Automatic Feature Extraction Research Articles

A Novel Image Classification Approach via MobileNet Model for Security Assessment of Power System

The computational complexities of conventional techniques used for security assessment of power system are addressed by proposing a maiden deep learning-based technique capable of boosting security calculations, increasing decision-making efficiency, and reducing hazards for real time operation. This approach uses MobileNet Convolutional Neural Network (MCNN) to evaluate security with reduced model size and computational burden as compared to traditional CNN architectures by leveraging special type of convolution technique. MCNN's automatic feature extraction and strong generalization allow the operator to determine whether the current operating state of power system is secured or not. All of the system’s critical operational scenarios are developed via load flow studies for generation of data while accounting for small signal stability with ℓ − 1 security and converted into RGB images of varied input dimensions for training and testing of the MCNN. Once trained, the MCNN is well-suited for real-time applications like assessing whether current state of the system is secured or not including unforeseen occurrences like topological changes in a computation-free manner. The comparative performance of the proposed method with customized CNN on three typical IEEE test system demonstrates its superior performance in terms of better accuracy and computational efficiency.

TrioConvTomatoNet: A robust CNN architecture for fast and accurate tomato leaf disease classification for real time application

Tomato leaf diseases are a common threat to long-term tomato protection that affects many farmers worldwide. Computer-assisted technology is more prevalent for early and accurate diagnosis of tomato leaf diseases, which may reduce the likelihood that the plant will suffer further harm. The advancement of deep learning techniques has a lot of potential for accurate classification of leaf diseases through an automated feature extraction topology. By changing the network layers and their parameters in the feature extraction topology, it is possible to extract the exact features and improve the classification accuracy. In this study, TrioConvTomatoNet, novel deep convolutional neural network architecture, is proposed, which includes a 3-series convolution layer under each stage of the feature extraction topology to capture and integrate the information from the leaf images efficiently. To improve the network's learning ability and perform accurate classifications, the proposed method incorporates the stochastic gradient descent optimizer. In order to facilitate proper learning, a separate dataset preparation strategy was followed for tomato leaf image dataset collection, which includes existing and real-time images. This research is carried out with varying the number of convolution layers to express the effectiveness of the proposed method in capturing and integrating information from the leaf images. Also, the extensive experiments on an unseen dataset collection with and without augmentation express the superiority and robustness of TrioConvTomatoNet in terms of both accuracy and speed when compared to state-of-the-art methods. The proposed method achieves remarkable accuracy of about 99.39% in disease classification while processing both existing database and real-time images, making it suitable for practical deployment in agricultural settings.

A Survey of Deep Learning Road Extraction Algorithms Using High-Resolution Remote Sensing Images.

Roads are the fundamental elements of transportation, connecting cities and rural areas, as well as people's lives and work. They play a significant role in various areas such as map updates, economic development, tourism, and disaster management. The automatic extraction of road features from high-resolution remote sensing images has always been a hot and challenging topic in the field of remote sensing, and deep learning network models are widely used to extract roads from remote sensing images in recent years. In light of this, this paper systematically reviews and summarizes the deep-learning-based techniques for automatic road extraction from high-resolution remote sensing images. It reviews the application of deep learning network models in road extraction tasks and classifies these models into fully supervised learning, semi-supervised learning, and weakly supervised learning based on their use of labels. Finally, a summary and outlook of the current development of deep learning techniques in road extraction are provided.

A robust multi-branch multi-attention-mechanism EEGNet for motor imagery BCI decoding

BackgroundMotor-Imagery-based Brain-Computer Interface (MI-BCI) is a promising technology to assist communication, movement, and neurological rehabilitation for motor-impaired individuals. Electroencephalography (EEG) decoding techniques using deep learning (DL) possess noteworthy advantages due to automatic feature extraction and end-to-end learning. However, the DL-based EEG decoding models tend to show large variations due to intersubject variability of EEG, which results from inconsistencies of different subjects’ optimal hyperparameters. New methodsThis study proposes a multi-branch multi-attention mechanism EEGNet model (MBMANet) for robust decoding. It applies the multi-branch EEGNet structure to achieve various feature extractions. Further, the different attention mechanisms introduced in each branch attain diverse adaptive weight adjustments. This combination of multi-branch and multi-attention mechanisms allows for multi-level feature fusion to provide robust decoding for different subjects. ResultsThe MBMANet model has a four-classification accuracy of 83.18% and kappa of 0.776 on the BCI Competition IV-2a dataset, which outperforms other eight CNN-based decoding models. This consistently satisfactory performance across all nine subjects indicates that the proposed model is robust. ConclusionsThe combine of multi-branch and multi-attention mechanisms empowers the DL-based models to adaptively learn different EEG features, which provides a feasible solution for dealing with data variability. It also gives the MBMANet model more accurate decoding of motion intentions and lower training costs, thus improving the MI-BCI’s utility and robustness.

