scholarly journals The Rock Burst Hazard Evaluation Using Statistical Learning Approaches

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Jie Chen ◽  
Jingkuan Gao ◽  
Yuanyuan Pu ◽  
Mingzhong Gao ◽  
Like Wei ◽  
...  
Keyword(s):  
Rock Burst ◽  
Statistical Learning ◽  
Learning Model ◽  
Impact Factors ◽  
Hazard Evaluation ◽  
Learning Approaches ◽  
Prior Distributions ◽  
Burst Hazard ◽  
Prediction And Prevention ◽  
Rock Burst Risk

The great threat and destructiveness brought by a rock burst make its prediction and prevention crucial in engineering. The rock burst hazard evaluation at project locations is an effective way of preventing rock burst since currently real-time prediction is not available. Since different control factors and discrimination conditions of rock burst were accepted by conventional risk determination methods, the rock burst risk determination in the same area may produce conflicting results. In this study, Naive Bayes statistical learning models based on different model prior distributions representing highly complicated nonlinear relationship between rock burst hazard and impact factors were built to evaluate the rock burst hazards. The results suggested that the Bayes statistical learning model based on a Gaussian prior has the strongest performance over four preset prior distributions. Combining the rock mechanics parameters measured in the laboratory and the stress data collected on the project sites, the proposed model was successfully employed to evaluate the kimberlite rock burst risk of a diamond mine in Canada. The Bayes statistical learning model exhibits its robustness and generalization in rock burst hazard evaluation, which can be generalized for similar engineering cases with enough supported data.

The results of rock burst prediction on the sokolovskaya underground mining by the acoustic emission method

2020 ◽  
pp. 149-160
Author(s):  
D. A. Mengel’
Keyword(s):  
Acoustic Emission ◽  
Rock Burst ◽  
Underground Mining ◽  
Technical Documentation ◽  
Acoustic Emission Method ◽  
Emission Method ◽  
Burst Hazard ◽  
High Tension ◽  
Rock Burst Risk ◽  
Rock Burst Prediction

The relevance of research. When developing underground mining, there is a risk of dynamic phenomenon of high rock pressure - rock burst. The local instrumental forecast of shock hazard by acoustic emission with the GS-01 device allows you to quickly identify high-tension zones during underground mining. The Research aim: generalization of the accumulated data of instrumental measurements of acoustic emission parameters throughout the Sokolovskaya mine; zoning of rock burst hazard in the deposit; use of zoning of rock burst hazard results for mining planning in terms of changing the principles and regulations for designing traces of mine workings in potentially impact hazardous areas; improving the methodology of instrumental forecasting of shock hazard by the acoustic emission method. Methods of research: analysis of the accumulated data of instrumental measurements of acoustic emission parameters during mining; theoretical studies (identifying patterns of changes in acoustic emission taking into account the influence of mining, the configuration of the excavation, etc.); measurement in situ (measurement of acoustic emission parameters when changing parameters underground working). Results of research: implementation of preventive measures (the so-called “relaxation” of the rock) to reduce the rock burst risk in hazard zones during mining; implementation of research results in normative and technical documentation.

scholarly journals An Efficient DenseNet-Based Deep Learning Model for Malware Detection

Entropy ◽  
2021 ◽  
Vol 23 (3) ◽  
pp. 344
Author(s):  
Jeyaprakash Hemalatha ◽  
S. Abijah Roseline ◽  
Subbiah Geetha ◽  
Seifedine Kadry ◽  
Robertas Damaševičius
Keyword(s):  
Deep Learning ◽  
Detection System ◽  
Malware Detection ◽  
Class Imbalance ◽  
Learning Model ◽  
Computational Time ◽  
Learning Approaches ◽  
Security Threat ◽  
Static And Dynamic Analysis ◽  
Deep Learning Model

Recently, there has been a huge rise in malware growth, which creates a significant security threat to organizations and individuals. Despite the incessant efforts of cybersecurity research to defend against malware threats, malware developers discover new ways to evade these defense techniques. Traditional static and dynamic analysis methods are ineffective in identifying new malware and pose high overhead in terms of memory and time. Typical machine learning approaches that train a classifier based on handcrafted features are also not sufficiently potent against these evasive techniques and require more efforts due to feature-engineering. Recent malware detectors indicate performance degradation due to class imbalance in malware datasets. To resolve these challenges, this work adopts a visualization-based method, where malware binaries are depicted as two-dimensional images and classified by a deep learning model. We propose an efficient malware detection system based on deep learning. The system uses a reweighted class-balanced loss function in the final classification layer of the DenseNet model to achieve significant performance improvements in classifying malware by handling imbalanced data issues. Comprehensive experiments performed on four benchmark malware datasets show that the proposed approach can detect new malware samples with higher accuracy (98.23% for the Malimg dataset, 98.46% for the BIG 2015 dataset, 98.21% for the MaleVis dataset, and 89.48% for the unseen Malicia dataset) and reduced false-positive rates when compared with conventional malware mitigation techniques while maintaining low computational time. The proposed malware detection solution is also reliable and effective against obfuscation attacks.

