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

829 篇 会议
303 篇 期刊文献
13 册 图书

馆藏范围

1,145 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

612 篇 工学
- 482 篇 计算机科学与技术...
- 401 篇 软件工程
- 174 篇 信息与通信工程
- 100 篇 生物工程
- 97 篇 控制科学与工程
- 62 篇 光学工程
- 61 篇 生物医学工程（可授...
- 58 篇 电气工程
- 45 篇 电子科学与技术（可...
- 39 篇 网络空间安全
- 32 篇 安全科学与工程
- 28 篇 动力工程及工程热...
- 28 篇 交通运输工程
- 25 篇 机械工程
- 21 篇 建筑学
- 20 篇 环境科学与工程（可...
329 篇 理学
- 171 篇 数学
- 106 篇 生物学
- 84 篇 物理学
- 73 篇 统计学（可授理学、...
- 27 篇 系统科学
189 篇 管理学
- 107 篇 图书情报与档案管...
- 98 篇 管理科学与工程(可...
- 59 篇 工商管理
68 篇 医学
- 51 篇 临床医学
- 50 篇 基础医学(可授医学...
- 38 篇 公共卫生与预防医...
- 32 篇 药学(可授医学、理...
39 篇 法学
- 34 篇 社会学
23 篇 经济学
- 23 篇 应用经济学
13 篇 农学
8 篇 教育学
6 篇 文学
2 篇 军事学

主题

93 篇 accuracy
55 篇 computational mo...
54 篇 real-time system...
52 篇 machine learning
46 篇 deep learning
37 篇 training
32 篇 feature extracti...
31 篇 predictive model...
30 篇 artificial intel...
30 篇 machine learning...
28 篇 convolutional ne...
27 篇 analytical model...
27 篇 data models
21 篇 reliability
21 篇 security
21 篇 technological in...
20 篇 support vector m...
20 篇 visualization
19 篇 forecasting
18 篇 medical services

机构

13 篇 center for data ...
13 篇 dept. of linguis...
11 篇 dept. of compute...
11 篇 computer science...
11 篇 dept. of compute...
10 篇 smart data & kno...
8 篇 center for data ...
8 篇 center for data ...
8 篇 school of data s...
7 篇 dept. of compute...
7 篇 deepwise ai lab
7 篇 dept. of compute...
6 篇 kalinga universi...
6 篇 fraunhofer insti...
6 篇 data science and...
6 篇 dept. of compute...
5 篇 university of th...
5 篇 g. s. sanyal sch...
5 篇 artificial intel...
5 篇 graphic era hill...

作者

21 篇 bowman samuel r.
13 篇 ashwini kodipall...
13 篇 günnemann stepha...
12 篇 dengel andreas
12 篇 schröder markus
12 篇 jilek christian
11 篇 kodipalli ashwin...
10 篇 ravindran balara...
7 篇 phang jason
7 篇 htut phu mon
7 篇 deepak upadhyay
7 篇 nookala venu
7 篇 wang yizhou
6 篇 shoeibi afshin
6 篇 vijayan priyesh
6 篇 seth pratinav
6 篇 farnadi golnoosh
6 篇 rao trupthi
6 篇 alizadehsani roo...
6 篇 yuyun

语言

842 篇 英文
301 篇 其他
1 篇 朝鲜文
1 篇 中文

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

共 1145 条记录，以下是31-40 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：

An Analysis of Human Factors in Malware Attacks

An Analysis of Human Factors in Malware Attacks

引用

2024 International Conference on computer and Applications, ICCA 2024

作者： Alotaibi, Jawza Al Ja'am, Jihad M. Hoda, Mohammed Oryx Universal College in Partnership with Liverpool John Moores University Dept. of Computing and Data Science Doha Qatar Kuwait College of Science and Technology Dept. of computer Science and Engineering Doha Kuwait

ISBN: (纸本)9798350367560

This research study investigates human responses to malware attacks, focusing on attacker motivations, individual reactions, and key factors influencing user vulnerability and resilience. Using a structured survey questionnaire, the study gathered data from 104 participants with diverse demographics across various occupations. The data was analyzed using SPSS statistical software and Python-based machine learning algorithms. The survey included demographic information and responses to cybersecurity-related questions about human behavior and experience. Machine learning techniques identified ten critical factors as significant predictors of vulnerability to malware attacks. The prediction model indicated that 55 out of 104 participants were vulnerable, with higher vulnerability observed among female participants and those in non-IT professions. This study contributes valuable insights into the human factors influencing cybersecurity resilience, providing a foundation for developing effective strategies to mitigate malware attack vulnerability. © 2024 IEEE.

