◽  
The Impact

Nowadays, there are many unsupervised and supervised machine learning techniques available for performing seismic facies classification. However, those classification methods either demand high computational costs or do not provide an accurate measure of confidence. Probabilistic neural networks (PNNs) overcome these limitations and have demonstrated their superiority among other algorithms. PNNs have been extensively applied for some prediction tasks, but not well studied regarding the prediction of seismic facies volumes using seismic attributes. We explore the capability of the PNN algorithm when classifying large- and small-scale seismic facies. Additionally, we evaluate the impact of user-chosen parameters on the final classification volumes. After performing seven tests, each with a parameter variation, we assess the impact of the parameter change on the resultant classification volumes. We show that the processing task can have a significant impact on the classification volumes, but also how the most geologically complex areas are the most challenging for the algorithm. Moreover, we demonstrate that even if the PNN technique is performing and producing considerably accurate results, it is possible to overcome those limitations and significantly improve the final classification volumes by including the geological insight provided by the geoscientist. We conclude by proposing a new workflow that can guide future geoscientists interested in applying PNNs, to obtain better seismic facies classification volumes by considering some initial steps and advice.

Convolutional Recurrent Neural Networks Based Speech Emotion Recognition

2020 ◽  
Vol 17 (8) ◽  
pp. 3786-3789
Author(s):  
P. Gayathri ◽  
P. Gowri Priya ◽  
L. Sravani ◽  
Sandra Johnson ◽  
Visanth Sampath
Keyword(s):  
Neural Networks ◽  
Emotion Recognition ◽  
Recurrent Neural Networks ◽  
Machine Learning Techniques ◽  
Speech Emotion Recognition ◽  
Emotional Information ◽  
Feature Representations ◽  
Emotional Factors ◽  
Learning Techniques ◽  
The Impact

Recognition of emotions is the aspect of speech recognition that is gaining more attention and the need for it is growing enormously. Although there are methods to identify emotion using machine learning techniques, we assume in this paper that calculating deltas and delta-deltas for customized features not only preserves effective emotional information, but also that the impact of irrelevant emotional factors, leading to a reduction in misclassification. Furthermore, Speech Emotion Recognition (SER) often suffers from the silent frames and irrelevant emotional frames. Meanwhile, the process of attention has demonstrated exceptional performance in learning related feature representations for specific tasks. Inspired by this, propose a Convolutionary Recurrent Neural Networks (ACRNN) based on Attention to learn discriminative features for SER, where the Mel-spectrogram with deltas and delta-deltas is used as input. Finally, experimental results show the feasibility of the proposed method and attain state-of-the-art performance in terms of unweighted average recall.

scholarly journals A Survey on Prevention of Overfitting in Convolution Neural Networks Using Machine Learning Techniques

2018 ◽  
Vol 7 (2.32) ◽  
pp. 177 ◽  
Author(s):  
Dr M.R.Narasinga Rao ◽  
V Venkatesh Prasad ◽  
P Sai Teja ◽  
Md Zindavali ◽  
O Phanindra Reddy
Keyword(s):  
Neural Networks ◽  
Neural Nets ◽  
Machine Learning Techniques ◽  
Test Time ◽  
Document Type ◽  
The Neural Network ◽  
Learning Techniques ◽  
Extreme Problem ◽  
Regularization Techniques ◽  
The Impact

Deep neural nets with a vast quantity of parameters are very effective machine getting to know structures. However, overfitting is an extreme problem in such networks. Massive networks are also sluggish to use, making it difficult to cope with overfitting by combining the predictions of many distinct large neural nets at test time. Dropout is a method for addressing this problem. The important thing concept is to randomly drop units (at the side of their connections) from the neural network for the duration of education. This prevents units from co-adapting an excessive amount of. during schooling, dropout samples from an exponential quantity of various "thinned" networks. At take a look at the time, it is simple to precise the impact of averaging the predictions of plenty of these thinned networks through in reality using a single unthinned network that has smaller weights. This considerably minimize overfitting and provides fundamental enhancements over other regularization techniques. We show that dropout enhance the overall performance of neural networks on manage gaining knowledge of obligations in imaginative and prescient, speech reputation, document type and computational biology, acquiring today's effects on many benchmark facts sets.  

