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

64,544 篇 会议
1,386 册 图书
573 篇 期刊文献
1 篇 科技报告

馆藏范围

66,502 篇 电子文献
2 种 纸本馆藏

日期分布

学科分类号

34,262 篇 工学
- 27,999 篇 计算机科学与技术...
- 22,309 篇 软件工程
- 6,758 篇 信息与通信工程
- 4,371 篇 控制科学与工程
- 4,132 篇 电气工程
- 2,566 篇 电子科学与技术（可...
- 2,327 篇 生物工程
- 2,127 篇 机械工程
- 1,588 篇 生物医学工程（可授...
- 1,504 篇 动力工程及工程热...
- 1,437 篇 仪器科学与技术
- 1,119 篇 光学工程
- 1,109 篇 安全科学与工程
- 971 篇 网络空间安全
- 952 篇 土木工程
- 945 篇 建筑学
11,242 篇 理学
- 6,411 篇 数学
- 2,524 篇 生物学
- 2,487 篇 物理学
- 1,555 篇 统计学（可授理学、...
- 1,349 篇 系统科学
- 920 篇 化学
8,251 篇 管理学
- 5,818 篇 管理科学与工程(可...
- 3,038 篇 图书情报与档案管...
- 2,695 篇 工商管理
1,411 篇 医学
- 1,180 篇 临床医学
- 1,064 篇 基础医学(可授医学...
1,268 篇 法学
- 946 篇 社会学
1,184 篇 教育学
- 1,161 篇 教育学
1,002 篇 经济学
- 996 篇 应用经济学
423 篇 农学
332 篇 文学
116 篇 军事学
79 篇 艺术学
6 篇 哲学
4 篇 历史学

主题

4,658 篇 application soft...
4,387 篇 software enginee...
4,106 篇 software
3,607 篇 cloud computing
2,677 篇 computer archite...
2,587 篇 computational mo...
2,442 篇 computer science
2,274 篇 hardware
1,906 篇 distributed comp...
1,269 篇 artificial intel...
1,224 篇 protocols
1,157 篇 concurrent compu...
1,151 篇 mobile computing
1,109 篇 educational inst...
1,078 篇 computer network...
1,057 篇 servers
1,027 篇 security
1,023 篇 quality of servi...
1,015 篇 costs
947 篇 testing

机构

216 篇 college of compu...
130 篇 school of comput...
89 篇 national enginee...
74 篇 school of comput...
69 篇 school of softwa...
68 篇 school of comput...
64 篇 school of comput...
59 篇 department of co...
50 篇 school of big da...
49 篇 state key labora...
48 篇 national enginee...
48 篇 school of softwa...
41 篇 beijing key labo...
41 篇 school of comput...
39 篇 institute of inf...
37 篇 department of co...
37 篇 school of inform...
36 篇 school of softwa...
34 篇 college of compu...
34 篇 department of co...

作者

41 篇 zhang lei
36 篇 lei wang
35 篇 wang lei
33 篇 m. takizawa
32 篇 jian wang
32 篇 zhu huibiao
31 篇 ruimin hu
29 篇 xiao liu
29 篇 li li
27 篇 jing zhang
25 篇 li yang
25 篇 yu wang
23 篇 wang jing
23 篇 jun zhang
23 篇 liu yang
22 篇 xin liu
21 篇 min huang
21 篇 bin wang
21 篇 bernady o. apduh...
20 篇 wei liu

语言

65,393 篇 英文
777 篇 其他
397 篇 中文
5 篇 葡萄牙文
3 篇 西班牙文
3 篇 俄文
2 篇 德文
1 篇 法文

检索条件"任意字段=International Conference on Multimedia, Software Engineering and Computing"

共 66504 条记录，以下是4911-4920 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：

Support Vector Machines Based Predictive Seizure Care using IoT-Wearable EEG Devices for Proactive Intervention in Epilepsy 2

Support Vector Machines Based Predictive Seizure Care using ...

