关键词： radiomics   magnetic resonance imaging   multiple myeloma   cytogenetics   classifier  

摘要： Background Radiomics has shown promising results in the diagnosis, efficacy, and prognostic assessments of multiple myeloma (MM). However, little evidence exists on the utility of radiomics in predicting a high-risk cytogenetic (HRC) status in MM. Purpose To develop and test a magnetic resonance imaging (MRI)-based radiomics model for predicting an HRC status in MM patients. Study Type Retrospective. Population Eighty-nine MM patients (HRC [n: 37] and non-HRC [n: 52]). Field Strength/Sequence A 3.0 T;fast spin-echo (FSE): T1-weighted image (T1WI) and fat-suppression T2WI (FS-T2WI). Assessment Overall, 1409 radiomics features were extracted from each volume of interest drawn by radiologists. Three sequential feature selection steps-variance threshold, SelectKBest, and least absolute shrinkage selection operator-were repeated 10 times with 5-fold cross-validation. Radiomics models were constructed with the top three frequency features of T1WI/T2WI/two-sequence MRI (T1WI and FS-T2WI). Radiomics models, clinical data (age and visually assessed MRI pattern), or radiomics combined with clinical data were used with six classifiers to distinguish between HRC and non-HRC statuses. Six classifiers used were support vector machine, random forest, logistic regression (LR), decision tree, k-nearest neighbor, and XGBoost. Model performance was evaluated with area under the curve (AUC) values. Statistical Tests Mann-Whitney U-test, Chi-squared test, Z test, and DeLong method. Results The LR classifier performed better than the other classifiers based on different data (AUC: 0.65-0.82;P < 0.05). The two-sequence MRI models performed better than the other data models using different classifiers (AUC: 0.68-0.82;P < 0.05). Thus, the LR two-sequence model yielded the best performance (AUC: 0.82 +/- 0.02;sensitivity: 84.1%;specificity: 68.1%;accuracy: 74.7%;P < 0.05). Conclusion The LR-based machine learning method appears superior to other classifier methods for assessing HRC in MM. Ra

关键词： applicability domain   computational toxicology   machine learning   mitochondrial toxicity   structural alert  

摘要： Mitochondria are important organelles in human cells, providing more than 95% of the energy. However, some drugs and environmental chemicals could induce mitochondrial dysfunction, which might cause complex diseases and even worsen the condition of patients with mitochondrial damage. Some drugs have been withdrawn from the market due to their severe mitochondrial toxicity, such as troglitazone. Therefore, there is an urgent need to develop models that could accurately predict the mitochondrial toxicity of chemicals. In this paper, suitable data were obtained from literature and databases first. Then nine types of fingerprints were used to characterize these compounds. Finally, different algorithms were used to build models. Meanwhile, the applicability domain of the prediction models was defined. We have also explored the structural alerts of mitochondrial toxicity, which would be helpful for medicinal chemists to better predict mitochondrial toxicity and further optimize lead compounds.

关键词： Permeability prediction   Computational fluid dynamics   Pore characterisation   Hybrid method   Artificial neural network   Genetic algorithm  

摘要： Permeability prediction is crucial in shale gas and CO2 geological sequestration. However, the intricate pore structure complicates the prediction of permeability. Machine learning (ML) is a promising approach for predicting inherent correlations in large data sets. In this paper, a hybrid ML method is proposed to implicitly build a nonlinear relationship between pore structure parameters and permeability. For the dataset preparation, an improved quartet structure generation set algorithm was firstly developed to generate 1000 porous media. Then, the pore structure parameters were extracted as input parameters and the permeability was calculated as the output parameter. For the ML modelling, a hybrid ML method was proposed using a combination of artificial neural network (ANN) and genetic algorithm (GA). The ANN was employed to learn the nonlinear relationships and GA was used to tune ANN architecture for the best performance. The prediction results show that the GA-ANN was robust in predicting permeability based on pore structure parameters. The ANN model with the optimum architecture could achieve an average R value of 0.998 on the training set and 0.999 on the testing set. Practically, the porous sample can be obtained through micro-computed tomography (CT) or nano-CT, and the proposed framework can be applied to real porous media. Fast prediction of permeability based on formation factors can provide some insights on reservoir evaluation and reservoir stimulation.

