Automatically Select Research Articles

Automatic plan selection using deep network-A prostate study.

Recently, high-dose-rate (HDR) brachytherapy treatment plans generation was improved with the development of multicriteria optimization (MCO) algorithms that can generate thousands of pareto optimal plans within seconds. This brings a shift, from the objective of generating an acceptable plan to choosing the best plans out ofthousands. In order to choose the best plans, new criteria beyond usual dosimetrics volumes histogram (DVH) metrics are introduced and a deep learning (DL) framework is added as an automatic plan selectionalgorithm. The new criteria are visual-like criteria implemented for the bladder, rectum, and urethra. One criterion also takes into account the cold spot in the prostate. Those criteria, along with commonly used DVH criteria, are used to form classes on which to train the algorithm. The algorithm is trained with an input of two 3D images, dose and mask of the anatomy, in order to rank and automatically select a plan. The confidence in the output is used for ranking and the automatic plan selection. The algorithm is trained on 835 previously treated prostate cancer patients and evaluated on a separated 20 patients cohort previously evaluated by two experts (clinical medical physicists) in an inter-observer MCOstudy. The deep network takes 10s to rank 2000 plans (vs. 5-10min for experts to rank 4 preferred plans). A total of four different networks are trained which offer different trade-offs. The key trade-offs are the target coverage or the organs at risk (OAR) sparing. The algorithm with the best network achieves no statistical difference with the plans chosen by the two experts for 6 and 9 criteria, respectively, out of 13 criteria (paired t-test with p 0.05) while the two experts have no statistical difference between them for 7criteria. The developed approach is flexible since it allows the modification or addition of criteria to obtain different trade-offs in plan quality, per the institution standard. The approach is fast and robust while adding negligible time to MCO planning. These results demonstrate potential for clinicaluse.

Automating urban soundscape enhancements with AI: In-situ assessment of quality and restorativeness in traffic-exposed residential areas

Formalized in ISO 12913, the “soundscape” approach is a paradigmatic shift towards perception-based urban sound management, aiming to alleviate the substantial socioeconomic costs of noise pollution to advance the United Nations Sustainable Development Goals. Focusing on traffic-exposed outdoor residential sites, we implemented an automatic masker selection system (AMSS) utilizing natural sounds to mask (or augment) traffic soundscapes. We employed a pre-trained AI model to automatically select the optimal masker and adjust its playback level, adapting to changes over time in the ambient environment to maximize “Pleasantness”, a perceptual dimension of soundscape quality in ISO 12913. Our validation study involving (N=68) residents revealed a significant 14.6% enhancement in “Pleasantness” after intervention, correlating with increased restorativeness and positive affect. Perceptual enhancements at the traffic-exposed site matched those at a quieter control site with 6dB(A) lower LA,eq and road traffic noise dominance, affirming the efficacy of AMSS as a soundscape intervention, while streamlining the labour-intensive assessment of “Pleasantness” with probabilistic AI prediction.

Smoothed empirical likelihood estimation and automatic variable selection for an expectile high-dimensional model

We consider a linear model which can have a large number of explanatory variables, the errors with an asymmetric distribution or the values of the explained variable are missing at random. In order to take in account these several situations, we consider the non parametric empirical likelihood (EL) estimation method. Because a constraint in EL contains an indicator function then a smoothed function instead of the indicator will be considered. Two smoothed expectile maximum EL methods are proposed, one of which will automatically select the explanatory variables. For each of the methods we obtain the convergence rate of the estimators and their asymptotic normality. The smoothed expectile empirical log-likelihood ratio process follow asymptotically a chi-square distribution and moreover the adaptive LASSO smoothed expectile maximum EL estimator satisfies the sparsity property which guarantees the automatic selection of zero model coefficients. In order to implement these methods, we propose four algorithms.

Suppression of negative transfer in motor imagery brain-computer interface based on mutual information and Pearson correlation coefficient.

