Search

Rhetorical sentence classification is an interesting approach for making extractive summaries but this technique still needs to be developed because the performance of automatic rhetorical sentence classification is still poor. Rhetorical sentences are sentences that contain rhetorical words or phrases. Rhetorical sentences not only appear in the contents of a paper but also in the title. In this study, features related to section class and title class that have been proposed in a previous research were further developed. Our method uses different techniques to reach automatic section class extraction for which we introduce new, format-based features. Furthermore, we propose automatic rhetoric phrase extraction from the title. The corpus we used was a collection of technical-experimental scientific papers. Our method uses the Support Vector Machine (SVM) algorithm and the Naïve Bayesian algorithm for classification. The four categories used were: Problem, Method, Data, and Result. It was hypothesized that these features would be able to improve classification accuracy compared to previous methods. The F-measure for these categories reached up to 14%. [ABSTRACT FROM AUTHOR]

Intelligent systems on Paper currency recognition and verification are inevitable for modern banking services. These systems are used in Auto-seller machines, vending machines etc. Extracting sufficient and reliable monetary characteristics are essential for accuracy and performance of such systems. This paper proposes a new intelligent system for paper currency recognition. Pakistani paper currency has been considered, as a case study, for intelligent recognition. This paper identifies, introduces, and extracts robust features from Pakistani banknotes. After extracting these features, the paper proposes to use three layers feed-forward Backpropagation Neural Network (BPN) for intelligent classification. The proposed technique and system are simple and comparatively less time consuming which makes it suitable for real-time applications. In order to evaluate the performance of the proposed technique, experiments have been conducted on 175 Pakistani banknotes. The results indicate that system has 100% recognition ability on properly captured images. [ABSTRACT FROM AUTHOR]

The semiconductor industry is the core industry of the information age. As a key link in the semiconductor industry, wafer fabrication plays a key role in its development. In the testing stage of the wafer, each die of the wafer is detected and marked, and a wafer map with a certain spatial pattern can be formed. The analysis and classification of these spatial patterns can identify the cause of wafer defects, thereby improving production yield. However, as wafer size increases, line widths become smaller, etc., the probability of a mixed defect mode wafer pattern increases. Moreover, the mixed defect mode wafer map is more difficult to identify and classify than the single defect mode wafer map. Therefore, this paper proposes an improved deep convolutional neural network (DCNN) structure model for the recognition and classification of mixed defect pattern wafer maps. From the perspective of increasing the width of the DCNN, the improved network structure can avoid problems such as over-fitting and limited extraction of features due to the continuous deepening of the DCNN. The network is called Multi-Path DCNN (MP-DCNN) structure. The experimental results show that the proposed Multi-Path DCNN structure has better performance and higher classification accuracy than existing methods. [ABSTRACT FROM AUTHOR]

With the advancement in living standards, there has been a significant surge in the quantity and diversity of household waste. To safeguard the environment and optimize resource utilization, there is an urgent demand for effective and cost-efficient intelligent waste classification methodologies. This study presents MRS-YOLO (Multi-Resolution Strategy-YOLO), a waste detection and classification model. The paper introduces the SlideLoss_IOU technique for detecting small objects, integrates RepViT of the Transformer mechanism, and devises a novel feature extraction strategy by amalgamating multi-dimensional and dynamic convolution mechanisms. These enhancements not only elevate the detection accuracy and speed but also bolster the robustness of the current YOLO model. Validation conducted on a dataset comprising 12,072 samples across 10 categories, including recyclable metal and paper, reveals a 3.6% enhancement in mAP50% accuracy compared to YOLOv8, coupled with a 15.09% reduction in volume. Furthermore, the model demonstrates improved accuracy in detecting small targets and exhibits comprehensive detection capabilities across diverse scenarios. For transparency and to facilitate further research, the source code and related datasets used in this study have been made publicly available at GitHub. [ABSTRACT FROM AUTHOR]

