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

1,730 篇 会议
45 册 图书
11 篇 期刊文献

馆藏范围

1,785 篇 电子文献
1 种 纸本馆藏

日期分布

学科分类号

1,223 篇 工学
- 1,093 篇 计算机科学与技术...
- 628 篇 软件工程
- 254 篇 信息与通信工程
- 199 篇 电气工程
- 177 篇 生物工程
- 99 篇 控制科学与工程
- 98 篇 生物医学工程（可授...
- 60 篇 光学工程
- 46 篇 化学工程与技术
- 45 篇 电子科学与技术（可...
- 41 篇 机械工程
- 40 篇 仪器科学与技术
- 40 篇 安全科学与工程
- 29 篇 交通运输工程
- 28 篇 动力工程及工程热...
- 27 篇 材料科学与工程（可...
455 篇 理学
- 199 篇 数学
- 196 篇 生物学
- 144 篇 物理学
- 83 篇 统计学（可授理学、...
- 51 篇 系统科学
- 45 篇 化学
233 篇 管理学
- 167 篇 图书情报与档案管...
- 86 篇 管理科学与工程(可...
- 43 篇 工商管理
182 篇 医学
- 160 篇 临床医学
- 72 篇 基础医学(可授医学...
- 39 篇 药学(可授医学、理...
- 32 篇 公共卫生与预防医...
34 篇 法学
- 30 篇 社会学
17 篇 经济学
14 篇 教育学
11 篇 农学
2 篇 艺术学
1 篇 军事学

主题

239 篇 machine learning
184 篇 deep learning
140 篇 pattern recognit...
110 篇 feature extracti...
91 篇 support vector m...
88 篇 data mining
79 篇 accuracy
75 篇 training
59 篇 convolutional ne...
51 篇 machine learning...
48 篇 data models
46 篇 artificial intel...
41 篇 computational mo...
40 篇 neural networks
39 篇 face recognition
36 篇 learning systems
36 篇 adversarial mach...
33 篇 predictive model...
32 篇 classification
32 篇 image recognitio...

机构

6 篇 college of compu...
5 篇 xi'an university...
4 篇 badji mokhtar un...
4 篇 shanghai jiao to...
4 篇 institute of big...
4 篇 chitkara centre ...
4 篇 chitkara univers...
4 篇 library shanghai...
3 篇 school of comput...
3 篇 univ chittagong ...
3 篇 chongqing univer...
3 篇 centre of resear...
3 篇 school of comput...
3 篇 school of comput...
3 篇 school of design...
3 篇 korea univ dept ...
3 篇 school of automa...
3 篇 university of mi...
3 篇 shenyang jianzhu...
3 篇 xi'an university...

作者

5 篇 venkatesh svetha
4 篇 ashwini kodipall...
4 篇 zhang yan
4 篇 srinivasa gowri
4 篇 kamel mohamed s.
4 篇 lall brejesh
4 篇 trupthi rao
3 篇 andersson karl
3 篇 peng yang
3 篇 malerba donato
3 篇 karl andersson
3 篇 chaudhury santan...
3 篇 de-shuang huang
3 篇 sekhar c. chandr...
3 篇 wang shuo
3 篇 kamata sei-ichir...
3 篇 mohammad shahada...
3 篇 azizi nabiha
3 篇 phung dinh
3 篇 krasotkina olga

语言

1,587 篇 英文
190 篇 其他
26 篇 中文

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

共 1786 条记录，以下是521-530 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：

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

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

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

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

A Practical Approach to detect Indoor and Outdoor Scene recognition 4

A Practical Approach to detect Indoor and Outdoor Scene Reco...

引用

4th international conference on Information Management and machine Intelligence, ICIMMI 2022

作者： Sharma, Vaishali Nagpal, Nitesh Shandilya, Ankit Dureja, Aman Dureja, Ajay Fet Pdm University India Department of Information Technology Bhagwan Parshuram Institute of Technology Rohini India Department of Information Technology Bharati Vidyapeeth's College of Engineering A-4 Paschim Vihar New Delhi India

