关键词： fiber tensile fracture   acoustic emission   feature extraction   spectrum analysis  

摘要： Energy release usually accompanies the single-fiber tensile fracture, and can be monitored using acoustic emission technology. Generated during the process of molecular structure fracture of various fibers, the acoustic emission signals can be extracted to identify different fracture types of fiber, which is especially important to the yarn formation process. In this study, a low-noise fiber-stretching device was employed to process the weak-intensity signal generated during fiber tensile fracture;in addition, the Hilbert-Huang transform (HHT), principal component analysis (PCA) and least squares support vector machine (LSSVM) algorithms were combined to identify the collected acoustic emission signals of polyester and cotton fibers. At the same time, it was verified that compared with the single-fiber breaking acoustic emission signal obtained by the electronic single-fiber strength tester, the signal acquisition device based on pneumatic components proposed in this paper can significantly improve the signal-to-noise ratio of the signal. According to the algorithm recognition results, the recognition rate of the two fibers increased from 74% to 95%.The experimental results indicate successful measurements of different fractures of two types of fiber.

关键词： Red blood cell   Sickle-cell disease   Microscopy image   Machine learning   Interpretability   Morphology analysis  

摘要： In this work we propose an approach to select the classification method and features, based on the state-of-the-art, with best performance for diagnostic support through peripheral blood smear images of red blood cells. In our case we used samples of patients with sickle-cell disease which can be generalized for other study cases. To trust the behavior of the proposed system, we also analyzed the interpretability. We pre-processed and segmented microscopic images, to ensure high feature quality. We applied the methods used in the literature to extract the features from blood cells and the machine learning methods to classify their morphology. Next, we searched for their best parameters from the resulting data in the feature extraction phase. Then, we found the best parameters for every classifier using Randomized and Grid search. For the sake of scientific progress, we published parameters for each classifier, the implemented code library, the confusion matrices with the raw data, and we used the public erythrocyteslDB dataset for validation. We also defined how to select the most important features for classification to decrease the complexity and the training time, and for interpretability purpose in opaque models. Finally, comparing the best performing classification methods with the state-of-the-art, we obtained better results even with interpretable model classifiers.

关键词： Artificial Intelligence   Banana detection   Deep learning   Disease detection   High-resolution satellite image   Surveillance   UAV images  

摘要： Front-line remote sensing tools, coupled with machine learning (ML), have a significant role in crop monitoring and disease surveillance. Crop type classification and a disease early warning system are some of these remote sensing applications that provide precise, timely, and cost-effective information at different spatial, temporal, and spectral resolutions. To our knowledge, most disease surveillance systems focus on a single-sensor based solutions and lagging the integration of multiple information sources. Moreover, monitoring larger landscapes using unmanned aerial vehicles (UAV) are challenging, and, therefore combining high resolution satellite imagery data with advanced machine learning (ML) models through the use of mobile apps could help detect and classify banana plants and provide more information on its overall health status. In this study, we classified banana under mixed-complex African landscapes through pixel-based classifications and ML models derived from multi-level satellite images (Sentinel 2, PlanetScope and WorldView-2) and UAV (MicaSense RedEdge) platforms. Our pixel-based classification from random forest (RF) model using combined features of vegetation indices (VIs) and principal component analysis (PCA) showed up to 97% overall accuracy (OA) with less than 10% omission and commission errors (OE and CE) and Kappa coefficient of 0.96 in high resolution multispectral images. We used UAV-RGB aerial images from DR Congo and Republic of Benin fields to develop a mixed-model system combining object detection model (RetinaNet) and a custom classifier for simultaneous banana localization and disease classification. Their accuracies were tested using different performance metrics. Our UAV-RGB mixed-model revealed that the developed object detection and classification model successfully classified healthy and diseased plants with 99.4%, 92.8%, 93.3% and 90.8% accuracy for the four classes: banana bunchy top disease (BBTD), Xanthomonas Wilt of Banan

