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

30,598 篇 会议
4,956 篇 期刊文献
248 册 图书

馆藏范围

35,802 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

22,248 篇 工学
- 15,837 篇 计算机科学与技术...
- 13,328 篇 软件工程
- 5,847 篇 信息与通信工程
- 3,722 篇 控制科学与工程
- 2,761 篇 电子科学与技术（可...
- 2,759 篇 电气工程
- 2,344 篇 生物工程
- 1,887 篇 生物医学工程（可授...
- 1,540 篇 光学工程
- 1,447 篇 机械工程
- 1,270 篇 动力工程及工程热...
- 1,250 篇 仪器科学与技术
- 1,049 篇 化学工程与技术
- 859 篇 安全科学与工程
- 748 篇 交通运输工程
11,423 篇 理学
- 6,205 篇 数学
- 3,136 篇 物理学
- 2,565 篇 生物学
- 2,047 篇 统计学（可授理学、...
- 1,408 篇 系统科学
- 1,110 篇 化学
5,668 篇 管理学
- 3,349 篇 管理科学与工程(可...
- 2,596 篇 图书情报与档案管...
- 1,822 篇 工商管理
1,586 篇 医学
- 1,374 篇 临床医学
- 1,281 篇 基础医学(可授医学...
- 850 篇 药学(可授医学、理...
- 656 篇 公共卫生与预防医...
1,025 篇 法学
- 826 篇 社会学
646 篇 经济学
- 646 篇 应用经济学
497 篇 农学
433 篇 教育学
155 篇 文学
81 篇 军事学
39 篇 艺术学
1 篇 哲学

主题

1,034 篇 accuracy
840 篇 feature extracti...
811 篇 computational mo...
684 篇 deep learning
681 篇 computer science
671 篇 training
510 篇 support vector m...
499 篇 real-time system...
499 篇 machine learning
440 篇 predictive model...
402 篇 internet of thin...
348 篇 computer archite...
343 篇 data models
330 篇 convolutional ne...
321 篇 reliability
310 篇 optimization
290 篇 data mining
278 篇 routing
260 篇 wireless sensor ...
248 篇 image segmentati...

机构

82 篇 north-west unive...
82 篇 institut univers...
80 篇 national centre ...
79 篇 infn sezione di ...
76 篇 infn sezione di ...
76 篇 institute of inf...
75 篇 tno technical sc...
66 篇 università di ca...
65 篇 université de st...
65 篇 dept. of informa...
61 篇 nikhef national ...
58 篇 dept. of informa...
56 篇 dept. of compute...
56 篇 dept. of compute...
54 篇 school of automa...
52 篇 leiden universit...
49 篇 dept. of compute...
49 篇 dept. of compute...
45 篇 dept. of electri...
44 篇 university würzb...

作者

85 篇 ferrara g.
85 篇 ardid m.
84 篇 calvo d.
84 篇 gal t.
84 篇 bozza c.
81 篇 chen a.
80 篇 kalekin o.
79 篇 díaz a.f.
79 篇 boumaaza j.
78 篇 coleiro a.
77 篇 gozzini s.r.
69 篇 dallier r.
68 篇 huang f.
62 篇 anguita m.
52 篇 kulikovskiy v.
44 篇 del burgo r.
41 篇 benoit d.m.
41 篇 eddymaoui a.
40 篇 gagliardini s.
40 篇 labalme m.

语言

34,525 篇 英文
819 篇 其他
348 篇 中文
57 篇 日文
51 篇 朝鲜文
2 篇 德文
1 篇 法文
1 篇 葡萄牙文
1 篇 斯洛文尼亚文

检索条件"机构=Dept. of Control Engineering and Computer Science"

共 35802 条记录，以下是51-60 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：

Exploring Advanced Approaches: A Comprehensive Analysis of Machine Learning and Deep Neural Networks in Spectrum Sensing Applications 4

Exploring Advanced Approaches: A Comprehensive Analysis of M...

引用

4th International Conference on Ubiquitous Computing and Intelligent Information Systems, ICUIS 2024

作者： Karthiga, G. Saravanan, K. Amc Engineering College Dept. of Computer Science and Engineering Karnataka Bengaluru India New Horizon College of Engineering Dept. of Computer Science and Engineering Karnataka Bengaluru India

ISBN: (数字)9798331529635

ISBN: (纸本)9798331529635

The increasing demand for wireless communication resources has intensified the need for effective spectrum sensing techniques to alleviate the scarcity of available spectral bands. This survey paper presents an indept. exploration and comparative analysis of various spectrum sensing methodologies, focusing on Machine Learning (ML) and Deep Neural Network (DNN) approaches. The study delineates the landscape of traditional spectrum sensing techniques, encompassing energy detection, matched filtering, cyclo-stationary feature detection, and cooperative spectrum sensing. This paper explains sensing using machine learning-based such as Support Vector Machines (SVM), Random Forest, Neural Networks, and Hidden Markov Models (HMM) are systematically elucidated. Moreover, the paper meticulously scrutinizes the advancements in deep learning-based spectrum sensing, encapsulating the utilization of Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Deep Reinforcement Learning (DRL) methodologies. The discussion encompasses their applications, advantages, and limitations in the context of spectrum sensing. © 2024 IEEE.

