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Multiplexing information in light's fundamental attributes to create supplementary orthogonal data channels has been well heralded as an effective means for optical data storage with greatly enhanced capacities. However, robust decoding methods against inevitable crosstalks associated with experimental noise and writing imperfections as the increase of multiplexing dimensions represent a major hurdle preventing the effective practice of multi-dimensional optical recording. Here, we propose a deep-learning-based retrieval approach for robust decoding multiplexed information. An artificial neural network is trained to learn the crosstalks from multiplexed recording in disordered gold nanorod aggregates with loosened orthogonality constraints. The acquired raw readout images are analyzed by the trained neural network, which allows quick, high-fidelity, and reliable information retrieval from polarization-, wavelength-, and 3D spatially multiplexed data. The smart decoding protocol paves the way toward the mass-production ready and wide-spread application of high-capacity multi-dimensional optical storage. [ABSTRACT FROM AUTHOR]

During the ignition process of a solid rocket motor, the pressure changes dramatically and the ignition process is very complex as it includes multiple reactions. Successful completion of the ignition process is essential for the proper operation of solid rocket motors. However, the measurement of pressure becomes extremely challenging due to several issues such as the enormity and high cost of conducting tests on solid rocket motors. Therefore, it needs to be investigated using numerical calculations and other methods. Currently, the fundamental theories concerning the ignition process have not been fully developed. In addition, numerical simulations require significant simplifications. To address these issues, this study proposes a solid rocket motor pressure prediction method based on bidirectional long short-term memory (CBiLSTM) combined with adaptive Gaussian noise (AGN). The method utilizes experimental pressure data and simulated pressure data as inputs for co-training to predict pressure data under new operating conditions. By comparison, the AGN-CBiLSTM method has a higher prediction accuracy with a percentage error of 3.27% between the predicted and actual data. This method provides an effective way to evaluate the performance of solid rocket motors and has a wide range of applications in the aerospace field. [ABSTRACT FROM AUTHOR]

Urban emergency shelters are a special type of public service facility, and their planning and construction are directly related to the safety of urban residents' lives and property, as well as to the sustainable development of the city. Existing research on the site selection of shelters is not ideal when dealing with large‐scale scenarios and fails to accurately reflect the actual situation. To address this issue, this study proposes an emergency shelter site selection model based on deep reinforcement learning, IAM‐PPO. This model constructs the site selection problem as a Markov Decision Process and uses deep learning to extract information from the site selection scenario. It finds the final solution for shelter locations through continuous exploration and learning. To improve the training efficiency of the model, the action masking process is innovatively applied to the model. The research results and ablation experiments using Shanghai as a case study prove that, owing to the diversity of shelter service ranges and action masking mechanism, the model proposed in this study can provide efficient and accurate shelter location services. Moreover, the customizability of this model provides meaningful reference value for other public facility location problems. [ABSTRACT FROM AUTHOR]

Background: Accurate pancreas and pancreatic tumor segmentation from abdominal scans is crucial for diagnosing and treating pancreatic diseases. Automated and reliable segmentation algorithms are highly desirable in both clinical practice and research. Purpose: Segmenting the pancreas and tumors is challenging due to their low contrast, irregular morphologies, and variable anatomical locations. Additionally, the substantial difference in size between the pancreas and small tumors makes this task difficult. This paper proposes an attention‐enhanced multiscale feature fusion network (AMFF‐Net) to address these issues via 3D attention and multiscale context fusion methods. Methods: First, to prevent missed segmentation of tumors, we design the residual depthwise attention modules (RDAMs) to extract global features by expanding receptive fields of shallow layers in the encoder. Second, hybrid transformer modules (HTMs) are proposed to model deep semantic features and suppress irrelevant regions while highlighting critical anatomical characteristics. Additionally, the multiscale feature fusion module (MFFM) fuses adjacent top and bottom scale semantic features to address the size imbalance issue. Results: The proposed AMFF‐Net was evaluated on the public MSD dataset, achieving 82.12% DSC for pancreas and 57.00% for tumors. It also demonstrated effective segmentation performance on the NIH and private datasets, outperforming previous State‐Of‐The‐Art (SOTA) methods. Ablation studies verify the effectiveness of RDAMs, HTMs, and MFFM. Conclusions: We propose an effective deep learning network for pancreas and tumor segmentation from abdominal CT scans. The proposed modules can better leverage global dependencies and semantic information and achieve significantly higher accuracy than the previous SOTA methods. [ABSTRACT FROM AUTHOR]

