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This study utilizes the Breast Ultrasound Image (BUSI) dataset to present a deep learning technique for breast tumor segmentation based on a modified UNet architecture. To improve segmentation accuracy, the model integrates attention mechanisms, such as the Convolutional Block Attention Module (CBAM) and Non-Local Attention, with advanced encoder architectures, including ResNet, DenseNet, and EfficientNet. These attention mechanisms enable the model to focus more effectively on relevant tumor areas, resulting in significant performance improvements. Models incorporating attention mechanisms outperformed those without, as reflected in superior evaluation metrics. The effects of Dice Loss and Binary Cross-Entropy (BCE) Loss on the model's performance were also analyzed. Dice Loss maximized the overlap between predicted and actual segmentation masks, leading to more precise boundary delineation, while BCE Loss achieved higher recall, improving the detection of tumor areas. Grad-CAM visualizations further demonstrated that attention-based models enhanced interpretability by accurately highlighting tumor areas. The findings denote that combining advanced encoder architectures, attention mechanisms, and the UNet framework can yield more reliable and accurate breast tumor segmentation. Future research will explore the use of multi-modal imaging, real-time deployment for clinical applications, and more advanced attention mechanisms to further improve segmentation performance., Competing Interests: Declarations. Competing interests: The authors declare that there are no conflicting or financial interests. Ethics approval: This article does not contain any studies with human participants or animals performed by any of the authors. Informed consent: Not applicable., (© 2025. The Author(s).)

Dictionary representations and deep learning Autoencoder (AE) models have proven effective in hyperspectral anomaly detection. Dictionary representations offer self-explanation but struggle with complex scenarios. Conversely, autoencoders can capture details in complex scenes but lack self-explanation. Complex scenarios often involve extensive spatial information, making its utilization crucial in hyperspectral anomaly detection. To effectively combine the advantages of both methods and address the insufficient use of spatial information, we propose an attention constrained low-rank and sparse autoencoder for hyperspectral anomaly detection. This model includes two encoders: an attention constrained low-rank autoencoder (AClrAE) trained with a background dictionary and incorporating a Global Self-Attention Module (GAM) to focus on global spatial information, resulting in improved background reconstruction; and an attention constrained sparse autoencoder (ACsAE) trained with an anomaly dictionary and incorporating a Local Self-Attention Module (LAM) to focus on local spatial information, resulting in enhanced anomaly reconstruction. Finally, to merge the detection results from both encoders, a nonlinear fusion scheme is employed. Experiments on multiple real and synthetic datasets demonstrate the effectiveness and feasibility of the proposed method., Competing Interests: Declaration of competing interest The authors declared that they have no conflicts of interest to this work., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Colon cancer poses a significant threat to human life with a high global mortality rate. Early and accurate detection is crucial for improving treatment quality and the survival rate. This paper presents a comprehensive approach to enhance colon cancer detection and classification. The histopathological images are gathered from the CRC-VAL-HE-7K dataset. The images undergo preprocessing to improve quality, followed by augmentation to increase dataset size and enhance model generalization. A deep learning based transformer model is designed for efficient feature extraction and enhancing classification by incorporating a convolutional neural network (CNN). A cross-transformation model captures long-range dependencies between regions, and an attention mechanism assigns weights to highlight crucial features. To boost classification accuracy, a Siamese network distinguishes colon cancer tissue classes based on probabilities. Optimization algorithms fine-tune model parameters, categorizing colon cancer tissues into different classes. The multi-class classification performance is evaluated in the experimental evaluation, which demonstrates that the proposed model provided highest accuracy rate of 98.84%. In this research article, the proposed method achieved better performance in all analyses by comparing with other existing methods. RESEARCH HIGHLIGHTS: Deep learning-based techniques are proposed. DL methods are used to enhance colon cancer detection and classification. CRC-VAL-HE-7K dataset is utilized to enhance image quality. Hybrid particle swarm optimization (PSO) and dwarf mongoose optimization (DMO) are used. The deep learning models are tuned by implementing the PSO-DMO algorithm., (© 2024 Wiley Periodicals LLC.)

Breast cancer is the most common cancer in women. Ultrasound is one of the most used techniques for diagnosis, but an expert in the field is necessary to interpret the test. Computer-aided diagnosis (CAD) systems aim to help physicians during this process. Experts use the Breast Imaging-Reporting and Data System (BI-RADS) to describe tumors according to several features (shape, margin, orientation...) and estimate their malignancy, with a common language. To aid in tumor diagnosis with BI-RADS explanations, this paper presents a deep neural network for tumor detection, description, and classification. An expert radiologist described with BI-RADS terms 749 nodules taken from public datasets. The YOLO detection algorithm is used to obtain Regions of Interest (ROIs), and then a model, based on a multi-class classification architecture, receives as input each ROI and outputs the BI-RADS descriptors, the BI-RADS classification (with 6 categories), and a Boolean classification of malignancy. Six hundred of the nodules were used for 10-fold cross-validation (CV) and 149 for testing. The accuracy of this model was compared with state-of-the-art CNNs for the same task. This model outperforms plain classifiers in the agreement with the expert (Cohen's kappa), with a mean over the descriptors of 0.58 in CV and 0.64 in testing, while the second best model yielded kappas of 0.55 and 0.59, respectively. Adding YOLO to the model significantly enhances the performance (0.16 in CV and 0.09 in testing). More importantly, training the model with BI-RADS descriptors enables the explainability of the Boolean malignancy classification without reducing accuracy., Competing Interests: Declarations. Conflict of interest: The authors declare no competing interests. Ethics Approval: All the data have been obtained from public datasets, so ethics approval was not required., (© 2024. The Author(s).)

Accurate medical image segmentation plays a vital role in clinical practice. Convolutional Neural Network and Transformer are mainstream architectures for this task. However, convolutional neural network lacks the ability of modeling global dependency while Transformer cannot extract local details. In this paper, we propose DATTNet, Dual ATTention Network, an encoder-decoder deep learning model for medical image segmentation. DATTNet is exploited in hierarchical fashion with two novel components: (1) Dual Attention module is designed to model global dependency in spatial and channel dimensions. (2) Context Fusion Bridge is presented to remix the feature maps with multiple scales and construct their correlations. The experiments on ACDC, Synapse and Kvasir-SEG datasets are conducted to evaluate the performance of DATTNet. Our proposed model shows superior performance, effectiveness and robustness compared to SOTA methods, with mean Dice Similarity Coefficient scores of 92.2%, 84.5% and 89.1% on cardiac, abdominal organs and gastrointestinal poly segmentation tasks. The quantitative and qualitative results demonstrate that our proposed DATTNet attains favorable capability across different modalities (MRI, CT, and endoscopy) and can be generalized to various tasks. Therefore, it is envisaged as being potential for practicable clinical applications. The code has been released on https://github.com/MhZhang123/DATTNet/tree/main ., (© 2024. The Author(s).)

Pediatric Sleep Apnea-Hypopnea (SAH) presents a significant health challenge, particularly in diagnostic contexts, where conventional Polysomnography (PSG) testing, although effective, can be distressing for children. Addressing this, our research proposes a less invasive method to assess pediatric SAH severity by analyzing blood oxygen saturation (SpO2) signals. We adopted two advanced deep learning architectures, namely ResNet-based and attention-augmented hybrid CNN-BiGRU models, to process SpO2 signals in a one-dimensional (1D) format for Apnea-Hypopnea Index (AHI) estimation in pediatric subjects. Employing the CHAT dataset, which includes 844 SpO2 signals, the data was partitioned into training (60%), testing (30%), and validation (10%) sets. A predefined validation subset was randomly selected to ensure the models' robustness via a threefold cross-validation approach. Comparative analysis revealed that while the ResNet model attained an average accuracy of 72.9% across four SAH severity categories with a kappa score of 0.57, the CNN-BiGRU-Attention model demonstrated superior performance, achieving an average accuracy of 75.95% and a kappa score of 0.63. This distinction underscores our method's efficacy in both estimating AHI and categorizing SAH severity levels with notable precision. Further, to evaluate diagnostic capabilities, the models were benchmarked against common AHI thresholds (1, 5, and 10 events/hour) in each test fold, affirming their effectiveness in identifying pediatric SAH. This study marks a significant advance in the field, offering a non-invasive, child-friendly alternative for pediatric SAH diagnosis. Although challenges persist in accurately estimating AHI, particularly in severe cases, our findings represent a critical stride towards improving diagnostic processes in pediatric SAH., (© 2024. The Author(s).)

