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

88,992 篇 会议
38,397 篇 期刊文献
1,305 册 图书

馆藏范围

128,694 篇 电子文献
3 种 纸本馆藏

日期分布

学科分类号

72,757 篇 工学
- 46,307 篇 计算机科学与技术...
- 37,027 篇 软件工程
- 18,655 篇 信息与通信工程
- 12,150 篇 控制科学与工程
- 11,749 篇 电气工程
- 11,460 篇 电子科学与技术（可...
- 8,305 篇 生物工程
- 7,083 篇 生物医学工程（可授...
- 6,854 篇 光学工程
- 5,088 篇 机械工程
- 4,644 篇 化学工程与技术
- 4,525 篇 仪器科学与技术
- 4,353 篇 动力工程及工程热...
- 4,096 篇 材料科学与工程（可...
- 3,249 篇 网络空间安全
- 2,982 篇 安全科学与工程
43,501 篇 理学
- 19,370 篇 数学
- 16,780 篇 物理学
- 9,542 篇 生物学
- 6,332 篇 统计学（可授理学、...
- 5,200 篇 化学
- 4,693 篇 系统科学
17,652 篇 管理学
- 11,094 篇 管理科学与工程(可...
- 7,332 篇 图书情报与档案管...
- 5,402 篇 工商管理
7,209 篇 医学
- 5,952 篇 临床医学
- 5,340 篇 基础医学(可授医学...
- 3,277 篇 药学(可授医学、理...
- 3,087 篇 公共卫生与预防医...
3,491 篇 法学
- 2,855 篇 社会学
2,234 篇 经济学
1,890 篇 农学
1,154 篇 教育学
557 篇 文学
281 篇 军事学
123 篇 艺术学
14 篇 哲学
8 篇 历史学

主题

3,873 篇 accuracy
3,118 篇 deep learning
2,915 篇 feature extracti...
2,831 篇 computer science
2,718 篇 machine learning
2,661 篇 computational mo...
2,286 篇 training
1,985 篇 real-time system...
1,875 篇 internet of thin...
1,854 篇 support vector m...
1,803 篇 convolutional ne...
1,763 篇 predictive model...
1,626 篇 artificial intel...
1,459 篇 optimization
1,395 篇 neural networks
1,350 篇 wireless sensor ...
1,258 篇 data mining
1,250 篇 security
1,218 篇 bandwidth
1,208 篇 computer archite...

机构

1,088 篇 cavendish labora...
1,018 篇 department of ph...
962 篇 physikalisches i...
953 篇 oliver lodge lab...
768 篇 university of ch...
678 篇 chitkara univers...
651 篇 department of el...
610 篇 infn laboratori ...
605 篇 infn sezione di ...
580 篇 infn sezione di ...
563 篇 infn sezione di ...
560 篇 fakultät physik ...
547 篇 school of physic...
539 篇 horia hulubei na...
531 篇 department of ph...
523 篇 school of physic...
516 篇 h.h. wills physi...
511 篇 imperial college...
503 篇 nikhef national ...
502 篇 stfc rutherford ...

作者

263 篇 barter w.
252 篇 a. gomes
244 篇 c. alexa
240 篇 j. m. izen
237 篇 j. wang
233 篇 c. amelung
233 篇 blake t.
232 篇 g. bella
232 篇 j. strandberg
231 篇 d. calvet
231 篇 beiter a.
227 篇 s. chen
224 篇 bellee v.
223 篇 amato s.
222 篇 betancourt c.
221 篇 bowcock t.j.v.
221 篇 boettcher t.
220 篇 borsato m.
219 篇 brundu d.
217 篇 bencivenni g.

