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

29,790 篇 会议
4,556 篇 期刊文献
234 册 图书

馆藏范围

34,580 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

21,416 篇 工学
- 15,483 篇 计算机科学与技术...
- 13,003 篇 软件工程
- 5,714 篇 信息与通信工程
- 3,392 篇 控制科学与工程
- 2,639 篇 电子科学与技术（可...
- 2,579 篇 电气工程
- 2,294 篇 生物工程
- 1,845 篇 生物医学工程（可授...
- 1,505 篇 光学工程
- 1,328 篇 机械工程
- 1,191 篇 动力工程及工程热...
- 1,181 篇 仪器科学与技术
- 995 篇 化学工程与技术
- 840 篇 安全科学与工程
- 708 篇 交通运输工程
10,963 篇 理学
- 5,947 篇 数学
- 3,000 篇 物理学
- 2,512 篇 生物学
- 1,970 篇 统计学（可授理学、...
- 1,303 篇 系统科学
- 1,064 篇 化学
5,529 篇 管理学
- 3,226 篇 管理科学与工程(可...
- 2,573 篇 图书情报与档案管...
- 1,787 篇 工商管理
1,559 篇 医学
- 1,353 篇 临床医学
- 1,264 篇 基础医学(可授医学...
- 828 篇 药学(可授医学、理...
- 657 篇 公共卫生与预防医...
1,010 篇 法学
- 810 篇 社会学
632 篇 经济学
- 632 篇 应用经济学
478 篇 农学
425 篇 教育学
155 篇 文学
76 篇 军事学
39 篇 艺术学
1 篇 哲学

主题

1,090 篇 accuracy
839 篇 computational mo...
829 篇 feature extracti...
720 篇 deep learning
676 篇 computer science
676 篇 training
523 篇 real-time system...
511 篇 support vector m...
497 篇 machine learning
437 篇 predictive model...
406 篇 internet of thin...
351 篇 computer archite...
351 篇 data models
344 篇 convolutional ne...
329 篇 reliability
309 篇 optimization
279 篇 data mining
274 篇 routing
242 篇 conferences
241 篇 wireless sensor ...

机构

84 篇 institut univers...
82 篇 north-west unive...
80 篇 national centre ...
80 篇 tno technical sc...
79 篇 infn sezione di ...
76 篇 infn sezione di ...
76 篇 institute of inf...
68 篇 université de st...
66 篇 università di ca...
65 篇 dept. of informa...
64 篇 nikhef national ...
60 篇 dept. of informa...
57 篇 dept. of compute...
56 篇 dept. of compute...
55 篇 dept. of compute...
52 篇 school of automa...
50 篇 leiden universit...
48 篇 dept. of compute...
47 篇 university würzb...
45 篇 dept. of electri...

作者

85 篇 ferrara g.
85 篇 ardid m.
84 篇 calvo d.
84 篇 gal t.
84 篇 bozza c.
81 篇 chen a.
81 篇 kalekin o.
80 篇 díaz a.f.
77 篇 boumaaza j.
76 篇 coleiro a.
75 篇 gozzini s.r.
72 篇 huang f.
69 篇 dallier r.
64 篇 anguita m.
54 篇 kulikovskiy v.
42 篇 benoit d.m.
42 篇 eddymaoui a.
42 篇 labalme m.
41 篇 gagliardini s.
41 篇 del burgo r.

语言

30,930 篇 英文
3,258 篇 其他
296 篇 中文
54 篇 日文
45 篇 朝鲜文
2 篇 德文
2 篇 斯洛文尼亚文
1 篇 法文
1 篇 葡萄牙文

检索条件"机构=Dept. of Computer Science& Engineering"

共 34580 条记录，以下是701-710 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：

Artificial Neural Network and ARIMA model to estimate ICT sector investment, a comparative approach 11

Artificial Neural Network and ARIMA model to estimate ICT se...