ISBN: (纸本)9781450399937

In computer vision recognition of scenes is a long-time research problem. Scene can be defined as the real-time environment view which consists of a lot of views (like road, tree, building, parks, etc.) in a meaningful manner. the problem of scene recognition can be explained as assessment of labels such as "road", "building", "hall", "bedroom"or in an extra simplified way "Indoor scene"and "Outdoor scenes is classified on an input image based on the object or environment of the image. A huge amount of data is created and available every second in this growing era of digital data. Scene recognition is still a rising area that did not attain much success as compared to image recognition due to the vast variability of features in the scenic environment. Because of this reason, there is not a lot of work being completed these days in this area. this project focuses on the evaluation of problem statements using all the literature surveys completed lately and offering solutions for that problem statement. For resolving the proposed problem declaration ResNet and VGG variants are used ResNet18, 50 and 152 & VGG16, 19, and VGG19 with batch normalization are implemented. the entire scene recognition procedure is discussed in this report and the main motive is to form a foundation that can be further continued in proposing a new algorithm. Before deep learning the design and implementation of the scene recognition model depended on the low dimensional portrayal of the scene. the utilization of deep learning especially CNN for scene recognition has gotten extraordinary attention from the computer vision community. © 2022 ACM.

关键词： Image recognition

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

4th international conference on Recent Trends in Image Processing and pattern recognition, RTIP2R 2021

4th International Conference on Recent Trends in Image Proce...

引用

4th international conference on Recent Trends in Image Processing and pattern recognition, RTIP2R 2021

ISBN: (纸本)9783031070044

the proceedings contain 33 papers presendted at a virtual meeting. the special focus in this conference is on Recent Trends in Image Processing and pattern recognition. the topics include: Real-Time Face recognition for Organisational Attendance Systems;Harnessing Sustainable Development in Image recognition through No-Code AI Applications: A Comparative Analysis;evaluating Performance of Adam Optimization by Proposing Energy Index;an Alignment-Free Fingerprint Template Protection Technique Based on Minutiae Triplets;early Prediction of Complex Business Processes Using Association Rule Based Mining;A Framework for Masked-Image recognition System in COVID-19 Era;A Deep-learning Based Automated COVID-19 Physical Distance Measurement System Using Surveillance Video;Detection of Male Fertility Using AI-Driven Tools;face Mask Detection Using Deep Hybrid Network Architectures;a Super Feature Transform for Small-Size Image Forgery Detection;UHTelHwCC: A dataset for Telugu Off-line Handwritten Character recognition;inflectional and Derivational Hybrid Stemmer for Sentiment Analysis: A Case Study with Marathi Tweets;adaptive threshold-Based database Preparation Method for Handwritten Image Classification;a Graph-Based Holistic recognition of Handwritten Devanagari Words: An Approach Based on Spectral Graph Embedding;Imagined Object recognition Using EEG-Based Neurological Brain Signals;a Fast and Efficient K-Nearest Neighbor Classifier Using a Convex Envelope;single Channel Speech Enhancement Using Masking Based on Sinusoidal Modeling;extraction of Temporal Features on Fibonacci Space for Audio Based Vehicle Classification;an Empirical Study of Vision Transformers for Cervical Precancer Detection;An Improved Technique for Preliminary Diagnosis of COVID-19 via Cough Audio Analysis;agricultural Field Analysis Using Satellite Hyperspectral data and Autoencoder;Development of NDVI Prediction Model Using Artificial Neural Networks;time Series Forecasting of Soil Moisture Using Sa

关键词：

来源：评论

学校读者我要写书评

暂无评论

引用

Ubiquitous Computing and Intelligent Information Systems (ICUIS), international conference on

作者： Dhruvi Srishwal Shibu PS Harsha Vardan Sajjad Ibrahim. A Gouthaman. P Department Of Networking And Communications School of Computing College of Engineering and Technology SRM Institute of Science and Technology Kattankulathur India Department Of Networking And Communications Faculty of Engineering and Technology and Technology SRM Institute of Science and Technology Kattankulathur India

ISBN: (数字)9798331529635

ISBN: (纸本)9798331529642

Heart arrhythmias, if not detected early, can lead to severe conditions like stroke or heart failure, making timely and accurate classification critical. Traditional methods of analyzing Electrocardiogram (ECG) data are manual, time-consuming, and prone to errors due to the complexity of ECG signals. this paper presents an automated approach for arrhythmia classification using vector databases and similarity search algorithms. By converting raw ECG time-series data into vectorized representations, the system enables efficient pattern recognition and real-time analysis. machine learning models applied to the vectorized data enhance classification accuracy, while the use of a vector database allows for quick similarity searches, facilitating real-time comparison of patient data with known arrhythmia patterns. the proposed method significantly reduces analysis time and increases precision, offering a robust framework for real-time monitoring and early detection, thereby improving patient outcomes and enabling timely medical interventions.

