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

527,939 篇 会议
6,093 册 图书
5,742 篇 期刊文献
6 篇 学位论文
2 篇 科技报告

馆藏范围

539,762 篇 电子文献
42 种 纸本馆藏

日期分布

学科分类号

321,217 篇 工学
- 226,559 篇 计算机科学与技术...
- 185,317 篇 软件工程
- 72,778 篇 信息与通信工程
- 46,664 篇 控制科学与工程
- 35,032 篇 电气工程
- 33,261 篇 生物工程
- 29,665 篇 电子科学与技术（可...
- 29,559 篇 机械工程
- 27,757 篇 生物医学工程（可授...
- 20,035 篇 光学工程
- 18,246 篇 仪器科学与技术
- 18,204 篇 化学工程与技术
- 17,042 篇 动力工程及工程热...
- 13,261 篇 安全科学与工程
- 12,992 篇 材料科学与工程（可...
- 11,775 篇 交通运输工程
- 10,817 篇 土木工程
147,084 篇 理学
- 79,521 篇 数学
- 36,883 篇 物理学
- 35,847 篇 生物学
- 24,347 篇 统计学（可授理学、...
- 17,121 篇 化学
- 17,113 篇 系统科学
85,991 篇 管理学
- 53,266 篇 管理科学与工程(可...
- 36,725 篇 图书情报与档案管...
- 25,745 篇 工商管理
22,807 篇 医学
- 19,397 篇 临床医学
- 18,508 篇 基础医学(可授医学...
- 12,261 篇 药学(可授医学、理...
14,222 篇 法学
- 11,616 篇 社会学
10,678 篇 经济学
10,665 篇 教育学
8,179 篇 农学
3,965 篇 文学
1,494 篇 军事学
1,233 篇 艺术学
62 篇 哲学
37 篇 历史学

主题

23,882 篇 computer science
14,394 篇 computational mo...
14,253 篇 feature extracti...
13,901 篇 accuracy
11,918 篇 training
9,815 篇 deep learning
8,782 篇 data mining
8,643 篇 computer archite...
8,600 篇 machine learning
8,049 篇 predictive model...
8,023 篇 support vector m...
7,181 篇 neural networks
7,102 篇 data models
6,991 篇 software enginee...
6,748 篇 real-time system...
6,740 篇 artificial intel...
6,502 篇 educational inst...
6,383 篇 convolutional ne...
5,906 篇 optimization
5,788 篇 visualization

机构

1,869 篇 chitkara univers...
1,327 篇 school of comput...
1,071 篇 school of comput...
1,033 篇 school of comput...
854 篇 school of comput...
660 篇 department of co...
638 篇 school of comput...
569 篇 lovely professio...
565 篇 department of co...
541 篇 school of comput...
532 篇 department of co...
528 篇 college of compu...
525 篇 college of compu...
524 篇 school of comput...
514 篇 maharishi school...
502 篇 school of comput...
469 篇 school of electr...
452 篇 college of compu...
452 篇 department of co...
419 篇 college of compu...

作者

278 篇 wang wei
253 篇 li wei
240 篇 liu yang
234 篇 yang yang
234 篇 china
228 篇 kukreja vinay
228 篇 vinay kukreja
228 篇 yang liu
206 篇 wei wang
163 篇 wei li
160 篇 li li
159 篇 zhang yu
156 篇 zhang wei
151 篇 wei zhang
143 篇 zhang lei
141 篇 wang lei
123 篇 li yang
122 篇 liu wei
110 篇 rahul chauhan
110 篇 yang li

语言

525,539 篇 英文
10,271 篇 其他
5,149 篇 中文
34 篇 西班牙文
31 篇 土耳其文
29 篇 法文
12 篇 葡萄牙文
9 篇 德文
6 篇 俄文
2 篇 乌克兰文
1 篇 阿拉伯文
1 篇 捷克文

检索条件"任意字段=International Conference on Advances in Computer Science and Engineering"

共 539786 条记录，以下是71-80 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：

Machine Learning-Driven QoS Prediction in 6G Networks Under α-η-μ Fading with Selection Combining and Co-Channel Interference Mitigation 1

Machine Learning-Driven QoS Prediction in 6G Networks Under ...

