关键词： inter-well connectivity   bottom-hole pressure   machine learning   artificial neural network   time lags   oil   gas reservoirs   petroleum engineering   petroleum wells-drilling   production   construction   wells-injection   oil   gas  

摘要： In the production and development of oil fields, production wells generally produce at a constant rate since the fixed production is easier to control than the fixed pressure. Thus, it is more feasible to use bottom-hole pressure data for connectivity analysis than historical injection and production data when producers are set in fixed rates. In this work, a practical procedure is proposed to infer inter-well connectivity based on the bottom-hole pressure data of injectors and producers. The procedure first preprocesses the bottom-hole pressure based on nonlinear diffusion filters to constitute the dataset for machine learning. An artificial neural network (ANN) is then generated and trained to simulate the connection relationship between the producer and its adjacent injectors. The genetic algorithm (GA) is also introduced to avoid the tedious process of determining time lags and other hyper-parameters of ANN. In particular, the time lag is normally determined by subjective judgment, which is optimized by GA for the first time. After optimizing the parameters, the sensitivity analysis is performed on the well-trained ANN to quantify inter-well connectivity. For the evaluation and verification purposes, the proposed GA and sensitivity analysis based ANN were applied to two synthetic reservoirs and one actual case from JD oilfields, China. The results show that the calculated connectivity conforms to known geological characteristics and tracer test results. And it demonstrates that the presented approach is an effective alternative way to characterize the reservoir connectivity and determine the flow direction of injected water.

关键词： 信用风险评估   xgboost-LR算法   随机森林   支持向量机  

摘要： 互联网金融在国内的兴起,使得个人信贷风险成为许多企业关注的热点。本文通过对LendingClub网站中个人信用贷款数据集的探索,构建基于xgboost和logistic regression组合算法xgboost-LR模型、随机森林和支持向量机算法三种机器学习方法,对个人信用风险进行全面的评估。通过实证数据分析,其中新构建的xgboost-LR算法评价效果最好,能够更加准确地预测个人信用风险。

关键词： RNA structure prediction   RNA scoring function   knowledge-based potentials   machine learning   convolutional neural networks  

摘要： RNAs play crucial and versatile roles in biological *** prediction approaches can help to understand RNA structures and their stabilizing factors,thus providing information on their functions,and facilitating the design of new *** learning(ML)techniques have made tremendous progress in many fields in the past few *** their usage in protein-related fields has a long history,the use of ML methods in predicting RNA tertiary structures is new and ***,we review the recent advances of using ML methods on RNA structure predictions and discuss the advantages and limitation,the difficulties and potentials of these approaches when applied in the field.

关键词： Imaging spectroscopy   Inversion   Gaussian processes   Radiative transfer modeling   Agricultural monitoring   EnMAP  

摘要： Hyperspectral acquisitions have proven to be the most informative Earth observation data source for the estimation of nitrogen (N) content, which is the main limiting nutrient for plant growth and thus agricultural production. In the past, empirical algorithms have been widely employed to retrieve information on this biochemical plant component from canopy reflectance. However, these approaches do not seek for a cause-effect relationship based on physical laws. Moreover, most studies solely relied on the correlation of chlorophyll content with nitrogen, and thus neglected the fact that most N is bound in proteins. Our study presents a hybrid retrieval method using a physically-based approach combined with machine learning regression to estimate crop N content. Within the workflow, the leaf optical properties model PROSPECT-PRO including the newly calibrated specific absorption coefficients (SAC) of proteins, was coupled with the canopy reflectance model 4SAIL to PROSAIL-PRO. The latter was then employed to generate a training database to be used for advanced probabilistic machine learning methods: a standard homoscedastic Gaussian process (GP) and a heteroscedastic GP regression that accounts for signal-to-noise relations. Both GP models have the property of providing confidence intervals for the estimates, which sets them apart from other machine learners. Moreover, a GP-based sequential backward band removal algorithm was employed to analyze the band-specific information content of PROSAIL-PRO simulated spectra for the estimation of aboveground N. Data from multiple hyperspectral field campaigns, carried out in the framework of the future satellite mission Environmental Mapping and Analysis Program (EnMAP), were exploited for validation. In these campaigns, corn and winter wheat spectra were acquired to simulate spectral EnMAP data. Moreover, destructive N measurements from leaves, stalks and fruits were collected separately to enable plant-organ-specific validati