Fruit fly species classification is a fine-grained task as there is a small gap between species. In order to effectively identify and improve the recognition of fruit flies, a fine-grained image-recognition method based on a multi-channel self-attention mechanism was studied and a network framework for fine-grained image recognition based on deep learning was designed in this paper. In this framework, long-term and short-term memory networks are used to extract the underlying features in fruit fly fine-grained images. By inputting the underlying features in the multi-channel self-attention mechanism module, the global and local attention feature maps can be obtained.The weighted attention feature map can also be obtained by multiplying the weight of each channel and the attention feature map. The fine-grained image features of fruit flies were obtained by summing the weighted attention feature map. A softmax classifier was used to process the features and complete the recognition of the fruit fly fine-grained images. Two fine-grained image datasets of fruit flies were applied as experimental objects. Dataset 1 and Dataset 2 contain 11,778 images and 20,580 images from 46 different categories of fruit flies, respectively. The Kappa coefficient was used as the evaluation index to identify fruit fly images with different targets using the method proposed herein. The experimental results showed that, as the number of attention channels increased, the Kappa coefficient gradually increased, suggesting an improvement in the accuracy of fine-grained image recognition. The fine-grained image features extracted by introducing a multi-channel self-attention mechanism exhibited more distinct boundaries with a small amount of overlap, demonstrating strong feature extraction capabilities. When dealing with fine-grained images with either simple or complex backgrounds, the method proposed in this paper has good performance and generalization ability. Even if the target is small and varied in shape, it can still achieve highly accurate recognition. [ABSTRACT FROM AUTHOR]

Since the dawn of civilization, handwriting has been one of the most important forms of communication. However, as handwriting differs from person to person, writer identification has become a promising application of pattern recognition to identify the actual writer of a handwritten document. Handwriting can be either online or offline, depending on how it was obtained. Users can write directly on tablets, smartphones, touch screens, PDAs, and other devices using input devices with online handwriting, whereas offline handwriting is done with a pen and paper. With the advent of artificial intelligence, and most importantly deep learning techniques, the development of writer identification systems based on offline handwritten documents has gained a lot of attention. Deep learning models have the capability of automatic feature extraction, which results in increased performance. From the literature survey, it was revealed that least attention has been paid towards the development of deep learning-based writer identification systems for offline Urdu handwritten documents, unlike the English and Arabic scripts. Therefore, in this paper, we proposed an offline Urdu handwritten writer identification system using a deep learning model inspired by the VGG-16 model of CNN. The model was trained and tested on a novel Urdu handwritten dataset contributed by 318 distinct Urdu writers, resulting in an overall training accuracy of 98.71% and a testing accuracy of 99.11%. The results achieved showed that the proposed model outperformed the already existing writer identification techniques. [ABSTRACT FROM AUTHOR]

With the growth of the Internet, more and more business is being done online, for example, online offices, online education and so on. While this makes people's lives more convenient, it also increases the risk of the network being attacked by malicious code. Therefore, it is important to identify malicious codes on computer systems efficiently. However, most of the existing malicious code detection methods have two problems: (1) The ability of the model to extract features is weak, resulting in poor model performance. (2) The large scale of model data leads to difficulties deploying on devices with limited resources. Therefore, this paper proposes a lightweight malicious code identification model Lightweight Malicious Code Classification Method Based on Improved SqueezeNet (LCMISNet). In this paper, the MFire lightweight feature extraction module is constructed by proposing a feature slicing module and a multi-size depthwise separable convolution module. The feature slicing module reduces the number of parameters by grouping features. The multi-size depthwise separable convolution module reduces the number of parameters and enhances the feature extraction capability by replacing the standard convolution with depthwise separable convolution with different convolution kernel sizes. In addition, this paper also proposes a feature splicing module to connect the MFire lightweight feature extraction module based on the feature reuse and constructs the lightweight model LCMISNet. The malicious code recognition accuracy of LCMISNet on the BIG 2015 dataset and the Malimg dataset reaches 98.90% and 99.58%, respectively. It proves that LCMISNet has a powerful malicious code recognition performance. In addition, compared with other network models, LCMISNet has better performance, and a lower number of parameters and computations. [ABSTRACT FROM AUTHOR]

