期刊文献+
任意字段
题名或关键词
题名
关键词
摘要
作者
第一作者
机构
刊名
分类号
参考文献
作者简介
基金资助
栏目信息
共找到679篇文章
< 1 2 … 34 >
每页显示 20 50 100
已选0条
引用分析
参考文献
引证文献
 统计分析
检索结果
已选文献
显示方式：
Traffic Vision:UAV-Based Vehicle Detection and Traffic Pattern Analysis via Deep Learning Classifier 认领 引用
1
作者 Mohammed Alnusayri Ghulam Mujtaba +4 位作者 Nouf Abdullah Almujally Shuoa S.Aitarbi Asaad Algarni Ahmad Jalal Jeongmin Park 《Computers, Materials & Continua》 SCIE EI 2026年第3期266-284,共19页
This paper presents a unified Unmanned Aerial Vehicle-based(UAV-based)traffic monitoring framework that integrates vehicle detection,tracking,counting,motion prediction,and classification in a modular and co-optimized... This paper presents a unified Unmanned Aerial Vehicle-based(UAV-based)traffic monitoring framework that integrates vehicle detection,tracking,counting,motion prediction,and classification in a modular and co-optimized pipeline.Unlike prior works that address these tasks in isolation,our approach combines You Only Look Once(YOLO)v10 detection,ByteTrack tracking,optical-flow density estimation,Long Short-Term Memory-based(LSTM-based)trajectory forecasting,and hybrid Speeded-Up Robust Feature(SURF)+Gray-Level Co-occurrence Matrix(GLCM)feature engineering with VGG16 classification.Upon the validation across datasets(UAVDT and UAVID)our framework achieved a detection accuracy of 94.2%,and 92.3%detection accuracy when conducting a real-time UAV field validation.Our comprehensive evaluations,including multi-metric analyses,ablation studies,and cross-dataset validations,confirm the framework’s accuracy,efficiency,and generalizability.These results highlight the novelty of integrating complementary methods into a single framework,offering a practical solution for accurate and efficient UAV-based traffic monitoring. 展开更多
关键词 Smart traffic system drone devices machine learner dynamic complex scenes VGG-16 classifier
暂未订购 下载PDF
Drone-Based Public Surveillance Using 3D Point Clouds and Neuro-Fuzzy Classifier 认领 引用 被引量：1
2
作者 Yawar Abbas Aisha Ahmed Alarfaj +3 位作者 Ebtisam Abdullah Alabdulqader Asaad Algarni Ahmad Jalal Hui Liu 《Computers, Materials & Continua》 SCIE EI 2025年第3期4759-4776,共18页
Human Activity Recognition(HAR)in drone-captured videos has become popular because of the interest in various fields such as video surveillance,sports analysis,and human-robot interaction.However,recognizing actions f... Human Activity Recognition(HAR)in drone-captured videos has become popular because of the interest in various fields such as video surveillance,sports analysis,and human-robot interaction.However,recognizing actions from such videos poses the following challenges:variations of human motion,the complexity of backdrops,motion blurs,occlusions,and restricted camera angles.This research presents a human activity recognition system to address these challenges by working with drones’red-green-blue(RGB)videos.The first step in the proposed system involves partitioning videos into frames and then using bilateral filtering to improve the quality of object foregrounds while reducing background interference before converting from RGB to grayscale images.The YOLO(You Only Look Once)algorithm detects and extracts humans from each frame,obtaining their skeletons for further processing.The joint angles,displacement and velocity,histogram of oriented gradients(HOG),3D points,and geodesic Distance are included.These features are optimized using Quadratic Discriminant Analysis(QDA)and utilized in a Neuro-Fuzzy Classifier(NFC)for activity classification.Real-world evaluations on the Drone-Action,Unmanned Aerial Vehicle(UAV)-Gesture,and Okutama-Action datasets substantiate the proposed system’s superiority in accuracy rates over existing methods.In particular,the system obtains recognition rates of 93%for drone action,97%for UAV gestures,and 81%for Okutama-action,demonstrating the system’s reliability and ability to learn human activity from drone videos. 展开更多
关键词 Activity recognition geodesic distance pattern recognition neuro fuzzy classifier
暂未订购 下载PDF
船海学术语篇摘要中名词词组形式表征的认知分析——以“Classifier +Thing”为例 认领 引用
3
作者 田苗 张宇新 《山东外语教学》 CSSCI 北大核心 2025年第3期19-29,共11页
“Classifier+Thing”结构在船海学术语篇摘要中俯拾皆是,其认知路径和理据亟待深入探究。本研究聚焦“Classifier+Thing”名词词组,分析船海学术语篇摘要中该词组的认知路径及理据。研究发现:(1)“Classifier+Thing”在概念结构-语义... “Classifier+Thing”结构在船海学术语篇摘要中俯拾皆是,其认知路径和理据亟待深入探究。本研究聚焦“Classifier+Thing”名词词组,分析船海学术语篇摘要中该词组的认知路径及理据。研究发现:(1)“Classifier+Thing”在概念结构-语义层的认知过程体现了语法转喻机制,船海摘要语料库中主要通过“过程-动作”“过程-结果”“用途-结构”实现概念结构-语义间的动、静态转换;(2)“Classifier+Thing”的形式表征过程为先确定“核心词(Thing)”,后在大脑词库中匹配“类别语(Classifier)”,遵循认知经济性原则;(3)该词组形式表征过程受学术语篇类型影响,遵循受限语言说。研究结果一定程度上深化了对学术语篇中名词词组的认识,提升学界对于船海学科学术话语的关注。 展开更多
关键词 “Classifier+Thing” 认知路径及理据 学术摘要 名词词组
