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Background: Breast cancer is one of the most common malignant tumors in women. A noninvasive ultrasound examination can identify mammary-gland-related diseases and is well tolerated by dense breast, making it a preferred method for breast cancer screening and of significant clinical value. However, the diagnosis of breast nodules or masses via ultrasound is performed by a doctor in real time, which is time-consuming and subjective. Junior doctors are prone to missed diagnoses, especially in remote areas or grass-roots hospitals, due to limited medical resources and other factors, which bring great risks to a patient's health. Therefore, there is an urgent need to develop fast and accurate ultrasound image analysis algorithms to assist diagnoses. Methods: We propose a breast ultrasound image-based assisted-diagnosis method based on convolutional neural networks, which can effectively improve the diagnostic speed and the early screening rate of breast cancer. Our method consists of two stages: tumor recognition and tumor classification. (1) Attention-based semantic segmentation is used to identify the location and size of the tumor; (2) the identified nodules are cropped to construct a training dataset. Then, a convolutional neural network for the diagnosis of benign and malignant breast nodules is trained on this dataset. We collected 2057 images from 1131 patients as the training and validation dataset, and 100 images of the patients with accurate pathological criteria were used as the test dataset. Results: The experimental results based on this dataset show that the MIoU of tumor location recognition is 0.89 and the average accuracy of benign and malignant diagnoses is 97%. The diagnosis performance of the developed diagnostic system is basically consistent with that of senior doctors and is superior to that of junior doctors. In addition, we can provide the doctor with a preliminary diagnosis so that it can be diagnosed quickly. Conclusion: Our proposed method can effectively improve diagnostic speed and the early screening rate of breast cancer. The system provides a valuable aid for the ultrasonic diagnosis of breast cancer. [ABSTRACT FROM AUTHOR]

Scientometric reviews, facilitated by computational and visual analytical approaches, allow researchers to gain a thorough understanding of research trends and areas of concentration from a large number of publications. With the fast development of satellite altimetry, which has been effectively applied to a wide range of research topics, it is timely to summarize the scientific achievements of the previous 50 years and identify future trends in this field. A comprehensive overview of satellite altimetry was presented using a total of 8541 publications from the Web of Science Core Collection covering the years from 1970 to 2021. We begin by presenting the fundamental statistical results of the publications, such as the annual number of papers, study categories, countries/regions, afflictions, journals, authors, and keywords, in order to provide a comprehensive picture of satellite altimetry research. We discuss the co-occurrence of the authors in order to reveal the global collaboration network of satellite altimetry research. Finally, we utilised co-citation networks to detect the development trend and associated crucial publications for various specific topics. The findings show that satellite altimetry research has been changed immensely during the last half-century. The United States, France, China, England, and Germany made the most significant contributions in the field of satellite altimetry. The analysis reveals a clear link between technology advancements and the trend in satellite altimetry research. As a result, wide swath altimetry, GNSS-reflectometry, laser altimetry, terrestrial hydrology, and deep learning are among the most frontier study subjects. The findings of this work could guide a thorough understanding of satellite altimetry's overall development and research front. [ABSTRACT FROM AUTHOR]

The enrollment rate of freshmen has always been a headache for colleges and universities. It is also very difficult to accurately predict the number of freshmen before they register. In recent years, deep learning and machine learning technology have made a breakthrough and are widely used in data processing, edge computing, and situation awareness. So far, no researcher has used machine learning methods to forecast the enrollment of new students, because the intuitive feeling is that the registration of a freshman is very subjective, dependent on several factors. To date, the number of freshmen in universities has always been forecasted using traditional methods, that is, phone calls and fee payment status inquiries. On the basis of the historical admission enrollment data of a university, this study used a variety of machine learning methods, including decision tree, random forest, and back propagation neural network, for forecasting. Based on the results of our research, it is possible to predict whether a freshman will register in a university, which makes this work very worthy of further study. [ABSTRACT FROM AUTHOR]

