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
93 册 图书
20 篇 期刊文献

馆藏范围

1,494 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

1,206 篇 工学
- 987 篇 计算机科学与技术...
- 809 篇 软件工程
- 268 篇 信息与通信工程
- 146 篇 生物工程
- 125 篇 生物医学工程（可授...
- 119 篇 控制科学与工程
- 82 篇 电气工程
- 71 篇 光学工程
- 70 篇 机械工程
- 67 篇 电子科学与技术（可...
- 49 篇 安全科学与工程
- 48 篇 化学工程与技术
- 44 篇 仪器科学与技术
- 40 篇 土木工程
536 篇 理学
- 286 篇 数学
- 166 篇 生物学
- 122 篇 物理学
- 108 篇 统计学（可授理学、...
- 65 篇 系统科学
- 44 篇 化学
343 篇 管理学
- 181 篇 管理科学与工程(可...
- 181 篇 图书情报与档案管...
- 117 篇 工商管理
113 篇 医学
- 110 篇 临床医学
- 103 篇 基础医学(可授医学...
- 64 篇 药学(可授医学、理...
- 45 篇 公共卫生与预防医...
71 篇 法学
- 61 篇 社会学
60 篇 教育学
- 60 篇 教育学
52 篇 农学
- 49 篇 作物学
33 篇 经济学
4 篇 军事学
2 篇 文学
1 篇 艺术学

主题

40 篇 artificial intel...
28 篇 computer communi...
23 篇 machine learning
22 篇 information syst...
22 篇 students
20 篇 internet of thin...
18 篇 computer vision
16 篇 support vector m...
16 篇 convolutional ne...
15 篇 computational in...
15 篇 semantics
14 篇 deep learning
13 篇 information stor...
12 篇 feature extracti...
12 篇 accuracy
12 篇 technological in...
11 篇 human computer i...
11 篇 wireless sensor ...
11 篇 visualization
11 篇 decision trees

机构

6 篇 department of co...
5 篇 beijing research...
5 篇 school of comput...
5 篇 department of co...
5 篇 department of co...
5 篇 department of co...
5 篇 institute of com...
5 篇 school of comput...
4 篇 university of sc...
4 篇 key laboratory o...
4 篇 gits udaipur
4 篇 jinan university
4 篇 lovely professio...
4 篇 insight sfi rese...
4 篇 department of co...
4 篇 mlr institute of...
4 篇 purdue universit...
4 篇 school of comput...
4 篇 jaypee universit...
4 篇 department of co...

作者

7 篇 sun fuchun
7 篇 harris frederick...
6 篇 bein doina
6 篇 vani v.
6 篇 karthik n.
5 篇 tyagi vipin
5 篇 constantine step...
5 篇 yang yujian
5 篇 margherita anton...
4 篇 rajalakshmi s.
4 篇 de-shuang huang
4 篇 jose joemon m.
4 篇 sharma ajay kuma...
4 篇 ootsu kanemitsu
4 篇 rathore narendra...
4 篇 patel mayank
4 篇 xiaorui zhang
4 篇 yokota takashi
4 篇 baba takanobu
4 篇 elisa bertino

语言

1,447 篇 英文
35 篇 其他
29 篇 中文

检索条件"任意字段=7th IASTED International Conference on Advances in Computer Science and Technology"

共 1494 条记录，以下是151-160 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：

Performance Evaluation of Several Transfer Learning Models for Classification of Road Surface State 1

引用

Proceedings of the 6th IFIP TC 12 international conference on Computational Intelligence in Data science, ICCIDS 2023

作者： Rahman, Fahim Ur Ahmed, Md. Tanvir Khan, Emran Rahman, Md Mahfuzur Ahamed, Shafin Mamun, Shahriar Hasan, Md Mehedi Department of Computer Science and Engineering Daffodil International University Dhaka Bangladesh Department of English Daffodil International University Dhaka Bangladesh

ISBN: (数字)9783031382963

ISBN: (纸本)9783031382956

Using a customized approach, automatic classification of road surface condition and categorized data storing are proposed using DenseNet201. Road surface distress is one of the main issues affecting transportation safety. the first indication of a catastrophic asphalt pavement collapsing is a surface crack, that can later develop into a pothole and result in high repair costs. By replacing the surveillance system with the automated software program that we are recommending in this analysis, the traditional methods for identifying cracks or degradation in a road's surface, which involved manual examination by people, can be eliminated. DenseNet201 has outperformed other compared models with an accuracy of 98.75% and the most minimal model loss while testing while classifying damaged and smooth road surfaces. Later, certain governing bodies responsible for preserving the quality of road infrastructure can use the model's classified images. © 2023, IFIP international Federation for Information Processing.