VSG-GAN: A high-fidelity image synthesis method with semantic manipulation in retinal fundus image

In recent years, advancements in retinal image analysis, driven by machine learning and deep learning techniques, have enhanced disease detection and diagnosis through automated feature extraction. However, challenges persist, including limited data set diversity due to privacy concerns and imbalanced sample pairs, hindering effective model training. To address these issues, we introduce the vessel and style guided generative adversarial network (VSG-GAN), an innovative algorithm building upon the foundational concept of GAN. In VSG-GAN, a generator and discriminator engage in an adversarial process to produce realistic retinal images. Our approach decouples retinal image generation into distinct modules: the vascular skeleton and background style. Leveraging style transformation and GAN inversion, our proposed hierarchical variational autoencoder module generates retinal images with diverse morphological traits. In addition, the spatially adaptive denormalization module ensures consistency between input and generated images. We evaluate our model on MESSIDOR and RITE data sets using various metrics, including structural similarity index measure, inception score, Fréchet inception distance, and kernel inception distance. Our results demonstrate the superiority of VSG-GAN, outperforming existing methods across all evaluation assessments. This underscores its effectiveness in addressing data set limitations and imbalances. Our algorithm provides a novel solution to challenges in retinal image analysis by offering diverse and realistic retinal image generation. Implementing the VSG-GAN augmentation approach on downstream diabetic retinopathy classification tasks has shown enhanced disease diagnosis accuracy, further advancing the utility of machine learning in this domain.

AUTOMATED DETECTION OF PARKINSON’S DISEASE BASED ON HYBRID CNN AND QUANTUM MACHINE LEARNING TECHNIQUES IN MRI IMAGES

Parkinson’s disease (PD) is a long-term neurological condition that causes severe neuronal degeneration in the motor cortex. Because of the constraints of medical applications determining the severity of PD, forecasting its development can be difficult. Recently research has focused on artificial intelligence to automatically diagnose PD from MRI images. The proposed study aims (a) to incorporate automated method for augmentation of the MRI images using the deep convolutional generative adversarial networks (DCGAN), (b) to perform feature extraction and classification by employing the pre-trained models such as VGG16, Xception, and InceptionV3 models, (c) to develop a hybrid model by fusing the InceptionV3 features and classification using QSVM model. A total of 60 real-time MRIs of normal (N = 30) and PD (N = 30) patients were included in this study. To increase the dataset, the proposed study employed the DCGAN approach. Thus, the dataset for this study was increased to 1000 (normal and PD) images. Automatic feature extraction and classification were performed utilizing different pre-trained models such as VGG16, Xception, and InceptionV3. Among the pre-trained models InceptionV3 architecture provided high accuracy of 74% compared to other models. The proposed work developed a hybrid model by fusing InceptionV3 features and a quantum support vector machine (QSVM) for the detection of PD and healthy groups. The proposed model attained a prediction accuracy of 87.5% and high precision of 95%, respectively. Additionally, the hybrid architecture provided high recall and F1-measure of 84% and 89%, respectively. The hybrid model provided lowest false negative and false positive of 4 and 1, respectively. Therefore, the hybrid model could be an effective diagnostic tool for the automated prediction of PD.

Accurate Capacity Prediction and Evaluation with Advanced SSA-CNN-BiLSTM Framework for Lithium-Ion Batteries

Lithium-ion batteries (LIBs) have been widely used for electric vehicles owing to their high energy density, light weight, and no memory effect. However, their health management problems remain unsolved in actual application. Therefore, this paper focuses on battery capacity as the key health indicator and proposes a data-driven method for capacity prediction. Specifically, this method mainly utilizes Convolutional Neural Network (CNN) for automatic feature extraction from raw data and combines it with the Bidirectional Long Short-Term Memory (BiLSTM) algorithm to realize the capacity prediction of LIBs. In addition, the sparrow search algorithm (SSA) is used to optimize the hyper-parameters of the neural network to further improve the prediction performance of original network structures. Ultimately, experiments with a public dataset of batteries are carried out to verify and evaluate the effectiveness of capacity prediction under two temperature conditions. The results show that the SSA-CNN-BiLSTM framework for capacity prediction of LIBs has higher accuracy compared with other original network structures during the multi-battery cycle experiments.