语言

105,470 篇 英文
21,928 篇 其他
1,506 篇 中文
119 篇 日文
26 篇 斯洛文尼亚文
25 篇 法文
24 篇 德文
8 篇 朝鲜文
6 篇 阿拉伯文
6 篇 土耳其文
4 篇 俄文
3 篇 荷兰文
1 篇 爱沙尼亚文
1 篇 意大利文
1 篇 波兰文
1 篇 葡萄牙文

检索条件"机构=Computer Technology Institute Department of Computer Engineering and Informatics"

共 128694 条记录，以下是4891-4900 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：

A Generalized Unknown Malware Classification 1

引用

18th EAI International Conference on Security and Privacy in Communication Networks, SecureComm 2022

作者： Rani, Nanda Mishra, Ayushi Kumar, Rahul Ghosh, Sarbajit Shukla, Sandeep K. Bagade, Priyanka Department of Computer Science and Engineering Indian Institute of Technology Kanpur Kanpur India

ISBN: (数字)9783031255380

ISBN: (纸本)9783031255373

Although state-of-the-art image-based malware classification models give the best performance, these models fail to consider real-world deployment challenges due to various reasons. We address three such problems through this work: limited dataset problems, imbalanced dataset problems, and lack of model generalizability. We employ a prototypical network-based few-shot learning method for a limited dataset problem and achieve 98.71% accuracy while training with only four malware samples of each class. To address the imbalanced dataset problem, we propose a class-weight technique to increase the weightage of minority classes during the training. The model performs well by improving precision and recall from 0% to close to 60% for the minority class. For the generalized model, we present a meta-learning-based approach and improve model performance from 48% to 72.06% accuracy. We report performances on five diverse datasets. The proposed solutions have the potential to set benchmark performance for their corresponding problem statements. © 2023, ICST institute for computer Sciences, Social informatics and Telecommunications engineering.

关键词： Malware

来源：评论

学校读者我要写书评

暂无评论

Quantum-Safe Federated Learning: Enhancing Data Privacy and Security 2

Quantum-Safe Federated Learning: Enhancing Data Privacy and ...

引用

2nd IEEE International Conference on Emerging Research in Computational Science, ICERCS 2024

作者： Kannan, E. Carmel Mary Belinda, M.J. Ravikumar, S. Alex David, S. Kannan, Sriram Vijay, K. Vel Tech Rangarajan Dr. Sagunthala RD Institute of Science and Technology Department of Computer Science and Engineering Chennai India Saveetha Institute of Medical And Technical Sciences Saveetha School of Engineering Department of Computer Science and Engineering Chennai India Fidelity Investments Chennai India Rajalakshmi Engineering College Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798331534950

Ensuring data security and privacy is highly pertinent in today's era marked by the dominance of collaborative machine learning. This study presents a novel framework, named"Quantum-Resilient Privacy-Preserving Federated Learning for Enhanced Data Security,"which leverages quantum computing to strengthen federated learning models against adversarial risks and enhance privacy safeguards. Our research investigates the incorporation of quantum key distribution and entanglement protocols into federated learning, therefore establishing a quantum- resilient methodology. The findings demonstrate the model's ability to effectively resist advanced threats, hence assuring the maintenance of strong data security measures. The utilization of quantum technology in this context serves to strengthen the fundamental principles of cryptography, while simultaneously introducing a novel approach to enhancing privacy. Moreover, the study explores the potential of the framework to accommodate various datasets from different cooperating entities, while also resolving the difficulties presented by the diversity of the data. The framework's success in accommodating multiple machine learning models and applications is demonstrated by evaluations of scalability and computing efficiency. In essence, the research paper titled"Quantum-Resilient Privacy-Preserving Federated Learning for Enhanced Data Security"represents a significant progression in the field of safeguarding collaborative machine learning. The quantum-resilient paradigm not only increases privacy but also offers a safe foundation for the future of collaborative machine learning. The issue at hand pertains to the growing apprehensions over the safeguarding of data privacy and security within the context of our technologically advanced day. © 2024 IEEE.

关键词： Federated learning

来源：评论

学校读者我要写书评

暂无评论

An Efficient Deep Learning Technique for Diabetes Classification and Prediction Based on Indian Diabetes Dataset 3

An Efficient Deep Learning Technique for Diabetes Classifica...