Protein-protein interactions (PPIs) are important for almost all cellular processes, including metabolic cycles, DNA transcription and replication, and signaling cascades. The experimental methods for identifying PPIs are always time-consuming and expensive. Therefore, it is important to develop computational approaches for predicting PPIs. In this paper, an improved model is proposed to use a machine learning method in the study of protein-protein interactions. With the consideration of the factors affecting the prediction of the PPIs, a method of feature extraction and fusion is proposed to improve the variety of the features to be considered in the prediction. Besides, with the consideration of the effect affected by the different input order of the two proteins, we propose a "Y-type" Bi-RNN model and train the network by using a method which both needs backward and forward training. In order to insure the training time caused on the extra training either a backward one or a forward one, this paper proposes a weight-sharing policy to minimize the parameters in the training. The experimental results show that the proposed method can achieve an accuracy of 99.57%, recall of 99.36%, sensitivity of 99.76%, precision of 99.74%, MCC of 99.14%, and AUC of 99.56% under the benchmark dataset. [ABSTRACT FROM AUTHOR]

Objective: To establish a model for predicting lymph node metastasis in bladder cancer (BCa) patients. Methods: We retroactively enrolled 239 patients who underwent three-phase CT and resection for BCa in two centers (training set, n = 185; external test set, n = 54). We reviewed the clinical characteristics and CT features to identify significant predictors to construct a clinical model. We extracted the hand-crafted radiomics features and deep learning features of the lesions. We used the Minimum Redundancy Maximum Relevance algorithm and the least absolute shrinkage and selection operator logistic regression algorithm to screen features. We used nine classifiers to establish the radiomics machine learning signatures. To compensate for the uneven distribution of the data, we used the synthetic minority over-sampling technique to retrain each machine-learning classifier. We constructed the combined model using the top-performing radiomics signature and clinical model, and finally presented as a nomogram. We evaluated the combined model’s performance using the area under the receiver operating characteristic, accuracy, calibration curves, and decision curve analysis. We used the Kaplan–Meier survival curve to analyze the prognosis of BCa patients. Results: The combined model incorporating radiomics signature and clinical model achieved an area under the receiver operating characteristic of 0.834 (95% CI: 0.659–1.000) for the external test set. The calibration curves and decision curve analysis demonstrated exceptional calibration and promising clinical use. The combined model showed good risk stratification performance for progression-free survival. Conclusion: The proposed CT-based combined model is effective and reliable for predicting lymph node status of BCa patients preoperatively. Critical relevance statement: Bladder cancer is a type of urogenital cancer that has a high morbidity and mortality rate. Lymph node metastasis is an independent risk factor for death in bladder cancer patients. This study aimed to investigate the performance of a deep learning radiomics model for preoperatively predicting lymph node metastasis in bladder cancer patients. Key points: • Conventional imaging is not sufficiently accurate to determine lymph node status. • Deep learning radiomics model accurately predicted bladder cancer lymph node metastasis. • The proposed method showed satisfactory patient risk stratification for progression-free survival. [ABSTRACT FROM AUTHOR]

Background: Breast fibroadenoma poses a significant health concern, particularly for young women. Computer-aided diagnosis has emerged as an effective and efficient method for the early and accurate detection of various solid tumors. Automatic segmentation of the breast fibroadenoma is important and potentially reduces unnecessary biopsies, but challenging due to the low image quality and presence of various artifacts in sonography. Methods: Human learning involves modularizing complete information and then integrating it through dense contextual connections in an intuitive and efficient way. Here, a human learning paradigm was introduced to guide the neural network by using two consecutive phases: the feature fragmentation stage and the information aggregation stage. To optimize this paradigm, three fragmentation attention mechanisms and information aggregation mechanisms were adapted according to the characteristics of sonography. The evaluation was conducted using a local dataset comprising 600 breast ultrasound images from 30 patients at Suining Central Hospital in China. Additionally, a public dataset consisting of 246 breast ultrasound images from Dataset_BUSI and DatasetB was used to further validate the robustness of the proposed network. Segmentation performance and inference speed were assessed by Dice similarity coefficient (DSC), Hausdorff distance (HD), and training time and then compared with those of the baseline model (TransUNet) and other state-of-the-art methods. Results: Most models guided by the human learning paradigm demonstrated improved segmentation on the local dataset with the best one (incorporating C3ECA and LogSparse Attention modules) outperforming the baseline model by 0.76% in DSC and 3.14 mm in HD and reducing the training time by 31.25%. Its robustness and efficiency on the public dataset are also confirmed, surpassing TransUNet by 0.42% in DSC and 5.13 mm in HD. Conclusions: Our proposed human learning paradigm has demonstrated the superiority and efficiency of ultrasound breast fibroadenoma segmentation across both public and local datasets. This intuitive and efficient learning paradigm as the core of neural networks holds immense potential in medical image processing. [ABSTRACT FROM AUTHOR]

