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

13,377 篇 会议
17 篇 期刊文献
16 册 图书

馆藏范围

13,410 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

7,105 篇 工学
- 6,398 篇 计算机科学与技术...
- 5,095 篇 软件工程
- 1,730 篇 信息与通信工程
- 1,008 篇 控制科学与工程
- 735 篇 生物工程
- 721 篇 电气工程
- 667 篇 生物医学工程（可授...
- 582 篇 网络空间安全
- 511 篇 光学工程
- 451 篇 安全科学与工程
- 398 篇 电子科学与技术（可...
- 344 篇 交通运输工程
- 328 篇 动力工程及工程热...
- 288 篇 机械工程
- 267 篇 仪器科学与技术
2,667 篇 理学
- 1,298 篇 数学
- 879 篇 生物学
- 637 篇 统计学（可授理学、...
- 535 篇 物理学
- 328 篇 系统科学
- 242 篇 化学
1,877 篇 管理学
- 1,300 篇 管理科学与工程(可...
- 789 篇 图书情报与档案管...
- 573 篇 工商管理
947 篇 医学
- 687 篇 临床医学
- 681 篇 基础医学(可授医学...
- 659 篇 公共卫生与预防医...
- 264 篇 药学(可授医学、理...
508 篇 法学
- 471 篇 社会学
262 篇 经济学
- 262 篇 应用经济学
223 篇 农学
210 篇 教育学
198 篇 文学
9 篇 军事学
1 篇 艺术学

主题

2,085 篇 accuracy
1,690 篇 deep learning
1,200 篇 machine learning
1,188 篇 computational mo...
1,044 篇 training
1,026 篇 data models
968 篇 predictive model...
724 篇 real-time system...
687 篇 convolutional ne...
684 篇 feature extracti...
676 篇 machine learning...
585 篇 support vector m...
531 篇 federated learni...
484 篇 adversarial mach...
330 篇 contrastive lear...
311 篇 medical services
295 篇 random forests
289 篇 analytical model...
278 篇 prediction algor...
271 篇 neural networks

机构

105 篇 lovely professio...
101 篇 chitkara univers...
57 篇 department of co...
47 篇 school of comput...
45 篇 school of comput...
45 篇 department of co...
42 篇 the islamic univ...
42 篇 department of co...
40 篇 shandong provinc...
36 篇 college of compu...
32 篇 university of ch...
28 篇 department of co...
27 篇 department of co...
26 篇 computer science...
26 篇 graphic era deem...
25 篇 department of co...
25 篇 amrita vishwa vi...
25 篇 the islamic univ...
23 篇 department of co...
23 篇 college of intel...

作者

23 篇 satvik vats
18 篇 shiva mehta
18 篇 kukreja vinay
18 篇 vats satvik
17 篇 anjali t.
17 篇 rishabh sharma
17 篇 deepak upadhyay
16 篇 senthil kumar th...
15 篇 singh jagendra
15 篇 venugopalan manj...
13 篇 manjunatha
13 篇 rakesh kumar
12 篇 ramya maranan
11 篇 selvanayaki kola...
11 篇 s. kaliappan
10 篇 andersson karl
10 篇 muthukumaravel a...
10 篇 yang yang
10 篇 ayodeji olalekan...
10 篇 surendran r.

语言

12,140 篇 英文
1,269 篇 其他
306 篇 中文
1 篇 西班牙文

检索条件"任意字段=2024 International Conference on Computing, Machine Learning and Data Science, CMLDS 2024"

共 13410 条记录，以下是171-180 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：

Advanced machine learning and Features Based data Analytics of Electricity Consumption in USA and India 4th

Advanced Machine Learning and Features Based Data Analytics ...

引用

4th international conference on Advanced Network Technologies and Intelligent computing-ANTIC-Annual

作者： Shukla, Adwitiya Univ Toledo Dept Elect Engn & Comp Sci Toledo OH 43606 USA

ISBN: (纸本)9783031837951;9783031837968

This study presents a comprehensive comparative analysis of electricity consumption patterns of USA and India, using a variety of basic and advanced machine learning (ML) techniques. ML can accurately predict and forecast the energy demand, allowing for more efficient management of energy grids. Herein, we utilized multiple basic and advanced ML models, including XGBoost, Convolutional Neural Networks, Long Short-Term Memory (LSTM) networks, Transformer models, and Reinforcement learning (RL) for electricity demand modeling. All ML models were evaluated for accuracy of electricity consumption prediction over short-term and long-term periods. Additional datasets of climate, economic indicators, and renewable energy metrics were also incorporated to enhance the prediction accuracy. Results show that the Transformer models outperformed all other models, due to their ability to accurately capture long-range temporal dependencies for both countries. LSTM networks also demonstrated robust performance, particularly for India, where frequent electricity demand fluctuations are more prevalent. XGBoost performed well for short-term predictions, owing to its capability to handle non-linear relationships between the drivers. Furthermore, RL provided an innovative solution for electricity usage planning and management. This paper presents key insights for energy policymakers and companies on improving grid efficiency, integrating renewable energy sources, future planning and pricing policies.