暂未订购 下载PDF
Selective Multiple Classifiers for Weakly Supervised Semantic Segmentation 认领 引用
4
作者 Zilin Guo Dongyue Wu +1 位作者 Changxin Gao Nong Sang 《CAAI Transactions on Intelligence Technology》 SCIE EI CSCD 2025年第6期1688-1702,共15页
Existing weakly supervised semantic segmentation(WSSS)methods based on image-level labels always rely on class activation maps(CAMs),which measure the relationships between features and classifiers.However,CAMs only f... Existing weakly supervised semantic segmentation(WSSS)methods based on image-level labels always rely on class activation maps(CAMs),which measure the relationships between features and classifiers.However,CAMs only focus on the most discriminative regions of images,resulting in their poor coverage performance.We attribute this to the deficiency in the recognition ability of a single classifier and the negative impacts caused by magnitudes during the CAMs normalisation process.To address the aforementioned issues,we propose to construct selective multiple classifiers(SMC).During the training process,we extract multiple prototypes for each class and store them in the corresponding memory bank.These prototypes are divided into foreground and background prototypes,with the former used to identify foreground objects and the latter aimed at preventing the false activation of background pixels.As for the inference stage,multiple prototypes are adaptively selected from the memory bank for each image as SMC.Subsequently,CAMs are generated by measuring the angle between SMC and features.We enhance the recognition ability of classifiers by adaptively constructing multiple classifiers for each image,while only relying on angle measurement to generate CAMs can alleviate the suppression phenomenon caused by magnitudes.Furthermore,SMC can be integrated into other WSSS approaches to help generate better CAMs.Extensive experiments conducted on standard WSSS benchmarks such as PASCAL VOC 2012 and MS COCO 2014 demonstrate the superiority of our proposed method. 展开更多
关键词 image segmentation multiple classifiers weakly supervised learning
暂未订购 下载PDF
A dual-approach to genomic predictions:leveraging convolutional networks and voting classifiers 认领 引用
5
作者 Raghad K.Mohammed Azmi Tawfeq Hussein Alrawi Ali Jbaeer Dawood 《Biomedical Engineering Communications》 CAS 2025年第1期3-11,共9页
Background:In the field of genetic diagnostics,DNA sequencing is an important tool because the depth and complexity of this field have major implications in light of the genetic architectures of diseases and the ident... Background:In the field of genetic diagnostics,DNA sequencing is an important tool because the depth and complexity of this field have major implications in light of the genetic architectures of diseases and the identification of risk factors associated with genetic disorders.Methods:Our study introduces a novel two-tiered analytical framework to raise the precision and reliability of genetic data interpretation.It is initiated by extracting and analyzing salient features from DNA sequences through a CNN-based feature analysis,taking advantage of the power inherent in Convolutional neural networks(CNNs)to attain complex patterns and minute mutations in genetic data.This study embraces an elite collection of machine learning classifiers interweaved through a stern voting mechanism,which synergistically joins the predictions made from multiple classifiers to generate comprehensive and well-balanced interpretations of the genetic data.Results:This state-of-the-art method was further tested by carrying out an empirical analysis on a variants'dataset of DNA sequences taken from patients affected by breast cancer,juxtaposed with a control group composed of healthy people.Thus,the integration of CNNs with a voting-based ensemble of classifiers returned outstanding outcomes,with performance metrics accuracy,precision,recall,and F1-scorereaching the outstanding rate of 0.88,outperforming previous models.Conclusions:This dual accomplishment underlines the transformative potential that integrating deep learning techniques with ensemble machine learning might provide in real added value for further genetic diagnostics and prognostics.These results from this study set a new benchmark in the accuracy of disease diagnosis through DNA sequencing and promise future studies on improved personalized medicine and healthcare approaches with precise genetic information. 展开更多
关键词 CNN DNA sequencing ensemble machine learning genetic disease voting classifier
暂未订购 下载PDF
Ensemble Classifier Design Based on Perturbation Binary Salp Swarm Algorithm for Classification 认领 引用
6