引用

2nd international conference on Computer, Communication and Control, IC4 2024

作者： Srinivas, Porandla Arulprakash, M. Vadivel, M. Anusha, N. Rajasekar, G. Srinivasan, C. Malla Reddy Institute of Engineering and Technology Department of Information Technology Telangana Hyderabad India Srm Institute of Science and Technology Department of Computing Technologies Kattankulathur Chenagalpattu Tamil Nadu Chennai India Vidya Jyothi Institute of Technology Department of Electronics and Communication Engineering Telangana Hyderabad India Vellore Institute of Technology School School of Computer Science and Engineering Department of IoT Tamil Nadu Vellore India Vinayaka Mission's Kirupananda Variyar Engineering College Department of Computer Science and Engineering Tamil Nadu Salem India Saveetha Institute of Medical and Technical Sciences Saveetha University Saveetha School of Engineering Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350387933

Epilepsy, a neurological illness that causes repeated seizures, can interfere with everyday life and needs prompt treatment. Internet of Things (IoT) wearable Electroencephalogram (EEG) devices and Support Vector Machines (SVM) for predictive analytics are used in this study to suggest a unique strategy for proactive seizure treatment. Wearable EEG devices feed real-time brain activity data. It uses SVM, a strong machine learning method, to build a prediction model using historical EEG data to identify and predict seizures. The prediction software analyses EEG data in real-time to detect pre-seizure patterns and initiate preventive treatments. The seizure prediction method uses SVM's capacity to handle high-dimensional data and catch complicated patterns to improve accuracy and reliability. Healthcare practitioners and caregivers may get timely warnings and react efficiently thanks to the IoT infrastructure's seamless connectivity between wearable devices and a centralized monitoring system. It discusses the ethical and privacy issues of installing such a system, stressing user permission and data protection. Pilot investigations show promising prediction accuracy and reaction time. SVM with IoT-wearable EEG sensors for predictive seizure care offers a forward-looking technique for enhancing epilepsy patients' quality of life by enabling individualized and proactive treatments. © 2024 IEEE.

关键词： Electroencephalography

来源：评论

学校读者我要写书评

暂无评论

Media Classification with Support Vector Machines

Media Classification with Support Vector Machines

引用

international conference on computing and Networking Technology (ICCNT)

作者： M Manjula Kasinath Jena Khushboo Sharma Department of Physics & Electronics School of Sciences JAIN (Deemed to be University) Bangalore Karnataka India Department of Electrical & Electronics Engineering ARKA JAIN University Jamshedpur Jharkhand India Department of Electrical Engineering Vivekananda Global University Jaipur India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Media type is a manner of robotically assigning a category or elegance label to a given media object. Many techniques have emerged for the reason of classifying multimedia content material, which include assist Vector Machines (SVM). aid Vector Machines are supervised mastering algorithms, which may be used for class issues. they may be primarily based at the idea of locating a hyperplane within the enter feature space which separates instructions of statistics into or extra categories. SVM can cope with numerous variables, so it is specifically well-appropriate for fixing multimedia categorization responsibilities. SVM may be used to routinely come across various gadgets and scenes in multimedia facts, which includes detecting gadgets in virtual snap shots or distinguishing distinctive audio events. moreover, SVM’s have been used for diverse types of multimedia content material analysis duties, inclusive of textual content categorization and audio/video area-unique class. SVM is a powerful tool for multimedia classification obligations, due to its capacity to research tremendously non-linear decision barriers at the same time as coping with a huge number of capabilities. it has been effectively used in a selection of real-world multimedia class duties.

关键词： Support vector machines Accuracy Media Vectors Classification algorithms Robots

来源：评论

学校读者我要写书评

暂无评论

Artificial Intelligence in System and software engineering for Auto Code Generation

Artificial Intelligence in System and Software Engineering f...

引用

Electrical Electronics and computing Technologies (ICEECT), international conference on

作者： Anupriya Sharma Ghai Vandna Rawat Vishan Kumar Gupta Kapil Ghai School of Computing Graphic Era Hill University Dehradun India Department of Computer Science Engineering Graphic Era Deemed to be University Dehradun India School of Engineering and Technology Amity University. Punjab Mohali India Department of Chemistry Graphic Era Hill University Dehradun India

ISBN: (数字)9798350378092

ISBN: (纸本)9798350378108

Artificial Intelligence has profoundly impacted system and software engineering by facilitating the automation of complicated tasks, reducing errors, and accelerating the development process. AI-driven tools help enhance efficiency, improve code quality, and promote the generation of more adaptive and innovative software solutions. The present research uses AI-generated tools to explore how AI transforms code synthesis. The study will include a comparative analysis of the performance of manual coding and other automated methods to explore how AI can accelerate code synthesis and make modern web development more competent or effective. It is suggested in the research that with algorithmic and contextual consideration, the quality of code developed by the AI would be higher in most cases than by people due to lower error rates and shorter synthesis. Overall, the central conclusion of the research is that AI plays a crucial role in transforming systems and software engineering. The paper also includes information on ethical dilemmas, robust validation mechanisms, conservative modes, and current research.