关键词： machine learning Electricity consumption Predictive modeling Features

来源：评论

学校读者我要写书评

暂无评论

A Survey on Deep learning-Based Medical Image Registration 5th

A Survey on Deep Learning-Based Medical Image Registration

引用

5th international conference on Neural computing for Advanced Applications (NCAA)

作者： Xu, Ronghao Liu, Chongxin Liu, Shuaitong Huang, Weijie Zhang, Menghua Univ Jinan Sch Elect Engn Jinan 250000 Peoples R China

ISBN: (纸本)9789819770007;9789819770014

In recent years, various methods have been proposed to address the fundamental task of medical image registration in medical image analysis. This paper systematically reviews the research progress in medical image registration using deep learning techniques. These methods utilize deep learning algorithms to achieve precise alignment of medical image data from different modalities, time points, or sources, thereby providing dependable help for clinical identification and care. This paper seeks to provide researchers and practitioners with the most current technological advances and potential applications in the realm of medical image registration.

关键词： Image registration Deep learning CNN Supervised learning

来源：评论

学校读者我要写书评

暂无评论

Seal-Tools: Self-instruct Tool learning dataset for Agent Tuning and Detailed Benchmark 13th

Seal-Tools: Self-instruct Tool Learning Dataset for Agent Tu...

引用

13th international conference on Natural Language Processing and Chinese computing

作者： Wu, Mengsong Zhu, Tong Han, Han Tan, Chuanyuan Zhang, Xiang Chen, Wenliang Soochow Univ Shizi St 1 Suzhou 215006 Peoples R China

ISBN: (纸本)9789819794331;9789819794348

This paper presents a new tool learning dataset Seal-Tools, which contains self-instruct API-like tools. Seal-Tools not only offers a large number of tools, but also includes instances which demonstrate the practical application of tools. Seeking to generate data on a large scale while ensuring reliability, we propose a self-instruct method to generate tools and instances, allowing precise control over the process. Moreover, our Seal-Tools contains hard instances that call multiple tools to complete the job, among which some are nested tool callings. For precise and comprehensive evaluation, we use strict format control and design three metrics from different dimensions. Therefore, Seal-Tools can serve as a new benchmark to evaluate the tool-calling ability of LLMs. Finally, we evaluate several prevalent LLMs and our finetuned model on Seal-Tools. The results show that current systems are far from perfect. The code, data and experiment results are available at https://***/fairyshine/Seal-Tools.

关键词： Tool learning Agent Self-Instruct

来源：评论

学校读者我要写书评

暂无评论

Efficient acne classification based on Resnet18 3

Efficient acne classification based on Resnet18

引用

3rd international conference on Algorithms, Network, and Communication Technology, ICANCT 2024

作者： Cheng, Shi Sun, Mingze Huang, Zhongyi Wang, Yushan Fu, Jiaxuan School of Engineering University of Manchester ManchesterM13 9PL United Kingdom School of Computing Science University of Glasgow GlasgowG12 8RZ United Kingdom School of Data Science and Big Data Nanjing Institute of Technology Nanjing210000 China The Affiliated High School Renmin University of China Beijing100083 China International Department Nanjing Jinling High School Nanjing21000 China

ISBN: (数字)9781510688902

ISBN: (纸本)9781510688896

In this study, we investigated the mechanical performance of lightweight composite slabs, specifically focusing on the application of an integrated face recognition and acne classification pipeline. We propose a comprehensive approach that commences with facial detection, proceeds to the acne region localization, and concludes with acne classification utilizing ResNet18 architecture. The aim is to develop a highly accurate and efficient system for acne classification, particularly beneficial for remote patients or those seeking a convenient self-assessment tool. Our dataset, though limited in size and diversity, is iteratively refined and augmented to mitigate data scarcity issues. Through experimental evaluations, we demonstrate that the combination of ResNet18 and Support Vector machine (SVM) achieves an accuracy rate of 96%, significantly outperforming traditional manual feature extraction methods. Additionally, visualization techniques such as Principal Component Analysis (PCA) and t-Distributed Stochastic Neighbor Embedding (t-SNE) are employed to gain insights into the data distribution and the effectiveness of automatically extracted features. This study highlights the potential of deep learning for acne detection and underscores the importance of dataset refinement for enhancing model performance. © 2025 SPIE.

