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

3,767 篇 会议
2,381 篇 期刊文献
50 册 图书

馆藏范围

6,198 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

3,641 篇 工学
- 2,719 篇 计算机科学与技术...
- 2,202 篇 软件工程
- 867 篇 信息与通信工程
- 743 篇 控制科学与工程
- 536 篇 生物工程
- 485 篇 电气工程
- 475 篇 生物医学工程（可授...
- 355 篇 光学工程
- 298 篇 电子科学与技术（可...
- 288 篇 网络空间安全
- 264 篇 机械工程
- 236 篇 仪器科学与技术
- 221 篇 安全科学与工程
- 195 篇 化学工程与技术
- 186 篇 交通运输工程
- 180 篇 动力工程及工程热...
2,001 篇 理学
- 900 篇 数学
- 632 篇 生物学
- 546 篇 物理学
- 367 篇 统计学（可授理学、...
- 296 篇 化学
- 161 篇 系统科学
1,166 篇 管理学
- 768 篇 管理科学与工程(可...
- 455 篇 图书情报与档案管...
- 328 篇 工商管理
597 篇 医学
- 479 篇 临床医学
- 401 篇 基础医学(可授医学...
- 293 篇 公共卫生与预防医...
- 219 篇 药学(可授医学、理...
217 篇 法学
- 193 篇 社会学
147 篇 农学
145 篇 经济学
59 篇 教育学
27 篇 文学
10 篇 军事学
5 篇 艺术学

主题

459 篇 accuracy
385 篇 deep learning
253 篇 machine learning
240 篇 real-time system...
215 篇 training
214 篇 feature extracti...
214 篇 convolutional ne...
179 篇 artificial intel...
169 篇 computational mo...
164 篇 predictive model...
151 篇 support vector m...
128 篇 data models
127 篇 optimization
123 篇 internet of thin...
119 篇 machine learning...
100 篇 medical services
92 篇 reliability
92 篇 security
89 篇 prediction algor...
89 篇 technological in...

机构

107 篇 faculty of infor...
81 篇 yunnan key labor...
67 篇 maharishi school...
65 篇 department of ar...
59 篇 vishwakarma inst...
58 篇 school of artifi...
53 篇 centre of interd...
29 篇 college of compu...
29 篇 university of ch...
28 篇 state key labora...
27 篇 school of data s...
26 篇 department of el...
25 篇 department of ar...
24 篇 school of artifi...
24 篇 shenzhen institu...
23 篇 centre of resear...
23 篇 department of co...
22 篇 chitkara centre ...
22 篇 national enginee...
21 篇 university of sc...

作者

41 篇 tao dacheng
35 篇 prateek verma
28 篇 el-sayed m.el-ke...
26 篇 ashwini kodipall...
23 篇 seifedine kadry
20 篇 mahalle parikshi...
19 篇 shen linlin
19 篇 verma prateek
18 篇 ding weiping
17 篇 moni mohammad al...
17 篇 wang meng
16 篇 chang xiaojun
16 篇 shanmugasundaram...
16 篇 lin chin-teng
15 篇 juanjuan li
15 篇 fei-yue wang
15 篇 kodipalli ashwin...
14 篇 bin wang
14 篇 mohanad a. deif
14 篇 vimal s.

语言

4,093 篇 英文
2,055 篇 其他
74 篇 中文
4 篇 德文
4 篇 法文
3 篇 日文
2 篇 斯洛文尼亚文

检索条件"机构=Artificial Intelligence and Data science Faculty of Engineering and Technology"

共 6198 条记录，以下是691-700 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：

Vehicle Prediction for BUS Identification Output from our Route Testing Real Time Algorithm 5

Vehicle Prediction for BUS Identification Output from our Ro...