The remote sensing ships' fine-grained classification technology makes it possible to identify certain ship types in remote sensing images, and it has broad application prospects in civil and military fields. However, the current model does not examine the properties of ship targets in remote sensing images with mixed multi-granularity features and a complicated backdrop. There is still an opportunity for future enhancement of the classification impact. To solve the challenges brought by the above characteristics, this paper proposes a Metaformer and Residual fusion network based on Visual Attention Network (VAN-MR) for fine-grained classification tasks. For the complex background of remote sensing images, the VAN-MR model adopts the parallel structure of large kernel attention and spatial attention to enhance the model's feature extraction ability of interest targets and improve the classification performance of remote sensing ship targets. For the problem of multi-grained feature mixing in remote sensing images, the VAN-MR model uses a Metaformer structure and a parallel network of residual modules to extract ship features. The parallel network has different depths, considering both high-level and low-level semantic information. The model achieves better classification performance in remote sensing ship images with multi-granularity mixing. Finally, the model achieves 88.73% and 94.56% accuracy on the public fine-grained ship collection-23 (FGSC-23) and FGSCR-42 datasets, respectively, while the parameter size is only 53.47 M, the floating point operations is 9.9 G. The experimental results show that the classification effect of VAN-MRis superior to that of traditional CNNs model and visual model with Transformer structure under the same parameter quantity. [ABSTRACT FROM AUTHOR]

Electroencephalography (EEG) is increasingly important in Brain-Computer Interface (BCI) systems due to its portability and simplicity. In this paper, we provide a comprehensive review of research on EEG signal processing techniques since 2021, with a focus on preprocessing, feature extraction, and classification methods. We analyzed 61 research articles retrieved from academic search engines, including CNKI, PubMed, Nature, IEEE Xplore, and Science Direct. For preprocessing, we focus on innovatively proposed preprocessing methods, channel selection, and data augmentation. Data augmentation is classified into conventional methods (sliding windows, segmentation and recombination, and noise injection) and deep learning methods [Generative Adversarial Networks (GAN) and Variation AutoEncoder (VAE)]. We also pay attention to the application of deep learning, and multi-method fusion approaches, including both conventional algorithm fusion and fusion between conventional algorithms and deep learning. Our analysis identifies 35 (57.4%), 18 (29.5%), and 37 (60.7%) studies in the directions of preprocessing, feature extraction, and classification, respectively. We find that preprocessing methods have become widely used in EEG classification (96.7% of reviewed papers) and comparative experiments have been conducted in some studies to validate preprocessing. We also discussed the adoption of channel selection and data augmentation and concluded several mentionable matters about data augmentation. Furthermore, deep learning methods have shown great promise in EEG classification, with Convolutional Neural Networks (CNNs) being the main structure of deep neural networks (92.3% of deep learning papers). We summarize and analyze several innovative neural networks, including CNNs and multi-structure fusion. However, we also identified several problems and limitations of current deep learning techniques in EEG classification, including inappropriate input, low cross-subject accuracy, unbalanced between parameters and time costs, and a lack of interpretability. Finally, we highlight the emerging trend of multi-method fusion approaches (49.2% of reviewed papers) and analyze the data and some examples. We also provide insights into some challenges of multi-method fusion. Our review lays a foundation for future studies to improve EEG classification performance. [ABSTRACT FROM AUTHOR]

Fine-grained management of rice fields can enhance the yield and quality of rice crops. Challenges in achieving fine classification include interference from similar vegetation, the irregularity of natural field shapes, and complex scale variations. This paper introduces Rice Attention Cascade Network (RACNet), for the fine classification of rice fields in high-resolution satellite remote sensing imagery. The network employs the Hybrid Task Cascade network as the base framework and uses spectral and indices mixed multimodal data as input to reinforce the feature differentiation of similar vegetation. Initially, a Channel Attention Deformable-ResNet (CAD-ResNet) was designed to enhance the feature representation of rice on different channels. Deformable convolution improves the ability of CAD-ResNet to capture irregular field shapes. Then, to address the issue of complex scale changes, the multi-scale features extracted by the CAD-ResNet are progressively fused using an Asymptotic Feature Pyramid, reducing the loss of scale information between non-adjacent layers. Experiments on the Meishan rice dataset show that the proposed method is capable of accurate instance segmentation for fragmented or irregularly shaped rice fields. The evaluation metric AP50 of RACNet reaches 50.8%. [ABSTRACT FROM AUTHOR]