The focus of this paper is on the main challenges in brain-computer interface transfer learning: how to address data characteristic length and the source domain sample selection problems caused by individual differences. To overcome the negative migration that results from feature length, we propose a migration algorithm based on mutual information transfer (MIT), which selects effective features by calculating the entropy value of the probability distribution and conditional distribution, thereby reducing negative migration and improving learning efficiency. Source domain participants who differ too much from the target domain distribution can affect the overall classification performance. On the basis of MIT, we propose the Pearson correlation coefficient source domain automatic selection algorithm (PDAS algorithm). The PDAS algorithm can automatically select the appropriate source domain participants according to the target domain distribution, which reduces the negative migration of participant data among the source domain participants, improves experimental accuracy, and greatly reduces training time. The two proposed algorithms were tested offline and online on two public datasets, and the results were compared with those from existing advanced algorithms. The experimental results showed that the MIT algorithm and the MIT + PDAS algorithm had obvious advantages.

Fuzzy Classification Approach to Select Learning Objects Based on Learning Styles in Intelligent E-Learning Systems

In e-learning systems, even though the automatic detection of learning styles is considered the key element in the adaptation process, it does not represent the main goal of this process at all. Indeed, to accomplish the task of adaptation, it is also necessary to be able to automatically select the learning objects according to the detected styles. The classification techniques are the most used techniques to automatically select the learning objects by processing data derived from learning object metadata. By using these classification techniques, considerable results are obtained via several approaches and consist of mapping the learning objects into different teaching strategies and then mapping these strategies into the identified learning styles. However, these approaches have some limitations related to robustness. Indeed, a common feature of these approaches is that they do not directly map learning object metadata elements to learning style dimensions. Moreover, they do not consider the fuzzy nature of learning objects. Indeed, any learning object can be suitable for different learning styles at varying degrees of suitability. This highlights the need to find a way to remedy this shortcoming. Our work is part of the automatic selection of learning objects. So, we will propose an approach that uses the fuzzy classification technique to select learning objects based on learning styles. In this approach, the metadata of each learning object that complies with the Institute of Electrical and Electronics Engineers (IEEE) standard are stored in a database as an Extensible Markup Language (XML) file. The Fuzzy C Means algorithm is used, on one hand, to assign fuzzy suitability rates to the stored learning objects and, on the other hand, to cluster them into the Felder and Silverman learning styles model categories. The experiment results show the performance of our approach.

Automatic Selection and Parameter Optimization of Mathematical Models Based on Machine Learning

With the rapid progress of machine learning (ML) technology, more and more ML algorithms have emerged, and the complexity of models is also constantly increasing. This development trend brings two significant challenges in practice: how to choose appropriate algorithm models and how to optimize hyperparameters for these models. In this context, the concept of Automatic Machine Learning (AutoML) has emerged. Due to the applicability of different algorithm models to different data types and problem scenarios, it is crucial to automatically select the most suitable model based on the characteristics of specific tasks. AutoML integrates multiple ML algorithms and automatically filters based on the statistical characteristics of data and task requirements, aiming to provide users with the best model selection solution. Hyperparameters are parameters that ML models need to set before training, such as learning rate, number of iterations, regularization strength, etc., which have a significant impact on the performance of the model. AutoML integrates advanced hyperparameter optimization techniques to automatically find the optimal parameter combination, thereby improving the model's generalization ability and prediction accuracy. This article studies the automatic selection and parameter optimization of mathematical models based on ML.

Automatic deep learning method for third lumbar selection and body composition evaluation on CT scans of cancer patients.

The importance of body composition and sarcopenia is well-recognized in cancer patient outcomes and treatment tolerance, yet routine evaluations are rare due to their time-intensive nature. While CT scans provide accurate measurements, they depend on manual processes. We developed and validated a deep learning algorithm to automatically select and segment abdominal muscles [SM], visceral fat [VAT], and subcutaneous fat [SAT] on CT scans. A total of 352 CT scans were collected from two cancer centers. The detection of the third lumbar vertebra and three different body tissues (SM, VAT, and SAT) were annotated manually. The 5-fold cross-validation method was used to develop the algorithm and validate its performance on the training cohort. The results were validated on an external, independent group of CT scans. The algorithm for automatic L3 slice selection had a mean absolute error of 4 mm for the internal validation dataset and 5.5 mm for the external validation dataset. The median DICE similarity coefficient for body composition was 0.94 for SM, 0.93 for VAT, and 0.86 for SAT in the internal validation dataset, whereas it was 0.93 for SM, 0.93 for VAT, and 0.85 for SAT in the external validation dataset. There were high correlation scores with sarcopenia metrics in both internal and external validation datasets. Our deep learning algorithm facilitates routine research use and could be integrated into electronic patient records, enhancing care through better monitoring and the incorporation of targeted supportive measures like exercise and nutrition.

