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

2,998 篇 会议
946 篇 期刊文献
24 册 图书

馆藏范围

3,968 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

2,331 篇 工学
- 1,937 篇 计算机科学与技术...
- 1,669 篇 软件工程
- 682 篇 信息与通信工程
- 424 篇 控制科学与工程
- 424 篇 生物工程
- 305 篇 生物医学工程（可授...
- 282 篇 电气工程
- 229 篇 光学工程
- 208 篇 电子科学与技术（可...
- 183 篇 安全科学与工程
- 179 篇 网络空间安全
- 165 篇 机械工程
- 146 篇 动力工程及工程热...
- 133 篇 化学工程与技术
- 130 篇 交通运输工程
- 124 篇 仪器科学与技术
1,345 篇 理学
- 606 篇 数学
- 467 篇 生物学
- 365 篇 物理学
- 234 篇 统计学（可授理学、...
- 163 篇 系统科学
- 150 篇 化学
764 篇 管理学
- 444 篇 管理科学与工程(可...
- 366 篇 图书情报与档案管...
- 234 篇 工商管理
390 篇 医学
- 332 篇 临床医学
- 311 篇 基础医学(可授医学...
- 212 篇 公共卫生与预防医...
- 172 篇 药学(可授医学、理...
164 篇 法学
- 136 篇 社会学
116 篇 农学
106 篇 经济学
44 篇 教育学
23 篇 文学
2 篇 军事学

主题

267 篇 accuracy
251 篇 deep learning
164 篇 machine learning
154 篇 convolutional ne...
143 篇 feature extracti...
123 篇 real-time system...
122 篇 support vector m...
120 篇 predictive model...
120 篇 training
118 篇 computational mo...
109 篇 internet of thin...
95 篇 machine learning...
81 篇 data models
77 篇 security
70 篇 artificial intel...
64 篇 technological in...
61 篇 medical services
59 篇 analytical model...
59 篇 sentiment analys...
59 篇 prediction algor...

机构

689 篇 school of comput...
495 篇 school of comput...
166 篇 vellore institut...
114 篇 vellore institut...
109 篇 school of comput...
106 篇 school of comput...
102 篇 school of comput...
93 篇 school of inform...
67 篇 school of comput...
59 篇 school of comput...
57 篇 school of electr...
50 篇 school of comput...
45 篇 school of electr...
35 篇 school of comput...
32 篇 school of comput...
31 篇 school of comput...
30 篇 vellore institut...
28 篇 school of comput...
27 篇 computer science...
26 篇 school of comput...

作者

39 篇 tyagi amit kumar
31 篇 gadekallu thippa...
21 篇 trilok nath pand...
20 篇 pandey trilok na...
18 篇 thippa reddy gad...
17 篇 kathiravan srini...
16 篇 harshith john
16 篇 maddikunta prave...
16 篇 dutta anurag
16 篇 sudhansu shekhar...
15 篇 bibhuti bhusan d...
15 篇 amit kumar tyagi
15 篇 sengan sudhakar
14 篇 pandiyaraju v.
14 篇 kannan a.
14 篇 dhanalakshmi r.
14 篇 geetha s.
14 篇 patra sudhansu s...
13 篇 kumaran k
13 篇 bhattacharya swe...

语言

3,508 篇 英文
457 篇 其他
25 篇 中文
1 篇 斯洛文尼亚文

检索条件"机构=Shool of Computer Science and Engineering Vellore Institute of Technology"

共 3968 条记录，以下是861-870 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：

Effective Ransomware Detection Method Using PE Header and YARA Rules

Effective Ransomware Detection Method Using PE Header and YA...

引用

International Conference on Network Security and Blockchain technology, ICNSBT 2023

作者： Hashwanth, S. Kirthica, S. Vellore Institute of Technology Chennai India Department of Computer Science and Engineering College of Engineering Guindy Anna University Chennai India

ISBN: (纸本)9789819944323

As information technology has become more ingrained in people’s lives, data protection has become more and more crucial. On the other hand, malicious programs are being created that could tamper with sensitive and important information and restrict the access to it. A perfect example for such is ransomware, it locks down a computer and prevents users from using it until a ransom is paid. Every 11–14 s, a brand-new organization gets assaulted. Faster recovery is facilitated by early ransomware detection. In this paper, to detect ransomware, several machine learning models are trained using information derived from portable executable (PE) file structure. The proposed approach classifies ransomware applications with 99.4% accuracy by using 14 features. These 14 features are important, and it is enough to provide the accurate classification result. And, to improve the efficiency, the classified file is further examined to check for any bitcoin addresses being present or not through YARA rules. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Static analysis

来源：评论

学校读者我要写书评

暂无评论

Privacy-Preserving Network Intrusion Detection using Federated Learning 14

Privacy-Preserving Network Intrusion Detection using Federat...

