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Due to the rapid advancement of the transportation industry and the continual increase in pavement infrastructure, it is difficult to keep up with the huge road maintenance task by relying only on the traditional manual detection method. Intelligent pavement detection technology with deep learning techniques is available for the research and industry areas by the gradual development of computer vision technology. Due to the different characteristics of pavement distress and the uncertainty of the external environment, this kind of object detection technology for distress classification and location still faces great challenges. This paper discusses the development of object detection technology and analyzes classical convolutional neural network (CNN) architecture. In addition to the one-stage and two-stage object detection frameworks, object detection without anchor frames is introduced, which is divided according to whether the anchor box is used or not. This paper also introduces attention mechanisms based on convolutional neural networks and emphasizes the performance of these mechanisms to further enhance the accuracy of object recognition. Lightweight network architecture is introduced for mobile and industrial deployment. Since stereo cameras and sensors are rapidly developed, a detailed summary of three-dimensional object detection algorithms is also provided. While reviewing the history of the development of object detection, the scope of this review is not only limited to the area of pavement crack detection but also guidance for researchers in related fields is shared. [ABSTRACT FROM AUTHOR]

Introduction: This paper introduces a deep learning approach based on Convolutional Neural Networks (CNN), Bidirectional Long Short-Term Memory Networks (BiLSTM), and attention mechanism for stock market prediction and investment decision making in financial management. These methods leverage the advantages of deep learning to capture complex patterns and dependencies in financial time series data. Stock market prediction and investment decisionmaking have always been important issues in financial management. Methods: Traditional statistical models often struggle to handle nonlinear relationships and complex temporal dependencies, thus necessitating the use of deep learning methods to improve prediction accuracy and decision effectiveness. This paper adopts a hybrid deep learning model incorporating CNN, BiLSTM, and attention mechanism. CNN can extract meaningful features from historical price or trading volume data, while BiLSTM can capture dependencies between past and future sequences. The attention mechanism allows the model to focus on the most relevant parts of the data. These methods are integrated to create a comprehensive stock market prediction model. We validate the effectiveness of the proposed methods through experiments on real stock market data. Compared to traditional models, the deep learning model utilizing CNN, BiLSTM, and attention mechanism demonstrates superior performance in stock market prediction and investment decision-making. Results and Discussion: Through ablation experiments on the dataset, our deep learning model achieves the best performance across all metrics. For example, the Mean Absolute Error (MAE) is 15.20, the Mean Absolute Percentage Error (MAPE) is 4.12%, the Root Mean Square Error (RMSE) is 2.13, and the Mean Squared Error (MSE) is 4.56. This indicates that these methods can predict stock market trends and price fluctuations more accurately, providing financial managers with more reliable decision guidance. This research holds significant implications for the field of financial management. It offers investors and financial institutions an innovative approach to better understand and predict stock market behavior, enabling them to make wiser investment decisions. [ABSTRACT FROM AUTHOR]

Introduction: In recent years, the use of EEG signals for seizure detection has gained widespread academic attention. Aiming at the problem of overfitting deep learning models due to the small number of EEG signal data during epilepsy detection, this paper proposes an epilepsy detection method that combines data augmentation and deep learning. Methods: First, the Adversarial and Mixup Data Augmentation (AMDA) method is used to realize the data augmentation, which effectively enriches the number of training samples. To further improve the classification accuracy and robustness of epilepsy detection, this paper proposes a one-dimensional convolutional neural network and gated recurrent unit (AM-1D CNN-GRU) network model based on attention mechanism for epilepsy detection. Results and discussion: The experimental results show that the performance of epilepsy detection achieved by using augmented data is significantly improved, and the accuracy, sensitivity, and area under the subject's working characteristic curve are up to 96.06, 95.48%, and 0.9637, respectively. Compared with the nonaugmented data, all indicators are increased by more than 6.2%. Meanwhile, the detection performance was significantly improved compared with other epilepsy detection methods. The results of this research can provide a reference for the clinical application of epilepsy detection. [ABSTRACT FROM AUTHOR]

Since plant diseases occurring during the growth process are a significant factor leading to the decline in both yield and quality, the classification and detection of plant leaf diseases, followed by timely prevention and control measures, are crucial for safeguarding plant productivity and quality. As the traditional convolutional neural network structure cannot effectively recognize similar plant leaf diseases, in order to more accurately identify the diseases on plant leaves, this paper proposes an effective plant disease image recognition method aECA-ResNet34. This method is based on ResNet34, and in the first and the last layers of this network, respectively, we add this paper's improved aECAnet with the symmetric structure. aECA-ResNet34 is compared with different plant disease classification models on the peanut dataset constructed in this paper and the open-source PlantVillage dataset. The experimental results show that the aECA-ResNet34 model proposed in this paper has higher accuracy, better performance, and better robustness. The results show that the aECA-ResNet34 model proposed in this paper is able to recognize diseases of multiple plant leaves very accurately. [ABSTRACT FROM AUTHOR]

Deep Self-Attention Network (Transformer) is an encoder–decoder architectural model that excels in establishing long-distance dependencies and is first applied in natural language processing. Due to its complementary nature with the inductive bias of convolutional neural network (CNN), Transformer has been gradually applied to medical image processing, including kidney image processing. It has become a hot research topic in recent years. To further explore new ideas and directions in the field of renal image processing, this paper outlines the characteristics of the Transformer network model and summarizes the application of the Transformer-based model in renal image segmentation, classification, detection, electronic medical records, and decision-making systems, and compared with CNN-based renal image processing algorithm, analyzing the advantages and disadvantages of this technique in renal image processing. In addition, this paper gives an outlook on the development trend of Transformer in renal image processing, which provides a valuable reference for a lot of renal image analysis. [ABSTRACT FROM AUTHOR]

An ultra-short-term multivariate load forecasting method under a microscopic perspective is proposed to address the characteristics of user-level integrated energy systems (UIES), which are small in scale and have large load fluctuations. Firstly, the spatio-temporal correlation of users' energy use behavior within the UIES is analyzed, and a multivariate load input feature set in the form of a class image is constructed based on the various types of load units. Secondly, in order to maintain the feature independence and temporal integrity of each load during the feature extraction process, a deep neural network architecture with spatiotemporal coupling characteristics is designed. Among them, the multi-channel parallel convolutional neural network (MCNN) performs independent spatial feature extraction of the 2D load component pixel images at each moment in time, and feature fusion of various types of load features in high dimensional space. A bidirectional long short-term memory network (BiLSTM) is used as a feature sharing layer to perform temporal feature extraction on the fused load sequences. In addition, a spatial attention layer and a temporal attention layer are designed in this paper for the original input load pixel images and the fused load sequences, respectively, so that the model can better capture the important information. Finally, a multi-task learning approach based on the hard sharing mechanism achieves joint prediction of each load. The measured load data of a UIES is analyzed as an example to verify the superiority of the method proposed in this paper. [ABSTRACT FROM AUTHOR]