Over the past four decades, Arctic sea ice coverage has steadily declined. This loss of sea ice has amplified solar radiation and heat absorption from the ocean, exacerbating both polar ice loss and global warming. It has also accelerated changes in sea ice movement, posing safety risks for ship navigation. In recent years, numerical prediction models have dominated the field of sea ice movement prediction. However, these models often rely on extensive data sources, which can be limited in specific time periods or regions, reducing their applicability. This study introduces a novel approach for predicting Arctic sea ice motion within a 10-day window. We employ a Self-Attention ConvLSTM deep learning network based on single-source data, specifically optical flow derived from the Advanced Microwave Scanning Radiometer Earth Observing System 36.5 GHz data, covering the entire Arctic region. Upon verification, our method shows a reduction of 0.80 to 1.18 km in average mean absolute error over a 10-day period when compared to ConvLSTM, demonstrating its improved ability to capture the spatiotemporal correlation of sea ice motion vector fields and provide accurate predictions. [ABSTRACT FROM AUTHOR]

Currently, intelligent crack detection is of great value for the maintenance of infrastructure, of which the most significant kind in China is roads. For pavement defects, the pavement can be repaired and maintained in a timely manner with an accurate defect detection task, which significantly reduces the occurrence of hazards. However, the detection of pavement defects remains a great challenge owing to many difficulties, for example, complex backgrounds, microdefects, various defect shapes and sizes, class imbalance issues, etc. Recently, deep learning has demonstrated its superior performance on pixelwise image segmentation, but some issues still exist on demanding pixelwise image segmentation, for instance, limited receptive field, insufficiency processing of local features, information loss issue generated by pooling operations, etc. Based on all of the above issues, a multiscale triple-attention network, named MST-Net, is proposed for end-to-end pixelwise crack detection. First, a multiscale input strategy is applied to the proposed segmentation network to capture more context information. Meanwhile, it can capably reduce the effect of the information loss issue generated by pooling operations. Second, to realize effective feature representation of local features, an additive attention fusion (AAF) block is proposed to guide feature learning to capture both global and local contexts. In addition, faced with the crack detection task with class imbalance issues, a triple attention (TA) block is proposed to detect spatial, channel and pixel attention information to suppress the background and useless information, which is conducive to the characterization of microcracks. Finally, aiming at the limited receptive field, a multiscale feature aggregation unit is proposed for feature fusion to increase the detection ability of multiscale defects. To better guide network training, a deep supervision mechanism is also introduced to speed up the convergence of the proposed segmentation model and improve the performance of defect segmentation. The related evaluation and detection experiments are carried out on three public datasets on crack segmentation, and the comparison experiments with the mainstream segmentation models show that the proposed segmentation network achieves excellent performance on pixelwise crack detection. • A multi-scale triple-attention network is proposed for end-to-end pixelwise crack detection. • An additive attention fusion block is proposed to capture both global and local contexts. • A triple attention block is proposed to suppress the background and useless information. • A multi-scale feature aggregation unit is proposed for accurate multi-scale crack detection. [ABSTRACT FROM AUTHOR]