引用

14th International Conference on Computing Communication and Networking Technologies, ICCCNT 2023

作者： Abhijit, Chunduru Sri Himmatramka, Rishav Bishoyi, Aum Shiva Rama Prasath, Lallith Subbulakshmi, T. Vellore Institute of Technology Dept. of Computer Science Chennai India Vellore Institute of Technology Dept. of Electronics Engineering Chennai India

ISBN: (纸本)9798350335095

Internet of Things (IoT) devices are more vulnerable to viruses, assaults, and harmful activities as they are developed in tandem with the rise in cybercrime. Network Intrusion Detection Systems (NIDS) are generally used to detect attacks in the IoT devices. Machine learning (ML) techniques are being employed to improve these systems. This study compares various ML techniques to develop NIDS using UNSW-NB15 dataset and proposes a federated learning (FL) approach to overcome the limitations of classical ML approaches. Feature selection is done on the basis of correlation between them and five ML approaches are implemented: Logistic Regression, K-Nearest Neighbors, Random Forest (RF), Decision Tree and Artificial Neural Network. RF was found to be the best performing model for both binary and multi-class classification. However, there are certain limitations in this centralized approach which assumes the presence of a central organisation to store and execute analysis on data from all participant devices. FL is a decentralized approach where each IoT device is considered as a client and models are trained on these devices without transmitting data. This preserves privacy of data and also reduces data transfer costs as only model weights are shared instead of huge data. To assess the effectiveness of the proposed method, we conduct extensive experiments on the UNSW-NB15 dataset. The proposed FL detection model is shown to be both accurate and superior to the centralised model in experiments and empirical analyses, demonstrating its resilience and benefits. © 2023 IEEE.

关键词： Internet of things

来源：评论

学校读者我要写书评

暂无评论

Feature Selection for Disease Prediction in Medical Diagnosis Using Machine Learning 2

Feature Selection for Disease Prediction in Medical Diagnosi...

引用

2nd IEEE International Conference on IoT, Communication and Automation technology, ICICAT 2024

作者： Devendran, K. Keerthika, P. Sagana, C. Abinaya, A. Sangeetha, M. Karuppuchamy, R. Bhuvaneshwari, R. Kongu Engineering College Department of Computer Science and Engineering Tamil Nadu Erode India Vellore Institute of Technology School of Computer Science and Engineering Tamil Nadu Vellore India Dr. N.G.P.Institute of Technology Department of Information Technology Tamil Nadu Coimbatore India

ISBN: (纸本)9798350368109

Feature selection is the technique widely used when experimented with huge dataset that can be used for dimensionality reduction to extract most recognizable features from the huge dataset and it also improves the performance of classification methods and helps in selecting relevant features. The most common method utilized in this work to extract the important characteristics from the COVID-19 dataset is feature selection, after which the derived dataset is utilized for disease prediction. The outcomes aim to offer nuanced insights into the applicability of these techniques for disease prediction, aiding healthcare professionals and researchers in selecting suitable models tailored to specific scenarios. © 2024 IEEE.

关键词： COVID-19

来源：评论

学校读者我要写书评

暂无评论

Enhancing Agile Development in Tech Companies: Backlog Management, Tool Integration, and Stakeholder Collaboration 6

Enhancing Agile Development in Tech Companies: Backlog Manag...

引用

6th International Conference on Recent Trends in Advance Computing, ICRTAC 2023

作者： Suganya, R. Banerjee, Dhiimayyi Mishra, Aarohi Subbulakshmi, T. Subramanian, Girish Vellore Institute of Technology School of Computer Science and Engineering Chennai India Vellore Institute of Technology Computer Science and Engineering Chennai India Penn State University Information Systems State College United States

ISBN: (纸本)9798350394702

This paper investigates the Agile development processes in tech companies, focusing on strategies for effective backlog management, tool utilization for progress tracking, Agile methodology integration, stakeholder involvement, and handling challenges in the tech domain. The study reveals the adaptability of Agile methodologies to diverse organizational needs and objectives, emphasizing their significance in the technical industry. Companies prioritize backlog tasks through subtask breakdown, Story Points, and priority tags, aligning with Agile principles. The integration of Agile involves Jira and Scrum, enhancing development efficiency. Stakeholders and team members engage through planning and design sessions, ensuring collaboration. Challenges are addressed through regular meetings, technical discussions, and transparency. Knowledge transfer sessions keep teams updated. Data security is maintained through NDA agreements and robust security measures. Metrics like story points and KPIs are tracked for Agile success evaluation. The paper concludes by highlighting the importance of aligning process models with organizational requirements. © 2023 IEEE.

