检索结果-内蒙古大学图书馆

您好，读者！请登录

内蒙古大学图书馆

首页
概况
党建
资源
服务
科研支持
- 论文收录引用证明
- 科技查新
知识产权
档案馆
帮助

咨询与建议

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

您的常用邮箱：*

您的手机号码：*

问题描述：

当前已输入0个字，您还可以输入200个字

全部搜索
期刊论文
图书
学位论文
标准
纸本馆藏
外文资源发现
数据库导航
超星发现

高级检索

时间限定

出版年份：

文献类型

图书期刊文献学位论文多媒体

馆藏选择

电子馆藏纸本馆藏

核心期刊

全部期刊 SCI 收录期刊 SSCI 收录期刊 EI 收录期刊 CSCD 收录期刊 CSSCI 收录期刊

语言

中文英文

文献类型

期刊文献图书学位论文标准纸本馆藏

帮助

文字说明：

T=题名（书名、题名），A=作者（责任者），K=主题词，P=出版物名称，PU=出版社名称，O=机构（作者单位、学位授予单位、专利申请人），L=中图分类号，C=学科分类号，U=全部字段，Y=年（出版发行年、学位年度、标准发布年）

检索规则说明：

AND代表“并且”；OR代表“或者”；NOT代表“不包含”；(注意必须大写,运算符两边需空一格)

检索范例：

范例一：(K=图书馆学 OR K=情报学) AND A=范并思 AND Y=1982-2016
范例二：P=计算机应用与软件 AND (U=C++ OR U=Basic) NOT K=Visual AND Y=2011-2016

分类表

所选分类

>> <<

限定检索结果

文献类型

2,600 篇 会议
177 册 图书
30 篇 期刊文献

馆藏范围

2,806 篇 电子文献
1 种 纸本馆藏

日期分布

学科分类号

1,871 篇 工学
- 1,693 篇 计算机科学与技术...
- 831 篇 软件工程
- 330 篇 信息与通信工程
- 270 篇 电气工程
- 191 篇 生物工程
- 176 篇 控制科学与工程
- 149 篇 生物医学工程（可授...
- 67 篇 安全科学与工程
- 65 篇 光学工程
- 63 篇 机械工程
- 63 篇 化学工程与技术
- 50 篇 交通运输工程
- 43 篇 仪器科学与技术
- 39 篇 网络空间安全
- 37 篇 电子科学与技术（可...
552 篇 理学
- 292 篇 数学
- 222 篇 生物学
- 112 篇 统计学（可授理学、...
- 103 篇 物理学
- 80 篇 系统科学
- 54 篇 化学
397 篇 管理学
- 266 篇 图书情报与档案管...
- 184 篇 管理科学与工程(可...
- 64 篇 工商管理
256 篇 医学
- 201 篇 临床医学
- 117 篇 基础医学(可授医学...
- 70 篇 药学(可授医学、理...
- 33 篇 公共卫生与预防医...
47 篇 法学
- 42 篇 社会学
39 篇 教育学
- 35 篇 教育学
29 篇 经济学
26 篇 农学
7 篇 文学
5 篇 艺术学
3 篇 军事学

主题

483 篇 data mining
482 篇 machine learning
272 篇 pattern recognit...
160 篇 feature extracti...
152 篇 deep learning
147 篇 artificial intel...
111 篇 support vector m...
106 篇 machine learning...
94 篇 data mining and ...
85 篇 training
76 篇 data models
74 篇 accuracy
57 篇 computer imaging...
56 篇 learning systems
55 篇 neural networks
47 篇 information syst...
47 篇 convolutional ne...
45 篇 predictive model...
44 篇 big data
44 篇 computer applica...