引用

5th International Conference on Sustainable Communication Networks and Application, ICSCNA 2024

作者： Misba, M. Ramya, R. Dickson, Joel Sharmila, L. Kavitha, J. Udayakumar, K. Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of Artificial Intelligence and Machine Learning Tamil Nadu India St. Joseph's College of Engineering Department of Artificial Intelligence and Data Science Chennai India Bethlehem Institute of Engineering Tamil Nadu Karungal India Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu India

ISBN: (纸本)9798331530013

Traffic generation in Indian cities is of a mixed nature, comprising over a dozen types of vehicles that can be broadly categorized into slow-moving and fast-moving vehicles. This study highlights the challenges associated with the inefficient operation of the existing bus system. GPS and mobile networks form integral components of the current system, enabling data storage and management using PostgreSQL. The system provides robust and reliable data to fulfill requests from other software applications. Operating on cloud platforms, it is compatible with all major web browsers. The proposed algorithm continuously tracks the real-time location of buses and their corresponding WMS (Web Map Service) location, alerting users through marker notifications. This paper also explores measures to evaluate the efficiency and effectiveness of bus services. Recognizing that an inadequate public transport system impedes economic growth and development, this study emphasizes the need to enhance the overall quality and accessibility of public transportation. The findings aim to improve the operational efficiency of bus services, contributing to a more effective and sustainable transport system. © 2024 IEEE.

关键词： Buses

来源：评论

学校读者我要写书评

暂无评论

Real-Time Sign Language Recognition System for Physically Challenged Community Using Deep Learning Technique 8th

Real-Time Sign Language Recognition System for Physically Ch...

引用

8th International Conference on Microelectronics Electromagnetics and Telecommunications, ICMEET 2023

作者： Devi, V. Anjana Bhuvaneswari, E. Tummala, Rama Krishna Professor Department of Computer Science and Engineering Rajalakshmi Institute of Technology Tamil Nadu Chennai India Associate Professor Department of Artificial Intelligence and Data Science Panimalar Engineering College Tamil Nadu Chennai India Accenture Solutions Pvt. Ltd Chennai India

ISBN: (纸本)9789819784219

Language disparities have always existed in human civilization. In their community, deaf individuals use sign languages to communicate with one another, but it can be difficult to do so when interacting with the wider population. The project comprises a real-time sign language recognition system that enables hearing-impaired individuals and persons who do not know sign language to effortlessly communicate. The most used sign language in South Asian nations is Indian sign language. Convolution neural networks, specifically Tensor Flow object detection, are utilized to assess and train the model on the images that are used. The transfer training approach is used to train the deep learning model with various hand movements. Loss is anticipated to be decreased. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

Risk Estimation in a Markov Cost Process: Lower and Upper Bounds 41

Risk Estimation in a Markov Cost Process: Lower and Upper Bo...

引用

41st International Conference on Machine Learning, ICML 2024

作者： Thoppe, Gugan Prashanth, L.A. Bhat, Sanjay P. Bengaluru India Robert Bosch Centre for Data Science and Artificial Intelligence IIT Madras Chennai India Dept. of Computer Science and Engineering Indian Institute of Technology Madras Chennai India TCS Research Hyderabad India

We tackle the problem of estimating risk measures of the infinite-horizon discounted cost of a Markov cost process. The risk measures we study include variance, Value-at-Risk (VaR), and Conditional Value-at-Risk (CVaR). First, we show that estimating any of these risk measures with ϵ-accuracy, either in expected or high-probability sense, requires at least Ω(1/ϵ2) samples. Then, using a truncation scheme, we derive an upper bound for the CVaR and variance estimation. This bound matches our lower bound up to logarithmic factors. Finally, we discuss an extension of our estimation scheme that covers more general risk measures satisfying a certain continuity criterion, such as spectral risk measures and utility-based shortfall risk. To the best of our knowledge, our work is the first to provide lower and upper bounds for estimating any risk measure beyond the mean within a Markovian setting. Our lower bounds also extend to the infinite-horizon discounted costs’ mean. Even in that case, our lower bound of Ω(1/ϵ2) improves upon the existing Ω(1/ϵ) bound (Metelli et al., 2023). Copyright 2024 by the author(s)

关键词： Cost estimating

来源：评论

学校读者我要写书评

暂无评论

Patch Target Guided Dual-Branch Deep Multiple Instance Learning for 3D MRI Analysis

Patch Target Guided Dual-Branch Deep Multiple Instance Learn...

