Seismic reliability analysis of building structures using subset simulation coupled with deep learning-based surrogate model

To identify the feasibility of deep learning-based diagnostic models for detecting and assessing lower-extremity fatigue fracture severity on plain radiographs. This retrospective study enrolled 1151 X-ray images (tibiofibula/foot: 682/469) of fatigue fractures and 2842 X-ray images (tibiofibula/foot: 2000/842) without abnormal presentations from two clinical centers. After labeling the lesions, images in a center (tibiofibula/foot: 2539/1180) were allocated at 7:1:2 for model construction, and the remaining images from another center (tibiofibula/foot: 143/131) for external validation. A ResNet-50 and a triplet branch network were adopted to construct diagnostic models for detecting and grading. The performances of detection models were evaluated with sensitivity, specificity, and area under the receiver operating characteristic curve (AUC), while grading models were evaluated with accuracy by confusion matrix. Visual estimations by radiologists were performed for comparisons with models. For the detection model on tibiofibula, a sensitivity of 95.4%/85.5%, a specificity of 80.1%/77.0%, and an AUC of 0.965/0.877 were achieved in the internal testing/external validation set. The detection model on foot reached a sensitivity of 96.4%/90.8%, a specificity of 76.0%/66.7%, and an AUC of 0.947/0.911. The detection models showed superior performance to the junior radiologist, comparable to the intermediate or senior radiologist. The overall accuracy of the diagnostic model was 78.5%/62.9% for tibiofibula and 74.7%/61.1% for foot in the internal testing/external validation set. The deep learning-based models could be applied to the radiological diagnosis of plain radiographs for assisting in the detection and grading of fatigue fractures on tibiofibula and foot. • Fatigue fractures on radiographs are relatively difficult to detect, and apt to be misdiagnosed. • Detection and grading models based on deep learning were constructed on a large cohort of radiographs with lower-extremity fatigue fractures. • The detection model with high sensitivity would help to reduce the misdiagnosis of lower-extremity fatigue fractures.

The violence detection is mostly achieved through handcrafted feature descriptors, while some researchers have also employed deep learning-based representation models for violent activity recognition. Deep learning-based models have achieved encouraging results for fight activity recognition on benchmark data sets such as hockey and movies. However, these models have limitations in learning discriminating features for violence activity classification with abrupt camera motion. This research work investigated deep representation models using transfer learning for handling the issue of abrupt camera motion. Consequently, a novel deep multi-net (DMN) architecture based on AlexNet and GoogleNet is proposed for violence detection in videos. AlexNet and GoogleNet are top-ranked pre-trained models for image classification with distinct pre-learnt potential features. The fusion of these models can yield superior performance. The proposed DMN unleashed the integrated potential by concurrently coalescing both networks. The results confirmed that DMN outperformed state-of-the-art methods by learning finest discriminating features and achieved 99.82% and 100% accuracy on hockey and movies data sets, respectively. Moreover, DMN has faster learning capability i.e. 1.33 and 2.28 times faster than AlexNet and GoogleNet, which makes it an effective learning architecture on images and videos.

COVID-19 pandemic has indeed plunged the global community especially African countries into an alarming difficult situationculminating into a great deal amounts of catastrophes such as economic recession, political instability and loss of jobs. Thepandemic spreads exponentially and causes loss of lives. Following the outbreak of the omicron new variant of concern,forecasting and identification of the COVID-19 infection cases is very vital for government at various levels. Hence, havingknowledge of the spread at a particular point in time, swift actions can be taken by government at various levels with a view toaccordingly formulate new policies and modalities towards minimizing the trajectory of the consequences of COVID-19 pandemicto both public health and economic sectors. Here, a potent combination of Convolutional Neural Network (CNN) learning algorithm along with Long Short Term Memory(LSTM) learning algorithm has been proposed in this work in order to produce a hybrid of a deep learning algorithm ConvolutionalNeural Network - Long Short Term Memory (CNN-LSTM) for forecasting COVID-19 infection cases particularly in Nigeria, SouthAfrica and Botswana. Forecasting models for COVID-19 infection cases in Nigeria, South Africa and Botswana, were developedfor 10 days using deep learning-based approaches namely CNN, LSTM and CNN-LSTM deep learning algorithm respectively. The models were evaluated on the basis of four standard performance evaluation metrics which include accuracy, MSE, MAEand RMSE respectively. However, the CNN-LSTM deep learning-based forecasting model achieved the best accuracy of98.30%, 97.60%, and 97.74% for Nigeria, South Africa and Botswana respectively; and in the same manner, achieved lesserMSE, MAE and RMSE values compared to models developed with CNN and LSTM respectively. Taken together, the CNN-LSTM deep learning-based forecasting model for COVID-19 infection cases in Nigeria, South Africaand Botswana dramatically surpasses the two other DL based forecasting models (CNN and LSTM) for COVID-19 infectioncases in Nigeria, South Africa and Botswana in terms of not only the best accuracy of with 98.30%, 97.60%, and 97.74% but alsoin terms of lesser MSE, MAE and RMSE.