引用

11th International Symposium on Telecommunication, IST 2024

作者： Mansourifar, Leila Moradhasel, Niloofar Borghei, Matin Sadat Heidari, Arman Dept. Strategic Studies and Digital Economics Center Tehran Iran Tehran Dept. Computer Science and Engineering Iran

ISBN: (纸本)9798350356250

The purpose of this research is to predict the required ICT sector for the time period leading up to . The ICT sector was predicted based on Autoregressive Integrated Moving Average (ARIMA) and Artificial Neural Networks (ANN). The major difference between the results of the two models indicates the difference between these two methods in forecasting, and it was shown that the ARIMA model is not a suitable method for program-oriented forecasts because it is not possible to consider variables in its calculations like economic growth. On the other hand, the neural network method can provide a more reliable prediction due to the inclusion of the economic growth variable in the learning process. © 2024 IEEE.

关键词： Neural network models

来源：评论

学校读者我要写书评

暂无评论

Reviewlogy: An Interface for Analyzing Sentiments 3

Reviewlogy: An Interface for Analyzing Sentiments

引用

3rd IEEE World Conference on Applied Intelligence and Computing, AIC 2024

作者： Shyam, Radhey Khanna, Shilpi Srivastava, Kavya Srivastava, Ajay Kumar Chaudhary, Ishika Bbd University Dept. of Computer Science & Engineering Lucknow India Srmcem Dept. of Information Technology Lucknow India

ISBN: (纸本)9798350384598

This paper investigates the impact of early reviews on product sales by analyzing the traits of initial reviewers on major e-commerce platforms. It segments product life-cycles into three phases: early, majority, and laggards, focusing on early reviewers who post reviews during the initial stages. The paper introduces an innovative interface for sentiment analysis to identify and predict early reviewers. Quantitative analysis reveals that early reviewers often provide more insightful comments and give higher overall ratings. Their ratings and helpfulness scores significantly affect product popularity. The methodology, tested on extensive datasets from Amazon and Yelp, extracts crucial attributes of early reviewers, including purchasing patterns and social impact. The interface employs a hybrid machine learning approach combining Naive Bayes, margin-based embedding ranking models (MERM), and support vector machines (SVMs) to predict potential early reviewers accurately. This approach balances probabilistic reasoning with robust classification, ensuring comprehensive forecasting. Evaluation metrics such as precision and accuracy demonstrate the effectiveness of the models over baseline methods. Furthermore, the study enhances the understanding of how early reviews influence consumer behavior and product success in e-commerce. The interface aids in refining product marketing strategies by enabling precise targeting of early reviewers, accelerating product adoption, amplifying sales, and optimizing marketing tactics in the competitive e-commerce landscape. © 2024 IEEE.

关键词： Support vector machines

来源：评论

学校读者我要写书评

暂无评论

Flood Detection from Satellite Images Using Self-Attention Empowered EfficientNetV2 Mechanism 7

Flood Detection from Satellite Images Using Self-Attention E...

引用

2023 7th International Conference On Computing, Communication, Control And Automation, ICCUBEA 2023

作者： Selva Jeba, G. Chitra, P. Thiagarajar College of Engineering Dept. of Computer Science and Engineering Madurai India Thiagarajar College of Engineering Dept. of Computer Applications Madurai India

ISBN: (纸本)9798350304268

Floods occur when water overflows onto normally dry land and are a destructive natural disaster. In recent times, deep learning models have demonstrated their remarkable capabilities in identifying objects and classifying images. As a result, the utilization of deep learning in remote sensing, especially for the crucial task of flood detection, has become an increasingly prominent and promising area of research and application. This study proposes a lightweight yet powerful feature extraction framework by leveraging a self-attention(SA) based EfficientNetV2. The EfficientNetV2 architecture serves as the backbone of the model and has exhibited effectiveness in image classification tasks. By incorporating self-attention mechanisms, the model can capture spatial relationships within remote sensing images, enhancing its accuracy and efficiency in flood detection. The model was trained and assessed using the SEN12-FLOOD dataset, and the outcomes demonstrate that the suggested framework exhibits enhanced flood detection performance, achieving an accuracy of 95.9%. © 2023 IEEE.

关键词： attention mechanism deep learning EfficientNetV2 flood detection remote sensing

来源：评论

学校读者我要写书评

暂无评论

Unveiling the Arsenal of User Data Protection Tools and Practices 1

Unveiling the Arsenal of User Data Protection Tools and Prac...