Individual Tree Detection-and-Counting (ITDC) is among the important tasks in town areas, and numerous methods are proposed in this direction. Despite their many advantages, still, the proposed methods are inadequate to provide robust results because they mostly rely on the direct field investigations. This paper presents a novel approach involving high-resolution imagery and the Canopy-Height-Model (CHM) data to solve the ITDC problem. The new approach is studied in six urban scenes: farmland, woodland, park, industrial land, road and residential areas. First, it identifies tree canopy regions using a deep learning network from high-resolution imagery. It then deploys the CHM-data to detect treetops of the canopy regions using a local maximum algorithm and individual tree canopies using the region growing. Finally, it calculates and describes the number of individual trees and tree canopies. The proposed approach is experimented with the data from Shanghai, China. Our results show that the individual tree detection method had an average overall accuracy of 0.953, with a precision of 0.987 for woodland scene. Meanwhile, the R2 value for canopy segmentation in different urban scenes is greater than 0.780 and 0.779 for canopy area and diameter size, respectively. These results confirm that the proposed method is robust enough for urban tree planning and management. [ABSTRACT FROM AUTHOR]

Introduction: Immune dysregulation plays a major role in cancer progression. The quantification of lymphocytic spatial inflammation may enable spatial system biology, improve understanding of therapeutic resistance, and contribute to prognostic imaging biomarkers. Methods: In this paper, we propose a knowledge-guided deep learning framework to measure the lymphocytic spatial architecture on human H&E tissue, where the fidelity of training labels is maximized through single-cell resolution image registration of H&E to IHC. We demonstrate that such an approach enables pixel-perfect ground-truth labeling of lymphocytes on H&E as measured by IHC. We then experimentally validate our technique in a genetically engineered, immune-compromised Rag2 mouse model, where Rag2 knockout mice lacking mature lymphocytes are used as a negative experimental control. Such experimental validation moves beyond the classical statistical testing of deep learning models and demonstrates feasibility of more rigorous validation strategies that integrate computational science and basic science. Results: Using our developed approach, we automatically annotated more than 111,000 human nuclei (45,611 CD3/CD20 positive lymphocytes) on H&E images to develop our model, which achieved an AUC of 0.78 and 0.71 on internal hold-out testing data and external testing on an independent dataset, respectively. As a measure of the global spatial architecture of the lymphocytic microenvironment, the average structural similarity between predicted lymphocytic density maps and ground truth lymphocytic density maps was 0.86 ± 0.06 on testing data. On experimental mouse model validation, we measured a lymphocytic density of 96.5 ± %1% in a Rag2+/- control mouse, compared to an average of 16.2 ± %5% in Rag2-/- immune knockout mice (p<0.0001, ANOVA-test). Discussion: These results demonstrate that CD3/CD20 positive lymphocytes can be accurately detected and characterized on H&E by deep learning and generalized across species. Collectively, these data suggest that our understanding of complex biological systems may benefit from computationally-derived spatial analysis, as well as integration of computational science and basic science. [ABSTRACT FROM AUTHOR]

For the intelligent vulnerability detection, the system extracts the graph structured source code slices according to the vulnerability characteristics from the program dependency graph of source code, and then presents the graph structured slice information to carry out vulnerability detection by using the graph neural network model. Slice level vulnerability detection was realized and the vulnerability line was located at the code line level. In order to verify the effectiveness of the system, compared with the static vulnerability detection systems, the vulnerability detection system based on serialized text information, and the vulnerability detection system based on graph structured information, the experimental results show that the proposed system has a high accuracy in the vulnerability detection capability and a good performance in the vulnerability code line prediction.

Product configurators are recognised as critical toolkits enabling customers to co-create products with companies. Most available product configurators require customers to select suitable product attributes from predefined options. However, customers usually find the selection processes frustrating due to their lack of product knowledge. In view of the fact that customers often express their needs in imprecise and vague natural language, we define a new needs-based configuration mechanism and propose an implementation approach based on text embeddings and multilayer perceptron. Specifically, we leverage the massive amount of product reviews by encoding them into text embeddings. A multilayer perceptron is trained to map text embeddings to product attribute options. Experiment results indicate that the mapping has good generalisation capability to map customer needs into product configurations. The performance of our approach is comparable to that of deep learning-based approaches but with much higher efficiency in terms of computational complexity. Our needs-based configuration thus provides a quick and effective means of facilitating product customisation. It also demonstrates an innovative way of utilising customer resources in unstructured text to co-create products with companies. [ABSTRACT FROM AUTHOR]