引用

1st international conference on advances in computer science, Electrical, Electronics, and Communication Technologies, CE2CT 2025

作者： Upadhyay, Deepak Rallapalli, Sreekanth Hussain, Zakir Sharma, Rashmi Venu, Nookala Ranswal, Aakash Singh Graphic Era Hill University Dept. of Computer Science and Engineering Dehradun India Nitte Meenakshi Institute of Technology Department of MCA Bengaluru India Department of CSE Nagpur Campus Pune India Centre for Internet Things Madhav Institute of Technology & Science Deemed University Gwalior India

ISBN: (纸本)9798331518578

In this paper, we study machine learning for resource utilization and QoS performance improvement in 6G networks under α-η-μ fading and co-channel interference (CCI). We conduct extensive experiments in simulation comparing our proposal with previous work and showing substantial improvements for both throughput, latency and resource efficiency over traditional as well as deep reinforcement learning based approaches. Their adaptive nature enables real-time optimization, and they run well in both dynamic and complex network conditions. Our experimental results illustrate that machine learned models significantly outperform state-of-the-art baselines on the following dimensions prediction accuracy, resource management and QoS metrics. This work underscores the importance of machine learning as a core enabler for sustainable 6G networks and providing better performance in future wireless communication systems. © 2025 IEEE.

关键词： Cochannel interference

来源：评论

学校读者我要写书评

暂无评论

Investigate the Vulnerabilities and Improvements in Biometric Authentication Systems Using Deep Learning Techniques 1

Investigate the Vulnerabilities and Improvements in Biometri...

引用

1st international conference on advances in computer science, Electrical, Electronics, and Communication Technologies, CE2CT 2025

作者： Vaidehi, M. Chaimanee, Mesith Sathiyanarayanan, Mithileysh Dayananda Sagar College of Engineering Visvesvaraya Technological University Bangalore India Shinawatra University Faculty of Engineering Thailand Shinawatra University Thailand MIT Square London United Kingdom

ISBN: (纸本)9798331518578

The advances in digitization globally, is essential to have efficient and effective system which would enable a secured data communication. The paradigm shift has led to many critical issues which has an impact on the performance of the system. Biometrics is one of the technologies to enhance authentication and security of the information technology systems. The biometric authentication systems have gained widespread adoption in various domains due to their potential to enhance security and user convenience. However, these systems are immune to vulnerabilities, understanding and addressing these weaknesses are crucial for ensuring the reliability of biometric authentication. The biometric technology has to ensure high security and reliability. In the current scenario the biometric verification and validation methods comprises of finger print recognition, iris scanning, facial recognition, voice recognition and other sophisticated approaches. The research focuses on the study and analysis of security vulnerabilities of the biometric technology. The vulnerabilities result in inaccurately identification or failure of identification of the individuals. This contribution focuses on to enhance the performance of the biometric system, we propose a multimodal biometric approach to enable secured and efficient system. Here we propose two approaches which can be applied to validate a biometric system. the fingerprint authentication and Iris scanning using the deep learning technology. © 2025 IEEE.

关键词： Deep learning

来源：评论

学校读者我要写书评

暂无评论

Employee Retention Analysis: Insights and Predictive Modeling With Machine Learning Techniques 1

Employee Retention Analysis: Insights and Predictive Modelin...

引用

1st international conference on advances in computer science, Electrical, Electronics, and Communication Technologies, CE2CT 2025

作者： Prajapati, Jyoti Vetrivendan, L. Mishra, Kanderp Narayan Radhakrishnan, R. Ranjith, M. School of Computer Science and Engineering Galgotias University Greater Noida India Alliance University India Sharda University India School of Computer Science Galgotias University Greater Noida India