引用

3rd International Conference on Technological Advancements in Computational Sciences, ICTACS 2023

作者： Singh, Prateeksha Silakari, Sanjay Agrawal, Shikha University Institute of Technology Rgpv Department of Computer Science & Engineering Bhopal India

ISBN: (纸本)9798350342338

Diabetes is a chronic metabolic disorder that affects millions of people worldwide, and early diagnosis and effective prediction of this disease are crucial for its management and prevention of associated complications. Recently, deep_learning shows effective results in various healthcare applications, including disease classification and prediction. This paper presents diabetes classification and prediction using machine learning approach. Proposed system is increased the accuracy of the results by diabetes prediction framework using artificial neural network (ANN) deep learning algorithms. The performance of the overall classification results is improved as a consequence of this. The use of machine learning (ML) algorithms is currently effective for sickness detection. © 2023 IEEE.

关键词： ANN Diabetes Classification Disease Machine Learning

来源：评论

学校读者我要写书评

暂无评论

Gui that Detects Liver Disease Using Machine Learning

Gui that Detects Liver Disease Using Machine Learning

引用

2023 International Conference on Innovations in engineering and technology, ICIET 2023

作者： Ashok Kumar, K. Sri Rama Subrahmanyam, K. Srinivasulu Reddy, M. Sathyabama Institute of Scienceand Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350309980

The liver is an important organ because it produces vitamins & substances necessary for the survival of the decaying body, including iron, toxins, proteins, & carbs. It aids in vitamin and mineral metabolism, tubular muscle growth, and blood coagulation. Factors including alcohol intake, opioid painkiller usage, poor nutrition, and underlying medical disorders have all been linked to the development of liver disease. The results of a patient's blood tests and physical exam may help physicians determine risk factors for liver disease. Data mining methods may help speed up the process of identifying liver illness. When more information is utilized, the vaccination becomes more precise. This design aims to address this issue by incorporating a method of device mastery into the process of evaluating liver disease. The primary motive of this system is to apply a wide angles set of concepts to perceive healthy folks that are honestly affected by liver disease. The design of this system also aims to evaluate and contrast different bracketing algorithms according to their operational characteristics. A Python-based graphical interface for Stoner is now being development to aid clinicians in the assessment of liver issues. The user interface may be used by soothsayers and medical interpreters as a web application for liver trials. © 2023 IEEE.

关键词： Blood

来源：评论

学校读者我要写书评

暂无评论

Brain Tumor Detection Using Convolutional Neural Network 5

Brain Tumor Detection Using Convolutional Neural Network

引用

5th IEEE International Conference on Cybernetics, Cognition and Machine Learning Applications, ICCCMLA 2023

作者： Uke, Shailaja Kaulwar, Mayuresh Kawtikwar, Yash Khadse, Samyak Khandare, Anurag Vishwakarma Institute of Technology Department of Computer Engineering Maharashtra Pune411037 India

ISBN: (纸本)9798350338287

Tumor is the abnormal development of cells in the body. The fairly significant overgrowth of brain cells is known as a brain tumor and classification manually takes time and is only possible at a few diagnostic facilities. Therefore, it is necessary to create a system that can identify the type of brain tumor based solely on the input MR images. To evaluate these malignancies, a common imaging approach is magnetic resonance imaging (MRI), however, manual segmentation cannot be done in a timely manner due to the volume of data generated by MRI, restricting the application of exact quantitative measurements in clinical practice. This study presents a novel hybrid methodology for the classification of brain cancers based on the analysis of magnetic resonance imaging (MRI) data. The methodology compares the performance of different classifiers, namely Convolutional Neural Networks (CNNs), K-nearest neighbor, Support Vector Machine and Logistic Regression in predicting the presence of brain tumors. The CNN model is specifically employed to get features from the MRI data and apply them to the classifiers. The results of the study show that the different CNN models, along with KNN, SVM, LR, and Long Short-Term Memory Networks were taken for tumor classification. The achieved accuracies were 82.35%, 78.43%, 61.26% and 74.28% respectively. Among these algorithms, KNN demonstrated the highest accuracy, indicating its effectiveness in accurately classifying brain tumors based on the MRI data. By integrating these classifiers and utilizing the CNN model for feature extraction, this hybrid methodology presents a promising method for classifying brain tumors from MRI data. The findings highlight the potential of combining deep learning techniques like CNNs with traditional machine learning algorithms to increase the reliability of diagnosing brain tumors and contribute to the field of medical image analysis. © 2023 IEEE.