引用

1st IEEE Karachi Section Humanitarian Technology Conference, Khi-HTC 2024

作者： Moiz, Syed Abro, Abdul Ahad Ebrahim, Mansoor Abro, Abdul Basit Iqra University Main Campus Dept. of Computer Science Karachi Pakistan Iqra University Main Campus Dept. of Software Engineering Karachi Pakistan Indus University Dept. of Computer Science Karachi Pakistan

ISBN: (纸本)9798350373042

In our rapidly evolving digital landscape, the imperative of safeguarding personal data has surged in significance. As lives increasingly intertwine with digital technologies, personal information has grown markedly, parallel to the escalating threats posed by cyberattacks and data breaches. This research paper provides a comprehensive exploration of data security and privacy, aiming to impart a profound understanding of the intrinsic value of data. It meticulously examines essential methodologies such as encryption, access control, multifactor authentication, and systems, all of which collectively form the bedrock of a resilient security framework. Additionally, this study delves into the intricate regulatory landscape that governs data protection, placing particular emphasis on well-established frameworks such as the General Data Protection Regulation and the Health Insurance Portability and Accountability Act. It highlights emergent trends in data security, including the integration of artificial intelligence and machine learning for threat detection, while also addressing the distinctive challenges posed by the proliferation of Internet of Things (IoT) devices. In conclusion, this paper underscores the paramount importance of personal data protection in our digital age, offering invaluable insights and guidance for organizations and individuals endeavoring to bolster their defenses and uphold data integrity within our interconnected world. © 2024 IEEE.

关键词： Data privacy

来源：评论

学校读者我要写书评

暂无评论

Onboard Near-Real-Time Detection of Harmful Algal Blooms Using a Convolutional Neural Network and Multispectral Imagery from a Satellite in Low Earth Orbit

Onboard Near-Real-Time Detection of Harmful Algal Blooms Usi...

引用

OCEANS 2024 - Halifax, OCEANS 2024

作者： Parsons, Jonathan D. Seto, Mae L. Dalhousie University Computer Science Dept. Halifax Canada Dalhousie University Mechanical Engineering Dept. Halifax Canada

ISBN: (纸本)9798331540081

Freshwater harmful algal blooms (HABs) pose significant ecological and public health risks worldwide. Detecting HABs soon after they form is critical to managing the damage they cause. While in-situ measurements are more accurate at detecting and measuring their toxicity levels, satellite imagery is more adept.at capturing the spatial and temporal dynamics of these blooms over large geographic regions. Satellites can also more persistently monitor for HABs. In the past, empirical methods and machine learning methods have used multispectral satellite imagery to estimate HAB biomass. To build upon the current body of research, this paper investigates an approach to expedite HAB detection by utilizing a convolutional neural network (CNN) deployed onboard a CubeSat in low Earth orbit to detect HABs in near-real-time. The CNN is trained with multispectral imagery from the Sentinel-2 satellite constellation aggregated with in-situ cyanobacteria cell counts from the Seabass CAML dataset. The results successfully demonstrated the capability of a CNN to detect cyanobacterial blooms using multispectral imagery. After classifying HAB predictions into 5 severity classes, the best performing model achieved a RMSE of 1.33 between HAB severity levels. Training the CNN on 30m GSD imagery with RGB and red edge (B05) bands achieved a RMSE of 1.83 between HAB severity levels, which was inadequate for detecting HABs in small inland water bodies. Improved performance was observed with 10m ground sample distance (GSD) band combinations. The best performing networks utilized all of Sentinel-2's 10m and 20m spectral bands. © 2024 IEEE.

关键词： Satellite imagery

来源：评论

学校读者我要写书评

暂无评论

IRS Controlling Method With Machine Learning Based on Position Information 13

IRS Controlling Method With Machine Learning Based on Positi...

