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

1,381 篇 会议
353 篇 期刊文献

馆藏范围

1,734 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

944 篇 工学
- 742 篇 计算机科学与技术...
- 633 篇 软件工程
- 254 篇 信息与通信工程
- 145 篇 生物工程
- 125 篇 控制科学与工程
- 98 篇 生物医学工程（可授...
- 88 篇 电气工程
- 82 篇 光学工程
- 80 篇 电子科学与技术（可...
- 49 篇 动力工程及工程热...
- 47 篇 安全科学与工程
- 47 篇 网络空间安全
- 38 篇 机械工程
- 38 篇 交通运输工程
- 33 篇 化学工程与技术
502 篇 理学
- 256 篇 数学
- 152 篇 生物学
- 125 篇 物理学
- 106 篇 统计学（可授理学、...
- 43 篇 系统科学
- 31 篇 地球物理学
275 篇 管理学
- 149 篇 图书情报与档案管...
- 135 篇 管理科学与工程(可...
- 88 篇 工商管理
100 篇 医学
- 79 篇 临床医学
- 75 篇 基础医学(可授医学...
- 55 篇 公共卫生与预防医...
- 48 篇 药学(可授医学、理...
65 篇 法学
- 55 篇 社会学
32 篇 经济学
- 32 篇 应用经济学
23 篇 农学
13 篇 教育学
8 篇 文学
3 篇 军事学

主题

130 篇 accuracy
72 篇 computational mo...
67 篇 deep learning
64 篇 real-time system...
64 篇 machine learning
57 篇 feature extracti...
56 篇 predictive model...
54 篇 convolutional ne...
51 篇 training
44 篇 data models
42 篇 machine learning...
37 篇 support vector m...
35 篇 analytical model...
35 篇 technological in...
32 篇 security
31 篇 internet of thin...
30 篇 visualization
27 篇 reliability
27 篇 data mining
27 篇 random forests

机构

15 篇 faculty of compu...
15 篇 gik institute of...
15 篇 university of eu...
15 篇 department of ph...
15 篇 dept. of technol...
14 篇 lovely professio...
13 篇 center for data ...
13 篇 bcmaterials basq...
13 篇 dept. of linguis...
11 篇 fermi national a...
11 篇 department of ph...
11 篇 dept. of compute...
11 篇 computer science...
11 篇 kavli institute ...
11 篇 dept. of compute...
11 篇 bcmaterials basq...
10 篇 madrid
10 篇 department of ph...
10 篇 smart data & kno...
9 篇 department of ph...

作者

21 篇 bowman samuel r.
17 篇 ali raja hashim
16 篇 zain ul abideen
15 篇 usama arshad
15 篇 arshad usama
15 篇 raja hashim ali
13 篇 ashwini kodipall...
13 篇 günnemann stepha...
12 篇 dengel andreas
12 篇 schröder markus
11 篇 gruendl r.a.
11 篇 ali zeeshan ijaz
11 篇 sanchez e.
11 篇 carretero j.
11 篇 kodipalli ashwin...
11 篇 jilek christian
10 篇 miquel r.
10 篇 ravindran balara...
10 篇 gruen d.
10 篇 doel p.

语言

1,639 篇 英文
88 篇 其他
6 篇 中文
1 篇 朝鲜文

检索条件"机构=Dept. of Data Science and Computer Applications"

共 1734 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：

Self Attention in U-Net for Semantic Segmentation of Low Resolution SAR Images 23

Self Attention in U-Net for Semantic Segmentation of Low Res...

