期刊文献+
共找到84,356篇文章
< 1 2 250 >
每页显示 20 50 100
Performance evaluation of seven multi-label classification methods on real-world patent and publication datasets
1
作者 Shuo Xu Yuefu Zhang +1 位作者 Xin An Sainan Pi 《Journal of Data and Information Science》 CSCD 2024年第2期81-103,共23页
Purpose:Many science,technology and innovation(STI)resources are attached with several different labels.To assign automatically the resulting labels to an interested instance,many approaches with good performance on t... Purpose:Many science,technology and innovation(STI)resources are attached with several different labels.To assign automatically the resulting labels to an interested instance,many approaches with good performance on the benchmark datasets have been proposed for multi-label classification task in the literature.Furthermore,several open-source tools implementing these approaches have also been developed.However,the characteristics of real-world multi-label patent and publication datasets are not completely in line with those of benchmark ones.Therefore,the main purpose of this paper is to evaluate comprehensively seven multi-label classification methods on real-world datasets.Research limitations:Three real-world datasets differ in the following aspects:statement,data quality,and purposes.Additionally,open-source tools designed for multi-label classification also have intrinsic differences in their approaches for data processing and feature selection,which in turn impacts the performance of a multi-label classification approach.In the near future,we will enhance experimental precision and reinforce the validity of conclusions by employing more rigorous control over variables through introducing expanded parameter settings.Practical implications:The observed Macro F1 and Micro F1 scores on real-world datasets typically fall short of those achieved on benchmark datasets,underscoring the complexity of real-world multi-label classification tasks.Approaches leveraging deep learning techniques offer promising solutions by accommodating the hierarchical relationships and interdependencies among labels.With ongoing enhancements in deep learning algorithms and large-scale models,it is expected that the efficacy of multi-label classification tasks will be significantly improved,reaching a level of practical utility in the foreseeable future.Originality/value:(1)Seven multi-label classification methods are comprehensively compared on three real-world datasets.(2)The TextCNN and TextRCNN models perform better on small-scale datasets with more complex hierarchical structure of labels and more balanced document-label distribution.(3)The MLkNN method works better on the larger-scale dataset with more unbalanced document-label distribution. 展开更多
关键词 Multi-label classification Real-World datasets Hierarchical structure classification system Label correlation Machine learning
下载PDF
Classification and rating of disintegrated dolomite strata for slope stability analysis
2
作者 Wenlian Liu Xinyue Gong +3 位作者 Jiaxing Dong Hanhua Xu Peixuan Dai Shengwei Zhang 《Journal of Rock Mechanics and Geotechnical Engineering》 SCIE CSCD 2024年第7期2552-2562,共11页
Although disintegrated dolomite,widely distributed across the globe,has conventionally been a focus of research in underground engineering,the issue of slope stability issues in disintegrated dolomite strata is gainin... Although disintegrated dolomite,widely distributed across the globe,has conventionally been a focus of research in underground engineering,the issue of slope stability issues in disintegrated dolomite strata is gaining increasing prominence.This is primarily due to their unique properties,including low strength and loose structure.Current methods for evaluating slope stability,such as basic quality(BQ)and slope stability probability classification(SSPC),do not adequately account for the poor integrity and structural fragmentation characteristic of disintegrated dolomite.To address this challenge,an analysis of the applicability of the limit equilibrium method(LEM),BQ,and SSPC methods was conducted on eight disintegrated dolomite slopes located in Baoshan,Southwest China.However,conflicting results were obtained.Therefore,this paper introduces a novel method,SMRDDS,to provide rapid and accurate assessment of disintegrated dolomite slope stability.This method incorporates parameters such as disintegrated grade,joint state,groundwater conditions,and excavation methods.The findings reveal that six slopes exhibit stability,while two are considered partially unstable.Notably,the proposed method demonstrates a closer match with the actual conditions and is more time-efficient compared with the BQ and SSPC methods.However,due to the limited research on disintegrated dolomite slopes,the results of the SMRDDS method tend to be conservative as a safety precaution.In conclusion,the SMRDDS method can quickly evaluate the current situation of disintegrated dolomite slopes in the field.This contributes significantly to disaster risk reduction for disintegrated dolomite slopes. 展开更多
关键词 Disintegrated dolomite slope Basic quality(BQ) Slope stability probability classification (SSPC) Rock mass quality classification Limit equilibrium method(LEM)
下载PDF
Point Cloud Classification Using Content-Based Transformer via Clustering in Feature Space 被引量:2
3
作者 Yahui Liu Bin Tian +2 位作者 Yisheng Lv Lingxi Li Fei-Yue Wang 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2024年第1期231-239,共9页
