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

3,150 篇 会议
171 册 图书
27 篇 期刊文献

馆藏范围

3,347 篇 电子文献
1 种 纸本馆藏

日期分布

学科分类号

2,240 篇 工学
- 2,038 篇 计算机科学与技术...
- 1,085 篇 软件工程
- 402 篇 信息与通信工程
- 356 篇 电气工程
- 249 篇 生物工程
- 169 篇 控制科学与工程
- 157 篇 生物医学工程（可授...
- 73 篇 光学工程
- 73 篇 化学工程与技术
- 68 篇 机械工程
- 62 篇 仪器科学与技术
- 59 篇 电子科学与技术（可...
- 58 篇 安全科学与工程
- 46 篇 交通运输工程
- 38 篇 材料科学与工程（可...
- 38 篇 动力工程及工程热...
717 篇 理学
- 377 篇 数学
- 272 篇 生物学
- 169 篇 物理学
- 147 篇 统计学（可授理学、...
- 104 篇 系统科学
- 69 篇 化学
459 篇 管理学
- 298 篇 图书情报与档案管...
- 199 篇 管理科学与工程(可...
- 95 篇 工商管理
300 篇 医学
- 273 篇 临床医学
- 125 篇 基础医学(可授医学...
- 70 篇 药学(可授医学、理...
- 53 篇 公共卫生与预防医...
64 篇 法学
- 57 篇 社会学
32 篇 经济学
31 篇 教育学
28 篇 农学
6 篇 艺术学
3 篇 军事学
1 篇 文学

主题

549 篇 data mining
547 篇 machine learning
289 篇 pattern recognit...
243 篇 deep learning
192 篇 feature extracti...
163 篇 support vector m...
156 篇 artificial intel...
127 篇 accuracy
123 篇 training
117 篇 machine learning...
99 篇 data models
91 篇 data mining and ...
75 篇 classification
73 篇 convolutional ne...
72 篇 neural networks
70 篇 predictive model...
69 篇 computational mo...
68 篇 big data
58 篇 sentiment analys...
55 篇 learning systems

机构

10 篇 chinese academy ...
9 篇 chitkara centre ...
8 篇 zhejiang univers...
7 篇 guangdong univer...
7 篇 centre of resear...
6 篇 central south un...
6 篇 unsw sydney
6 篇 college of compu...
5 篇 badji mokhtar un...
5 篇 xi'an university...
5 篇 indian statistic...
5 篇 southwest jiaoto...
5 篇 kobe university ...
5 篇 southern univers...
4 篇 columbia univers...
4 篇 chitkara univers...
4 篇 shanghai jiao to...
4 篇 institute of big...
4 篇 centre for patte...
4 篇 shandong institu...

作者

12 篇 de-shuang huang
12 篇 venkatesh svetha
8 篇 petra perner
7 篇 phung dinh
7 篇 jacek m. zurada
7 篇 krasotkina olga
7 篇 mottl vadim
6 篇 zheng-guang wu
6 篇 ryszard tadeusie...
6 篇 marcin korytkows...
6 篇 hongyi li
6 篇 chaojie li
6 篇 biao luo
6 篇 lotfi a. zadeh
6 篇 kang-hyun jo
6 篇 leszek rutkowski
6 篇 long cheng
6 篇 zhongzhi shi
5 篇 zhang yan
5 篇 vitoantonio bevi...

语言

3,033 篇 英文
298 篇 其他
38 篇 中文

检索条件"任意字段=4th International Conference on Machine Learning and Data Mining in Pattern Recognition"

共 3348 条记录，以下是831-840 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：

Resume Parsing and Skill Extraction using Custom pattern Matching algorithm and Gemini API

Resume Parsing and Skill Extraction using Custom Pattern Mat...

引用

international conference on Computing Communication Control and Automation (ICCUBEA)

作者： Atharva Joshi Shailaja Pede Prathmesh Kolekar Aditya Metkar Department of Computer Science Engineering (Artificial Intelligence and Machine Learning) PCCOE SPPU India Pune India

ISBN: (数字)9798350391770

ISBN: (纸本)9798350391787

In the realm of contemporary recruitment, efficient resume parsing is essential for talent acquisition. this paper investigates resume parsing using advanced OCR technology and NLP methods. We begin with preprocessing, enhancing scanned resumes for legibility. Leveraging the Tesseract OCR engine, we convert images to machine-readable text. through NLP techniques with spaCy, we extract key information like personal details, education, experience, and *** approach integrates the Gemini Pro Vision API for semantic analysis and question-answering. this enriches our framework, enabling deeper insights and implicit attribute inference. Empirical validation across diverse datasets demonstrates the robustness of our methodology. this work sheds light on performance metrics and challenges in automated resume parsing, paving the way for future enhancements.