引用

2023 Asia Conference on computer Vision, Image Processing and Pattern Recognition, CVIPPR 2023

作者： Yadav, Hrishikesh Singh Panchal, Priyanshu Manawat, Divyanshu Girisha, S. Shreesha, S. Dept. of Data Science and Computer Applications Manipal Institute of Technology Manipal Academy of Higher Education Manipal India Dept. of Information and Communication Technology Manipal Institute of Technology Manipal Academy of Higher Education Manipal India

ISBN: (纸本)9798400700033

The SAR image semantic segmentation using computer vision techniques has gained much popularity in the research community due to their wide applications. Despite the advancements in Deep Learning for image analysis, these models still struggle to segment SAR images due to the existence of speckle noise and a poor feature extractor. Moreover, deep learning models are challenging to train on small datasets and the performance of the model is significantly impacted by the quality of the data. This calls for the development of an effective network that can draw out critical information from the low resolution SAR images. In this regard, the present work proposes a unique Self attention module in U-Net for the semantic segmentation of low resolution SAR images.. The Self Attention Model makes use of Laplacian kernel to highlight the sharp discontinuities in the features that define the boundaries of the objects. The proposed model, employs dilated convolution layers at the initial layers, enabling the model to more effectively capture larger contextual information. With an accuracy of 0.84 and an F1-score of 0.83, the proposed model outperforms the state-of-the-art techniques in semantic segmentation of low resolution SAR images. The results clearly demonstrate the importance of the self attention module and the consideration of dilated convolution layers in the initial layers in semantic segmentation of low resolution SAR images. © 2023 ACM.

关键词： Synthetic aperture radar

来源：评论

学校读者我要写书评

暂无评论

Early Cancer Detection and Diagnosis using Deep Learning Models in Medical Imaging 4

Early Cancer Detection and Diagnosis using Deep Learning Mod...

引用

4th International Conference on Advancement in Electronics and Communication Engineering, AECE 2024

作者： Sikarwar, Shailendra Singh Rathore, Rachna Kumar, Sharma Sonu Monalisha, Mitali Satapathy, Lopamudra Nisha, M. Jiwaji University Dept. of Computer Science Pgv College Gwalior India Jiwaji University Dept. of Mathematics Gwalior India Mangalayatan University Dept. of Computer Engineering and Applications Aligarh India Usha Martin University Dept. of Paramedical Science Ranchi India Usha Martin University Dept. of Agriculture Ranchi India Dhanalakshmi Srinivasan University Dept. of Cse Coimbatore India

ISBN: (纸本)9798350364729

Breast cancer is a leading cause of mortality among women globally. While its exact cause remains uncertain, no definitive methods exist for its prevention or treatment. However, early detection through mammography plays a crucial role in improving management strategies and increasing the likelihood of recovery. Mammography aids not only in early cancer detection but also in identifying various conditions and distinguishing between cancer types - benign, malignant, or normal. This study explores an advanced machine learning and image processing framework for detecting and classifying breast cancer. The approach incorporates machine learning techniques alongside feature extraction, selection, and image preprocessing to classify breast cancer and related skin diseases effectively. Key advancements include the use of a geometric mean filter to enhance image quality and AlexNet for feature extraction. The relief algorithm is employed for feature selection, and classification is performed using machine learning models such as random forest, K-nearest neighbors (KNN), least square support vector machine (LS-SVM), and Naïve Bayes. This innovative methodology provides an accurate and efficient solution for breast cancer detection through advanced image analysis techniques. © 2024 IEEE.

关键词： Lung cancer

来源：评论

学校读者我要写书评

暂无评论

Cybersecurity for Industrial IoT: A Review on Vulnerabilities and Attacks 8

Cybersecurity for Industrial IoT: A Review on Vulnerabilitie...

引用

8th International Conference on Electronics, Materials Engineering and Nano-Technology, IEMENTech 2025

作者： Kumari, Reetu Kumar, Sunil Amandeep Dutta, Kamlesh Kumari, Aparna Guru Jambheshwar University of Science and Technology Dept. of Computer Science & Engineering Haryana Hisar India Guru Jambheshwar University of Science and Technology Dept. of AI & Data Science Haryana Hisar India National Institute of Technology Hamirpur Dept. of Computer Science & Engineering Himachal Pardesh India Institute of Technology Nirma University Dept. of Computer Science & Engineering Gujarat Ahmedabad India

