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

13,794 篇 会议
7,684 篇 期刊文献
257 册 图书

馆藏范围

21,735 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

13,436 篇 工学
- 10,390 篇 计算机科学与技术...
- 8,665 篇 软件工程
- 3,411 篇 信息与通信工程
- 2,162 篇 控制科学与工程
- 1,904 篇 生物工程
- 1,594 篇 电气工程
- 1,407 篇 生物医学工程（可授...
- 1,175 篇 光学工程
- 1,170 篇 电子科学与技术（可...
- 834 篇 网络空间安全
- 791 篇 机械工程
- 677 篇 安全科学与工程
- 633 篇 动力工程及工程热...
- 592 篇 交通运输工程
- 584 篇 仪器科学与技术
- 556 篇 化学工程与技术
6,839 篇 理学
- 3,489 篇 数学
- 2,090 篇 生物学
- 1,772 篇 物理学
- 1,216 篇 统计学（可授理学、...
- 742 篇 系统科学
- 653 篇 化学
4,015 篇 管理学
- 2,427 篇 管理科学与工程(可...
- 1,769 篇 图书情报与档案管...
- 1,067 篇 工商管理
1,565 篇 医学
- 1,223 篇 临床医学
- 1,120 篇 基础医学(可授医学...
- 820 篇 公共卫生与预防医...
- 655 篇 药学(可授医学、理...
849 篇 法学
- 701 篇 社会学
373 篇 经济学
334 篇 农学
236 篇 教育学
183 篇 文学
32 篇 军事学
28 篇 艺术学
5 篇 哲学
3 篇 历史学

主题

922 篇 deep learning
848 篇 accuracy
539 篇 computational mo...
491 篇 machine learning
459 篇 feature extracti...
443 篇 training
394 篇 real-time system...
382 篇 predictive model...
371 篇 internet of thin...
321 篇 convolutional ne...
316 篇 support vector m...
285 篇 computer science
269 篇 data models
264 篇 semantics
259 篇 optimization
231 篇 neural networks
220 篇 computer archite...
214 篇 reliability
214 篇 scalability
204 篇 image segmentati...

机构

261 篇 department of co...
166 篇 school of comput...
138 篇 school of comput...
136 篇 school of comput...
122 篇 department of co...
118 篇 school of comput...
112 篇 school of comput...
106 篇 department of co...
105 篇 department of co...
103 篇 school of comput...
101 篇 college of compu...
101 篇 national enginee...
99 篇 department of co...
98 篇 school of comput...
94 篇 department of co...
93 篇 north-west unive...
93 篇 institut univers...
87 篇 infn sezione di ...
87 篇 national centre ...
82 篇 tno technical sc...

作者

122 篇 niyato dusit
96 篇 ferrara g.
96 篇 ardid m.
95 篇 calvo d.
92 篇 chen a.
88 篇 gal t.
88 篇 bozza c.
88 篇 díaz a.f.
88 篇 boumaaza j.
86 篇 coleiro a.
85 篇 gozzini s.r.
84 篇 zheng wei-shi
82 篇 anjali t.
81 篇 dallier r.
73 篇 kalekin o.
65 篇 anguita m.
64 篇 boettcher m.
61 篇 huang f.
56 篇 kulikovskiy v.
53 篇 benoit d.m.

语言

17,458 篇 英文
4,166 篇 其他
168 篇 中文
2 篇 法文
1 篇 德文
1 篇 日文
1 篇 朝鲜文
1 篇 斯洛文尼亚文

检索条件"机构=MTech Computer Science and Engineering School of Computing Science and Engineering"

共 21735 条记录，以下是841-850 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：

CRAYM: Neural Field Optimization via Camera RAY Matching 38

CRAYM: Neural Field Optimization via Camera RAY Matching

引用

38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Lin, Liqiang Wu, Wenpeng Fu, Chi-Wing Zhang, Hao Huang, Hui College of Computer Science and Software Engineering Shenzhen University China Department of Computer Science and Engineering The Chinese University of Hong Kong Hong Kong School of Computing Science Simon Fraser University Canada

We introduce camera ray matching (CRAYM) into the joint optimization of camera poses and neural fields from multi-view images. The optimized field, referred to as a feature volume, can be "probed" by the camera rays for novel view synthesis (NVS) and 3D geometry reconstruction. One key reason for matching camera rays, instead of pixels as in prior works, is that the camera rays can be parameterized by the feature volume to carry both geometric and photometric information. Multi-view consistencies involving the camera rays and scene rendering can be naturally integrated into the joint optimization and network training, to impose physically meaningful constraints to improve the final quality of both the geometric reconstruction and photorealistic rendering. We formulate our per-ray optimization and matched ray coherence by focusing on camera rays passing through keypoints in the input images to elevate both the efficiency and accuracy of scene correspondences. Accumulated ray features along the feature volume provide a means to discount the coherence constraint amid erroneous ray matching. We demonstrate the effectiveness of CRAYM for both NVS and geometry reconstruction, over dense- or sparse-view settings, with qualitative and quantitative comparisons to state-of-the-art alternatives. © 2024 Neural information processing systems foundation. All rights reserved.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Recursive Descent Parser for Abstract Syntax Tree Visualization of Mathematical Expressions 7

Recursive Descent Parser for Abstract Syntax Tree Visualizat...