关键词： Visualization Technological innovation machine learning algorithms Resumes Optical character recognition Semantics Robustness data mining pattern matching Recruitment

来源：评论

学校读者我要写书评

暂无评论

4th international conference on Advanced Network Technologies and Intelligent Computing, ANTIC 2024

4th International Conference on Advanced Network Technologie...

引用

4th international conference on Advanced Network Technologies and Intelligent Computing, ANTIC 2024

ISBN: (纸本)9783031837951

the proceedings contain 109 papers. the special focus in this conference is on Advanced Network Technologies and Intelligent Computing. the topics include: Exploratory Study on Different Transformer Models;visionLingua: Empowering Multimodal Understanding with Cutting-Edge Language Models;human Action recognition Using Mediapipe Holistic Keypoints: A Deep learning Approach;machine learning for Waste-to-Energy: Optimization and Predictive Analytics;enhanced Traffic Forecasting for Urban Planning Using DeepWalk Embeddings and Spatio-Temporal Graph Ordinary Differential Equation Model;fault Detection in Semiconductor Manufacturing Using Advanced machine learning Techniques with Interpretability;fine-Tuning RetinaNet for Few-Shot Fruit Detection;Smart Street: AI-Powered Traffic Flow Enhancement with Adaptive Signal Control;a Hybrid machine learning Approach for Accurate Milk Quality Classification;Designing VoiceGPT Chatterbox Concierge: A Personal AI Assistant for Daily Services Using Supervised machine learning Based Speech recognition Techniques;improved Classification Techniques Using Convolutional Neural Networks for Rice Plant Disease Detection;Enhancing Customer Segmentation and Behavior Prediction through machine learning and Medium Gaussian SVM;Grapevine SDLC Model for Real-Time Fake News Classification;Detecting AI-Generated Text Using Fine-Tuned Transformers: A Study on Academic Integrity;MoReco: AI Model Recommendation and Optimization for B5G Networks;file Prefetching Accuracy Enhancement Using Deep learning Approaches;Mamba Based Conditional GAN for Optical Cloud Removal of Satellite Images;a Hybrid Quantum machine learning Model for Multi-class Classifier;adaptive Resource Management in Massive machine-Type Communications Using Reinforcement learning;Advanced machine learning and Features Based data Analytics of Electricity Consumption in USA and India;cross-Domain Adversarial Attacks and Robust Defense Mechanisms for Multimodal Neural Networks;deep Featu

关键词：

来源：评论

学校读者我要写书评

暂无评论

A Detailed Analysis of machine learning Algorithm Performance in Heart Disease Prediction

A Detailed Analysis of Machine Learning Algorithm Performanc...

引用

international conference on Robotics,Electrical and Signal Processing Techniques (ICREST)

作者： Mohammad Ali Rabbi Rokibul Hasan Rijon Sharmin Sultana Akhi Alomgir Hossain Samiha Maisha Jeba Dept of Computer Science and Engineering International University of Business Agriculture and Technology Bangladesh Dept of Computer Science Monroe University USA

ISBN: (数字)9798331530952

ISBN: (纸本)9798331530969

Human deaths are natural events, but heart disease remains a leading cause of mortality worldwide. Predicting heart disease using machine learning can significantly benefit global health, though it presents clinical challenges requiring high accuracy and efficiency. Many countries face a shortage of cardiovascular experts and a high rate of misdiagnoses, which can be mitigated by accurately determining the stage of coronary disease and supporting medical decisions with automated patient records. this paper addresses the prediction of coronary disease based on input attributes using data mining techniques. the results showed that the CatBoost Classifier achieved the highest accuracy at 87.2%, followed closely by the Ridge Classifier at 86.3%. Other models, such as XGBoost and Gradient Boosting, performed similarly, achieving 84.0%, while the RandomForest Classifier had a slightly better performance with an accuracy of 85.6%.

关键词： Heart Accuracy machine learning algorithms Transfer learning Signal processing algorithms Signal processing Boosting Robots Random forests Diseases

来源：评论

学校读者我要写书评

暂无评论

4th international conference on Advanced Network Technologies and Intelligent Computing, ANTIC 2024

4th International Conference on Advanced Network Technologie...

引用

4th international conference on Advanced Network Technologies and Intelligent Computing, ANTIC 2024

ISBN: (纸本)9783031837920

关键词：

来源：评论

学校读者我要写书评

暂无评论

FingerPrintInsight:Unveiling Crime pattern through Deep Fingerprint Analysis

FingerPrintInsight:Unveiling Crime Pattern Through Deep Fing...