ISBN: (纸本)9798331518578

Brain tumors are one of the most serious and potentially fatal diseases in the world, to have any hope of being successfully treated it is imperative that they be discovered early. Conventional diagnostic methods such as MRI and CT images often rely on manual interpretation;this can take a long time and is prone to human error. To address this situation, we present a deep learning-based approach which uses a Deep Convolutional Neural Network for automatic detection of brain tumors. The resultant model takes advantage of the powerful feature extraction abilities of CNNs in order to divide brain images into tumor and non-tumor categories with high precision. The dataset consists of MRI scans and has been preprocessed to enhance image quality while removing noise. Employing advanced techniques such as data augmentation, transfer learning and hyperparameter tuning, the structure of this CNN network for brain tumor detection is optimized. Experimental results prove that the combination is superior to traditional machine learning methods. In medical imaging, this research shows that the potential for deep learning is enormous and will help pave the way for easier, accurate brain tumor detection systems. © 2025 IEEE.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

An Efficient Algorithm for Generating Interactive Visualization of Large Datasets 1

An Efficient Algorithm for Generating Interactive Visualizat...

引用

1st international conference on advances in computer science, Electrical, Electronics, and Communication Technologies, CE2CT 2025

作者： Sharma, Chandani Babu Chilluri, Venkata Suresh Kumar, Ankit Goyal, Shweta CSE Department Mullana Haryana Ambala India Intuit Inc. Mountain ViewCA94043 United States Chandigarh University Department of Computer Science and Engineering Mohali140413 India Graphic Era Deemed to be University Department of Electrical Engineering Dehradun248002 India

ISBN: (纸本)9798331518578

Easy-to-use visualisation tools are essential for identifying patterns in unstructured data as it grows larger and more complicated. The majority of large data visualisation techniques either knowledge of a programming language or access to commercial tools. These days, data is growing in size, and statistical visualisation of the data needs numerous displays to represent all of the data's viewpoints. Model tuning, optimisation, and quick decision-making are greatly aided by the intriguing patterns produced by the models that are visualised. However, traditional techniques like histograms, pie charts, box plots, and bar graphs are typically insufficient to adequately communicate the intriguing pattern that needs to be extracted from massive datasets. As a result, this paper introduces a tree-plot method that uses the h2o package's in-memory node technique to store the enormous dataset in memory. Data. Tree's charting capabilities were employed to display the modelled trees, which were built using an Ada Boosted Model as the underlying learning methods. Visualising the modelled tree and evaluating the effectiveness of the data training process are based on the AUC, RMSE, execution procedure time, and MSE results. © 2025 IEEE.

关键词： Large datasets

来源：评论

学校读者我要写书评

暂无评论

Metamaterial-Loaded Circular Patch Antenna for UAV Applications 1

Metamaterial-Loaded Circular Patch Antenna for UAV Applicati...

引用

1st international conference on advances in computer science, Electrical, Electronics, and Communication Technologies, CE2CT 2025

作者： Kumari, Runa Vaishnavi, M.S. Suresh, C.H. Rowe, Wayne S.T. Choudhury, Balamati BITS Pilani EEE Department India School of Electrical and Computer Engineering RMIT University Centre for Electromagnetics MelbourneVIC3001 Australia Centre for Electromagnetics CSIR-National Aerospace Laboratories Bangalore India

ISBN: (纸本)9798331518578

The paper presents a circular patch antenna loaded with a double circular split ring resonator designed for Unmanned Aerial Vehicle (UAV) applications. Here the coaxial probe feed excites the patch antenna. Coaxial probe feed has low spurious radiation when compared to other feeding mechanisms. The antenna offers a bandwidth of 2.37 to 2.43 GHz, which is suitable for UAV applications. The parametric study involves changing the ground plane's dimension, varying the substrate's thickness, and adding a split ring resonator structure. The metamaterial-based patch antenna achieves a gain of 10.4 dBi and omnidirectional radiation patterns at a resonant frequency of 2.4 GHz. © 2025 IEEE.

关键词： Unmanned aerial vehicles (UAV)

来源：评论

学校读者我要写书评

暂无评论

Deep Learning-Based Facial Emotion Recognition for Advanced Human-computer Interaction 1

Deep Learning-Based Facial Emotion Recognition for Advanced ...