The mesoscale eddies are prevalent oceanic circulation phenomena, exerting significant influence on various aspects of the marine environment including energy transfer, material transport and ecosystem dynamics in the Northwest Pacific Ocean. However, due to sparse vertical observational data, the understanding of the three-dimensional temperature structure of individual cases of mesoscale eddies remains limited. In recent years, utilizing surface remote sensing observations to estimate subsurface temperature anomaly has been crucial for comprehending the intricate multi-dimensional dynamic processes in the ocean. Consequently, this paper proposes an eddy residual multi-channel attention convolution network (ERCACN) with the adaptive threshold and designs the combination of various surface features to estimate the eddy subsurface temperature anomaly (ESTA). By integrating results with climatic temperature, thermal structures containing 46 levels at depths up to 1000 m could be obtained, achieving excellent daily temporal resolution and 0.25° spatial resolution. Validation using independent Argo profiles from 2016 to 2017 reveals that the combination of multiple surface variables outperforms univariate methods, and the ERCACN model demonstrates superior performance compared to other approaches. Overall, with an 8% error deemed acceptable, the ERCACN model achieves a precision of 88.08% in estimating ESTA. This method provides a novel perspective for other essential oceanic variables, contributing to a better perception of the global climate system. [ABSTRACT FROM AUTHOR]

To recommend useful information to users more efficiently, this paper proposes a dual-path recommendation algorithm which combines multilayer Convolutional Neural Network (CNN) and attention-enhanced long short-term memory network (Attention-LSTM). Firstly, the matrix factorisation technique is used for learning the long-term preferences of users. Secondly, a dual-path network based on CNN and LSTM is constructed to perform feature extraction on the rating matrix. The dual-path network can learn the long-term preferences of users while capturing their dynamic preferences in changing preferences. The algorithm is tested on the public dataset MovieLens-1M, and the MAE value reflects the accuracy of the algorithm. [ABSTRACT FROM AUTHOR]

With the rapid development of electric power systems, load estimation plays an important role in system operation and planning. Usually, load estimation techniques contain traditional, time series, regression analysis-based, and machine learning-based estimation. Since the machine learning-based method can lead to better performance, in this paper, a deep learning-based load estimation algorithm using image fingerprint and attention mechanism is proposed. First, an image fingerprint construction is proposed for training data. After the data preprocessing, the training data matrix is constructed by the cyclic shift and cubic spline interpolation. Then, the linear mapping and the gray-color transformation method are proposed to form the color image fingerprint. Second, a convolutional neural network (CNN) combined with an attentionmechanism is proposed for training performance improvement. At last, an experiment is carried out to evaluate the estimation performance. Compared with the support vector machine method, CNN method and long short-term memory method, the proposed algorithm has the best load estimation performance. [ABSTRACT FROM AUTHOR]

Although deep learning has been proven to significantly outperform most traditional methods in the classification of large-scale balanced image datasets, collecting enough samples for defect classification is extremely time-consuming and costly. In this paper, we propose a lightweight defect classification method based on few-shot image generation and self-attention fused convolution features. We constructed a 4-class dataset using welding seam images collected from a solar cell module packaging production line. To address the issue of limited defect samples, especially for classes with less than 10 images, we implemented two strategies. Geometric enhancement techniques were first used to extend the defective images. Secondly, multi-scale feature fusion Generative Adversarial Networks (GANs) were utilized to further enhance the dataset. We then performed the feature-level fusion of convolution neural networks and self-attention networks, achieving a classification accuracy of 98.19%. Our experimental results demonstrate that the proposed model performs well in small sample defect classification tasks. And, it can be effectively applied to product quality inspection tasks in industrial production lines. [ABSTRACT FROM AUTHOR]

Deep learning has shown superior performance in various applications. The emergence of graph convolution neural networks (GCNs) enables deep learning to learn the latent representation from graph-structured data with rich attributes. To be specific, the message passing mechanism of GCNs can aggregate and update messages through the topological relationship between nodes in a graph. The graph attention network (GAT) introduces the attention mechanism into GCNs when aggregating messages and achieves significant performance on the node classification task. However, focusing on each node in the neighborhood, GAT becomes extremely complex. In addition, although stacking network layers could obtain a wider receptive field, it also brings high time cost and leads to the difficulty of training. To handle this problem, this paper only divides the messages into two types, i.e. self message and neighborhood message. The neighborhood message comes from the neighborhood with(out) self-loop while the self message comes from the node itself. Then, we design a light attention mechanism that only focuses on two weights, one for the self message, and the other for the neighborhood message, to adaptively reveal the different contributions of messages from a node as well as its neighborhood. In addition, we also adopt linear propagation, a shallow and efficient method, to aggregate messages from distant neighbors and thus obtain a wider neighborhood receiving field. To verify the effectiveness of our proposed approach, extensive experiments have been conducted on the semi-supervised node classification task. Results show that our proposed approach achieves comparable or even better performance than the baseline methods with complicated GCN structures on the benchmark datasets. Specifically, the proposed light attention mechanism focusing on message passing exhibits a great efficiency improvement with the training time cost less than half of GAT. [ABSTRACT FROM AUTHOR]

Fire is one of the most harmful hazards that affect daily life. The existing fire detection methods have the problems of large computation, slow detection speed, and low detection accuracy to varying degrees, and do not achieve a better trade-off between model complexity, accuracy, and detection speed. In this paper, a multiscale fire image detection method combining Convolutional Neural Network(CNN) and Transformer is proposed. In the shallow layer of the model, the CNN-based multiscale feature extraction module is used to obtain rich fire image information. In the deep layers of the model, the powerful global learning ability of the Transformer is used to carry out overall perception and macroscopic understanding of images. The experimental results show that the best detection accuracy of the model can reach 94.62%, and the fastest detection speed can reach 158.12FPS, F1 score is stable at around 94%, which is fully capable of real-time and accurate detection of fire. Compared with the existing detection methods, this method has higher detection accuracy under similar model complexity and detection speed. With similar detection accuracy, our method has a faster detection speed. The proposed method achieves a better balance between model complexity, detection speed, and accuracy. [ABSTRACT FROM AUTHOR]