引用

7th IEEE International Conference on Computational Systems and Information Technology for Sustainable Solutions, CSITSS 2023

作者： Pecheti, Shiva Teja Basavadeepthi, M.H. Kodurupaka, Nithin Belwal, Meena Amrita School of Computing Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350343144

This study presents a novel method for finding and evaluating mathematical expressions using Abstract Syntax Trees (AST). The approach is particularly adaptable for both academic and professional applications since it accurately parses complex equations and accommodates a large number of mathematical operations. The AST model ensures a hierarchical expression structure, enabling accurate and efficient evaluation. Parsed expressions are improved by incorporating interactive graph vi-sualisation, which simplifies mathematical computations and engineering tasks for understanding and application. The project's complete approach to detecting mathematical expressions stands out since it takes into account a wide range of functions and variables and ultimately enhances the system's accuracy and adaptability. The visual representation of AST also helps with debugging and checking the accuracy of parsed expressions, which adds to its significance in the field of mathematical expression recognition. © 2023 IEEE.

关键词： Syntactics

来源：评论

学校读者我要写书评

暂无评论

Health and Environment Monitoring System for Viral Respiratory Diseases 14

Health and Environment Monitoring System for Viral Respirato...

引用

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

作者： Shwetha, S. Nithuna, N. Oviya, B. Swetha, J. Harini, N. Amrita School of Computing Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350335095

The proposed system in this work is a contactless health and environment monitoring system designed after conducting a literature review on the most recent research on viral respiratory diseases and their impact on businesses, risk factors of a location, and surveillance of people for various safety measures and causes leading to its spread. The proposed system takes the form of an Android Mobile Application, designed to function as a safety entry checkpoint for various organizations, including businesses and educational institutions. It aims to detect viral respiratory diseases by leveraging the capabilities of a smartphone camera without requiring any additional external hardware specifications. Furthermore, the system incorporates users' vaccination information, enabling a comprehensive assessment of health and safety. The key algorithms presented in this work focus on measuring critical body vital signs, including heart rate, temperature, and oxygen saturation (Oxygen saturation (SpO2)) levels. By utilizing advanced techniques such as mask detection and social distance monitoring, the system ensures the recognition of disease indicators and triggers appropriate actions to minimize the risk of transmission. To assess the safety of an area or environment, the proposed system employs the Euclidean distance metric and MobileNetV2, a state-of-the-art deep learning model. This combination enables real-time evaluation and analysis of the surroundings, providing crucial information for making informed decisions regarding the level of safety in a given location. © 2023 IEEE.

关键词： Python

来源：评论

学校读者我要写书评

暂无评论

Hybrid Actor-Action Relation Extraction: A MachineLearning Approach 5

Hybrid Actor-Action Relation Extraction: A MachineLearning A...

引用

5th International Conference on Innovative Data Communication Technologies and Application, ICIDCA 2024

作者： Nair, Reshma P. Thushara, M.G. Department of Computer Science and Engineering Amrita Vishwa Vidhyapeetham Amrita School of Computing Amritapuri India Department of Computer Science and Applications Amrita Vishwa Vidhyapeetham Amrita School of Computing Amritapuri India

Software architects and developers face challenges while trying to explain the complex functional relationships between actors and systems in the process of system design. The field of natural language processing (NLP) and information extraction is always growing. One of the biggest problems that comes up time and time again is finding relationships between entities and actions in textual data. Actor-action relation extraction (AARE) is a hybrid model that combines NLP with rule-based and machine-learning models. It effectively analyzes unstructured data and takes into account different contextual factors, saving time and reducing errors. The proposed approach extracts Actors, actions, entities, and relationships (AARE) from natural language text more accurately and completely using machine learning techniques and rule-based systems. The hybrid model handles unstructured input and adapts to changing linguistic signals using machine learning. The hybrid approach uses Named Entity Recognition, rule-based extraction, and machine learning principles to convert unstructured data into structured format. It uses tokenization, part-of-speech tagging, nlp, and semantic role labeling for relationship categorization. The model has a 93% accuracy rate and is effective in extracting actor-action relations. Future research should focus on improving rule-based techniques, semantic learning, and addressing complexities in UML diagrams. © 2024 The Authors. Published by Elsevier B.V.