引用

1st international conference on advances in computer science, Electrical, Electronics, and Communication Technologies, CE2CT 2025

作者： Chindiyababy, U. Kakkar, Preetish Vedula, Jagadeesh Yunus, Jumaniyozov Umidbek, Abdalov Sharma, Subhash SRM Valliammai Engineering College Dept. of Information Technology Chennai India India Digital Transformation Lead Meghaz Inc. HoustonTX United States Urgench state university Department of psychology and pedagogy Urgench Uzbekistan Mamun university Department of History Khiva Uzbekistan Tula's Institute Department of Computer Science and Engineering Dehradun India

ISBN: (纸本)9798331518578

Deep learning technology establishes advanced methods for enhancing Human-computer Interaction systems by providing machines with the ability to sense and understand human emotional states. The capability's value stems from its applications, including healthcare situations alongside virtual assistants, gaming interfaces, and educational platforms seeking personalized functions. We present a deep learning framework that unites multimodal data fusion with Convolutional Neural Network(CNN) to enhance emotion recognition systems while improving performance and interpretability. The proposed framework undergoes testing against three benchmark emotion datasets: FER2013, AffectNet, and CK+ dataset. Our experimental findings show that the proposed system delivers a 92.5% accuracy rate for emotion recognition which exceeds traditional unimodal models by a maximum of 12% improvement. Context-aware modeling achieves a 15% performance boost during real-world applications while lightweight architectural design reduces system processing requirements by 25% to support edge device runtime operations. Through explainable AI integration users can see how the model functions to make decisions therefore establishing both trust and ethical practices in use. This research delivers substantial progress in emotion recognition study through an approach that establishes a deployable system that maintains both operational effectiveness and ethical operations across multiple Human-computer Interaction application domains. © 2025 IEEE.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

Generate Topics and Filter Novelties at Scale with Pre-Trained Language Models: A GPT-2 Method 1

Generate Topics and Filter Novelties at Scale with Pre-Train...

引用

1st international conference on advances in computer science, Electrical, Electronics, and Communication Technologies, CE2CT 2025

作者： Jeevashri, B. Uma Priyadarsini, P.S. Saveetha University Saveetha Schoole of Engineering Dept of Computer Science and Engineering Chennai India

ISBN: (纸本)9798331518578

The exponential rise of digital material calls for the development of more sophisticated approaches to the formation of topics that guarantee semantic richness, diversity, and scalability within the content. This research introduces an innovative pipeline employing the pre-trained Generative Pre-trained Transformer 2 (GPT-2) model to tackle issues related to topic coherence, redundancy, and the effective processing of extensive textual datasets to integrates text segmentation, novelty filtration, and semantic coherence enhancement. Text chunking facilitates the processing of extensive datasets by partitioning information into manageable chunks, whereas novelty filtering utilizes Term Frequency-Inverse Document Frequency (TF-IDF) vectorization and cosine similarity measures to remove redundancy. Experiments on a news dataset illustrated the pipeline's efficacy, with filtered GPT-2 outputs attaining higher coherence scores (0.85) than raw GPT-2 (0.75) and conventional approaches such as Latent Dirichlet Allocation (LDA) (0.45). Chunking was essential for sustaining scalability, with an ideal size of 800 tokens achieving a compromise between computational efficiency and precision. The methodology substantially surpassed traditional methodologies in producing distinctive, contextually relevant themes, rendering it appropriate for applications including content analysis, document organization, and knowledge discovery. Future endeavors will concentrate on optimizing for domain-specific datasets, multilingual applications, and minimizing computational overhead to improve adaptability and efficiency. © 2025 IEEE.

关键词： Scalability

来源：评论

学校读者我要写书评

暂无评论

Personalized Healthcare Monitoring Through AR Wearables and Predictive Analytics Algorithms 1

Personalized Healthcare Monitoring Through AR Wearables and ...