Comparison of statistical learning approaches for cerebral aneurysm rupture assessment

2019 ◽  
Vol 15 (1) ◽  
pp. 141-150 ◽  
Author(s):  
Felicitas J. Detmer ◽  
Daniel Lückehe ◽  
Fernando Mut ◽  
Martin Slawski ◽  
Sven Hirsch ◽  
...  
Keyword(s):  
Cerebral Aneurysm ◽  
Statistical Learning ◽  
Aneurysm Rupture ◽  
Learning Approaches

scholarly journals PlotMI: visualization of pairwise interactions and positional preferences learned by a deep learning model from sequence data

2021 ◽  
Author(s):  
Tuomo Hartonen ◽  
Teemu Kivioja ◽  
Jussi Taipale
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Sequence Data ◽  
Predictive Performance ◽  
Learning Model ◽  
Biological Research ◽  
Learning Approaches ◽  
Learning Models ◽  
Model Interpretation ◽  
Pairwise Interactions

Deep learning models have in recent years gained success in various tasks related to understanding information coded in the DNA sequence. Rapidly developing genome-wide measurement technologies provide large quantities of data ideally suited for modeling using deep learning or other powerful machine learning approaches. Although offering state-of-the art predictive performance, the predictions made by deep learning models can be difficult to understand. In virtually all biological research, the understanding of how a predictive model works is as important as the raw predictive performance. Thus interpretation of deep learning models is an emerging hot topic especially in context of biological research. Here we describe plotMI, a mutual information based model interpretation strategy that can intuitively visualize positional preferences and pairwise interactions learned by any machine learning model trained on sequence data with a defined alphabet as input. PlotMI is freely available at https://github.com/hartonen/plotMI.

scholarly journals Comparison between Deep Learning and Tree-Based Machine Learning Approaches for Landslide Susceptibility Mapping

Water ◽  
2021 ◽  
Vol 13 (19) ◽  
pp. 2664
Author(s):  
Sunil Saha ◽  
Jagabandhu Roy ◽  
Tusar Kanti Hembram ◽  
Biswajeet Pradhan ◽  
Abhirup Dikshit ◽  
...  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Deep Learning ◽  
Landslide Susceptibility ◽  
Learning Model ◽  
Susceptibility Mapping ◽  
Landslide Susceptibility Mapping ◽  
Learning Approaches ◽  
Statistical Measures ◽  
Deep Learning Model

The efficiency of deep learning and tree-based machine learning approaches has gained immense popularity in various fields. One deep learning model viz. convolution neural network (CNN), artificial neural network (ANN) and four tree-based machine learning models, namely, alternative decision tree (ADTree), classification and regression tree (CART), functional tree and logistic model tree (LMT), were used for landslide susceptibility mapping in the East Sikkim Himalaya region of India, and the results were compared. Landslide areas were delimited and mapped as landslide inventory (LIM) after gathering information from historical records and periodic field investigations. In LIM, 91 landslides were plotted and classified into training (64 landslides) and testing (27 landslides) subsets randomly to train and validate the models. A total of 21 landslide conditioning factors (LCFs) were considered as model inputs, and the results of each model were categorised under five susceptibility classes. The receiver operating characteristics curve and 21 statistical measures were used to evaluate and prioritise the models. The CNN deep learning model achieved the priority rank 1 with area under the curve of 0.918 and 0.933 by using the training and testing data, quantifying 23.02% and 14.40% area as very high and highly susceptible followed by ANN, ADtree, CART, FTree and LMT models. This research might be useful in landslide studies, especially in locations with comparable geophysical and climatological characteristics, to aid in decision making for land use planning.

Symbolic and Statistical Learning Approaches to Speech Summarization: A Scoping Review

2021 ◽  
pp. 101305
Author(s):  
Dana Rezazadegan ◽  
Shlomo Berkovsky ◽  
Juan C. Quiroz ◽  
A. Baki Kocaballi ◽  
Ying Wang ◽  
...  
Keyword(s):  
Statistical Learning ◽  
Scoping Review ◽  
Learning Approaches ◽  
Speech Summarization

scholarly journals GA-Based Early Warning Method for Rock Burst with Microseismic and Acoustic Emission in Steeply Inclined Coal Seam

2020 ◽  
Vol 2020 ◽  
pp. 1-16
Author(s):  
Yarong Xue ◽  
Dazhao Song ◽  
Zhenlei Li ◽  
Jianqiang Chen ◽  
Xueqiu He ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Rock Burst ◽  
Coal Mine ◽  
Early Warning ◽  
Characteristic Parameter ◽  
Control Measures ◽  
Risk Level ◽  
Parameter System ◽  
Fitness Value ◽  
Rock Burst Risk

Aiming at problem of low efficacy of early warning of rock burst in coal mine, a multisystem and multiparameter integrated early warning method based on genetic algorithm (GA) is proposed. In this method, firstly, the temporal-spatial-intensity information of energy incubation process of rock burst is deeply mined, and the multidimensional precursory characteristic parameter system of rock burst is constructed. Secondly, the genetic algorithm is used to train the historical monitoring data to obtain the optimal critical value and fitness value of each precursory characteristic parameter, and then the early warning index WC of each monitoring system is calculated. Finally, the integrated rock burst early warning index IC is obtained by synthesizing the early warning index WC of each system. The value of IC corresponds to the specific rock burst risk level of the mine. This method is applied to Wudong coal mine in Xinjiang, China. Based on the actual situation of the mine, a multidimensional precursory characteristic parameter system of rock burst is constructed, which includes energy deviation (DE), frequency ratio (Fr), frequency deviation (DF), degree of dispersion (DS), and total high value of energy deviation (DH). After analyzing the rock burst danger status and risk level in the monitoring area, the early warning capability of this method is found to reach 0.896. Combining with the specific prevention and control measures corresponding to different rock burst risk levels, it can provide effective guidance for the field work.

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