关键词： Ethics Codes Error analysis software algorithms Transforms Manuals software Encoding Artificial intelligence software engineering

来源：评论

学校读者我要写书评

暂无评论

Optimizing Bevy-ECS Using Predictive JSSP Approach 5

Optimizing Bevy-ECS Using Predictive JSSP Approach

引用

5th IEEE international conference on Power, Electronics and Computer Applications, ICPECA 2025

作者： Ma, Benchi Du, Yugen Software Engineering Institution East China Normal University Shanghai China Shanghai Key Laboratory of Trustworthy Computing Shanghai200062 China

ISBN: (纸本)9798331533694

Entity-Component-System (ECS) is a fundamental architecture of game engines. It consists of entities (identities), components (data), and systems (logical units). It schedules the system graph to drive the update loop. The most popular open-sourced game engine on ***, the Bevy engine, is designed in an ECS architecture, and Bevy-ECS is its ECS implementation. While Bevy-ECS is a flexible implementation and easy to expand, performance issues can be found with its original scheduler. The original Bevy-ECS scheduler is based on a greedy algorithm, the system's runtime is unknown to the scheduler, which leads to a poor scheduler in terms of CPU utilization and update loop time. In this paper, we found Bevy-ECS's update loop isomorphic to a traditional NP-Hard job shop scheduling problem (JSSP), then we researched JSSP solutions' approach. With reference to the predictive JSSP solution, we represented a multi-threaded ECS scheduler based on the predictive method in Bevy-ECS. We implement genetic methods for predicting system runtime and use the predicted runtime to schedule the systems. We set up test cases using Bevy-ECS's built-in samples, and then we did performance tests using the profiling tool Tracy. In our performance tests, we use CPU utilization and total runtime to evaluate the performance of the scheduler. The results showed that our optimized scheduler achieved higher CPU utilization and faster update loop time. © 2025 IEEE.

关键词： Job shop scheduling

来源：评论

学校读者我要写书评

暂无评论

Graph Contrastive Learning with Decoupled Augmentation

Graph Contrastive Learning with Decoupled Augmentation

引用

2025 IEEE international conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Gao, Shihao Li, Caoshuo Mao, Cunli Zhang, Xulong Qu, Xiaoyang Jin, Taisong Wang, Jianzong Key Laboratory of Multimedia Trusted Perception and Efficient Computing Ministry of Education of China School of Informatics Xiamen University China Faculty of Information Engineering and Automation Kunming University of Science and Technology China Yunnan Key Laboratory of Artificial Intelligence Kunming University of Science and Technology China Key Laboratory of Oracle Bone Inscriptions Information Processing Ministry of Education of China Anyang Normal University China Ping An Technology Co. Ltd. Shenzhen China

ISBN: (纸本)9798350368741

Graph contrastive learning based on augmentation strategies has recently demonstrated remarkable performance. Existing methods typically jointly leverage attribute and structural augmentations to generate graph views, learning data invariance information through contrasting sample pairs. However, this joint approach may deviate from the expectation of semantically similar before and after augmentation. The propagation of attribute information in graphs usually occurs through their structure, meaning that structural and attribute augmentations can interfere with each other and potentially distort the graph's semantics. To address this, we propose a decoupled augmentation framework for graph contrastive learning, which eliminates the mutual interference between the two levels of augmentation while fully exploring graph information. Specifically, our framework employs separate encoders to learn data invariance under different augmentation levels, and it considers the positive gains generated between these levels. Experimental results on five public datasets show that the proposed method is more competitive than state-of-the-art approaches. © 2025 IEEE.

关键词： Contrastive learning Graph representation Self-supervised learning

来源：评论

学校读者我要写书评

暂无评论

Analysis of Heart Disease Prediction using Various Machine Learning Techniques: A Review Study

Analysis of Heart Disease Prediction using Various Machine L...