引用

1st international conference on advances in computer science, Electrical, Electronics, and Communication Technologies, CE2CT 2025

作者： Singh, Jagbir Ragothaman, Hariharan Jaggi, Arjun Solaev, Ogabek Dilbar, Urazbayeva Chauhan, Akanksha HCLTech India Urgench state university Department of Pedagogy and psychology Urgench Uzbekistan Mamun university Khive Uzbekistan Tula's Institute Department of Computer Science and Engineering Dehradun India India

ISBN: (纸本)9798331518578

Wearable Augmented Reality integrated with predictive analytics is revolutionizing the healthcare monitoring system innovative and individualized. This paper outlines a new paradigm integrating AR devices for capturing and, especially, showing user health statistics and using algorithms to predict health issues. The AR visualization integration with data analytics ensures that users can observe important signs such as pulse, blood oxygen, and activity in real-time. Some of them are device calibration, synchronized data, and the prophecy of the model that forms the framework's design to provide a sound professional health monitoring system. The users' data is submitted to the comprehensive predictive analytics algorithms to define certain patterns to create specific recommendations and ensure early health management. The performance analysis shows a high percentage of system accuracy, and the provided findings show its effectiveness during clinical application or everyday use. This approach reflects a major advancement in improving healthcare delivery by employing innovative wearable technology and data analysis. © 2025 IEEE.

关键词： Electronic health record

来源：评论

学校读者我要写书评

暂无评论

Scalable Performance Testing for Distributed Big Data Frameworks 1

Scalable Performance Testing for Distributed Big Data Framew...

引用

1st international conference on advances in computer science, Electrical, Electronics, and Communication Technologies, CE2CT 2025

作者： Ramdass, Karthikeyan Mulka, Arun Agarwal, Nitin Avvaru, Sugeetha Gupta, Gopal Kumar Chauhan, Sansar Singh Anna University Chennai India Kakatiya University Telangana Warangal India Galgotia's College of Engineering and Technology Uttar Pradesh Technical University Uttar Pradesh India Nagpur Campus Pune440008 India G.L. Bajaj Institute of Technology and Management Department of Computer Science and Engineering Greater Noida201306 India

ISBN: (纸本)9798331518578

The development of distributed Big Data frameworks relies heavily on performance testing because this part is what decides the robustness and scalability of systems. There is no scale performance testing methods that could support the enormous volume of data in the new era of Big Data frameworks. Thus, a scalable performance testing approach can monitor the system's performance and expose potential bottlenecks. The suggested solution is a Scalable Performance Testing framework to test big-scale available systems. The framework is built on a distributed architecture that models real-world scenarios and creates a high-volume workload for testing. Think of it as a driver-assist that uses one of the most advanced data generation techniques to generate data sets that look like real-world data sets, making it easier to measure performance. SPT is flexible, extensible, and will be out-of-the-box compatible with most of the Big Data frameworks and deployment scenarios. Such scalability guarantees that the framework is adapted for the volatility of Big Data environments. Also, it remains a reliable tool for performance measurements in the long run when the system scales. © 2025 IEEE.

关键词： Development Framework Generation Performance Techniques

来源：评论

学校读者我要写书评

暂无评论

Natural Language Generation in AI: Developing Human-Like Text Through Deep Learning 1

Natural Language Generation in AI: Developing Human-Like Tex...

引用

1st international conference on advances in computer science, Electrical, Electronics, and Communication Technologies, CE2CT 2025

作者： Arabelli, Rajeshwarrao Gupta, Suneet Prakash, Nilima Ali, Zainab School of Engineering SR University Department of Electronics and Communications Engineerin Warangal India School of Computer Science and Engineering Galgotias University Greater Noida India Northcap University Gurugram India Vivekananda Global University Department of Journalism & Mass Communication Jaipur India

ISBN: (纸本)9798331518578

Natural Language Generation (NLG) is a subset of artificial intelligence that has as its goal to develop techniques to synthesize human language. This paper focuses on the finding of NLG as a field of study as well as how it benefitted from ideas such as deep learning. Next, we consider the development of NLG models, from standard Recurrent Neural Networks to Long Short-Term Memory networks and from Transformers like GPT. Transformer models have proven to be magnificent for the NLG as they remove the drawbacks of previous models and have improved the long-range dependencies and have synthesized appropriate topic-based content. This paper assesses the merits and the demerit of these models and the areas where that they are used such as healthcare, content creation and customer care. Transformer models are particularly good at producing high-quality text, for which it comes with severe difficulties though. The research for this paper aims to consider future developments of NLG with regards to scalability and the potential for change across fields. © 2025 IEEE.

关键词： Contrastive Learning

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 << < 4 5 6 7 8 9 10 11 12 13 > >>

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：