Recently, there have been some attempts of Transformer in 3D point cloud classification. In order to reduce computations, most existing methods focus on local spatial attention,but ignore their content and fail to est... Recently, there have been some attempts of Transformer in 3D point cloud classification. In order to reduce computations, most existing methods focus on local spatial attention,but ignore their content and fail to establish relationships between distant but relevant points. To overcome the limitation of local spatial attention, we propose a point content-based Transformer architecture, called PointConT for short. It exploits the locality of points in the feature space(content-based), which clusters the sampled points with similar features into the same class and computes the self-attention within each class, thus enabling an effective trade-off between capturing long-range dependencies and computational complexity. We further introduce an inception feature aggregator for point cloud classification, which uses parallel structures to aggregate high-frequency and low-frequency information in each branch separately. Extensive experiments show that our PointConT model achieves a remarkable performance on point cloud shape classification. Especially, our method exhibits 90.3% Top-1 accuracy on the hardest setting of ScanObjectN N. Source code of this paper is available at https://github.com/yahuiliu99/PointC onT. 展开更多
关键词 Content-based Transformer deep learning feature aggregator local attention point cloud classification
下载PDF
An Imbalanced Data Classification Method Based on Hybrid Resampling and Fine Cost Sensitive Support Vector Machine 被引量:1
4
作者 Bo Zhu Xiaona Jing +1 位作者 Lan Qiu Runbo Li 《Computers, Materials & Continua》 SCIE EI 2024年第6期3977-3999,共23页
When building a classification model,the scenario where the samples of one class are significantly more than those of the other class is called data imbalance.Data imbalance causes the trained classification model to ... When building a classification model,the scenario where the samples of one class are significantly more than those of the other class is called data imbalance.Data imbalance causes the trained classification model to be in favor of the majority class(usually defined as the negative class),which may do harm to the accuracy of the minority class(usually defined as the positive class),and then lead to poor overall performance of the model.A method called MSHR-FCSSVM for solving imbalanced data classification is proposed in this article,which is based on a new hybrid resampling approach(MSHR)and a new fine cost-sensitive support vector machine(CS-SVM)classifier(FCSSVM).The MSHR measures the separability of each negative sample through its Silhouette value calculated by Mahalanobis distance between samples,based on which,the so-called pseudo-negative samples are screened out to generate new positive samples(over-sampling step)through linear interpolation and are deleted finally(under-sampling step).This approach replaces pseudo-negative samples with generated new positive samples one by one to clear up the inter-class overlap on the borderline,without changing the overall scale of the dataset.The FCSSVM is an improved version of the traditional CS-SVM.It considers influences of both the imbalance of sample number and the class distribution on classification simultaneously,and through finely tuning the class cost weights by using the efficient optimization algorithm based on the physical phenomenon of rime-ice(RIME)algorithm with cross-validation accuracy as the fitness function to accurately adjust the classification borderline.To verify the effectiveness of the proposed method,a series of experiments are carried out based on 20 imbalanced datasets including both mildly and extremely imbalanced datasets.The experimental results show that the MSHR-FCSSVM method performs better than the methods for comparison in most cases,and both the MSHR and the FCSSVM played significant roles. 展开更多
关键词 Imbalanced data classification Silhouette value Mahalanobis distance RIME algorithm CS-SVM
下载PDF
Intrahepatic portal venous systems in adult patients with cavernous transformation of portal vein: Imaging features and a new classification 被引量:1
5
作者 Xin Huang Qian Lu +5 位作者 Yue-Wei Zhang Lin Zhang Zhi-Zhong Ren Xiao-Wei Yang Ying Liu Rui Tang 《Hepatobiliary & Pancreatic Diseases International》 SCIE CAS CSCD 2024年第5期481-486,共6页
Background: Cavernous transformation of the portal vein(CTPV) due to portal vein obstruction is a rare vascular anomaly defined as the formation of multiple collateral vessels in the hepatic hilum. This study aimed to... Background: Cavernous transformation of the portal vein(CTPV) due to portal vein obstruction is a rare vascular anomaly defined as the formation of multiple collateral vessels in the hepatic hilum. This study aimed to investigate the imaging features of intrahepatic portal vein in adult patients with CTPV and establish the relationship between the manifestations of intrahepatic portal vein and the progression of CTPV. Methods: We retrospectively analyzed 14 CTPV patients in Beijing Tsinghua Changgung Hospital. All patients underwent both direct portal venography(DPV) and computed tomography angiography(CTA) to reveal the manifestations of the portal venous system. The vessels measured included the left portal vein(LPV), right portal vein(RPV), main portal vein(MPV) and the portal vein bifurcation(PVB). Results: Nine males and 5 females, with a median age of 40.5 years, were included in the study. No significant difference was found in the diameters of the LPV or RPV measured by DPV and CTA. The visualization in terms of LPV, RPV and PVB measured by DPV was higher than that by CTA. There was a significant association between LPV/RPV and PVB/MPV in term of visibility revealed with DPV( P = 0.01), while this association was not observed with CTA. According to the imaging features of the portal vein measured by DPV, CTPV was classified into three categories to facilitate the diagnosis and treatment. Conclusions: DPV was more accurate than CTA for revealing the course of the intrahepatic portal vein in patients with CTPV. The classification of CTPV, that originated from the imaging features of the portal vein revealed by DPV, may provide a new perspective for the diagnosis and treatment of CTPV. 展开更多