ISBN: (纸本)9798331520762

Internet has evolved from a network of connecting people to a network of connecting things, leading to a more complex and sophisticated network of Industrial Things, known as Industrial IoT (IIoT) today. This evolution has revolutionized the traditional Industrial system into real-time smarter systems that enhanced overall efficiency and productivity. Despite its benefits, the interconnected nature of IIoT, expanded the attack surface, exposing the systems to new crucial and complex cybersecurity threats. Several real time attacks on various industries as mentioned in this paper and the increasing cybersecurity cost over the years, clearly states the need of robust security solutions. To deeply understand the cybersecurity aspect, we provided a comprehensive study of attacks and vulnerabilities in this paper. In this paper, we did a indept. study of various IIoT related severe attacks and aspects related to these attacks, like vulnerabilities, impacts, potential solutions etc. We also provided future research directions to help the research community, to work upon and develop more robust and efficient cybersecurity solutions. © 2025 IEEE.

关键词： attacks cybersecurity etc IIoT Industrial IoT IoT vulnerabilities

来源：评论

学校读者我要写书评

暂无评论

Heart disease Classification Utilising Machine Learning Techniques 3

Heart disease Classification Utilising Machine Learning Tech...

引用

3rd International Conference for Advancement in Technology, ICONAT 2024

作者： Dhondiyal, Shiv Ashish Verma, Himanshu Kumar, Rohit Sawan, Vikash Uniyal, Ishita Graphic Era Deemed to Be University Dept. of Computer Science and Engineering Dehradun India Haridwar University Roorkee Dept. of Computer Science and Engineering India GLA University Department of Computer Engineering & Applications Mathura India Era Hill University Department of Electrical Engineering Graphic India

ISBN: (纸本)9798350354171

Heart diseases comprise a extensive Various heart conditions and are commonly referred to as cardio vascular diseases. These encompass issues with the rhythm of the heart, blood vessel diseases, and congenital cardiac defects. It has been the world's biggest cause of death for the last few decades. Finding a reliable and accurate way to automate the process of early disease diagnosis and efficient disease management is urgently needed. data science facilitates the medical sciences in analysing vast quantities of data. Researchers utilize various data Mining and Machine Learning methods to evaluate extensive datasets in order to accurately forecast heart illness. This study examines the supervised learning models of Random Forest, K-NN, and SVM to provide a comparative analysis of their efficiency. It is discovered that, when compared to other algorithms, SVM offers the highest accuracy at 0.994. © 2024 IEEE.

关键词： Random forests

来源：评论

学校读者我要写书评

暂无评论

Simplified Micropayment Mechanism to Eliminate the Risk of Double Payment in E-Commerce

Simplified Micropayment Mechanism to Eliminate the Risk of D...

引用

2023 International Conference on Advances in Intelligent Computing and applications, AICAPS 2023

作者： Chahar, Narendra Kumar Singh, Krishan Pal Hussain, Muzzammil Central University of Rajasthan Dept. of Data Science and Analytics Ajmer India Malaviya National Institute of Technology Dept. of Computer Science and Engineering Jaipur India Central University of Rajasthan Dept. of Computer Science and Engineering Ajmer India

ISBN: (纸本)9798350333817

In recent years, the emergence of the Internet and E-commerce has steered significant growth in digital transactions. Businesses today need mobile wallets, credit and debit cards, and e-cash to digitize payments. Digital payment systems are in transition and promise amazing advancements, but they also pose many risks and as the number of online transactions is increasing tremendously, we need a security system that follows all security norms. In this paper, we study digital transaction systems and evaluate various components of E-commerce plat-forms to address the security of these services. We evaluate the attributes that affect the security of digital payment processes and identify several barriers that hinder their performance to propose a simplified payment mechanism for micro-payments that eliminates the double payment problem. © 2023 IEEE.

关键词： Online systems

来源：评论

学校读者我要写书评

暂无评论

A Blockchain-Based Secure and Transparent Global Radioactive Source Lifecycle Management System

A Blockchain-Based Secure and Transparent Global Radioactive...