Efficient mitosis detection: leveraging pre-trained faster R-CNN and cell-level classification

The assessment of mitotic activity is an integral part of the comprehensive evaluation of breast cancer pathology. Understanding the level of tumor dissemination is essential for assessing the severity of the malignancy and guiding appropriate treatment strategies. A pathologist must manually perform the intricate and time-consuming task of counting mitoses by examining biopsy slices stained with Hematoxylin and Eosin (H&E) under a microscope. Mitotic cells can be challenging to distinguish in H&E-stained sections due to limited available datasets and similarities among mitotic and non-mitotic cells. Computer-assisted mitosis detection approaches have simplified the whole procedure by selecting, detecting, and labeling mitotic cells. Traditional detection strategies rely on image processing techniques that apply custom criteria to distinguish between different aspects of an image. Additionally, the automatic feature extraction from histopathology images that exhibit mitosis using neural networks.Additionally, the possibility of automatically extracting features from histopathological images using deep neural networks was investigated. This study examines mitosis detection as an object detection problem using multiple neural networks. From a medical standpoint, mitosis at the tissue level was also investigated utilising pre-trained Faster R-CNN and raw image data. Experiments were done on the MITOS-ATYPIA- 14 dataset and TUPAC16 dataset, and the results were compared to those of other methods described in the literature.

Deep Ensemble Learning Model for Diagnosis of Lung Diseases from Chest X -Ray Images

Objectives: This study aims to develop a robust medical recognition system using deep learning for the identification of various lung diseases, including COVID-19, pneumonia, lung opacity, and normal states, from chest X-ray images. The focus is on implementing ensemble fixed features learning methods to enhance diagnostic capabilities, contributing to the development of a cost-effective and reliable diagnostic tool for combating the global epidemic of lung disorders. Methods: The study utilizes a Kaggle dataset containing COVID-19 chest radiography images. Raw X-ray images undergo preprocessing for contrast enhancement and noise removal while addressing dataset imbalance through near-miss resampling. Ensemble learning techniques, including two and three-level methods, are employed to harness the strengths of individual base learners—VGG16, InceptionV3, and MobileNetV2. The model's performance is evaluated using metrics such as accuracy, recall, precision, and F1-score. For remote access, a user interface and a shared web link are developed using Python Gradio. Findings: In two-level ensembles, features from base learners are concatenated and classified using a support vector machine. Three-level ensembles use concatenated features classified by three machine learning classifiers, employing a majority voting system for the final prediction. The two-level method achieved 93% accuracy, precision, recall, and F1 score. The three-level ensemble model demonstrates superior performance, achieving 94% accuracy in detecting four lung diseases, namely COVID-19, pneumonia, lung opacity, and normal states. Novelty: This research contributes to the field by showcasing the efficacy of deep learning technology, particularly ensemble learning, in enhancing the detection of lung diseases from raw chest X-ray images. The model employs three modified and efficient pretrained networks for automatic feature extraction, eliminating the need for manual feature engineering. The developed model stands as a promising decision-support tool for healthcare professionals, particularly in low-resource environments. Keywords: Convolutional Neural Network (CNN), Deep Learning (DL), Transfer Learning (TL), Ensemble learning (EL), Fixed feature extraction, Chest X­rays (CXR), Lung diseases

Multi-Feature Automatic Extraction for Detecting Obstructive Sleep Apnea Based on Single-Lead Electrocardiography Signals.

Obstructive sleep apnea (OSA), a prevalent sleep disorder, is intimately associated with various other diseases, particularly cardiovascular conditions. The conventional diagnostic method, nocturnal polysomnography (PSG), despite its widespread use, faces challenges due to its high cost and prolonged duration. Recent developments in electrocardiogram-based diagnostic techniques have opened new avenues for addressing these challenges, although they often require a deep understanding of feature engineering. In this study, we introduce an innovative method for OSA classification that combines a composite deep convolutional neural network model with a multimodal strategy for automatic feature extraction. This approach involves transforming the original dataset into scalogram images that reflect heart rate variability attributes and Gramian angular field matrix images that reveal temporal characteristics, aiming to enhance the diversity and richness of data features. The model comprises automatic feature extraction and feature enhancement components and has been trained and validated on the PhysioNet Apnea-ECG database. The experimental results demonstrate the model's exceptional performance in diagnosing OSA, achieving an accuracy of 96.37%, a sensitivity of 94.67%, a specificity of 97.44%, and an AUC of 0.96. These outcomes underscore the potential of our proposed model as an efficient, accurate, and convenient tool for OSA diagnosis.