关键词： computer vision

来源：评论

学校读者我要写书评

暂无评论

Lattice based Ring Signature Scheme for Secure Cloud-based EMR Sharing 7

Lattice based Ring Signature Scheme for Secure Cloud-based E...

引用

7th international conference on computer and Communication Systems, ICCCS 2022

作者： Hu, Wenxin Chai, Yidan Chen, Xue Zheng, Chao North China University of Technology Beijing China

ISBN: (数字)9781665450607

ISBN: (纸本)9781665450607

advances in medical science and technology continue to drive the development of e-healthcare, with Electronic Medical Records (EMRs) consisting of medical information such as patient consultation records and bodily data being gradually popularized in the form of cloud sharing. the sharing of EMR effectively enhances the effectiveness and reliability of healthcare services. However, the highly sensitive EMRs are susceptible to tampering and misuse during the EMR sharing, which poses a tremendous threat to patients' privacy and the security of e-health. Further, EMRs are extremely vulnerable to quantum computing attacks during the process of sharing in cloud, which will be the most significant bottleneck in the advancement of e-healthcare systems. To tackle the foregoing issues, we present a lattice-based ring signature scheme for secure EMR sharing on the cloud. To begin with, we implement EMR sharing with medical cloud. then, we set a lattice-based ring signature scheme to ensure user anonymity, EMR and signature unforgeability, while resisting quantum attacks. Further, we require all EMR-related users to perform multiparty confirmation signatures on the EMR. In conlusion, the security analysis and performance evaluation indicate that our scheme guarantees correctness, anonymity, and unforgeability, while outperforming other existing schemes. © 2022 IEEE.

关键词： Health risks

来源：评论

学校读者我要写书评

暂无评论

Campus Drive Portal on Career Advancement for College Students 7th

Campus Drive Portal on Career Advancement for College Studen...

引用

7th IFIP TC 12 international conference on Computational Intelligence in Data science, ICCIDS 2024

作者： Nandhini, S. Praganya, P. Karthick, V. Ariyamala, V. Department of Computer Science and Engineering Rajalakshmi Engineering College Tamil Nadu Chennai India

ISBN: (纸本)9783031699856

the Campus Drive Portal is a user-friendly website designed to assist both students and Placement Admins in managing campus recruitment events. this application provides the facility to view upcoming campus drives. the two users of our application are Student and Placement Admin. From Placement Perspective, the portal simplifies the management of student information for upcoming drives. this includes creating, updating, viewing, and deleting student records, with the ability to filter based on criteria set by companies. Admin can update the details like Company’s name, drive date and eligibility criteria for the upcoming placement drives. the portal also incorporates a prediction tool to assess a student’s chances of securing placement and further the trend analysis is used to help students by providing better training programs. the placement administrator has the facility to collect the offer letters from the placed candidates, which will be helpful in storing the details and analyzing the trends. From a Students’ Perspective, Students can receive information regarding the upcoming drives. After interviews, students could contribute valuable feedback about their experiences with companies. this feedback not only benefits the individual student but also aids the placement department in making informed decisions about future collaborations with companies. the system further includes a prediction tool that allows students to evaluate their interview preparedness and offers guidance on improving their skills. Students can also gain valuable insights from their seniors about different interview rounds, promoting a collaborative learning environment. © IFIP international Federation for Information Processing 2024.

关键词： Students

来源：评论

学校读者我要写书评

暂无评论

Innovative Approaches to Reduce Carbon Footprint and Air Pollution: the Role of AI, ML, Cloud Computing, and IoT 4th

Innovative Approaches to Reduce Carbon Footprint and Air Pol...

