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
In this paper, decision mechanism of credit-risk for banks is studied when the loan interest rate is fixed with asymmetry information in credit market. We give out the designs of rationing and non-rationing on credit ...In this paper, decision mechanism of credit-risk for banks is studied when the loan interest rate is fixed with asymmetry information in credit market. We give out the designs of rationing and non-rationing on credit risky decision mechanism when collateral value provided by an entrepreneur is not less than the minimum demands of the bank. It shows that under the action of the mechanism, banks could efficiently identify the risk size of the project. Finally, the condition of the project investigation of bank is given over again.展开更多
A review of signal processing algorithms employing Wi-Fi signals for positioning and recognition of human activities is presented.The principles of how channel state information(CSI)is used and how the Wi-Fi sensing s...A review of signal processing algorithms employing Wi-Fi signals for positioning and recognition of human activities is presented.The principles of how channel state information(CSI)is used and how the Wi-Fi sensing systems operate are reviewed.It provides a brief introduction to the algorithms that perform signal processing,feature extraction and recognitions,including location,activity recognition,physiological signal detection and personal identification.Challenges and future trends of Wi-Fi sensing are also discussed in the end.展开更多
A novel classification algorithm based on abnormal magnetic signals is proposed for ground moving targets which are made of ferromagnetic material. According to the effect of diverse targets on earth's magnetism,t...A novel classification algorithm based on abnormal magnetic signals is proposed for ground moving targets which are made of ferromagnetic material. According to the effect of diverse targets on earth's magnetism,the moving targets are detected by a magnetic sensor and classified with a simple computation method. The detection sensor is used for collecting a disturbance signal of earth magnetic field from an undetermined target. An optimum category match pattern of target signature is tested by training some statistical samples and designing a classification machine. Three ordinary targets are researched in the paper. The experimental results show that the algorithm has a low computation cost and a better sorting accuracy. This classification method can be applied to ground reconnaissance and target intrusion detection.展开更多
The article discusses the use of Fourier descriptors for the analysis and classification of blood cells. A model describing the contour boundaries in the form of two-dimensional numerical sequence Fourier descriptors....The article discusses the use of Fourier descriptors for the analysis and classification of blood cells. A model describing the contour boundaries in the form of two-dimensional numerical sequence Fourier descriptors. The influence of the shape and orientation of the figures on the parameters of the Fourier descriptors. Explore ways to ensure the invariance of the Fourier descriptors with respect to geometric transformations. A model of the graphical representation of the Fourier descriptors of computer graphics tools. A method of forming a space of informative features based on Fourier descriptors for the neural network, classifying the contours of borders image segments.展开更多
As Natural Language Processing(NLP)continues to advance,driven by the emergence of sophisticated large language models such as ChatGPT,there has been a notable growth in research activity.This rapid uptake reflects in...As Natural Language Processing(NLP)continues to advance,driven by the emergence of sophisticated large language models such as ChatGPT,there has been a notable growth in research activity.This rapid uptake reflects increasing interest in the field and induces critical inquiries into ChatGPT’s applicability in the NLP domain.This review paper systematically investigates the role of ChatGPT in diverse NLP tasks,including information extraction,Name Entity Recognition(NER),event extraction,relation extraction,Part of Speech(PoS)tagging,text classification,sentiment analysis,emotion recognition and text annotation.The novelty of this work lies in its comprehensive analysis of the existing literature,addressing a critical gap in understanding ChatGPT’s adaptability,limitations,and optimal application.In this paper,we employed a systematic stepwise approach following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses(PRISMA)framework to direct our search process and seek relevant studies.Our review reveals ChatGPT’s significant potential in enhancing various NLP tasks.Its adaptability in information extraction tasks,sentiment analysis,and text classification showcases its ability to comprehend diverse contexts and extract meaningful details.Additionally,ChatGPT’s flexibility in annotation tasks reducesmanual efforts and accelerates the annotation process,making it a valuable asset in NLP development and research.Furthermore,GPT-4 and prompt engineering emerge as a complementary mechanism,empowering users to guide the model and enhance overall accuracy.Despite its promising potential,challenges persist.The performance of ChatGP Tneeds tobe testedusingmore extensivedatasets anddiversedata structures.Subsequently,its limitations in handling domain-specific language and the need for fine-tuning in specific applications highlight the importance of further investigations to address these issues.展开更多