Defect detection is a very important link for much manufacturing and processing applications which could be used for quality control and precise maintenance decision. However, faced with the weak-texture and low-contrast industrial environment, high-precision defect detection still faces a certain challenge due to diverse and complex of defects. Meanwhile, due to a minimal portion image pixels of defects, the pixel-level defect detection task is always against class-unbalance issue which also will affect the detection performance. Recently, with the strong automatic feature representation ability, deep learning has shown an excellent detection performance on defect identification and location. Nevertheless, it still has some demerits, such as insufficient processing of feature maps, lack of temporal modeling information, etc. To address these issues, on the basis of the encoder–decoder architecture, a pixel-level deep segmentation network is proposed for automatic defect detection to construct an end-to-end defect segmentation model. To realize effective feature representation, a residual attention network is proposed to construct the backbone network, which could also make the segmentation network better emphasize target regions. Meanwhile, to improve the network propagation ability of subtle context features, a bidirectional convolutional long short-term memory (ConvLSTM) block is introduced to optimize the skip connections to learn long-range spatial contexts. Besides, a weighted loss function is proposed for model training to address the class-unbalance issue. Combined with multiple public data sets, through qualitative and quantitative analysis, experimental results demonstrate that the proposed defect segmentation network achieves a better performance compared to other state-of-the-art segmentation methods. [ABSTRACT FROM AUTHOR]

Automatic power line extraction is a crucial task for the safe navigation of inspection robots. Nevertheless, power lines are always against complicated natural backgrounds which bring a certain challenge for accurate power line extraction. Meanwhile, the power lines always occupy a minimal portion image pixels in the aerial images compared with backgrounds which causes serious class imbalance issue. Therefore, the robust and accurate power line segmentation from aerial images is one of the most frequently stated problems faced with these factors. Recently, the deep learning has got wide applications on different segmentation tasks with effective contextual feature generation ability. However, these methods show poor ability on the samples with class imbalance due to insufficient process of local contextual features. To address these issues, combined with the encoder–decoder framework, a novel power line extraction network (PLE-Net) is proposed in this paper to construct an end-to-end attention-based segmentation method for automatic power line extraction from aerial images with a self-attention block and a multi-scale feature enhance block. To capture rich contextual relationships from local feature maps, a feature enhance block is proposed for multi-scale feature expression. And a self-attention block is proposed to embed into the proposed segmentation network to emphasize the regions about power lines. Further, the hybrid loss function with binary cross-entropy (BCE) and Dice is set as the loss function to address the class imbalance issue. Combined with the public datasets of power lines, the proposed segmentation network shows a better segmentation performance on vision images and infrared images through the ablation analysis and comparison experiments. • A novel power line extraction network (PLE-Net) is proposed to construct an endto-end segmentation method. • A feature enhance block is proposed to capture rich contextual relationships from local feature maps. • A self-attention block is proposed to embed into the proposed PLE-Net network to emphasize the regions about power lines. [ABSTRACT FROM AUTHOR]

With the development of convolutional neural networks, the effect of pedestrian detection has been greatly improved by deep learning models. However, the presence of pseudo pedestrians will lead to accuracy reduction in pedestrian detection. To solve the problem that the existing pedestrian detection algorithms cannot distinguish pseudo pedestrians from real pedestrians, a real and pseudo pedestrian detection method with CA-YOLOv5s based on stereo image fusion is proposed in this paper. Firstly, the two-view images of the pedestrian are captured by a binocular stereo camera. Then, a proposed CA-YOLOv5s pedestrian detection algorithm is used for the left-view and right-view images, respectively, to detect the respective pedestrian regions. Afterwards, the detected left-view and right-view pedestrian regions are matched to obtain the feature point set, and the 3D spatial coordinates of the feature point set are calculated with Zhengyou Zhang's calibration method. Finally, the RANSAC plane-fitting algorithm is adopted to extract the 3D features of the feature point set, and the real and pseudo pedestrian detection is achieved by the trained SVM. The proposed real and pseudo pedestrian detection method with CA-YOLOv5s based on stereo image fusion effectively solves the pseudo pedestrian detection problem and efficiently improves the accuracy. Experimental results also show that for the dataset with real and pseudo pedestrians, the proposed method significantly outperforms other existing pedestrian detection algorithms in terms of accuracy and precision. [ABSTRACT FROM AUTHOR]