Emotion detection holds significant importance in facilitating human–computer interaction, enhancing the depth of engagement. By integrating this capability, we pave the way for forthcoming AI technologies to possess a blend of cognitive and emotional understanding, bridging the divide between machine functionality and human emotional complexity. This progress has the potential to reshape how machines perceive and respond to human emotions, ushering in an era of empathetic and intuitive artificial systems. The primary research challenge involves developing models that can accurately interpret and analyze emotions from both auditory and textual data, whereby auditory data require optimizing CNNs to detect subtle and intense emotional fluctuations in speech, and textual data necessitate access to large, diverse datasets to effectively capture nuanced emotional cues in written language. This paper introduces a novel approach to multimodal emotion recognition, seamlessly integrating speech and text modalities to accurately infer emotional states. Employing CNNs, we meticulously analyze speech using Mel spectrograms, while a BERT-based model processes the textual component, leveraging its bidirectional layers to enable profound semantic comprehension. The outputs from both modalities are combined using an attention-based fusion mechanism that optimally weighs their contributions. The proposed method here undergoes meticulous testing on two distinct datasets: Carnegie Mellon University's Multimodal Opinion Sentiment and Emotion Intensity (CMU-MOSEI) dataset and the Multimodal Emotion Lines Dataset (MELD). The results demonstrate superior efficacy compared to existing frameworks, achieving an accuracy of 88.4% and an F1-score of 87.9% on the CMU-MOSEI dataset, and a notable weighted accuracy (WA) of 67.81% and a weighted F1 (WF1) score of 66.32% on the MELD dataset. This comprehensive system offers precise emotion detection and introduces several significant advancements in the field. [ABSTRACT FROM AUTHOR]

Explore how neural networks combine attention mechanisms and their variants to better learn complex and implicit relationships between users and items, thereby improving the accuracy and personalization of recommendations. Starting from six typical types of neural networks: multi-layer perceptron, convolutional neural network, recurrent neural network, autoencoder, graph neural network, and backpropagation neural network, this paper studies the process of combining them with the attention mechanism for recommendation. Specifically, the advantages and disadvantages are analyzed based on typical application scenarios such as click-through rate prediction, tag recommendation, and review rating prediction. By combining neural networks with attention mechanisms, the model can focus on key information in the input data, reduce attention to secondary information, and even directly filter out irrelevant information. Existing recommendation models that combine attention mechanisms with neural networks, to a large extent, can meet the needs of common recommendation tasks. However, this type of model still faces some challenges in complex recommendation scenarios such as cross-domain recommendation, deep reinforcement learning recommendation, and multi-modal recommendation. For example, cross-domain recommendation requires the model with the ability of transfer learning, and reinforcement learning recommendation requires long-term reward modeling. [ABSTRACT FROM AUTHOR]

The object of this study is the process of recognizing intrusions in computer networks. Network intrusion detection systems (NIDS) have become an important area of research as they are used to protect computer systems from hacker attacks. Deep learning is becoming increasingly popular for detecting and classifying malicious network traffic, including for building NIDS. In this paper, we propose a network intrusion detection model CNN-BiGRU-Attention based on a time-based approach to deep learning using the attention mechanism. The main goal of the study is to build an effective combined deep learning model that can detect various network cyber threats. A 1D convolutional neural network is implemented to extract high-level representations of intrusion information features. A bidirectional gated recurrent unit (BiGRU) with an attention mechanism for traffic data classification has been designed. The attention mechanism plays a key role in the constructed model as it allows the system to focus only on important aspects of network traffic and allows the model to adapt to new types of threats. The results of the study show that using a combination of CNN and BiGRU with the attention mechanism speeds up and improves the process of classifying network attacks. On the NSL-KDD and UNSW-NB15 training datasets, the model shows an accuracy of 99.81 % and 97.80 %. On the NSL-KDD and UNSW-NB15 test datasets, the model demonstrates 82.16 % and 97.72 % accuracy. The proposed NIDS model will be considered for implementation in a real-time corporate network security system. In general, the results of the study provide a new perspective on improving the performance of NIDS and are quite relevant in terms of using attention mechanisms to classify network traffic. [ABSTRACT FROM AUTHOR]

This paper proposes a novel deep learning model incorporating attention mechanisms for the analysis of slope stochastic fields. Initially, a deep learning model is designed to digitally image the stochastic field features of soil strength variability. This is achieved by discretizing the slope soil stochastic field using the Karhunen-Loeve expansion method and transforming the discrete results into digital images. These images are then used to establish a Convolutional Neural Network (CNN) surrogate model that maps the implicit relationship between stochastic field images and slope functional function values, thus calculating the probability of slope failure. The precision of the CNN surrogate model is enhanced through Bayesian optimization and five-fold cross-validation. Moreover, to overcome the limitations of existing data-driven landslide stability prediction models, this study also introduces a Spatial-Temporal Attention (STA) mechanism. By combining the CNN with Long Short-Term Memory (LSTM) networks, the model can accurately approximate the actual results of slope stability calculations in scenarios of high-dimensional representation imaging of stochastic fields and low-probability slope instability. Consequently, this significantly improves the computational efficiency of slope reliability analysis considering stochastic field simulations. [ABSTRACT FROM AUTHOR]

Assessing the strength of complex networks is crucial for evaluating their functionality and monitoring system security. Two primary perspectives for measuring network robustness are connectivity and controllability, which quantify how well a network maintains connectivity and controllability after experiencing complete attacks. However, conventional robustness calculation methods that use simulation attacks can be impractical for large-scale networks. Recent research has explored the use of deep learning methods for network robustness prediction, including PCR (short for predictor of controllability robustness), which uses a VGG (short for visual geometry group)-based convolutional neural network (CNN) and a linear filter. However, PCR has limitations, including inaccurate edge importance differentiation and fluctuating output curves. This paper presents ATTRP, an attention-based robustness predictor, which addresses these limitations. ATTRP uses a novel convolutional neural network with channel and spatial attention mechanisms to improve prediction accuracy and a robustness filter based on Savitzky–Golay smoothing to rectify the robustness curve. Experimental studies demonstrate that ATTRP outperforms other predictors, with much lower errors, while its computational speed is far superior to that of traditional attacking simulation. Overall, our research provides a more effective and efficient means of predicting network robustness, with potential applications in monitoring system security. [ABSTRACT FROM AUTHOR]

This paper delves into the realm of human activity recognition (HAR) by leveraging the capabilities of Siamese neural networks (SNNs), focusing on the comparative effectiveness of contrastive and triplet learning approaches. Against the backdrop of HAR's growing importance in healthcare, sports, and smart environments, the need for advanced models capable of accurately recognizing and classifying complex human activities has become paramount. Addressing this, we have introduced a Siamese network architecture integrated with convolutional neural networks (CNNs) for spatial feature extraction, bidirectional LSTM (Bi-LSTM) for temporal dependency capture, and attention mechanisms to prioritize salient features. Employing both contrastive and triplet loss functions, we meticulously analyze the impact of these learning approaches on the network's ability to generate discriminative embeddings for HAR tasks. Through extensive experimentation, the study reveals that Siamese networks, particularly those utilizing triplet loss functions, demonstrate superior performance in activity recognition accuracy and F1 scores compared with baseline deep learning models. The inclusion of a stacking meta-classifier further amplifies classification efficacy, showcasing the robustness and adaptability of our proposed model. Conclusively, our findings underscore the potential of Siamese networks with advanced learning paradigms in enhancing HAR systems, paving the way for future research in model optimization and application expansion. [ABSTRACT FROM AUTHOR]