Markov State Models: To Optimize or Not to Optimize.

Markov state models (MSM) are a popular statistical method for analyzing the conformational dynamics of proteins including protein folding. With all statistical and machine learning (ML) models, choices must be made about the modeling pipeline that cannot be directly learned from the data. These choices, or hyperparameters, are often evaluated by expert judgment or, in the case of MSMs, by maximizing variational scores such as the VAMP-2 score. Modern ML and statistical pipelines often use automatic hyperparameter selection techniques ranging from the simple, choosing the best score from a random selection of hyperparameters, to the complex, optimization via, e.g., Bayesian optimization. In this work, we ask whether it is possible to automatically select MSM models this way by estimating and analyzing over 16,000,000 observations from over 280,000 estimated MSMs. We find that differences in hyperparameters can change the physical interpretation of the optimization objective, making automatic selection difficult. In addition, we find that enforcing conditions of equilibrium in the VAMP scores can result in inconsistent model selection. However, other parameters that specify the VAMP-2 score (lag time and number of relaxation processes scored) have only a negligible influence on model selection. We suggest that model observables and variational scores should be only a guide to model selection and that a full investigation of the MSM properties should be undertaken when selecting hyperparameters.

A method for the systematic selection of enzyme panel candidates by solving the maximum diversity problem

Enzymes are being increasingly exploited for their potential as industrial biocatalysts. Establishing a portfolio of useful biocatalysts from large and diverse protein family is challenging and a systematic method for candidate selection promises to aid in this task. Moreover, accurate enzyme functional annotation can only be confidently guaranteed through experimental characterisation in the laboratory. The selection of catalytically diverse enzyme panels for experimental characterisation is also an important step for shedding light on the currently unannotated proteins in enzyme families. Current selection methods often lack efficiency and scalability, and are usually non-systematic. We present a novel algorithm for the automatic selection of subsets from enzyme families. A tabu search algorithm solving the maximum diversity problem for sequence identity was designed and implemented, and applied to three diverse enzyme families. We show that this approach automatically selects panels of enzymes that contain high richness and relative abundance of the known catalytic functions, and outperforms other methods such as k-medoids.

Attentive transformer deep learning algorithm for intrusion detection on IoT systems using automatic Xplainable feature selection.

Recent years have witnessed an in-depth proliferation of the Internet of Things (IoT) and Industrial Internet of Things (IIoT) systems linked to Industry 4.0 technology. The increasing rate of IoT device usage is associated with rising security risks resulting from malicious network flows during data exchange between the connected devices. Various security threats have shown high adverse effects on the availability, functionality, and usability of the devices among which denial of service (DoS) and distributed denial of service (DDoS), which attempt to exhaust the capacity of the IoT network (gateway), thereby causing failure in the functionality of the system have been more pronounced. Various machine learning and deep learning algorithms have been used to propose intelligent intrusion detection systems (IDS) to mitigate the challenging effects of these network threats. One concern is that although deep learning algorithms have shown good accuracy results on tabular data, not all deep learning algorithms can perform well on tabular datasets, which happen to be the most commonly available format of datasets for machine learning tasks. Again, there is also the challenge of model explainability and feature selection, which affect model performance. In this regard, we propose a model for IDS that uses attentive mechanisms to automatically select salient features from a dataset to train the IDS model and provide explainable results, the TabNet-IDS. We implement the proposed model using the TabNet algorithm based on PyTorch which is a deep-learning framework. The results obtained show that the TabNet architecture can be used on tabular datasets for IoT security to achieve good results comparable to those of neural networks, reaching an accuracy of 97% on CIC-IDS2017, 95% on CSE-CICIDS2018 and 98% on CIC-DDoS2019 datasets.