引用

2025 IEEE SoutheastCon, SoutheastCon 2025

作者： Poudel, Susan Kausar, Firdous Das, Debashis Chatterjee, Pushpita Hussain, Sajid Ghosh, Uttam Fisk University Dept. of Mathematics and Computer Science NashvilleTN United States Meharry Medical College Department of Computer Science and Data Science NashvilleTN United States

ISBN: (纸本)9798331504847

The management of radioactive sources is a critical process that ensures the safe and responsible handling of radioactive materials throughout their lifecycle. These sources require careful management from production to disposal, such as real-time tracking and radiation monitoring to follow everyone's safety rules and protect people and the environment. However, these sources present significant global challenges, especially regarding safety, security, and transparency. Current systems face limitations such as fragmented oversight, lack of accountability, and risks of unauthorized access. To address these limitations, this paper proposes a blockchain-based radioactive source lifecycle management system to manage the lifecycle of radioactive sources. Blockchain's decentralized and tamper-proof ledger secures data throughout the entire lifecycle of radioactive materials. By using smart contracts and access controls, the system ensures that only authorized parties can monitor and verify transactions in real-time, which reduces human error and prevents unauthorized changes to the data. Users can perform key operations such as retrieving source details, transferring ownership, updating source locations, and adding observers to our proposed system. Our experiment in designing and testing a blockchain application has proven the potential for a secure and transparent system that enhances global cooperation in managing radioactive sources. Overall, the proposed system not only addresses current challenges in radioactive source management but also enhances the tracking and monitoring of radioactive materials. © 2025 IEEE.

关键词： Radioactive wastes

来源：评论

学校读者我要写书评

暂无评论

SCORIGAMI: SIMULATING THE DISTRIBUTION AND ASSESSING THE RARITY OF NATIONAL FOOTBALL LEAGUE SCORES

SCORIGAMI: SIMULATING THE DISTRIBUTION AND ASSESSING THE RAR...

引用

2024 Winter Simulation Conference, WSC 2024

作者： Moyer, Liam Railey, Jameson Daw, Andrew Gutekunst, Samuel C. Dept. of Computer Science Bucknell University LewisburgPA United States Dept. of Computer Science Stevens Institute of Technology HobokenNJ United States Dept. of Data Sciences and Operations University of Southern California Los AngelesCA United States Depts. of Computer Science and Mathematics Bucknell University LewisburgPA United States

ISBN: (纸本)9798331534202

NFL Scorigamis have a cult-like following, and occur whenever a game ends in a new, never-before-seen score. While substantial research has gone into simulating and predicting NFL game scores, most work has been in relation to winner prediction and betting spreads. We analyze a Poisson random variable model for the distribution of NFL game scores and show that it fails to incorporate important game dynamics. Through an analysis of extensive play-by-play data, we extend this to a non-stationary, state-dependent Poisson process model. This latter model more closely fits real NFL score data, and we use it in NFL score simulations to forecast likely future Scorigamis. © 2024 IEEE.

关键词： Poisson distribution

来源：评论

学校读者我要写书评

暂无评论

Predictive Maintenance with Large Language Models and Transformer-based Survival Analysis 50

Predictive Maintenance with Large Language Models and Transf...

引用

50th Annual Conference of the IEEE Industrial Electronics Society, IECON 2024

作者： Esteban, Aurora Cobilean, Victor Nawaratne, Rashmika Dept. of Computer Science and Numerical Analysis University of Cordoba Cordoba Spain Dept. of Computer Science Virginia Commonwealth University RichmondVA United States Research Center for Data Analytics and Cognition La Trobe University VIC Australia

ISBN: (纸本)9781665464543

Predictive maintenance involves proactive monitoring and prediction of equipment failures to optimize maintenance schedules to minimize downtime, reduce losses, and ensure optimal equipment performance. However, traditional methods often rely on classification or regression, overlooking the temporal aspect of failure prediction. Survival analysis is focused on modeling time-to-event data, making it well-suited for predicting the time until equipment failures. The primary goal of this paper is to evaluate the feasibility and effectiveness of Transformer-based model for survival analysis in the industrial domain. Additionally, this work aims to explore the integration of Large Language Models as virtual assistants to facilitate user interaction and decision-making in the predictive maintenance process. The proposed system was tested on a public predictive maintenance problem and the Transformer-based model obtained better survival predictions than the baseline statistical and machine learning models, with improvements of up to 20%. Furthermore, the virtual assistant based on the large language model demonstrated the ability to retrieve precise information based on natural language prompts. © 2024 IEEE.