引用

4th international conference on Machine Learning, advances in Computing, Renewable Energy and Communication, MARC 2023

作者： Ghalwan, Manish Bisht, Sapna Pant, Rajesh Lande, Jayapal Pant, Shivani Swami, Siddharth Department of Civil Engineering Uttaranchal Institute of Technology Uttaranchal University Uttarakhand Dehradun India Uttranchal Institute of Management Uttaranchal University Uttarakhand Dehradun India School of Computer Science and AI SR University Warangal India Department of Computer Science Engineering Uttaranchal Institute of Technology Uttaranchal University Uttarakhand Dehradun India School of Environment and Natural Resources Doon University Uttarakhand Dehradun India

ISBN: (纸本)9789819752300

In recent years, the phrase "carbon footprint" has gained enormous popularity. the environment is being disrupted by severe global warming and its accompanying effects due to increased greenhouse gases in the atmosphere. this review’s objectives are to highlight recent developments in our understanding, summarise the body of available information, and propose research goals. the background material in the article covers the causes, trends in emissions and their effect on the environment. In this regard, the incorporation of carbon footprint and air pollution 4.0 with various technologies (Internet of things (IoT), artificial intelligence (AI), blockchain, and big data for acquiring data and current industrial breakthroughs. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Carbon capture and utilization

来源：评论

学校读者我要写书评

暂无评论

A Comparison of Various Class Balancing and Dimensionality Reduction Techniques on Customer Churn Prediction 7

A Comparison of Various Class Balancing and Dimensionality R...

引用

7th IEEE international conference on Recent advances and Innovations in Engineering, ICRAIE 2022

作者： Bhushan Mada, Sri Phani thimmireddygari, Nandini Tata, Rahul thondapu, Snehatejasree Reddy Saleti, Sumalatha SRM University Department of Computer Science Engineering AP India

ISBN: (纸本)9781665489102

With the advancement of technology, companies are able to foresee the customers who are going to leave their organization much before. this problem of customer churn prediction is handled in the current work. In the real-world, data is not balanced, having more observations with respect to few classes and less observations in case of other classes. But, giving equal importance to each class is really significant to build an efficient prediction model. Moreover, real-world data contains many attributes meaning that the dimensionality is high. In the current paper, we discussed three data balancing techniques and two methods of dimensionality reduction i.e. feature selection and feature extraction. Further, selecting the best machine learning model for churn prediction is an important issue. this has been dealt in the current paper. Also, we aim to improve the efficiency of customer churn prediction by evaluating various class balancing and dimensionality reduction techniques. Moreover, we evaluated the performance of the models using AUC curves and K-fold cross-validation techniques. © 2022 IEEE.

关键词： Forecasting

来源：评论

学校读者我要写书评

暂无评论

CNN-Based Skin Lesion Classification for Melanoma Detection 7th

CNN-Based Skin Lesion Classification for Melanoma Detection

引用

7th IFIP TC 12 international conference on Computational Intelligence in Data science, ICCIDS 2024

作者： Havirbhavi, P. Ashwini, K. Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Thiruvallur Chennai India

ISBN: (纸本)9783031699818

the significant worldwide health risk posed by skin cancer, especially melanoma, highlights the urgent need for sophisticated diagnostic techniques to facilitate early diagnosis. the goal of this research is to improve the efficiency and accuracy of melanoma diagnosis by doing a thorough analysis of dermatological pictures using CNNs. Two different CNN models are implemented: one uses the ResNet50 architecture for feature extraction, while the other is a specially created CNN for the classification of skin lesions. thorough preparation of the data, including rotation, flipping, and sophisticated methods like loading, scaling, and normalizing of images, guarantees the best possible model performance. Treating the underlying imbalances in skin lesion datasets is one of the main goals. this is accomplished by computing and adding class weights during training, which is especially important for data that isn’t balanced. Properly calibrated learning rates, accuracy as the evaluation measure, categorical cross-entropy loss, and the Adam optimizer are all used in the model optimization process. One special feature of this project is the incorporation of a progress indicator based on TQDM, which offers real-time information about model convergence when training. through the careful preparation of data, careful CNN designs, and efficient techniques for handling unbalanced data, this program seeks to substantially enhance the field of skin cancer diagnosis. As the fight against skin cancer never ends, the conclusive goal is to enhance patient outcomes through more precise and early melanoma detection. © IFIP international Federation for Information Processing 2024.