引用

data Engineering and Communication Systems (ICDECS), international conference on

作者： T. Kavitha Revappa Sachin B Handignur Y M Sachin Sumit S Purandare C.R. Rathish Dept. of Computer Engineering New Horizon College Of engineering Bangalore India

A fingerprint identification system employing deep machine learning and Convolutional Neural Networks could automate analysis. Images from crime scene investigation techniques are entered into a database. Partial latent prints are often difficult to classify. the system operates in three phases: preprocessing, feature extraction, and matching. Preprocessing enhances image quality before feature extraction identifies distinctive minutiae points. False minutiae removal further refines the data. the preprocessed fingerprints serve as input to train and test the model. As new prints are incorporated along with confirmed matches to improve accuracy, the system facilitates identification. the approach scales without proportionate increase in human effort. Accelerated evidence analysis could potentially solve more crimes by linking cases in a database.

关键词：

来源：评论

学校读者我要写书评

暂无评论

IBDR-Net: A Computerized Framework for Irregular Brain Signal data recognition 13th

IBDR-Net: A Computerized Framework for Irregular Brain Signa...

引用

13th international conference on Health Information Science, HIS 2024

作者： Alvi, Ashik Mostafa Manami, Nishat Tasnim Siuly, Siuly Sun, Lili Victoria University MelbourneVIC Australia

ISBN: (纸本)9789819655960

Brain signal data are records of the electrical activity of the brain made with electroencephalography (EEG). EEG data mining is one of the key topics which has received lots of attention. State-of-art machine learning (ML) methods are incompetent to dive deep and learn the EEG data pattern. Moreover, additional feature extraction methods are employed for feature extraction when a classical ML is in action which adds up additional computational burden. Lastly, the multi-class EEG data classification performance is not quite satisfactory. To overcome these issues, we propose a computerized framework for efficient and effective irregular brain signal data recognition network (IBDR-Net). Our suggested IBDR-Net is made up of four steps: (1) EEG data acquisition, (2) Pre-processing, (3) recognition of irregular brain signal data using IBDR-Net, and (4) Performance evaluation. IBDR-Net framework has been tested and trained with real human EEG dataset and this study has set a new milestone for the EEG researchers and boosted the multi-class performance. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Deep learning

来源：评论

学校读者我要写书评

暂无评论

Cross-Modality Image Quality Prediction for Time-Resolved CT from Breathing Signals

Cross-Modality Image Quality Prediction for Time-Resolved C...

引用

Workshop on Longitudinal Disease Tracking and Modeling with Medical Images and data, LDTM 2024, 5th international Workshop on Multiscale Multimodal Medical Imaging, MMMI 2024, 1st Workshop on machine learning for Multimodal/-sensor Healthcare data, ML4MHD2024 and Workshop on Multimodal learning and Fusion Across Scales for Clinical Decision Support, ML-CDS 2024 held in conjunction with the 27th international conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2024

作者： Schwarz, Annette Dickmann, Jannis Hofmann, Christian Szkitsak, Juliane Bert, Christoph Maier, Andreas Arias-Vergara, Tomás Pattern Recognition Lab Friedrich-Alexander-Universität Erlangen-Nürnberg Erlangen Germany Siemens Healthineers AG Forchheim Germany Department of Radiation Oncology Universitätsklinikum Erlangen Friedrich-Alexander-Universität Erlangen-Nürnberg Erlangen Germany Erlangen Germany

ISBN: (纸本)9783031845246

Four-dimensional computed tomography (4DCT) is a time-resolved, multi-modal imaging method that captures respiratory signals synchronised with the CT scan in order to track the movement of the lung. It is routinely used in radiation therapy treatment planning for lung or liver cancer patients. However, image artifacts during 4DCT scans caused by irregular patient breathing negatively impact treatment outcomes. this work proposes a method to automatically detect patients at high risk for severe image artifacts even before the scan is conducted based on an pre-scan analysis of their breathing. this can help to take proactive measures to improve image quality, such as changing the scan mode or providing in-depth patient coaching. A deep neural network is trained to predict the image quality score of 28 lung CT phantom scans, each rated by ten clinical experts. Different pretrained networks are investigated for feature generation and combined with two linear output heads to predict the average expert image quality score of unseen scans. We were able to predict the quality of a 4DCT with a mean absolute error of 8% using only the one-dimensional breathing signal as input. this accuracy is comparable to the rating consistency of our clinical experts, which were rating the images directly. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Radiotherapy

来源：评论

学校读者我要写书评

暂无评论

5th international conference on Inventive Computation Technologies, ICICT 2022 - Proceedings

5th International Conference on Inventive Computation Techno...