关键词： Cyber attacks

来源：评论

学校读者我要写书评

暂无评论

Handling the Challenges of Microbiome data through Supervised Autoencoders for the Non-invasive Disease Diagnosis

Handling the Challenges of Microbiome Data through Supervise...

引用

2024 Discovery science Late Breaking Contributions, DS-LB 2024

作者： Buttaro, Veronica Ceci, Michelangelo Pio, Gianvito Dept. of Computer Science University of Bari Aldo Moro Bari Italy Data Science Lab CINI Consortium Rome Italy Dept. of Knowledge Technologies Jozef Stefan Institute Ljubljana Slovenia

来源：评论

学校读者我要写书评

暂无评论

Analysis and Ranking Resume using Machine Learning Algorithms and NLP 1

Analysis and Ranking Resume using Machine Learning Algorithm...

引用

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

作者： Deshmukh, Pratik Chandrasen Bendre, M.R. Pravara Rural Engineering College Dept. of Data Science Engineering Loni India Pravara Rural Engineering College Dept. of Computer Engineering Loni India

ISBN: (纸本)9798350356816

Specialized recruiting firms have emerged as a result of the expansion of the Indian employment market. These agencies use machine learning models to expedite the hiring process and provide their clients with the best candidates. Thus, the suggested methodology offers the potential to improve the hiring process by incorporating the advantages of machine learning in addition to natural language processing(NLP). Employing NLP techniques, one may accurately grade resumes according to the company's criteria and extract useful information from them. After thorough testing and improvement, the web portal might prove to be an invaluable tool for hiring managers and job seekers ***, one might think about adding features to the CV classifier and ranker, such as customized feedback and recommendations for enhancements that might assist candidates in understanding where they might be lacking and increase their chances of getting recruited. © 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

Multiclass Brain Tumor Detection using Deep Transfer Learning

Multiclass Brain Tumor Detection using Deep Transfer Learnin...

引用

2024 MIT Art, Design and Technology School of Computing International Conference, MITADTSoCiCon 2024

作者： Deshmukh, Aishwarya V. Bendre, M.R. Prawara Rural Engineering College Dept. of Data Science Engineering Loni India Prawara Rural Engineering College Dept. of Computer Engineering Loni India

ISBN: (纸本)9798350362879

Brain tumors, identified as abnormal growth of cell inside the brain, necessitate accurate categorization for tailored treatment plans. This classification aids in determining the severity of the condition and guiding therapeutic decisions. Iimaging technologies in medical like Magnetic Resonance Imaging (MRI) are instrumental in identifying brain pathologies without emitting ionizing radiation. Deep learning, a subset of AI, enhances brain tumor detection from MRI scans, improving prediction rates. Digital image processing is vital for medical image analysis and diagnosis. Brain tumors manifest in various forms, categorized as benign or malignant based on their tissue characteristics. Convolutional Neural Networks (CNNs), particularly VGG-16, Inception-v3, and ResNet-50 are prominent in brain tumor analysis, segmentation, and classification. These models are compared for their efficacy in identifying and forecasting brain tumor cells. Common brain tumor types include glioma, meningioma, and pituitary tumors, each requiring specific treatment approaches. © 2024 IEEE.

关键词： Tumors

来源：评论

学校读者我要写书评

暂无评论

Youth Unemployment Mitigation: Leveraging Machine Learning and Exploratory data Analysis for Evidence-Driven Strategies

Youth Unemployment Mitigation: Leveraging Machine Learning a...