作者 Xuhui Zhu Pingfan Xia +2 位作者 Qizhi He Zhiwei Ni Liping Ni 《Computer Modeling in Engineering & Sciences》 SCIE EI 2023年第4期653-671,共19页
Multiple classifier system exhibits strong classification capacity compared with single classifiers,but they require significant computational resources.Selective ensemble system aims to attain equivalent or better cl... Multiple classifier system exhibits strong classification capacity compared with single classifiers,but they require significant computational resources.Selective ensemble system aims to attain equivalent or better classification accuracy with fewer classifiers.However,current methods fail to identify precise solutions for constructing an ensemble classifier.In this study,we propose an ensemble classifier design technique based on the perturbation binary salp swarm algorithm(ECDPB).Considering that extreme learning machines(ELMs)have rapid learning rates and good generalization ability,they can serve as the basic classifier for creating multiple candidates while using fewer computational resources.Meanwhile,we introduce a combined diversity measure by taking the complementarity and accuracy of ELMs into account;it is used to identify the ELMs that have good diversity and low error.In addition,we propose an ECDPB with powerful optimizing ability;it is employed to find the optimal subset of ELMs.The selected ELMs can then be used to forman ensemble classifier.Experiments on 10 benchmark datasets have been conducted,and the results demonstrate that the proposed ECDPB delivers superior classification capacity when compared with alternative methods. 展开更多
关键词 Ensemble classifier salp swarmalgorithm diversity measure multiple classifiers system extreme learning machine
暂未订购 下载PDF
Performance Evaluation of Multiple Classifiers for Predicting Fake News 认领 引用
7
作者 Arzina Tasnim Md. Saiduzzaman +2 位作者 Mohammad Arafat Rahman Jesmin Akhter Abu Sayed Md. Mostafizur Rahaman 《Journal of Computer and Communications》 2022年第9期1-21,共21页
The rise of fake news on social media has had a detrimental effect on society. Numerous performance evaluations on classifiers that can detect fake news have previously been undertaken by researchers in this area. To ... The rise of fake news on social media has had a detrimental effect on society. Numerous performance evaluations on classifiers that can detect fake news have previously been undertaken by researchers in this area. To assess their performance, we used 14 different classifiers in this study. Secondly, we looked at how soft voting and hard voting classifiers performed in a mixture of distinct individual classifiers. Finally, heuristics are used to create 9 models of stacking classifiers. The F1 score, prediction, recall, and accuracy have all been used to assess performance. Models 6 and 7 achieved the best accuracy of 96.13 while having a larger computational complexity. For benchmarking purposes, other individual classifiers are also tested. 展开更多
关键词 Fake News Machine Learning TF-IDF Classifier Estimator F1 Score Recall Precision Voting Classifiers Stacking Classifier Soft Voting Hard Voting
暂未订购 下载PDF
Mapping of cropland,cropping patterns and crop types by combining optical remote sensing images with decision tree classifier and random forest 认领 引用 被引量：23
8
作者 Aqil Tariq Jianguo Yan +2 位作者 Alexandre S.Gagnon Mobushir Riaz Khan Faisal Mumtaz 《Geo-Spatial Information Science》 SCIE EI CSCD 2023年第3期302-320,共19页
Mapping and monitoring the distribution of croplands and crop types support policymakers and international organizations by reducing the risks to food security,notably from climate change and,for that purpose,remote s... Mapping and monitoring the distribution of croplands and crop types support policymakers and international organizations by reducing the risks to food security,notably from climate change and,for that purpose,remote sensing is routinely used.However,identifying specific crop types,cropland,and cropping patterns using space-based observations is challenging because different crop types and cropping patterns have similarity spectral signatures.This study applied a methodology to identify cropland and specific crop types,including tobacco,wheat,barley,and gram,as well as the following cropping patterns:wheat-tobacco,wheat-gram,wheat-barley,and wheat-maize,which are common in Gujranwala District,Pakistan,the study region.The methodology consists of combining optical remote sensing images from Sentinel-2 and Landsat-8 with Machine Learning(ML)methods,namely a Decision Tree Classifier(DTC)and a Random Forest(RF)algorithm.The best time-periods for differentiating cropland from other land cover types were identified,and then