关键词 Cavernous transformation of the portal vein classifICATION Direct portal venography Intrahepatic portal venous system
下载PDF
DeepSVDNet:A Deep Learning-Based Approach for Detecting and Classifying Vision-Threatening Diabetic Retinopathy in Retinal Fundus Images 被引量:1
6
作者 Anas Bilal Azhar Imran +4 位作者 Talha Imtiaz Baig Xiaowen Liu Haixia Long Abdulkareem Alzahrani Muhammad Shafiq 《Computer Systems Science & Engineering》 2024年第2期511-528,共18页
Artificial Intelligence(AI)is being increasingly used for diagnosing Vision-Threatening Diabetic Retinopathy(VTDR),which is a leading cause of visual impairment and blindness worldwide.However,previous automated VTDR ... Artificial Intelligence(AI)is being increasingly used for diagnosing Vision-Threatening Diabetic Retinopathy(VTDR),which is a leading cause of visual impairment and blindness worldwide.However,previous automated VTDR detection methods have mainly relied on manual feature extraction and classification,leading to errors.This paper proposes a novel VTDR detection and classification model that combines different models through majority voting.Our proposed methodology involves preprocessing,data augmentation,feature extraction,and classification stages.We use a hybrid convolutional neural network-singular value decomposition(CNN-SVD)model for feature extraction and selection and an improved SVM-RBF with a Decision Tree(DT)and K-Nearest Neighbor(KNN)for classification.We tested our model on the IDRiD dataset and achieved an accuracy of 98.06%,a sensitivity of 83.67%,and a specificity of 100%for DR detection and evaluation tests,respectively.Our proposed approach outperforms baseline techniques and provides a more robust and accurate method for VTDR detection. 展开更多
关键词 Diabetic retinopathy(DR) fundus images(FIs) support vector machine(SVM) medical image analysis convolutional neural networks(CNN) singular value decomposition(SVD) classification
下载PDF
Comprehensive understanding of glioblastoma molecular phenotypes:classification,characteristics,and transition
7
作者 Can Xu Pengyu Hou +7 位作者 Xiang Li Menglin Xiao Ziqi Zhang Ziru Li Jianglong Xu Guoming Liu Yanli Tan Chuan Fang 《Cancer Biology & Medicine》 SCIE CAS CSCD 2024年第5期363-381,共19页
Among central nervous system-associated malignancies,glioblastoma(GBM)is the most common and has the highest mortality rate.The high heterogeneity of GBM cell types and the complex tumor microenvironment frequently le... Among central nervous system-associated malignancies,glioblastoma(GBM)is the most common and has the highest mortality rate.The high heterogeneity of GBM cell types and the complex tumor microenvironment frequently lead to tumor recurrence and sudden relapse in patients treated with temozolomide.In precision medicine,research on GBM treatment is increasingly focusing on molecular subtyping to precisely characterize the cellular and molecular heterogeneity,as well as the refractory nature of GBM toward therapy.Deep understanding of the different molecular expression patterns of GBM subtypes is critical.Researchers have recently proposed tetra fractional or tripartite methods for detecting GBM molecular subtypes.The various molecular subtypes of GBM show significant differences in gene expression patterns and biological behaviors.These subtypes also exhibit high plasticity in their regulatory pathways,oncogene expression,tumor microenvironment alterations,and differential responses to standard therapy.Herein,we summarize the current molecular typing scheme of GBM and the major molecular/genetic characteristics of each subtype.Furthermore,we review the mesenchymal transition mechanisms of GBM under various regulators. 展开更多
关键词 GLIOBLASTOMA molecular phenotype classifICATION CHARACTERISTIC mesenchymal transition
下载PDF
Classification of Sailboat Tell Tail Based on Deep Learning
8
作者 CHANG Xiaofeng YU Jintao +3 位作者 GAO Ying DING Hongchen LIU Yulong YU Huaming 《Journal of Ocean University of China》 SCIE CAS CSCD 2024年第3期710-720,共11页