Feature extraction is the key to a successfully trained classifier. Although many automatic methods exist for traditional data, other data types (e.g., sequences, graphs) usually require dedicated approaches. In this paper, we study a universal feature extraction method based on distance from reference points. First, we formalize this process and provide an instantiation based on network centrality. To reliably select the best reference points, we introduce the notion of θ-neighborhood which allows us to navigate the topography of fully connected graphs. Our experiments show that the proposed peak selection method is significantly better than a traditional top-k approach for centrality-based reference points and that the quality of the reference points is much less important than their quantity. Finally, we provide an alternative, neural network interpretation of reference points, which paves a path to optimization-based selection methods, together with a new type of neuron, called the Euclidean neuron, and the necessary modifications to backpropagation. [ABSTRACT FROM AUTHOR]

In the field of object detection, small object detection in remote sensing images is an important and challenging task. Due to limitations in size and resolution, most existing methods often suffer from localization blurring. To address the above problem, this paper proposes a remote sensing small object detection network based on adaptive channel attention and large kernel adaptation. This approach aims to enhance multi-channel information mining and multi-scale feature extraction to alleviate the problem of localization blurring. To enhance the model's focus on the features of small objects in remote sensing at varying scales, this paper introduces an adaptive channel attention block. This block applies adaptive attention weighting based on the input feature dimensions, guiding the model to better focus on local information. To mitigate the loss of local information by large kernel convolutions, a large kernel adaptive block is designed. The block dynamically adjusts the surrounding spatial receptive field based on the context around the detection area, improving the model's ability to extract information around remote sensing small objects. To address the recognition confusion during the sample classification process, a layer batch normalization method is proposed. This method enhances the consistency analysis capabilities of adaptive learning, thereby reducing the decline in the model's classification accuracy caused by sample misclassification. Experiments on the DOTA-v2.0, SODA-A and VisDrone datasets show that the proposed method achieves state-of-the-art performance. [ABSTRACT FROM AUTHOR]

Supervised learning has been the cornerstone of many successful medical imaging applications. However, its reliance on large labeled datasets poses significant challenges, especially in the medical domain, where data annotation is time-consuming and expensive. In response, self-supervised learning (SSL) has emerged as a promising alternative, leveraging unlabeled data to learn meaningful representations without explicit supervision. This paper provides a detailed overview of supervised learning and its limitations in medical imaging, underscoring the need for more efficient and scalable approaches. The study emphasizes the importance of the area under the curve (AUC) as a key evaluation metric in assessing SSL performance. The AUC offers a comprehensive measure of model performance across different operating points, which is crucial in medical applications, where false positives and negatives have significant consequences. Evaluating SSL methods based on the AUC allows for robust comparisons and ensures that models generalize well to real-world scenarios. This paper reviews recent advances in SSL for medical imaging, demonstrating their potential to revolutionize the field by mitigating challenges associated with supervised learning. Key results show that SSL techniques, by leveraging unlabeled data and optimizing performance metrics like the AUC, can significantly improve the diagnostic accuracy, scalability, and efficiency in medical image analysis. The findings highlight SSL's capability to reduce the dependency on labeled datasets and present a path forward for more scalable and effective medical imaging solutions. [ABSTRACT FROM AUTHOR]