引用

2023 IEEE Conference on Engineering Informatics, EI 2023

作者： Aarthi, S. Dharmita, A. Pragathi, G. Elakkiya, S. Srm Institute of Science and Technology Dept. of Computer Science Engineering Chennai India Srm Institute of Science and Technology Dept. Computer Science Engineering Specialization with Big Data Analytics Chennai India

ISBN: (纸本)9798350338522

Youth unemployment represents an urgent global socioeconomic challenge. This study combines machine learning and exploratory data analysis to understand and solve this problem comprehensively. Traditional approaches often rely on qualitative assessments and historical data requiring a data-driven transition to more effective solutions. Our approach integrates machine learning algorithms and Exploratory data Analysis techniques to analyze diverse data sets, identify influencing factors, and recommend targeted interventions. The analysis process includes data collection, preprocessing, feature selection, machine learning modeling, and Exploratory data Analysis techniques to understand the dynamics of the Better understanding unemployment among young people. Preliminary results reveal precise predictive patterns and previously unrecognized relationships, providing evidence-based solutions to inform policy and support the economic well-being of younger generations. Further research and validation are needed to increase applicability. © 2023 IEEE.

关键词： data visualization

来源：评论

学校读者我要写书评

暂无评论

Remote Sensing Aircraft Classification Harnessing Deep Learning Advancements 18

Remote Sensing Aircraft Classification Harnessing Deep Learn...

引用

18th IEEE International Conference on Emerging Technologies, ICET 2023

作者： Saeed, Ahmad Atif, Haasha Bin Habib, Usman Bilal, Mohsin National University of Computer and Emerging Sciences Dept. of Ai & Data Science Islamabad Pakistan

ISBN: (纸本)9798350328172

Remote sensing imagery is challenging to analyze due to its diverse sources, object image variability and contextual backgrounds. In current era, Aviation Industry is continuously progressing from specific domain of aircraft classification to improving in its safety measures, maintenance procedures and diverse flight operations by utilizing the potential of satellite imagery and Deep Learning methods. This paper highlights and explores the potential of Transfer Learning with state-of-the-art Deep Learning architectures on publicly available Multi Type Aircraft Remote Sensing Imagery datasets. In the same context, our experiments and training mechanism on the state-of-the-art models, like Vision Transformers, ResNet50v2, EfficientNetB0 and InceptionNetV3 outperforms earlier methods on the benchmark remote sensing military aircraft dataset. Our experiments on mentioned Deep learning models yielded, 95%, 93.4%, 92% and 80% classification accuracy due to advance architecture design and fine tuning techniques by allowing them to capture more intricate features and patterns of multifaceted military platforms. This research will provide a valuable resource in the aviation industry in the perspective of aircraft recognition domain by utilizing the power of advance Deep Learning methods to handle the complexity and diversity of remote sensing imagery. © 2023 IEEE.

关键词： Remote sensing

来源：评论

学校读者我要写书评

暂无评论

Windmill Pitch Prediction And Error Correction With Adaptive Control And Current Generation Analysis Using data Analysis

Windmill Pitch Prediction And Error Correction With Adaptive...

引用

2023 International Conference on Energy, Materials and Communication Engineering, ICEMCE 2023

作者： Minu, M.S. Rohit Suryaa, S. Jovan Titus, J. Sharan, S. Srm Institute of Science and Technology Dept. of Computer Science Engineering Chennai India Srm Institute of Science and Technology Dept. Computer Science Engineering Specialization with Big Data Analytics Chennai India

ISBN: (纸本)9798350393378

The growing prominence of wind energy as an eco-friendly power alternative amplifies the need for optimal turbine performance. Central to this is the ability to accurately predict and modulate the wind turbine pitch. Our research focuses on integrating adaptive control strategies with advanced power generation analytics to enhance pitch prediction precision. We have processed and analyzed vast amounts of wind data using state-of-the-art data analytics to discern patterns in wind velocity changes accurately. The continuous stream of real-time data is the backbone of our adaptive controls, ensuring perpetual pitch adjustments that optimize energy capture. In tandem with this, we present a thorough analysis linking momentary wind shifts to power production, ensuring consistent and efficient power output. The culmination of these methodologies offers a breakthrough solution in wind energy optimization. Preliminary results highlight the paramount importance of real-time data analytics in revolutionizing green energy advancements. © 2023 IEEE.

关键词： Wind power

来源：评论

学校读者我要写书评

暂无评论

The Role of data Visualization in Improving the Sustainability of Smallholdings 2

The Role of Data Visualization in Improving the Sustainabili...

