scholarly journals A Novel Approach for Predicting the Height of the Water-Flow Fracture Zone in Undersea Safety Mining

2020 ◽  
Vol 12 (3) ◽  
pp. 358
Author(s):  
Bing Dai ◽  
Ying Chen
Keyword(s):  
Water Flow ◽  
Fracture Zone ◽  
Absolute Error ◽  
Mine Safety ◽  
Support Vector ◽  
Novel Approach ◽  
Crown Pillar ◽  
Rof Model ◽  
Undersea Mining ◽  
The Impact

The height of the water-flow fracture zone (WFZ) is an important reference for designing the size of a waterproof crown pillar. Once the WFZ is connected with the sea, there will be catastrophic consequences, especially for undersea mining. This study suggests using a rotating forest (RoF) model to predict the height of the WFZ for the evaluation of the size of a waterproof crown pillar. To train and test the RoF model, five indicators with major influencing factors on undersea safety mining were determined, 107 field-measured mining datasets were collected, 75 (70%) datasets were used for training, and 32 (30%) datasets were used for model testing. At the same time, the random forest ensemble algorithm (RFR) and support vector machine (SVM) models were introduced for comparison and verification; in the end, the tested results were evaluated by RMSE (root-mean-square error) and R2. The comparison shows that the predicted results from the RoF model are significantly better than those from the RFR and SVM models. An importance analysis of the impact indicators shows that the mining height and depth have significant impacts on the prediction results. The development height of the WFZ in undersea safety mining was predicted via the RoF model. The predicted results via the RoF model were verified by field observations using panoramic borehole televiewers. The RoF prediction results are consistent with the observation results at all depths. Compared with the other two models, the RoF model has the smallest average absolute error at 2.87%. The results show that the RoF model can be applied to predict the height of the WFZ in undersea mining, which could be an effective way of minimizing the mineral resource waste in the study area and in other similar areas in the world under the premise of mine safety.

Using Context to Discern User Tasks on Desktop

2014 ◽  
Vol 519-520 ◽  
pp. 318-321
Author(s):  
Ning Lv ◽  
Jing Li Zhou ◽  
Lei Hua Qin
Keyword(s):  
Support Vector Machine ◽  
Information Management ◽  
User Behavior ◽  
Personal Information ◽  
Contextual Information ◽  
Support Vector ◽  
Novel Approach ◽  
Information User ◽  
User Tasks ◽  
The Impact

The precise context of user tasks helps to ameliorate personal information management on desktop. This paper introduces a novel approach to discern user tasks using contextual information which is divided into two categories, user behavior based context and text based context. With the contextual information, user tasks are discerned by support vector machine (SVM) method. Experimental results showed the impact of distinct attributes of files on the performance of user task identification.

scholarly journals TOWARDS PREDICTING RICE LOSS DUE TO TYPHOONS IN THE PHILIPPINES

2019 ◽  
Vol XLII-4/W19 ◽  
pp. 63-70 ◽  
Author(s):  
S. Boeke ◽  
M. J. C. van den Homberg ◽  
A. Teklesadik ◽  
J. L. D. Fabila ◽  
D. Riquet ◽  
...  
Keyword(s):  
Binary Classification ◽  
Open Data ◽  
Absolute Error ◽  
The Philippines ◽  
Classification Model ◽  
Support Vector ◽  
Average Value ◽  
Early Action ◽  
Support Vector Regressor ◽  
The Impact