引用

2023 IEEE international conference on Device Intelligence, computing and Communication Technologies, DICCT 2023

作者： Joshi, Kapil Reddy, G. Abhishek Kumar, Sachin Anandaram, Harishchander Gupta, Ashulekha Gupta, Himanshu Uttaranchal Institute of Technology Uttaranchal University Department of Cse Dehradun India Ms Ramaiah Institute of Technology Electronics and Communication India Amrita School of Engineering Centre for Computational Engineering and Networking Coimbatore India Department of Management Studies Uttarakhand Dehradun India College of Engineering Roorkee Department of Cse Roorkee India

ISBN: (纸本)9781665474924

Heart-related illnesses, often known as Cardiovascular diseases (CVDs) have over the past few decades become the major cause of death worldwide and the most dangerous ailment in both India and the rest of the world. The medicinal services industry contains large a measure of knowledge. This large measure of information, the infection is frequently anticipated, distinguished, or might be relieved. A serious risk to humanity is caused by infections such as cardiovascular disease, cancerous development, tumour, and so forth. Within this article, We attempt to focus on coronary illness forecast, utilizing AI methods, coronary illness is regularly anticipated. The knowledge likes pulse, hypertension, diabetes, cigarette smoking is used as information, and these highlights are then shown as a forecast. The calculations like Random Forest, KNN, and choice tree are use. In this paper, we predict the complete analysis of heart disease. The precision of the prototype is to investigate using each computation. Because of the model for predicting the infection of daringness, action is conducted at that point with greater precision. The use of machine learning algorithms by researchers is speeding up the creation of software that can help physicians with the diagnosis and prognosis of heart ailment. The main objective of this work is to forecast a patient's heart state using machine learning techniques. © 2023 IEEE.

关键词： Decision trees

来源：评论

学校读者我要写书评

暂无评论

Exploitability Analysis of Public Component Library Vulnerabilities Based on Taint Analysis 7

Exploitability Analysis of Public Component Library Vulnerab...

引用

7th international conference on Intelligent computing and Signal Processing, ICSP 2022

作者： Yuan, Huijie Wang, Yunchao Zong, Guoxiao Lv, Zhuo State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China State Grid Electric Power Research Institute Zhengzhou China

ISBN: (纸本)9781665478571

The reuse of public component libraries has contributed to the field of computer science because of its low implementation time and high production efficiency. However, the vulnerabilities in libraries can be more serious than those in real-world software because they can affect various software which uses these libraries. Therefore, the technologies of vulnerability mining of public component libraries have received extensive attention, such as fuzzing. But the number of crashes generated by fuzzing is huge, and only an exceedingly small number of public component library vulnerabilities are exploitable in real-world software. To solve this problem, we use taint analysis techniques to analyze the exploitability of library vulnerabilities in real-world software. We first use Pin binary instrumentation technology to instrument the consumer program, analyze the process of calling the library in the consumer program through the taint analysis, and convert the extracted execution path and parameter information into an adjacency matrix. Then we analyze the execution path and crash scene of the crash file, convert the exploitability analysis into path reachability analysis, and determine whether the crash can reach the vulnerable pointer of the software through reachability. Finally, we divide the library vulnerabilities into three levels: directly exploitable, indirectly exploitable, and unexploitable. We design and implement a prototype tool, LibExp-T, to analyze nine public component libraries and four real-world software containing multiple attack surfaces such as images, audio, video, fonts, etc. And we compare them with automatic exploit generation tools CRAX and REX. The results show that LibExp-T can effectively verify the exploitability of component library vulnerabilities in real-world software with low overhead. © 2022 IEEE.

关键词： Libraries

来源：评论

学校读者我要写书评

暂无评论

An AI Chatbot for Explaining Deep Reinforcement Learning Decisions of Service-Oriented Systems 21st

An AI Chatbot for Explaining Deep Reinforcement Learning Dec...