关键词： Support vector machines

来源：评论

学校读者我要写书评

暂无评论

Enhancing Performance of machine learning Models in Healthcare: An Analytical Framework for Assessing and Improving data Quality 5th

Enhancing Performance of Machine Learning Models in Healthca...

引用

5th international conference on Advances in Information Communication Technology and computing, AICTC 2024

作者： Al-Hgaish, Areen Atan, Rodziah Yaakob, Razali Osman, Mohd Hafeez Department of Software Engineering and Information Systems Faculty of Computer Science and Information Technology Universiti Putra Malaysia Serdang44330 Malaysia Department of Computer Science Faculty of Computer Science and Information Technology Universiti Putra Malaysia Serdang44330 Malaysia

ISBN: (纸本)9789819761050

The utilization of data-Driven machine learning (DDML) models in the healthcare sector poses unique challenges due to the crucial nature of clinical decision-making and its impact on patient outcomes. A primary concern in this domain is the data quality that forms the basis for these models, which is essential for their effectiveness. While numerous studies are dedicated to leveraging machine learning (ML) to enhance patient care, a substantial gap exists in understanding and evaluating the quality of training data before training ML models. This issue is exacerbated by the lack of well-defined standards to ensure that data characteristics align with the specific goals of ML in healthcare. To address this, our study introduces a conceptual three-dimensional framework focusing on data accuracy, completeness, and consistency aimed at assessing and improving the quality of training data. This framework is designed to enhance the performance, predictability, and interpretability of healthcare-specific ML models and reduce biases, thus bridging current research gaps and laying the groundwork for future developments in ML-driven healthcare systems. This paper presents the initial step for our proposed data Quality Framework (DQF), which addresses the three dimensions mentioned above. It discusses its potential implications and applications in healthcare data, and its main goal is to provide a comprehensive overview of the proposed DQF, illustrating its significance in advancing healthcare data quality and ultimately improving patient outcomes. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： data accuracy

来源：评论

学校读者我要写书评

暂无评论

A novel stable feature selection algorithm for machine learning based intrusion detection system 4

A novel stable feature selection algorithm for machine learn...

引用

4th international conference on Evolutionary computing and Mobile Sustainable Networks, ICECMSN 2024

作者： Sowmya, T. Mary Anita, E.A. Department of Computer Science and Engineering Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Karnataka Manipal576104 India Bangalore India Karnataka Bengaluru560074 India

The advent of new technologies like artificial intelligence, and big data has influenced many cyber attackers to launch their attacks on the network. Hence researchers have already proposed Intrusion Detection Systems by incorporating machine learning as well. Building an effective IDS is still a challenging task because of low accuracy. Managing high dimensional data is another major problem that occurs in IDS. Hence in this paper, an efficient machine learning based Intrusion Detection System is developed by means of a novel stable feature selection strategy called IV-RFE. The proposed methodology aims to select only the relevant features that contribute to the attack, which is purely based on relative variance, and weight factor in combination with RFE. This methodology increases the performance in terms of accuracy and maintains a stable set of features. Previous studies only focussed on the feature selection strategy and their performance. The feature stability also has to be considered which is an equally important metric, especially in the field of Intrusion Detection Systems. Hence in the current study, an efficient ML based IDS is proposed which selects only the relevant and stable features. Experimental results also revealed that the proposed IV-RFE outperformed well for three attacks with respect to accuracy and stability metrics also. The results show that stability is also an important indicator in selecting the features in the field of Intrusion Detection Systems. © 2025 The Authors. Published by Elsevier B.V.

关键词： Network intrusion

来源：评论

学校读者我要写书评

暂无评论

Microservices Architecture to Improve the Performance of machine learning Applications in eHealth 10th

Microservices Architecture to Improve the Performance of Mac...