Wireless capsule endoscopy is a non-invasive medical imaging modality used for diagnosing and monitoring digestive tract diseases. However, the analysis of images obtained from wireless capsule endoscopy is a challenging task, as the images are of low resolution and often contain a large number of artifacts. In recent years, deep learning has shown great promise in the analysis of medical images, including wireless capsule endoscopy images. This paper provides a review of the current trends and future directions in deep learning for wireless capsule endoscopy. We focus on the recent advances in transfer learning, attention mechanisms, multi-modal learning, automated lesion detection, interpretability and explainability, data augmentation, and edge computing. We also highlight the challenges and limitations of current deep learning methods and discuss the potential future directions for the field. Our review provides insights into the ongoing research and development efforts in the field of deep learning for wireless capsule endoscopy, and can serve as a reference for researchers, clinicians, and engineers working in this area inspection process., Competing Interests: No competing interests were disclosed., (Copyright: © 2024 Habe TT et al.)

The classification of missense variant pathogenicity continues to pose significant challenges in human genetics, necessitating precise predictions of functional impacts for effective disease diagnosis and personalized treatment strategies. Traditional methods, often compromised by suboptimal feature selection and limited generalizability, are outpaced by the enhanced classification model, MissenseNet (Missense Classification Network). This model, advancing beyond standard predictive features, incorporates structural insights from AlphaFold2 protein predictions, thus optimizing structural data utilization. MissenseNet, built on the ShuffleNet architecture, incorporates an encoder-decoder framework and a Squeeze-and-Excitation (SE) module designed to adaptively adjust channel weights and enhance feature fusion and interaction. The model's efficacy in classifying pathogenicity has been validated through superior accuracy compared to conventional methods and by achieving the highest areas under the Receiver Operating Characteristic (ROC) and Precision-Recall (PR) curves (Area Under the Curve and Area Under the Precision-Recall Curve) in an independent test set, thus underscoring its superiority.

Multi-task learning (MTL) methods are widely applied in breast imaging for lesion area perception and classification to assist in breast cancer diagnosis and personalized treatment. A typical paradigm of MTL is the shared-backbone network architecture, which can lead to information sharing conflicts and result in the decline or even failure of the main task's performance. Therefore, extracting richer lesion features and alleviating information-sharing conflicts has become a significant challenge for breast cancer classification. This study proposes a novel Multi-Feature Fusion Multi-Task (MFFMT) model to effectively address this issue. Firstly, in order to better capture the local and global feature relationships of lesion areas, a Contextual Lesion Enhancement Perception (CLEP) module is designed, which integrates channel attention mechanisms with detailed spatial positional information to extract more comprehensive lesion feature information. Secondly, a novel Multi-Feature Fusion (MFF) module is presented. The MFF module effectively extracts differential features that distinguish between lesion-specific characteristics and the semantic features used for tumor classification, and enhances the common feature information of them as well. Experimental results on two public breast ultrasound imaging datasets validate the effectiveness of our proposed method. Additionally, a comprehensive study on the impact of various factors on the model's performance is conducted to gain a deeper understanding of the working mechanism of the proposed framework.

Background: Transcranial sonography (TCS) plays a crucial role in diagnosing Parkinson's disease. However, the intricate nature of TCS pathological features, the lack of consistent diagnostic criteria, and the dependence on physicians' expertise can hinder accurate diagnosis. Current TCS-based diagnostic methods, which rely on machine learning, often involve complex feature engineering and may struggle to capture deep image features. While deep learning offers advantages in image processing, it has not been tailored to address specific TCS and movement disorder considerations. Consequently, there is a scarcity of research on deep learning algorithms for TCS-based PD diagnosis., Methods: This study introduces a deep learning residual network model, augmented with attention mechanisms and multi-scale feature extraction, termed AMSNet, to assist in accurate diagnosis. Initially, a multi-scale feature extraction module is implemented to robustly handle the irregular morphological features and significant area information present in TCS images. This module effectively mitigates the effects of artifacts and noise. When combined with a convolutional attention module, it enhances the model's ability to learn features of lesion areas. Subsequently, a residual network architecture, integrated with channel attention, is utilized to capture hierarchical and detailed textures within the images, further enhancing the model's feature representation capabilities., Results: The study compiled TCS images and personal data from 1109 participants. Experiments conducted on this dataset demonstrated that AMSNet achieved remarkable classification accuracy (92.79%), precision (95.42%), and specificity (93.1%). It surpassed the performance of previously employed machine learning algorithms in this domain, as well as current general-purpose deep learning models., Conclusion: The AMSNet proposed in this study deviates from traditional machine learning approaches that necessitate intricate feature engineering. It is capable of automatically extracting and learning deep pathological features, and has the capacity to comprehend and articulate complex data. This underscores the substantial potential of deep learning methods in the application of TCS images for the diagnosis of movement disorders., (© 2024. The Author(s).)

The intricacy of the Deep Learning (DL) landscape, brimming with a variety of models, applications, and platforms, poses considerable challenges for the optimal design, optimization, or selection of suitable DL models. One promising avenue to address this challenge is the development of accurate performance prediction methods. However, existing methods reveal critical limitations. Operator-level methods, proficient at predicting the performance of individual operators, often neglect broader graph features, which results in inaccuracies in full network performance predictions. On the contrary, graph-level methods excel in overall network prediction by leveraging these graph features but lack the ability to predict the performance of individual operators. To bridge these gaps, we propose SLAPP, a novel subgraph-level performance prediction method. Central to SLAPP is an innovative variant of Graph Neural Networks (GNNs) that we developed, named the Edge Aware Graph Attention Network (EAGAT). This specially designed GNN enables superior encoding of both node and edge features. Through this approach, SLAPP effectively captures both graph and operator features, thereby providing precise performance predictions for individual operators and entire networks. Moreover, we introduce a mixed loss design with dynamic weight adjustment to reconcile the predictive accuracy between individual operators and entire networks. In our experimental evaluation, SLAPP consistently outperforms traditional approaches in prediction accuracy, including the ability to handle unseen models effectively. Moreover, when compared to existing research, our method demonstrates a superior predictive performance across multiple DL models., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier Ltd. All rights reserved.)

Cybersecurity is a present and growing concern that needs to be addressed with both behavioral and design-oriented research. Public cloud providers such as Amazon Web Services and federal funding agencies such as the National Science Foundation have invested billions of dollars into developing high-performance computing resources accessible to users through configurable virtual machine (VM) images. This approach offers users the flexibility of changing and updating their environment for their computational needs. Despite the substantial benefits, users often introduce thousands of vulnerabilities by installing open-source software packages and misconfiguring file systems. Given the scale of vulnerabilities, security personnel struggle to identify and prioritize vulnerable assets for remediation. In this research, we designed a novel unsupervised deep learning-based Multi-View Combinatorial-Attentive Autoencoder (MV-CAAE) to capture multi-dimensional vulnerability data and automatically identify groups of similar vulnerable compute instances to help facilitate the development of targeted remediation strategies. We rigorously evaluated the proposed MV-CAAE against state-of-the-art methods in three technical clustering experiments. Experiment results indicate that the MV-CAAE achieves V-measure scores (metric of cluster quality) 8 percent-48 percent higher than benchmark methods. We demonstrated the practical value through a comprehensive case study by clustering vulnerable VMs and gathering qualitative feedback from experienced security professionals through semi-structured interviews. The results indicated that clustering vulnerable assets can help prioritize vulnerable instances for remediation and enhance decision-making tasks. The present design-research work also contributes to our theoretical knowledge of cyber-defense. [ABSTRACT FROM AUTHOR]