Railway signals' fault text data contain a substantial amount of expert maintenance experience. Extracting valuable information from these fault text data can enhance the efficiency of fault diagnosis for signal equipment, thereby contributing to the advancement of intelligent railway operations and maintenance technology. Considering that the characteristics of different signal equipment in actual operation can easily lead to a lack of fault data, a fault diagnosis method for railway signal equipment based on data augmentation and an improved attention mechanism (DEIAM) is proposed in this paper. Firstly, the original fault dataset is preprocessed based on data augmentation technology and retained noun and verb operations. Then, the neural network is constructed by integrating a bidirectional long short-term memory (BiLSTM) model with an attention mechanism and a convolutional neural network (CNN) model enhanced with a channel attention mechanism. The DEIAM method can more effectively capture the important text features and sequence features in fault text data, thereby facilitating the diagnosis and classification of such data. Consequently, it enhances onsite fault maintenance experience by providing more precise insights. An empirical study was conducted on a 10-year fault dataset of signal equipment produced by a railway bureau. The experimental results demonstrate that in comparison with the benchmark model, the DEIAM model exhibits enhanced performance in terms of accuracy, precision, recall, and F1. [ABSTRACT FROM AUTHOR]

With the rapid advancements in computer vision, using deep learning for strawberry disease recognition has emerged as a new trend. However, traditional identification methods heavily rely on manual discernment, consuming valuable time and imposing significant financial losses on growers. To address these challenges, this paper presents BerryNet-Lite, a lightweight network designed for precise strawberry disease identification. First, a comprehensive dataset, encompassing various strawberry diseases at different maturity levels, is curated. Second, BerryNet-Lite is proposed, utilizing transfer learning to expedite convergence through pre-training on extensive datasets. Subsequently, we introduce expansion convolution into the receptive field expansion, promoting more robust feature extraction and ensuring accurate recognition. Furthermore, we adopt the efficient channel attention (ECA) as the attention mechanism module. Additionally, we incorporate a multilayer perceptron (MLP) module to enhance the generalization capability and better capture the abstract features. Finally, we present a novel classification head design approach which effectively combines the ECA and MLP modules. Experimental results demonstrate that BerryNet-Lite achieves an impressive accuracy of 99.45%. Compared to classic networks like ResNet34, VGG16, and AlexNet, BerryNet-Lite showcases superiority across metrics, including loss value, accuracy, precision, F1-score, and parameters. It holds significant promise for applications in strawberry disease identification. [ABSTRACT FROM AUTHOR]

Summary: Internet data thefts, intrusions and DDoS attacks are some of the big concerns for the network security today. Detection of these anomalies, is gaining tremendous impetus with the development of machine learning and artificial intelligence. Even now researchers are shifting the base from machine learning to the deep neural architectures with auto‐feature selection capabilities. We in this paper propose multiple deep neural network architectures which can select, co‐learn and teach the gradients of the neural network by itself with no human intervention. This is what we call as meta‐learning. The models are configured in both many to one and many to many design architectures. We combine long short‐term memory (LSTM), bi‐directional long short‐term memory (BiLSTM), convolutional neural network (CNN) layers along with attention mechanism to achieve the higher accuracy values among all the available deep learning model architectures. LSTMs overcomes the vanishing and exploding gradient problem of RNN and attention mechanism mimics the human cognitive attention that screens the network flow to obtain the key features for network traffic classification. In addition, we also add multiple convolutional layers to get the key features for network traffic classification. We get the time series analysis of the traffic done for the possibility of a DDoS attack without using any feature selection techniques and without balancing the dataset. The performance analysis is done based on confusion matrix scores, that is, accuracy, false alarm rate (FAR), sensitivity, specificity, false‐positive rate (FPR), F1 score, area under curve (AUC) analysis and loss functions on well‐known public benchmark KDD Cup'99 data set. The results of our experiments reveal that our models outperform existing techniques, showing their superiority in performance. [ABSTRACT FROM AUTHOR]

It is of great significance to accurately grasp ground-level ozone concentration on a wide scale in order to cope with the increasingly serious ground-level ozone pollution problem. Currently, satellite technology can be used to monitor ground-level ozone concentration on a wide scale, but there is the problem of insufficient spatial resolution. The use of ground monitoring stations to detect ground-level ozone concentration can obtain more accurate ozone concentration around the station, but there are problems of limited number of stations and sparse distribution. Therefore, it is still a challenge to accurately grasp the ground-level ozone concentration at a large scale and high spatial resolution. To address this issue, this paper proposes a ground-level ozone concentration inversion algorithm based on deep learning methods, incorporating data from remote sensing satellites, ground monitoring stations, meteorological conditions, geography, and land use. Taking Yunnan Province in China as the experimental region and utilizing five-fold cross-validation, the model's Mean Square Error (MSE) reached 340.45 and Root Mean Square Error (RMSE) reached 18.45. Compared to machine learning models, the error decreased by 14.53%, outperforming Random Forest models, Convolution Neural Networks, and Vision Transformers. This study offers a referential approach for accurately capturing ground-level ozone concentrations at a large scale with high spatial resolution. [ABSTRACT FROM AUTHOR]

Online chatter detection is crucial to ensure the quality and safety of the high-speed milling process. The rapid development of the deep learning community provides a promising tool for chatter detection. However, most previous chatter detection studies rely on complex signal processing techniques, leading to the separation of feature extraction and chatter detection and reducing detection efficiency. Additionally, these studies are developed for a limited range of machining conditions because the development of their model relies on experimental data, while performing experiments with numerous combinations of machining parameters is expensive and time-consuming. To tackle these drawbacks, this paper proposes a transfer learning chatter detection framework that doesn't rely on any experimental data. The proposed framework is composed of the dynamic milling process model, the Double Attention-driven One-Dimension Convolutional Neural Networks (DAOCNN), and the ensemble prediction strategy. Firstly, a dynamic milling process model is established to generate simulated cutting force signals over a wide range of machining parameters, providing abundant training data and saving experimental costs. Then, without any complex signal processing method, the detection results are directly predicted by the proposed DAOCNN from the time-domain signals, eliminating the separation of feature extraction and chatter detection. Finally, a novel ensemble prediction strategy is proposed to ensure an accurate and robust prediction. To validate the effectiveness of the proposed framework, actual milling experiments are carried out under different cutting conditions. Moreover, contrastive studies with other detection approaches and ensemble methods are also performed. The results demonstrate that the milling stability is correctly predicted by the proposed method in an accurate and efficient manner, which indicates the proposed framework can be a promising tool for online chatter detection. [ABSTRACT FROM AUTHOR]