Objective: Modic changes (MCs) are the most prevalent classification system for describing intravertebral MRI signal intensity changes. However, interpreting these intricate MRI images is a complex and time‐consuming process. This study investigates the performance of single shot multibox detector (SSD) and ResNet18 network‐based automatic detection and classification of MCs. Additionally, it compares the inter‐observer agreement and observer‐classifier agreement in MCs diagnosis to validate the feasibility of deep learning network‐assisted detection of classified MCs. Method: A retrospective analysis of 140 patients with MCs who underwent MRI diagnosis and met the inclusion and exclusion criteria in Tianjin Hospital from June 2020 to June 2021 was used as the internal dataset. This group consisted of 55 males and 85 females, aged 25 to 89 years, with a mean age of (59.0 ± 13.7) years. An external test dataset of 28 patients, who met the same criteria and were assessed using different MRI equipment at Tianjin Hospital, was also gathered, including 11 males and 17 females, aged 31 to 84 years, with a mean age of 62.7 ± 10.9 years. After Physician 1 (with 15 years of experience) annotated all MRI images, the internal dataset was imported into the deep learning model for training. The model comprises an SSD network for lesion localization and a ResNet18 network for lesion classification. Performance metrics, including accuracy, recall, precision, F1 score, confusion matrix, and inter‐observer agreement parameter Kappa value, were used to evaluate the model's performance on the internal and external datasets. Physician 2 (with 1 year of experience) re‐labeled the internal and external test datasets to compare the inter‐observer agreement and observer‐classifier agreement. Results: In the internal dataset, when models were utilized for the detection and classification of MCs, the accuracy, recall, precision and F1 score reached 86.25%, 87.77%, 84.92% and 85.60%, respectively. The Kappa value of the inter‐observer agreement was 0.768 (95% CI: 0.656, 0.847),while observer‐classifier agreement was 0.717 (95% CI: 0.589, 0.809).In the external test dataset, the model's the accuracy, recall, precision and F1 scores for diagnosing MCs reached 75%, 77.08%, 77.80% and 74.97%, respectively. The inter‐observer agreement was 0.681 (95% CI: 0.512, 0.677), and observer‐classifier agreement was 0.519 (95% CI: 0.290, 0.690). Conclusion: The model demonstrated strong performance in detecting and classifying MCs, achieving high agreement with physicians in MCs diagnosis. These results suggest that deep learning models have the potential to facilitate the application of intelligent assisted diagnosis techniques in the field of spine research. [ABSTRACT FROM AUTHOR]

Automatic extraction of flame area plays an important role in forest fire detection, which can accurately understand the spatial distribution and development trend of forest fire, so as to effectively realize the protection of forest resources. However, due to the instability and spread of fires, and the complexity of the background, accurate early fire detection is extremely challenging. At the same time, the image pixel proportion of the flame area in early stage is much smaller than that in the background, which causes a serious class imbalance problem. With the fast development of deep learning, some achievements have been made in flame extraction, but there are still some deficiencies in the existing networks, such as limited feature representation, poor feature capturing ability on micro objects, insufficiency processing of local features, etc. This paper proposes an attention-based dual-encoding segmentation network, abbreviated as ADE-Net, for pixelwise early fire detection. To realize strong feature representation, a dual-encoding path, consisting of semantic units and spatial units, is introduced to extract richer features, and an attention fusion module (AFM) is introduced to fully integrate spatial and semantic information and achieve effective feature aggregation. In addition, faced with the class imbalance problem, a multi-attention fusion (MAF) module is introduced to obtain more discriminating features to make the segmentation network to focus on the key pixel areas. Furthermore, a feature enhancement module, named attention-guided enhancement (AGE) module, is proposed to enrich the feature representation of local feature maps. Finally, to realize better multi-scale global feature extraction and fusion, a global context fusion (GCF) module is proposed into the bottleneck layer for multi-scale feature enhancement. Experimental results show that the proposed ADE-Net has a good early fire detection ability from remote sensing images, and it has obtained a competitive advantage compared with advanced segmentation models. [ABSTRACT FROM AUTHOR]