A real-time automatic fire emergency evacuation route selection model based on decision-making processes of pedestrians

After a fire occurs, it is imperative that people in danger evacuate as soon as possible. However, the current emergency plan based on the pre-established static exiting route is unable to considering the real-time fire scenario. In addition, the selection of evacuation routes significantly relies on the decision-maker's experiences. These issues seriously affect evacuation efficiency, decreasing the likelihood of survival. This paper developed an effective real-time evacuation guidance method that can automatically select the evacuation route in accordance with real-time fire scenarios. The model is established based on the on-policy learning algorithm SARSA (State–action–reward–state–action), an algorithm for learning a Markov decision process policy, which could mimic the decision-making of pedestrian behaviors in an emergency. In addition, two types of radar (exit radar and fire radar) are introduced into the SARSA algorithm to facilitate the wayfinding process, which formulated the so-called Radar-assisted SARSA (RSARSA). The results have shown that RSARSA can swiftly decide a safer evacuation route for pedestrians or crowd at arbitrary location. The convergence time of initial successful route planning is between 0.05 and 4.5 s under the tests in this paper. The evacuation route determined by this algorithm can well consider the fire, and timely avoid routes with potential dangerous. Moreover, RSARSA can flexibly respond to different fires under various heat release rates and development speeds. By applying this technology, fire evacuation can be guided by routes that are more attuned to the mindset of pedestrians. It can provide a good basis for route selection of crowd evacuation.

A Bayesian deep learning pipeline for lithium‐ion battery SOH estimation with uncertainty quantification

AbstractIn recent years, deep learning (DL) methods for state of health (SOH) estimation of lithium‐ion (Li‐ion) batteries have attracted great attention. However, most existing DL‐based methods for SOH estimation were designed using specific battery dataset. To gain better performance, the procedure can be labor‐intensive, involving designing intricate features and fine‐tuning complex DL model, which significantly limits the applications of these methods. Moreover, uncertainty quantification, that is how much uncertainty these DL‐based methods can have on their SOH estimation, cannot be addressed with only point estimation. To this end, this paper proposes a Bayesian DL pipeline for Li‐ion battery SOH estimation targeting at automatic feature extraction and uncertainty‐aware DL‐model developing for general application to batteries under different working conditions. The proposed pipeline is composed of three modules accounting for common feature extraction, automatic feature selection, and Bayesian neural network (BNN) model construction. Specifically, a frequently‐used feature set is designed using the voltage, current and time data of constant current and constant voltage (CC‐CV) charging mode, while the recursive feature elimination (RFE) with cross validation (RFECV) algorithm is used to automatically select features based on relevance. Based on the automatically selected features, DL model is automatically constructed without too much human intervention. The uncertainty of SOH estimation is quantified by BNN model using Monte Carlo (MC) dropout method, which is subsequently used to calculate the confidence of the estimation results. Four battery datasets, covering regular charging, fast charging, and second‐use applications, are used to demonstrate the performance of the proposed method. The proposed pipeline shows superior performance to state‐of‐art DL‐based methods in terms of application capability to different working conditions and uncertainty quantification for SOH estimation.

Automatic feature selection for performing Unit 2 of vault in wheel gymnastics.

We propose a framework to analyze the relationship between the movement features of a wheel gymnast around the mounting phase of Unit 2 of the vault event and execution (E-score) deductions from a machine-learning perspective. We first developed an automation system from a video of a wheel gymnast performing a tuck-front somersault to extract the four frames highlighting its Unit 2 performance of the vault event, such as take-off, pike-mount, the starting point of time on the wheel, and final position before the thrust. We implemented this automation using recurrent all-pairs field transforms (RAFT) and XMem, i.e., deep network architectures respectively for optical flow estimation and video object segmentation. We then used a markerless pose-estimation system called OpenPose to acquire the coordinates of the gymnast's body joints, such as shoulders, hips, and knees then calculate the joint angles at the extracted video frames. Finally, we constructed a regression model to estimate the E-score deductions during Unit 2 on the basis of the joint angles using an ensemble learning algorithm called Random Forests, with which we could automatically select a small number of features with the nonzero values of feature importances. By applying our framework of markerless motion analysis to videos of male wheel gymnasts performing the vault, we achieved precise estimation of the E-score deductions during Unit 2 with a determination coefficient of 0.79. We found the two movement features of particular importance for them to avoid significant deductions: time on the wheel and angles of knees at the pike-mount position. The selected features well reflected the maturity of the gymnast's skills related to the motions of riding the wheel, easily noticeable to the judges, and their branching conditions were almost consistent with the general vault regulations.

Automated isocenter optimization approach for treatment planning for gyroscopic radiosurgery.