关键词： Aneurysmal subarachnoid hemorrhage   Outcome prediction   Deep learning   Artificial neural net   Tree boosting  

摘要： Reliable prediction of outcomes of aneurysmal subarachnoid hemorrhage (aSAH) based on factors available at patient admission may support responsible allocation of resources as well as treatment decisions. Radiographic and clinical scoring systems may help clinicians estimate disease severity, but their predictive value is limited, especially in devising treatment strategies. In this study, we aimed to examine whether a machine learning (ML) approach using variables available on admission may improve outcome prediction in aSAH compared to established scoring systems. Combined clinical and radiographic features as well as standard scores (Hunt & Hess, WFNS, BNI, Fisher, and VASOGRADE) available on patient admission were analyzed using a consecutive single-center database of patients that presented with aSAH (n = 388). Different ML models (seven algorithms including three types of traditional generalized linear models, as well as a tree bosting algorithm, a support vector machine classifier (SVMC), a Naive Bayes (NB) classifier, and a multilayer perceptron (MLP) artificial neural net) were trained for single features, scores, and combined features with a random split into training and test sets (4:1 ratio), ten-fold cross-validation, and 50 shuffles. For combined features, feature importance was calculated. There was no difference in performance between traditional and other ML applications using traditional clinico-radiographic features. Also, no relevant difference was identified between a combined set of clinico-radiological features available on admission (highest AUC 0.78, tree boosting) and the best performing clinical score GCS (highest AUC 0.76, tree boosting). GCS and age were the most important variables for the feature combination. In this cohort of patients with aSAH, the performance of functional outcome prediction by machine learning techniques was comparable to traditional methods and established clinical scores. Future work is necessary to examine input

关键词： binaries: eclipsing   stars: evolution   stars: fundamental parameters   starspots  

摘要： The parameter space for modelling stellar systems is vast and complicated. To find best-fitting models for a star one needs a statistically robust way of exploring this space. We present a new machine-learning approach to predict the modelling parameters for detached double-lined eclipsing binary systems, including the system age, based on observable quantities. Our method allows for the estimation of the importance of several physical effects which are included in a parametrized form in stellar models, such as convective core overshoot or stellar spot coverage. The method yields probability distribution functions for the predicted parameters which take into account the statistical and, to a certain extent, the systematic errors which is very difficult to do using other methods. We employ two different approaches to investigate the two components of the system either independently or in a combined manner. Furthermore, two different grids are used as training data. We apply the method to 26 selected objects and test the predicted best solutions with an on-the-fly optimization routine which generates full hydrostatic models. While we do encounter failures of the predictions, our method can serve as a rapid estimate for stellar ages of detached eclipsing binaries taking full account of the uncertainties in the observables.

关键词： leaf chlorophyll content   hyperspectral remote sensing   RF   SVR  

摘要： Leaf chlorophyll content (LCC) is one of the most important factors affecting photosynthetic capacity and nitrogen status, both of which influence crop harvest. However, the development of rapid and nondestructive methods for leaf chlorophyll estimation is a topic of much interest. Hence, this study explored the use of the machine learning approach to enhance the estimation of leaf chlorophyll from spectral reflectance data. The objective of this study was to evaluate four different approaches for estimating the LCC of apple tree leaves at five growth stages (the 1st, 2nd, 3rd, 4th and 5th growth stages): (1) univariate linear regression (ULR);(2) multivariate linear regression (MLR);(3) support vector regression (SVR);and (4) random forest (RF) regression. Samples were collected from the leaves on the eastern, western, southern and northern sides of apple trees five times (1st, 2nd, 3rd, 4th and 5th growth stages) over three consecutive years (2016-2018), and experiments were conducted in 10-20-year-old apple tree orchards. Correlation analysis results showed that LCC and ST, LCC and vegetation indices (VIs), and LCC and three edge parameters (TEP) had high correlations with the first-order differential spectrum (FODS) (0.86), leaf chlorophyll index (LCI) (0.87), and (SDr - SDb)/ (SDr + SDb) (0.88) at the 3rd, 3rd, and 4th growth stages, respectively. The prediction models of different growth stages were relatively good. The MLR and SVR models in the LCC assessment of different growth stages only reached the highest R-2 values of 0.79 and 0.82, and the lowest RMSEs were 2.27 and 2.02, respectively. However, the RF model evaluation was significantly better than above models. The R-2 value was greater than 0.94 and RMSE was less than 1.37 at different growth stages. The prediction accuracy of the 1st growth stage (R-2 = 0.96, RMSE = 0.95) was best with the RF model. This result could provide a theoretical basis for orchard management. In the future, more models based

关键词： Maize yield prediction   Machine learning methods   Gradient boosting (XGBoost)   Random fields   Yield influencing variables   Decision support in crop production  