This paper suggests a text emotion analysis approach built on the BMCBMA model for use in an online education environment aiming at the issues of poor text utilization, challenge in effective information extraction, and a failure to effectively recognize word polysemy in existing text emotion analysis task research. Initially, a language model is constructed using Bidirectional Encoder Representations from Transformers (BERT) pre-training. The word vector trained by BERT pre-training is selected to supplant the word vector trained in the conventional manner, and the semantic vector is generated dynamically using the context of the word. Second, the Bi-directional Long Short-Term Memory (BiLSTM) model is employed to record semantic information by simultaneously integrating both positive and negative orientations to assess the emotional polarity information for every text in its entirety. The Multi-channel Convolutional Neural Networks (CNN) model is then utilized to derive textual local feature information of text. Lastly, the attention mechanism is used to cause all types of features to interact and to derive a deeper semantic association within the context. Experiments indicate that the suggested method has accuracy, F1, recall, and precision values of 0.915, 0.911, 0.915 and 0.908, correspondingly. The suggested approach performs significantly better in text emotion analysis than the comparison method. [ABSTRACT FROM AUTHOR]

Dynamic monitoring of cropland using high spatial resolution remote sensing images is a powerful means to protect cropland resources. However, when a change detection method based on a convolutional neural network employs a large number of convolution and pooling operations to mine the deep features of cropland, the accumulation of irrelevant features and the loss of key features will lead to poor detection results. To effectively solve this problem, a novel cropland change detection network (CroplandCDNet) is proposed in this paper; this network combines an adaptive receptive field and multiscale feature transmission fusion to achieve accurate detection of cropland change information. CroplandCDNet first effectively extracts the multiscale features of cropland from bitemporal remote sensing images through the feature extraction module and subsequently embeds the receptive field adaptive SK attention (SKA) module to emphasize cropland change. Moreover, the SKA module effectively uses spatial context information for the dynamic adjustment of the convolution kernel size of cropland features at different scales. Finally, multiscale features and difference features are transmitted and fused layer by layer to obtain the content of cropland change. In the experiments, the proposed method is compared with six advanced change detection methods using the cropland change detection dataset (CLCD). The experimental results show that CroplandCDNet achieves the best F1 and OA at 76.04% and 94.47%, respectively. Its precision and recall are second best of all models at 76.46% and 75.63%, respectively. Moreover, a generalization experiment was carried out using the Jilin-1 dataset, which effectively verified the reliability of CroplandCDNet in cropland change detection. [ABSTRACT FROM AUTHOR]

The recognition of steel microstructure images plays a crucial role in the metallographic analysis process. Although some progress has been made through the application of artificial intelligence algorithms, several challenges remain. First, existing algorithms exhibit weak nonlinear feature extraction capabilities and noticeable limitations. Second, they overlook the intrinsic noise and redundant interference present in microscopic images. To address these issues, this paper investigates the automatic recognition of metallographic tissues by leveraging residual structures in deep neural networks. An enhanced residual network model based on transfer learning is proposed, which utilizes the pre-trained weights from the ImageNet dataset to facilitate learning with small sample data. This network offers higher classification accuracy and higher F1 scores. In addition, a deep residual shrinkage network model based on an attention mechanism is proposed. This model incorporates an attention sub-network into the original residual module and employs a soft threshold function to eliminate redundant features, including noise. The proposed algorithms are evaluated against various convolutional neural networks using 20 types of metallographic test sets. The experimental results showed that both methods have high accuracy rates of 95% and 94.44%, respectively, and F1 scores of 0.9464 and 0.9419. While maintaining the complexity of the model, there has been a significant improvement in accuracy, and the models exhibit strong generalization capabilities. Our research contributes to enhancing production efficiency, strengthening quality control, and improving material performance through computer vision technology. [ABSTRACT FROM AUTHOR]

Acoustic signal analysis is an important component of transformer online monitoring. Currently, traditional methods have problems such as low spectral resolution, imbalanced sample distribution, and unsatisfactory classification performance. This article first introduces the matrix pencil algorithm for time-frequency spectrum analysis of acoustic signals, and then uses the SMOTE algorithm to expand the imbalanced samples. Then, an ACmix hybrid deep neural network model is constructed to classify 11 types of transformer operation and environmental acoustic signals. Finally, detailed experiments were conducted on the method proposed in this paper, and the experimental results showed that the matrix pencil algorithm has high time-frequency resolution and good noise resistance performance. The SMOTE sample expansion method can significantly improve the recognition accuracy by more than 2%. Overall accuracy of the proposed method in acoustic signal classification tasks reaches 91.81%. [ABSTRACT FROM AUTHOR]

Enhancers play an important role in the process of gene expression regulation. In DNA sequence abundance or absence of enhancers and irregularities in the strength of enhancers affects gene expression process that leads to the initiation and propagation of diverse types of genetic diseases such as hemophilia, bladder cancer, diabetes and congenital disorders. Enhancer identification and strength prediction through experimental approaches is expensive, time-consuming and error-prone. To accelerate and expedite the research related to enhancers identification and strength prediction, around 19 computational frameworks have been proposed. These frameworks used machine and deep learning methods that take raw DNA sequences and predict enhancer's presence and strength. However, these frameworks still lack in performance and are not useful in real time analysis. This paper presents a novel deep learning framework that uses language modeling strategies for transforming DNA sequences into statistical feature space. It applies transfer learning by training a language model in an unsupervised fashion by predicting a group of nucleotides also known as k-mers based on the context of existing k-mers in a sequence. At the classification stage, it presents a novel classifier that reaps the benefits of two different architectures: convolutional neural network and attention mechanism. The proposed framework is evaluated over the enhancer identification benchmark dataset where it outperforms the existing best-performing framework by 5%, and 9% in terms of accuracy and MCC. Similarly, when evaluated over the enhancer strength prediction benchmark dataset, it outperforms the existing best-performing framework by 4%, and 7% in terms of accuracy and MCC. [ABSTRACT FROM AUTHOR]

In recent years, deep learning techniques have been increasingly utilized to automate and reduce human errors in leukocyte classification. However, reliability and accuracy issues remain a challenge. This paper proposes CAB‐Net, an end‐to‐end deep learning‐based model that integrates a convolutional neural network with an attention mechanism and BLSTM. The model uses a stack of convolutional and pooling layers to extract initial features from input images, which are then fed into the attention block to generate class‐specific discriminative features and BLSTM employed to model deterministic class correlations between highlighted discriminative features and labels. Experimental results show that CAB‐Net outperforms state‐of‐the‐art methods in term of accuracy, precision, recall, F1‐Score, and AUC‐ROC. In addition, the CAB‐Net model demonstrates good performance even with low‐resolution images. Consequently, CAB‐Net can be considered as the current state‐of‐the‐art prediction model for improving leukocyte classification in clinical environments. [ABSTRACT FROM AUTHOR]