Despite significant advancements in deep learning-based DOA estimation, such models exhibit critical vulnerabilities to adversarial attacks. Previous studies of attacks on DOA estimation models typically require full knowledge of the target model, which is impractical in real-world black-box scenarios. In this paper, we explore two black-box adversarial attack strategies for deep learning-based DOA models: transfer-based and decision-based attacks. Decision-based attacks iteratively generate adversarial samples by querying the model’s hard-label predictions, while transfer-based attacks craft perturbations using surrogate models with accessible architectures and data. We systematically investigate how the architecture, depth, and input of the surrogate model affect attack transferability. Under an 8-element uniform linear array setting, experimental results reveal that both strategies can substantially degrade the performance of deep learning-based DOA models and reach attack success rates above 88% in black-box settings. Crucially, we further demonstrate that adversarial samples designed for deep learning models also impair classical subspace-based DOA estimation methods, yielding RMSE values that are 1.39 to 50.77 times higher than those under AWGN, depending on the attack type.

Accurate and effective classification of plant diseases is critical for increasing yield and quality in agricultural production, minimizing economic losses through early detection of diseases, and implementing sustainable agriculture approaches. In this study, an approach for the detection and classification of plant leaf diseases is presented. We compare the performance of machine learning and deep learning-based models, and we use GAN-based data synthesis methods on a dataset we created to improve the model performance. For machine learning methods, ResNet-based feature extraction is performed and XGBoost, Random Forest, SVM, and InceptionV3 models are evaluated. In contrast, AlexNet, VGG16, VGG19, DenseNet, and ResNet models are examined within the scope of deep learning. The study was analyzed in three classes Phytophthora Infestans, Potassium Deficiency, and Healthy, and tested on data obtained from 21 different plant species. According to the model performances obtained, the deep learning-based ResNet model showed the highest success in all performance metrics and achieved 99% accuracy, showing superior performance compared to other methods. In the study, a comprehensive evaluation of multiple classification, GAN-based data synthesis, machine learning, and deep learning models was carried out. A valuable contribution was made to the existing studies in the literature.

Heart failure is a clinical syndrome that occurs when the heart is too weak or stiff and cannot pump enough blood that our body needs. It is one of the most expensive diseases due to frequent hospitalizations and emergency room visits. Reducing unnecessary rehospitalizations is also an important and challenging task that has the potential of saving healthcare costs, enabling discharge planning, and identifying patients at high risk. Therefore, this paper proposes a deep learning-based prediction model of heart failure rehospitalization during 6, 12, 24-month follow-ups after hospital discharge in patients with acute myocardial infarction (AMI). We used the Korea Acute Myocardial Infarction-National Institutes of Health (KAMIR-NIH) registry which included 13,104 patient records and 551 features. The proposed deep learning-based rehospitalization prediction model outperformed traditional machine learning algorithms such as logistic regression, support vector machine, AdaBoost, gradient boosting machine, and random forest. The performance of the proposed model was accuracy, the area under the curve, precision, recall, specificity, and F1 score of 99.37%, 99.90%, 96.86%, 98.61%, 99.49%, and 97.73%, respectively. This study showed the potential of a deep learning-based model for cardiology, which can be used for decision-making and medical diagnosis tool of heart failure rehospitalization in patients with AMI.

To increase the productivity, it is important to manage yield and reduce defects in the semiconductor industry. One of the efforts is to identify defect patterns and control the cause factors that affects the defects. Many engineers inspect the quality of each chip and check the defect pattern on the wafer bin maps. To get the accurate and consistent classification results regardless of the level for domain knowledge or experience of engineers, deep learning-based models have recently been studied. Since most previous studies aim to classify the single-type defect patterns, it is needed to consider the mixed-type defect patterns together. Also, they require a lot of labeled data to train the deep learning-based classification model. However, defects occur extremely rarely in actual manufacturing process. Therefore, the method securing the higher accuracy in a situation where enough labeled data are not given is needed. This paper proposes a deep convolutional generative adversarial network for wafer map synthesis (DCGAN-WS) which generates the mixed-type patterns by synthesizing the single-type pattern and adding the pixel-wise summation. To maintain the characteristics of the binary pixel of the wafer bin maps, a thresholding technique is added. MixedWM38 dataset is used for the experiments, and it was verified that the mixed-type patterns were synthesized well. It helps to construct more robust model for single-type pattern classification and to generate the mixed-type patterns that have not occurred before. In the future, it is expected that this model addresses the problem of the lack of labeled data for defect pattern classification models.

