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

62,387 篇 会议
29,271 篇 期刊文献
981 册 图书

馆藏范围

92,639 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

53,435 篇 工学
- 35,480 篇 计算机科学与技术...
- 28,589 篇 软件工程
- 13,738 篇 信息与通信工程
- 8,988 篇 控制科学与工程
- 8,250 篇 电气工程
- 7,683 篇 电子科学与技术（可...
- 6,544 篇 生物工程
- 5,475 篇 生物医学工程（可授...
- 5,122 篇 光学工程
- 3,560 篇 机械工程
- 3,419 篇 化学工程与技术
- 3,261 篇 动力工程及工程热...
- 3,137 篇 仪器科学与技术
- 3,018 篇 材料科学与工程（可...
- 2,798 篇 网络空间安全
- 2,384 篇 安全科学与工程
32,525 篇 理学
- 14,327 篇 数学
- 12,392 篇 物理学
- 7,552 篇 生物学
- 4,710 篇 统计学（可授理学、...
- 3,941 篇 化学
- 3,308 篇 系统科学
13,693 篇 管理学
- 8,595 篇 管理科学与工程(可...
- 5,710 篇 图书情报与档案管...
- 4,158 篇 工商管理
5,665 篇 医学
- 4,684 篇 临床医学
- 4,252 篇 基础医学(可授医学...
- 2,588 篇 药学(可授医学、理...
- 2,545 篇 公共卫生与预防医...
2,733 篇 法学
- 2,237 篇 社会学
1,697 篇 经济学
1,516 篇 农学
829 篇 教育学
420 篇 文学
189 篇 军事学
87 篇 艺术学
9 篇 哲学
7 篇 历史学

主题

3,145 篇 accuracy
2,681 篇 computer science
2,641 篇 deep learning
2,247 篇 feature extracti...
2,224 篇 machine learning
1,933 篇 computational mo...
1,704 篇 training
1,553 篇 internet of thin...
1,553 篇 real-time system...
1,535 篇 support vector m...
1,513 篇 convolutional ne...
1,377 篇 predictive model...
1,178 篇 artificial intel...
1,074 篇 optimization
986 篇 neural networks
946 篇 wireless sensor ...
905 篇 security
882 篇 data mining
847 篇 computer archite...
801 篇 data models

机构

1,098 篇 cavendish labora...
1,022 篇 department of ph...
971 篇 physikalisches i...
964 篇 oliver lodge lab...
721 篇 university of ch...
631 篇 infn laboratori ...
611 篇 infn sezione di ...
590 篇 infn sezione di ...
579 篇 fakultät physik ...
574 篇 chitkara univers...
566 篇 infn sezione di ...
553 篇 school of physic...
542 篇 horia hulubei na...
531 篇 school of physic...
530 篇 department of ph...
516 篇 h.h. wills physi...
515 篇 imperial college...
514 篇 nikhef national ...
506 篇 stfc rutherford ...
499 篇 school of physic...

作者

261 篇 barter w.
258 篇 a. gomes
245 篇 c. alexa
245 篇 beiter a.
243 篇 j. m. izen
235 篇 g. bella
235 篇 j. strandberg
235 篇 d. calvet
235 篇 c. amelung
234 篇 j. wang
231 篇 s. chen
229 篇 blake t.
225 篇 bellee v.
223 篇 g. spigo
219 篇 bowcock t.j.v.
219 篇 betancourt c.
218 篇 amato s.
217 篇 f. siegert
217 篇 brundu d.
217 篇 borsato m.