引用

2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Dai, Mingzhe Shi, Xiaoshuang Zhu, Xiaofeng Pan, Tingrui Li, Kang University of Science and Technology of China School of Software Engineering Hefei China West China Hospital of Sichuan University West China Biomedical Big Data Center Chengdu China University of Electronic Science and Technology of China School of Computer Science and Engineering Chengdu China Sichuan Artificial Intelligence Research Institute Yibin China University of Science and Technology of China Suzhou Institute for Advanced Research Suzhou China Shanghai Artificial Intelligence Laboratory Shanghai China

ISBN: (纸本)9798350386226

Deep multiple instance learning (MIL) has attracted considerable attention in medical image analysis, since it only requires image-level labels for model training without using fine-grained (or patch) annotations. Unfortunately, MIL-based methods might lose some significant patch features. Although pseudo-label-based methods, which assign a pre-defined label to each patch, can explore more patch-level features, they might bring label noise and make the patch-level features lose diversity, thereby possibly restricting the model performance. To overcome this issue, we propose a novel gradient-based patch target generation (PTG) module to dynamically produce a feature vector for each patch as its target. Additionally, based on the PTG module, we propose a patch-target guided dual-branch deep MIL framework for 3D MRI data analysis, where both the two branches consist of a CNN model to extract patch-level features, an attention module to interpret the significance of patches, and a bag-level classifier, while the second branch also contains the PTG module to generate patch targets of patches. Moreover, the two branches are alternatively updated in our framework, resulting in a bi-level optimization problem, and thus we design a bi-level optimization algorithm to solve our proposed objective function. Extensive experiments demonstrate the superior classification and interpretation performance of the proposed framework over recent state-of-the-art methods. Codes are available at https://***/daimz1213/mil2024. © 2024 IEEE.

关键词： Image annotation

来源：评论

学校读者我要写书评

暂无评论

Towards Instruction-Tuned Verification for Improving Biomedical Information Extraction with Large Language Models

Towards Instruction-Tuned Verification for Improving Biomedi...

引用

2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Li, Jin Yuan, Ruifan Tian, Yu Li, Jingsong Nanjing University of Information Science and Technology School of Artificial Intelligence Nanjing China Zhejiang University College of Biomedical Engineering & Instrumental Science Hangzhou China Research Center for Data Hub and Security Zhejiang Lab Hangzhou China

ISBN: (纸本)9798350386226

Biomedical information extraction, including tasks like Named Entity Recognition (NER) and Relationship Extraction (RE), plays a critical role in analyzing unstructured biomedical data, facilitating more accurate downstream applications such as clinical decision support and personalized medicine. Although Large Language Models (LLMs) excel in various domains, they often struggle with issues like hallucinations and domain-specific inaccuracies, limiting their effectiveness in biomedical applications. To tackle these challenges, we propose a novel framework that incorporates an external dual-stage verifier to enhance biomedical information extraction. This verifier is prompted to first identify and complete missing entities and relations, and then filter out incorrect extractions, thereby enhancing both precision and recall in information extraction. Furthermore, we proposed an approach for constructing the instruction-tuning dataset using both positive and negative demonstrations, which improves the verifier's correction capabilities through Parameter-Efficient Fine-Tuning (PEFT). Our extensive experiments on three public benchmark datasets cover both biomedical NER and RE tasks, comparing four LLMs ranging from 7 to 175 billion parameters to validate the effectiveness and scalability of our approach. The results demonstrate that by integrating an external verifier specifically finetuned for biomedical tasks, we achieve substantial improvements in precision, recall, and F1 scores for NER and RE tasks. Our method consistently outperforms In-Context Learning (ICL) approaches in F1 scores, achieving increases of up to 20% in some cases. Furthermore, ablation studies demonstrate the effectiveness of both stages in the verification process. The proposed framework provides valuable insights for accurate biomedical information extraction using LLMs, highlighting its potential applications in real-world clinical settings. © 2024 IEEE.