scholarly journals Seismic facies analysis using machine learning

Geophysics ◽  
2018 ◽  
Vol 83 (5) ◽  
pp. O83-O95 ◽  
Author(s):  
Thilo Wrona ◽  
Indranil Pan ◽  
Robert L. Gawthorpe ◽  
Haakon Fossen
Keyword(s):  
Machine Learning ◽  
Seismic Facies ◽  
Machine Learning Techniques ◽  
Seismic Section ◽  
Seismic Reflection Data ◽  
Reflection Data ◽  
Facies Classification ◽  
Learning Techniques ◽  
Seismic Facies Analysis ◽  
Northern North Sea

Seismic interpretations are, by definition, subjective and often require significant time and expertise from the interpreter. We are convinced that machine-learning techniques can help address these problems by performing seismic facies analyses in a rigorous, repeatable way. For this purpose, we use state-of-the-art 3D broadband seismic reflection data of the northern North Sea. Our workflow includes five basic steps. First, we extract seismic attributes to highlight features in the data. Second, we perform a manual seismic facies classification on 10,000 examples. Third, we use some of these examples to train a range of models to predict seismic facies. Fourth, we analyze the performance of these models on the remaining examples. Fifth, we select the “best” model (i.e., highest accuracy) and apply it to a seismic section. As such, we highlight that machine-learning techniques can increase the efficiency of seismic facies analyses.

scholarly journals Phishing Website Detection using Supervised Deep Learning

2019 ◽  
Vol 8 (4) ◽  
pp. 4844-4846
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Deep Neural Networks ◽  
Personal Information ◽  
Supervised Machine Learning ◽  
Machine Learning Techniques ◽  
Web Pages ◽  
Web Page ◽  
Algorithmic Approach ◽  
Learning Techniques

The website phishing is the tremendously growing problem over the internet which will lead to the loss of personal information. This process will run like, when ever user clicks a website link it will lead them to the web page that is created by the phisher to deceive the user. After this phishing has been started in order to stop it many techniques came into existence to detect the phished web site and help the user from being deceived by the attacker. Even though many techniques have adapted to stop the attackers, it is difficult because as many phished web pages are generated by the attackers within few hours. Most of the techniques to detect these phishing websites are not able to decide the fake website with legitimate one because the accuracy of getting results are much less. There are many supervised machine learning techniques which are supervised, where a primary set of data is given to the algorithm and depending on that set the algorithm will be trained and it will predict the results for the same. One of the most important techniques that is deep Learning classifiers is applied with significant features to detect phishing websites. By using this algorithm we can classify the phishing websites from genuine websites by using effective features. In this algorithmic approach to detect genuine websites a feature set is used so by analyzing these features using deep neural networks we can detect a website is phished or not. We can also increase the accuracy of our algorithm by adding certain more features and increasing the hidden layers in neural networks.

scholarly journals The application of volume texture extraction to three-dimensional seismic data – lithofacies structures exploration within the Miocene deposits of the Carpathian Foredeep

2021 ◽  
Vol 46 (4) ◽  
pp. 301-313
Author(s):  
Mariusz Łukaszewski
Keyword(s):  
Machine Learning ◽  
Seismic Data ◽  
Seismic Analysis ◽  
Seismic Facies ◽  
Machine Learning Techniques ◽  
Small Scale ◽  
Facies Classification ◽  
Carpathian Foredeep ◽  
Texture Attributes ◽  
Self Learning

There are numerous conventional fields of natural gas in the Carpathian Foredeep, and there is also evidence to suggest that unconventional gas accumulations may occur in this region. The different seismic sig-natures of these geological forms, the small scale of amplitude variation, and the large amount of data make the process of geological interpretation extremely time-consuming. Moreover, the dispersed nature of information in a large block of seismic data increasingly requires automatic, self-learning cognitive processes. Recent developments with Machine Learning have added new capabilities to seismic interpretation, especially to multi-attribute seismic analysis. Each case requires a proper selection of attributes. In this paper, the Grey Level Co-occurrence Matrix method is presented and its two texture attributes Energy and Entropy. Haralick’s two texture parameters were applied to an advanced interpretation of the interval of Miocene deposits in order to discover the subtle geological features hidden between the seismic traces. As a result, a submarine-slope channel system was delineated leading to the discovery of unknown earlier relationships between gas boreholes and the geological environment. The Miocene deposits filling the Carpathian Foredeep, due to their lithological and facies diversity, provide excellent conditions for testing and implementing Machine Learning techniques. The presented texture attributes are the desired input components for self-learning systems for seismic facies classification.