Sentinel-2 and Landsat 8 NDVI-based time-series were linked to phenological parameters to determine the different crop types and cropping patterns over the study region using their temporal indices and ML algorithms.The methodology was subsequently evaluated using Landsat images,crop statistical data for 2020 and 2021,and field data on cropping patterns.The results highlight the high level of accuracy of the methodological approach presented using Sentinel-2 and Landsat-8 images,together with ML techniques,for mapping not only the distribution of cropland,but also crop types and cropping patterns when validated at the county level.These results reveal that this methodology has benefits for monitoring and evaluating food security in Pakistan,adding to the evidence base of other studies on the use of remote sensing to identify crop types and cropping patterns in other countries. 展开更多
关键词 Sentinel-2 Random Forest cropland crop types cropping patterns Decision Tree Classifier
暂未订购 下载PDF
Adaptive target and jamming recognition for the pulse doppler radar fuze based on a time-frequency joint feature and an online-updated naive bayesian classifier with minimal risk 认领 引用 被引量：16
9
作者 Jian Dai Xin-hong Hao +2 位作者 Ze Li Ping Li Xiao-peng Yan 《Defence Technology（防务技术）》 SCIE EI CAS CSCD 2022年第3期457-466,共10页
This paper considers the problem of target and jamming recognition for the pulse Doppler radar fuze(PDRF).To solve the problem,the matched filter outputs of the PDRF under the action of target and jamming are analyzed... This paper considers the problem of target and jamming recognition for the pulse Doppler radar fuze(PDRF).To solve the problem,the matched filter outputs of the PDRF under the action of target and jamming are analyzed.Then,the frequency entropy and peak-to-peak ratio are extracted from the matched filter output of the PDRF,and the time-frequency joint feature is constructed.Based on the time-frequency joint feature,the naive Bayesian classifier(NBC)with minimal risk is established for target and jamming recognition.To improve the adaptability of the proposed method in complex environments,an online update process that adaptively modifies the classifier in the duration of the work of the PDRF is proposed.The experiments show that the PDRF can maintain high recognition accuracy when the signal-to-noise ratio(SNR)decreases and the jamming-to-signal ratio(JSR)increases.Moreover,the applicable analysis shows that he ONBCMR method has low computational complexity and can fully meet the real-time requirements of PDRF. 展开更多
关键词 Pulse Doppler radar fuze(PDRF) Target and jamming recognition Time-frequency joint feature Online-update naive Bayesian classifier minimal risk(ONBCMR)
暂未订购 下载PDF
Real and Altered Fingerprint Classification Based on Various Features and Classifiers 认领 引用 被引量：1
10
作者 Saif Saad Hameed Ismail Taha Ahmed Omar Munthir Al Okashi 《Computers, Materials & Continua》 SCIE EI 2023年第1期327-340,共14页
Biometric recognition refers to the identification of individuals through their unique behavioral features(e.g.,fingerprint,face,and iris).We need distinguishing characteristics to identify people,such as fingerprints... Biometric recognition refers to the identification of individuals through their unique behavioral features(e.g.,fingerprint,face,and iris).We need distinguishing characteristics to identify people,such as fingerprints,which are world-renowned as the most reliablemethod to identify people.The recognition of fingerprints has become a standard procedure in forensics,and different techniques are available for this purpose.Most current techniques lack interest in image enhancement and rely on high-dimensional features to generate classification models.Therefore,we proposed an effective fingerprint classification method for classifying the fingerprint image as authentic or altered since criminals and hackers routinely change their fingerprints to generate fake ones.In order to improve fingerprint classification accuracy,our proposed method used the most effective texture features and classifiers.Discriminant Analysis(DCA)and Gaussian Discriminant Analysis(GDA)are employed as classifiers,along with Histogram of Oriented Gradient(HOG)and Segmentation-based Feature Texture Analysis(SFTA)feature vectors as inputs.The performance of the classifiers is determined by assessing a range of feature sets,and the most accurate results are obtained.The proposed method is tested using a Sokoto Coventry Fingerprint Dataset(SOCOFing).The SOCOFing project includes 6,000 fingerprint images collected from 600 African people whose fingerprints were taken ten times.Three distinct degrees of obliteration,central rotation,and z-cut have been performed to obtain synthetically altered replicas of the genuine fingerprints.The proposal achieved massive success with a classification accuracy reaching 99%.The experimental results indicate that the proposed method for fingerprint classification is feasible and effective.The experiments also showed that the proposed SFTA-based GDA method outperformed state-of-art approaches in feature dimension and classification accuracy. 展开更多