机构

15 篇 national univers...
15 篇 the islamic univ...
15 篇 fujian provincia...
13 篇 medical technica...
13 篇 school of inform...
13 篇 altoosi universi...
11 篇 guangdong univer...
10 篇 indian statistic...
10 篇 school of manage...
10 篇 department of bu...
9 篇 institute of env...
9 篇 department of to...
8 篇 xi'an university...
8 篇 al-zahraa univer...
8 篇 college of mecha...
8 篇 chinese academy ...
7 篇 school of inform...
7 篇 zhejiang univers...
7 篇 institute of aut...
7 篇 unsw sydney

作者

17 篇 chang kuo-chi
12 篇 de-shuang huang
10 篇 chu kai-chun
10 篇 mottl vadim
9 篇 chang fu-hsiang
9 篇 wang hsiao-chuan
8 篇 zheng-guang wu
8 篇 hongyi li
8 篇 chaojie li
8 篇 biao luo
8 篇 petra perner
8 篇 long cheng
7 篇 yuhui shi
7 篇 krasotkina olga
7 篇 alexander gelbuk...
7 篇 sung tien-wen
7 篇 ying tan
7 篇 lin yuh-chung
6 篇 ryszard tadeusie...
6 篇 marcin korytkows...

语言

2,717 篇 英文
75 篇 其他
35 篇 中文
1 篇 土耳其文

检索条件"任意字段=6th International Conference on Machine Learning and Data Mining in Pattern Recognition"

共 2807 条记录，以下是101-110 订阅

全选清除本页清除全部题录导出标记到"检索档案"

详细简洁

排序：

Enhancing Text Extraction Using OCR with AWS Textract Service 6th

Enhancing Text Extraction Using OCR with AWS Textract Servic...

引用

6th international conference on Advanced Informatics for Computing Research, ICAICR 2023

作者： Bawasker, Aakash Dedania, Yash Karia, Vrushti Bhatt, Nirav Prajapati, Purvi Bhatt, Nikita Chandubhai S. Patel Institue of Technology Charotar University of Science and Technology Gujarat Anand India

ISBN: (纸本)9783031840616

the advent of Optical Character recognition (OCR) technology has revolutionized document handling for businesses, enabling the conversion of handwritten text into machine-readable content. this project explores Amazon Web Services Textract, a cutting-edge OCR service integrating advanced machine learning and deep learning models. Emphasis is placed on dissecting Textract's architecture, features, and practical applications, showcasing its proficiency in extracting text, forms, and tables from diverse document types such as scanned papers, PDFs, and images. Real-world examples demonstrate Textract's seamless integration with document management systems, facilitating data analysis and ***, the article conducts a comprehensive evaluation of the efficiency and accuracy of three OCR solutions: Tesseract (English text), Amazon Textract (Arabic text), and Google DocumentAI (English text). through a comparative analysis of their performance and identification of distinctions in handling common text imperfections, the study aids researchers in informed decision-making for OCR solutions aligned with specific research ***, the project underscores the utility and benefits of incorporating AWS Textract into OCR applications. It highlights how Textract enhances document processing efficiency, improves data accessibility, and ensures compliance adherence. Valuable insights are provided for organizations seeking to optimize document management workflows and leverage OCR capabilities through AWS Textract. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Optical data processing

来源：评论

学校读者我要写书评

暂无评论

Foundational Techniques and Principles for VLSI Testing Using machine learning

Foundational Techniques and Principles for VLSI Testing Usin...

引用

Mobile Computing and Sustainable Informatics (ICMCSI), international conference on

作者： Sindhu Nalla G. Nagarajan Dept of Electronic Engineering Satyabhama Institute of Science and Technology Chennai India Dept. of CSE Satyabhama institute of Science and Technology Chennai India

ISBN: (数字)9798331522667

ISBN: (纸本)9798331522674

With the advent of Very-Large-Scale Integration (VLSI), testing has turned out to be much more troublesome as their size develops. Effective as these traditional VLSI testing methods are in simplification, implementing when it comes to modern circuits teeming strewn with millions of components is suitable and this is where they often falter. machine learning (ML), as a recent sibling in the family of curtain technologies has opened doors to innovative solutions that may improve VLSI testing, ranging from faster detection and lower fake p/f ratio causing masking costs. A systematic survey of existing methods, techniques and frameworks that utilize ML for VLSI testing is presented in this paper. the survey has encompassed a wide set of ML paradigms, many under the umbrella of supervised/unsupervised/reinforcement learning to study their relevance in different VLSI testing phases (e.g., for fault diagnosis/test pattern generation/design-for-testability considerations). Lastly, the paper discusses how ML frameworks have to be integrated into established VLSI testing workflows and what possible opportunities/trade-offs with current incorporation practices. Performance Metrics: It also aims to evaluate key performance metrics, such as the accuracy/efficiency of a method or its scalability that may be thoroughly analyzed for an ML-based VLSI testing practice. the survey also points out vital gaps in the existing research and suggests possible ways to fill in these gaps by focusing on maybe developing more scalable, explainable & universally applicable ML models for VLSI testing. In addition, by organizing the vast body of work in this area and providing insights into how different approaches have been employed towards achieving specific testing objectives, it is envisaged that researchers and practitioners working on VLSI test research using machine learning will find a useful resource for advancing their contributions.