关键词： Runoff model   Nature-inspired optimizers   Malaysia  

摘要： Rainfall and runoff are considered the main components in the hydrological cycle. Developing an accurate model to capture the dynamic connection between rainfall and runoff remains a problematic task for engineers. Several studies have been carried out to develop models to accurately predict the changes in runoff from rainfall. However, these models have limitations in terms of accuracy and complexity when large numbers of parameters are needed. Therefore, recently, with the advancement of data-driven techniques, a vast number of hydrologists have adopted models to predict changes in runoff. However, data-driven models still encounter several limitations related to hyperparameter optimization and overfitting. Hence, there is a need to improve these models to overcome these limitations. In this study, data-driven techniques such as a Multi-Layer Perceptron (MLP) neural network and Least Squares Support Vector Machine (LSSVM) are integrated with an advanced nature-inspired optimizer, namely, Harris Hawks Optimization (HHO) to model the rainfall-runoff relationship. Five different scenarios will be examined based on the autocorrelation function (ACF), cross-correlation function (CCF) and partial autocorrelation function (PACF). Finally, for comprehensive analysis, the performance of the proposed model will then be compared with integrated data-driven techniques with particle swarm optimization (PSO). The results revealed that all the augmented models with HHO outperformed other integrated models with PSO in predicting the changes in runoff. In addition, a high level of accuracy in predicting runoff values was achieved when HHO was integrated with LSSVM.

关键词： Metaheuristic evolutionary   Classification   Landslide perdition  

摘要： The present study aims to assess the superiority of the metaheuristic evolutionary when compared to the conventional machine learning classification techniques for landslide occurrence estimation. To evaluate and compare the applicability of these metaheuristic algorithms, a real-world problem of landslide assessment (i.e., including 266 records and fifteen landslide conditioning factors) is selected. In the first step, seven of the most common traditional classification techniques are applied. Then, after introducing the elite model, it is optimized using six state-of-the-art metaheuristic evolutionary techniques. The results show that applying the proposed evolutionary algorithms effectively increases the prediction accuracy from 81.6 to the range (87.8-98.3%) and the classification ratio from 58.3% to the range (60.1-85.0%).

关键词： concurrent   interleaved   RNN   BiLSTM   SCCRF   activity recognition   Smart Home  

摘要： Human activity recognition has become an important research topic within the field of pervasive computing, ambient assistive living (AAL), robotics, health-care monitoring, and many more. Techniques for recognizing simple and single activities are typical for now, but recognizing complex activities such as concurrent and interleaving activity is still a major challenging issue. In this paper, we propose a two-phase hybrid deep machine learning approach using bi-directional Long-Short Term Memory (BiLSTM) and Skip-Chain Conditional random field (SCCRF) to recognize the complex activity. BiLSTM is a sequential generative deep learning inherited from Recurrent Neural Network (RNN). SCCRFs is a distinctive feature of conditional random field (CRF) that can represent long term dependencies. In the first phase of the proposed approach, we recognized the concurrent activities using the BiLSTM technique, and in the second phase, SCCRF identifies the interleaved activity. Accuracy of the proposed framework against the counterpart state-of-art methods using the publicly available datasets in a smart home environment is analyzed. Our experiment's result surpasses the previously proposed approaches with an average accuracy of more than 93%.