In recent years, deep learning technology has developed significantly in the judicial field. More and more scholars are introducing deep learning to solve problems in the judicial field and improve the efficiency of judicial agencies in handling cases. An important task of judicial work is to identify the key elements of complex cases, establish the relationship between entities in the document, accurately grasp the development of the case, provide a basis for the understanding, analysis, and ruling of the case, and increase the interpretability of the case results. However, judging from the actual application situation, the application of deep learning technology in the judicial field is not yet mature and still faces huge challenges. On the one hand, the existing legal text element identification methods only consider the identification of core elements and ignore the basic elements. However, the basic elements contain necessary legal-related information, which is basic case information unique to the legal field and has important reference value for judicial staff to analyze cases. On the other hand, the traditional method of identifying key elements does not consider the contextual semantic relationship and ignores the element semantic information and global semantic information is lost, resulting in poor recognition of key elements of legal text. Therefore, in the identification of key elements of legal texts, not only the core elements and basic elements must be considered, but also the semantic features of sentences and the semantic features of elements must be comprehensively considered, thereby improving the effect of identifying key elements of legal texts and promoting the efficiency of judicial organs in handling cases. Aiming at the problem of identifying the basic elements of legal texts, this paper proposes a model for identifying the basic elements of legal texts based on dynamic representation. This algorithm uses the dynamic representation capability of the BERT model to vectorize text and considers global domain semantic information during the pre-training process to achieve a more comprehensive vector representation. Secondly, the memory component of the bidirectional long short-term memory network is used to integrate all the feature information between long-distance words in the legal text, effectively express the meaning of the word in the context, and realize the recognition of the basic elements of the legal text. In addition, the conditional random field model is used to learn the transfer rules of labels between entities and output the label sequence that best conforms to the actual rules. Finally, the basic element identification model proposed in this article has been significantly improved by comparing it with the current better methods. [ABSTRACT FROM AUTHOR]

The article introduces a new model called APF-GAN that improves upon existing generative adversarial network (GAN)-based models for image semantic segmentation. The model incorporates various strategies such as asymmetric pre-training and fine-tuning, progressive growth training, and semantic spatially-guided noise to enhance image quality and training efficiency. Experimental results demonstrate that the APF-GAN outperforms other models in terms of segmentation accuracy and aesthetics. The article also provides examples of images generated by the APF-GAN and discusses potential areas for future research. [Extracted from the article]

Machine learning (ML) has been broadly applied for vadose zone applications in recent years. This article provides a comprehensive review of such developments. ML applications for variables corresponding to different complex vadose zone processes are summarized mostly in a prediction context. By analyzing and assessing these applications, we discovered extensive usages of classic ML models with relatively limited applications of deep learning (DL) approaches in general. We also recognized a lack of benchmark datasets for soil property research as well as limited integration of physics‐based vadose zone principles into the ML approaches. To facilitate this interdisciplinary research of ML in vadose zone characterization and processes, a paradigm of knowledge‐guided machine learning is suggested along with other data‐driven and ML model‐based research suggestions to advance future research. Core Ideas: Random forest and artificial neural network are two widely applied machine learning options for predicting vadose zone studies.A benchmark dataset is missing in soil property studies.We suggest vadose zone scientists explore more deep learning options and expand knowledge‐guided machine learning implementations. [ABSTRACT FROM AUTHOR]

The challenge of spatial resource allocation is pervasive across various domains such as transportation, industry, and daily life. As the scale of real-world issues continues to expand and demands for real-time solutions increase, traditional algorithms face significant computational pressures, struggling to achieve optimal efficiency and real-time capabilities. In recent years, with the escalating computational power of computers, the remarkable achievements of reinforcement learning in domains like Go and robotics have demonstrated its robust learning and sequential decision-making capabilities. Given these advancements, there has been a surge in novel methods employing reinforcement learning to tackle spatial resource allocation problems. These methods exhibit advantages such as rapid solution convergence and strong model generalization abilities, offering a new perspective on resolving spatial resource allocation problems. Despite the progress, reinforcement learning still faces hurdles when it comes to spatial resource allocation. There remains a gap in its ability to fully grasp the diversity and intricacy of real-world resources. The environmental models used in reinforcement learning may not always capture the spatial dynamics accurately. Moreover, in situations laden with strict and numerous constraints, reinforcement learning can sometimes fall short in offering feasible strategies. Consequently, this paper is dedicated to summarizing and reviewing current theoretical approaches and practical research that utilize reinforcement learning to address issues pertaining to spatial resource allocation. In addition, the paper accentuates several unresolved challenges that urgently necessitate future focus and exploration within this realm and proposes viable approaches for these challenges. This research furnishes valuable insights that may assist scholars in gaining a more nuanced understanding of the problems, opportunities, and potential directions concerning the application of reinforcement learning in spatial resource allocation. [ABSTRACT FROM AUTHOR]