Breast Cancer Classification from Histopathological Images using Future Search Optimization Algorithm and Deep Learning

In medical imaging, precise recognition of Breast Cancer (BC) is a challenge due to the complications of breast tissues. Histopathological detection is still considered the standard in BC detection. Still, the dramatic increase in workload and the complexity of histopathological image (HPI) make this task labor-intensive and dependent on the pathologist, making the advance of automated and precise HPI analysis techniques needed. Due to the automated feature extraction capability, Deep Learning (DL) methods have been effectively used in different sectors, particularly in the medical imaging sector. This study develops the future search algorithm with a DL-based breast cancer detection and classification (FSADL-BCDC) method. The FSADL-BCDC technique examines HPIs to detect and classify BC. To achieve this, the FSADL-BCDC technique implements Wiener Filtering (WF)-based preprocessing to eliminate the noise in the images. Additionally, the FSADL-BCDC uses the ResNeXt method for feature extraction with a Future Search Algorithm (FSA)-based tuning procedure. For BCDC, the FSADL-BCDC technique employs a Hybrid Convolutional Neural Network along with the Long Short-Term Memory (HCNN-LSTM) approach. Finally, the Sunflower Optimization (SFO) approach adjusts the hyperparameter values of the HCNN-LSTM. The outcomes of the FSADL-BCDC are inspected on a standard medical image dataset. Extensive relational studies highlighted the improved performance of the FSADL-BCDC approach in comparison with known methods by exhibiting an output of 96.94% and 98.69% under diverse datasets.

An Exploration of Deep Learning Techniques for the Detection of Grape Diseases

Abstract: Plant diseases are one of the major contributors to economic loss in the agriculture industry worldwide. Detection of disease at early stages can help in the reduction of this loss. In recent times, a lot of emphasis has been done on disease detection due to the overall increase in production as well as the loss of grape number. With deep learning, having a promising future and having the advantages of automatic learning and feature extraction, the use of these techniques has now been widely spread. This paper reviewed the existing deep-learning techniques available for grape disease detection. Firstly, covering the various steps in a grape disease detection model ranging from the various sources of image acquisition, the different image augmentation techniques and the various models used, and the parameters required to evaluate. Secondly, the study summarizes the important findings of all literature available on the theme. The paper also tries to highlight the various challenges faced by the researchers and the common trend among them, so that future research on the topic can achieve higher performance.

Author identification of literary works based on text analysis and deep learning

With the development of science, speech, picture, and other analysis, problems have been gradually better solved, but the study of Chinese text has been a complex problem to overcome. Chinese text analysis requires not only statistics but also semantic comprehension analysis. Different text types need other language style feature modeling to obtain good recognition results. In this study, we use the deep learning method to construct an automatic text feature extraction model and classify it with the author as a classification label. This study presents a literature author recognition model based on deep learning, which is mainly divided into three phases: text preprocessing, feature extraction, and classification. Each part consists of several small modules or steps. First, we input the corpus to Word2Vec to generate the new word vector. Then, the improved text feature extractor based on CNN and Attention extracts the text features and uses them as the input of the CNN convolution layer. After convolution, the text is combined with bits to get Window Feature Sequence. It is the text feature vector. Next, based on LSTM and Softmax classification output, Window Feature Sequence is used as the input of LSTM to obtain two one-dimensional vectors spliced by concatenate layer. Finally, the result is classified through the fully connected layer, Batch Normalization layer, and Softmax. The performance of the proposed model in recognizing authors of Chinese literature was evaluated using two datasets. In the research process, the data we collected included works of different forms, such as prose and fiction. The research results show that the proposed model can effectively identify author identity. The classification accuracy of our proposed algorithm is significantly better than that of the benchmark model.