关键词： Oncology

来源：评论

学校读者我要写书评

暂无评论

Predictive EQCi-Optimized Load Scheduling for Heterogeneous IoT-Data in Fog Computing Environments 7

Predictive EQCi-Optimized Load Scheduling for Heterogeneous ...

引用

7th international conference on Image Information Processing, ICIIP 2023

作者： Madasamy, Sridhar Vikkram, R. Basi Reddy, A. Nandhini, T. Gupta, Shipra Nagamani, A. Cognizant Technology Solution 500 Frank W. Burr Boulevard TeaneckNJ07666 United States Karpagam College of Engineering Department of Information Technology Tamilnadu Coimbatore India Mohan Babu University School of Computing Department of Computer Science And Engineering Andhra Pradesh Tirupati India KSR Institute For Engineering And Technology Department of Information Technology TamilNadu Tiruchengode637 215 India Graphic Era Hill University School of Management Uttarakhand Dehradun India Mlr Institute of Technology Department of Computer Science And Engineering Telangana Hyderabad500043 India

ISBN: (纸本)9798350371406

this paper presents a pioneering predictive EQCi-optimized load scheduling technique tailored to the complexities of heterogeneous IoT data within fog computing environments. the proposed method strategically assigns tasks to fog nodes by considering Load Scheduling Policies (LSPs) and the computing capabilities of fog nodes. Incorporating an EQCi-technique, the model enhances load allocation precision for various LSPs, facilitated by representing LSPs as quantum bits (qubits). this representation enables efficient assessment of probabilistic Execution Index values and Node Availability Index (NAI) for fog nodes, facilitating real-time task allocation. Furthermore, the introduction of EQCi-NN introduces a predictive model that anticipates the optimal fog node for specific tasks with given LSPs. this predictive capability minimizes execution delays and enhances throughput within the fog computing environment. the proposed technique's performance is rigorously validated through simulations using the iFogSim toolkit, and its efficacy is demonstrated against existing load scheduling methods. Comparative analysis with other predictive techniques highlights the precision and efficiency of the proposed prediction model. the research underscores the presented system's effectiveness in predicting optimal computational nodes within fog computing platforms, particularly in scenarios necessitating real-time analytical outcomes. By synergizing quantum-inspired representation and predictive modeling, this study advances load scheduling methodologies in fog computing, optimizing resource utilization and bolstering the responsiveness of IoT-driven applications. © 2023 IEEE.

关键词： Fog computing

来源：评论

学校读者我要写书评

暂无评论

Sentiment Analysis of Various Ride Sharing Applications Reviews: A Comparative Analysis Between Deep Learning and Machine Learning Algorithms 7th

Sentiment Analysis of Various Ride Sharing Applications Revi...

引用

7th IFIP TC 12 international conference on Computational Intelligence in Data science, ICCIDS 2024

作者： Ahammad, Md. Saymon Sinthia, Sadia Akter Chowdhury, Muaj Asif, Nur-A-All Afsarikram, Nurul Department of Computer Science and Engineering Daffodil International University Dhaka Bangladesh