Multi-exposure fusion (MEF) is widely researched and applied to high dynamic range (HDR) imaging, where one of the most challenging problems is the artifacts caused by the motion of objects between input images. Recently, deep learning methods have been widely applied for HDR imaging with excellent results, showing significant advantages. However, many methods cannot avoid artifacts due to inaccurate alignment before merging HDR images. In this paper, we propose an end-to-end network (C-ED-GMNET) with a channel and spatial attention network, an encoder–decoder network and a gradual merging network for generating artifact-free HDR images in dynamic scenes. The attention module consists of two submodules to identify useful features and exclude harmful components in inputs from both channel and spatial dimensions, respectively. The attention-guided feature maps are sent to the encoders for further feature extraction, and then, the outputs are sent to a gradual merging module consisting of two steps to generate deep features progressively. Besides, the differences between the merged image and the original images are identified by applying global residual learning with all the inputs, and the merged image feature is recovered by the decoder to obtain the final HDR image. Quantitative and qualitative experiments on two public datasets show that the proposed C-ED-GMNET produces better results than existing state-of-the-art methods and significantly reduces the runtime due to the encoders which reduce the amount of computation. [ABSTRACT FROM AUTHOR]

In the domain of AI-driven healthcare, deep learning models have markedly advanced pneumonia diagnosis through X-ray image analysis, thus indicating a significant stride in the efficacy of medical decision systems. This paper presents a novel approach utilizing a deep convolutional neural network that effectively amalgamates the strengths of EfficientNetB0 and DenseNet121, and it is enhanced by a suite of attention mechanisms for refined pneumonia image classification. Leveraging pre-trained models, our network employs multi-head, self-attention modules for meticulous feature extraction from X-ray images. The model's integration and processing efficiency are further augmented by a channel-attention-based feature fusion strategy, one that is complemented by a residual block and an attention-augmented feature enhancement and dynamic pooling strategy. Our used dataset, which comprises a comprehensive collection of chest X-ray images, represents both healthy individuals and those affected by pneumonia, and it serves as the foundation for this research. This study delves deep into the algorithms, architectural details, and operational intricacies of the proposed model. The empirical outcomes of our model are noteworthy, with an exceptional performance marked by an accuracy of 95.19%, a precision of 98.38%, a recall of 93.84%, an F1 score of 96.06%, a specificity of 97.43%, and an AUC of 0.9564 on the test dataset. These results not only affirm the model's high diagnostic accuracy, but also highlight its promising potential for real-world clinical deployment. [ABSTRACT FROM AUTHOR]

Accurate identification of vehicles and estimating density in traffic surveillance systems is a challenging task, particularly in scenarios with closely spaced lanes. Single Shot MultiBox Detector (SSD) is introduced in vehicle detection and classification due to its speed and accuracy. It utilizes a transfer learning technique that enables them to utilize features from pretrained Convolutional Neural Networks (CNNs). Although the utilization of multi-scale feature maps in SSD has achieved efficient results in vehicle detection, it struggles to identify key features of vehicles due to its one-stage detection approach. Furthermore, the use of VGG16 as the backbone network in these approaches leads to the loss of fine-grained details, posing challenges in accurately localizing and classifying small vehicles. Also, there is no interaction between high- and low-level features, which restricts the networks ability to effectively integrate and utilize both features for accurate vehicle detection. To overcome these limitations, an improved SSD approach is introduced in this paper, leveraging interactive multi-scale attention characteristics to accurately detect and classify vehicles. This approach utilizes ResNet50 as the network backbone to overcome the limitations of traditional SSDs in detecting small vehicles. Also, an attention block is incorporated into it to focus on key details and assign higher attention to relevant pixels within the feature map. Also, the network employs a parallel detection framework and shares multi-scale layers (both high and low), enabling efficient detection of vehicles with various sizes. Then, traffic density is estimated based on the weights assigned to the categorized vehicles obtained from the improved SSD and the recorded area. Finally, traffic is classified as high, low, or moderate by comparing the estimated density to a threshold value. By adding attention characteristics of different scales to the original detection branch and replacing the VGG16 with ResNet50 of the SSD technique using our method, the feature representation capability and detection accuracy are both significantly improved. [ABSTRACT FROM AUTHOR]

High-resolution remote sensing images (HRRSI) have important theoretical and practical value in urban planning. However, current segmentation methods often struggle with issues like blurred edges and loss of detailed information due to the intricate backgrounds and rich semantics in high-resolution remote sensing images. To tackle these challenges, this paper proposes an end-to-end attention-based Convolutional Neural Network (CNN) called Double Hybrid Attention U-Net (DHAU-Net). We designed a new Double Hybrid Attention structure consisting of dual-parallel hybrid attention modules to replace the skip connections in U-Net, which can eliminate redundant information interference and enhances the collection and utilization of important shallow features. Comprehensive experiments on the Massachusetts remote sensing building dataset and the Inria aerial image labeling dataset demonstrate that our proposed method achieves effective pixel-level building segmentation in urban remote sensing images by eliminating redundant information interference and making full use of shallow features, and improves the segmentation performance without significant time costs (approximately 15%). The evaluation metrics reveal significant results, with an accuracy rate of 0.9808, precision reaching 0.9300, an F1 score of 0.9112, a mean intersection over union (mIoU) of 0.9088, and a recall rate of 0.8932. [ABSTRACT FROM AUTHOR]

Now days, with the fast improvement in internet technology and social media network, user give his/her feedback on many things and companies also asked to give feedback about their services as product or service delivered to the customers in the form of review. User’s review about product or service playing major role in improving services as user pleasure. In this paper we present a sentiment analysis model, which is constructed with the help of sentiment lexicon with association of attention-based Bidirectional Gated Recurrent Unit (BiGRU) and CNN (Convolutional Neural Network). For experimental work we used movie review dataset and matched the sentiment analysis results of the proposed method with the mutual sentiment analysis models (SVM, Naïve Bayes, Decision Tree, Random Forest, CNN, and BiGRU) on the dataset. The Experiment result shows that proposed model perform better than other classifiers. [ABSTRACT FROM AUTHOR]

Coal–gangue sorting is a vital component of intelligent mine construction. As intelligent manufacturing continued to advance, data-driven coal–gangue recognition emerged as a prominent research topic. However, conventional data-driven methods for coal–gangue recognition heavily rely on expert-extracted features. The process of feature extraction is labor-intensive and significantly impacts the final outcome. Deep learning (DL) offers an effective approach to automatically extract features from raw data. Among the various DL techniques, convolutional neural networks (CNNs) have proven to be particularly effective. In this paper, we propose an intelligent method for recognizing coal–rock by fusing multiple preprocessing techniques applied to near-infrared spectra and employing dual attention. Initially, a signal-to-RGB image conversion method is applied to fuse three types of preprocessing data, namely first-order differential, second-order differential, and standard normal transform, into an RGB image representation. Subsequently, we propose a neural network model (CBAM-VIT) that integrates the convolutional block attention mechanism (CBAM) and Vision Transformer (VIT). When evaluated on the coal–rock dataset, this model achieves an accuracy of 98.5%, surpassing the performance of VIT (95.3%), VGG-16 (89%), and AlexNet (82%). The comparative results clearly demonstrate that the proposed coal–gangue recognition method yields significant improvements in classification outcomes. [ABSTRACT FROM AUTHOR]