In recent years, with the rapid development of deep learning technology, a large number of excellent convolutional neural networks (CNNs) have been proposed, many of which are based on improvements to classical methods. Based on the Inception family of methods, depthwise separable convolution was applied to Xception to achieve lightweighting, and Inception-ResNet introduces residual connections to accelerate model convergence. However, existing improvements for the Inception module often neglect further enhancement of its receptive field, while increasing the receptive field of CNNs has been widely studied and proven to be effective in improving classification performance. Motivated by this fact, three effective expansion modules are proposed in this paper. The first expansion module, Inception expand (Inception-e) module, is proposed to improve the classification accuracy by concatenating more and deeper convolutional branches. To reduce the number of parameters for Inception e, this paper proposes a second expansion module—Equivalent Inception-e (Eception) module, which is equivalent to Inception-e in terms of feature extraction capability, but which suppresses the growth of the parameter quantity brought by the expansion by effectively reducing the redundant convolutional layers; on the basis of Eception, this paper proposes a third expansion module—Lightweight Eception (Lception) module, which crosses depthwise convolution with ordinary convolution to further effectively reduce the number of parameters. The three proposed modules have been validated on the Cifar10 dataset. The experimental results show that all these extensions are effective in improving the classification accuracy of the models, and the most significant effect is the Lception module, where Lception (rank = 4) on the Cifar10 dataset improves the accuracy by 1.5% compared to the baseline model (Inception module A) by using only 0.15 M more parameters. [ABSTRACT FROM AUTHOR]

Copyright of Baghdad Science Journal is the property of Republic of Iraq Ministry of Higher Education & Scientific Research (MOHESR) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In biomedical research, Electromyography (EMG) data play a crucial role as a bridge between human motions and machine interpretation, offering valuable insights into muscle activation. EMG signals give vital information on hand movements in the context of applications like gesture recognition, prosthetic control, and rehabilitation. This paper describes the classification of EMG signals based on muscle motions, which makes it simpler to identify distinct gestures or movements. A Linear Discriminant Analysis (LDA) classifier is used to differentiate between various classes of muscle activity. In order to record EMG signals during hand motions, surface electrodes are carefully positioned on pertinent muscles. Muscle activity may be tracked in real time with these non-invasive electrodes. In order to extract meaningful information from these signals, which are complex and frequently contaminated by noise, strong feature extraction techniques are needed. When working with noisy signals, denoising is a commonly used approach to restoring the original quality of the source data. It attempts to maintain relevant information by reducing noise in the raw EMG signals. In order to retrieve only the pertinent information from the original EMG signal data, any unnecessary noise must first be removed. Through the identification of key characteristics in the time, frequency, and time-frequency domains, it transforms unstructured EMG data. This procedure improves the next step of classification, which is the identification and classification of patterns in the EMG signals. Ultimately, the obtained information is employed to classify signals by the Linear Discriminant Analysis (LDA) classifier, demonstrating a distinction between various muscle motions with over 80% accuracy. [ABSTRACT FROM AUTHOR]

The intelligent appearance quality classification method for Auricularia auricula is of great significance to promote this industry. This paper proposes an appearance quality classification method for Auricularia auricula based on the improved Faster Region-based Convolutional Neural Networks (improved Faster RCNN) framework. The original Faster RCNN is improved by establishing a multiscale feature fusion detection model to improve the accuracy and real-time performance of the model. The multiscale feature fusion detection model makes full use of shallow feature information to complete target detection. It fuses shallow features with rich detailed information with deep features rich in strong semantic information. Since the fusion algorithm directly uses the existing information of the feature extraction network, there is no additional calculation. The fused features contain more original detailed feature information. Therefore, the improved Faster RCNN can improve the final detection rate without sacrificing speed. By comparing with the original Faster RCNN model, the mean average precision (mAP) of the improved Faster RCNN is increased by 2.13%. The average precision (AP) of the first-level Auricularia auricula is almost unchanged at a high level. The AP of the second-level Auricularia auricula is increased by nearly 5%. And the third-level Auricularia auricula AP is increased by 1%. The improved Faster RCNN improves the frames per second from 6.81 of the original Faster RCNN to 13.5. Meanwhile, the influence of complex environment and image resolution on the Auricularia auricula detection is explored. [ABSTRACT FROM AUTHOR]