引用

2nd International Conference on Technology Innovation and Its Applications, ICTIIA 2024

作者： Maulana, Ahmad Candra, Ade Amalia, Amalia Universitas Sumatera Utara Dept. of Data Science and Artificial Intelligence Medan Indonesia Universitas Sumatera Utara Dept. of Information Technology Medan Indonesia Universitas Sumatera Utara Dept. of Computer Science Medan Indonesia

ISBN: (纸本)9798350351613

Indonesia is the largest palm oil producer in the world, with smallholder oil palm plantations being a significant contributor. However, information regarding smallholder oil palm plantations is very limited. This research aims to provide an alternative solution by designing a dataset that can be used as a basis for decision-making. This research analyzed data from 2818 smallholders in 64 sub-districts in Sumatra. The level of sustainability was visualized and analyzed using QGIS based on three factors, namely plant age, group status, and certification status. The results show that the districts of Nassau, Sungai Apit, Kerumutan, Batang Asam, and Batang Toru have the highest level, while Batang Lubu Sutam, Salapian, Torgamba, Pematang Bandar, and Bilah Hulu subdistricts have the lowest level of sustainability. The sustainability levels per sub-district were visualized to cluster the level of sustainability per region for large-scale smallholding programs. © 2024 IEEE.

关键词： Information management

来源：评论

学校读者我要写书评

暂无评论

Brain-Powered Vehicles for Individuals with Disabilities Leveraging AI 4

Brain-Powered Vehicles for Individuals with Disabilities Lev...

引用

4th International Conference on Technological Advancements in Computational sciences, ICTACS 2024

作者： Jaison, T. Jisna Thambi, Priyanka E. Pinheiro, Nikita Scms School of Engineering and Technology Dept. of Artificial Intelligence & Data Science Karukutty India Kalamasery India St. Paul's College Dept. of Computer Science Kalamasery India

ISBN: (纸本)9798350387490

This paper provides a novel method for enhancing mobility and independence for people with disabilities via integrating Brain-pc interface (BCI) generation with artificial intelligence (AI) for vehicle control. The proposed device permits customers to control important vehicle features which includes steerage, braking, and acceleration the usage of their Brain indicators, captured through non-invasive EEG sensors. Preliminary checking out with diverse person agencies established an outstanding accuracy price of over ninety% in deciphering person intentions and efficaciously controlling the automobile. The device adapts to person neural styles, ensuring personalized and dynamic control, and extensively improves person autonomy and self-assurance. However, demanding situations which include sign noise, user variability, and long-time period device stability stay. This studies demonstrates the capacity for BCIs and AI to revolutionize transportation for individuals with disabilities, supplying an exceptional level of independence. Future trends will focus on improving signal clarity, improving machine stability, and addressing regulatory issues to make sure safe, real-world application. © 2024 IEEE.

关键词： Neurons

来源：评论

学校读者我要写书评

暂无评论

Estimating Surface Soil Moisture based on Remote Sensing data and Crop Yield Classification using Machine Learning 9

Estimating Surface Soil Moisture based on Remote Sensing Dat...

引用

9th International Conference on Communication and Electronics Systems, ICCES 2024

作者： Anuradha, G. Susrith, Maddi Siddhardha, Kondreddy Gnana Velagapudi Ramakrishna Siddhartha Engineering College Dept. of computer science and engineering Vijayawada India Velagapudi Ramakrishna Siddhartha Engineering College Dept. of Artificial Intelligence and Data Science Vijayawada India

ISBN: (纸本)9798350377972

Soil moisture plays a critical role in environmental monitoring and sustainable agricultural practices due to its significant impact on crop growth, water availability, and overall ecosystem health. Properly understanding and managing soil moisture is vital for efficient water usage, especially in agriculture. Traditional methods in measuring soil moisture have been confined to ground-based small-scale methods, which are high price due to high labor intensity and limited spatial coverage. This project addresses its limitation in the spatial view by integrating remote sensing data from the Sentinel-1 satellite. Sentinel-1 is particularly effective in monitoring the soil moisture as it captures data in all climate conditions. By combining the satellite data with Google Earth Engine (GEE) we create a soil moisture dataset. For analyzing the joint satellite and ground truth data, the machine learning technique using Random Forest (RF) is applied and classification of crop yield is done. © 2024 IEEE.

关键词： Soil moisture

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115页 << < 1 2 3 4 5 6 7 8 9 10 > >>

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：