关键词 Fingerprint classification HOG SFTA discriminant analysis(DCA)classifier gaussian discriminant analysis(GDA)classifier SOCOFing
暂未订购 下载PDF
Classification performance of model coal mill classifiers with swirling and non-swirling inlets 认领 引用 被引量：6
11
作者 Lele Feng Hai Zhang +4 位作者 Lilin Hu Yang Zhang Yuxin Wu Yuzhao Wang Hairui Yang 《Chinese Journal of Chemical Engineering》 SCIE EI CAS CSCD 2020年第3期777-784,共8页
The classification performance of model coal mill classifiers with different bottom incoming flow inlets was experimentally and numerically studied.The flow field adjacent to two neighboring impeller blades was measur... The classification performance of model coal mill classifiers with different bottom incoming flow inlets was experimentally and numerically studied.The flow field adjacent to two neighboring impeller blades was measured using the particle image velocimetry technique.The results showed that the flow field adjacent to two neighboring blades with the swirling inlet was significantly different from that with the non-swirling inlet.With the swirling inlet,there was a vortex located between two neighboring blades,while with the nonswirling inlet,the vortex was attached to the blade tip.The vorticity of the vortex with the non-swirling inlet was much lower than that with the swirling inlet.The classifier with the non-swirling inlet demonstrated a larger cut size than that with the swirling inlet when the impeller was stationary(~0 r·min-1).As the impeller rotational speed increased,the cut size of the cases with non-swirling and swirling inlets both decreased,and the one with the non-swirling inlet decreased more dramatically.The values of the cut size of the two classifiers were close to each other at a high impeller rotational speed(≥120 r·min-1).The overall separation efficiency of the classifier with the non-swirling inlet was lower than that with the swirling inlet,and monotonically increased as the impeller rotational speed increased.With the swirling inlet,the overall separation efficiency first increased with the impeller rotational speed and then decreased when the rotational speed was above 120 r·min-1,and the variation trend of the separation efficiency was more moderate.As the initial particle concentration increased,the cut sizes of both swirling and non-swirling inlet cases decreased first and then barely changed.At a low initial particle concentration(b 0.04 kg·m-3),the classifier with the swirling inlet had a larger cut size than that with the non-swirling inlet. 展开更多
关键词 Coal mill classifier Cut size Non-swirling inlet Particle image velocimetry Impeller rotational speed
暂未订购 下载PDF
Effect of rotor cage rotary speed on classification accuracy in turbo air classifier 认领 引用 被引量：13
12
作者 高利苹 于源 刘家祥 《化工学报》 EI CSCD 北大核心 2012年第4期1056-1062,共7页
The numerical simulation of flow field was performed with Fluent, in order to indicate the effect of rotor cage rotary speed on classification accuracy in turbo air classifier. With the same wind speed and different r... The numerical simulation of flow field was performed with Fluent, in order to indicate the effect of rotor cage rotary speed on classification accuracy in turbo air classifier. With the same wind speed and different rotary speeds of rotor cage, airflow velocity distribution in the rotor classifier was analyzed and compared, and a reasonable rotor cage rotary speed was obtained, which made a small velocity gradient both in the annular zone and between the neighboring blades, and a stable flow field. Moreover, the pressure distributions between the neighboring blades were analyzed. In this reasonable range of rotor cage rotary speed, the pressure between onward surface and regression surface of rotor blades was evenly distributed, and the formed vortex was weak, which helped to improve the accuracy of classification. Pulvis talci and quartz sand material experiments showed that when wind speed was 8 m· s^-1 , rotating speed was 600-800 r· min^-1, the accuracy of turbo air classifier was high. These actual experimental results confirmed the simulation results using Fluent. Through this simulation, the corresponding reasonable range of the rotor cage speeds with different wind speeds could be obtained. For example, when wind speed was 20 m - s^-1 , reasonable rotating speed was 1000-1400 r· rain^-1 and when the wind speed was 30 m · s^-1, reasonable rotating speed was 1500-2000 r ·min^- 1. This provides a method to obtain suitable operating parameters of turbo air classifier. 展开更多