Application of Machine Learning Techniques to Predict Binding Affinity for Drug Targets: A Study of Cyclin-Dependent Kinase 2

2020 ◽  
Vol 28 (2) ◽  
pp. 253-265 ◽  
Author(s):  
Gabriela Bitencourt-Ferreira ◽  
Amauri Duarte da Silva ◽  
Walter Filgueira de Azevedo
Keyword(s):  
Machine Learning ◽  
Binding Affinity ◽  
Predictive Performance ◽  
Supervised Machine Learning ◽  
Machine Learning Techniques ◽  
Scoring Functions ◽  
Cyclin Dependent Kinase ◽  
Learning Models ◽  
Learning Techniques ◽  
Machine Learning Models

Background: The elucidation of the structure of cyclin-dependent kinase 2 (CDK2) made it possible to develop targeted scoring functions for virtual screening aimed to identify new inhibitors for this enzyme. CDK2 is a protein target for the development of drugs intended to modulate cellcycle progression and control. Such drugs have potential anticancer activities. Objective: Our goal here is to review recent applications of machine learning methods to predict ligand- binding affinity for protein targets. To assess the predictive performance of classical scoring functions and targeted scoring functions, we focused our analysis on CDK2 structures. Methods: We have experimental structural data for hundreds of binary complexes of CDK2 with different ligands, many of them with inhibition constant information. We investigate here computational methods to calculate the binding affinity of CDK2 through classical scoring functions and machine- learning models. Results: Analysis of the predictive performance of classical scoring functions available in docking programs such as Molegro Virtual Docker, AutoDock4, and Autodock Vina indicated that these methods failed to predict binding affinity with significant correlation with experimental data. Targeted scoring functions developed through supervised machine learning techniques showed a significant correlation with experimental data. Conclusion: Here, we described the application of supervised machine learning techniques to generate a scoring function to predict binding affinity. Machine learning models showed superior predictive performance when compared with classical scoring functions. Analysis of the computational models obtained through machine learning could capture essential structural features responsible for binding affinity against CDK2.

scholarly journals Local mortality estimates during the COVID-19 pandemic in Italy

2021 ◽  
Author(s):  
Augusto Cerqua ◽  
Roberta Di Stefano ◽  
Marco Letta ◽  
Sara Miccoli
Keyword(s):  
Machine Learning ◽  
Excess Mortality ◽  
Control Method ◽  
Local Level ◽  
Supervised Machine Learning ◽  
Machine Learning Techniques ◽  
Mortality Data ◽  
Official Method ◽  
Learning Techniques ◽  
Mortality Estimates

AbstractEstimates of the real death toll of the COVID-19 pandemic have proven to be problematic in many countries, Italy being no exception. Mortality estimates at the local level are even more uncertain as they require stringent conditions, such as granularity and accuracy of the data at hand, which are rarely met. The “official” approach adopted by public institutions to estimate the “excess mortality” during the pandemic draws on a comparison between observed all-cause mortality data for 2020 and averages of mortality figures in the past years for the same period. In this paper, we apply the recently developed machine learning control method to build a more realistic counterfactual scenario of mortality in the absence of COVID-19. We demonstrate that supervised machine learning techniques outperform the official method by substantially improving the prediction accuracy of the local mortality in “ordinary” years, especially in small- and medium-sized municipalities. We then apply the best-performing algorithms to derive estimates of local excess mortality for the period between February and September 2020. Such estimates allow us to provide insights about the demographic evolution of the first wave of the pandemic throughout the country. To help improve diagnostic and monitoring efforts, our dataset is freely available to the research community.

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