The increasing societal concern for consumer information privacy has led to the enforcement of privacy regulations worldwide. In an effort to adhere to privacy regulations such as the General Data Protection Regulation (GDPR), many companies’ privacy policies have become increasingly lengthy and complex. In this study, we adopted the computational design science paradigm to design a novel privacy policy evolution analytics framework to help identify how companies change and present their privacy policies based on privacy regulations. The framework includes a self-attentive annotation system (SAAS) that automatically annotates paragraph-length segments in privacy policies to help stakeholders identify data practices of interest for further investigation. We rigorously evaluated SAAS against state-of-the-art machine learning (ML) and deep learning (DL)-based methods on a well-established privacy policy dataset, OPP-115. SAAS outperformed conventional ML and DL models in terms of F1-score by statistically significant margins. We demonstrate the proposed framework’s practical utility with an in-depth case study of GDPR’s impact on Amazon’s privacy policies. The case study results indicate that Amazon’s post-GDPR privacy policy potentially violates a fundamental principle of GDPR by causing consumers to exert more effort to find information about first-party data collection. Given the increasing importance of consumer information privacy, the proposed framework has important implications for regulators and companies. We discuss several design principles followed by the SAAS that can help guide future design science-based e-commerce, health, and privacy research. [ABSTRACT FROM AUTHOR]

Retinal vessel segmentation is a computer-aided diagnostic method for ophthalmic disease analysis. Owing to the complex structure of the retinal vasculature, it is difficult for the segmentation network to capture effective features, and the semantic gap between different layers of features leads to insufficient feature fusion and thus makes segmentation difficult. In this paper, we propose a new segmentation network called CFFANet. Firstly, to capture accurate and sufficient global and local features, we design a Multi-scale Residual Pooling Module. In addition, a Category Feature Fusion Module is proposed to fuse category features at different stages to reduce the semantic gap between layers. Finally, a Frequency Channel Fusion Cross Attention Module is incorporated to reduce redundant semantic information during feature fusion. We conducted experiments on the DRIVE, CHASEDB1 and STARE datasets. The Dice and MIoU scores of the CFFANet network on the above datasets are 83.0, 82.9, 84.2, 84.1, and 84.1, 84.5. The ablation experiments also validate the effectiveness of the main modules in the network. The experiments show the value of the method in retinal vessel segmentation tasks. [ABSTRACT FROM AUTHOR]

Featured Application: This proposed method could potentially be applied to musical instrument classification tasks, contributing to the organization and analysis of audio data where identifying instruments is required. This may be useful in research, music information retrieval systems, or other related applications. Musical instrument recognition is a relatively unexplored area of machine learning due to the need to analyze complex spatial–temporal audio features. Traditional methods using individual spectrograms, like STFT, Log-Mel, and MFCC, often miss the full range of features. Here, we propose a hierarchical residual attention network using a scaled combination of multiple spectrograms, including STFT, Log-Mel, MFCC, and CST features (Chroma, Spectral contrast, and Tonnetz), to create a comprehensive sound representation. This model enhances the focus on relevant spectrogram parts through attention mechanisms. Experimental results with the OpenMIC-2018 dataset show significant improvement in classification accuracy, especially with the "Magnified 1/4 Size" configuration. Future work will optimize CST feature scaling, explore advanced attention mechanisms, and apply the model to other audio tasks to assess its generalizability. [ABSTRACT FROM AUTHOR]

Image super-resolution has experienced significant advancements with the emergence of deep learning technology. However, deploying highly complex super-resolution networks on resource-constrained devices poses a challenge due to their substantial computational requirements. This paper presents the Adaptive Dynamic Shuffle Convolutional Parallel Network (ADSCPN), a novel lightweight super-resolution model designed to achieve an optimal balance between computational efficiency and image reconstruction quality. The ADSCPN framework employs large-kernel parallel depthwise separable convolutions, dynamic convolutions, and an enhanced attention mechanism to optimize feature extraction and improve detail preservation. Extensive evaluations on standard benchmark datasets demonstrate that ADSCPN achieves state-of-the-art performance while significantly reducing computational complexity, making it well-suited for practical applications on devices with limited computational resources. [ABSTRACT FROM AUTHOR]

Survival analysis is employed to scrutinize time-to-event data, with emphasis on comprehending the duration until the occurrence of a specific event. In this article, we introduce two novel survival prediction models: CosAttnSurv and CosAttnSurv + DyACT. CosAttnSurv model leverages transformer-based architecture and a softmax-free kernel attention mechanism for survival prediction. Our second model, CosAttnSurv + DyACT, enhances CosAttnSurv with Dynamic Adaptive Computation Time (DyACT) control, optimizing computation efficiency. The proposed models are validated using two public clinical datasets related to heart disease patients. When compared to other state-of-the-art models, our models demonstrated an enhanced discriminative and calibration performance. Furthermore, in comparison to other transformer architecture-based models, our proposed models demonstrate comparable performance while exhibiting significant reduction in both time and memory requirements. Overall, our models offer significant advancements in the field of survival analysis and emphasize the importance of computationally effective time-based predictions, with promising implications for medical decision-making and patient care. [ABSTRACT FROM AUTHOR]

Text classification is a crucial domain within natural language processing (NLP), with applications ranging from document categorization to sentiment analysis. In this context, the use of attention mechanisms in neural networks has emerged as an effective method to enhance the performance of classification models. This comparative study focuses on the application of these mechanisms to CV classification, adopting a distributed approach with Apache Spark to handle large datasets. We explore several neural network architectures, including recurrent neural networks (RNNs) and transformer neural networks, integrating various attention mechanisms such as global attention, contextual attention, and multi-head attention. The performance of these models is compared to traditional text classification methods such as SVMs, taking into account the scalability and processing speed offered by Spark. Experiments are conducted on a diverse dataset of CVs. Results show that models based on neural networks with attention mechanisms, combined with a distributed Spark architecture, significantly outperform traditional approaches. Additionally, we analyze the impact of various Spark configuration parameters, such as the number of nodes and allocated memory, on model performance. In conclusion, this study demonstrates the effectiveness of attention mechanisms in the specific context of CV classification, highlighting the advantages of a distributed approach for efficient processing of large textual datasets. [ABSTRACT FROM AUTHOR]

Skin lesion segmentation and classification from dermoscopic images have emerged as pivotal research topics, playing vibrant role in early detection and diagnosis of skin diseases, including melanoma. Previous studies have employed various deep learning models for skin lesion segmentation and classification, enabling the automatic learning of complex and discriminative features from dermoscopic images. However, inherent challenges arise due to the variance in skin lesion shape, size, and contrast, leading to intrinsic limitations of former models, such as Isolated Representation Learning, Uniform Attention, Limited Model Generalization, Reduced Model Interpretability, and Uncertainty. To address these limitations and propel the field forward, this paper introduces a novel frameworkcalled AMTLUS that leverages Multi-Task Learning (MTL) in conjunction with deep Attention Mechanisms and Uncertainty Estimation. The integration of MTL facilitates joint training of segmentation and classification tasks, enabling shared representation learning and efficient utilization of data. Incorporating attention mechanisms dynamically focuses on informative regions within dermoscopic images, improving segmentation accuracy and feature extraction for classification. Uncertainty estimation techniques quantify model confidence, offering probabilistic interpretations for improved reliability and interpretability. Our widespread experiments conducted on the ISIC-2016 dataset demonstrate superior accuracy and reliability, showcasing the proposed model's capability to identify challenging cases. This deep learning framework represents a significant advancement in automated skin lesion analysis, enhancing early detection and diagnosis of skin diseases, including melanoma. [ABSTRACT FROM AUTHOR]

Hyperspectral image (HSI) classification is a crucial technique that assigns each pixel in an image to a specific land cover category by leveraging both spectral and spatial information. In recent years, HSI classification methods based on convolutional neural networks (CNNs) and Transformers have significantly improved performance due to their strong feature extraction capabilities. However, these improvements often come with increased model complexity, leading to higher computational costs. To address this, we propose a compact and efficient spectral-spatial feature extraction and attention-based neural network (SSFAN) for HSI classification. The SSFAN model consists of three core modules: the Parallel Spectral-Spatial Feature Extraction Block (PSSB), the Scan Block, and the Squeeze-and-Excitation MLP Block (SEMB). After preprocessing the HSI data, it is fed into the PSSB module, which contains two parallel streams, each comprising a 3D convolutional layer and a 2D convolutional layer. The 3D convolutional layer extracts spectral and spatial features from the input hyperspectral data, while the 2D convolutional layer further enhances the spatial feature representation. Next, the Scan Block module employs a layered scanning strategy to extract spatial information at different scales from the central pixel outward, enabling the model to capture both local and global spatial relationships. The SEMB module combines the Spectral-Spatial Recurrent Block (SSRB) and the MLP Block. The SSRB, with its adaptive weight assignment mechanism in the SToken Module, flexibly handles time steps and feature dimensions, performing deep spectral and spatial feature extraction through multiple state updates. Finally, the MLP Block processes the input features through a series of linear transformations, GELU activation functions, and Dropout layers, capturing complex patterns and relationships within the data, and concludes with an argmax layer for classification. Experimental results show that the proposed SSFAN model delivers superior classification performance, outperforming the second-best method by 1.72 % , 5.19 % , and 1.94 % in OA, AA, and Kappa coefficient, respectively, on the Indian Pines dataset. Additionally, it requires less training and testing time compared to other state-of-the-art deep learning methods. [ABSTRACT FROM AUTHOR]