关键词： Pore-pressure prediction   Geomechanics   Multivariate model   Machine learning   Random forest  

摘要： Accurate pore-pressure prediction is of essential importance to hydrocarbon exploration and development. A multivariate prediction model of multiple petrophysical data is required to adequately reflect variations in pore pressure. However, the parametric multivariate models with assumptions on lithology, predominantly sand or shale, are theoretically inaccurate for mixed lithologies and require a tedious calibration process. Here, we propose a new method of utilizing machine learning (ML) techniques for pore-pressure prediction with a nonparametric multivariate model of petrophysical properties (sonic velocity, porosity, and shale volume). The training dataset for the ML models is constructed using petrophysical properties extracted from well log data and theoretical effective stress in the normally compacted interval. Bowers' unloading relation is invoked herein to account for abnormal pressure generated by unloading. Four ML algorithms, including the multilayer perceptron neural network, support vector machine, random forest, and gradient boosting machine, are applied to well log data from a set of offshore exploration wells. The results suggest that the proposed method using ML makes pore-pressure predictions in good agreement with pore-pressure measurement, and random forest outperforms the other ML algorithms in terms of goodness-of-fit, generalizability, and prediction accuracy. Compared with methods based on parametric models, the proposed method based on ML produces more accurate pore-pressure prediction and better capture the onset of overpressure.

关键词： Novel coronavirus   COVID-19   Simple linear regression   Polynomial regression   Support vector regression model   Pearson   Active cases   Recoveries  

摘要： In this paper, we are working on a pandemic of novel coronavirus (COVID-19). COVID-19 is an infectious disease, it creates severe damage in the lungs. COVID-19 causes illness in humans and has killed many people in the entire world. However, this virus is reported as a pandemic by the World Health Organization (WHO) and all countries are trying to control and lockdown all places. The main objective of this work is to solve the five different tasks such as I) Predicting the spread of coronavirus across regions. II) Analyzing the growth rates and the types of mitigation across countries. III) Predicting how the epidemic will end. IV) Analyzing the transmission rate of the virus. V) Correlating the coronavirus and weather conditions. The advantage of doing these tasks to minimize the virus spread by various mitigation, how well the mitigations are working, how many cases have been prevented by this mitigations, an idea about the number of patients that will recover from the infection with old medication, understand how much time will it take to for this pandemic to end, we will be able to understand and analyze how fast or slow the virus is spreading among regions and the infected patient to reduce the spread based clear understanding of the correlation between the spread and weather conditions. In this paper, we propose a novel Support Vector Regression method to analysis five different tasks related to novel coronavirus. In this work, instead of simple regression line we use the supported vectors also to get better classification accuracy. Our approach is evaluated and compared with other well-known regression models on standard available datasets. The promising results demonstrate its superiority in both efficiency and accuracy. (C) 2020 Elsevier Ltd. All rights reserved.

关键词： Knee   Replacement   Prediction   Machine learning   Arthroplasty   Artificial intelligence   Algorithms  

摘要： Purpose Machine-learning methods are flexible prediction algorithms with potential advantages over conventional regression. This study aimed to use machine learning methods to predict post-total knee arthroplasty (TKA) walking limitation, and to compare their performance with that of logistic regression. Methods From the department's clinical registry, a cohort of 4026 patients who underwent elective, primary TKA between July 2013 and July 2017 was identified. Candidate predictors included demographics and preoperative clinical, psychosocial, and outcome measures. The primary outcome was severe walking limitation at 6 months post-TKA, defined as a maximum walk time <= 15 min. Eight common regression (logistic, penalized logistic, and ordinal logistic with natural splines) and ensemble machine learning (random forest, extreme gradient boosting, and SuperLearner) methods were implemented to predict the probability of severe walking limitation. Models were compared on discrimination and calibration metrics. Results At 6 months post-TKA, 13% of patients had severe walking limitation. Machine learning and logistic regression models performed moderately [mean area under the ROC curves (AUC) 0.73-0.75]. Overall, the ordinal logistic regression model performed best while the SuperLearner performed best among machine learning methods, with negligible differences between them (Brier score difference, < 0.001;95% CI [- 0.0025, 0.002]). Conclusions When predicting post-TKA physical function, several machine learning methods did not outperform logistic regression-in particular, ordinal logistic regression that does not assume linearity in its predictors.