引用

21st international conference on Service-Oriented computing (ICSOC)

作者： Metzger, Andreas Bartel, Jone Laufer, Jan Univ Duisburg Essen Paluno Ruhr Inst Software Technol Essen Germany

ISBN: (纸本)9783031484209;9783031484216

Deep Reinforcement Learning (Deep RL) is increasingly used to cope with the open-world assumption in service-oriented systems. Deep RL was successfully applied to problems such as dynamic service composition, job scheduling, and service adaptation. While Deep RL offers many benefits, understanding the decision-making of Deep RL is challenging because the action-selection policy that underlies its decision-making essentially appears as a black box. Yet, understanding the decision-making of Deep RL is key to help service developers perform debugging, support service providers to comply with relevant legal frameworks, and facilitate service users to build trust. We introduce Chat4XAI to provide natural-language explanations of the decision-making of Deep RL. Compared with visual explanations, the reported benefits of natural-language explanations include better understandability for non-technical users, increased user acceptance, and more efficient explanations. Chat4XAI leverages modern AI chatbot technology and dedicated prompt engineering. Compared to earlier work on natural-language explanations using classical software-based dialogue systems, using an AI chatbot eliminates the need for eliciting and defining potential questions and answers up-front. We prototypically realize Chat4XAI using OpenAI's ChatGPT API and evaluate the fidelity and stability of its explanations using an adaptive service exemplar.

关键词： chatbot explainable AI reinforcement learning service engineering service adaptation

来源：评论

学校读者我要写书评

暂无评论

A Practical Approach to software Metrics in Beehive Requirement engineering Process Model 7th

A Practical Approach to Software Metrics in Beehive Requirem...

引用

7th international conference on Emerging Research in computing, Information, Communication and Applications, ERCICA 2022

作者： Swarnalatha, K.S. Department of Information Science and Engineering Nitte Meenakshi Institute of Technology Bangalore560064 India

ISBN: (纸本)9789811954818

Requirements specification says what the framework ought to do, how it must act under the given requirements, the qualities it must show and have. Necessity building stage manages specialized information, hierarchical, administrative, monetary and social issues. It assumes an imperative part in keeping up nature of the product. software quality can be overseen and kept up by verifying quality characteristics in the requirements report. RE process has certain stages: they are requirement elicitation, requirement analysis and management, requirement specification and requirement verification and validation, with some interleaving between them. Requirement engineering includes various measurements to effectively oversee, execute and finish the product ventures. Prerequisite measurements are greatly valuable in distinguishing the dangers of a venture by finding blunders or peculiarities in the necessity report. The proposed beehive model (RE process model) (Swarnalatha et al. in Int J Comput Eng Technol (IJCET) 5(7):48–54, 2014 [1]) finds a center ground between the ordinary models of necessity assembling and investigation, viz. waterfall, spiral and agile techniques. In this paper, we investigate how measurements help in examining the bee hive RE process model with the existing software RE process models. The focal point of this exploration is to feature the significance of different execution measurements which could be taken after for examining beehive requirement engineering. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Requirements engineering

来源：评论

学校读者我要写书评

暂无评论

Fused Semantic Information and Hierarchical Attention Network for course Recommendation 3

Fused Semantic Information and Hierarchical Attention Networ...

引用

3rd international conference on Artificial Intelligence, Automation, and High-Performance computing, AIAHPC 2023

作者： Liu, Tieyuan Zhou, Rupeng Zhu, Chuangying Chang, Liang School of Electronic Engineering and Automation Guilin University of Electronic Technology Guangxi Guilin541004 China Guangxi Key Laboratory of Trusted Software Guilin University of Electronic Technology Guangxi Guilin541004 China

ISBN: (数字)9781510666580

ISBN: (纸本)9781510666573

With the development of smart education, personalized course recommendations are getting more and more attention. Recently, some in-depth learning based solutions have been proposed. The main principle is to make sequential recommendations based on the learner's historical learning records, rarely use the semantic information covered by the course profile and user profile to complete the recommendation task. User preferences change over time. To solve these problems, a course recommendation model (FIHA) that integrates semantic information and hierarchical attention network is proposed. next class. Specifically, TextCNN is used to perform semantic processing on the course introduction to generate vectors, and then the learner's historical access course records are used to capture the learner's long-term and short-term preferences using hierarchical attention mechanism, and the learner's dynamic preference representation is obtained. The two are fused and recommendations are made based on their scores to courses. Experiments on real datasets show that this method is superior to other mainstream advanced methods. © 2023 SPIE.

关键词： Recommender systems

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 << < 488 489 490 491 492 493 494 495 496 497 > >>

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：