Radiosurgery is a well-established treatment for various intracranial tumors. In contrast to other established radiosurgery platforms, the new ZAP-X® allows for self-shielding gyroscopic radiosurgery. Here, treatment beams with variable beam-on times are targeted towards a small number of isocenters. The existing planning framework relies on a heuristic based on random selection or manual selection of isocenters, which often leads to a higher plan quality in clinical practice. The purpose of this work is to study an improved approach for radiosurgery treatment planning, which automatically selects the isocenter locations for the treatment of brain tumors and diseases in the head and neck area using the new system ZAP-X® . We propose a new method to automatically obtain the locations of the isocenters, which are essential in gyroscopic radiosurgery treatment planning. First, an optimal treatment plan is created based on a randomly selected nonisocentric candidate beam set. The intersections of the resulting subset of weighted beams are then clustered to find isocenters. This approach is compared to sphere-packing, random selection, and selection by an expert planner for generating isocenters. We retrospectively evaluate plan quality on 10 acoustic neuroma cases. Isocenters acquired by the method of clustering result in clinically viable plans for all 10 test cases. When using the same number of isocenters, the clustering approach improves coverage on average by 31 percentage points compared to random selection, 15 percentage points compared to sphere packing and 2 percentage points compared to the coverage achieved with the expert selected isocenters. The automatic determination of location and number of isocenters leads, on average, to a coverage of 97 ± 3% with a conformity index of 1.22 ± 0.22, while using 2.46 ± 3.60 fewer isocenters than manually selected. In terms of algorithm performance, all plans were calculated in less than 2 min with an average runtime of 75 ± 25s. This study demonstrates the feasibility of an automatic isocenter selection by clustering in the treatment planning process with the ZAP-X® system. Even in complex cases where the existing approaches fail to produce feasible plans, the clustering method generates plans that are comparable to those produced by expert selected isocenters. Therefore, our approach can help reduce the effort and time required for treatment planning in gyroscopicradiosurgery.

EFFECT: Explainable framework for meta-learning in automatic classification algorithm selection

With the growing convergence of artificial intelligence and daily life scenarios, the application scenarios for intelligent decision methods are becoming increasingly complex. The development of various machine learning algorithms has benefited all disciplines of study, but choosing which algorithm is most suitable for a certain problem among a large number of algorithms is a challenge that every field must overcome. Another challenge at the practical application level is that machine learning algorithms currently trained with large amounts of data are primarily black-box and uninterpretable. This indicates that these methods pose potential risks and are difficult to rely on, thus hindering their application in sensitive fields such as finance and healthcare. The first challenge can be overcome by using meta-learning to combine data and prior knowledge to efficiently and automatically select the machine learning models. The second challenge remains to be addressed due to the lack of interpretability of traditional meta-learning techniques and deficiencies in transparency and fairness. Achieving the interpretability of meta-learning in autonomous algorithm selection for classification is crucial to balance the need for high accuracy and transparency of machine learning models in practical application scenarios. This paper proposes EFFECT, an interpretable meta-learning framework that can explain the recommendation results of meta-learning algorithm selection and provide a more complete and accurate explanation of the recommendation algorithm’s performance on specific datasets combined with business scenarios. Extensive experiments have demonstrated the validity and correctness of this framework.

A new algorithm for support vector regression with automatic selection of hyperparameters

The hyperparameters in support vector regression (SVR) determine the effectiveness of the support vectors with fitting and predictions. However, the choice of these hyperparameters has always been challenging in both theory and practice. The ν-support vector regression eliminates the need to specify an ϵ value elegantly, but at the cost of specifying or postulating a ν value. We propose an extended primal objective function arising from probability regularization leading to an automatic selection of ϵ, and we can express ν as an explicit function of ϵ. The resultant hyperparameter values can be interpreted as ‘working’ values required only in training but not testing or prediction. This regularized algorithm, namely ϵ*-SVR, automatically provides a data-dependent ϵ and is found to have a close connection to the ν-support vector regression in the sense that ν as a fraction is a sensible function of ϵ. The ϵ*-SVR automatically selects both ν and ϵ values. We illustrate these findings with some public benchmark datasets.