语言

85,618 篇 英文
6,134 篇 其他
951 篇 中文
84 篇 日文
14 篇 法文
11 篇 德文
10 篇 斯洛文尼亚文
7 篇 朝鲜文
3 篇 荷兰文
2 篇 阿拉伯文
1 篇 意大利文
1 篇 俄文

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

共 92639 条记录，以下是1101-1110 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：

Efficient Parallel Algorithms For Exact SimRank Computations 31

Efficient Parallel Algorithms For Exact SimRank Computations

引用

31st IEEE International Conference on High Performance Computing, Data, and Analytics Workshops, HiPCW 2024

作者： Mundhara, Aditya Nijhara, Prajjwal Banerjee, Dip Sankar Department of Computer Science and Engineering Indian Institute of Technology Jodhpur India

ISBN: (纸本)9798331509118

SimRank is a popular measure for evaluating node similarities in graphs, but its high computational cost limits scalability for large graphs. The ExactSim [1] algorithm achieves precise single-source SimRank similarities but suffers from significant computation time. In this work, we parallelize key components of ExactSim - Personalized PageRank (PPR) computations and SimRank calculations on a CPU-based shared memory multi-core system. By optimizing these modules, we significantly reduce runtime, making ExactSim more practical for large-scale applications. Our results (on graph sizes ranging from 0.1 million edges to 100 million edges) demonstrate the effectiveness of parallelization in improving both efficiency and scalability. © 2024 IEEE

关键词： Graph algorithms

来源：评论

学校读者我要写书评

暂无评论

Graph Neural Networks for Optimizing Reliability in Vehicle-to-Infrastructure Communication in Autonomous Systems 9

Graph Neural Networks for Optimizing Reliability in Vehicle-...

引用

9th International Conference on Communication and Electronics Systems, ICCES 2024

作者： Neelamegam, G. Revathy, J. Kannukkiniyal, M. Devendran, M. Akshya, J. Choudhry, Mani Deepak Vsb College of Engineering and Technology Department of Computer Science and Engineering Coimbatore India Christ the King Engineering College Department of Artificial Intelligence and Data Science Coimbatore India Kongu Engineering College Department of Computer Science and Engineering Erode India Hindustan institute of technology Department of Computer Science and Engineering Coimbatore India Srm Institute of Science and Technology Department of Computational Intelligence Chennai India Srm Institute of Science and Technology Department of Computing Technologies Chennai India

ISBN: (纸本)9798350377972

High reliance on autonomous systems necessitates efficient and reliable data exchange through Vehicle-to-Infrastructure (V2I) communication to have appropriate and stable network performance in dynamic vehicular environments. With heuristic approaches, Dijkstra's algorithm, and Deep Q-Networks (DQN), several methods are proposed for optimizing latency reduction, packet delivery rate improvement, and power efficiency in the direction of V2I communications. However, these approaches suffer from important challenges: low adaptability of the network to real-time dynamics, suboptimal resource usage, and difficulties in scalability towards complex large-scale networks. In this scenario, we propose a framework that incorporates a GNN inside autonomous systems for V2I communication to optimize performance. The GNN will process real-time graph features, including signal strength, packet loss, and mobility speed, as it dynamically changes in adaptation to the evolution of network behaviour and other functions. This work shows the superiority of the proposed GNN model for the efficiency of experimentally conducted simulations compared to the current models;it achieved latency reduced by up to 28%, 5-10% PDR, and 20% energy efficiency across the various scenarios with urban, rural, highway, and suburban networks. The results validate robustness and scalability for practical implementations by the proposed approach in real-world autonomous systems. Further development is possible with this framework: for example, the use of multi-agent reinforcement learning techniques within cooperative decision-making and the enhancement through blockchain-based security mechanisms to ensure the integrity of the data. © 2024 IEEE.

关键词： Graph neural networks

来源：评论

学校读者我要写书评

暂无评论

Review On Text Classification Using Improved Deep Learning Models 2

Review On Text Classification Using Improved Deep Learning M...

引用

2nd International Conference on computer, Communication and Control, IC4 2024

作者： Gaikwad, Rajni Mulchandani, Mona Thakur, Reena Jhulelal Institute of Technology Department of Computer Science and Engineering Nagpur India

ISBN: (纸本)9798350387933

Modern deep learning models have exhibited better performance than conventional machine learning methods across a range of text classification tasks, encompassing natural language inference, sentiment analysis, news categorization, and question answering. The application of text classification in natural language processing extends to tasks like spam detection, news categorization, information retrieval, sentiment analysis, and intent assessment. Traditional text classifiers utilizing machine learning techniques encounter challenges such as sparse data, dimensionality issues, and limited generalization capabilities. In contrast, classifiers based on deep learning effectively tackle these limitations, eliminating the need for intricate feature extraction processes, and providing improved learning capabilities along with enhanced prediction accuracy. One notable instance of Convolutional neural networks are used in this way (CNNs). This paper delineates the text classification process, emphasizing the application of deep learning methodologies for text classification. © 2024 IEEE.