While the world struggles to recover from the devastation wrought by the widespread spread of COVID-19, monkeypox virus has emerged as a new global pandemic threat. In this paper, a high precision and lightweight classification network MpoxNet based on ConvNext is proposed to meet the need of fast and safe detection of monkeypox classification. In this method, a two-branch depthseparable convolution residual Squeeze and Excitation module is designed. This design aims to extract more feature information with two branches, and greatly reduces the number of parameters in the model by using depth-separable convolution. In addition, our method introduces a convolutional attention module to enhance the extraction of key features within the receptive field. The experimental results show that MpoxNet has achieved remarkable results in monkeypox disease classification, the accuracy rate is 95.28%, the precision rate is 96.40%, the recall rate is 93.00%, and the F1-Score is 95.80%. This is significantly better than the current mainstream classification model. It is worth noting that the FLOPS and the number of parameters of MpoxNet are only 30.68% and 31.87% of those of ConvNext-Tiny, indicating that the model has a small computational burden and model complexity while efficient performance. [ABSTRACT FROM AUTHOR]

With the increasing demand for high-resolution images, handling high-dimensional image data has become a key aspect of intelligence algorithms. One effective approach is to preserve the high-dimensional manifold structure of the data and find the accurate mappings in a lower-dimensional space. However, various non-sparse, high-energy occlusions in real-world images can lead to erroneous calculations of sample relationships, invalidating the existing distance-based manifold dimensionality reduction techniques. Many types of noise are difficult to capture and filter in the original domain but can be effectively separated in the frequency domain. Inspired by this idea, a novel approach is proposed in this paper, which obtains the high-dimensional manifold structure according to the correlationships between data points in the frequency domain and accurately maps it to a lower-dimensional space, named Frequency domain-based Manifold Dimensionality Reduction (FMDR). In FMDR, samples are first transformed into frequency domains. Then, interference is filtered based on the distribution in the frequency domain, thereby emphasizing discriminative features. Subsequently, an innovative kernel function is proposed for measuring the similarities between samples according to the correlationships in the frequency domain. With the assistance of these correlationships, a graph structure can be constructed and utilized to find the mapping in a low-dimensional space. To further demonstrate the effectiveness of the proposed algorithm, FMDR is employed for the semi-supervised classification problems in this paper. Experiments using public image datasets indicate that, compared to baseline algorithms and state-of-the-art methods, our approach achieves superior recognition performance. Even with very few labeled data, the advantages of FMDR are still maintained. The effectiveness of FMDR in dimensionality reduction and feature extraction of images makes it widely applicable in fields such as image processing and image recognition. [ABSTRACT FROM AUTHOR]

In recent years, classification and identification of Earth's surface materials has been a challenging research topic in the field of earth science and remote sensing (RS). Although deep learning techniques have achieved some results in remote sensing image classification, there are still some challenges for multimodal remote sensing data classification, such as information redundancy between multimodal remote sensing images. In this paper, we propose a multimodal remote sensing data classification method IFF-Net based on irregular feature fusion, called IFF-Net. The IFF-Net architecture utilizes weight-shared residual blocks for feature extraction while maintaining the independent batch normalization (BN) layer. During the training phase, the redundancy of the current channel is determined by evaluating the judgement factor of the BN layer. If this judgment factor falls below a predefined threshold, it indicates that the current channel information is redundant and should be substituted with another channel. Sparse constraints are imposed on some of the judgment factors in order to remove extra channels and enhance generalization. Furthermore, a module for feature normalization and calibration has been devised to leverage the spatial interdependence of multimodal features in order to achieve improved discrimination. Two standard datasets are used in the experiments to validate the effectiveness of the proposed method. The experimental results show that the IFF-NET method proposed in this paper exhibits significantly superior performance compared to the state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Being a significant repository of Buddhist imagery, Thangka images are valuable historical materials of Tibetan studies, which covers many domains such as Tibetan history, politics, culture, social life and even traditional medicine and astronomy. Thangka cultural element images are the essence of Thangka images. Hence, Thangka cultural element images classification is one of the most important works of knowledge representation and mining in the field of Thangka and is the foundation of digital protection of Thangka images. However, due to the limited quantity, high complexity and the intricate textures of Thangka images, the classification of Thangka images is limited to a small number of categories and coarse granularity. Thus, a novel fusion texture feature dual-branch Thangka cultural elements classification model based on the attention mechanism and self-supervised contrastive learning has been proposed in this paper. Specifically, to address the issue of insufficient labeled samples and improve the classification performance, this method utilizes a large amount of unlabeled irrelevant data to pre-train the feature extractor through self-supervised learning. During the fine-tuning stage of the downstream task, a dual-branch feature extraction structure incorporating texture features has been designed, and MS Triplet Attention proposed by us is used for the integration of important features. Additionally, to address the problem of sample imbalance and the existence of a large number of difficult samples in the Thangka cultural element dataset, the Gradient Harmonizing Mechanism Loss has been adopted, and it has been improved by introducing a self-designed adaptive mechanism. The experimental results on Thangka cultural elements dataset prove the superiority of the proposed method over the state-of-the-art methods. The source code of our proposed algorithm and the related datasets is available at https://github.com/WiniTang/MS-BiCLR. [ABSTRACT FROM AUTHOR]