The tell tail is usually placed on the triangular sail to display the running state of the air flow on the sail surface.It is of great significance to make accurate judgement on the drift of the tell tail of the sailb... The tell tail is usually placed on the triangular sail to display the running state of the air flow on the sail surface.It is of great significance to make accurate judgement on the drift of the tell tail of the sailboat during sailing for the best sailing effect.Normally it is difficult for sailors to keep an eye for a long time on the tell sail for accurate judging its changes,affected by strong sunlight and visual fatigue.In this case,we adopt computer vision technology in hope of helping the sailors judge the changes of the tell tail in ease with ease.This paper proposes for the first time a method to classify sailboat tell tails based on deep learning and an expert guidance system,supported by a sailboat tell tail classification data set on the expert guidance system of interpreting the tell tails states in different sea wind conditions,including the feature extraction performance.Considering the expression capabilities that vary with the computational features in different visual tasks,the paper focuses on five tell tail computing features,which are recoded by an automatic encoder and classified by a SVM classifier.All experimental samples were randomly divided into five groups,and four groups were selected from each group as the training set to train the classifier.The remaining one group was used as the test set for testing.The highest resolution value of the ResNet network was 80.26%.To achieve better operational results on the basis of deep computing features obtained through the ResNet network in the experiments.The method can be used to assist the sailors in making better judgement about the tell tail changes during sailing. 展开更多
关键词 tell tail sailboat classifICATION deep learning
下载PDF
Empowering Diagnosis: Cutting-Edge Segmentation and Classification in Lung Cancer Analysis
9
作者 Iftikhar Naseer Tehreem Masood +4 位作者 Sheeraz Akram Zulfiqar Ali Awais Ahmad Shafiq Ur Rehman Arfan Jaffar 《Computers, Materials & Continua》 SCIE EI 2024年第6期4963-4977,共15页
Lung cancer is a leading cause of global mortality rates.Early detection of pulmonary tumors can significantly enhance the survival rate of patients.Recently,various Computer-Aided Diagnostic(CAD)methods have been dev... Lung cancer is a leading cause of global mortality rates.Early detection of pulmonary tumors can significantly enhance the survival rate of patients.Recently,various Computer-Aided Diagnostic(CAD)methods have been developed to enhance the detection of pulmonary nodules with high accuracy.Nevertheless,the existing method-ologies cannot obtain a high level of specificity and sensitivity.The present study introduces a novel model for Lung Cancer Segmentation and Classification(LCSC),which incorporates two improved architectures,namely the improved U-Net architecture and the improved AlexNet architecture.The LCSC model comprises two distinct stages.The first stage involves the utilization of an improved U-Net architecture to segment candidate nodules extracted from the lung lobes.Subsequently,an improved AlexNet architecture is employed to classify lung cancer.During the first stage,the proposed model demonstrates a dice accuracy of 0.855,a precision of 0.933,and a recall of 0.789 for the segmentation of candidate nodules.The suggested improved AlexNet architecture attains 97.06%accuracy,a true positive rate of 96.36%,a true negative rate of 97.77%,a positive predictive value of 97.74%,and a negative predictive value of 96.41%for classifying pulmonary cancer as either benign or malignant.The proposed LCSC model is tested and evaluated employing the publically available dataset furnished by the Lung Image Database Consortium and Image Database Resource Initiative(LIDC-IDRI).This proposed technique exhibits remarkable performance compared to the existing methods by using various evaluation parameters. 展开更多
关键词 Lung cancer SEGMENTATION AlexNet U-Net classification
下载PDF
Orchard Sports Injury and Illness Classification System (OSIICS) Version 15
10
作者 John W.Orchard Ebonie Rio +2 位作者 Kay M.Crossley Jessica J.Orchard Margo Mountjoy 《Journal of Sport and Health Science》 SCIE CAS CSCD 2024年第4期599-604,共6页
Background:Sports medicine(injury and illnesses)requires distinct coding systems because the International Classification of Diseases is insuf-ficient for sports medicine coding.The Orchard Sports Injury and Illness C... Background:Sports medicine(injury and illnesses)requires distinct coding systems because the International Classification of Diseases is insuf-ficient for sports medicine coding.The Orchard Sports Injury and Illness Classification System(OSIICS)is one of two sports medicine coding systems recommended by the International Olympic Committee.Regular updates of coding systems are required.Methods:For Version 15,updates for mental health conditions in athletes,sports cardiology,concussion sub-types,infectious diseases,and skin and eye conditions were considered particularly important.Results:Recommended codes were added from a recent International Olympic Committee consensus statement on mental health conditions in athletes.Two landmark sports cardiology papers were used to update a more comprehensive list of sports cardiology codes.Rugby union protocols on head injury assessment were used to create additional concussion codes.Conclusion:It is planned that OSIICS Version 15 will be translated into multiple new languages in a timely fashion to facilitate international accessibility.The large number of recently published sport-specific and discipline-specific consensus statements on athlete surveillance warrant regular updating of OSIICS. 展开更多
关键词 Sports cardiology DERMATOLOGY Eye injuries CONCUSSION Infectious diseases Sports injury classification
下载PDF
Stochastic seismic inversion and Bayesian facies classification applied to porosity modeling and igneous rock identification
11
作者 Fábio Júnior Damasceno Fernandes Leonardo Teixeira +1 位作者 Antonio Fernando Menezes Freire Wagner Moreira Lupinacci 《Petroleum Science》 SCIE EI CAS CSCD 2024年第2期918-935,共18页