Background: In radiotherapy, real‐time tumor tracking can verify tumor position during beam delivery, guide the radiation beam to target the tumor, and reduce the chance of a geometric miss. Markerless kV x‐ray image‐based tumor tracking is challenging due to the low tumor visibility caused by tumor‐obscuring structures. Developing a new method to enhance tumor visibility for real‐time tumor tracking is essential. Purpose: To introduce a novel method for markerless kV image‐based tracking of lung tumors via deep learning‐based target decomposition. Methods: We utilized a conditional Generative Adversarial Network (cGAN), known as Pix2Pix, to build a patient‐specific model and generate the synthetic decomposed target image (sDTI) to enhance tumor visibility on the real‐time kV projection images acquired by the onboard kV imager equipped on modern linear accelerators. We used 4DCT simulation images to generate the digitally reconstructed radiograph (DRR) and DTI image pairs for model training. We augmented the training dataset by randomly shifting the 4DCT in the superior‐inferior, anterior‐posterior, and left‐right directions during the DRR and DTI generation process. We performed real‐time 2D tumor tracking via template matching between the DTI generated from the CT simulation and the sDTI generated from the real‐time kV projection images. We validated the proposed method using nine patients' datasets with implanted beacons near the tumor. Results: The sDTI can effectively improve the image contrast around the lung tumors on the kV projection images for the nine patients. With the beacon motion as ground truth, the tracking errors were on average 0.8 ± 0.7 mm in the superior‐inferior (SI) direction and 0.9 ± 0.8 mm in the in‐plane left‐right (IPLR) direction. The percentage of successful tracking, defined as a tracking error less than 2 mm in the SI direction, is 92.2% on the 4312 tested images. The patient‐specific model took approximately 12 h to train. During testing, it took approximately 35 ms to generate one sDTI, and 13 ms to perform the tumor tracking using template matching. Conclusions: Our method offers the potential solution for nearly real‐time markerless lung tumor tracking. It achieved a high level of accuracy and an impressive tracking rate. Further development of 3D lung tumor tracking is warranted. [ABSTRACT FROM AUTHOR]

With the development of computer technology, fire image processing technology combining computer vision, machine learning, deep learning and other technologies has been widely studied and applied. Aiming at the complex preprocessing process and high false positive rate of traditional image processing methods, this paper proposes a method based on deep convolutional neural network model for fire detection, which reduces complex preprocessing links and integrates the whole fire identification process into one single depth neural network for easy training and optimization. In view of the problem of fire detection caused by similar fire scenes in the identification process, this paper uses the motion characteristics of fire to innovatively propose the combination of fire frame position changes before and after the fire video to eliminate the interference of lights and other similar fire scenes. After comparing many open learning open source frameworks, this paper chooses Caffe framework for training and testing. The experimental results show that the method realizes the recognition and localization of fire images. This method is suitable for different fire scenarios and has good generalization ability and anti-interference ability.

The spatial resolution of precipitation predicted by general circulation models is too coarse to meet current research and operational needs. Downscaling is one way to provide finer resolution data at local scales. The single-image super-resolution method in the computer vision field has made great strides lately and has been applied in various fields. In this article, we propose a novel reference-based and gradient-guided deep learning model (RBGGM) to downscale daily precipitation considering the discontinuity of precipitation and ill-posed nature of downscaling. Global Precipitation Measurement Mission (GPM) precipitation data, variables in ERA5 re-analysis data, and topographic data are selected to perform the downscaling, and a residual dense attention block is constructed to extract features of them. By exploring the discontinuous feature of precipitation, we introduce gradient feature to reconstruct precipitation distribution. We also extract the feature of high-resolution monthly precipitation as a reference feature to resolve the ill-posed nature of downscaling. Extensive experimental results on benchmark data sets demonstrate that our proposed model performs better than other baseline methods. Furthermore, we construct a daily precipitation downscaling data set based on GPM precipitation data, ERA5 re-analysis data and topographic data.