关键词： Surveys Adaptation models Systematics Scalability Computational modeling Very large scale integration data models Complexity theory Integrated circuit modeling Testing

来源：评论

学校读者我要写书评

暂无评论

Artificial Intelligence Techniques for the Performance Analysis of Indian Mutual Fund Industry and Market Trends

Artificial Intelligence Techniques for the Performance Analy...

引用

Mobile Computing and Sustainable Informatics (ICMCSI), international conference on

作者： S. Silvia Priscila M. Sakthivanitha M. Yogeshwari T. Shirley Devakirubai M. Mohamed Sirajudeen Department of Computer Science Bharath Institute of Higher Education and Research Tamil Nadu India Department of Information Technology VEL'S Institute of Science Technology and Advanced Studies Chennai Tamil Nadu India Department of Data Analyst Saveetha Liberal Arts and Science College Chennai Tamil Nadu India Department of Commerce Madras Christian College Chennai Tamil Nadu India Department of Computer Science Nilgiri College of Arts and Science (Autonomous) The Nilgiris Tamil Nadu India

ISBN: (数字)9798331522667

ISBN: (纸本)9798331522674

A basic examination of a mutual fund's strategy (growth or value), median market cap, rolling returns, standard deviation, and maybe a portfolio breakdown by industry, geography, and other factors make up a typical mutual fund analysis. Mutual fund analysis typically involves calculating the amount of management contribution to the portfolio and ranking equities according to industry. Analyzing a mutual fund portfolio involves a few phases, one of which comprehends the fund's design. this study examine the basic mutual fund parameters and explore the use of artificial intelligence (AI) methods, including data mining, machine learning(ML), blockchain and predictive analytics in predicting market trends and assessing mutual fund performance. the purpose of this study is to give users a clear, concise, and professional overview of how to analyze the performance of mutual funds using AI Techniques

关键词： Industries Mutual funds Benchmark testing Market research Performance analysis Artificial intelligence Predictive analytics Portfolios Standards Investment

来源：评论

学校读者我要写书评

暂无评论

A Study on Applications of Natural Language Processing in Medical data 15th

A Study on Applications of Natural Language Processing in Me...

引用

15th international conference on Soft Computing and pattern recognition, SoCPaR 2023 and 14th World Congress on Nature and Biologically Inspired Computing, NaBIC 2023

作者： Cherish, Reshma Rachel Lovesum, S. P. Jeno Christ Deemed to be University Karnataka Kumbalgodu Bangalore 560074 India Ramaiah Institute of Technology MSR Nagar MSRIT Post Karnataka Bangalore 560054 India

ISBN: (纸本)9783031810824

Natural Language Processing (NLP) is unique to several other fields because it facilitates human-machine interaction by enabling machines to comprehend human language. this interaction is important as it changes the lifestyle of humans due to its diverse applications in machine translation, email spam detection, information extraction, text summarization, medicine, and question-answering. the significance in medicine is in terms of clinical documentation, disease diagnostics, clinical research, chatbots for patient-facing interfaces, medical transcription and dictation, and clinical coding. this article provides a review of the NLP applications in the medical field in accordance with the research works that were conducted specifically on the application, the challenges faced by healthcare professionals and consumers after the influence of the internet era, a review of the available NLP approaches, a review of the NLP tasks specific to the medical field, and overall, the importance of further development of NLP models that improve healthcare. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： biomarkers clinical decision support systems (CDSS) Electronic Health Records (EHRs) Federated learning (FL) free-text KDD medical coding medical notes medical procedures NLP omics data telemedicine wearable devices

来源：评论

学校读者我要写书评

暂无评论

A Revolutionary GPU-Based Healthcare System for Blood Sample Analysis Performance Evaluation 6th

A Revolutionary GPU-Based Healthcare System for Blood Sample...