We apply stochastic seismic inversion and Bayesian facies classification for porosity modeling and igneous rock identification in the presalt interval of the Santos Basin. This integration of seismic and well-derived ... We apply stochastic seismic inversion and Bayesian facies classification for porosity modeling and igneous rock identification in the presalt interval of the Santos Basin. This integration of seismic and well-derived information enhances reservoir characterization. Stochastic inversion and Bayesian classification are powerful tools because they permit addressing the uncertainties in the model. We used the ES-MDA algorithm to achieve the realizations equivalent to the percentiles P10, P50, and P90 of acoustic impedance, a novel method for acoustic inversion in presalt. The facies were divided into five: reservoir 1,reservoir 2, tight carbonates, clayey rocks, and igneous rocks. To deal with the overlaps in acoustic impedance values of facies, we included geological information using a priori probability, indicating that structural highs are reservoir-dominated. To illustrate our approach, we conducted porosity modeling using facies-related rock-physics models for rock-physics inversion in an area with a well drilled in a coquina bank and evaluated the thickness and extension of an igneous intrusion near the carbonate-salt interface. The modeled porosity and the classified seismic facies are in good agreement with the ones observed in the wells. Notably, the coquinas bank presents an improvement in the porosity towards the top. The a priori probability model was crucial for limiting the clayey rocks to the structural lows. In Well B, the hit rate of the igneous rock in the three scenarios is higher than 60%, showing an excellent thickness-prediction capability. 展开更多
关键词 Stochastic inversion Bayesian classification Porosity modeling Carbonate reservoirs Igneous rocks
下载PDF
A New Algorithm of Rain Type Classification for GPM Dual-Frequency Precipitation Radar in Summer Tibetan Plateau
12
作者 Yunfei FU Liu YANG +4 位作者 Zhenhao WU Peng ZHANG Songyan GU Lin CHEN Sun NAN 《Advances in Atmospheric Sciences》 SCIE CAS CSCD 2024年第11期2093-2111,共19页
In this study, a new rain type classification algorithm for the Dual-Frequency Precipitation Radar(DPR) suitable over the Tibetan Plateau(TP) was proposed by analyzing Global Precipitation Measurement(GPM) DPR Level-2... In this study, a new rain type classification algorithm for the Dual-Frequency Precipitation Radar(DPR) suitable over the Tibetan Plateau(TP) was proposed by analyzing Global Precipitation Measurement(GPM) DPR Level-2 data in summer from 2014 to 2020. It was found that the DPR rain type classification algorithm(simply called DPR algorithm) has mis-identification problems in two aspects in summer TP. In the new algorithm of rain type classification in summer TP,four rain types are classified by using new thresholds, such as the maximum reflectivity factor, the difference between the maximum reflectivity factor and the background maximum reflectivity factor, and the echo top height. In the threshold of the maximum reflectivity factors, 30 d BZ and 18 d BZ are both thresholds to separate strong convective precipitation, weak convective precipitation and weak precipitation. The results illustrate obvious differences of radar reflectivity factor and vertical velocity among the three rain types in summer TP, such as the reflectivity factor of most strong convective precipitation distributes from 15 d BZ to near 35 d BZ from 4 km to 13 km, and increases almost linearly with the decrease in height. For most weak convective precipitation, the reflectivity factor distributes from 15 d BZ to 28 d BZ with the height from 4 km to 9 km. For weak precipitation, the reflectivity factor mainly distributes in range of 15–25 d BZ with height within 4–10 km. It is also shows that weak precipitation is the dominant rain type in summer TP, accounting for 40%–80%,followed by weak convective precipitation(25%–40%), and strong convective precipitation has the least proportion(less than 30%). 展开更多
关键词 satellite precipitation radar rain type classification method Tibetan Plateau strong convective precipitation
下载PDF
BACNN: Multi-scale feature fusion-based bilinear attention convolutional neural network for wood NIR classification
13
作者 Zihao Wan Hong Yang +2 位作者 Jipan Xu Hongbo Mu Dawei Qi 《Journal of Forestry Research》 SCIE EI CAS CSCD 2024年第4期202-214,共13页
Effective development and utilization of wood resources is critical.Wood modification research has become an integral dimension of wood science research,however,the similarities between modified wood and original wood... Effective development and utilization of wood resources is critical.Wood modification research has become an integral dimension of wood science research,however,the similarities between modified wood and original wood render it challenging for accurate identification and classification using conventional image classification techniques.So,the development of efficient and accurate wood classification techniques is inevitable.This paper presents a one-dimensional,convolutional neural network(i.e.,BACNN)that combines near-infrared spectroscopy and deep learning techniques to classify poplar,tung,and balsa woods,and PVA,nano-silica-sol and PVA-nano silica sol modified woods of poplar.The results show that BACNN achieves an accuracy of 99.3%on the test set,higher than the 52.9%of the BP neural network and 98.7%of Support Vector Machine compared with traditional machine learning methods and deep learning based methods;it is also higher than the 97.6%of LeNet,98.7%of AlexNet and 99.1%of VGGNet-11.Therefore,the classification method proposed offers potential applications in wood classification,especially with homogeneous modified wood,and it also provides a basis for subsequent wood properties studies. 展开更多