关键词： Personalized medicine

来源：评论

学校读者我要写书评

暂无评论

An API Field Design Method Based on Pre-trained Model 14

An API Field Design Method Based on Pre-trained Model

引用

14th International Conference on Software technology and engineering, ICSTE 2024

作者： Wang, Hai Lou, Yihua Yang, Yi Xu, Ranbin Yang, Jingbo Liao, Xingchuang Center of Civil Aviation Big Data TravelSky Technology Limited Beijing Engineering Research Beijing China School of Computer Science and Engineering Beihang University Beijing China School of Artificial Intelligence Beihang University Beijing China

ISBN: (纸本)9798350378955

Application programming interface (API) plays an important role in modern software development, a well designed API can provide tremendous convenience for developers. However, designing an excellent API is not a trivial task, each field in the API should avoid any ambiguity. Besides, in some special cases, for instance, a Chinese developer might not accurately translate the meaning of a Chinese API field into its English counterpart. Therefore, there exists a tremendous requirement in designing high quality API field. In this paper, a fine-tuning pre-trained model based method is proposed for API field design task. Firstly, we consider the design of API fields as a translation task, converting the fields from another language into English. Secondly, we fine-tune a pre-trained model for API field design task. In the end, we curate two datasets to illustrate our method and the experiment results demonstrate that our method has a better performance in designing API field. © 2024 IEEE.

关键词： Application programs

来源：评论

学校读者我要写书评

暂无评论

Utilizing Machine Learning Techniques for Forecasting Agricultural Commodity Prices 2

Utilizing Machine Learning Techniques for Forecasting Agricu...

引用

2nd International Conference on Intelligent data Communication Technologies and Internet of Things, IDCIoT 2024

作者： Benjamin, Minu Inba Shanthini Watson Gowtham, D. Reddy, S. Anand Kumar Krishna, D. Durga Radha Vel Tech Rangarajan Dr.Sagunthala R and D Institute of Science and Technology Department of Computer Science and Engineering Chennai India Vel Tech Rangarajan Dr.Sagunthala R and D Institute of Science and Technology Department of Artificial Intelligence and Data Science Chennai India

ISBN: (纸本)9798350327533

Crop prices in India are vulnerable to environmental factors such as rainfall and temperature which can cause supply variations and price shifts. Government policies are intended to keep prices stable during severe weather occurrences. An extensive crop price prediction approach utilizing diverse machine learning techniques and encompassing features such as temperature extremes, humidity, precipitation, and probabilities is introduced in this research work. Employing algorithms including linear regression, decision trees, random forests, K-Nearest Neighbors (KNN), XGBoost, and Bayesian regression, the research conducts comprehensive evaluation and comparative analysis to identify optimal techniques for crop price prediction, thereby yielding valuable insights for agricultural applications. Amid the intricacies of contemporary agricultural markets, where numerous factors influence prices, machine learning emerges as a potent tool capable of capturing intricate influences. By exploring a range of machine learning algorithms, complex relationships within data are unveiled, establishing a basis for evidence-based methodology selection. The research results heighten forecasting precision and empower stakeholders with actionable insights, facilitating informed decisions, risk mitigation, and capitalization on opportunities. This signifies a substantial stride towards the convergence of machine learning and agriculture, fostering enhanced predictive accuracy in crop price forecasting. © 2024 IEEE.

关键词： Bayesian regression Decision tree Ensemble methods exploratory data analysis hybrid models K-Nearest Neighbors linear regression Machine learning Random Forest XGBoost

来源：评论

学校读者我要写书评

暂无评论

Revolutionizing Cardiovascular Attack Prediction: A Comprehensive Machine Learning Approach for Accurate and Timely Detection 4

Revolutionizing Cardiovascular Attack Prediction: A Comprehe...

引用

4th International Conference on Expert Clouds and Applications, ICOECA 2024

作者： Durai, S. Jaganathan, D. Prabhu, V. Harini, R. Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Avadi Dept. of CSE Chennai India Madanapalle Institute of Technology & Science Department of Artificial Intelligence Andhra Pradesh India R.M.K. Engineering College Department of Computer Science and Engineering Thiruvallur Kavaraipettai India vel Tech Multi Tech Dr.Rangarajan Dr. Sakunthala Engineering College Artificial Intelligence & Data Science Chennai India

ISBN: (纸本)9798350385793

Cardiac attacks are a leading cause of mortality worldwide, underscoring the critical need for timely and accurate healthcare detection. Predicting cardiovascular attacks is a formidable challenge in clinical data analysis. Machine Learning (ML) proves to be a valuable tool for analyzing vast healthcare data. This innovative approach focuses on employing ML techniques to identify significant features, thereby improving the accuracy of cardiovascular attack prediction. A variety of characteristics and well-known classifiers, including Naïve Bayes (NB), K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Random Forest (RF), Decision Tree (DT), and Logistic Regression (LR), are used to create the final prediction model. Through collaborative efforts, these algorithms pave the way for binary classification, effectively distinguishing between true and false outcomes. By analyzing their accuracies, this approach enables the possibilities for successful predictions. © 2024 IEEE.