Multi-task Gaussian process upper confidence bound for hyperparameter tuning and its application for simulation studies of additive manufacturing

In many scientific and engineering applications, Bayesian Optimization (BO) is a powerful tool for hyperparameter tuning of a machine learning model, materials design and discovery, etc. Multi-task BO is a general method to efficiently optimize multiple different, but correlated, “black-box” functions. The objective of this work is to develop an algorithm for multi-task BO with automatic task selection so that only one task evaluation is needed per query round. Specifically, a new algorithm, namely, Multi-Task Gaussian Process Upper Confidence Bound (MT-GPUCB), is proposed to achieve this objective. The MT-GPUCB is a two-step algorithm, where the first step chooses which query point to evaluate, and the second step automatically selects the most informative task to evaluate. Under the bandit setting, a theoretical analysis is provided to show that our proposed MT-GPUCB is no-regret under some mild conditions. Our proposed algorithm is verified experimentally on a range of synthetic functions. In addition, our algorithm is applied to Additive Manufacturing simulation software, namely, Flow-3D Weld, to determine material property values, ensuring the quality of simulation output. The results clearly show the advantages of our query strategy for both design point and task.

A Graph Adaptive Density Peaks Clustering algorithm for automatic centroid selection and effective aggregation

As a clustering approach based on density, Density Peaks Clustering algorithm (DPC) has conspicuous superiorities in searching and finding density peaks. Nevertheless, DPC has obvious deficiencies in centroid selection and aggregation process affected by differences in data shape and density distribution, which can easily cause problems in centroid selection and trigger domino effect. Therefore, a Graph Adaptive Density Peaks Clustering algorithm based on Graph Theory (called GADPC) is proposed to automatically select centroid and aggregate more effectively. The improvement of GADPC can be subdivided into the two steps. First, the clustering centroids are automatically selected based on the turning angle θ and the graph connectivity of centroids. Second, the remaining points are aggregated towards the corresponding clustering centroid. According to the improved principle, they belong to the closer point which has stronger graph connectivity and higher density. Theoretical analyses and experimental data indicate that GADPC, compared with DBSCAN, K-means and DPC, is more feasible and effective in processing some data sets with varying density and non-spherical distribution such as Jain and Spiral.

Automatic Interferogram Selection for SBAS-InSAR Based on Deep Convolutional Neural Networks

The small baseline subset of spaceborne interferometric synthetic aperture radar (SBAS-InSAR) technology has become a classical method for monitoring slow deformations through time series analysis with an accuracy in the centimeter or even millimeter range. Thereby, the selection of high-quality interferograms calculated is one of the key operations for the method, since it mainly determines the credibility of the deformation information. Especially in the era of big data, the demand for an automatic and effective selection method of high-quality interferograms in SBAS-InSAR technology is growing. In this paper, a deep convolutional neural network (DCNN) for automatichigh-quality interferogram selection is proposed that provides more efficient image feature extraction capabilities and a better classification performance. Therefore, the ResNet50 (a kind of DCNN) is used to identify and delete interferograms that are severely contaminated. According to simulation experiments and calculated Sentinel-1A data of Shenzhen, China, the proposed approach can significantly separate interferograms affected by turbulences in the atmosphere and by the decorrelation phase. The remarkable performance of the DCNN method is validated by the analysis of the standard deviation of interferograms and the local deformation information compared with the traditional selection method. It is concluded that DCNN algorithms can automatically select high quality interferogram for the SBAS-InSAR method and thus have a significant impact on the precision of surface deformation monitoring.

Automatic layer selection for transfer learning and quantitative evaluation of layer effectiveness

The performance of transfer learning in convolutional neural networks depends on the selection of which layer to update and fix. Because the number of layers is increasing, it is becoming increasingly difficult for humans to select layers. Therefore, in this study, we propose a method to automatically select effective update layers for transfer learning using a genetic algorithm. In our experiments, we conducted transfer learning from InceptionV3 pretrained with ImageNet to Canadian Institute for Advanced Research-100 dataset, The Street View House Numbers dataset and Food-101 dataset. We found that the test accuracy obtained by an ensemble of models selected by the genetic algorithm was greater than that obtained by from-scratch and fine-tuning for all target dataset. The distribution of the layers selected by the genetic algorithm as effective update layers was spread over the entire network. We also employed the optimal transport distance to evaluate whether each convolutional layer is an effective update layer for transfer learning. In our experiments, we compared the layer importance values and the accuracy of transfer learning. The layer importance was then correlated with the test accuracy of transfer learning, and the results demonstrate that the proposed method can quantitatively evaluate how well each network layer can detect general features in the target datasets.