Gathering pipelines are one of the key upstream infrastructures in the gas industry that link production well to the processing plant. Leak detection is critical for ensuring the safety of pipeline transmission. The detection of small leakage in gathering pipelines consistently poses a formidable challenge. In this paper, a process model is built based on health data of supervisory control and data acquisition system from the actual operating pipeline. In the model structure, the convolutional neural network is used to extract the spatial features, the bi-directional long short-term memory is used to extract the temporal features, and the attention mechanism is employed to allocate the model's attention resources reasonably. Next, the residual between the entity pipeline's output data and the process model's output data is used as a monitoring indicator of the operating state of the pipeline. A clustering-based boundary determination method is proposed to recognize the centroid of normal and small leak conditions, and pipeline leak detection is performed by the Euclidean distance between the monitoring indicator and the centroid. This paper explores the feasibility of fast modeling and leak detection with limited hardware. Field tests for the validation of the proposed methods were implemented in two in-service natural gas gathering pipeline. The experimental results demonstrate that the proposed method significantly enhances the detection performance of small-size leak. The leak detection rates of 94.06% and 92.16% evinces the potency of the proposed method applied in the leak detection of gathering pipelines across diverse real-world scenarios. [ABSTRACT FROM AUTHOR]

Electricity load exhibits daily and weekly cyclical patterns as well as random characteristics. At present, prevailing deep learning models cannot learn electricity load cyclical and stochastic features adequately. This results in insufficient prediction accuracy and the scalability of current methods. To tackle these difficulties, this paper proposes a framework for electrical load prediction based on an Attention Mechanism Time Series Depthwise Separable Convolutional Neural Network (ELPF-ATDSCN). The framework starts by using the Maximum Information Coefficient for exogenous variable selection. It then incorporates a seasonal decomposition algorithm with manual feature engineering to extract the cyclical and stochastic features of the electrical load. Subsequently, the framework employs the ATDSCN to learn the cyclical and stochastic features of the electrical load. In addition, the Bayesian algorithm optimizes model hyperparameters for optimal model performance. Experimental results of point and interval load prediction on datasets from the US and Nordic power markets reveal that the ATDSCN model proposed in this paper enhances load prediction accuracy compared with other models. It can provide more reliable predictions for power system operation and dispatch. • A new attention mechanism is designed to improve the model memory capability. • The ATDSCN model is proposed for learning periodic and stochastic features of electricity loads. • A framework for electricity load prediction is proposed to improve the accuracy of electricity load forecasting. • The effectiveness of the proposed method is validated on the real world dataset. [ABSTRACT FROM AUTHOR]

• We proposed a delineating field parcel model (TSANet) based on satellite image time series. • TSANet learn the relevance of spatial-spectral-temporal feature representation. • TSANet is robust across space and time. • TSANet performs better than pervious methods. • The time series data from the main growing period has a greater impact. Satellite image time series (SITS), such as Sentinel-2 imagery, plays a crucial role in the delineation of agricultural fields by reducing the impacts of ambiguities due to the spatial arrangement of field boundaries. Existing delineate field parcel models rely extensively on spatial features derived from single-date imagery. However, several studies have exploited the potential of SITS to effectively tackle the complexities associated with the intrinsic consistency between agricultural fields and their boundaries. This paper proposes a novel Two-Stream Attention convolutional neural network (TSANet) to capture the subtle difference between agricultural fields from SITS. Specifically, a field temporal semantic stream is introduced to adaptively leverage the significance of spatial-spectral-temporal feature representation associated with the location of agricultural parcels, especially where transitions in crop types take place. Considering the consistency between field parcels and their boundaries, we developed a field boundary prediction stream to enhance the extraction of edge features, particularly for the extraction of small and irregular agricultural parcels. Moreover, a field parcel refining block is employed to further enhance the geometric accuracy of agricultural fields. We conducted experiments on Sentinel-2 images from the Netherlands. Results showed that our approach produced a better layout of agricultural fields, with an average F1-score of 0.91 than the existing 3D-UNet, U-TEA, and BiConvLSTM. In addition, through the analysis of both quantitative and qualitative results, the stronger robustness of the model compared to other algorithms has been verified by temporal transfer and large-scale spatial prediction. We compared the difference between SITS and the corresponding composite images, which further verified the influence of temporal variation on the proposed approach. This paper provides a general guide for delineating agricultural parcels using SITS. [ABSTRACT FROM AUTHOR]

• This paper proposed a novel deep graph learning framework for airspace complexity prediction that can comprehensively capture the spatio-temporal dependencies between large-scale airspace sectors. • This paper constructed a multimodal adaptive graph convolution module that can effectively explore the multiple spatial dependencies and adaptively adjust the impact of different spatial modes on airspace complexity in a data-driven learning manner. • This paper designed a temporal convolution module based on a multiple-time-step self-attention mechanism to extract the mixed short- and long-term temporal patterns from both local and global perspectives. • This paper constructed a comprehensive dataset from real-world air traffic data within Chinese domestic airspace, ensuring its reliability through rigorous calibration with experienced air traffic controllers. Airspace complexity is defined as an essential indicator to comprehensively measure the safety of air traffic operational situations. A reliable prediction of airspace complexity can provide practical guidance for formulating air traffic management strategies and resource allocation. Although extensive efforts have been devoted to computing airspace complexity, previous studies can rarely model the multi-dimensional and combined spatio-temporal features within airspace complexity data. In this paper, we propose a multimodal adaptive spatio-temporal graph neural network to simultaneously explore the spatio-temporal dependencies in the airspace sector network. Specifically, we design a multimodal adaptive graph convolution module to effectively learn the diverse spatial relationships and adaptively adjust the impact of different spatial modes on airspace complexity in a data-driven manner. To model dynamic long-short-term temporal patterns, we develop a dilated causal convolution layer with a multiple-time-step self-attention mechanism to accurately predict airspace complexity over a longer time horizon. Extensive experiments on real-world air traffic datasets show that the proposed approach can harness differing spatial modes in achieving higher generalization performance across different temporal patterns, outperforming state-of-the-art methods in all prediction time horizons. [ABSTRACT FROM AUTHOR]