关键词 turbo air classifier airflow velocity distribution flow pressure classification accuracy
暂未订购 下载PDF
SDN-based intrusion detection system for IoT using deep learning classifier (IDSIoT-SDL) 认领 引用 被引量：16
13
作者 Azka Wani Revathi S Rubeena Khaliq 《CAAI Transactions on Intelligence Technology》 SCIE EI 2021年第3期281-290,共10页
The participation of ordinary devices in networking has created a world of connected devices rapidly.The Internet of Things(IoT)includes heterogeneous devices from every field.There are no definite protocols or standa... The participation of ordinary devices in networking has created a world of connected devices rapidly.The Internet of Things(IoT)includes heterogeneous devices from every field.There are no definite protocols or standards for IoT communication,and most of the IoT devices have limited resources.Enabling a complete security measure for such devices is a challenging task,yet necessary.Many lightweight security solutions have surfaced lately for IoT.The lightweight security protocols are unable to provide an optimum protection against prevailing powerful threats in cyber world.It is also hard to deploy any traditional security protocol on resource-constrained IoT devices.Software-defined networking introduces a centralized control in computer networks.SDN has a programmable approach towards networking that decouples control and data planes.An SDN-based intrusion detection system is proposed which uses deep learning classifier for detection of anomalies in IoT.The proposed intrusion detection system does not burden the IoT devices with security profiles.The proposed work is executed on the simulated environment.The results of the simulation test are evaluated using various matrices and compared with other relevant methods. 展开更多
关键词 IoT classifier system
暂未订购 下载PDF
Construction of Influenza Early Warning Model Based on Combinatorial Judgment Classifier:A Case Study of Seasonal Influenza in Hong Kong 认领 引用 被引量：3
14
作者 Zi-xiao WANG James NTAMBARA +3 位作者 Yan LU Wei DAI Rui-jun MENG Dan-min QIAN 《Current Medical Science》 SCIE CAS 2022年第1期226-236,共11页
Objective:The annual influenza epidemic is a heavy burden on the health care system,and has increasingly become a major public health problem in some areas,such as Hong Kong(China).Therefore,based on a variety of mach... Objective:The annual influenza epidemic is a heavy burden on the health care system,and has increasingly become a major public health problem in some areas,such as Hong Kong(China).Therefore,based on a variety of machine learning methods,and considering the seasonal influenza in Hong Kong,the study aims to establish a Combinatorial Judgment Classifier(CJC)model to classify the epidemic trend and improve the accuracy of influenza epidemic early warning. 展开更多
关键词 influenza prediction data-driven Support Vector Machine Discriminant Analysis Ensemble Classifier
暂未订购 下载PDF
Comparative study for machine learning classifier recommendation to predict political affiliation based on online 认领 引用 被引量：3
15
作者 Hayat Ullah Bashir Ahmad +4 位作者 Iqra Sana Anum Sattar Aurangzeb Khan Saima Akbar Muhammad Zubair Asghar 《CAAI Transactions on Intelligence Technology》 SCIE EI 2021年第3期251-264,共14页
In the current era of social media,different platforms such as Twitter and Facebook have frequently been used by leaders and the followers of political parties to participate in political events,campaigns,and election... In the current era of social media,different platforms such as Twitter and Facebook have frequently been used by leaders and the followers of political parties to participate in political events,campaigns,and elections.The acquisition,analysis,and presentation of such content have received considerable attention from opinion-mining researchers.For this purpose,different supervised and unsupervised techniques have been used.However,they have produced less efficient results,which need to be improved by incorporating additional classifiers with the extended data sets.The authors investigate different su-pervised machine learning classifiers for classifying the political affiliations of users.For this purpose,a data set of political reviews is acquired from Twitter and annotated with different polarity classes.After pre-processing,different machine learning classifiers like K-nearest neighbor,naïve Bayes,support vector machine,extreme gradient boosting,and others,are applied.Experimental results illustrate that support vector machine and extreme gradient boosting have shown promising results for predicting political affiliations. 展开更多