Abstract. Reliable predictions of the impact of natural hazards turning into a disaster is important for better targeting humanitarian response as well as for triggering early action. Open data and machine learning can be used to predict loss and damage to the houses and livelihoods of affected people. This research focuses on agricultural loss, more specifically rice loss in the Philippines due to typhoons. Regression and binary classification algorithms are trained using feature selection methods to find the most important explanatory features. Both geographical data from every province, and typhoon specific features of 11 historical typhoons are used as input. The percentage of lost rice area is considered as the output, with an average value of 7.1%. As for the regression task, the support vector regressor performed best with a Mean Absolute Error of 6.83 percentage points. For the classification model, thresholds of 20%, 30% and 40% are tested in order to find the best performing model. These thresholds represent different levels of lost rice fields for triggering anticipatory action towards farmers. The binary classifiers are trained to increase its ability to rightly predict the positive samples. In all three cases, the support vector classifier performed the best with a recall score of 88%, 75% and 81.82%, respectively. However, the precision score for each of these models was low: 17.05%, 14.46% and 10.84%, respectively. For both the support vector regressor and classifier, of all 14 available input features, only wind speed was selected as explanatory feature. Yet, for the other algorithms that were trained in this study, other sets of features were selected depending also on the hyperparameter settings. This variation in selected feature sets as well as the imprecise predictions were consequences of the small dataset that was used for this study. It is therefore important that data for more typhoons as well as data on other explanatory variables are gathered in order to make more robust and accurate predictions. Also, if loss data becomes available on municipality-level, rather than province-level, the models will become more accurate and valuable for operationalization.

A case study on the height of a water-flow fracture zone above undersea mining: Sanshandao Gold Mine, China

2019 ◽  
Vol 78 (4) ◽  
Author(s):  
Ying Chen ◽  
Guoyan Zhao ◽  
Shaofeng Wang ◽  
Hao Wu ◽  
Shaowei Wang
Keyword(s):  
Water Flow ◽  
Fracture Zone ◽  
Gold Mine ◽  
Undersea Mining

A Novel Approach to Quantitative Evaluation of Tangle Formations for Seaweeds in Stirrer Cultivation

2016 ◽  
Vol 28 (1) ◽  
pp. 40-49
Author(s):  
Jun Ogawa ◽  
◽  
Hiroyuki Iizuka ◽  
Masahito Yamamoto ◽  
Masashi Furukawa ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Water Flow ◽  
Quantitative Evaluation ◽  
Real World ◽  
Physical Simulation ◽  
Support Vector ◽  
The Real ◽  
Novel Approach ◽  
Novel Approaches ◽  
Nonlinear Support

[abstFig src='/00280001/04.jpg' width=""300"" text='Seaweed tangle formation' ]We discuss novel approaches to the control of seaweed tangle formations in stirrer cultivation. Cultivating seaweed is one important way to avoid such formation. Because defining such formation is difficult based on human recognition alone, there is currently no quantitative evaluation criterion for formation. We develop physical simulation for analyzing formations in a water flow field and model three factors – physical, geometric and time – for characterizing formations. Our criterion is that formations are created by using these factors as input to a nonlinear support vector machine. To show the effectiveness of our simulation and criteria, we confirm the control effects of the water flow in simulation and the real world. Results show that our simulation model is useful for avoiding such formation in the real world.

scholarly journals Machine Learning Methods for Herschel–Bulkley Fluids in Annulus: Pressure Drop Predictions and Algorithm Performance Evaluation

2020 ◽  
Vol 10 (7) ◽  
pp. 2588
Author(s):  
Abhishek Kumar ◽  
Syahrir Ridha ◽  
Tarek Ganet ◽  
Pandian Vasant ◽  
Suhaib Umer Ilyas
Keyword(s):  
Neural Network ◽  
Performance Evaluation ◽  
Pressure Drop ◽  
Pressure Loss ◽  
Oil And Gas ◽  
Input Parameter ◽  
Absolute Error ◽  
Support Vector ◽  
Bayesian Neural Network ◽  
The Impact

Accurate measurement of pressure drop in energy sectors especially oil and gas exploration is a challenging and crucial parameter for optimization of the extraction process. Many empirical and analytical solutions have been developed to anticipate pressure loss for non-Newtonian fluids in concentric and eccentric pipes. Numerous attempts have been made to extend these models to forecast pressure loss in the annulus. However, there remains a void in the experimental and theoretical studies to establish a model capable of estimating it with higher accuracy and lower computation. Rheology of fluid and geometry of system cumulatively dominate the pressure gradient in an annulus. In the present research, the prediction for Herschel–Bulkley fluids is analyzed by Bayesian Neural Network (BNN), random forest (RF), artificial neural network (ANN), and support vector machines (SVM) for pressure loss in the concentric and eccentric annulus. This study emphasizes on the performance evaluation of given algorithms and their pitfalls in predicting accurate pressure drop. The predictions of BNN and RF exhibit the least mean absolute error of 3.2% and 2.57%, respectively, and both can generalize the pressure loss calculation. The impact of each input parameter affecting the pressure drop is quantified using the RF algorithm.