关键词： Knowledge management

来源：评论

学校读者我要写书评

暂无评论

Fruits Classification and Analysis in Hyperspectral Imaging Using Deep Convolutional Neural Networks 6th

Fruits Classification and Analysis in Hyperspectral Imaging ...

引用

6th International Conference on Advanced Informatics for Computing Research, ICAICR 2023

作者： Verma, Divyanshu Vijayarajan, V. Rajkumar, S. Ramesh Pathy, M. Shool of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Vellore632014 India School of Bio Sciences and Technology Vellore Institute of Technology Tamil Nadu Vellore632014 India

ISBN: (纸本)9783031840616

Hyperspectral imaging (HSI) emerges as a highly efficient means of information retention, particularly when leveraging the power of deep learning. The intricate task of non-destructively identifying rotten fruits is addressed through HSI, offering a non-invasive method for data collection that significantly streamlines the sorting process using advanced algorithms. Deep learning, employing neural network methodologies, facilitates the exploration of the intricate connection between spatial and spectral contexts within the data. The HSI system captures the wavelength information of refracted, reflected, and absorbed light across multiple bands. To address noise introduced by the machinery, a Standard Normal Variate (SNV) filter is applied, resulting in a refined dataset represented as a hyperspectral cube (3D). The workflow involves the utilization of an image pre-training convolutional neural network (CNN) on the collected dataset, subsequently fine-tuned to serve as a robust classifier. In the proposed model, a substantial majority of the dataset (70%) is dedicated to training the neural network, with the remaining portion (30%) reserved for the validation process. The findings demonstrate that this analysis approach, leveraging the CNN algorithm, yields remarkably accurate results (>90%) and proves to be highly effective in discerning fruit quality. The presented methodology showcases a promising synergy between hyperspectral imaging and deep learning, offering a reliable solution for non-destructive fruit quality assessment. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

Smart Image Recognition System for The Visually Impaired People

Smart Image Recognition System for The Visually Impaired Peo...

引用

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

作者： Khekare, Ganesh Midhunchakkravarthy Vellore Institute of Technology School of Computer Science and Engineering Vellore India Lincoln University College Faculty of Computer Science and Multimedia Petaling Jaya Malaysia

ISBN: (纸本)9798350393378

The inclusion of people with specifics in society has become a big challenge. Vision is extremely relevant in the relationship with objects, space, and others, and their absence can negatively affect the quality of life. Object detection can be applied in various forms, such as autonomous cars, industrial 4.0, and people control. This research work aims to objectively the application of computer vision techniques to present a proposal for an application of image recognition for visually impaired people and give real-time voice feedback of the object. It is possible to build an application of recognition of images that can help visually impaired people to easily surround things and live independently. The application uses a special algorithm based on machine learning and deep learning. © 2023 IEEE.

关键词： Object detection

来源：评论

学校读者我要写书评

暂无评论

A Knowledge engineering Framework Addressing High Incidence of Farmer Suicides 2nd

A Knowledge Engineering Framework Addressing High Incidence ...

引用

2nd International Conference on Speech and Language Technologies for Low-Resource Languages, SPELLL 2023

作者： Bokinala, Vivek Tirunagari, Santosh Mohan, Senthilkumar Department of Computer Science University of Surrey Guildford United Kingdom Rishi M.S. Institute of Engineering and Technology for Women Hyderabad India Department of Computer Science Middlesex University London United Kingdom School of Information Technology and Engineering Vellore Institute of Technology Vellore India

ISBN: (纸本)9783031584947

This study addresses the pressing issue of farmer suicides in India, a problem that has escalated significantly since 1995, with over 0.3 million recorded cases. While scholars and activists have proposed various factors contributing to these suicides, quantitative insights into the influence and probabilistic significance of these factors remain elusive. Consequently, we introduce a pioneering two-tier knowledge engineering framework to tackle this challenge. In the first tier, we employ natural language processing to gather a comprehensive array of suicide-causing factors from news articles and blog posts on the World Wide Web. In the second tier, we undertake a meticulous analysis of the causal factors by calculating probabilities and categorising the identified causal factors into distinct groups: social, economic, socio-economic, nature-related, health-related, and governmental policies. Our analysis reveals that health-related causal factors account for over 50% of farmer suicides, while economic, social, and socio-economic factors contribute to 26%. By meticulously investigating these causal elements, our approach has the potential to substantially mitigate future instances of farmer suicides. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Natural language processing systems

来源：评论

学校读者我要写书评

暂无评论

An Intrusion Detection System for Securing IoT Based Sensor Networks from Routing Attacks 7th

An Intrusion Detection System for Securing IoT Based Sensor ...