关键词： Adversarial machine learning

来源：评论

学校读者我要写书评

暂无评论

Machine Learning-based Biomedical Antenna for Brain Tumor Detection

Machine Learning-based Biomedical Antenna for Brain Tumor De...

引用

2023 International Conference on Next-Generation Computing, IoT and Machine Learning, NCIM 2023

作者： Hasan, Nafiul Aktar, Mousumi Rana, Md. Masud Moni, Mohammad Ali Rajshahi University of Engineering and Technology Department of Electrical and Electronic Engineering Rajshahi Bangladesh School of Health and Rehabilitation Sciences The University of Queensland Artificial Intelligence & Data Science St Lucia Australia

ISBN: (纸本)9798350316001

Automated identification and categorization of brain anomalies like tumors is crucial to understanding and monitoring patient outcomes. In this study, we applied machine learning models to improve the effectiveness of a step-constant tapered slot antenna (STSA) for diagnosing gliomas in the brain. We have performed simulation with the CST Microwave Studio 2015 to a dataset consisting of astrocytoma (tumor radius 5 mm, skin-tumor distance 24 mm) and matching healthy controls. Phantom, an artificial human head, has four antennas placed at right angles to it. Later on, several machine learning models including the Support Vector Machine (SVM), K Nearest Neighbor (KNN), Random Forest Classifier (RFC), and artificial Neural Network (ANN) were applied to detect and diagnose brain cancers. There was a rigorous investigation of the scattering (S), admittance (Y), and impedance (Z) parameters and we observed the best result (99.60% accuracy) from the application of ANN and the Z parameter. The proposed technology could be applied for brain tumor detection accurately and clinical applications for a more efficient healthcare system based on biomedical antennas. © 2023 IEEE.

关键词： Support vector machines

来源：评论

学校读者我要写书评

暂无评论

Early Diabetes prediction with optimal feature selection using ML based Prediction Framework 4

Early Diabetes prediction with optimal feature selection usi...

引用

4th International Conference on Signal Processing and Communication, ICSPC 2023

作者： Ashisha, G.R. Mary, X. Anitha Ashif, H. Mohamed Karthikeyan, I. Roshan, J. Karunya Institute of Technology and Sciences Electronics and Instrumentation Engineering Coimbatore India Karunya Institute of Technology and Sciences Robotics Engineering Coimbatore India Karunya Institute of Technology and Sciences Artificial Intelligence and Data Science Coimbatore India

ISBN: (纸本)9798350300772

The notable developments in healthcare sciences and biotechnology have led to a significant increase in information technology, for instance, clinical data and high efficiency genetic information, produced from intensive Electronic Medical data. The use of data mining and machine learning techniques are essential in the biomedical sciences to distinctly transform all accessible health records into useful knowledge. Early detection of diseases like diabetes is crucial as the growing number of people with diabetes is rising rapidly. According to an International Diabetes Federation report, the number of diabetic cases globally is expected to reach 642 million by 2040. This growing disease requires a lot of attention. Machine learning has quickly advanced, and many facets of medical health have benefited from its use. Due to their capacity for prediction, machine learning algorithms are presently significant in the healthcare industry. The major goal of this work is to design a model that can more accurately predict patients' diabetes. The research work analyzed the hospital clinical examination data obtained from Sree Guru Hospital, Kanyakumari district, TamilNadu. To improve the accuracy of the system, imbalanced data in the real time dataset were eliminated using oversampling technique. Extra Trees classifier model, and Random Forest (RF) was used to predict diabetes. The result of this work shows that prediction of diabetes with the Extra Trees classifier had the highest accuracy of 98%. Finally, this work effectively determines the pervasiveness and detection of diabetes. © 2023 IEEE.

关键词： Forecasting

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 << < 66 67 68 69 70 71 72 73 74 75 > >>

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：