Accurate classification of substation equipment images remains challenging due to various factors such as unexpected illumination, viewing angles, scale variations, shadows, surface contaminants, and different elements sharing similar appearances. This paper presents a novel two‐stage substation equipment classification method based on dual‐scale attention. Leveraging the region proposal technique from Faster‐regions with CNN features (RCNN), the input images are initially decomposed into multiple scales to capture latent features. A dual‐scale attention module is introduced to enhance the precision of feature extraction. Furthermore, a two‐stage network is proposed to address the challenge of classifying closely similar substation equipment. A multi‐layer perceptron performs a coarse classification to categorize the equipment into broad categories. Then, a lightweight classifier is employed for fine‐grained subclassification, further distinguishing equipment within the same broad category. To mitigate the issue of limited training data, a specialized dataset is collected and annotated for the substation equipment classification. Experimental results demonstrate that the proposed method achieves remarkable accuracy, recall, and F1‐score surpassing 0.91, outperforming mainstream approaches in terms of recall and F1 scores. Ablation experiments further validate the significant contributions of both the dual‐scale attention and the two‐stage classification module in improving the overall performance of the classification network. [ABSTRACT FROM AUTHOR]

Various types of foreign fibres may be mixed in the planting, transportation, and production processes of cotton, which not only cause equipment to be out of control, but also leads to a decrease in the quality of cotton textile products and economic losses. The machine vision based detection method for cotton foreign fibres is widely used. Based on existing related research, we construct a classification dataset for cotton foreign fibres in practical scenarios, named the CF2113‐10 dataset. The authors design a basic foreign fibre classification network called CottonNet that balances performance and efficiency. The classification accuracy on the validation set reached 94.2%. In order to enhance the high‐level feature extraction ability, this paper improves the feature fusion method of residual networks and proposes CottonNet‐Res, which improves the classification accuracy to 95.1%. Finally, a classification model based on feature difference fitting, CottonNet‐Fusion, is proposed to address the classification problem of foreign fibre images sampled in complex environments. The classification accuracy of foreign fibre images sampled in ordinary scenes has improved to 97.4%, while the images sampled in complex environments maintain an accuracy of 90.3%. [ABSTRACT FROM AUTHOR]

At present, the commonly used Bag of Words (BOW) expression ignores the semantic information of text and the problems of high dimension and high sparsity of feature extraction. This paper presents a multi-class text representation and classification algorithm. This project is based on the vector expression of keywords and takes the multi-category classification problem as the research object. Then, a hybrid Deep Location network (HDBN) is constructed by combining DBN with Boltzmann (DBM). Then, this paper does a lot of tests on the algorithm and proves the effectiveness of the algorithm. In addition, the 2D visual experiment is carried out with HDBN, and then the high-level text expression based on HDBN is obtained. The expression has strong cohesion and weak coupling. [ABSTRACT FROM AUTHOR]