Research on behavior recognition based on feature fusion of automatic coder and recurrent neural network

2020 ◽  
Vol 39 (6) ◽  
pp. 8927-8935
Author(s):  
Bing Zheng ◽  
Dawei Yun ◽  
Yan Liang
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Behavior Pattern ◽  
Rapid Development ◽  
Video Data ◽  
Support Vector ◽  
Behavior Recognition ◽  
Learning Methods ◽  
The Impact ◽  
Internet Of Things Technology

Under the impact of COVID-19, research on behavior recognition are highly needed. In this paper, we combine the algorithm of self-adaptive coder and recurrent neural network to realize the research of behavior pattern recognition. At present, most of the research of human behavior recognition is focused on the video data, which is based on the video number. At the same time, due to the complexity of video image data, it is easy to violate personal privacy. With the rapid development of Internet of things technology, it has attracted the attention of a large number of experts and scholars. Researchers have tried to use many machine learning methods, such as random forest, support vector machine and other shallow learning methods, which perform well in the laboratory environment, but there is still a long way to go from practical application. In this paper, a recursive neural network algorithm based on long and short term memory (LSTM) is proposed to realize the recognition of behavior patterns, so as to improve the accuracy of human activity behavior recognition.

scholarly journals Automated Confirmation of Protein Annotation Using NLP and the UniProtKB Database

2020 ◽  
Vol 11 (1) ◽  
pp. 24
Author(s):  
Jin Tao ◽  
Kelly Brayton ◽  
Shira Broschat
Keyword(s):  
Language Processing ◽  
Fine Tuning ◽  
Support Vector ◽  
Protein Annotation ◽  
Computing Power ◽  
Journal Publication ◽  
Novel Approach ◽  
Uniprotkb Database ◽  
Public Repositories ◽  
Annotation Errors

Advances in genome sequencing technology and computing power have brought about the explosive growth of sequenced genomes in public repositories with a concomitant increase in annotation errors. Many protein sequences are annotated using computational analysis rather than experimental verification, leading to inaccuracies in annotation. Confirmation of existing protein annotations is urgently needed before misannotation becomes even more prevalent due to error propagation. In this work we present a novel approach for automatically confirming the existence of manually curated information with experimental evidence of protein annotation. Our ensemble learning method uses a combination of recurrent convolutional neural network, logistic regression, and support vector machine models. Natural language processing in the form of word embeddings is used with journal publication titles retrieved from the UniProtKB database. Importantly, we use recall as our most significant metric to ensure the maximum number of verifications possible; results are reported to a human curator for confirmation. Our ensemble model achieves 91.25% recall, 71.26% accuracy, 65.19% precision, and an F1 score of 76.05% and outperforms the Bidirectional Encoder Representations from Transformers for Biomedical Text Mining (BioBERT) model with fine-tuning using the same data.

scholarly journals Brain drain and brain gain in Russia: Analyzing international migration of researchers by discipline using Scopus bibliometric data 1996–2020

2021 ◽  
Author(s):  
Alexander Subbotin ◽  
Samin Aref
Keyword(s):  
Brain Drain ◽  
Policy Development ◽  
Donor Country ◽  
International Mobility ◽  
Science System ◽  
Migration Rates ◽  
Net Migration ◽  
Novel Approach ◽  
Five Disciplines ◽  
The Impact