Eye health has become a significant concern in recent years, given the rising prevalence of visual impairment resulting from various eye disorders and related factors. Global surveys suggest that approximately 2.2 billion individuals are visually impaired, with at least 1 billion affected by treatable diseases or ailments. Early detection, treatment, and screening for fundus diseases are crucial in addressing these challenges. In this study, we propose a novel segmentation model for retinal vascular delineation aimed at diagnosing diabetic retinopathy. The model integrates CBAM (Channel-Attention and Spatial-Attention) for enhanced feature representation, JPU (Joint Pyramid Upsampling) for multi-scale feature fusion, and transformer blocks for contextual understanding. Leveraging deep-learning techniques, our proposed model outperforms existing approaches in retinal vascular segmentation, like achieving a Mean IOU of 0.8047, Recall of 0.7254, Precision of 0.8492, F1 Score of 0.7824, and Specificity of 0.9892 for CHASEDB1 dataset. Extensive evaluations on benchmark datasets demonstrate its efficacy, highlighting its potential for automated diabetic retinopathy screening. [ABSTRACT FROM AUTHOR]

The rapid and accurate detection of fire scenes in various environments is crucial for effective disaster management and mitigation. Fire scene classification is a critical aspect of modern fire detection systems that directly affects public safety and property preservation. This research introduced a novel hybrid deep learning model designed to enhance the accuracy and efficiency of fire scene classification across diverse environments. The proposed model integrates advanced convolutional neural networks with multiscale feature extraction, attention mechanisms, and ensemble learning to achieve superior performance in real-time fire detection. By leveraging the strengths of pre-trained networks such as ResNet50, VGG16, and EfficientNet-B3, the model captures detailed features at multiple scales, ensuring robust detection capabilities. Including spatial and channel attention mechanisms further refines the focus on critical areas within the input images, reducing false positives and improving detection precision. Extensive experiments on a comprehensive dataset encompassing wildfires, building fires, vehicle fires, and non-fire scenes demonstrate that the proposed framework outperforms existing cutting-edge techniques. The model also exhibited reduced computational complexity and enhanced inference speed, making it suitable for deployment in real-time applications on various hardware platforms. This study sets a new benchmark for fire detection and offers a powerful tool for early warning systems and emergency response initiatives. [ABSTRACT FROM AUTHOR]

Diabetic retinopathy is a complication of diabetes and one of the leading causes of vision loss. Early detection and treatment are essential to prevent vision loss. Deep learning has been making great strides in the field of medical image processing and can be used as an aid for medical practitioners. However, unbalanced datasets, sparse focal areas, small differences between adjacent disease grades, and varied manifestations of the same grade disease challenge deep learning model training. Generalization performance and robustness are inadequate. To address the problem of unbalanced sample numbers between classes in the dataset, this work proposes using VQ‐VAE for reconstructing affine transformed images to enrich and balance the dataset. Test results show the model's average reconstruction error is 0.0001, and the mean structural similarity between reconstructed and original images is 0.967. This proves reconstructed images differ from originals yet belong to the same category, expanding and diversifying the dataset. Addressing the issues of focal area sparsity and disease grade disparity, this work utilizes ResNeXt50 as the backbone network and constructs diverse attention networks by modifying the network structure and embedding different attention modules. Experiments demonstrate that the convolutional attention network outperforms ResNeXt50 in terms of Precision, Sensitivity, Specificity, F1 Score, Quadratic Weighted Kappa Coefficient, Accuracy, and robustness against Salt and Pepper noise, Gaussian noise, and gradient perturbation. Finally, the heat maps of each model recognizing the fundus image were plotted using the Grad‐CAM method. The heat maps show that the attentional network is more effective than the non‐attentional network ResNeXt50 at attending to the fundus image. [ABSTRACT FROM AUTHOR]

This study addresses the problem of detecting occluded apples in complex unstructured environments in orchards and proposes an apple detection and segmentation model based on improved YOLOv8n-SGW-YOLOv8n. The model improves apple detection and segmentation by combining the SPD-Conv convolution module, the GAM global attention mechanism, and the Wise-IoU loss function, which enhances the accuracy and robustness. The SPD-Conv module preserves fine-grained features in the image by converting spatial information into channel information, which is particularly suitable for small target detection. The GAM global attention mechanism enhances the recognition of occluded targets by strengthening the feature representation of channel and spatial dimensions. The Wise-IoU loss function further optimises the regression accuracy of the target frame. Finally, the pre-prepared dataset is used for model training and validation. The results show that the SGW-YOLOv8n model significantly improves relative to the original YOLOv8n in target detection and instance segmentation tasks, especially in occlusion scenes. The model improves the detection mAP to 75.9% and the segmentation mAP to 75.7% and maintains a processing speed of 44.37 FPS, which can meet the real-time requirements, providing effective technical support for the detection and segmentation of fruits in complex unstructured environments for fruit harvesting robots. [ABSTRACT FROM AUTHOR]

To address the issue of low accuracy in facial expression recognition due to insufficient consideration of semantic features and individual facial characteristics in most current facial expression extraction methods, a highly efficient facial expression recognition method based on feature decoding (FER-FD) is proposed. This method consists of two modules, namely the feature decoupling module (FFD) and the semantic enhancement module (VTS). Firstly, the FFD module employs two deep 2 D convolutional neural networks to extract facial and expression features from input images, where the facial feature decoupler disentangles facial features from expression features to minimize the influence of individual facial characteristics. Secondly, the VTS module adopts two key ideas to automatically capture facial movements in an unsupervised manner, thereby acquiring deep semantic information of the global facial region. Finally, concatenating the features from both modules enables more accurate prediction of facial expressions of samples. Experimental results demonstrate that the proposed feature decoding method achieves 98.78% accuracy on the CK + dataset, exhibiting scalability, adaptability to different scenarios, and good generalization capability. [ABSTRACT FROM AUTHOR]

The widespread adoption of deep learning approaches in natural language processing is largely attributed to their exceptional performance across diverse tasks. Notably, Transformer-based models, such as BERT, have gained popularity for their remarkable efficacy and their ease of adaptation (via fine-tuning) across various domains. Despite their success, fine-tuning these models for informal language, particularly instances involving offensive expressions, presents a major challenge due to limitations in vocabulary coverage and contextual information for such tasks. To address these challenges, we propose the domain adaptation of the BERT language model for the task of detecting abusive language. Our approach involves constraining the language model with the adaptation and paradigm shift of two default pre-trained tasks, the design of two datasets specifically engineered to support the adapted pre-training tasks, and the proposal of a dynamic weighting loss function. The evaluation of these adapted configurations on six datasets dedicated to abusive language detection reveals promising outcomes, with a significant enhancement observed compared to the base model. Furthermore, our proposed methods yield competitive results when compared to state-of-the-art approaches, establishing a robust and easily trainable model for the effective identification of abusive language. • The use of tailored pre-training tasks enhances the detection of abusive language. • Carefully designed pre-training tasks can reduce the large volumes of training data. • The joint retraining of the pre-training tasks competes with contemporary benchmarks. [ABSTRACT FROM AUTHOR]