关键词： Hidden Markov models

来源：评论

学校读者我要写书评

暂无评论

Personalized Movie Recommendation Based on User Preferences Using Optimized Sequential Transformer Model

Personalized Movie Recommendation Based on User Preferences ...

引用

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

作者： Ravikumar, R.N. Jain, Sanjay Sarkar, Manash Amity University Rajasthan Dept. of Computer Science and Engineering Jaipur India Atria Institute of Technology Dept. of Computer Science and Engineering Banglore India

ISBN: (纸本)9798350379716

This study explores the application of a Transformer based model to the 'MovieLens 1M' and 'MovieLens 10M' datasets provided by GroupLens, which includes one million and ten movie ratings. The Transformer model, known for its proficiency in handling sequence-to-sequence tasks, employs multi-head self-attention mechanisms and position-wise feed-forward networks. Our implementation, developed in PyTorch, benefits from its capacity for automatic differentiation and GPU acceleration, which significantly enhances the training efficiency. The model underwent rigorous hyperparameter tuning and optimization to adapt optimally to the characteristics of the dataset. Performance evaluation using the Normalized Discounted Cumulative Gain (NDCG) metric demonstrates that the Transformer model substantially outperforms a conventional popular recommendation system across various top-K rankings. Specifically, for the MovieLens 1M dataset, the Transformer model achieved NDCG scores of 0.0455, 0.0805, 0.0995, and 0.1273 at top 1, 3, 5, and 10, respectively. Comparatively, the popular recommendation system scored 0.0023, 0.0044, 0.0061, and 0.0091 at these same rankings. This trend of superior performance by the Transformer model is also consistent across the MovieLens 10M dataset, further confirming its effectiveness in ranking highly relevant items at the top of the list. Also, to address quality and utility of recommendations, MAP, Precision, Recall, F1 Score, Coverage and Serendipity metrics evaluated. These results underscore the effectiveness of Transformer models in extracting and leveraging complex patterns in sequence data, demonstrating a clear superiority over traditional methods in recommendation systems. This study highlights the potential of deep learning architectures to revolutionize fields reliant on understanding user preferences and sequential data interactions. © 2024 IEEE.

关键词： Recommender systems

来源：评论

学校读者我要写书评

暂无评论

Predictive Modeling and Dynamic Analysis of Price Trends in eCommerce using ML Technique 3

Predictive Modeling and Dynamic Analysis of Price Trends in ...

引用

3rd IEEE North Karnataka Subsection Flagship International Conference, NKCon 2024

作者： Banerjee, Pallab Prasad, Vishal Thakur, Kanika Mitra, Dipra Gaurav, Kumar Kanrar, Soumen Amity University Dept. of Computer Science Engineering Jharkhand Ranchi India Galgotias University Dept. of Computer Science Engineering Greater Noida India

ISBN: (纸本)9798350364569

In the ever-evolving landscape of eCommerce, staying ahead of pricing trends is paramount for both consumers and sellers. This project presents an advanced Ecommerce Product Price Tracker with Price Prediction capabilities, aimed at empowering users with foresight into future pricing dynamics. Supervised Machine Learning algorithms, encompassing regression and time series analysis, form the backbone of predictive modeling, enabling accurate forecasts of forthcoming price movements. The resultant tool not only provides real-time tracking of current product prices but also furnishes predictive analytics, empowering users with actionable insights. By bridging the gap between data-driven insights and eCommerce dynamics, this project contributes to fostering efficiency and efficacy in the vibrant eCommerce ecosystem. © 2024 IEEE.

关键词： Predictive analytics

来源：评论

学校读者我要写书评

暂无评论

Breast Cancer Classification via the Use of ML and DL: A Comprehensive Review 3

Breast Cancer Classification via the Use of ML and DL: A Com...