引用

6th international conference on Smart Computing and Informatics, SCI 2024

作者： Sujani, B. Ranadeep Reddy, D. Malsoru, Vankudothu Sri Harsha, V.N.V. Navanitha, Dubbaka Ramasamy, M. Department of CSE(AI&ML) CMR College of Engineering and Technology Telangana Hyderabad India Department of CSE CMR College of Engineering and Technology Telangana Hyderabad India Department of IT CMR Technical Campus Telangana Hyderabad India Department of CSE(AI&ML) CMR Technical Campus Telangana Hyderabad India Department of CSE CMR Engineering College Telangana Hyderabad India Department of CSE(CS) CMR Engineering College Telangana Hyderabad India

ISBN: (纸本)9789819619801

Numerous studies in the biological sciences have been conducted as a result of the demand for innovative medical treatments and ongoing efforts to better understand the biological causes of diseases. Large datasets that contain a lot of text and images can lead to a lot of practical issues. the classification of this data requires the use of neural networks and other related architectures. Deep learning's potential for machine learning has made it more significant. the present endeavor aims to develop an approach for acute leukemia categorization using a deep learning technique. It demonstrates to be a potent supplementary clinical test for determining the severity of a patient's illness. GPU processing capability is used to develop a computerized recognition and categorization system for acute leukemia identification from peripheral blood pictures. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Convolution neural network Deep learning Image analysis Leukemia and deep learning

来源：评论

学校读者我要写书评

暂无评论

Kendall’s Rank Correlation Coefficient-Based Monotonic Decision Tree for the Analysis of Students’ Stress Factors 6th

Kendall’s Rank Correlation Coefficient-Based Monotonic Deci...

引用

6th international conference on Computer Science and Educational Informatization, CSEI 2024

作者： Mu, Yashuang Sun, Yan Wang, Lidong Zheng, Yueyang Wang, Jingjing School of Artificial Intelligence and Big Data Henan University of Technology Zhengzhou450001 China College of Information Science and Engineering Henan University of Technology Zhengzhou450001 China School of Science Dalian Maritime University Dalian116026 China

ISBN: (纸本)9789819637348

data mining technology can extract important information from numerical data to help students develop better, and a good mental state can help improve students’ academic performance. A decision tree is a machine learning technique that can be utilized to complete data mining tasks and predict students’ mental health. Traditional decision trees can complete this task, but they pay less attention to the monotonic constraint relationship between students’ personal reasons and external environment and the degree of psychological stress. To address this problem, this paper proposes a Kendall’s rank correlation coefficient-based monotonic decision tree model (KCC-tree). this algorithm can consider monotonic constraints in the data mining process, which accurately predicts the stress level of students. With the use of Kendall’s rank correlation coefficient, the proposed KCC-tree method can be used to evaluate the monotonic consistency of features and labels. this approach allows for the selection of qualified candidate features, which are then used to determine the split point. Moreover, the analysis of classification accuracy, mean absolute error and precision reveals that the monotonic classification decision tree method discussed here is feasible for predicting the stress level of students. In addition, this paper illustrates the effectiveness and feasibility of the algorithm by comparing the KCC-tree with classic decision trees such as BFT, C4.5, LAD, and SC. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Decision Tree Education data mining Monotonic Classification Prediction Model

来源：评论

学校读者我要写书评

暂无评论

Novel Hybrid Feature Selection Method and Globalization Technique for Text Classification 4th

Novel Hybrid Feature Selection Method and Globalization Tec...

引用

4th international conference on Intelligent Systems and pattern recognition, ISPR 2024

作者： Mekhzoumi, Dalila Boulkaboul, Sahar Amroun, Kamal CERIST Research Center Algiers Ben Aknoun Algeria Abderrahmane Mira University Bejaia Algeria

ISBN: (纸本)9783031821523

Feature selection in text classification refers to the critical process of identifying and selecting the most relevant and informative features such as words, phrases, or other linguistic elements from a text dataset. this process, which has been a research topic for decades and finds applications across various fields such as bioinformatics, image recognition, image retrieval, text mining, etc., is essential for optimizing classification accuracy and efficiency. Addressing the challenge of high dimensionality in text data, stemming from the abundance of features like words or n-grams, is crucial to mitigate computational inefficiency and overfitting. Furthermore, the presence of irrelevant or redundant features in text datasets poses another significant challenge, as these features can introduce noise or irrelevant information, thereby undermining the performance of classifier. In this paper, we proposed a new approach GL-SMCHI using improved features selection method to reduce the CHI value of high-frequency words, and globalization technique to incorporate both feature and class information when evaluating the importance of each feature. We compared the results obtained from our proposal against those of existing robust alternatives, the simulation results show that the proposed method outperforms the standard CHI squared, the improved feature selection methods, and the globalization method in term of F-Score and accuracy. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Adversarial machine learning