关键词： Sentiment analysis

来源：评论

学校读者我要写书评

暂无评论

A Transfer Learning-Based DenseNet with Multi kernel aggregated Channel Attention Block for COVID-19 Classification using CT scan images

A Transfer Learning-Based DenseNet with Multi kernel aggrega...

引用

2024 OPJU International technology Conference on Smart Computing for Innovation and Advancement in Industry 4.0, OTCON 2024

作者： Girepunje, Shashank Singh, Pradeep National Institute of Technology Department of Computer Science & Engineering Raipur India

ISBN: (纸本)9798350373783

Accurate diagnosis of COVID-19 using chest X-ray imaging is pivotal for large-scale screening and epidemic containment efforts. However, discerning COVID-19 from CT scan images poses formidable challenges due to the heterogeneous and intricate radiographic features, such as widespread ground-glass opacities and diffuse reticular-nodular opacities. Secondly, the limited number of samples also poses a big challenge for deep learning models that require large number of samples to perform efficiently. In this study, we propose a novel approach for COVID-19 classification using a pre-trained DenseNet architecture combined with a MKACAB (multi-kernel aggregated Channel attention Block). Leveraging the expressive power of DenseNet and the attention mechanism's ability to highlight informative features, our model aims to effectively discriminate between COVID-19 and non-COVID-19 cases. Comprehensive experimental evaluations conducted on publicly available dataset attest to the superior performance of our architecture in the domain of COVID-19 classification. © 2024 IEEE.

关键词： COVID-19

来源：评论

学校读者我要写书评

暂无评论

Unwinding the Significance of Federated Learning for Smart Agriculture

Unwinding the Significance of Federated Learning for Smart A...

引用

2024 International Conference on Artificial Intelligence and Emerging technology, Global AI Summit 2024

作者： Bhat, Asif Nazir Sahil Kumar, Gautam National Institute of Technology Department of Computer Science and Engineering Delhi India

ISBN: (纸本)9798350379716

Smart agriculture leverages cutting-edge information and communication technologies to optimize agricultural practices, and machine learning plays a significant role in achieving this goal. However, due to the conventional nature of centralized learning, machine learning-oriented solutions in agriculture face significant challenges, primarily the network overburden, time-delayed response and limited computational capabilities of edge nodes. This leads to the requirement of a more efficient and decentralized approach for learning, which can distribute the computational load and enable real-time data processing closer to the data sources. The presented paper un-winds the significance of Federated Learning in addressing these challenges associated with Centralized Learning, particularly in agriculture. The paper explores Federated Learning's ability to leverage the collective intelligence of distributed data sources while maintaining computational efficiency and scalability. It presents it as an ideal solution for the agricultural sector, where data is often fragmented and collected in diverse environments. In this work, a methodology is adopted to compare and analyze the performance of Federated Learning with Centralized Learning using classification-based statistical parameters viz. Accuracy, Precision, Recall and F1-Score and hosting nodes' parameters viz. Memory usage, CPU utilization, Model Converging Time and Node Energy Consumption. The experimental results clearly depict the edge of Federated Learning in analyzing the crop recommendation, as compared to the Centralized Learning. © 2024 IEEE.