引用

13th IEEE Global Conference on Consumer Electronic, GCCE 2024

作者： Tsuchiya, Yoshihiko Suga, Norisato Uruma, Kazunori Fujisawa, Masaya Tokyo University of Science Dept. of Information and Computer Technology Tokyo Japan Shibaura Institute of Technology Dept. of Information and Communication Engineering Tokyo Japan Kogakuin University Dept. of Computer Science Tokyo Japan

ISBN: (纸本)9798350355079

Intelligent Reflecting Surfaces (IRS) is attracting attention for wireless communications at high-frequency band. IRS can control radio propagation by reflecting radio waves and shifting their phase. As one of the methods to control IRS, location-based methods have been proposed. However, to achieve highly accurate IRS control by the methods, the assumed propagation model must match the actual propagation environment. This study proposes a method to control the IRS from location information adapted to the propagation environment by using machine learning. Numerical experiments confirm that the proposed method can appropriately control IRS regardless of the propagation environment. © 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

Leveraging Deep Learning for Identification of Medicinal Plant Species 2

Leveraging Deep Learning for Identification of Medicinal Pla...

引用

2nd IEEE International Conference on Data science and Network Security, ICDSNS 2024

作者： Vidya, H.A. Narasimha Murthy, M.S. Thara, D.K. KIT Tiptur Vtu Dept. of Computer Science and Engineering Belagavi India BMSIT&M Bengaluru Vtu Dept. of Information Science and Engineering Belagavi India CIT Gubbi Vtu Dept. of Information Science and Engineering Belagavi India

ISBN: (数字)9798350373110

ISBN: (纸本)9798350373110

Identifying medicinal plant species is crucial in plant taxonomy because it allows us to harness the therapeutic properties of medicinal plants for human health. This information can be beneficial for the advancement of new drugs and treatments and for promoting the sustainable use of medicinal plants. Allopathic medicine has side effects in many cases. Traditional medicine, like Ayurveda, provides alternative treatments with minor side effects and helps protect human health. The decline of many medicinal plant species highlights the need for botanical research and biodiversity conservation. Artificial intelligence builds a gap between applications of computer technology in plant taxonomy. The goal of the study is to develop a reliable deep learning system for the recognition of medicinal plant species, which can subsequently aid in their preservation and support ongoing research efforts in this field. This paper focuses on a deep learning approach based on Convolutional Neural Network (CNN) to identify three distinct categories of medicinal plants. The model proposed uses CNN architecture and two pre-trained models, InceptionV3 and ResNet50, to classify three classes of medicinal plants, namely neem, peepal, and tulsi. The CNN model has an accuracy of 97.14% and a loss of 0.1274, the ResNet50 model has an accuracy of 97.06% and a loss of 0.1474, and the InceptionV3 model has an accuracy of 100% and a loss of 0.0199 in identifying the medicinal plant species. © 2024 IEEE.

关键词： Medicinal chemistry

来源：评论

学校读者我要写书评

暂无评论

Predictive Analytics in Agriculture: Unraveling the Determinants of Crop Yield with Machine Learning 26

Predictive Analytics in Agriculture: Unraveling the Determin...

引用

26th International Conference on computer and Information Technology, ICCIT 2023

作者： Sakib, Shadman Hossain, Md. Rakib Hasan, S. M. Mahedy Akter, Farzana Srizon, Azmain Yakin Faruk, Md. Farukuzzaman Basak, Anindya Dept. of Electronics & Telecommunication Engineering Rajshahi Bangladesh Dept. of Computer Science & Engineering Rajshahi Bangladesh

ISBN: (纸本)9798350359015

In the context of Bangladesh, the precision in forecasting crop yields has been enhanced significantly through the integrative approach of amalgamating historical data, environmental variables, and cutting-edge technology. This integrative methodology underscores the fortification of the nation's agricultural domain, integral to both its economic fabric and the populace's sustenance. This study presents an integrative approach leveraging historical data, environmental variables, and advanced technology to enhance crop yield forecasting. Traditional agricultural methods, while valuable, often lack comprehensive understanding of climatic factors, limiting agricultural refinement. Employing an Advanced computer-Aided Decision Support System, this research meticulously analyzes extensive datasets to uncover intricate correlations between historical yields and environmental nuances. In this study, a novel interpretative machine learning model is formulated, emphasizing data pre-processing and optimal feature transformation protocols tailored to diverse model archetypes. The study delves into the influence of distinct variables on predictive acumen and the impact of individual observational values on forecast outcomes. The study's contributions include a transparent machine learning model elucidating key variables affecting crop yield predictions, paving the way for informed agricultural practices. Future endeavors could focus on leveraging real-time data and expanding datasets to further refine predictive models in this crucial domain. © 2023 IEEE.