摘要： The reference evapotranspiration (ET0) estimates is important for water resources and irrigation management. The Penman-Monteith equation is known for its accuracy but requires a high number of climatic parameters that are not always available. Thus, this study aimed to evaluate the performance of machine learning techniques (cubist regression, artificial neural network with Bayesian regularization, support vector machine with linear kernel function) and stepwise multiple linear regression method to estimate daily ET(0)with limited weather data in a Brazilian agricultural frontier (MATOPIBA). Climatic data from 2000 to 2016 obtained from 23 weather stations were used. Five data scenarios were evaluated: (i) all variables, (ii) radiation and temperature, (iii) temperature and relative humidity, (iv) wind speed and temperature, and (v) temperature. The results showed that the machine learning methods are robust in estimating ET0, even in the absence of some variables. Among the methods evaluated using only temperature data, the cubist regression showed better performance. When estimating water demand for soybean and maize crops using only temperature, the cubist regression and calibrated Hargreaves-Samani equation showed the smallest errors.

关键词： Machine learning (ML)   nanodevice   nonequilibrium   Green's function (NEGF) simulations  

摘要： As the device size scalesdown to the nanometer regime, quantum effects play an important role in device characteristicsand performance. Quantum transport device simulation based on the nonequilibrium Green's function (NEGF) has been extensively applied to simulate the nanoscaledevices. TheNEGF simulations, however, can be computationally expensive, especially in the presence of scattering. In this study, a machine learning (ML)-based framework is developed, targeting on replacing the computationally intensive NEGF simulations. This framework first learns a sparse representation of a quantum transport property of interest and then trains a model to describe the quantitative mapping relation between the device parameters and properties. Also, the accuracy is further improved with the application of feature engineering. As an example, a graphene-ferroelectric-metal (GFM) ferroelectric tunnel junction (FTJ) is simulated. The results show that the ML-based framework allows circumventing the NEGF calculation and simultaneously maintaining high accuracy in quantum transmissions and tunneling I - V characteristics. This ML-based framework can be applied to speed up the quantum transport device simulations and enable efficient tunneling device design.

关键词： Debris flow   Himalayas area   Bayes discriminant analysis   Logistic regression   Random forest   Susceptibility  

摘要： Debris flows destroys the facilities and seriously threatens human lives, especially in mountainous area. Susceptibility mapping is the key for hazard prevention. The aim of the present study is to compare the performance of three methods including Bayes discriminant analysis (BDA), logistic regression (LR) and random forest (RF) for debris flow susceptibility mapping from three aspects: applicability, analyticity and accuracy. Nyalam county, a debris flow-prone area, located in Southern Tibet, was selected as the study area. Firstly, the dataset containing 49 debris flow inventories and 16 conditioning factors was prepared. Subsequently, divided the dataset into two groups with a ratio of 70/30 for training and validation purposes, and repeated 5 times to obtain 5 different groups. Then, 16 factors were involved in the modeling of RF, of which 11 factors with low linear correlation were for BDA and LR. Finally, receiver operating characteristic curves, the area under curve (AUC) and contingency tables were applied to evaluated the accuracy performance of the 3 models. The prediction rates were 74.6-81.8%, 74.6-83.6% and 80-92.7%, for the BDA, LR and FR, while the AUC values of three models were 0.72-0.78, 0.82-0.92 and 0.90-0.99, respectively. Compare to LR an BDA, RF not only effectively process and preserved dataset without priori assumption and the obtained susceptibility zoning map and major factors were reasonable. The conclusion of the current study is useful for risk mitigation and land use planning in the study area and provide related references to other researches.

关键词： Machine learning   Mutation   Position specific mutation   One-hot encoding   Haemophilia   Factor VIII  

摘要： Haemophilia is an X-linked genetic disorder in which A and B types are the most common that occur due to absence or lack of protein factors VIII and IX, respectively. Severity of the disease depends on mutation. Available Machine Learning (ML) methods that predict the mutational severity by using traditional encoding approaches, generally have high time complexity and compromised accuracy. In this study, Haemophilia 'A' patient mutation dataset containing 7784 mutations was processed by the proposed Position-Specific Mutation (PSM) and One-Hot Encoding (OHE) technique to predict the disease severity. The dataset processed by PSM and OHE methods was analyzed and trained for classification of mutation severity level using various ML algorithms. Surprisingly, PSM outperformed OHE, both in terms of time efficiency and accuracy, with training and prediction time improvement in the range of approximately 91 to 98% and 80 to 99% respectively. The severity prediction accuracy also improved by using PSM with different ML algorithms.