Background: Lumbar spinal stenosis (LSS) is a common cause of low back pain, especially in the elderly, and accurate diagnosis is critical for effective treatment. However, manual diagnosis using MRI images is time consuming and subjective, leading to a need for automated methods. Objective: This study aims to develop a convolutional neural network (CNN)-based deep learning model integrated with multiple attention mechanisms to improve the accuracy and robustness of LSS classification via MRI images. Methods: The proposed model is trained on a standardized MRI dataset sourced from multiple institutions, encompassing various lumbar degenerative conditions. During preprocessing, techniques such as image normalization and data augmentation are employed to enhance the model's performance. The network incorporates a Multi-Headed Self-Attention Module, a Slot Attention Module, and a Channel and Spatial Attention Module, each contributing to better feature extraction and classification. Results: The model achieved 95.2% classification accuracy, 94.7% precision, 94.3% recall, and 94.5% F1 score on the validation set. Ablation experiments confirmed the significant impact of the attention mechanisms in improving the model's classification capabilities. Conclusion: The integration of multiple attention mechanisms enhances the model's ability to accurately classify LSS in MRI images, demonstrating its potential as a tool for automated diagnosis. This study paves the way for future research in applying attention mechanisms to the automated diagnosis of lumbar spinal stenosis and other complex spinal conditions. [ABSTRACT FROM AUTHOR]

Illegal waste dumping not only encroaches on land resources but also threatens the health of the surrounding residents. The traditional artificial waste monitoring solution requires professional workers to conduct field investigations. This solution not only requires high labor resources and economic costs but also demands a prolonged cycle for updating the monitoring status. Therefore, some scholars use deep learning to achieve automatic waste detection from satellite imagery. However, relevant models cannot effectively capture multi-scale features and enhance key information. To further bolster the monitoring efficiency of urban solid waste, we propose a novel multi-scale context fusion network for solid waste detection in remote sensing images, which can quickly collect waste distribution information in a large-scale range. Specifically, it introduces a new guidance fusion module that leverages spatial attention mechanisms alongside the use of large kernel convolutions. This module helps guide shallow features to retain useful details and adaptively adjust multi-scale spatial receptive fields. Meanwhile, it proposes a novel context awareness module based on heterogeneous convolutions and gating mechanisms. This module can effectively capture richer context information and provide anisotropic features for waste localization. In addition, it also designs an effective multi-scale interaction module based on cross-guidance and coordinate perception. This module not only enhances critical information but also fuses multi-scale semantic features. To substantiate the effectiveness of our approach, we conducted a series of comprehensive experiments on two representative urban waste detection datasets. The outcomes of relevant experiments indicate that our methodology surpasses other deep learning models. As plug-and-play components, these modules can be flexibly integrated into existing object detection frameworks, thereby delivering consistent enhancements in performance. Overall, we provide an efficient solution for monitoring illegal waste dumping, which contributes to promoting eco-friendly development. [ABSTRACT FROM AUTHOR]

The objective of deep learning-based object detection is to accurately localize and recognize objects of interest from images or videos using neural networks. However, the detection and localization of non-salient objects pose challenges due to their small proportions, low contrast, and occlusion in images. To address this, we propose an improved object detection method, namely hierarchical coordinate attention (HCA)-YOLO, based on the YOLOv8 architecture. Specifically, we enhance the model's attention towards non-salient objects by introducing HCA, building upon the optimized YOLOv8 baseline. Additionally, we propose a novel object regression loss metric, β-VIoU, to improve YOLOv8's perception of non-salient object positions. Our method achieves competitive results on multiple metrics with two widely adopted open-source datasets, MS COCO 2017 and CrowdHuman. Compared to the YOLOv8x baseline model, HCA-YOLO improves the average precision (mAP) by 3.3% and 3.7% on these two datasets, respectively. [ABSTRACT FROM AUTHOR]

In this paper, we propose a representation of the visual information about Multiplayer Online Battle Arena (MOBA) video games using an adapted unsupervised deep learning architecture called Convolutional Accordion Auto-Encoder (Conv_A2E). Our study includes a presentation of current representations of MOBA video game information and why our proposal offers a novel and useful solution to this task. This approach aims to achieve dimensional reduction and refined feature extraction of the visual data. To enhance the model's performance, we tested several attention mechanisms for computer vision, evaluating algorithms from the channel attention and spatial attention families, and their combination. Through experimentation, we found that the best reconstruction of the visual information with the Conv_A2E was achieved when using a spatial attention mechanism, deformable convolution, as its mean squared error (MSE) during testing was the lowest, reaching a value of 0.003893, which means that its dimensional reduction is the most generalist and representative for this case study. This paper presents one of the first approaches to applying attention mechanisms to the case study of MOBA video games, representing a new horizon of possibilities for research. [ABSTRACT FROM AUTHOR]

Illegal cultivation and reclamation activities are expressly prohibited in the area of reclamation of steep slopes. Large areas of sloping farmland have posed a high risk of soil erosion to reduce land productivity, even seriously threatening food security. However, manual ground surveys have been confined to investigating the illegal cultivation on the steep slopes and the prohibition of reclamation, due mainly to time-consuming and laborious. Meanwhile, the steep slopes are often distributed in dangerous areas, such as mountains and hills, leading to delays in the manual supervision of regional soil and water conservation. Fortunately, remote sensing interpretation and detection can be applied to monitor soil and water conservation in steep slopes, particularly with the rapid development of remote sensing and unmanned aerial vehicle (UAV) technology in recent years. Deep learning has been used to extract sloping farmland in the field of cultivated land identification. However, it is still lacking to consider the terrain slope, which has a great impact on soil erosion. At the same time, the large-scale remote sensing images often contain both concentrated contiguous fields and scattered small and micro fields; There are quite different directions and shapes of the fields, due to the differences in topography and landform, leading to the missed, false detection and low confidence in the detection of convolutional neural networks. The low detection efficiency and high time cost can also be caused by manual visual inspection in the supervision of illegal cultivation areas. In this study, a new model was designed to detect the remote sensing images from the illegal cultivation areas on steep slopes using the improved YOLOv9. The datasets were used from the GF-1 satellite and ALOS PALSAR. Firstly, 12.5 m resolution DEM was calculated to extract the potential slope farmland areas using ALOS data. Then the GF-1 satellite remote sensing images in the potential slope farmland area were utilized as experimental data. The specific procedures and main contribution were as follows :1) The lightweight self-attention mechanism (SimAM) was introduced into the backbone network of YOLOv9 for the richer background and the better discrimination of features; 2) The ultra-lightweight upsampling operator (DySample) was selected to replace the kernel-based dynamic one, in order to reduce the number of network parameters for the high recognition speed and accuracy; 3) The recognition accuracy of YOLO was verified for the illegal tillage areas after the introduction of SimAM and DySample; Finally, the comparison was made on the Faster RCNN, YOLOv5, YOLOv7, and Yolov8 models. Field investigation and remote-sensing image migration experiments were also carried out to verify the effectiveness of the improved model. The results show that the average recognition accuracy of the improved YOLOv9 model was 82.27%, which was better than that of mainstream object detection, such as Faster RCNN, YOLOv7, and YOLOv8. The high reliability and effectiveness were achieved in the improved model. The ablation test showed that the accuracy, recall, mean average accuracy, and F1 score of the improved model increased by 3.62, 3.78, 1.86, and 3.7 percentage points, respectively, compared with the original model. The findings can provide high-quality data support for soil and water conservation, particularly for the decision-making on the regional control of soil erosion, ecological environmental protection, as well as green and low-carbon sustainable production. [ABSTRACT FROM AUTHOR]

The real-time instance segmentation task based on deep learning aims to accurately identify and distinguish all instance objects from images or videos. However, due to the existence of problems such as mutual occlusion between instances, limitations in model receptive fields, etc., achieving accurate and real-time segmentation continues to pose a formidable challenge. To alleviate the aforementioned issues, this paper proposes a real-time instance segmentation method based on a dual-branch structure, called GHA-Inst. Specifically, we made improvements to the feature fusion module (Neck) and output end (Head) of the YOLOv7-seg real-time instance segmentation framework to mitigate the accuracy reduction caused by feature loss and reduce the interference of background noise on the model. Secondly, we introduced a Global Hybrid-Domain Attention (GHA) module to improve the model's focus on significant information while retaining more original spatial features, alleviate incomplete segmentation caused by instance occlusion, and improve the quality of generated masks. Finally, our method achieved competitive results on multiple metrics of the MS COCO 2017 and KINS open-source datasets. Compared with the YOLOv7-seg baseline model, GHA-Inst improved the average precision (AP) by 3.4% and 2.6% on the two datasets, respectively. [ABSTRACT FROM AUTHOR]