摘要： In order to meet the requirements of sustainability and to determine yield drivers and limiting factors, it is now more likely that traditional yield modelling will be carried out using artificial intelligence (AI). The aim of this study was to predict maize yields using AI that uses spatio-temporal training data. The paper has advanced a new method of maize yield prediction, which is based on spatio-temporal data mining. To find the best solution, various models were used: counter-propagation artificial neural networks (CP-ANNs), XY-fused Querynetworks (XY-Fs), supervised Kohonen networks (SKNs), neural networks with Rectangular Linear Activations (ReLU), extreme gradient boosting (XGBoost), support-vector machine (SVM), and different subsets of the independent variables in five vegetation periods. Input variables for modelling included: soil parameters (pH, P2O5, K2O, Zn, clay content, ECa, draught force, Cone index), micro-relief averages, and meteorological parameters for the 63 treatment units in a 15.3 ha research field. The best performing method (XGBoost) reached 92.1% and 95.3% accuracy on the training and the test sets. Additionally, a novel method was introduced to treat individual units in a lattice system. The lattice-based smoothing performed an additional increase in Area under the curve (AUC) to 97.5% over the individual predictions of the XGBoost model. The models were developed using 48 different subsets of variables to determine which variables consistently contributed to prediction accuracy. By comparing the resulting models, it was shown that the best regression model was Extreme Gradient Boosting Trees, with 92.1% accuracy (on the training set). In addition, the method calculates the influence of the spatial distribution of site-specific soil fertility on maize grain yields. This paper provides a new method of spatio-temporal data analyses, taking the most important influencing factors on maize yields into account.

关键词： Pumpkin seed   Logistic regression   Multilayer peceptrons   Random forest   Classification   Support vector machine   Thresholding  

摘要： Pumpkin seeds are frequently consumed as confection worldwide because of their adequate amount of protein, fat, carbohydrate, and mineral contents. This study was carried out on the two most important and quality types of pumpkin seeds, "urgup Sivrisi" and "cercevelik", generally grown in urgup and Karacaoren regions in Turkey. However, morphological measurements of 2500 pumpkin seeds of both varieties were made possible by using the gray and binary forms of threshold techniques. Considering morphological features, all the data were modeled with five different machine learning methods: Logistic Regression (LR), Multilayer Perceptrons (MLP), Support Vector Machine (SVM) and Random Forest (RF), and k-Nearest Neighbor (k-NN), which further determined the most successful method for classifying pumpkin seed varieties. However, the performances of the models were determined with the help of the 10 k-fold cross-validation method. The accuracy rates of the classifiers were obtained as LR 87.92 percent, MLP 88.52 percent, SVM 88.64 percent, RF 87.56 percent, and k-NN 87.64 percent.

关键词： Underwater acoustic communications   Channel quality prediction   Energy consumption   Machine learning  

摘要： Due to the limited energy supply of sensor nodes in underwater acoustic communication networks (UACNs), energy optimization for underwater acoustic transmissions is critical to prolong the network lifetime and improve network performance. Machine learning is a powerful and promising method that can be used to optimize energy consumption of UACNs. In this paper, we propose a machine learning based environment-aware communication channel quality prediction (ML-ECQP) method for UACNs. In ML-ECQP, the logistic regression (LR) algorithm is used to predict the communication channel quality (which is measured according to the bit error rate) between a transmitter and a receiver based on the perceived underwater acoustic channel environmental parameters (such as signal-to-noise ratio, underwater temperature, wind speed, etc.). Based on the predicted communication quality, each transmitter can optimize the acoustic data transmissions in order to minimize the energy waste caused by retransmissions, thus significantly reducing the energy consumption of UACNs. Extensive experiments are conducted in the Furong Lake at Xiamen University to demonstrate the performance (in terms of the feasibility, channel condition predication accuracy, and energy consumption reduction) of the proposed ML-ECQP method. (C) 2021 Elsevier Ltd. All rights reserved.

关键词： pressure sensor   pressure mapping   sitting posture recognition   wheelchair seat cushion   machine learning algorithms  

摘要： This paper presents a posture recognition system aimed at detecting sitting postures of a wheelchair user. The main goals of the proposed system are to identify and inform irregular and improper posture to prevent sitting-related health issues such as pressure ulcers, with the potential that it could also be used for individuals without mobility issues. In the proposed monitoring system, an array of 16 screen printed pressure sensor units was employed to obtain pressure data, which are sampled and processed in real-time using read-out electronics. The posture recognition was performed for four sitting positions: right-, left-, forward- and backward leaning based on k-nearest neighbors (k-NN), support vector machines (SVM), random forest (RF), decision tree (DT) and LightGBM machine learning algorithms. As a result, a posture classification accuracy of up to 99.03 percent can be achieved. Experimental studies illustrate that the system can provide real-time pressure distribution value in the form of a pressure map on a standard PC and also on a raspberry pi system equipped with a touchscreen monitor. The stored pressure distribution data can later be shared with healthcare professionals so that abnormalities in sitting patterns can be identified by employing a post-processing unit. The proposed system could be used for risk assessments related to pressure ulcers. It may be served as a benchmark by recording and identifying individuals' sitting patterns and the possibility of being realized as a lightweight portable health monitoring device.