关键词 boosting classifier extreme
暂未订购 下载PDF
Combination of classifiers with incomplete frames of discernment 认领 引用 被引量：2
16
作者 Zhunga LIU Jingfei DUAN +2 位作者 Linqing HUANG Jean DEZERT Yongqiang ZHAO 《Chinese Journal of Aeronautics》 SCIE EI CAS CSCD 2022年第5期145-157,共13页
The methods for combining multiple classifiers based on belief functions require to work with a common and complete(closed)Frame of Discernment(Fo D)on which the belief functions are defined before making their combin... The methods for combining multiple classifiers based on belief functions require to work with a common and complete(closed)Frame of Discernment(Fo D)on which the belief functions are defined before making their combination.This theoretical requirement is however difficult to satisfy in practice because some abnormal(or unknown)objects that do not belong to any predefined class of the Fo D can appear in real classification applications.The classifiers learnt using different attributes information can provide complementary knowledge which is very useful for making the classification but they are usually based on different Fo Ds.In order to clearly identify the specific class of the abnormal objects,we propose a new method for combination of classifiers working with incomplete frames of discernment,named CCIF for short.This is a progressive detection method that select and add the detected abnormal objects to the training data set.Because one pattern can be considered as an abnormal object by one classifier and be committed to a specific class by another one,a weighted evidence combination method is proposed to fuse the classification results of multiple classifiers.This new method offers the advantage to make a refined classification of abnormal objects,and to improve the classification accuracy thanks to the complementarity of the classifiers.Some experimental results are given to validate the effectiveness of the proposed method using real data sets. 展开更多
关键词 Abnormal object Belief functions Classifier fusion Evidence theory Detection
暂未订购 下载PDF
An Improved LSTM-PCA Ensemble Classifier for SQL Injection and XSS Attack Detection 认领 引用 被引量：2
17
作者 Deris Stiawan Ali Bardadi +7 位作者 Nurul Afifah Lisa Melinda Ahmad Heryanto Tri Wanda Septian Mohd Yazid Idris Imam Much Ibnu Subroto Lukman Rahmat Budiarto 《Computer Systems Science & Engineering》 SCIE EI 2023年第8期1759-1774,共16页
The Repository Mahasiswa(RAMA)is a national repository of research reports in the form of final assignments,student projects,theses,dissertations,and research reports of lecturers or researchers that have not yet been... The Repository Mahasiswa(RAMA)is a national repository of research reports in the form of final assignments,student projects,theses,dissertations,and research reports of lecturers or researchers that have not yet been published in journals,conferences,or integrated books from the scientific repository of universities and research institutes in Indonesia.The increasing popularity of the RAMA Repository leads to security issues,including the two most widespread,vulnerable attacks i.e.,Structured Query Language(SQL)injection and cross-site scripting(XSS)attacks.An attacker gaining access to data and performing unauthorized data modifications is extremely dangerous.This paper aims to provide an attack detection system for securing the repository portal from the abovementioned attacks.The proposed system combines a Long Short–Term Memory and Principal Component Analysis(LSTM-PCA)model as a classifier.This model can effectively solve the vanishing gradient problem caused by excessive positive samples.The experiment results show that the proposed system achieves an accuracy of 96.85%using an 80%:20%ratio of training data and testing data.The rationale for this best achievement is that the LSTM’s Forget Gate works very well as the PCA supplies only selected features that are significantly relevant to the attacks’patterns.The Forget Gate in LSTM is responsible for deciding which information should be kept for computing the cell state and which one is not relevant and can be discarded.In addition,the LSTM’s Input Gate assists in finding out crucial information and stores specific relevant data in the memory. 展开更多
关键词 LSTM PCA ensemble classifier SQL injection XSS
暂未订购 下载PDF
Detection of Turbulence Anomalies Using a Symbolic Classifier Algorithm in Airborne Quick Access Record(QAR)Data Analysis 认领 引用 被引量：2