引用

10th international conference on Technologies and Innovation

作者： Calderon-Gomez, Huriviades Mendoza-Pitti, Luis Gomez-Pulido, Jose Manuel Vargas-Lombardo, Miguel Univ Tecnolog Panama E Hlth & Supercomputing Res Grp GISES Panama City 081907289 Panama Univ Alcala Dept Comp Sci Hlth Comp & Intelligent Syst Res Grp HCIS Alcala De Henares 28805 Spain Ramon & Cajal Inst Hlth Res IRYCIS Renal & Vasc Physiol & Pathophysiol Res Grp Colmenar Viejo Spain

ISBN: (纸本)9783031757013;9783031757020

The objective of this paper is to propose an intergenerational software architecture, designated "mu e-Health 4.0," for the integration and interconnection of traditional eHealth applications utilized within medical institutions. These applications are commonly developed in accordance with the service-oriented architecture architectural style. Furthermore, in conjunction with modern applications, the microservice architecture style has been adapted to accommodate multiple machine learning-based inference systems, which are fed by a large database that integrates diverse and heterogeneous dynamic data sources. Proper standardization of these data sources is achieved using metadata. In contrast to other proposals, our approach prioritizes the replicability, scalability, and interoperability of the specialized services that comprise this architecture, leveraging our versatile, high-performance software features. To demonstrate the feasibility of mu eHealth 4.0, it was necessary to adapt this proposal to a real eHealth use case, specifically to the prediction of the occurrence of intradialytic hypotension during a hemodialysis session. These results were validated by quantitative values obtained from rigorous tests, such as response time, efficient use of the infrastructure, and network consumption. In conclusion, it can be posited that mu eHealth 4.0 has the potential to create an eHealth ecosystem in conjunction with the integration of multiple machine learning-based inference systems, such as logistic regression, random forest, or XGBoost, to perform specialized decision support tasks in the prevention, monitoring, and treatment of hypotension prediction in hemodialysis. It should be noted that mu eHealth 4.0 was developed with the intention of being adapted to other eHealth domains, including diabetes and colon cancer.

关键词： Cloud-native computing eHealth Intergenerational architecture software Interoperability machine learning-based inference system Microservices architecture Versatile high-performance

来源：评论

学校读者我要写书评

暂无评论

3rd international Workshop on Cancer Prevention, detection and intervenTion, CaPTion 2024, held in Conjunction with 27th international conference on Medical Image computing and Computer Assisted Intervention, MICCAI 2024

3rd International Workshop on Cancer Prevention, detection a...

引用

3rd international Workshop on Cancer Prevention, detection and intervenTion, CaPTion 2024, held in Conjunction with 27th international conference on Medical Image computing and Computer Assisted Intervention, MICCAI 2024

ISBN: (纸本)9783031733758

The proceedings contain 22 papers. The special focus in this conference is on Cancer Prevention, detection and intervenTion. The topics include: Classification of Endoscopy and Video Capsule Images Using CNN-Transformer Model;multimodal Deep learning-Based Prediction of Immune Checkpoint Inhibitor Efficacy in Brain Metastases;seeing More with Less: Meta-learning and Diffusion Models for Tumor Characterization in Low-data Settings;performance Evaluation of Deep learning and Transformer Models Using Multimodal data for Breast Cancer Classification;on Undesired Emergent Behaviors in Compound Prostate Cancer Detection Systems;optimizing Multi-expert Consensus for Classification and Precise Localization of Barrett’s Neoplasia;Automated Hepatocellular Carcinoma Analysis in Multi-phase CT with Deep learning;Refining Deep learning Segmentation Maps with a Local Thresholding Approach: Application to Liver Surface Nodularity Quantification in CT;Uncertainty-Aware Deep learning Classification for MRI-Based Prostate Cancer Detection;Generalized Polyp Detection from Colonoscopy Frames Using Proposed EDF-YOLO8 Network;AI-Assisted Laryngeal Examination System;UltraWeak: Enhancing Breast Ultrasound Cancer Detection with Deformable DETR and Weak Supervision;SelectiveKD: A Semi-supervised Framework for Cancer Detection in DBT Through Knowledge Distillation and Pseudo-labeling;AI Age Discrepancy: A Novel Parameter for Frailty Assessment in Kidney Tumor Patients;deep Neural Networks for Predicting Recurrence and Survival in Patients with Esophageal Cancer After Surgery;treatment Efficacy Prediction of Focused Ultrasound Therapies Using Multi-parametric Magnetic Resonance Imaging;SurRecNet: A Multi-task Model with Integrating MRI and Diagnostic Descriptions for Rectal Cancer Survival Analysis;improved Prediction of Recurrence After Prostate Cancer Radiotherapy Using Multimodal data and in Silico simulations;autoDoseRank: Automated Dosimetry-Informed Segmentation Ranking for Radiotherapy

关键词：

来源：评论

学校读者我要写书评

暂无评论

Secure Resource Allocation via Constrained Deep Reinforcement learning 6th

Secure Resource Allocation via Constrained Deep Reinforceme...