关键词： Smart agriculture

来源：评论

学校读者我要写书评

暂无评论

Validating δ-Currency Using Model Checking

Validating δ-Currency Using Model Checking

引用

International Conference on Data science and Network engineering, ICDSNE 2023

作者： Prabhu, Shreekanth M. Department of Computer Science and Engineering CMR Institute of Technology Bengaluru India

ISBN: (纸本)9789819967544

In recent years, there is a surfeit of digital currencies, virtual currencies, and cryptocurrencies. These currencies serve as alternatives to fiat currencies in the form of physical currencies or deposits in banks. Some of the common characteristics that differentiate these currencies are how they gain and maintain their value, the anonymity of transactions, and considerations of security, data integrity, and transaction performance in a distributed computing scenario. Digital Currencies are generally issued by Central Banks raising privacy concerns. Cryptocurrencies work outside such formal mechanisms raising concerns about volatility and the possibility of loss. In this paper, we discuss a new variant called δ-Currency which attempts to navigate these concerns and arrive at an alternative mechanism that addresses privacy and financial security considerations in a novel manner. The δ-Currency is currently at a conceptual stage. We make use of Model Checking to validate the architecture, design principles, and implementation approach of δ-Currency. Model Checking focuses on fundamental building blocks of computation and is generally used to validate complex systems operating in an environment that enables a high degree of concurrency. In this paper, we make use of Model Checking to arrive at a common vocabulary, abstractions, and framework to understand seemingly disparate systems which nevertheless achieve the same objective. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Model checking

来源：评论

学校读者我要写书评

暂无评论

Multimodal Hate Speech Detection using Fine-Tuned Llama 2 Model

Multimodal Hate Speech Detection using Fine-Tuned Llama 2 Mo...

引用

2024 International Conference on Intelligent Algorithms for Computational Intelligence Systems, IACIS 2024

作者： Sasidaran, Keerthana Geetha, J. Ramaiah Institute of Technology Department of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798350360660

Hate speech detection is crucial for secure online environments, especially with recent policy changes on social media. This study enhances the Llama 2 model by fine-tuning it with a comprehensive Twitter dataset to improve hate speech detection accuracy. The system's frontend allows users to upload or record audio, upload videos, or provide YouTube URLs, with processing managed by the Google Speech Recognition API and the Whisper library. The backend integrates advanced speech recognition and video processing technologies. The refined Llama 2 model achieved an F1 score of 0.94, with accuracy, precision, and recall metrics surpassing other state-of-the-art models. These results demonstrate the model's high effectiveness and reliability in hate speech detection. This research highlights the potential of transformer-based models in enhancing online safety and inclusivity. Future work will focus on expanding the dataset, improving model generalization, supporting additional languages, and enhancing system scalability to better handle evolving hate speech forms. © 2024 IEEE.

关键词： Social networking (online)

来源：评论

学校读者我要写书评

暂无评论

A Hybrid Channel Access Mechanism for NR-U to Improve the Coexistence Performance 16

A Hybrid Channel Access Mechanism for NR-U to Improve the Co...

引用

16th International Conference on COMmunication Systems and NETworkS, COMSNETS 2024

作者： Akbar Basha, K.M. Gumasthi, Nikhil Baswade, Anand M. Indian Institute of Technology Bhilai Department of Computer Science and Engineering India

ISBN: (纸本)9798350383119

The saturated licensed spectrum is insufficient to meet the increasing demand for data traffic, which paves the path for exploring new bands. Since the unlicensed bands are free of cost, efforts are made to offload the cellular data to unlicensed bands, which results in unlicensed versions of cellular technologies. New Radio Unlicensed (NR-U) is the unlicensed version of New Radio (NR). Since anyone can access unlicensed frequency, the devices follow listen before talk (LBT) to gain access to the unlicensed band. Unlike WiFi, the dominant technology in unlicensed bands, the cellular node can not transmit data immediately after the LBT procedure. The node has to wait for the next licensed slot boundary to occur since it is scheduled technology. However, the LBT procedure can end before the occurrence of slot boundary;the period after LBT to the slot bound boundary by sending a reservation signal (RS) or staying idle using a gap. The RS approach yields high fairness with less efficiency, whereas the gap-based approach gives better fairness with comparatively less fairness. To deal with the tradeoff in fairness and efficiency, we have proposed a hybrid channel access method that uses both gap and RS approaches. In the proposed method, the duration of RS is limited by a threshold, and the RS is transmitted as fragments. The proposed method achieves 54 % to 128 % higher fairness than the gap-based approach and 10 % to 28 % than the RS-based approach for the given simulation scenario. © 2024 IEEE.