关键词： Predictive maintenance

来源：评论

学校读者我要写书评

暂无评论

Enhancing Drug Detection Strategies for Post-Rehabilitated Juveniles: A Comprehensive Review and Empirical Study 1

Enhancing Drug Detection Strategies for Post-Rehabilitated J...

引用

1st IEEE International Conference on Advances in Computing, Communication and Networking, ICAC2N 2024

作者： Kaushik, Priyanka Gour, Prabha Bir, Lovish Ansari, Lubna Samnata, Rojalini Soundarya, R. Chandigarh University Dept. of Computer Science Engineering Chandigarh India Madhav University Dept. of Humanities and S.Science Pindwara India Chandigarh University Dept. of of Com. Science Engineering Chandigarh India Mangalayatan University Dept. of Computer Eng. and Applications Aligarh India Usha Martin University Dept. of Pharamcy Jharkhand India Dhanalakshmi Srinivasan College Dept. of Biomedical Engineering Coimbatore India

ISBN: (纸本)9798350356816

Drug addiction is a complex problem that will affect individuals of all ages, including juveniles. It is a chronic and often relapsing disease that requires continued care and support to help individuals achieve and maintain long-term recovery. While rehabilitation programs are designed to help individuals overcome addiction, relapse rates remain high, making post-rehabilitation drug detection an important component of continued care. This study aims to investigate the efficacy of various drug detection techniques for juveniles who have completed their rehabilitation. The success of rehabilitation program for young drug addicts depends on the creation of efficient drug detection rules and procedures that can assist avoid relapse and increase program effectiveness overall. The research technique comprises both an empirical investigation of post-rehabilitated juvenile participants and a thorough evaluation of the literature of past studies on drug detection for young people. The results of this study will have a big impact on how well drug detection policies and practises for young people who have completed their rehabilitation are developed. © 2024 IEEE.

关键词： Addiction Drug Post Rehabilitated Rehabilitation

来源：评论

学校读者我要写书评

暂无评论

Exploring data Visualization Techniques for Large datasets 1

Exploring Data Visualization Techniques for Large Datasets

引用

1st IEEE International Conference on Advances in Computing, Communication and Networking, ICAC2N 2024

作者： Sikarwar, Shailendra Singh Kumawate, Bhawesh Vaishnav, Arun Kumar, Love Bushra, Seema Devi, S. Teitsana PGV College Dept. of Computer Science Gwalior India Madhav University Dept. of CSE Pindwara India Mangalayatan University Dept. of Computer Eng. and Applications Beswan India Deen Academics Lingayas Vidyapeeth Faridabad India Dhanalakshmi Srinivasan University Dept. of CSE Coimbatore India

ISBN: (纸本)9798350356816

In a world of Big data, being able to easily visualize large data sets can make the difference between understanding & wisely judging that data - or not. Big data is the word with which not many people are aware of, but these success stories make sure that organizations sit back & take notice to what this large data can show then when it collated together over time from different sources. The methods traditionally used for visualization rarely fit into the Big-data world because they do not have scalability, given that volume (huge amounts), velocity (high speed), or variety (complex details) are now more prevalent in big-data processing. This paper then explores data visualization techniques that can be used to manage very large datasets, including streaming data visualization, dimensionality reduction (e.g. T-SNE), & hierarchical clustering among other Node XL analyses. In this paper, based on accumulated observations & implementations of these techniques over the years, an extensive review is provided for researchers or practitioners to select suitable visualization methodologies that not only suit the scale but also discover patterns in their data. The results highlight the need for enabling tools, methods, & practices to make sense of big data in an interactive visual context that enables better orchestration between complex systems using diverse hardware/software stacks. Additionally, the pros & cons of each method are explained, with real-world use cases & case studies demonstrating their practicality. © 2024 IEEE.

关键词： data assimilation

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174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：