关键词： Magnetic resonance imaging

来源：评论

学校读者我要写书评

暂无评论

Privacy-Preserving and Verifiable Decentralized Federated Learning 5

Privacy-Preserving and Verifiable Decentralized Federated Le...

引用

5th International Conference on Energy, Power, and Environment, ICEPE 2023

作者： Kumar, Sanjipogu Sandeep Joshith, Telugu Sai Lokesh, Duddupudi Daya Jahnavi, Dasari Mahato, Ganesh Kumar Chakraborty, Swarnendu Kumar National Institute of Technology Department of Computer Science & Engineering Arunachal Pradesh India

ISBN: (纸本)9798350313123

Federated learning is a machine learning technique that allows multiple devices to collaboratively train a machine learning model without sharing their data with a central server. The data is kept on the local device, and the model is trained on the device itself. It also reduces the risk of data breaches and enhances privacy by keeping the data local to the device. It can improve the speed and efficiency of training large-scale models by distributing the workload across multiple devices. This paper proposes a solution to the security challenges faced by the federated learning framework, which allows global model construction without sharing raw data. Privacy-preserving and verifiable decentralized federated learning (PPVD-FL) is a framework designed for secure deep learning model training. It's a decentralized federated learning framework that pre-serves privacy and ensures verification. The framework uses an efficient and verifiable cipher-based matrix multiplication algorithm along with a suite of decentralized algorithms. It maintains the confidentiality of the global model and local updates and verifies every training step. PPVD-FL can protect privacy against various inference attacks and ensure training integrity, as shown by security analysis. Real-world experiments on datasets demonstrate its accuracy and practical performance. © 2023 IEEE.

关键词： Deep learning

来源：评论

学校读者我要写书评

暂无评论

Sweep Coverage with Faults 5

Sweep Coverage with Faults

引用

5th International Conference on Recent Advances in Information technology, RAIT 2023

作者： Dhar, Amit Kumar Gorain, Barun Mahawar, Madhuri Indian Institute of Technology Department of Electrical Engineering and Computer Science Bhilai India

ISBN: (纸本)9798350335705

Time variant coverage, called sweep coverage in wireless sensor networks has got attention from various re-searchers in recent time. In this problem, a set of mobile sensors are collectively monitoring certain area of interest (AoI). In various applications periodic monitoring of the AoI is sufficient. For a given time period, the objective of the sweep coverage problem is to find minimum number of mobile sensors so that the periodic monitoring can be guaranteed. This problem has been studied by various researchers in the context of point, area and barrier coverage problems. The sweep coverage problem on a graph is studied in [11]. In this problem, for a given input graph, minimum number of mobile sensors must be deployed such a way that all nodes of the graph must be visited by at least one mobile sensor. The sweep coverage problem is NP-hard and can not be approximated within a factor of 2 [16]. In this paper, we propose a fault-tolerant sweep coverage algorithm. For a given integer, using our proposed algorithm, the non-faulty sensors adjusted their movement and guarantees sweep coverage of all the nodes of an undirected weighted graph if at most sensors becomes faulty. © 2023 IEEE.

关键词： Fault tolerance

来源：评论

学校读者我要写书评

暂无评论

A Paradigm Shift in Brain Tumor Classification: Harnessing the Potential of Capsule Networks 2

A Paradigm Shift in Brain Tumor Classification: Harnessing t...