关键词： Machine learning

来源：评论

学校读者我要写书评

暂无评论

Surveying Keyword Extractors: Classification, Applications, and Empirical Analysis 6

Surveying Keyword Extractors: Classification, Applications, ...

引用

6th Parul International Conference on engineering and Technology, PICET 2024

作者： Musunuru, Varun Nagam, Sai Pavan Krishna Thushara, M.G. Villuri, Praneeth Vasantha, Gopikrishna Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Amritapuri India Department of Computer Science and Applications Amrita School of Computing Amrita Vishwa Vidyapeetham Amritapuri India

ISBN: (纸本)9798350369748

Natural Language Processing (NLP) has seen some rapid developments. One of the areas is keyword Extraction which has seen rapid growth. Many varied and complex extractors have been brought to light recently, and several extractors have been upgraded. These can be used in several NLP applications. The analysis and working of some of the newest and benchmark extractors that shed new light in this field have been discussed. The goodness of any extractor can only be measured by how it works on real-life data. Instead of going down the set route, a detour was taken, and the working of keyword extractors on movie plots was observed and discussed. Thus for the analysis part, a new dataset has been created from several movie plots taken from various credible sources like IMDB, Rotten Tomatoes, etc. © 2024 IEEE.

关键词： Natural language processing systems

来源：评论

学校读者我要写书评

暂无评论

Deep learning based approaches for accurate automated nuclei segmentation of Pap smear images 14

Deep learning based approaches for accurate automated nuclei...

引用

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

作者： Hridya Nair, S. Nair, Kasthuri A. S. Padmanabhan, Niharika Ratnakaran, Riya Vinitha Panicker, J. Amrita School of Computing Department of Computer Science and Engineering Amritapuri India

ISBN: (纸本)9798350335095

Cervical cancer is a type of cancer that occurs among women. The cancerous cells are developed in the cells of the cervix which is the lower part of the uterus. The human papillomavirus (HPV) is responsible for the vast majority of cervical cancer cases. A Papanicolaou test (pap smear test) is the most reliable screening method for identifying cancerous cells. Pathologists visually examine the slide after performing the test to look for malignancy. But this is a time-consuming and challenging task, often limited by the availability of certified professionals. Additionally, the diagnostic efficacy of visual screening is compromised by the increased workload of pathologists, leading to decreased diagnostic accuracy. Thus, a computer-aided diagnosis system is essential to enhance the quality of the Pap test results and enable early diagnosis. Nuclei segmentation is a crucial step in automated cervical cancer detection, as the nucleus carries essential information about the alterations occurring during the disease's progression. Thus, automated segmentation of nuclei can significantly and ultimately influence the diagnostic results. Recent surveys found that neural networks outperformed various traditional segmentation methods. We conducted experiments on the Herlev dataset using U-Net with ResNet and EfficientNet encoders, PAN (Pyramid Attention Network), and LinkNet with a ResNet encoder. For each of these models, our results showed IOU scores of 0.76, 0.938, 0.79, and 0.69, respectively. © 2023 IEEE.

关键词： Diseases

来源：评论

学校读者我要写书评

暂无评论

AST-Based and Token-Based Neural Networks for Source Code Classification: A Comparative Performance Analysis 15

AST-Based and Token-Based Neural Networks for Source Code Cl...

引用

15th International Conference on computing Communication and Networking Technologies, ICCCNT 2024

作者： Parvathy, R. Thushara, Mg Amrita School of Computing Department of Computer Science & Engineering Amrita vishwa Vidyapeetham Amritapuri India Amrita School of Computing Department of Computer Science & Applications Amrita vishwa Vidyapeetham Amritapuri India

ISBN: (纸本)9798350370249

Programming languages function as a medium for expressing instructions in the context of computer program creation. A prevalent convention in programming is categorizing source code into fragments, a strategy that enhances understanding and simplifies the upkeep process. This research study presents a comparative examination of two distinct models utilized to classify source code. The first model employs a neural network approach that heavily relies on Syntax Trees (ASTs). In contrast, the second model is built on a neural network framework that utilizes the tokenization process. The AST-based model employs a methodology that entails decomposing extensive ASTs into smaller clusters of statement trees. These clusters are subsequently transformed into vectors by capturing the lexical and syntactical characteristics of the statements. The naturalness of certain statements is captured by employing a bidirectional Recurrent Neural Network (RNN) model, which utilizes a collection of statement vectors. In contrast, the token-based paradigm diverges from ASTs and emphasizes tokens obtained through tokenization. The tokens undergo preprocessing and vectorization before inputting into a bidirectional RNN model to evaluate the naturalness of the statements. To choose the optimal model, the accuracy of the two models are compared. © 2024 IEEE.

关键词： Recurrent neural networks

来源：评论

学校读者我要写书评

暂无评论

Fake Social Media Profile Identification and Report Using Machine Learning

Fake Social Media Profile Identification and Report Using Ma...