18
作者 Zibo ZHUANG Kunyun LIN +1 位作者 Hongying ZHANG Pak-Wai CHAN 《Advances in Atmospheric Sciences》 SCIE CAS CSCD 2024年第7期1438-1449,共12页
As the risks associated with air turbulence are intensified by climate change and the growth of the aviation industry,it has become imperative to monitor and mitigate these threats to ensure civil aviation safety.The ... As the risks associated with air turbulence are intensified by climate change and the growth of the aviation industry,it has become imperative to monitor and mitigate these threats to ensure civil aviation safety.The eddy dissipation rate(EDR)has been established as the standard metric for quantifying turbulence in civil aviation.This study aims to explore a universally applicable symbolic classification approach based on genetic programming to detect turbulence anomalies using quick access recorder(QAR)data.The detection of atmospheric turbulence is approached as an anomaly detection problem.Comparative evaluations demonstrate that this approach performs on par with direct EDR calculation methods in identifying turbulence events.Moreover,comparisons with alternative machine learning techniques indicate that the proposed technique is the optimal methodology currently available.In summary,the use of symbolic classification via genetic programming enables accurate turbulence detection from QAR data,comparable to that with established EDR approaches and surpassing that achieved with machine learning algorithms.This finding highlights the potential of integrating symbolic classifiers into turbulence monitoring systems to enhance civil aviation safety amidst rising environmental and operational hazards. 展开更多
关键词 turbulence detection symbolic classifier quick access recorder data
暂未订购 下载PDF
Ship detection in optical remote sensing image based on visual saliency and AdaBoost classifier 认领 引用 被引量：12
19
作者 王慧利 朱明 +1 位作者 蔺春波 陈典兵 《Optoelectronics Letters》 EI 2017年第2期151-155,共5页
In this paper, firstly, target candidate regions are extracted by combining maximum symmetric surround saliency detection algorithm with a cellular automata dynamic evolution model. Secondly, an eigenvector independen... In this paper, firstly, target candidate regions are extracted by combining maximum symmetric surround saliency detection algorithm with a cellular automata dynamic evolution model. Secondly, an eigenvector independent of the ship target size is constructed by combining the shape feature with ship histogram of oriented gradient(S-HOG) feature, and the target can be recognized by Ada Boost classifier. As demonstrated in our experiments, the proposed method with the detection accuracy of over 96% outperforms the state-of-the-art method. efficiency switch and modulation. 展开更多
关键词 classifier AdaBoost histogram automata symmetric pixel candidate similarity surround segmentation
暂未订购 下载PDF
An Alignment-Based Approach to L2 Learning of Chinese Numeral Classifiers 认领 引用 被引量：8
20
作者 Chuming WANG Wei HONG 《Chinese Journal of Applied Linguistics》 2021年第3期335-350,431,共16页
This study investigated the efficiency of learning the Chinese numeral classifiers by L2 Chinese learners by means of an alignment-oriented task. Participants were a total of 96 intermediate learners of L2 Chinese, wh... This study investigated the efficiency of learning the Chinese numeral classifiers by L2 Chinese learners by means of an alignment-oriented task. Participants were a total of 96 intermediate learners of L2 Chinese, who were randomly assigned to two experimental groups and one control group, with each group consisting of 32 participants. The continuation task used in this study consisted of a picture-based Chinese text depicting a room with an array of objects, which necessitates the use of classifiers. The two experimental groups were both required to first read the text and then write to describe their own rooms in comparison with the one in the text. One group was instructed to use the classifiers from the text as much as possible in their writing, whereas the other was not required to do so. Participants in the control group were first given the picture to look at in the absence of the text and then asked to describe their own rooms. The results showed that the continuation task significantly enhanced participants’ retention of the Chinese numeral classifiers, suggesting that the alignment-based approach is an effective way to learn difficult linguistic categories such as the Chinese classifiers. 展开更多
关键词 alignment interaction continuation task learn-together-use-together(LTUT)principle classifiers
暂未订购 下载PDF
已选0条
引用分析
参考文献
引证文献
 统计分析
检索结果
已选文献
上一页 1 2 … 34 下一页 到第 页
在线咨询 使用帮助 返回顶部 意见反馈