ISBN: (纸本)9783031699856

the exponential growth of ride sharing platforms in developing countries has significantly transformed the landscape of urban transportation. It is crucial for service providers to comprehend customer feedback and opinions in order to enhance the user experience and for passengers to select a safe and secure ride. A comprehensive sentiment analysis was conducted on user evaluations of ridesharing platforms such as Grab, Uber, Indrive, Pathao, Jatri, and Obhai. An aggregate of 12052 data points (Negative, Positive, Neutral) were extracted from ride sharing reviews available on the Google Play Store. By utilizing cutting-edge deep learning models (LSTM, GRU, BiLSTM, and BiGRU) in addition to traditional machine learning models (Random Forest, Decision Tree, Gradient Boosting Classifier, Logistic Regression, KNN, Naive Bayes, SVM, AdaBoost, and LightGBM), our research endeavors to offer substantial insights into the determinants of customer satisfaction. Support Vector Machine is the most effective machine learning algorithm in this context, attaining an accuracy rate of 76.70%. Bidirectional Gated Recurrent Unit emerged as the best-performing model in the domain of deep learning, attaining an exceptional accuracy rate of 94.97%, this algorithm demonstrates the efficacy of deep learning methodologies in the context of sentiment analysis. Our research indicates that deep learning algorithms demonstrate significant superiority when compared to machine learning algorithms. © IFIP international Federation for Information Processing 2024

关键词： Customer satisfaction

来源：评论

学校读者我要写书评

暂无评论

Gesture Me: A Machine Learning Tool for Designers to Train Gesture Classifiers 7th

Gesture Me: A Machine Learning Tool for Designers to Train G...

引用

7th international conference on computer-Human Interaction Research and Applications, CHIRA 2023

作者： Winter, Marcus Jackson, Phil Fallahkhair, Sanaz School of Architecture Technology and Engineering University of Brighton Brighton United Kingdom

ISBN: (纸本)9783031494246

this paper contributes to the body of work examining how designers can be supported in integrating machine learning (ML) capabilities into their designs for novel applications and services. It presents an online tool enabling designers and other non-specialist audiences to define body gestures, interactively and iteratively train and test a classifier to recognise these gestures, and integrate the trained classifier into a template web application. An empirical evaluation with MSc User Experience Design students and practitioners, all of whom had previous experience in web development but not in ML, found that the tool enables them to define, train and test a gesture recognition classifier with little or no help, and that engagement with the tool advances their understanding of the capabilities, limitations and operational aspects of ML. the evaluation confirmed the value of visualising the ML perspective and encouraging designers to experiment with ML to support their experiential learning. the study led to design recommendations that can inform the development of tools supporting designers to ideate and prototype ML-enhanced applications. © 2023, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Gesture recognition Machine learning UX design

来源：评论

学校读者我要写书评

暂无评论

VEL@LT-EDI-2023: Detecting Homophobia and Transphobia in Code-Mixed Spanish Social Media Comments 3

VEL@LT-EDI-2023: Detecting Homophobia and Transphobia in Cod...

引用

3rd Workshop on Language technology for Equality, Diversity and Inclusion, LTEDI 2023

作者： Kumaresan, Prasanna Kumar Ponnusamy, Kishore Kumar Shanmugavadivel, Kogilavani Navaneethakrishnan, Subalalitha Chinnaudayar Priyadharshini, Ruba Chakravarthi, Bharathi Raja Insight SFI Research Centre for Data Analytics University of Galway Ireland Guru Nanak College Tamil Nadu Chennai India Kongu Engineering College Tamil Nadu India Department of Computer Science & Engineering SRM Institute of Science and Technology Tamil Nadu India Gandhigram Rural Institute-Deemed to be University Tamil Nadu India Insight SFI Research Centre for Data Analytics School of Computer Science University of Galway Ireland

ISBN: (纸本)9789544520847

Our research aims to address the task of detecting homophobia and transphobia in social media code-mixed comments written in Spanish. Code-mixed text in social media often violates strict grammar rules and incorporates non-native scripts, posing challenges for identification. To tackle this problem, we perform pre-processing by removing unnecessary content and establishing a baseline for detecting homophobia and transphobia. Furthermore, we explore the effectiveness of various traditional machine-learning models with feature extraction and pre-trained transformer model techniques. Our best configurations achieve weighted F1 scores of 0.86 on the test set and 0.86 on the development set for Spanish, demonstrating promising results in detecting instances of homophobia and transphobia in code-mixed comments. © 2023 LTEDI 2023 - 3rd Workshop on Language technology for Equality, Diversity and Inclusion, associated with the 14th international conference on Recent advances in Natural Language Processing, RANLP 2023 - Proceedings. All rights reserved.

关键词： Social networking (online)

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150页 << < 12 13 14 15 16 17 18 19 20 21 > >>

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：