Objective: For several years, computer technology has been utilized to diagnose lung nodules. When compared to traditional machine learning methods for image processing, deep-learning methods can improve the accuracy of lung nodule diagnosis by avoiding the laborious pre-processing step of the picture (extraction of fake features, etc.). Our goal is to investigate how well deep-learning approaches classify lung nodule malignancy. Method: We evaluated the performance of deep-learning methods on lung nodule malignancy classification via a systematic literature search. We conducted searches for appropriate articles in the PubMed and ISI Web of Science databases and chose those that employed deep learning to classify or predict lung nodule malignancy for our investigation. The figures were plotted, and the data were extracted using SAS version 9.4 and Microsoft Excel 2010, respectively. Results: Sixteen studies that met the criteria were included in this study. The articles classified or predicted pulmonary nodule malignancy using classification and summarization, using convolutional neural network (CNN), autoencoder (AE), and deep belief network (DBN). The AUC of deep-learning models is typically greater than 90% in articles. It demonstrated that deep learning performed well in the diagnosis and forecasting of lung nodules. Conclusion: It is a thorough analysis of the most recent advancements in lung nodule deep-learning technologies. The advancement of image processing techniques, traditional machine learning techniques, deep-learning techniques, and other techniques have all been applied to the technology for pulmonary nodule diagnosis. Although the deep-learning model has demonstrated distinct advantages in the detection of pulmonary nodules, it also carries significant drawbacks that warrant additional research. [ABSTRACT FROM AUTHOR]

With the development of deep learning techniques, convolutional neural networks (CNN) are increasingly being used in image recognition for marine surveys and underwater object classification. Automatic recognition of targets on side-scan sonar (SSS) images using CNN can improve recognition accuracy and efficiency. However, the vast selection of CNN models makes it challenging to select models for target recognition in SSS images. Therefore, this paper aims to compare different CNN models' prediction accuracy and computational performance comprehensively. First, four traditional CNN models were applied to train and predict the same submarine SSS dataset using both the original model and models with transfer learning methods. Then, we examined and studied the prediction accuracy and computation performance of four CNN models. Results showed that transfer learning enhances the accuracy of all CNN models, with lesser improvements for AlexNet and VGG-16 and greater improvements for GoogleNet and ResNet101. GoogleNet has the highest prediction of accuracy (100% in the train dataset and 94.27% in the test dataset) and good computational difficulty. The findings of this work are useful for future model selection in target recognition in SSS images. [ABSTRACT FROM AUTHOR]

Solar radio observation is a method used to study the Sun. It is very important for space weather early warning and solar physics research to automatically classify solar radio spectrums in real time and judge whether there is a solar radio burst. As the number of solar radio burst spectrums is small and uneven, this paper proposes a classification method for solar radio spectrums based on the Swin transformer. First, the method transfers the parameters of the pretrained model to the Swin transformer model. Then, the hidden layer weights of the Swin transformer are frozen, and the fully connected layer of the Swin transformer is trained on the target dataset. Finally, parameter tuning is performed. The experimental results show that the method can achieve a true positive rate of 100%, which is more accurate than previous methods. Moreover, the number of our model parameters is only 20 million, which is 80% lower than that of the traditional VGG16 convolutional neural network with more than 130 million parameters. [ABSTRACT FROM AUTHOR]

In the Bering Sea slope, ocean eddies are essential physical processes that carry nutrients to the shelf. The development of the satellite altimeter has facilitated the observation of oceanic eddies. Attention networks are used as the core algorithm for eddy detection to suppress feature responses in irrelevant non-eddy areas, which can address the issue of sample imbalance in high-latitude ocean eddies. Furthermore, data from both the sea surface height (SSH) and geostrophic velocity were employed as model inputs to integrate more eddy-related properties. The results of ocean eddy detection using this method and the dataset allowed more eddies to be detected than with traditional vector geometry-based methods and only SSH-based models. This study also incorporated the results of multiple deep learning models to increase both the overall and single-day eddy detection efficiency. As a result, the algorithms in this paper show that attention networks and geostrophic velocity data are both appropriate for high-latitude ocean eddy identification. This makes a contribution to the further application of deep learning methods to satellite altimetry data. [ABSTRACT FROM AUTHOR]