To smoothly realize the information conversion from the original language information to the target language, this paper constructs a deep learning-based fuzzy translation model for news reports so that the translated text can faithfully convey the meaning of the original language text information and achieve natural semantic equivalence. A neural probabilistic language model is used to construct objective functions in speech recognition and lexical annotation so that the translated text can provide a more appropriate linguistic representation of the polysemantic words in the original language text according to the differences in contextual morphology. A deep learning occurrence mechanism model is constructed through fuzzy semantic reasoning and fuzzy translation logic, and learning state indicators such as emotional interaction are designed to evaluate the occurrence status of fuzzy translation accurately. The simulation results show that the natural language processing (GLUE) test score of the deep learning-based fuzzy translation model for news reports is 89.8, 9.2, and 6.9 points higher than 80.6 and 82.9 for the other two models, respectively. The average error discrimination ability of the model designed in this paper is 93.57, and the average training set, development set, and test set values are 98.425, 10.16, and 45.95, respectively. Thus, it can be seen that the deep learning-based fuzzy translation model for news reports can more naturally and accurately respond to the dynamic changes in language, which promotes the rapid development of translation theory and practice.

This paper compares the classification performance of machine learning classifiers vs. deep learning-based handcrafted models and various pretrained deep networks. The proposed study performs a comprehensive analysis of object classification techniques implemented on low-altitude UAV datasets using various machine and deep learning models. Multiple UAV object classification is performed through widely deployed machine learning-based classifiers such as K nearest neighbor, decision trees, naïve Bayes, random forest, a deep handcrafted model based on convolutional layers, and pretrained deep models. The best result obtained using random forest classifiers on the UAV dataset is 90%. The handcrafted deep model's accuracy score suggests the efficacy of deep models over machine learning-based classifiers in low-altitude aerial images. This model attains 92.48% accuracy, which is a significant improvement over machine learning-based classifiers. Thereafter, we analyze several pretrained deep learning models, such as VGG-D, InceptionV3, DenseNet, Inception-ResNetV4, and Xception. The experimental assessment demonstrates nearly 100% accuracy values using pretrained VGG16- and VGG19-based deep networks. This paper provides a compilation of machine learning-based classifiers and pretrained deep learning models and a comprehensive classification report for the respective performance measures.

Bounds-based structure reliability analysis of bogie frame under variable load cases

The anticipated increases in urban climate vulnerabilities resulting from climate shifts have led to various mitigation efforts, such as adopting green or cool roofs in urban areas. Therefore, determining the most effective roofing strategies, including type and distribution, is crucial given associated costs. This preliminary assessment typically involves employing statistical or numerical models with diverse scenario simulations, which are computationally intensive and time-consuming. In this study, we introduce a deep learning-based surrogate model designed to optimize roofing strategies for urban climate risk mitigation in the Greater Seoul area, South Korea. First, we implemented the Weather Research and Forecasting model coupled with Urban Canopy Modeling (WRF-UCM) while assigning one type of roof (e.g., 100% green roof scheme or cool roof schemes ranging from 25% to 100%) to urban grids within the study region under the business-as-usual climate scenario (RCP8.5) during the period 2090-2099. Using the outputs from the WRF-UCM model, we calculated three objective indices (heat stress index, flash flood index, and wind speed index), and trained a deep learning algorithm, Multi-residual networks (Multi-ResNet), to construct a surrogate model of the WRF-UCM. To reduce the total number of scenarios, we applied the Mini Batch K-mean method to cluster 379 urban grids into nine. Afterwards, we generated multi-type roof scenarios by assigning each roof scheme to every urban cluster (four roof schemes across nine clusters, totaling 0.3M scenarios). We then employed the surrogate model to compute the three objectives (i.e., heat, flood, and wind) for each multi-type roof scenario. Finally, we present the optimal roof configurations lying on the Pareto front, reflecting the trade-offs among objectives including cost reduction, heat mitigation, flash flood prevention, and wind speed enhancement. Our results demonstrate the potential of deep learning-based surrogate models as an effective framework for urban planning to mitigate climate risks.This study is supported by the National Research Foundation of Korea (NRF) grants funded by the Korean government (MSIT) (2020R1A2C2007670, 2020R1C1C1014886, and 2022R1C1C2009543) and Korea Environment Industry & Technology Institute (KEITI) through the R&D Program for Innovative Flood Protection Technologies against Climate Crisis funded by the Korean Ministry of Environment (MOE) (No. RS-2023-00218873). 