关键词： Channel access Coexistence LBT NR-U Unlicensed bands Wireless networks

来源：评论

学校读者我要写书评

暂无评论

Bridging the Knowledge Gap: Improving BERT models for answering MCQs by using Ontology-generated synthetic MCQA Dataset 37

Bridging the Knowledge Gap: Improving BERT models for answer...

引用

37th International Florida Artificial Intelligence Research Society Conference, FLAIRS 2024

作者： Sahil, Sahil Kumar, P. Sreenivasa Department of Computer Science and Engineering Indian Institute of Technology Madras India

BERT-based models possess impressive language understanding capabilities but often lack domain-specific knowledge, limiting their performance on specialised tasks such as medical multiple-choice question answering (MCQA). In this paper, we study how biomedical ontologies, rich repositories of medical knowledge, can be harnessed to enhance BERT-based models for medical MCQA task. Our contributions include OntoMCQA-Gen, a system which leverages different biomedical ontologies to construct BioOntoMCQA, a large synthetic MCQA dataset. OntoMCQA-Gen exploits the subclass-class relationships, definitions of concepts, and also synonym relationships from the ontologies to create this dataset of MCQs automatically. We then use this synthetic dataset to fine-tune various BERT-based models to answer medical MCQs. We evaluated these fine-tuned BERT models on the challenging MedMCQA and MedQA datasets of questions from admission examinations for medical degrees in India and USA, respectively. Our evaluation study on these datasets shows that fine-tuning the BERT-based models on BioOntoMCQA results in significantly improved accuracy scores. BioBERT and PubMedBERT, pretrained on the large medical corpus, have also shown significant improvements with our technique of fine-tuning ontology-generated synthetic data. This finding highlights the effectiveness of incorporating biomedical ontologies to enhance the BERT-based model in the medical domain. Moreover, our results underscore the importance of using ontology-generated data along with model adaptation for specialised domains, contributing to a novel advancement in natural language processing. Copyright © 2024 by the authors.

关键词： Ontology

来源：评论

学校读者我要写书评

暂无评论

Ovulytics: A Machine Learning Approach for Precision Diagnosis of PCOD, PCOD and Infertility 1

Ovulytics: A Machine Learning Approach for Precision Diagnos...

引用

1st International Conference for Women in Computing, InCoWoCo 2024

作者： Jha, Tanushree Sirisha, M. Bhargavi, M.S. Bangalore Institute of Technology Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798331518943

Polycystic Ovary Syndrome (PCDS) and Polycystic Ovarian Disease (PCOD) are common endocrine disorders that affect a significant percentage of women of reproductive age, often leading to complications such as infertility. Early detection and differentiation of these conditions are important for timely intervention and effective management. This research proposes a machine learning-based solution to classify and predict the occurrence of PCDS, PCOD, and PCDS with infertility using Random Forest, Logistic Regression, and XGBoost. Each algorithm is trained and tested to predict the level of presence of PCDS, PCOD, and infertility-related PCDS. Among the models, Random Forest demonstrates superior performance. However, the importance of our work extends beyond performance metrics. This study illustrates how machine learning can be applied to complex, real-world healthcare challenges, such as early diagnosis and differentiation of overlapping conditions like PCDS and PCOD. The ability to leverage machine learning in this context aids earlier detection of these conditions, allowing for timely interventions that can have the potential to prevent further complications, such as infertility, cardiovascular diseases, and metabolic disorders. The potential impact of this research is substantial, offering scalable, non-invasive, and cost-effective diagnostic solutions, thus improving outcomes for millions of women worldwide and transforming reproductive healthcare practices. © 2024 IEEE.

关键词： Logistic regression

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 << < 107 108 109 110 111 112 113 114 115 116 > >>

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：