关键词： Structural dynamics   Physics-guided machine learning   Data-driven machine learning   Recurrent neural network  

摘要： Physics-guided machine learning (ML) is an emerging paradigm that combines both data-driven ML models and physics-based models together to fully take advantage of the data discovery ability of ML without losing the valuable physics/domain knowledge. This paper proposes a novel physics-guided ML method based on recurrent neural network (RNN) and multilayer perceptron (MLP) for structural dynamics simulation. The key idea is to integrate the underlying physics of structural dynamics into data-enabled ML models to `guide' the training and prediction of ML models. First, structural dynamics formulation and the use of data-driven RNN and MLP for modeling dynamical systems are briefly reviewed, which leads to the development of the proposed physics-guided ML model. Physics-guided ML model contains physics-based layers to encode the known physics and data-driven layers to approximate the unknown relationships. Thus, the data-driven RNN and MLP are augmented with existing physics knowledge for better performance in simulations. Following this, several numerical case studies for structural dynamics are presented to demonstrate the proposed methodology. It is observed that: (1) compared with purely data-driven ML method, the proposed physics-guided ML method has better generalization ability and reduced training costs;(2) compared with physics-based modeling, the proposed method has improved computational efficiency and can handle partially unknown physics. Finally, conclusions and future works are presented based on the proposed study.

关键词： image classification   feature extraction   biological tissues   support vector machines   medical image processing   learning (artificial intelligence)   pattern classification   cancer   permittivity   normal tissues   malignant colorectal tissues   dielectric properties   machine learning methods   malignant tissues   computer-aided diagnostic technologies   neighbour   support vector machine classifiers  

摘要： Numerous researchers approved discrepancies in dielectric properties between malignant and normal tissues. Such discrepancies serve as a foundation for the development of computer-aided diagnostic technologies. In this study, machine learning methods were proposed for discrimination between normal and malignant colorectal tissues based on discrepancies in their dielectric properties. To do so, first, two independent-sample t-tests and receiver operating characteristic curve analysis were utilised to examine discrimination power with respect to three types of features, namely, permittivity, conductivity and Cole-Cole fitting parameters. K-nearest neighbour and support vector machine classifiers were used to assess the possibility of combining these features for better classification accuracy. Obtained k-fold cross-validation accuracy reached 88.2%. The obtained accuracy indicated the potential capability of discrimination between normal and malignant colorectal tissues based on discrepancies in their dielectric properties.

关键词： Diffusion   Activation energy   Machine learning   Support vector machine  

摘要： We evaluate the performance of a popular machine learning (ML) method support vector machine (SVM) for modeling and predicting the solute diffusion activation energies in fcc, bcc, and hcp metallic hosts. The diffusion activation energies of 408 host-solute systems from ab-initio calculations are made as our dataset. We obtain an optimal set of features by combining prior physics knowledge and combination ranking based on the LeaveGroup-Out (LOG) cross-validation (CV) method, including solute migration barrier, atomic volume of host, elastic modulus of host, melting point of host, unpaired d electrons of host, and the corresponding parameters of solute. We present the results of LOG/10-fold/5-fold/3-fold CV, with the corresponding root mean squared error (RMSE) of 0.128/0.106 +/- 0.014/0.107 +/- 0.004/0.110 +/- 0.005 eV. SVM gives an about 0.1 eV errors when extrapolating to new host-solute systems for main hosts. We further make predictions on the activation energies of thousands of new systems with quite small computational cost. Our work demonstrates that the ML method is a promising method to accelerate materials science researches.

关键词： Streamflow forecast   Artificial neural network   Long short term memory   Long lead-time   Machine learning  