During the design process of an autonomous underwater vehicle (AUV), the pressure vessel has a critical role. The pressure vessel contains dry electronics, power sources, and other sensors that cannot be flooded. A traditional design approach for a pressure vessel design involves running multiple Finite Element Analysis (FEA) based simulations and optimizing the design to find the best suitable design which meets the requirement. Running these FEAs are computationally very costly for any optimization process and it becomes difficult to run even hundreds of evaluation. In such a case, a better approach is the surrogate design with the goal of replacing FEA-based prediction with some learning-based regressor. Once the surrogate is trained for a class of problem, then the learned response surface can be used to analyze the stress effect without running the FEA for that class of problem. The challenge of creating a surrogate for a class of problems is data generation. Since the process is computationally costly, it is not possible to densely sample the design space and the learning response surface on sparse data set becomes difficult. During experimentation, we observed that a Deep Learning-based surrogate outperforms other regression models on such sparse data. In the present work, we are utilizing the Deep Learning-based model to replace the costly finite element analysis-based simulation process. By creating the surrogate, we speed up the prediction on the other design much faster than direct Finite element Analysis. We also compared our DL-based surrogate with other classical Machine Learning (ML) based regression models (random forest and Gradient Boost regressor). We observed on the sparser data, the DL-based surrogate performs much better than other regression models.

The seismic reliability analysis of complex structure is carried out based on the response surface method and finite element method. Firstly, the appropriate design points are selected based on the mean values and standard deviations of the basic random variables. Secondly, the finite element method is employed to obtain the values of the limit state function of the complex structure. Thirdly, with selected design points and the obtained values of the limit state function of the complex structure, a polynomial function is constructed to approximate the original implicit limit state function. Then, with the established explicit polynomial limit state function and available methods of structural reliability analysis, the seismic reliability of the complex structure is estimated. Numerical analyses show that the established method is simple to use for the evaluation of the reliability analysis of complex structure.

To develop an intelligent recognition model based on deep learning algorithms of unmanned aerial vehicle (UAV) images, and to preliminarily explore the value of this model for remote identification, monitoring and management of cattle, a source of Schistosoma japonicum infection. Oncomelania hupensis snail-infested marshlands around the Poyang Lake area were selected as the study area. Image datasets of the study area were captured by aerial photography with UAV and subjected to augmentation. Cattle in the sample database were annotated with the annotation software VGG Image Annotator to create the morphological recognition labels for cattle. A model was created for intelligent recognition of livestock based on deep learning-based Mask R-convolutional neural network (CNN) algorithms. The performance of the model for cattle recognition was evaluated with accuracy, precision, recall, F1 score and mean precision. A total of 200 original UAV images were obtained, and 410 images were yielded following data augmentation. A total of 2 860 training samples of cattle recognition were labeled. The created deep learning-based Mask R-CNN model converged following 200 iterations, with an accuracy of 88.01%, precision of 92.33%, recall of 94.06%, F1 score of 93.19%, and mean precision of 92.27%, and the model was effective to detect and segment the morphological features of cattle. The deep learning-based Mask R-CNN model is highly accurate for recognition of cattle based on UAV images, which is feasible for remote intelligent recognition, monitoring, and management of the source of S. japonicum infection.

The flexibility and volatility experienced in exchange rates affect many financial activities in the world. In order to follow this situation, countries and multinational companies need to follow financial indicators in the world economy. There is a need for important decision systems that will follow all these systems with a reliable prediction model together with the developing technology. In this study, deep learning-based models that will accurately predict the movement of gold, dollar, and euro exchange rates are proposed. Time Series methods were used to analyze the data and make predictions. In addition to deep learning models such as LSTM, GRU, Bi-LSTM and RNN, hybrid models of these methods were also used to compare their prediction performances. The data set includes USD/TRY and EUR/TRY exchange rates and monthly prices of bullion gold in Turkish Lira. Data between the years 2000-2024 were included in the analyses, and a six-month future prediction of each rate was also made. In the study, the best results were obtained with a 98.39% f1 score in gold rate prediction with the GRU-RNN hybrid model. It was observed that hybrid models in particular provided higher accuracy in predictions in general. The findings show that optimizing model parameters has a significant impact on the success of financial forecasts.