Deep-learning (DL)-based object detection algorithms can greatly benefit the community at large in fighting fires, advancing climate intelligence, and reducing health complications caused by hazardous smoke particles. Existing DL-based techniques, which are mostly based on convolutional networks, have proven to be effective in wildfire detection. However, there is still room for improvement. First, existing methods tend to have some commercial aspects, with limited publicly available data and models. In addition, studies aiming at the detection of wildfires at the incipient stage are rare. Smoke columns at this stage tend to be small, shallow, and often far from view, with low visibility. This makes finding and labeling enough data to train an efficient deep learning model very challenging. Finally, the inherent locality of convolution operators limits their ability to model long-range correlations between objects in an image. Recently, encoder–decoder transformers have emerged as interesting solutions beyond natural language processing to help capture global dependencies via self- and inter-attention mechanisms. We propose Nemo: a set of evolving, free, and open-source datasets, processed in standard COCO format, and wildfire smoke and fine-grained smoke density detectors, for use by the research community. We adapt Facebook's DEtection TRansformer (DETR) to wildfire detection, which results in a much simpler technique, where the detection does not rely on convolution filters and anchors. Nemo is the first open-source benchmark for wildfire smoke density detection and Transformer-based wildfire smoke detection tailored to the early incipient stage. Two popular object detection algorithms (Faster R-CNN and RetinaNet) are used as alternatives and baselines for extensive evaluation. Our results confirm the superior performance of the transformer-based method in wildfire smoke detection across different object sizes. Moreover, we tested our model with 95 video sequences of wildfire starts from the public HPWREN database. Our model detected 97.9% of the fires in the incipient stage and 80% within 5 min from the start. On average, our model detected wildfire smoke within 3.6 min from the start, outperforming the baselines. [ABSTRACT FROM AUTHOR]

As the core machining process element in the overall manufacturing process of a part, the machining process route plays an important role in improving final manufacturing quality. In the current CAPP system, the decision-making of the process route still depends on human-computer interaction and essentially depends on human intelligence. In the past decade, deep learning technology architecture has been gradually improved, which provides a new enabling technology for intelligent process planning. Recently, some researchers have applied deep learning to process route decision-making. However, due to the challenges of data representation and deep learning network construction, this promising solution is still at infancy. To address the two challenges, this paper presents a novel process route generation approach based on deep learning. First, we propose a fourth-order tensor model to represent the geometry and technological requirements of a part. And the relation matrix is constructed to represent the relationships among machining features. The process route is represented as a sequential set of one-hot vectors. Then, we construct an encoder-decoder neural architecture to automatically generate the machining process route for the part. The 3D convolution neuron network-based encoder converts the geometry, technological requirements, and the information of the relationships among machining features into a higher layer of vector representation, and the long short-term memory network-based decoder maps this representation to the process route. The whole neural architecture including the encoder and decoder is jointly trained to maximize the conditional probability of the target process route given the training part. Finally, the paper takes slot cavity parts as examples to verify the feasibility and effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

Since the outbreak of COVID-19, in order to reduce people's contact, the takeaway business has been developed rapidly, bringing a large demand for disposable and degradable tableware (e.g., wooden spoon). However, in the production process of wooden spoon, the selection of crack spoons still relies on manual labour. Therefore, in order to detect cracked wooden spoons more effectively and reduce production costs, we propose a wooden spoon crack detection method by using machine vision techniques and apply it in real-world industrial factory. In the production system, the captured color of crack regions is black while the good region shows normal log color. The positions of crack regions are located frequently in the central or marginal areas of spoons and their directions of cracks are often same due to the extrusion of the mold in the production process. Based on these two types of prior knowledge (i.e., color and spatial prior information), three modules are designed to explore these priors by jointly integrating with the current mainstream detection network of YOLO-v5, which satisfies the speed and accuracy for detecting cracks. The color fusion module is designed to explore the color difference between good regions and crack regions. The attention and orientation modules are then combined and embedded into the backbone of deep architecture. Reported experiments on our collected database show that our proposed detection method can locate the spoon cracks very well and significantly outperforms the model of YOLO-v5 with the protocols of R e c a l l , P r e c i s i o n and m e a n A v e r a g e P r e c i s i o n (m A P). • A method with prior analysis is proposed for wooden spoon crack detection. • The color and spatial priors are analyzed for inspecting cracks. • A vision-based system has been deployed and achieved promising performance. [ABSTRACT FROM AUTHOR]