Toward automated hail disaster weather recognition based on spatio-temporal sequence of radar images

Abstract Hail, an intense convective catastrophic weather, is seriously hazardous to people’s lives and properties. This article proposes a multi-step cyclone hail weather recognition model, called long short-term memory (LSTM)-C3D, based on radar images, integrating attention mechanism and network voting optimization characteristics to achieve intelligent recognition and accurate classification of hailstorm weather based on long short-term memory networks. Based on radar echo data in the strong-echo region, LSTM-C3D can selectively fuse the long short-term time feature information of hail meteorological images and effectively focus on the significant features to achieve intelligent recognition of hail disaster weather. The meteorological scans of 11 Doppler weather radars deployed in various regions of the Hunan Province of China are used as the specific experimental and application objects for extensive validation and comparison experiments. The results show that the proposed method can realize the automatic extraction of radar reflectivity image features, and the accuracy of hail identification in the strong-echo region reaches 91.3%. It can also effectively realize the prediction of convective storm movement trends, laying the theoretical foundation for reducing the misjudgment of extreme disaster weather.

Therapy-induced modulation of tumor vasculature and oxygenation in a murine glioblastoma model quantified by deep learning-based feature extraction

Glioblastoma presents characteristically with an exuberant, poorly functional vasculature that causes malperfusion, hypoxia and necrosis. Despite limited clinical efficacy, anti-angiogenesis resulting in vascular normalization remains a promising therapeutic approach. Yet, fundamental questions concerning anti-angiogenic therapy remain unanswered, partly due to the scale and resolution gap between microscopy and clinical imaging and a lack of quantitative data readouts. To what extend does treatment lead to vessel regression or vessel normalization and does it ameliorate or aggravate hypoxia? Clearly, a better understanding of the underlying mechanisms would greatly benefit the development of desperately needed improved treatment regimens. Here, using orthotopic transplantation of Gli36 cells, a widely used murine glioma model, we present a mesoscopic approach based on light sheet fluorescence microscopic imaging of wholemount stained tumors. Deep learning-based segmentation followed by automated feature extraction allowed quantitative analyses of the entire tumor vasculature and oxygenation statuses. Unexpectedly in this model, the response to both cytotoxic and anti-angiogenic therapy was dominated by vessel normalization with little evidence for vessel regression. Equally surprising, only cytotoxic therapy resulted in a significant alleviation of hypoxia. Taken together, we provide and evaluate a quantitative workflow that addresses some of the most urgent mechanistic questions in anti-angiogenic therapy.

Lactylation prediction models based on protein sequence and structural feature fusion.

Lysine lactylation (Kla) is a newly discovered posttranslational modification that is involved in important life activities, such as glycolysis-related cell function, macrophage polarization and nervous system regulation, and has received widespread attention due to the Warburg effect in tumor cells. In this work, we first design a natural language processing method to automatically extract the 3D structural features of Kla sites, avoiding potential biases caused by manually designed structural features. Then, we establish two Kla prediction frameworks, Attention-based feature fusion Kla model (ABFF-Kla) and EBFF-Kla, to integrate the sequence features and the structure features based on the attention layer and embedding layer, respectively. The results indicate that ABFF-Kla and Embedding-based feature fusion Kla model (EBFF-Kla), which fuse features from protein sequences and spatial structures, have better predictive performance than that of models that use only sequence features. Our work provides an approach for the automatic extraction of protein structural features, as well as a flexible framework for Kla prediction. The source code and the training data of the ABFF-Kla and the EBFF-Kla are publicly deposited at: https://github.com/ispotato/Lactylation_model.

Empowering robust biometric authentication: The fusion of deep learning and security image analysis

Many societal entities now have more excellent standards for the efficacy and dependability of identification systems due to the ongoing advancement of computer technology. Traditional identification methods, such as keys and smart cards, have been supplanted by biometric systems in highly secure environments. This research presents a smart computational method for automatically authenticating fingerprints for identity (ID) verification and personal identification. Compared to more traditional machine learning algorithms, the results from applying Deep learning (DL) in areas like computer vision, image identification, robotics, and voice processing have generally been positive. Due to their capacity to analyse big data size and deal with fluctuations in biometric data (such as ageing or expression problems), DL has been heavily used by the artificial intelligence research community. Several biometric systems have succeeded with automatic feature extraction employing deep learning approaches like Convolutional Neural Networks (CNNs). In this research, we provide a biometric process that uses convolutional neural networks. This work introduces a deep learning-based biometric identification system that uses Monte Carlo Dropout (MC Dropout). Combining these two systems makes the authentication process more secure and dependable. Fingerprint image enhancement techniques involve the application of Gabor filters and structure-adaptive anisotropic filters, which have proven to be effective in enhancing the clarity and distinctiveness of fingerprint patterns. To improve the efficiency of deep learning models, this work proposes the Inception-Augmentation GAN (IAGAN) model for data augmentation. The study adds to security development by integrating novel biometric identification and authentication approaches with cutting-edge neural network technology. In this research, we provide a new activation function to speed up the convergence of deep neural networks. The results of 99.6% on Gabor filters and 99.8% on the structure-adaptive anisotropic filter with GACNN with MCD show that deep neural networks can excel over competing approaches with enough training data.