引用

2024 International Conference on Signal Processing, Computation, Electronics, Power and Telecommunication, IConSCEPT 2024

作者： Chinnasamy, P. Ayyasamy, Ramesh Kumar Subramaniam, Suthashini Sangodiah, Anbuselvan Iftikhar, Aqsa Kiran, Ajmeera Kalsalingam Academy of Research and Education Department of Computer Science and Engineering School of Computing TamilNadu Srivilliputtur India Kampar Malaysia School of Computing Faculty of Computing and Engineering Quest International University Perak Ipoh30250 Malaysia MLR Institute of Technology Department of Computer Science and Engineering Hyderabad India

ISBN: (纸本)9798331540685

In the digital age, Online Social Networking platforms are integral to daily life, but they also attract malicious bots that mimic human behaviour and pose cybersecurity threats. This literature review focuses on recent Machine Learning-based techniques for detecting and classifying bots on major social media platforms. It covers supervised, semi-supervised, and unsupervised methods, dataset details, and feature categories. While the technology is advancing, an ongoing challenge lies in the creation of ever- evolving bots to evade detection. The study offers insights into the evolving landscape of cybersecurity and the need for continual research in this field, but potential drawbacks may include data privacy concerns, The pervasive influence of the internet and social media as platforms for real-time information dissemination and their potential to shape perceptions and lives. It highlights the concern that unverified and malicious content can significantly impact users' perceptions. The paper offers a taxonomy for classifying malicious information at various stages and presents technologies to combat the issue from content generation to containment. The results illustrate the efficacy of machine learning in improving the dependability and safety of social media platforms, offering a scalable approach to address the increasing problem of online deception. © 2024 IEEE.

关键词： Bot (Internet)

来源：评论

学校读者我要写书评

暂无评论

Federated Ensemble for Lung Pneumonia Detection 15

Federated Ensemble for Lung Pneumonia Detection

引用

15th International Conference on computing Communication and Networking Technologies, ICCCNT 2024

作者： Sharma, Vishwash Challa, VenkataHemant Kumar Reddy Pranavi, Pasupuleti Singh, Tripty Nair, Rekha R. Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Bengaluru India Alliance University Alliance School of Advanced Computing Department of Computer Science and Enginnering Bengaluru India

ISBN: (纸本)9798350370249

In the realm of medical imaging, the detection of lung pneumonia through automated methods has garnered significant attention due to its potential to augment diagnostic processes. This study proposes a novel approach, termed Federated CNN Ensemble, aiming to enhance the accuracy and robustness of pneumonia detection from chest X -ray images through federated learning and ensemble techniques. The proposed method leverages convolutional neural networks trained locally at distributed medical institutions, thereby preserving data privacy and security while harnessing collectiveintelligence. By aggregating the learned models through an ensemble strategy, our approach achieves superior performance in pneumonia detection compared to standalone CNN models. This research contributes to the advancement of automated medical diagnosis systems, offering a promising avenue for scalable and collaborative disease detection in healthcare settings. © 2024 IEEE.

关键词： DeepLearning Ensemble models Federated Learning

来源：评论

学校读者我要写书评

暂无评论

Time Series Forecasting for COVID-19 Confirmed Cases Using Transformer Based Stacked LSTM Model 8th

Time Series Forecasting for COVID-19 Confirmed Cases Using T...

引用

8th IFIP TC 12 International Conference on computer, Communication and Signal Processing, ICCCSP 2024

作者： Penumadi, Sri Naga Jathin Nambiar, Vaishakh Vignesh, G.S. Singh, Rimjhim Padam Kanchan, Sneha Department of Computer Science and Engineering Amrita School of Computing Bengaluru Amrita Vishwa Vidyapeetham Bengaluru India Department of Internet Engineering and Computer Science Universiti Tunku Abdul Rahman Perak Malaysia

ISBN: (纸本)9783031736162

The worldwide impact of COVID-19 necessitates accurate forecasting for informed decision-making by governments and health entities. This study proposes neural network architectures for time series forecasting of confirmed COVID-19 cases based on historical data. The models, including variants of LSTM networks, CNN-GRU hybrids, RBMs, DBNs, and self-attention mechanisms, are trained on comprehensive datasets of confirmed cases, deaths, recoveries, and other variables across various countries. Comparative analysis indicates that CNN-GRU and LSTM models exhibit superior performance in forecasting accuracy, surpassing RBM, DBN, and self-attention models across metrics like Mean Absolute Error and Root Mean Squared Error. This research emphasizes the viability of employing advanced neural networks for precise COVID-19 spread predictions, pivotal in shaping public health policies during global crises. © IFIP International Federation for Information Processing 2025.

关键词： Time series

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 << < 81 82 83 84 85 86 87 88 89 90 > >>

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：