To address issues such as misdetection and omission due to low light, image defocus, and worker occlusion in coal-rock image recognition, a new method called YOLOv8-Coal, based on YOLOv8, is introduced to enhance recognition accuracy and processing speed. The Deformable Convolution Network version 3 enhances object feature extraction by adjusting sampling positions with offsets and aligning them closely with the object's shape. The Polarized Self-Attention module in the feature fusion network emphasizes crucial features and suppresses unnecessary information to minimize irrelevant factors. Additionally, the lightweight C2fGhost module combines the strengths of GhostNet and the C2f module, further decreasing model parameters and computational load. The empirical findings indicate that YOLOv8-Coal has achieved substantial enhancements in all metrics on the coal rock image dataset. More precisely, the values for AP50, AP50:95, and AR50:95 were improved to 77.7%, 62.8%, and 75.0% respectively. In addition, optimal localization recall precision (oLRP) were decreased to 45.6%. In addition, the model parameters were decreased to 2.59M and the FLOPs were reduced to 6.9G. Finally, the size of the model weight file is a mere 5.2 MB. The enhanced algorithm's advantage is further demonstrated when compared to other commonly used algorithms. [ABSTRACT FROM AUTHOR]

Forest fire is an ecological catastrophe with great damage and rapid spread, which inflicts significant damage upon the ecological balance of the forests and poses a threat to human well-being. Given the current problems of low forest fire recognition accuracy and weak local detection, an improved forest fire detection algorithm Catch Fire YOLO-based neural networks (CF-YOLO) based on YOLOv7 model is studied. In global information processing, the plug-and-play coordinate attention mechanism is introduced into the YOLOv7 model, which enhances the visual depiction of the receptive field, while aggregate features along different spatial directions to improve the depiction of the focal interest. We present the three parallel max-pooling operations in the SPPCSPC module of the Neck to a serial mode, where the output of each pooling is used as the next pooling input. In local information processing, we prepare a feature fusion module to replace the partial high-efficiency layer aggregation network (ELAN), so that the network further improves the detection accuracy while speeding up the calculation. The proposed model was trained and verified on a forest fire dataset, the experimental results demonstrate an improved detection capability, especially for small targets, and can meet the requirements of edge deployment in forest fire scenarios. [ABSTRACT FROM AUTHOR]

The hole segmentation of core images is important for oil exploration. In order to solve the problems of discontinuous hole edge segmentation, low segmentation accuracy and large number of parameters, an improved model based on UNet network MSHAM-UNet was proposed. Firstly, aiming at the problem of semantic information loss caused by the hopping connection of UNet models to feature maps of different scales, a multi-scale hybrid fusion attention module (MSHAM) combined with a hybrid attention mechanism (HAM) was designed, which performed attention feature fusion on shallow feature maps with spatial information and deep feature maps with semantic information, so as to enhanced the ability of the network to aggregate information at different scales. Secondly, the GPbneck module was used to replace part of the ordinary convolution, which can reduce the number of model parameters and deepen the network, and enhance the network feature extraction ability. The experimental results show that the F1-score, intersection over union ( IoU) and mean intersection over union (MIoU) of the MSHAM-UNet network on the core hole dataset reach 87.35%, 77.27% and 90.21%, respectively, which are 5.29%, 4.02% and 4. 84% higher than the original model, and are also improved compared with the mainstream semantic segmentation model, which provides a new idea for core pore research. [ABSTRACT FROM AUTHOR]

Introduction: Soil pollution, which includes a variety of contaminants such as heavy metals and organic compounds, poses significant environmental and health risks, making effective prediction and assessment techniques essential. Current predictive models often struggle with the complexity and diversity of soil contaminant behaviors, leading to limitations in their accuracy and applicability. Methods: Recognizing the importance of capturing the temporal dynamics influenced by seasonal variations and agricultural practices, our study introduces an SSA-optimized Attention-ConvGRU model. This model integrates convolutional neural networks, gated recurrent units, and attention mechanisms, enhanced through optimization with the Sparrow Search Algorithm to improve predictive performance. Results: Experimental results confirm that our model significantly outperforms traditional methods, demonstrating over 30% improvement in prediction accuracy across multiple datasets. Discussion: This research underscores the potential of advanced machine learning techniques to revolutionize the assessment of soil pollution, providing substantial benefits for environmental management and public health protection. [ABSTRACT FROM AUTHOR]

Facial expression recognition (FER) plays a crucial role in affective computing, enhancing human-computer interaction by enabling machines to understand and respond to human emotions. Despite advancements in deep learning, current FER systems often struggle with challenges such as occlusions, head pose variations, and motion blur in natural environments. These challenges highlight the need for more robust FER solutions. To address these issues, we propose the Attention-Enhanced Multi-Layer Transformer (AEMT) model, which integrates a dual-branch Convolutional Neural Network (CNN), an Attentional Selective Fusion (ASF) module, and a Multi-Layer Transformer Encoder (MTE) with transfer learning. The dual-branch CNN captures detailed texture and color information by processing RGB and Local Binary Pattern (LBP) features separately. The ASF module selectively enhances relevant features by applying global and local attention mechanisms to the extracted features. The MTE captures long-range dependencies and models the complex relationships between features, collectively improving feature representation and classification accuracy. Our model was evaluated on the RAF-DB and AffectNet datasets. Experimental results demonstrate that the AEMT model achieved an accuracy of 81.45% on RAF-DB and 71.23% on AffectNet, significantly outperforming existing state-of-the-art methods. These results indicate that our model effectively addresses the challenges of FER in natural environments, providing a more robust and accurate solution. The AEMT model significantly advances the field of FER by improving the robustness and accuracy of emotion recognition in complex real-world scenarios. This work not only enhances the capabilities of affective computing systems but also opens new avenues for future research in improving model efficiency and expanding multimodal data integration. [ABSTRACT FROM AUTHOR]

Tomato disease image recognition plays a crucial role in agricultural production. Today, whilemachine vision methods based on deep learning have achieved some success indiseaserecognition, theystill faceseveralchallenges. These include issues such as imbalanced datasets, unclear disease features, small inter-class differences, and large intra-class variations. To address these challenges, this paper proposes a method for classifying and recognizing tomato leaf diseases based on machine vision. First, to enhance the disease feature details in images, a piecewise linear transformation method is used for image enhancement, and oversampling is employed to expand the dataset, compensating for the imbalanced dataset. Next, this paper introduces a convolutional block with a dual attention mechanismcalled DAC Block, which is used to construct a lightweight model named LDAMNet. The DAC Block innovatively uses Hybrid Channel Attention (HCA) and Coordinate Attention (CSA) to process channel information and spatial information of input images respectively, enhancing the model's feature extraction capabilities. Additionally, this paper proposes a Robust Cross-Entropy (RCE) loss function that is robust tonoisylabels, aimedat reducing the impact of noisy labels on the LDAM Net model during training. Experimental results show that this method achieves an average recognition accuracy of 98.71% on the tomato disease dataset, effectively retaining disease information in images and capturing disease areas. Furthermore, the method also demonstrates strong recognition capabilities on rice crop disease datasets, indicating good generalization performance and the ability to function effectively in disease recognition across different crops. The research findings of this paper provide new ideas and methods for the field of crop disease recognition. However, future research needs to further optimize the model's structure and computational efficiency, and validate its application effects in more practical scenarios. [ABSTRACT FROM AUTHOR]

As artificial intelligence technology progresses, deep learning models are increasingly utilized for machine fault classification. However, a significant drawback of current state-of-the-art models is their high computational complexity, rendering them unsuitable for deployment in portable devices. This paper presents a compact fault diagnosis model that integrates a self-attention SqueezeNet architecture with a hybrid texture representation technique utilizing empirical mode decomposition (EMD) and a gammatone spectrogram (GS) filter. In the model, the dominant signal is first isolated from the audio fault signals by discarding lower intrinsic mode functions (IMFs) from EMD, and subsequently, the dominant signals are transformed into 2D texture maps using the GS filter. These generated texture maps feed as input into the modified self-attention SqueezeNet classifier, featuring reduced model width and depth, for training and validation. Different attention modules were tested in the paper, including the self-attention, channel attention, spatial attention, and convolutional block attention module (CBAM). The models were tested on the MIMII and ToyADMOS datasets. The experimental results demonstrated that the self-attention mechanism with SqueezeNet achieved an accuracy of 97% on the previously unseen MIMII and ToyADMOS datasets. Furthermore, the proposed model outperformed the SqueezeNet attention model with other attention mechanisms and state-of-the-art deep architectures, exhibiting a higher precision, recall, and F1-score. Lastly, t-SNE is applied to visualize the features of the self-attention SqueezeNet for different fault classes of both MIMII and ToyADMOS. [ABSTRACT FROM AUTHOR]