An Affective EEG Analysis Method Without Feature Engineering

Emotional electroencephalography (EEG) signals are a primary means of recording emotional brain activity.Currently, the most effective methods for analyzing emotional EEG signals involve feature engineering and neuralnetworks. However, neural networks possess a strong ability for automatic feature extraction. Is it possible to discardfeature engineering and directly employ neural networks for end-to-end recognition? Based on the characteristics of EEGsignals, this paper proposes an end-to-end feature extraction and classification method for a dynamic self-attention network(DySAT). The study reveals significant differences in brain activity patterns associated with different emotions acrossvarious experimenters and time periods. The results of this experiment can provide insights into the reasons behind thesedifferences.

Early Diagnosing and Transformation Prediction of Alzheimer's Disease Using Multi-Scaled Self-Attention Network on Structural MRI Images with Occlusion Sensitivity Analysis.

Structural magnetic resonance imaging (sMRI) is vital for early Alzheimer's disease (AD) diagnosis, though confirming specific biomarkers remains challenging. Our proposed Multi-Scale Self-Attention Network (MUSAN) enhances classification of cognitively normal (CN) and AD individuals, distinguishing stable (sMCI) from progressive mild cognitive impairment (pMCI). This study leverages AD structural atrophy properties to achieve precise AD classification, combining different scales of brain region features. The ultimate goal is an interpretable algorithm for this method. The MUSAN takes whole-brain sMRI as input, enabling automatic extraction of brain region features and modeling of correlations between different scales of brain regions, and achieves personalized disease interpretation of brain regions. Furthermore, we also employed an occlusion sensitivity algorithm to localize and visualize brain regions sensitive to disease. Our method is applied to ADNI-1, ADNI-2, and ADNI-3, and achieves high performance on the classification of CN from AD with accuracy (0.93), specificity (0.82), sensitivity (0.96), and area under curve (AUC) (0.95), as well as notable performance on the distinguish of sMCI from pMCI with accuracy (0.85), specificity (0.84), sensitivity (0.74), and AUC (0.86). Our sensitivity masking algorithm identified key regions in distinguishing CN from AD: hippocampus, amygdala, and vermis. Moreover, cingulum, pallidum, and inferior frontal gyrus are crucial for sMCI and pMCI discrimination. These discoveries align with existing literature, confirming the dependability of our model in AD research. Our method provides an effective AD diagnostic and conversion prediction method. The occlusion sensitivity algorithm enhances deep learning interpretability, bolstering AD research reliability.

Electromyographic hand gesture recognition using convolutional neural network with multi-attention

Electromyographic hand gesture recognition can be used to develop myoelectric prosthetic hands for disabled people when the acquired myoelectric signals are accurately classified. Recently, the developed end-to-end deep learning methods are widely used in hand gesture recognition for their automatic extraction of features from raw signals. But not all extracted features are useful and unimportant information can impede gesture recognition. In this study, a deep learning model using multi-attention for electromyographic hand gesture recognition is proposed. The attention mechanism inspired by the biological system enables deep learning models to focus on the important parts of input data while ignoring unimportant information. At first, a convolutional neural network is employed as the feature extractor, enabling the learning of high-level discriminative features from input signals. Then, multi-attention blocks, including channel attention, spatial attention, and temporal attention, are integrated into the convolutional layers, enhancing the extraction of pertinent features by emphasizing critical information through weight recalibration. Finally, the proposed model is evaluated on both the public NinaPro dataset and the public Myo dataset, demonstrating superior performance compared to baseline models in terms of recognition accuracy. The experimental results reveal that the model with attention mechanism achieves an average recognition accuracy of 91.64%, representing a 1.1% improvement over the model without attention mechanism.