关键词 Wood classification Near infrared spectroscopy Bilinear network SE module Anti-noise algorithm
下载PDF
A High Resolution Convolutional Neural Network with Squeeze and Excitation Module for Automatic Modulation Classification
14
作者 Duan Ruifeng Zhao Yuanlin +3 位作者 Zhang Haiyan Li Xinze Cheng Peng Li Yonghui 《China Communications》 SCIE CSCD 2024年第10期132-147,共16页
Automatic modulation classification(AMC) technology is one of the cutting-edge technologies in cognitive radio communications. AMC based on deep learning has recently attracted much attention due to its superior perfo... Automatic modulation classification(AMC) technology is one of the cutting-edge technologies in cognitive radio communications. AMC based on deep learning has recently attracted much attention due to its superior performances in classification accuracy and robustness. In this paper, we propose a novel, high resolution and multi-scale feature fusion convolutional neural network model with a squeeze-excitation block, referred to as HRSENet,to classify different kinds of modulation signals.The proposed model establishes a parallel computing mechanism of multi-resolution feature maps through the multi-layer convolution operation, which effectively reduces the information loss caused by downsampling convolution. Moreover, through dense skipconnecting at the same resolution and up-sampling or down-sampling connection at different resolutions, the low resolution representation of the deep feature maps and the high resolution representation of the shallow feature maps are simultaneously extracted and fully integrated, which is benificial to mine signal multilevel features. Finally, the feature squeeze and excitation module embedded in the decoder is used to adjust the response weights between channels, further improving classification accuracy of proposed model.The proposed HRSENet significantly outperforms existing methods in terms of classification accuracy on the public dataset “Over the Air” in signal-to-noise(SNR) ranging from-2dB to 20dB. The classification accuracy in the proposed model achieves 85.36% and97.30% at 4dB and 10dB, respectively, with the improvement by 9.71% and 5.82% compared to LWNet.Furthermore, the model also has a moderate computation complexity compared with several state-of-the-art methods. 展开更多
关键词 automatic modulation classification deep learning feature squeeze-and-excitation HIGH-RESOLUTION MULTI-SCALE
下载PDF
A Robust Approach for Multi Classification-Based Intrusion Detection through Stacking Deep Learning Models
15
作者 Samia Allaoua Chelloug 《Computers, Materials & Continua》 SCIE EI 2024年第6期4845-4861,共17页
Intrusion detection is a predominant task that monitors and protects the network infrastructure.Therefore,many datasets have been published and investigated by researchers to analyze and understand the problem of intr... Intrusion detection is a predominant task that monitors and protects the network infrastructure.Therefore,many datasets have been published and investigated by researchers to analyze and understand the problem of intrusion prediction and detection.In particular,the Network Security Laboratory-Knowledge Discovery in Databases(NSL-KDD)is an extensively used benchmark dataset for evaluating intrusion detection systems(IDSs)as it incorporates various network traffic attacks.It is worth mentioning that a large number of studies have tackled the problem of intrusion detection using machine learning models,but the performance of these models often decreases when evaluated on new attacks.This has led to the utilization of deep learning techniques,which have showcased significant potential for processing large datasets and therefore improving detection accuracy.For that reason,this paper focuses on the role of stacking deep learning models,including convolution neural network(CNN)and deep neural network(DNN)for improving the intrusion detection rate of the NSL-KDD dataset.Each base model is trained on the NSL-KDD dataset to extract significant features.Once the base models have been trained,the stacking process proceeds to the second stage,where a simple meta-model has been trained on the predictions generated from the proposed base models.The combination of the predictions allows the meta-model to distinguish different classes of attacks and increase the detection rate.Our experimental evaluations using the NSL-KDD dataset have shown the efficacy of stacking deep learning models for intrusion detection.The performance of the ensemble of base models,combined with the meta-model,exceeds the performance of individual models.Our stacking model has attained an accuracy of 99%and an average F1-score of 93%for the multi-classification scenario.Besides,the training time of the proposed ensemble model is lower than the training time of benchmark techniques,demonstrating its efficiency and robustness. 展开更多
关键词 Intrusion detection multi classification deep learning STACKING NSL-KDD
下载PDF
Explainable Artificial Intelligence(XAI)Model for Cancer Image Classification
16
作者 Amit Singhal Krishna Kant Agrawal +3 位作者 Angeles Quezada Adrian Rodriguez Aguiñaga Samantha Jiménez Satya Prakash Yadav 《Computer Modeling in Engineering & Sciences》 SCIE EI 2024年第10期401-441,共41页