In both plant breeding and crop management, interpretability plays a crucial role in instilling trust in AI-driven approaches and enabling the provision of actionable insights. The primary objective of this research is to explore and evaluate the potential contributions of deep learning network architectures that employ stacked LSTM for end-of-season maize grain yield prediction. A secondary aim is to expand the capabilities of these networks by adapting them to better accommodate and leverage the multi-modality properties of remote sensing data. In this study, a multi-modal deep learning architecture that assimilates inputs from heterogeneous data streams, including high-resolution hyperspectral imagery, LiDAR point clouds, and environmental data, is proposed to forecast maize crop yields. The architecture includes attention mechanisms that assign varying levels of importance to different modalities and temporal features that, reflect the dynamics of plant growth and environmental interactions. The interpretability of the attention weights is investigated in multi-modal networks that seek to both improve predictions and attribute crop yield outcomes to genetic and environmental variables. This approach also contributes to increased interpretability of the model's predictions. The temporal attention weight distributions highlighted relevant factors and critical growth stages that contribute to the predictions. The results of this study affirm that the attention weights are consistent with recognized biological growth stages, thereby substantiating the network's capability to learn biologically interpretable features. Accuracies of the model's predictions of yield ranged from 0.82-0.93 R2 ref in this genetics-focused study, further highlighting the potential of attention-based models. Further, this research facilitates understanding of how multi-modality remote sensing aligns with the physiological stages of maize. The proposed architecture shows promise in improving predictions and offering interpretable insights into the factors affecting maize crop yields, while demonstrating the impact of data collection by different modalities through the growing season. By identifying relevant factors and critical growth stages, the model's attention weights provide valuable information that can be used in both plant breeding and crop management. The consistency of attention weights with biological growth stages reinforces the potential of deep learning networks in agricultural applications, particularly in leveraging remote sensing data for yield prediction. To the best of our knowledge, this is the first study that investigates the use of hyperspectral and LiDAR UAV time series data for explaining/interpreting plant growth stages within deep learning networks and forecasting plot-level maize grain yield using late fusion modalities with attention mechanisms. [ABSTRACT FROM AUTHOR]

In recent years, attention mechanisms have shown great potential in various computer vision tasks. However, most existing methods focus on developing more complex attention modules for better performance, which inevitably increases the complexity of the model. To overcome performance and complexity tradeoffs, this paper proposes efficient spatial and channel attention (ESCA), a symmetric, comprehensive, and efficient attention module. By analyzing squeeze-and-excitation (SE), convolutional block attention module (CBAM), coordinate attention (CA), and efficient channel attention (ECA) modules, we abandon the dimension-reduction operation of SE module, verify the negative impact of global max pooling (GMP) on the model, and apply a local cross-channel interaction strategy without dimension reduction to learn attention. We not only care about the channel features of the image, we also care about the spatial location of the target on the image, and we take into account the effectiveness of channel attention, so we designed the symmetric ESCA module. The ESCA module is effective, as demonstrated by its application in the ResNet-50 classification benchmark. With 26.26 M parameters and 8.545 G FLOPs, it introduces a mere 0.14% increment in FLOPs while achieving over 6.33% improvement in Top-1 accuracy and exceeding 3.25% gain in Top-5 accuracy. We perform image classification and object detection tasks on ResNet, MobileNet, YOLO, and other architectures on popular datasets such as Mini ImageNet, CIFAR-10, and VOC 2007. Experiments show that ESCA can achieve great improvement in model accuracy at a very small cost, and it performs well among similar models. [ABSTRACT FROM AUTHOR]

Sericulture has a pivotal position in the agricultural production of China. The number of silkworms is one of the key indicators in the process of silkworm breeding, which is crucial to the feeding and healthy growth of silkworms. Accurate detection of the number of silkworms can ensure the quality of sericulture products. However, manual counting has not fully met the needs of large-scale production in recent years. There is a high demand to combine AI technology for silkworm counting. In this study, silkworm detection and counting were studied from the perspective of deep learning. Several challenges were solved in the detection of silkworms. Since the silkworms belonged to the small targets, it was difficult to detect the fewer features; When the number of silkworms was high, serious masking occurred to cause interference in the detection of densely distributed silkworms; It was difficult to ensure that the clear images of silkworms were captured from the actual production. The blur images were easy to deteriorate the accuracy of detection. Two tasks were covered as follows. 1) The image dataset of silkworms was constructed; 2) MAM-YOLO, a silkworm detection and counting model was proposed using multi-dimensional perception. The pictures of silkworms were first taken from the Guangxi Sericulture Technology Promotion Station. These pictures of silkworms were preliminarily processed. The polygon annotation was then selected, according to the body and distribution of silkworms. The pictures of silkworms were annotated with the open-source tool (Labelme). Finally, the dataset was expanded using image transformation and noise addition. The silkworm picture dataset was obtained for the subsequent training, verification, and testing. An improved YOLOv8 (MAM-YOLO) was proposed to detect the silkworm, in order to realize the accurate detection and counting. A silkworm detection and counting model was proposed using space-to-depth transformation and image deblurring. The fine-grained detection performance of blurred images was improved in the MAMYOLO model. The space-to-depth transformed convolution was utilized instead of stride convolution for the pooling layer in the feature extraction. The fine-grained features were extracted from the images, in order to further improve the detection of the low-resolution and small targets. In addition, the impact of blurred images on the detection performance was reduced by introducing a deblurring module into the model. RCS-OSA module was added to the YOLOv8 model. The network feature extraction was improved to integrate the information of different sizes in the receptive fields. The detection head of the original network was improved into the multi-dimensional sensing head. The three-dimensional features of size, space, and channel were aggregated to enhance the detection of silkworm targets. The loss function was replaced with the EIOU loss function. The regression accuracy of the detection frame was improved to reduce the object occlusion. Experimental results show that the MAM-YOLO more accurately detected the silkworm targets. The detection speed of mAP@0.5:0.95 reached 74.8% and 22.2 frames per second, indicating the better functions of silkworm detection and counting with high precision and high rate. The detection requirements were fully met in the actual work of the Guangxi sericulture station. The results show that the silkworm detection and counting system of the technical indicators met the application requirements. The finding can provide the detection services for the actual production work of the silkworm breeding station. In the end, a generalization experiment was conducted on the Global Wheat Challenge 2021 dataset. It was found that the MAM-YOLO was also achieved in the better detection performance of wheat ears, particularly on the small targets similar to dense occlusion.Sericulture has a pivotal position in China's agricultural production. The number of silkworms is a key indicator in the process of silkworm breeding, which is crucial to the feeding and healthy growth of silkworms, and accurate detection of the number of silkworms can ensure the quality of sericulture products. The traditional way of counting silkworms manually is inefficient, and it needs to be combined with technology to intelligently complete this kind of heavy work. In this thesis, from the perspective of deep learning, a silkworm detection and counting method is studied, and several problems in silkworm detection are solved: silkworms belong to small targets, which have fewer features so that they are not easy to detect; silkworms are densely distributed, and when the number of silkworms is high, serious masking phenomenon occurs which causes interference in the detection; it is difficult to ensure that the images of silkworms obtained in the actual production are clear, and blur images will make the detection affected. The main research content of this thesis is as follows: This chapter completed two main tasks: 1) Completed the production of silkworm image data set; 2) MAM-YOLO, a silkworm detection and counting model based on multi-dimensional perception, was proposed.In this paper, pictures of silkworms were taken by workers from Guangxi Sericulture Technology Promotion Station. First, these pictures of silkworms were preliminically processed. Then, according to the characteristics of the body and distribution of silkworms, the polygon annotation method was selected, and the pictures of silkworms were annotated with the open-source tool Labelme. Finally, the data set is expanded by means of image transformation and noise addition, and finally the silkworm picture data set is obtained for subsequent training, verification and testing.This chapter proposes an improved silkworm detection method based on YOLOv8, MAM-YOLO, which realizes accurate detection and counting of silkworm. Secondly, aiming at the problems of loss of fine-grained information and poor detection performance of blurred images in the MAM-YOLO model, a silkworm detection and counting model based on spaceto-depth transformation and image deblurring is proposed. The model utilizes space-to-depth transformed convolution instead of stride convolution and pooling layer in the feature extraction process, so that fine-grained features in the image can be extracted, which further improves the detection capability for low-resolution and small targets. In addition, the impact of blurred images on the detection performance is reduced by introducing a deblurring module into the model. By adding RCSOSA module to YOLOv8 model, the capability of network feature extraction is improved, and the information of different size receptive fields is integrated. The detection head of the original network is improved into multi-dimensional sensing head, and the three-dimensional characteristics of size, space and channel are aggregated to enhance the detection ability of silkworm targets. By replacing the loss function with EIOU loss function, the regression accuracy of detection frame is improved and the problem caused by object occlusion is reduced. Experiments show that MAM-YOLO can more accurately detect silkworm targets, and the detection speed of mAP@0.5:0.95 reaches 74.8% and 22.2 frames per second, which realizes the functions of silkworm detection and counting with high precision and high rate, and can meet the detection requirements in the actual work of Guangxi sericulture station. results show that the silkworm detection and counting system of the technical indicators have met the application requirements, and can provide detection services for the actual production work of silkworm breeding station.In the end, a generalization experiment was conducted on the Global Wheat Challenge 2021 data set, and it was found that MAM-YOLO also showed a high improvement in the detection performance of wheat ears, proving that the proposed method can generalize to other small targets similar to dense occlusion. [ABSTRACT FROM AUTHOR]