The JTC technology deals with the problem of target tracking and target classification simultaneously within a unified framework.The fundamental idea of the JTC technology is that by taking advantage of the mutual exc...The JTC technology deals with the problem of target tracking and target classification simultaneously within a unified framework.The fundamental idea of the JTC technology is that by taking advantage of the mutual exchange of useful information between the tracker and classifier,significant improvements in performance of both target tracking and target classification can be expected.The principle of JTC technology is introduced.The existing JTC technologies are broadly categorized into two classes,i.e.,point-target-motion-model-based JTC and rigid-target-motion-based JTC,which are then compared in detail.The advance of the JTC technology is surveyed with comments on some related literatures.Finally,some opening topics of the JTC technology are discussed.展开更多
传统的档案信息提取方法主要依赖人工操作,这不仅耗时费力,还易出现错误,影响数据的准确性和可靠性。随着自然语言处理(Natural Language Processing,NLP)技术的迅速发展,医院档案信息提取的效率得到了显著提升。文章探讨了如何应用NLP...传统的档案信息提取方法主要依赖人工操作,这不仅耗时费力,还易出现错误,影响数据的准确性和可靠性。随着自然语言处理(Natural Language Processing,NLP)技术的迅速发展,医院档案信息提取的效率得到了显著提升。文章探讨了如何应用NLP技术来提高医院档案信息提取的效率,重点介绍了文本分类、命名实体识别和关系抽取等关键技术。其中,文本分类可以自动对档案进行分类,有效组织信息;命名实体识别用于识别和提取关键信息,如患者姓名、疾病名称和药物等;关系抽取则可以揭示不同信息间的关系,帮助建立完整的信息网络。展开更多
情感分类是一项具有较大实用价值的分类技术,它可以在一定程度上解决网络评论信息杂乱的现象,方便用户准确定位所需信息。目前针对中文情感分类的研究相对较少,其中各种有监督学习方法的分类效果以及文本特征表示方法和特征选择机制等...情感分类是一项具有较大实用价值的分类技术,它可以在一定程度上解决网络评论信息杂乱的现象,方便用户准确定位所需信息。目前针对中文情感分类的研究相对较少,其中各种有监督学习方法的分类效果以及文本特征表示方法和特征选择机制等因素对分类性能的影响更是亟待研究的问题。本文以n-gram以及名词、动词、形容词、副词作为不同的文本表示特征,以互信息、信息增益、CHI统计量和文档频率作为不同的特征选择方法,以中心向量法、KNN、Winnow、Na ve Bayes和SVM作为不同的文本分类方法,在不同的特征数量和不同规模的训练集情况下,分别进行了中文情感分类实验,并对实验结果进行了比较,对比结果表明:采用Bi Grams特征表示方法、信息增益特征选择方法和SVM分类方法,在足够大训练集和选择适当数量特征的情况下,情感分类能取得较好的效果。展开更多
基金This research was funded by Prince Sattam bin Abdulaziz University(Project Number PSAU/2023/01/25387).
文摘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.
基金This project was supported by Fubangs Science & Technology Company Ltd.
文摘In this paper, decision mechanism of credit-risk for banks is studied when the loan interest rate is fixed with asymmetry information in credit market. We give out the designs of rationing and non-rationing on credit risky decision mechanism when collateral value provided by an entrepreneur is not less than the minimum demands of the bank. It shows that under the action of the mechanism, banks could efficiently identify the risk size of the project. Finally, the condition of the project investigation of bank is given over again.
基金National Natural Science Foundation of China(NSFC)under Grant No.61401100Natural Science Foundation of Fuji⁃an Province under Grant No.2018J01805+1 种基金Youth Research Project of Fujian Provincial Department of Education under Grant No.JAT190011and Fuzhou University Scientific Research Fund Project under Grant No.GXRC-18074.
文摘A review of signal processing algorithms employing Wi-Fi signals for positioning and recognition of human activities is presented.The principles of how channel state information(CSI)is used and how the Wi-Fi sensing systems operate are reviewed.It provides a brief introduction to the algorithms that perform signal processing,feature extraction and recognitions,including location,activity recognition,physiological signal detection and personal identification.Challenges and future trends of Wi-Fi sensing are also discussed in the end.
基金Sponsored by the National Natural Science Foundation of China (60773129)the Excellent Youth Science and Technology Foundation of Anhui Province of China ( 08040106808)
文摘A novel classification algorithm based on abnormal magnetic signals is proposed for ground moving targets which are made of ferromagnetic material. According to the effect of diverse targets on earth's magnetism,the moving targets are detected by a magnetic sensor and classified with a simple computation method. The detection sensor is used for collecting a disturbance signal of earth magnetic field from an undetermined target. An optimum category match pattern of target signature is tested by training some statistical samples and designing a classification machine. Three ordinary targets are researched in the paper. The experimental results show that the algorithm has a low computation cost and a better sorting accuracy. This classification method can be applied to ground reconnaissance and target intrusion detection.