AbstractWe study international mobility in academia, with a focus on the migration of published researchers to and from Russia. Using an exhaustive set of over 2.4 million Scopus publications, we analyze all researchers who have published with a Russian affiliation address in Scopus-indexed sources in 1996–2020. The migration of researchers is observed through the changes in their affiliation addresses, which altered their mode countries of affiliation across different years. While only 5.2% of these researchers were internationally mobile, they accounted for a substantial proportion of citations. Our estimates of net migration rates indicate that while Russia was a donor country in the late 1990s and early 2000s, it has experienced a relatively balanced circulation of researchers in more recent years. These findings suggest that the current trends in scholarly migration in Russia could be better framed as brain circulation, rather than as brain drain. Overall, researchers emigrating from Russia outnumbered and outperformed researchers immigrating to Russia. Our analysis on the subject categories of publication venues shows that in the past 25 years, Russia has, overall, suffered a net loss in most disciplines, and most notably in the five disciplines of neuroscience, decision sciences, mathematics, biochemistry, and pharmacology. We demonstrate the robustness of our main findings under random exclusion of data and changes in numeric parameters. Our substantive results shed light on new aspects of international mobility in academia, and on the impact of this mobility on a national science system, which have direct implications for policy development. Methodologically, our novel approach to handling big data can be adopted as a framework of analysis for studying scholarly migration in other countries.

New Multi-View Classification Method with Uncertain Data

2021 ◽  
Vol 16 (1) ◽  
pp. 1-23
Author(s):  
Bo Liu ◽  
Haowen Zhong ◽  
Yanshan Xiao
Keyword(s):  
Learning Strategy ◽  
State Of The Art ◽  
Uncertain Data ◽  
Real Life ◽  
Support Vector ◽  
Classification Methods ◽  
Complementary Information ◽  
Novel Approach ◽  
Svm Model ◽  
Iterative Framework

Multi-view classification aims at designing a multi-view learning strategy to train a classifier from multi-view data, which are easily collected in practice. Most of the existing works focus on multi-view classification by assuming the multi-view data are collected with precise information. However, we always collect the uncertain multi-view data due to the collection process is corrupted with noise in real-life application. In this case, this article proposes a novel approach, called uncertain multi-view learning with support vector machine (UMV-SVM) to cope with the problem of multi-view learning with uncertain data. The method first enforces the agreement among all the views to seek complementary information of multi-view data and takes the uncertainty of the multi-view data into consideration by modeling reachability area of the noise. Then it proposes an iterative framework to solve the proposed UMV-SVM model such that we can obtain the multi-view classifier for prediction. Extensive experiments on real-life datasets have shown that the proposed UMV-SVM can achieve a better performance for uncertain multi-view classification in comparison to the state-of-the-art multi-view classification methods.

scholarly journals Impact of Dataset Size on Classification Performance: An Empirical Evaluation in the Medical Domain

2021 ◽  
Vol 11 (2) ◽  
pp. 796
Author(s):  
Alhanoof Althnian ◽  
Duaa AlSaeed ◽  
Heyam Al-Baity ◽  
Amani Samha ◽  
Alanoud Bin Dris ◽  
...  
Keyword(s):  
Empirical Evaluation ◽  
Classification Performance ◽  
Support Vector ◽  
Robust Model ◽  
Original Distribution ◽  
C4.5 Decision Tree ◽  
Dataset Size ◽  
Overall Performance ◽  
Medical Domain ◽  
The Impact

Dataset size is considered a major concern in the medical domain, where lack of data is a common occurrence. This study aims to investigate the impact of dataset size on the overall performance of supervised classification models. We examined the performance of six widely-used models in the medical field, including support vector machine (SVM), neural networks (NN), C4.5 decision tree (DT), random forest (RF), adaboost (AB), and naïve Bayes (NB) on eighteen small medical UCI datasets. We further implemented three dataset size reduction scenarios on two large datasets and analyze the performance of the models when trained on each resulting dataset with respect to accuracy, precision, recall, f-score, specificity, and area under the ROC curve (AUC). Our results indicated that the overall performance of classifiers depend on how much a dataset represents the original distribution rather than its size. Moreover, we found that the most robust model for limited medical data is AB and NB, followed by SVM, and then RF and NN, while the least robust model is DT. Furthermore, an interesting observation is that a robust machine learning model to limited dataset does not necessary imply that it provides the best performance compared to other models.

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