The use of Explainable Artificial Intelligence(XAI)models becomes increasingly important for making decisions in smart healthcare environments.It is to make sure that decisions are based on trustworthy algorithms and ... The use of Explainable Artificial Intelligence(XAI)models becomes increasingly important for making decisions in smart healthcare environments.It is to make sure that decisions are based on trustworthy algorithms and that healthcare workers understand the decisions made by these algorithms.These models can potentially enhance interpretability and explainability in decision-making processes that rely on artificial intelligence.Nevertheless,the intricate nature of the healthcare field necessitates the utilization of sophisticated models to classify cancer images.This research presents an advanced investigation of XAI models to classify cancer images.It describes the different levels of explainability and interpretability associated with XAI models and the challenges faced in deploying them in healthcare applications.In addition,this study proposes a novel framework for cancer image classification that incorporates XAI models with deep learning and advanced medical imaging techniques.The proposed model integrates several techniques,including end-to-end explainable evaluation,rule-based explanation,and useradaptive explanation.The proposed XAI reaches 97.72%accuracy,90.72%precision,93.72%recall,96.72%F1-score,9.55%FDR,9.66%FOR,and 91.18%DOR.It will discuss the potential applications of the proposed XAI models in the smart healthcare environment.It will help ensure trust and accountability in AI-based decisions,which is essential for achieving a safe and reliable smart healthcare environment. 展开更多
关键词 Explainable artificial intelligence artificial intelligence XAI healthcare CANCER image classification
下载PDF
Effective separation of coal gasification fine slag: Role of classification and ultrasonication in enhancing flotation
17
作者 Rui Han Anning Zhou +4 位作者 Ningning Zhang Zhen Li Mengyan Cheng Xiaoyi Chen Tianhao Nan 《International Journal of Mining Science and Technology》 SCIE EI CAS CSCD 2024年第6期867-880,共14页
Effective separation of residual carbon and ash is the basis for the resource utilization of coal gasification fine slag(CGFS).The conventional flotation process of CGFS has the bottlenecks of low carbon recovery and ... Effective separation of residual carbon and ash is the basis for the resource utilization of coal gasification fine slag(CGFS).The conventional flotation process of CGFS has the bottlenecks of low carbon recovery and high collector dosage.In order to address these issues,CGFS sample taken from Shaanxi,China was used as the study object in this paper.A new process of size classification-fine grain ultrasonic pretreatment flotation(SC-FGUF)was proposed and its separation effect was compared with that of wholegrain flotation(WGF)as well as size classification-fine grain flotation(SC-FGF).The mechanism of its enhanced separation effect was revealed through flotation kinetic fitting,flotation flow foam layer stability,particle size composition,surface morphology,pore structure,and surface chemical property analysis.The results showed that compared with WGF,pre-classification could reduce the collector dosage by 84.09%and the combination of pre-classification and ultrasonic pretreatment could increase the combustible recovery by 17.29%and up to 93.46%.The SC-FGUF process allows the ineffective adsorption of coarse residual carbon to collector during flotation stage to be reduced by pre-classification,and the tightly embedded state of fine CGFS particles is disrupted and surface oxidizing functional group occupancy was reduced by ultrasonic pretreatment,thus carbon and ash is easier to be separated in the flotation process.In addition,some of the residual carbon particles were broken down to smaller sizes in the ultrasonic pretreatment,which led to an increase in the stability of flotation flow foam layer and a decrease in the probability of detachment of residual carbon particles from the bubbles.Therefore,SCFGUF could increase the residual carbon recovery and reduce the flotation collector dosage,which is an innovative method for carbon-ash separation of CGFS with good application prospect. 展开更多
关键词 Coal gasification fine slag Size classification Ultrasonic pretreatment FLOTATION Carbon recovery
下载PDF
Monitoring Surface Water Change in Northeast China in 1999–2020:Evidence from Satellite Observation and Refined Classification
18
作者 LIU Kai ZHANG Dapeng +3 位作者 CHEN Tan CUI Peipei FAN Chenyu SONG Chunqiao 《Chinese Geographical Science》 SCIE CSCD 2024年第1期106-117,共12页
As a typical region with high water demand for agricultural production,understanding the spatiotemporal surface water changes in Northeast China is critical for water resources management and sustainable development.H... As a typical region with high water demand for agricultural production,understanding the spatiotemporal surface water changes in Northeast China is critical for water resources management and sustainable development.However,the long-term variation characteristics of surface water of different water body types in Northeast China remain rarely explored.This study investigated how surface water bodies of different types(e.g.,lake,reservoir,river,coastal aquaculture,marsh wetland,ephemeral water) changed during1999–2020 in Northeast China based on various remote sensing-based datasets.The results showed that surface water in Northeast China grew dramatically in the past two decades,with an equivalent area increasing from 24 394 km^(2) in 1999 to 34 595 km^(2) in 2020.The surge of ephemeral water is the primary driver of surface water expansion,which could ascribe to shifted precipitation pattern.Marsh wetlands,rivers,and reservoirs experienced a similar trend,with an approximate 20% increase at the interdecadal scale.By contrast,coastal aquacultures and natural lakes remain relatively stable.This study is expected to provide a more comprehensive investigation of the surface water variability in Northeast China and has important practical significance for the scientific management of different types of surface water. 展开更多