来源：评论

学校读者我要写书评

暂无评论

Heuristic Model to Improve Feature Selection Based on machine learning in data mining 6

Heuristic Model to Improve Feature Selection Based on Machin...

引用

6th international conference on Cloud System and Big data Engineering (Confluence)

作者： Majumdar, Jahin Mal, Anwesha Gupta, Shruti Am Univ ASET Dept CSE Noida India

ISBN: (纸本)9781467382038

data mining and machine learning is one of the most popular research areas in computer science that is relevant in today's world of unfathomable data. To keep up with the rising size of data, there arises a need to quickly extract knowledge from data sources to aid data analysis research and improve industry and market needs. Primary data mining algorithms like k-means, Apriori, PageRank etc. are used today, but machine learning techniques can enhance the same by learning from the complex patterns. this paper focuses on the various existing approaches where machine learning algorithms have been used to improve data classification and pattern recognition in data mining especially for Feature Selection. It compares and contrasts the existing techniques and finds out the best one among them. Further, the paper proposes a heuristic approach to theoretically overcome most of the limitations in existing algorithms.

关键词： data mining machine learning Fuzzy Knowledge Discovery

来源：评论

学校读者我要写书评

暂无评论

Optimal Double-Kernel Combination for Classification

引用

6th international conference on machine learning and data mining in pattern recognition

作者： Wang, Feng Zhang, Hongbin Beijing Univ Sci & Technol Coll Comp Sci Beijing 100124 Peoples R China

ISBN: (纸本)9783642030697

Traditional kernelised classification methods Could not perforin well sometimes because of the using of a single and fixed kernel, especially oil sonic complicated data sets. In this paper. a novel optimal double-kernel combination (ODKC) method is proposed for complicated classification tasks. Firstly, data sets are mapped by two basic kernels into different feature spaces respectively, and then three kinds of optimal composite kernels are constructed by integrating information of the two feature spaces. Comparative experiments demonstrate the effectiveness of our methods.

关键词： Classification kernel learning pattern recognition

来源：评论

学校读者我要写书评

暂无评论

An Evidence-Driven Probabilistic Inference Framework for Semantic Image Understanding

引用

6th international conference on machine learning and data mining in pattern recognition

作者： Nikolopoulos, Spiros Papadopoulos, Georgios th. Kompatsiaris, Ioannis Patras, Ioannis CERTH Informat & Telemat Inst 6th Km Charilaou Thermi Rd Thessaloniki Greece Univ London Queen Mary Sch Elect Engn & Comp Sci London E1 4NS England

ISBN: (纸本)9783642030697

this work presents an image analysis framework driven by emerging evidence and constrained by the semantics expressed in an ontology. Human perception, apart from visual stimulus and pattern recognition, relies also on general knowledge and application context for understanding visual content in conceptual terms. Our work is an attempt to imitate this behavior by devising an evidence driven probabilistic, inference framework using ontologies and bayesian networks. Experiments conducted for two different image analysis, tasks showed improvement performance, compared to the case where computer vision techniques act isolated from any type of knowledge or context.

关键词： Image analysis

来源：评论

学校读者我要写书评

暂无评论

没有更多数据了...

全选清除本页清除全部题录导出标记到“检索档案”

共281页 << < 7 8 9 10 11 12 13 14 15 16 > >>

检索报告对象比较合并检索0

隐藏清空

合并搜索

回到顶部

执行限定条件

内容：

评分：

请选择保存的检索档案：

请选择收藏分类：

订阅名称：

通借通还

温馨提示：

图书名称：

借书校区：

取书校区：

手机号码：

邮箱地址：

一卡通帐号：

电话和邮箱必须正确填写，我们会与您联系确认。

联系人：

所在院系：

联系邮箱：

联系电话：

内蒙古自治区呼和浩特市赛罕区大学西街235号邮编: 010021

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：