引用

2nd IEEE International Conference on Data, Decision and Systems, ICDDS 2023

作者： Raythatha, Yashvi Vani, M. National Institute of Technology Department of Computer Science and Engineering Karnataka Surathkal India

ISBN: (纸本)9798350314168

Accurate and timely classification of brain tumors is critical for developing effective treatment plans and predicting treatment outcomes. However, CNN-based models commonly used for this task have limitations, such as their reliance on large amounts of training data and difficulties with input orientation and transformations. To address these limitations, we propose a CapsNet-based model for brain tumor classification designed to effectively handle limited datasets, class imbalance, and input transformations. CapsNet relies on 'capsules,' groups of neurons that work together to represent specific input image features and are resistant to input orientation and transformations. Our study compares the performance of the proposed CapsNet-based model with state-of-The-Art CNN models, and our results demonstrate that the CapsNet-based model outperforms CNN models in terms of accuracy and robustness to input orientation and transformations. These findings suggest that CapsNet has the potential to be a promising alternative to CNNs for accurate and efficient brain tumor classification. © 2023 IEEE.

关键词： Classification (of information)

来源：评论

学校读者我要写书评

暂无评论

Revolutionizing E-commerce with 3D Visualization: An Experimental Assessment of Behavioural Shopper Responses to Augmented Reality in Online Shopping 4

Revolutionizing E-commerce with 3D Visualization: An Experim...

引用

4th International Conference for Emerging technology, INCET 2023

作者： Dethe, Harshal Sujit Joy, Emmanuel Karunya Institute of Technology and Sciences Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350335750

Augmented Reality (AR) has shown great potential to improve consumer purchasing decisions and experiences. By adding more information and functionality to mobile shopping, AR enables customers to browse and test goods virtually. The objective of this paper is to explore the use of augmented reality in e-commerce to enhance customers' experiences and to analyze customer behavior and satisfaction with product purchases. The methodology used in this paper is a form of markerless AR, enabling the 3D models to be placed freely in environments for all product categories with the ability to resize, rotate, and navigate as per customer needs. The study also finds how significantly the use of AR will affect the e-commerce sector in terms of purchase decisions and return rates in the future. The novelty of this work lies in providing a new AR feature to various product categories in e-commerce stores, thereby enhancing the product experience and improving the purchase decision of the customers. © 2023 IEEE.

关键词： Augmented reality

来源：评论

学校读者我要写书评

暂无评论

Self-Attention Vision Transformer with Transfer Learning for Efficient Crops and Weeds Classification 6

Self-Attention Vision Transformer with Transfer Learning for...

引用

6th International Conference on Information Systems and computer Networks, ISCON 2023

作者： Sharma, Shubham Vardhan, Manu National Institute of Technology Department of Computer Science and Engineering Raipur492010 India

ISBN: (纸本)9798350346961

Classifying and mapping weeds are crucial for ensuring that plantations remain free of unwanted plants, particularly in cases where plant spacing is uneven, making weed detection more challenging than it is for crops. To address this issue, Deep Neural Networks (DNNs) are often used in agriculture to identify the unique features of weeds and classify them based on low-resolution imaging, which helps to control weed populations. Convolutional Neural Networks (CNN) in Deep Learning (DL) models have advanced, achieving remarkable performances in plant-weed classification. Despite Convolutional Neural Networks (CNNs) success in computer vision, they still need help with massive labeled datasets, intra-class classification, and high processing costs when used in classification tasks. Meanwhile, transformer-based models have shown significant modeling capabilities globally but are yet to be thoroughly investigated in agriculture. In this study, we explore Self-Attention Vision Transformers (ViT) using a Transfer Learning (TL) approach to classify weeds in benchmark datasets. The proposed model, ViT-S16, was fine-tuned on the ImageNet-1k dataset, pretrained on the ImageNet-21k, and evaluated on the public benchmark agriculture dataset DeepWeed, which contains plants grown with various weed species under different conditions. Our experimental results show that ViT-S16 outperforms state-of-the-art CNN models ResNeXt-50 and EfficientNet-B7, achieving an utmost accuracy of 98.54%. These results show how helpful ViT can be in different image classification tasks. In the future, researchers can look into the strengths and weaknesses of various ViT models.. © 2023 IEEE.

关键词： Convolutional neural networks

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 << < 486 487 488 489 490 491 492 493 494 495 > >>

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：