引用

5th international conference on Inventive Computation Technologies, ICICT 2022

ISBN: (纸本)9781665408370

the proceedings contain 243 papers. the topics discussed include: provably secure pseudonyms based authentication protocol for wearable ubiquitous computing environment;analysis of dc motor speed control and interfacing it with drowsiness detection;height and weight estimation of an individual from virtual visuals;projective synchronization of uncertain chaotic systems with different dimensions and external noise disturbance;design and implementation of a rescue and surveillance robot using cross-platform application;design of voice controlled multifunctional computer numerical control (CNC) machine;Bangla font recognition using transfer learning method;electroencephalography (EEG) based home automation for physically challenged people using brain computer interface (BCI);optimization of redirection algorithm based on cache technology in programming implementation of college students' mental health education information platform;a resistance detection device for microelectronic semiconductors: from signal detection to fusion analysis;and research on positioning accuracy of welding robot based on visual data mining and pattern fusion analysis algorithm.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Using Deep Transfer learning to Predict Student Engagement in Online Courses 4th

Using Deep Transfer Learning to Predict Student Engagement i...

引用

4th international conference on machine learning, Image Processing, Network Security and data Sciences (MIND)

作者： Ahmad, Naeem Gupta, Anirudh Singh, Deepak Natl Inst Technol Raipur Dept Comp Applicat CG Raipur Madhya Pradesh India Natl Inst Technol Raipur Dept Comp Sci & Engn CG Raipur Madhya Pradesh India

ISBN: (纸本)9783031243660;9783031243677

the use of online courses is growing worldwide that has opened the door for the interested students to learn comfortably from their locations especially, during Covid-19 pandemic. However, an important aspect of traditional classroom is real-time students' feedback for content delivery and interactive sessions, which is missing in online courses. the aim of this work is to bridge this gap by providing an automatic recognition system for engagement level of the students during online courses using deep transfer learning. In this paper, a CNN based method is proposed to predict the level of engagement while watching online class sessions. the CNN based method consists of two different modalities including: (1) pre-trained network based transfer learning for feature extraction from image data, (2) support vector machine (SVM) classifier for classification. Ten different pre-trained networks are used in the proposed method. the superiority of the method is evaluated on the dataset created using images of graduate students. Of all pre-trained networks, Resnet50 and VGG16 achieved highest classification accuracy of 72.34% and 71.77% using the proposed approach respectively.

关键词： Deep learning MOOCs Student engagement E-learning Deep transfer learning

来源：评论

学校读者我要写书评

暂无评论

Explore, Enhance and Reveal: A Dynamic Framework for Long-Tailed Classification

Explore, Enhance and Reveal: A Dynamic Framework for Long-Ta...

引用

pattern recognition and machine learning (PRML), IEEE international conference on

作者： Jiahui Lv Jiaxin Yang Jun Lei Chao Chen Hong Duan Shuohao Li Laboratory for Big Data and Decision National University of Defense Technology Changsha Hunan National University of Defense Technology Changsha Hunan

ISBN: (数字)9798350355925

ISBN: (纸本)9798350355932

In addressing the inherent challenge of imbalanced data distribution within long-tailed classification problems and the limitations of traditional classification models in fully extracting intricate information from input images, this paper introduces a pioneering approach: the Tail-Vigor Network (TVNet). Specifically tailored for long-tailed image classification tasks, TVNet aims to explore rare patterns, enhance features, and reveal hidden information. To achieve this, a novel multi-branch network is incorporated within the ResNet architecture to extract and integrate information from various hierarchical levels. Furthermore, this approach involves integrating primitive features from different hierarchical levels into an Adaptive-Context Module (ACM) for feature enhancement, which combines adaptive receptive fields with cross-scale contextual attention mechanisms. Additionally, a novel ReLU activation function is custom-designed to address challenges posed by long-tailed data distributions as it encompasses both linear response stages and nonlinear saturation stages. Empirical validation across multiple public datasets demonstrates that the proposed model yields noteworthy performance enhancements in the realm of long-tailed classification tasks, thus substantiating the efficacy of the proposed approach.

关键词： Training Technological innovation Heavily-tailed distribution Attention mechanisms Benchmark testing Feature extraction data models Complexity theory data mining Image classification

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335页 << < 80 81 82 83 84 85 86 87 88 89 > >>

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：