关键词： Crop Yield Prediction Feature Importance Machine Learning Parameter Optimization

来源：评论

学校读者我要写书评

暂无评论

Machine Learning Framework for Women Safety Prediction using Decision Tree 5

Machine Learning Framework for Women Safety Prediction using...

引用

5th International Conference on Smart Systems and Inventive Technology, ICSSIT 2023

作者： Sowmika, P. Sareena Rao, S.Siva Nageswara Rafi, Shaik Dept. of Computer Science and Engineering Andhra Pradesh India

ISBN: (纸本)9781665474672

In every city, harassment and violence becomes one of the major problems for women. Further, women's personal life is suffered by the bullying and abusive content presented in Online Social Networking (OSN). Therefore, it is necessary to identify the women safety in OSN environment. When it came to predicting the maximum safety analysis, however, traditional methodologies came up short. This study, then, employs a decision tree (WSP-DT) classifier to make predictions about women's safety. After considering the Twitter dataset for system implementation, it is pre-processed to get rid of the blanks and the unknowns. The tweets were then processed by a natural language toolkit (NLTK) that handled tasks including tokenization, case-conversion, stop-word detection, stemming, and lemmatization. Next, we create a text blob protocol to determine the positive, negative, and neutral polarity of pre-processed tweets. To further extract the data characteristics based on word and character frequency, term frequency-inverse document frequency (TF-IDF) is used. At last, a decision tree classifier was used, based on several rounds of training, to determine if a tweet was phoney or real. Testing on the Twitter dataset demonstrates that the proposed WSP-DT classifier outperforms the competition in simulations. © 2023 IEEE.

关键词： Decision trees

来源：评论

学校读者我要写书评

暂无评论

Utilizing Advanced Machine Learning Techniques for Effective Prediction and Control of Pandemic Outbreaks 2

Utilizing Advanced Machine Learning Techniques for Effective...

引用

2nd IEEE International Conference on Trends in Quantum Computing and Emerging Business Technologies, TQCEBT 2024

作者： Rajasekaran, R. Thalapathi Shanmugavalli, H. Jayalakshmi, P. Ambika, S. Balaji, V. Saveetha School of Engineering Simats Dept. of Computer Science and Engineering Chennai India Grt Institute of Engineering and Technology Dept. of Computer Science and Engineering Tamil Nadu Tiruttani India Sri Krishna College of Engineering Dept. of Computer Science and Engineering Arakkonam India E.G.S. Pillay Engineering College Department of Information Technology Tamil Nadu Nagapattinam India Chennai India

ISBN: (纸本)9798350384277

The health and social implications of pandemic epidemics are substantial. Accurate forecasting and management of such epidemics are of utmost importance to lessen the blow of such epidemics. To tackle this pressing problem, we employ state-of-the-art machine learning methods in our research. In this paper, we investigate the potential of cutting-edge algorithms for modeling, predicting, and controlling the spread of pandemics. These algorithms include LSTM, Random Forest, GNN, RL, DRL, Bayesian Networks, and SVM. We run a battery of experiments and analyses using real-world data sources, such as COVID-19 case data, mobility data, contact tracking data, vaccination distribution data, intervention strategy data, and resource allocation data. These advanced machine-learning approaches have the ability to enhance the accuracy of pandemic predictions, optimize the allocation of resources, decrease transmission rates, and enable more effective intervention measures, according to our findings. For example, our RL algorithm optimized vaccination distribution by 20% while our LSTM model averaged a 92% success rate in forecasting new cases each day. Our research further emphasizes the significance of early detection and intervention. With a 90% success rate, the SVM-based early detection system could spot epidemics ten days before conventional approaches could, buying precious time for focused and efficient responses. © 2024 IEEE.

关键词： COVID-19

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 << < 67 68 69 70 71 72 73 74 75 76 > >>

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：