关键词： Gene expression analysis   Machine learning   Metastasis   Prostate cancer  

摘要： The present study aimed to identify the genes associated with the involvement of adjunct lymph nodes of patients with prostate cancer (PCa) and to provide valuable information for the identification of potential diagnostic biomarkers and pathological genes in PCa metastasis. The most important candidate genes were identified through several machine learning approaches including K-means clustering, neural network, Naive Bayesian classifications and PCA with or without downsampling. In total, 21 genes associated with lymph nodes involvement were identified. Among them, nine genes have been identified in metastatic prostate cancer, six have been found in the other metastatic cancers and four in other local cancers. The amplification of the candidate genes was evaluated in the other PCa datasets. Besides, we identified a validated set of genes involved in the PCa metastasis. The amplification of SPAG1 and PLEKHF2 genes were associated with decreased survival in patients with PCa.

关键词： australian real house price index   autoregression   forecasting   machine learning   neural networks   time series  

摘要： We employ 47 different algorithms to forecast Australian log real house prices and growth rates, and compare their ability to produce accurate out-of-sample predictions. The algorithms, which are specified in both single- and multi-equation frameworks, consist of traditional time series models, machine learning (ML) procedures, and deep learning neural networks. A method is adopted to compute iterated multistep forecasts from nonlinear ML specifications. While the rankings of forecast accuracy depend on the length of the forecast horizon, as well as on the choice of the dependent variable (log price or growth rate), a few generalizations can be made. For one- and two-quarter-ahead forecasts we find a large number of algorithms that outperform the random walk with drift benchmark. We also report several such outperformances at longer horizons of four and eight quarters, although these are not statistically significant at any conventional level. Six of the eight top forecasts (4 horizons x 2 dependent variables) are generated by the same algorithm, namely a linear support vector regressor (SVR). The other two highest ranked forecasts are produced as simple mean forecast combinations. Linear autoregressive moving average and vector autoregression models produce accurate olne-quarter-ahead predictions, while forecasts generated by deep learning nets rank well across medium and long forecast horizons.

关键词： Relapsed/refractory DLBCL   Imbalanced data   Classification and possibility prediction   Machine learning   Indicators  

摘要： Background: Treatments are limited for patients with relapsed/refractory Diffuse large B-cell lymphoma (DLBCL), and their survival rate is low. Prediction of the recurrence hazard for each patient could provide a reference regarding chemotherapy regimens for clinicians to extend patients' period of long-term remission. As current strategies cannot satisfy such need, we have established predictive models to classify patients with DLBCL with complete remission who had recurrences in 2 years from ones who did not. Methods: We assessed 518 patients with DLBCL and measured 52 variables of each patient. They were treated between January 2011 and July 2016. 17 variables were first selected by variable selection methods (including Lasso, Adaptive Lasso, and Elastic net). Then, we set classifiers and probability models for imbalanced data by combining the SMOTE sampling, cost-sensitive, and ensemble learning (consisting of AdaBoost, voting strategy, and Stacking) methods with the machine learning methods (Support Vector Machine, BackPropagation Artificial Neural Network, Random Forest), respectively. Last, assessed their performance. Results: The disease stage and other 5 variables are significant indicators for recurrence. The SVM with AdaBoost ensemble learning method modeling by SMOTE data performs the best (Sensitivity=97.3%, AUC=96%, RMSE=19.6%, G-mean=96%) in all classifiers. The SVM with AdaBoost method(AUC=98.7%, RMSE=17.7%, MXE=12.7%, Cal mean=3.2%, 1350=2.5%, 1351=4%, BSALL=3.1%) and random forest (AUC=99.5%, RMSE=19.8%, MXE=16.2%, Cal mean=9.1%, 1350=4.8%, 1351=2.9%, BSALL=3.9%) both modeling by SMOTE sampling data perform well in probability models. Conclusions: This predictive model has high accuracy for almost all DLBCL patients and the six indicators can be recurrence signals. (C) 2020 Elsevier B.V. All rights reserved.