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Runoff prediction is essential in water resource management, environmental protection, and agricultural development. Due to the large randomness, high non-stationarity, and low prediction accuracy of nonlinear effects of the traditional model, this study proposes a runoff prediction model based on the improved vector weighted average algorithm (INFO) to optimize the convolutional neural network (CNN)-bidirectional long short-term memory (Bi-LSTM)-Attention mechanism. First, the historical data are analyzed and normalized. Secondly, CNN combined with Attention is used to extract the depth local features of the input data and optimize the input weights of Bi-LSTM. Then, Bi-LSTM is used to study the time series feature depth analysis data from both positive and negative directions simultaneously. The INFO parameters are optimized to provide the optimal parameter guarantee for the CNN-Bi-LSTM-Attention model. Based on a hydrology station's water level and flow data, the influence of three main models and two optimization algorithms on the prediction accuracy of the CNN-Bi-LSTM-Attention model is compared and analyzed. The results show that the fitting coefficient, R2, of the proposed model is 0.948, which is 7.91% and 3.38% higher than that of Bi-LSTM and CNN-Bi-LSTM, respectively. The R2 of the vector-weighted average optimization algorithm (INFO) optimization model is 0.993, which is 0.61% higher than that of the Bayesian optimization algorithm (BOA), indicating that the method adopted in this paper has more significant forecasting ability and can be used as a reliable tool for long-term runoff prediction. [ABSTRACT FROM AUTHOR]

Background: Diagnosing lung diseases accurately is crucial for proper treatment. Convolutional neural networks (CNNs) have advanced medical image processing, but challenges remain in their accurate explainability and reliability. This study combines U-Net with attention and Vision Transformers (ViTs) to enhance lung disease segmentation and classification. We hypothesize that Attention U-Net will enhance segmentation accuracy and that ViTs will improve classification performance. The explainability methodologies will shed light on model decision-making processes, aiding in clinical acceptance. Methodology: A comparative approach was used to evaluate deep learning models for segmenting and classifying lung illnesses using chest X-rays. The Attention U-Net model is used for segmentation, and architectures consisting of four CNNs and four ViTs were investigated for classification. Methods like Gradient-weighted Class Activation Mapping plus plus (Grad-CAM++) and Layer-wise Relevance Propagation (LRP) provide explainability by identifying crucial areas influencing model decisions. Results: The results support the conclusion that ViTs are outstanding in identifying lung disorders. Attention U-Net obtained a Dice Coefficient of 98.54% and a Jaccard Index of 97.12%. ViTs outperformed CNNs in classification tasks by 9.26%, reaching an accuracy of 98.52% with MobileViT. An 8.3% increase in accuracy was seen while moving from raw data classification to segmented image classification. Techniques like Grad-CAM++ and LRP provided insights into the decision-making processes of the models. Conclusions: This study highlights the benefits of integrating Attention U-Net and ViTs for analyzing lung diseases, demonstrating their importance in clinical settings. Emphasizing explainability clarifies deep learning processes, enhancing confidence in AI solutions and perhaps enhancing clinical acceptance for improved healthcare results. [ABSTRACT FROM AUTHOR]

Multimodal sentiment analysis combines data from different sources, like text and images, to improve the accuracy of sentiment predictions. Our research tackles two critical challenges in MSA: missing data and effective fusion through attention mechanisms. The challenge of missing data can be a significant obstacle in real-time applications. We propose a method to handle cases where either text or images are missing by utilizing the knowledge of available data to fill in the gaps. Our approach begins with feature extraction. For text, we utilize advanced natural language processing models to obtain rich, context-aware representations. For images, we employ deep convolutional neural networks to capture detailed visual features. After extracting these features, we calculate sentiment scores for both modalities to identify the most relevant modality. These sentiment scores play a crucial role in determining attention weights, allowing the model to focus dynamically on the most significant features from each modality. We then concatenate the text and image features according to these attention weights, ensuring a robust and accurate fusion of information. These fused features are fed into a classification algorithm to predict the overall sentiment. Our method outperforms previous approaches, demonstrating the effectiveness of using attention-based fusion networks for multimodal sentiment analysis. This framework also underscores the importance of effectively handling missing data to maintain robust performance in real-time scenarios. It shows the potential for improving sentiment analysis in practical applications by intelligently combining multimodal data using attention-weighted fusion. [ABSTRACT FROM AUTHOR]

Nonalcoholic fatty liver disease (NAFLD) is a prevalent liver disorder affecting approximately 25.2% of the global population, posing risks of liver fibrosis, cancer, and metabolic disturbances. Despite its increasing prevalence, many facets of NAFLD's pathogenesis remain elusive, and there are currently no approved therapeutic drugs, underscoring the critical need for a comprehensive understanding of its pathophysiology to enable early diagnosis and intervention. Experimental animal studies play a pivotal role in elucidating the mechanisms underlying NAFLD and in the exploration of novel pharmacotherapies. Despite the widespread integration of deep learning techniques in human histopathology, their application to scrutinize histological features in animal models warrants exploration. This study presents a pioneering NAFLD assessment system leveraging IFNet and ResNet34 architectures. This automated system adeptly identifies inflammatory cell foci and hepatic steatosis in histopathology sections of rat livers. Remarkably, our approach achieved an impressive 95.6% accuracy in the assessment of hepatic steatosis and 77.7% in the evaluation of inflammation cell foci. By introducing a novel histopathology scoring system, our methodology mitigated subjective variations inherent in traditional pathologist evaluations, concurrently streamlining time and labor costs. This system ensured a standardized and precise assessment of rat liver histology in NAFLD and represented a significant stride toward enhancing the efficiency and objectivity of experimental outcomes. [ABSTRACT FROM AUTHOR]

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Underwater images often exhibit color deviation, reduced contrast, distortion, and other issues due to light refraction, scattering, and absorption. Therefore, restoring detailed information in underwater images and obtaining high-quality results are primary objectives in underwater image enhancement tasks. Recently, deep learning-based methods have shown promising results, but handling details in low-light underwater image processing remains challenging. In this paper, we propose an attention-based color consistency underwater image enhancement network. The method consists of three components: illumination detail network, balance stretch module, and prediction learning module. The illumination detail network is responsible for generating the texture structure and detail information of the image. We introduce a novel color restoration module to better match color and content feature information, maintaining color consistency. The balance stretch module compensates using pixel mean and maximum values, adaptively adjusting color distribution. Finally, the prediction learning module facilitates context feature interaction to obtain a reliable and effective underwater enhancement model. Experiments conducted on three real underwater datasets demonstrate that our approach produces more natural enhanced images, performing well compared to state-of-the-art methods. [ABSTRACT FROM AUTHOR]

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