引用

7th International Conference on computer, Communication and Signal Processing, ICCCSP 2023

作者： Subramani, Shalini Selvi, M. Kumar, S. V. N. Santhosh Thangaramya, K. Anand, M. Kannan, A. School of Computer Science and Engineering Vellore Institute of Technology Vellore632014 India School of Information Technology and Engineering Vellore Institute of Technology Vellore632014 India Computer Science and Engineering College of Engineering Guindy Anna University Chennai600025 India Department of Electronics and Communication Saveetha Engineering College Chennai602105 India

ISBN: (纸本)9783031398100

Internet of Things is made up of sensor nodes that can sense the given environment, gather the data from environment and then communicates the data to the sink node and the internet through wireless communication. As it follows the routing protocols used in Wireless Sensor Networks, the Denial of Service (DoS) attacks including selective forwarding and flooding are launched by the attackers through Routing Protocol for Low-Power and Lossy Network (RPL) attacks. Intrusion detection systems are able to handle the RPL based DoS attacks and can prevent the attackers from participation in the routing process. Therefore, we propose a new fuzzy temporal random forest algorithm for detecting the attacks more efficiently. In addition, a new intelligent agent based intrusion prevention system which uses fuzzy temporal inference rules for intrusion prevention is also proposed in this work. The proposed model has been compared with other machine learning algorithms such as decision trees, logistic regression, random forest, K-Nearest Neighbor and also the deep learning algorithm namely the Convolution neural networks. Based on the experimentations carried out, we could prove that the proposed algorithm provides higher intrusion detection accuracy, reduces the false positive rate and detection time when compared with the related work. © IFIP International Federation for Information Processing 2023.

关键词： Denial-of-service attack

来源：评论

学校读者我要写书评

暂无评论

Interpretable Deep Learning For Automated Retinal Damage Detection in Optical Coherence Tomography

Interpretable Deep Learning For Automated Retinal Damage Det...

引用

2024 IEEE International Conference on Big Data, BigData 2024

作者： Suganya, R. Sharma, Abhiram Srivats, R. Kalyanasundaram, V. Shanmugam, Selvanayaki Kolandapalayam Vellore Institute of Technology School of Computer Science and Engineering Chennai India Ashland University Department of the Mathematics and Computer Science AshlandOH United States

ISBN: (纸本)9798350362480

This work portrays developing and evaluating deep learning models for automatically identifying retinal injuries using Optical Coherence Tomography (OCT) images. In particular, this study optimizes three pre-trained Convolutional Neural Networks (CNNs), VGG16, EfficientNetB3, and ResNet50, using transfer learning to classify retinal images into four categories, Drusen, Normal, Diabetic Macular Edema (DME), and Choroidal Neovascularization (CNV). In addition to high-performance classification, the study integrates Explainable Artificial Intelligence (XAI) methods, utilizing Gradient-weighted Class Activation Mapping (Grad-CAM) to highlight the retinal regions influencing the model's predictions visually. This offers critical interpretability for clinical settings. The Key performance metrics indicators include accuracy, precision, and F1-score and help to evaluate the models. With the available selected models, EfficientNetB3 achieved the highest performance with an accuracy of 93%, outperforming VGG16 and ResNet50. The application of Grad-CAM highlights the increase in transparency and prediction trust of this model's decision-making process. This study demonstrated the profound learning model efficacy, particularly EfficientNetB3, for accurate retinal injury detection and provides valuable insights into model interpretability for clinical use. © 2024 IEEE.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

Machine Learning Models for PCB Defect Detection: A Survey 6

Machine Learning Models for PCB Defect Detection: A Survey

引用

6th International Conference on Recent Trends in Advance Computing, ICRTAC 2023

作者： Subbulakshmi, T. Ramachandran, Vishwath Anand, Philip Madhavan, Rohit Revathi, M. Subramanian, Girish H. Vellore Institute of Technology School of Computer Science and Engineering Chennai India Vellore Institute of Technology Computer Science and Engineering Chennai India Pennsylvania State University School of Business Administration PA United States

ISBN: (纸本)9798350394702

Almost every commonly used electronic device relies on a Printed Circuit Board (PCB) produced through Surface-Mount technology (SMT). Thanks to advancements that have significantly reduced the size of electronic components, PCBs have dramatically decreased in size. Consequently, it is crucial to conduct the application and examination of solder paste during SMT with a strong emphasis on speed and precision. This research survey delves into an analysis of Solder Paste Inspection (SPI) methods, drawing insights from more than 30 research papers spanning several decades. Initially, the survey identifies various PCB defect detection methods and assembles relevant papers for each approach. Subsequently, it provides a brief assessment of these papers, comparing their algorithms, performance, advantages, and challenges. This comprehensive overview aims to offer fresh perspectives to researchers and readers interested in gaining a deeper understanding of the latest developments in solder paste inspection and the detection of defects in PCBs. © 2023 IEEE.

关键词： Printed circuit boards

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397页 << < 83 84 85 86 87 88 89 90 91 92 > >>

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：