关键词： Heart Accuracy databases Arrhythmia Electrocardiography Ubiquitous computing Vectors Real-time systems pattern recognition Monitoring

来源：评论

学校读者我要写书评

暂无评论

Motivating students by visualizing the learning process in college english classes 4

Motivating students by visualizing the learning process in c...

引用

4th international conference on pattern recognition and Artificial Intelligence, PRAI 2021

作者： Li, Ming Zhengzhou Sias University Zhengzhou Kansas International School China

ISBN: (纸本)9781665413220

this study examined how the AI platform influenced students' motivation in college English classes. the participants were junior English majors in a university in central China. the students belonged to two groups, one for the control and the other for the treatment (N=210). the key strategies mainly included the visualization of the class activities, such as class attendance, group summary of the given passage, peer-instruction and peer assessment. Independent samples t-tests were used for data analysis. the results showed that classes incorporating visualizing strategies on the AI platform demonstrated more interest, sense of success and sense of power in their learning process. these results were significant for the instructional design in college English if the professors want to motivate their students in their learning. Implications and limitations were discussed. © 2021 IEEE.

关键词： Students

来源：评论

学校读者我要写书评

暂无评论

Speaker Identification Using Deep learning Models for Enhanced Voice Biometrics

Speaker Identification Using Deep Learning Models for Enhanc...

引用

Sentiment Analysis and Deep learning (ICSADL), international conference on

作者： Akhilesh Mamatha Balipa Anmol S Shetty Department of MCA Nitte Deemed to be University NMAM Institute of Technology Nitte

ISBN: (数字)9798331523923

ISBN: (纸本)9798331523930

the goal of this project is to create a sophisticated voice recognition system that accurately recognizes and categorizes spoken data by applying machine learning and audio processing techniques. It uses a deep learning model (model.h5) for high-accuracy audio input categorization and incorporates tools like Librosa for comprehensive audio feature extraction (such as Mel-frequency cepstral coefficients, MFCCs, and spectral contrast). data preprocessing, which includes gathering unprocessed audio data and using methods like feature extraction, noise reduction, and conversion to machine-readable forms, is the first step in the implementation process. through feature extraction, important aspects of the voice are recorded, allowing the system to recognize distinct voice signatures. A Python-based framework (***) that provides both batch and real-time processing options is used to deploy the trained model. the complete method, including model training, validation, and assessment metrics, is documented in detail in a companion Jupyter Notebook. Because predictions are produced in an organized JSON format, integrating them into bigger systems is simple. Speaker verification, tailored user experiences, and biometric authentication are some of the system's primary uses. It may be scaled to accommodate both enterprise and consumer-level service requirements. the study demonstrates how artificial intelligence may revolutionize voice-based identification and presents opportunities for additional enhancements, including support for other languages, environmental adaptability, and integration with IoT devices.

关键词： Deep learning Training Accuracy Biological system modeling Speech recognition Predictive models Feature extraction data models Speaker recognition Security

来源：评论

学校读者我要写书评

暂无评论

An Optimized Transfer Attack Framework Towards Multi-Modal machine learning 4

An Optimized Transfer Attack Framework Towards Multi-Modal M...

引用

4th international conference on data-Driven Optimization of Complex Systems, DOCS 2022

作者： Zhang, Yinjie Xiao, Geyang Bai, Bin Wang, Zhiyu Sun, Caijun Tu, Yonggang Intelligent Network Research Institute Zhejiang Laboratory Hangzhou China

ISBN: (数字)9781665459822

ISBN: (纸本)9781665459822

Deep neural networks (DNNs) have excelled at a wide range of tasks, including computer vision (CV), natural language processing (NLP), and speech recognition. However, past research has demonstrated that DNNs are vulnerable to adversarial examples, which are deliberately meant to trick models into making incorrect predictions by adding subtle perturbations into inputs. Adversarial examples create an exponential threat to multi-modal models that can accept a variety of inputs. By attacking substitute models, we provide a transferable attack framework. the suggested framework optimizes the attack process by modifying the prompt templates and simultaneously raising the attack on multiple inputs. Our experiments demonstrate that the proposed attack framework can significantly improve the success rate of transferable attacks, and adversarial examples are rarely noticed by humans. Meanwhile, experiments show that in transferable attacks, coarse-grained adversarial examples can achieve higher attack success rates than fine-grained ones, and the multi-modal models has some robustness against uni-modal attacks. © 2022 IEEE.

关键词： Deep neural networks

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179页 << < 49 50 51 52 53 54 55 56 57 58 > >>

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：