引用

6th international conference on machine learning for Cyber Security, ML4CS 2024

作者： Sun, Jianfei Gao, Qiang Wu, Cong Li, Yuxian Wang, Jiacheng Niyato, Dusit School of Computing and Information Systems Singapore Management University Singapore188065 Singapore School of Computing and Artificial Intelligence Southwestern University of Finance and Economics Chengdu611130 China College of Computing and Data Science Nanyang Technological University Singapore639798 Singapore

ISBN: (纸本)9789819645657

The proliferation of Internet of Things (IoT) devices and the advent of 6G technologies have introduced computationally intensive tasks that often surpass the processing capabilities of user devices. Efficient and secure resource allocation in serverless multi-cloud edge computing environments is essential for supporting these demands and advancing distributed computing. However, existing solutions frequently struggle with the complexity of multi-cloud infrastructures, robust security integration, and effective application of traditional deep reinforcement learning (DRL) techniques under system constraints. To address these challenges, we present SARMTO, a novel framework that integrates an action-constrained DRL model. SARMTO dynamically balances resource allocation, task offloading, security, and performance by utilizing a Markov decision process formulation, an adaptive security mechanism, and sophisticated optimization techniques. Extensive simulations across varying scenarios-including different task loads, data sizes, and MEC capacities-show that SARMTO consistently outperforms five baseline approaches, achieving up to a 40% reduction in system costs and a 41.5% improvement in energy efficiency over state-of-the-art methods. These enhancements highlight SARMTO ’s potential to revolutionize resource management in intricate distributed computing environments, opening the door to more efficient and secure IoT and edge computing applications. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Deep reinforcement learning

来源：评论

学校读者我要写书评

暂无评论

2nd international Workshop on Fairness of AI in Medical Imaging, FAIMI 2024, and 3rd international Workshop on Ethical and Philosophical Issues in Medical Imaging, EPIMI 2024, Held in Conjunction with the international conference on Medical Image computing and Computer Assisted Interventions, MICCAI 2024

2nd International Workshop on Fairness of AI in Medical Imag...

引用

2nd international Workshop on Fairness of AI in Medical Imaging, FAIMI 2024, and 3rd international Workshop on Ethical and Philosophical Issues in Medical Imaging, EPIMI 2024, Held in Conjunction with the international conference on Medical Image computing and Computer Assisted Interventions, MICCAI 2024

ISBN: (纸本)9783031727863

The proceedings contain 17 papers. The special focus in this conference is on Fairness of AI in Medical Imaging. The topics include: dataset Distribution Impacts Model Fairness: Single Vs. Multi-task learning;AI Fairness in Medical Imaging: Controlling for Disease Severity;Fair and Private CT Contrast Agent Detection;Mitigating Overdiagnosis Bias in CNN-Based Alzheimer’s Disease Diagnosis for the Elderly;Positive-Sum Fairness: Leveraging Demographic Attributes to Achieve Fair AI Outcomes Without Sacrificing Group Gains;all You Need Is a Guiding Hand: Mitigating Shortcut Bias in Deep learning Models for Medical Imaging;exploring Fairness in State-of-the-Art Pulmonary Nodule Detection Algorithms;quantifying the Impact of Population Shift Across Age and Sex for Abdominal Organ Segmentation;BMFT: Achieving Fairness via Bias-Based Weight Masking Fine-Tuning;using Backbone Foundation Model for Evaluating Fairness in Chest Radiography Without Demographic data;do Sites Benefit Equally from Distributed learning in Medical Image Analysis?;Cycle-GANs Generated Difference Maps to Interpret Race Prediction from Medical Images;On Biases in a UK Biobank-Based Retinal Image Classification Model;investigating Gender Bias in Lymph-Node Segmentation with Anatomical Priors;Assessing the Impact of Sociotechnical Harms in AI-Based Medical Image Analysis;Practical and Ethical Considerations for Generative AI in Medical Imaging.

关键词：

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500页 << < 14 15 16 17 18 19 20 21 22 23 > >>

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：