摘要： Long lead-time streamflow forecasting is of great significance for water resources planning and management in both the short and long terms. Despite of some studies using machine learning methods in streamflow forecasting, only few studies have been conducted to explore long lead-time forecasting capabilities of these methods, and gain an insight into systematic comparison of model forecasting performance in both the short and long terms. In this work, an artificial neural network (ANN) and a long short term memory (LSTM), a powerful tool for learning long-term temporal dependencies and capturing nonlinear relationship, have been adopted to forecast streamflow at daily and monthly scales for a long lead-time period. For long lead-time streamflow forecasting, a recursive forecasting procedure, which takes the last one-step-ahead forecast as a new input for the next-step-ahead forecast, is used in the ANN and LSTM forecasting systems. Two models are trained and validated for streamflow forecasting using the rainfall and runoff datasets collected from the Nan River Basin and Ping River Basin, Thailand, covering the period 1974 to 2014. To further explore the impact of parameter settings on model performance, two parameters, i.e. the length of time lag and the number of maximum epochs, are examined in the ANN and LSTM models. The main findings are highlighted here. First, with an optimal setting up of model parameters, both the ANN and LSTM model can provide accurate daily forecasting (up to 20 days ahead). Second, in comparison to the ANN model, the LSTM model exhibits better model performance in long lead-time daily forecasting, but less satisfactory in multi-monthly forecasting due to lack of large monthly training dataset. Third, the selection of the length of the time lag and number of maximum epochs used in both ANN and LSTM modelling are the key for long lead-time streamflow forecasting at daily and monthly scales. These findings suggest that the LSTM could be ad

关键词： machine learning   drug discovery   deep learning   in silico screening  

摘要： The advancements of information technology and related processing techniques have created a fertile base for progress in many scientific fields and industries. In the fields of drug discovery and development, machine learning techniques have been used for the development of novel drug candidates. The methods for designing drug targets and novel drug discovery now routinely combine machine learning and deep learning algorithms to enhance the efficiency, efficacy, and quality of developed outputs. The generation and incorporation of big data, through technologies such as high-throughput screening and high through-put computational analysis of databases used for both lead and target discovery, has increased the reliability of the machine learning and deep learning incorporated techniques. The use of these virtual screening and encompassing online information has also been highlighted in developing lead synthesis pathways. In this review, machine learning and deep learning algorithms utilized in drug discovery and associated techniques will be discussed. The applications that produce promising results and methods will be reviewed.

关键词： family designs   gene-gene interaction   ischemic stroke   sparse factor analysis   unsupervised machine learning  

摘要： Gene-gene interaction (G x G) is thought to fill the gap between the estimated heritability of complex diseases and the limited genetic proportion explained by identified single-nucleotide polymorphisms. The current tools for exploring G x G were often developed for case-control designs with less considerations for their applications in families. Family-based studies are robust against bias led from population stratification in genetic studies and helpful in understanding G x G. We proposed a new algorithm epistasis sparse factor analysis (EPISFA) and epistasis sparse factor analysis for linkage disequilibrium (EPISFA-LD) based on unsupervised machine learning to screen G x G. Extensive simulations were performed to compare EPISFA/EPISFA-LD with a classical family-based algorithm FAM-MDR (family-based multifactor dimensionality reduction). The results showed that EPISFA/EPISFA-LD is a tool of both high power and computational efficiency that could be applied in family designs and is applicable within high-dimensionality datasets. Finally, we applied EPISFA/EPISFA-LD to a real dataset drawn from the Fangshan/family-based Ischemic Stroke Study in China. Five pairs of G x G were discovered by EPISFA/EPISFA-LD, including three pairs verified by other algorithms (FAM-MDR and logistic), and an additional two pairs uniquely identified by EPISFA/EPISFA-LD only. The results from EPISFA might offer new insights for understanding the genetic etiology of complex diseases. EPISFA/EPISFA-LD was implemented in R. All relevant source code as well as simulated data could be freely downloaded from .

关键词： Visible Light Positioning   robust   machine learning   multi layer perceptron   gaussian processes  

摘要： In this work, the use of Machine Learning methods for robust Received Signal Strength (RSS)-based Visible Light Positioning (VLP) is experimentally evaluated. The performance of Multilayer Perceptron (MLP) models and Gaussian processes (GP) is investigated when using relative RSS input features. The experimental set-up for the RSS-based VLP technology uses light-emitting diodes (LEDs) transmitting intensity modulated light and a single photodiode (PD) as a receiver. The experiments focus on achieving robustness to cope with unknown received signal strength modifications over time. Therefore, several datasets were collected, where per dataset either the LEDs transmitting power is modified or the PD aperture is partly obfuscated by dust particles. Two relative RSS schemes are investigated. The first scheme uses the maximum received light intensity to normalize the received RSS vector, while the second approach obtains RSS ratios by combining all possible unique pairs of received intensities. The Machine Learning (ML) methods are compared to a relative multilateration implementation. It is demonstrated that the adopted MLP and GP models exhibit superior performance and higher robustness when compared to the multilateration strategies. Furthermore, when comparing the investigated ML models, the GP model is proven to be more robust than the MLP for the considered scenarios.

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