The main objective of this study was to build a rich and high-quality thyroid ultrasound image database (TUD) for computer-aided diagnosis (CAD) systems to support accurate diagnosis and prognostic modeling of thyroid disorders. Because most of the raw thyroid ultrasound images contain artificial markers, which seriously affect the robustness of CAD systems because of their strong prior location information, we propose a marker mask inpainting (MMI) method to erase artificial markers and improve image quality. First, a set of thyroid ultrasound images were collected from the General Hospital of the Northern Theater Command. Then, two modules were designed in MMI, namely, the marker detection (MD) module and marker erasure (ME) module. The MD module detects all markers in the image and stores them in a binary mask. According to the binary mask, the ME module erases the markers and generates an unmarked image. Finally, a new TUD based on the marked images and unmarked images was built. The TUD is carefully annotated and statistically analyzed by professional physicians to ensure accuracy and consistency. Moreover, several normal thyroid gland images and some ancillary information on benign and malignant nodules are provided. Several typical segmentation models were evaluated on the TUD. The experimental results revealed that our TUD can facilitate the development of more accurate CAD systems for the analysis of thyroid nodule-related lesions in ultrasound images. The effectiveness of our MMI method was determined in quantitative experiments. The rich and high-quality resource TUD promotes the development of more effective diagnostic and treatment methods for thyroid diseases. Furthermore, MMI for erasing artificial markers and generating unmarked images is proposed to improve the quality of thyroid ultrasound images. Our TUD database is available at https://github.com/NEU-LX/TUD-Datebase. [ABSTRACT FROM AUTHOR]

Recent machine learning models for bandgap prediction that explicitly encode the structure information to the model feature set significantly improve the model accuracy compared to both traditional machine learning and non-graph-based deep learning methods. The ongoing rapid growth of open-access bandgap databases can benefit such model construction not only by expanding their domain of applicability but also by requiring constant updating of the model. Here, we build a new state-of-the-art multi-fidelity graph network model for bandgap prediction of crystalline compounds from a large bandgap database of experimental and density functional theory (DFT) computed bandgaps with over 806 600 entries (1500 experimental, 775 700 low-fidelity DFT, and 29 400 high-fidelity DFT). The model predicts bandgaps with a 0.23 eV mean absolute error in cross validation for high-fidelity data, and including the mixed data from all different fidelities improves the prediction of the high-fidelity data. The prediction error is smaller for high-symmetry crystals than for low symmetry crystals. Our data are published through a new cloud-based computing environment, called the "Foundry," which supports easy creation and revision of standardized data structures and will enable cloud accessible containerized models, allowing for continuous model development and data accumulation in the future. [ABSTRACT FROM AUTHOR]

Realistic image composition aims to composite new images by fusing a source object into a target image. It is a challenging problem due to the complex multi-task framework, including sensible object placement, appearance consistency, shadow generation, etc. Most existing researchers attempt to address one of the issues. Especially before compositing, there is no matching assignment between the source object and target image, which often leads to unreasonable results. To address the issues above, we consider image composition as an image generation problem and propose a deep adversarial learning network via spatial position analysis. We target the analysis network segment and classify the objects in target images. A spatial alignment network matches the segmented objects with the source objects, and predicts a sensible placement position, and an adversarial network generates a realistic composite image with the shadow and reflection of the source object. Furthermore, we use the classification information of target objects to filter out unreasonable image compositing. Moreover, we introduce a new test set to evaluate the network generalization for our multi-task image composition dataset. Extensive experimental results of the SHU (Shanghai University) dataset demonstrate that our deep spatial position analysis network remarkably enhances the compositing performance in realistic, shadow, and reflection generations. [ABSTRACT FROM AUTHOR]