Accurate fault identification provides an important basis for well location deployment, oil and gas resource development. However, obtaining a large number of fault samples through manual labeling or interpretation is difficult. Conventional convolutional neural networks have certain challenges in finely characterizing low-order faults and fault continuity. This paper studies a 3D intelligent fault identification method that integrates deep learning with attention mechanism. Based on 3D-UNet network, the method constructs a dual attention fault identification model with an encoder-decoder structure, which focuses on enhancing low-order fault information and extracting key features. An adaptive hybrid loss function is proposed to address the imbalance problem of fault proportions in seismic amplitude data. The function introduces an adjustment coefficient to adaptively enhance the attention to high-order or low-order faults according to the actual situation of the fault, improving the accuracy and continuity of fault identification. Furthermore, a model re-training learning optimization method is presented to capture the missed fault information in the first training and repair the connectivity of low-order faults. Applying this method to the synthetic data set, the accuracy can be improved to 0.9732, and the loss can be converged to 0.0982. The results of shale reservoirs in the North Sea Basin and Kerry-3D indicate that the method can reduce the situation of fault missing and misidentification, effectively improve fault identification accuracy, and enhance low-order fault identification by obtaining more contextual fault features. • A seismic fault image segmentation network is developed with attention mechanism. • Construct an adaptive hybrid loss function to solve the problem of data imbalance. • A method retraining learning that integrates theory and predictive data is proposed. [ABSTRACT FROM AUTHOR]

Recent studies focus on the utilization of deep learning approaches to recognize depression from facial videos. However, these approaches have been hindered by their limited performance, which can be attributed to the inadequate consideration of global spatial–temporal relationships in significant local regions within faces. In this paper, we propose Spatial–Temporal Attention Depression Recognition Network (STA-DRN) for depression recognition to enhance feature extraction and increase the relevance of depression recognition by capturing the global and local spatial–temporal information. Our proposed approach includes a novel Spatial–Temporal Attention (STA) mechanism, which generates spatial and temporal attention vectors to capture the global and local spatial–temporal relationships of features. To the best of our knowledge, this is the first attempt to incorporate pixel-wise STA mechanisms for depression recognition based on 3D video analysis. Additionally, we propose an attention vector-wise fusion strategy in the STA module, which combines information from both spatial and temporal domains. We then design the STA-DRN by stacking STA modules ResNet-style. The experimental results on AVEC 2013 and AVEC 2014 show that our method achieves competitive performance, with mean absolute error/root mean square error (MAE/RMSE) scores of 6.15/7.98 and 6.00/7.75, respectively. Moreover, visualization analysis demonstrates that the STA-DRN responds significantly in specific locations related to depression. The code is available at: https://github.com/divertingPan/STA-DRN. [ABSTRACT FROM AUTHOR]

Induction motors are widely used in various industrial applications due to their simplicity, robustness, and high efficiency. In recent years, deep learning approaches have shown great potential for detecting and diagnosing faults in induction motors. However, it was observed that the convolutional neural network (CNN) might miss the crucial long-range temporal dependencies of the significant features. At the same time, the recursive neural network (RNN) may face issues such as vanishing gradient and less computational efficiency if the data is too long with complex patterns. This paper proposes a novel deep-learning model that combines a one-dimensional CNN and an RNN into two independent pipelines. The spatial, temporal, long-term, and short-term features can be extracted from the raw input signal. A multi-head mechanism, a state-of-the-art deep learning technique in natural language processing (NLP), enables the model to focus more on relevant features and prevent overfitting. Specifically, our method simplifies the conventional fault diagnosis workflow while maintaining competitive accuracy compared to several state-of-the-art methods. By leveraging the ability to detect faults directly from raw, unprocessed electrical signals, our approach offers a practical, cost-effective solution for real-time fault detection in industrial applications. This innovation has the potential to significantly reduce downtime and the associated operational costs, thereby providing immediate practical value in various industrial settings. [ABSTRACT FROM AUTHOR]

Skin cancer is the most prevalent type of cancer worldwide. Early detection is essential as it could be fatal at later stages. The classification of skin lesions is challenging since there are many variations, including changes in color, shape, size, high intra-class variation, and high inter-class similarity. In this paper, a unique class-wise attention method is proposed that considers each class equally while extracting additional discriminative information of skin lesions. The proposed attention mechanism is employed in a progressive manner to incorporate discriminative feature information from multiple scales. The proposed approach obtained competitive performance against more than 15 state-of-the-art methods including HAM1000 and ISIC 2019 leaderboard winners. The proposed method achieved 97.40% accuracy on the HAM10000 and 94.9% accuracy on the ISIC 2019 dataset. • A novel attention based mechanism is proposed for skin lesions classification. • The problem of significant intra-class variance, high inter-class similarity, and class imbalance is addressed by a class-wise attention mechanism. • The important features for the classification are extracted without the burden of additional parameters using a progressive class-wise attention mechanism. • The final three layers of the baseline model are discarded and the global average pooling (GAP) and classification output layers are added to reduce the number of parameters, while maintaining performance. • The proposed network is robust, end-to-end trainable and has good generalization on unseen data. [ABSTRACT FROM AUTHOR]

Brain tumor is the deadliest type of cancer and has the lowest survival rate when compared with other cancers. Hence, timely detection of brain tumor is indispensable for patients to make better treatment plans, leading to improved life expectancy. However, accurate classification of different brain tumor types from MR images is challenging due to high inter-class similarities. Though deep learning architectures, mainly CNNs, have shown promising performance compared to traditional approaches, such models often demand huge parameters and lead to overfitting while dealing with limited training samples. Further, the state-of-the-art CNN models cannot capture the subtle lesion size and shape variations among different classes. To cope with these issues, in this paper, we propose an attention-based residual multiscale CNN called ARM-Net for multiclass brain tumor classification. In particular, we propose a lightweight residual multiscale CNN dubbed RM-Net to capture high-level feature representations at different receptive fields. Further, a lightweight global attention module (LGAM) is proposed to selectively learn more discriminative features. The LGAM is placed on the top of RM-Net and is introduced to capture wide-range feature dependencies. Experimental results on two benchmark datasets indicate the superiority of our ARM-Net over the state-of-the-art CNN architectures and existing methods. The ARM-Net achieves an accuracy of 96.64% and 97.11% on MBTD and BraTS 2020 dataset, respectively. The ablation studies, Grad-CAM, and Grad-CAM++ visualization results confirm the effectiveness of our proposed LGAM. In addition, our ARM-Net is lightweight, end-to-end learnable, and hence more suitable for real-time brain tumor classification. • An attention-based residual multiscale CNN 'ARM-Net' is proposed. • A lightweight global attention module is proposed to learn class-specific features. • A comparative analysis with contemporary attention mechanisms is performed. • Results indicate the effectiveness of ARM-Net over existing methods. • The proposed ARM-Net model is lightweight and end-to-end learnable. [ABSTRACT FROM AUTHOR]