Accurate predictions of remaining useful life (RUL) of important components play a crucial role in system reliability, which is the basis of prognostics and health management (PHM). This paper proposed an integrated deep learning approach for RUL prediction of a turbofan engine by integrating an autoencoder (AE) with a deep convolutional generative adversarial network (DCGAN). In the pretraining stage, the reconstructed data of the AE not only participate in its error reconstruction but also take part in the DCGAN parameter training as the generated data of the DCGAN. Through double-error reconstructions, the capability of feature extraction is enhanced, and high-level abstract information is obtained. In the fine-tuning stage, a long short-term memory (LSTM) network is used to extract the sequential information from the features to predict the RUL. The effectiveness of the proposed scheme is verified on the NASA commercial modular aero-propulsion system simulation (C-MAPSS) dataset. The superiority of the proposed method is demonstrated via excellent prediction performance and comparisons with other existing state-of-the-art prognostics. The results of this study suggest that the proposed data-driven prognostic method offers a new and promising prediction approach and an efficient feature extraction scheme. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) technology is increasingly used for digital orthodontics, but one of the challenges is to automatically and accurately detect tooth landmarks and axes. While many attempts have been made to streamline the orthodontic treatments by automatic technologies, the detection of the tooth landmarks and axes is still left to experienced dental practitioners and performed manually. This is partly because of sophisticated geometric definitions of them, and partly due to large variations among individual tooth and across different types of tooth. As such, we propose a deep learning approach with a labeled dataset by professional dentists to the tooth landmark/axis detection on tooth model that are crucial for orthodontic treatments. Our method can extract not only tooth landmarks in the form of points (e.g. cusps), but also axes that measure the tooth angulation and inclination. The proposed network takes as input a 3D tooth model and predicts various types of the tooth landmarks and axes. Specifically, we encode the landmarks and axes as dense fields defined on the surface of the tooth model. This design choice and a set of added components make the proposed network more suitable for extracting sparse landmarks from a given 3D tooth model. Extensive evaluation of the proposed method was conducted on a set of dental models prepared by experienced dentists. Results show that our method can produce tooth landmarks with high accuracy. Our method was examined and justified via comparison with the state-of-the-art methods as well as the ablation studies. • A novel framework is presented for robustly detecting tooth landmarks and axes from a 3D tooth model. • A new mechanism is proposed for encoding sparse geometric entities (i.e. points and axes) into dense representation. • A novel feature aggregation module is proposed to aggregate features in local and non-local spaces. [ABSTRACT FROM AUTHOR]

Many COVID-19 patients infected by SARS-CoV-2 virus develop pneumonia (called novel coronavirus pneumonia, NCP) and rapidly progress to respiratory failure. However, rapid diagnosis and identification of high-risk patients for early intervention are challenging. Using a large computed tomography (CT) database from 3,777 patients, we developed an AI system that can diagnose NCP and differentiate it from other common pneumonia and normal controls. The AI system can assist radiologists and physicians in performing a quick diagnosis especially when the health system is overloaded. Significantly, our AI system identified important clinical markers that correlated with the NCP lesion properties. Together with the clinical data, our AI system was able to provide accurate clinical prognosis that can aid clinicians to consider appropriate early clinical management and allocate resources appropriately. We have made this AI system available globally to assist the clinicians to combat COVID-19. • AI system that can diagnose COVID-19 pneumonia using CT scans • Prediction of progression to critical illness • Potential to improve performance of junior radiologists to the senior level • Can assist evaluation of drug treatment effects with CT quantification Zhang et al. present an AI-based system, based on hundreds of thousands of human lung CT scan images, that can aid in distinguishing patients NCP versus other common pneumonia and can help to predict the prognosis of COVID-19 patients. [ABSTRACT FROM AUTHOR]