文摘The article discusses the use of Fourier descriptors for the analysis and classification of blood cells. A model describing the contour boundaries in the form of two-dimensional numerical sequence Fourier descriptors. The influence of the shape and orientation of the figures on the parameters of the Fourier descriptors. Explore ways to ensure the invariance of the Fourier descriptors with respect to geometric transformations. A model of the graphical representation of the Fourier descriptors of computer graphics tools. A method of forming a space of informative features based on Fourier descriptors for the neural network, classifying the contours of borders image segments.
文摘As Natural Language Processing(NLP)continues to advance,driven by the emergence of sophisticated large language models such as ChatGPT,there has been a notable growth in research activity.This rapid uptake reflects increasing interest in the field and induces critical inquiries into ChatGPT’s applicability in the NLP domain.This review paper systematically investigates the role of ChatGPT in diverse NLP tasks,including information extraction,Name Entity Recognition(NER),event extraction,relation extraction,Part of Speech(PoS)tagging,text classification,sentiment analysis,emotion recognition and text annotation.The novelty of this work lies in its comprehensive analysis of the existing literature,addressing a critical gap in understanding ChatGPT’s adaptability,limitations,and optimal application.In this paper,we employed a systematic stepwise approach following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses(PRISMA)framework to direct our search process and seek relevant studies.Our review reveals ChatGPT’s significant potential in enhancing various NLP tasks.Its adaptability in information extraction tasks,sentiment analysis,and text classification showcases its ability to comprehend diverse contexts and extract meaningful details.Additionally,ChatGPT’s flexibility in annotation tasks reducesmanual efforts and accelerates the annotation process,making it a valuable asset in NLP development and research.Furthermore,GPT-4 and prompt engineering emerge as a complementary mechanism,empowering users to guide the model and enhance overall accuracy.Despite its promising potential,challenges persist.The performance of ChatGP Tneeds tobe testedusingmore extensivedatasets anddiversedata structures.Subsequently,its limitations in handling domain-specific language and the need for fine-tuning in specific applications highlight the importance of further investigations to address these issues.
文摘The JTC technology deals with the problem of target tracking and target classification simultaneously within a unified framework.The fundamental idea of the JTC technology is that by taking advantage of the mutual exchange of useful information between the tracker and classifier,significant improvements in performance of both target tracking and target classification can be expected.The principle of JTC technology is introduced.The existing JTC technologies are broadly categorized into two classes,i.e.,point-target-motion-model-based JTC and rigid-target-motion-based JTC,which are then compared in detail.The advance of the JTC technology is surveyed with comments on some related literatures.Finally,some opening topics of the JTC technology are discussed.
文摘传统的档案信息提取方法主要依赖人工操作,这不仅耗时费力,还易出现错误,影响数据的准确性和可靠性。随着自然语言处理(Natural Language Processing,NLP)技术的迅速发展,医院档案信息提取的效率得到了显著提升。文章探讨了如何应用NLP技术来提高医院档案信息提取的效率,重点介绍了文本分类、命名实体识别和关系抽取等关键技术。其中,文本分类可以自动对档案进行分类,有效组织信息;命名实体识别用于识别和提取关键信息,如患者姓名、疾病名称和药物等;关系抽取则可以揭示不同信息间的关系,帮助建立完整的信息网络。
文摘情感分类是一项具有较大实用价值的分类技术,它可以在一定程度上解决网络评论信息杂乱的现象,方便用户准确定位所需信息。目前针对中文情感分类的研究相对较少,其中各种有监督学习方法的分类效果以及文本特征表示方法和特征选择机制等因素对分类性能的影响更是亟待研究的问题。本文以n-gram以及名词、动词、形容词、副词作为不同的文本表示特征,以互信息、信息增益、CHI统计量和文档频率作为不同的特征选择方法,以中心向量法、KNN、Winnow、Na ve Bayes和SVM作为不同的文本分类方法,在不同的特征数量和不同规模的训练集情况下,分别进行了中文情感分类实验,并对实验结果进行了比较,对比结果表明:采用Bi Grams特征表示方法、信息增益特征选择方法和SVM分类方法,在足够大训练集和选择适当数量特征的情况下,情感分类能取得较好的效果。