关键词 surface water spatiotemporal variation water body classification remote sensing Northeast China
下载PDF
Curve Classification Based onMean-Variance Feature Weighting and Its Application
19
作者 Zewen Zhang Sheng Zhou Chunzheng Cao 《Computers, Materials & Continua》 SCIE EI 2024年第5期2465-2480,共16页
The classification of functional data has drawn much attention in recent years.The main challenge is representing infinite-dimensional functional data by finite-dimensional features while utilizing those features to a... The classification of functional data has drawn much attention in recent years.The main challenge is representing infinite-dimensional functional data by finite-dimensional features while utilizing those features to achieve better classification accuracy.In this paper,we propose a mean-variance-based(MV)feature weighting method for classifying functional data or functional curves.In the feature extraction stage,each sample curve is approximated by B-splines to transfer features to the coefficients of the spline basis.After that,a feature weighting approach based on statistical principles is introduced by comprehensively considering the between-class differences and within-class variations of the coefficients.We also introduce a scaling parameter to adjust the gap between the weights of features.The new feature weighting approach can adaptively enhance noteworthy local features while mitigating the impact of confusing features.The algorithms for feature weighted K-nearest neighbor and support vector machine classifiers are both provided.Moreover,the new approach can be well integrated into existing functional data classifiers,such as the generalized functional linear model and functional linear discriminant analysis,resulting in a more accurate classification.The performance of the mean-variance-based classifiers is evaluated by simulation studies and real data.The results show that the newfeatureweighting approach significantly improves the classification accuracy for complex functional data. 展开更多
关键词 Functional data analysis classifICATION feature weighting B-SPLINES
下载PDF
Improving the Effectiveness of Image Classification Structural Methods by Compressing the Description According to the Information Content Criterion
20
作者 Yousef Ibrahim Daradkeh Volodymyr Gorokhovatskyi +1 位作者 Iryna Tvoroshenko Medien Zeghid 《Computers, Materials & Continua》 SCIE EI 2024年第8期3085-3106,共22页
The research aims to improve the performance of image recognition methods based on a description in the form of a set of keypoint descriptors.The main focus is on increasing the speed of establishing the relevance of ... The research aims to improve the performance of image recognition methods based on a description in the form of a set of keypoint descriptors.The main focus is on increasing the speed of establishing the relevance of object and etalon descriptions while maintaining the required level of classification efficiency.The class to be recognized is represented by an infinite set of images obtained from the etalon by applying arbitrary geometric transformations.It is proposed to reduce the descriptions for the etalon database by selecting the most significant descriptor components according to the information content criterion.The informativeness of an etalon descriptor is estimated by the difference of the closest distances to its own and other descriptions.The developed method determines the relevance of the full description of the recognized object with the reduced description of the etalons.Several practical models of the classifier with different options for establishing the correspondence between object descriptors and etalons are considered.The results of the experimental modeling of the proposed methods for a database including images of museum jewelry are presented.The test sample is formed as a set of images from the etalon database and out of the database with the application of geometric transformations of scale and rotation in the field of view.The practical problems of determining the threshold for the number of votes,based on which a classification decision is made,have been researched.Modeling has revealed the practical possibility of tenfold reducing descriptions with full preservation of classification accuracy.Reducing the descriptions by twenty times in the experiment leads to slightly decreased accuracy.The speed of the analysis increases in proportion to the degree of reduction.The use of reduction by the informativeness criterion confirmed the possibility of obtaining the most significant subset of features for classification,which guarantees a decent level of accuracy. 展开更多
关键词 Description reduction description relevance DESCRIPTOR image classification information content keypoint processing speed
下载PDF
上一页 1 2 250 下一页 到第
使用帮助 返回顶部