SUMMARY: Spartina alterniflora is a halophyte that can survive in high‐salinity environments, and it is phylogenetically close to important cereal crops, such as maize and rice. It is of scientific interest to understand why S. alterniflora can live under such extremely stressful conditions. The molecular mechanism underlying its high‐saline tolerance is still largely unknown. Here we investigated the possibility that high‐affinity K+ transporters (HKTs), which function in salt tolerance and maintenance of ion homeostasis in plants, are responsible for salt tolerance in S. alterniflora. To overcome the imprecision and unstable of the gene screening method caused by the conventional sequence alignment, we used a deep learning method, DeepGOPlus, to automatically extract sequence and protein characteristics from our newly assemble S. alterniflora genome to identify SaHKTs. Results showed that a total of 16 HKT genes were identified. The number of S. alterniflora HKTs (SaHKTs) is larger than that in all other investigated plant species except wheat. Phylogenetically related SaHKT members had similar gene structures, conserved protein domains and cis‐elements. Expression profiling showed that most SaHKT genes are expressed in specific tissues and are differentially expressed under salt stress. Yeast complementation expression analysis showed that type I members SaHKT1;2, SaHKT1;3 and SaHKT1;8 and type II members SaHKT2;1, SaHKT2;3 and SaHKT2;4 had low‐affinity K+ uptake ability and that type II members showed stronger K+ affinity than rice and Arabidopsis HKTs, as well as most SaHKTs showed preference for Na+ transport. We believe the deep learning‐based methods are powerful approaches to uncovering new functional genes, and the SaHKT genes identified are important resources for breeding new varieties of salt‐tolerant crops. Significance Statement: The great potential of deep learning‐based methods for uncovering the HKT genes in S. alterniflora was demonstrated, which opens the door for utilizing SaHKTs in a wide range of species to increase the stress resilience of staple crops. [ABSTRACT FROM AUTHOR]

Until recently, conventional biochemical staining had the undisputed status as well-established benchmark for most biomedical problems related to clinical diagnostics, fundamental research and biotechnology. Despite this role as gold-standard, staining protocols face several challenges, such as a need for extensive, manual processing of samples, substantial time delays, altered tissue homeostasis, limited choice of contrast agents, 2D imaging instead of 3D tomography and many more. Label-free optical technologies, on the other hand, do not rely on exogenous and artificial markers, by exploiting intrinsic optical contrast mechanisms, where the specificity is typically less obvious to the human observer. Over the past few years, digital staining has emerged as a promising concept to use modern deep learning for the translation from optical contrast to established biochemical contrast of actual stainings. In this review article, we provide an in-depth analysis of the current state-of-the-art in this field, suggest methods of good practice, identify pitfalls and challenges and postulate promising advances towards potential future implementations and applications. [ABSTRACT FROM AUTHOR]

(1) Background: Cell proliferation (Ki-67) has important clinical value in the treatment and prognosis of non-small cell lung cancer (NSCLC). However, current detection methods for Ki-67 are invasive and can lead to incorrect results. This study aimed to explore a deep learning classification model for the prediction of Ki-67 and the prognosis of NSCLC based on FDG-PET/CT images. (2) Methods: The FDG-PET/CT scan results of 159 patients with NSCLC confirmed via pathology were analyzed retrospectively, and the prediction models for the Ki-67 expression level based on PET images, CT images and PET/CT combined images were constructed using Densenet201. Based on a Ki-67 high expression score (HES) obtained from the prediction model, the survival rate of patients with NSCLC was analyzed using Kaplan–Meier and univariate Cox regression. (3) Results: The statistical analysis showed that Ki-67 expression was significantly correlated with clinical features of NSCLC, including age, gender, differentiation state and histopathological type. After a comparison of the three models (i.e., the PET model, the CT model, and the FDG-PET/CT combined model), the combined model was found to have the greatest advantage in Ki-67 prediction in terms of AUC (0.891), accuracy (0.822), precision (0.776) and specificity (0.902). Meanwhile, our results indicated that HES was a risk factor for prognosis and could be used for the survival prediction of NSCLC patients. (4) Conclusions: The deep-learning-based FDG-PET/CT radiomics classifier provided a novel non-invasive strategy with which to evaluate the malignancy and prognosis of NSCLC. [ABSTRACT FROM AUTHOR]

This paper proposes a novel domain-generation-algorithm detection framework based on statistical learning that integrates the detection capabilities of multiple heterogeneous models. The framework includes both traditional machine learning methods based on artificial features and deep learning methods, comprehensively analyzing 34 artificial features and advanced features extracted from deep neural networks. Additionally, the framework evaluates the predictions of the base models based on the fit of the samples to each type of sample set and a predefined significance level. The predictions of the base models are statistically analyzed, and the final decision is made using strategies such as voting, confidence, and credibility. Experimental results demonstrate that the DGA detection framework based on statistical learning achieves a higher detection rate compared to the underlying base models, with accuracy, precision, recall, and F1 scores reaching 0.979, 0.977, 0.981, and 0.979, respectively. The framework also exhibits a stronger adaptability to unknown domains and a certain level of robustness against concept drift attacks. [ABSTRACT FROM AUTHOR]