引用

3rd International Conference on Automation, Computing and Renewable Systems, ICACRS 2024

作者： Sarang, Ruvanshi Dabhi, Vipul Shah, Arpit Dept. of Computer Science Engineering Parul University Vadodara India Dept. of Computer Engineering Parul University Vadodara India

ISBN: (纸本)9798331532420

New and improved methods of diagnosis are needed because breast cancer is still the leading cancer-related killer worldwide. Updates to the methods used to categorize breast cancer have emerged because of recent advances in DL and ML. This review paper’s goal in conducting this research is to bring together existing breast cancer diagnostic and classification methods that make use of deep learning and machine learning techniques. We examine the many approaches, datasets, and performance indicators used in the industry and point out the pros and cons of each as part of the review paper. This review aims to thoroughly examine all the methods that are now in use, spot any trends, and propose new areas for research. This review helps researchers and practitioners understand the current state of breast cancer categorization by combining the findings of past research. Discuss recent advances in ML/DL techniques for improving classification, such as ensemble methods, transfer learning, or hybrid models. Additionally, propose the use of fine-tuning of hyperparameters or novel architectures to enhance classification. © 2024 IEEE.

关键词： Adversarial machine learning

来源：评论

学校读者我要写书评

暂无评论

From Image to Emotion: Exploring CNN Architectures for Facial Emotion Recognition

From Image to Emotion: Exploring CNN Architectures for Facia...

引用

2024 IEEE International Conference on Computing, Applications and Systems, COMPAS 2024

作者： Mahmud Rafi, M Shahriar Alam, Tazrian Chowdhury, Linkon Roy Chowdhury, Bristy East Delta University Dept. of Computer Science and Engineering Chattogram Bangladesh Bgc Trust University Dept. of Computer Science and Engineering Chattogram Bangladesh

ISBN: (纸本)9798331529765

Facial Emotion Recognition (FER) has made significant accomplishments over the past few years while the implications of it are quite extensive and are found in different domains. In this paper, a deep learning-based architecture is proposed for FER where we mainly focus on the main components, including dataset preparation;preprocessing methods;feature extraction techniques;classification algorithm;and XAI approaches. The proposed model based on convolutional neural networks (CNNs) operates with feature extraction from facial images. Preprocessing techniques such as resizing, normalization, and augmentation are used to elevate the quality and variety of the dataset. Due to the architecture of VGG16 and ResNet50, we implement three CNN models to extract discriminant features from facial expressions. Meanwhile, each model works as a classifier so that it can make accurate classifications. On the other hand, we use saliency maps to give the model the computation process in a more interpretable manner. Through multiple experimentation and evaluation, the established model shown the high accuracy of the regional analysis of facial emotions, and, as a result, FE R systems can be updated by the improved performance and explainability. © 2024 IEEE.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

Enhancing 5G Efficiency: Regression Analysis for Resource Allocation Optimization in Wireless Networks

Enhancing 5G Efficiency: Regression Analysis for Resource Al...

引用

2024 International Conference on Electrical, Electronics and Computing Technologies, ICEECT 2024

作者： Padmageetha, B.G. Kumar Naik, Pramod Patil, Mallanagouda Dayananda Sagar University Dept. of Computer Applications Bengaluru India Dayananda Sagar University Dept. of Computer Science and Engineering Bengaluru India RVITM Dept. of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350378092

The advent of 5G networks signifies a major breakthrough in mobile communications, providing better data rates, low latency, and improved connectivity. Efficient resource allocation within 5G networks is paramount to fully leverage these capabilities, ensuring optimal performance and user satisfaction. Traditional resource allocation methods often struggle to keep pace with the dynamic and complex nature of 5G environments. The potential benefits of machine learning (ML) is explored in this research to enhance resource allocation in 5G networks. We begin by examining the unique challenges posed by 5G networks, by ML approaches employed for resource allocation, including supervised learning such as Trees, SVM, Neural Networks. Our study we train the model using MATLAB simulator. Analysis on the response and residual plot is conducted for predicted response. Analysis and comparative study is made to find the best performance metric suitable for the predicting the model for resource allocation. The study shows the improvisation in the metrics of regression learner. Simulation results demonstrate significant improvements in network performance. Finally, we have done the comparison between the results of MATLAB simulation. This study gives better decisions to service providers to enhance the overall performance of network © 2024 IEEE.

关键词： MATLAB

来源：评论

学校读者我要写书评

暂无评论

The Impact of Compiler Warnings on Code Quality in C++ Projects 32

The Impact of Compiler Warnings on Code Quality in C++ Proje...

引用

32nd IEEE/ACM International Conference on Program Comprehension, ICPC 2024

作者： Johansson, Albin Holmberg, Carl Neto, Francisco G. De Oliveira Leitner, Philipp Chalmers University of Technology Dept. of Computer Science and Engineering Gothenburg Sweden Chalmers - University of Gothenburg Dept. of Computer Science and Engineering Gothenburg Sweden

ISBN: (纸本)9798400705861

Modern compilers often offer a variety of warning flags, which developers can enable to get feedback on code that, while syntactically correct, may be problematic. In the case of C++, one example of such 'correct but problematic' code is code that leads to undefined behavior (UB). The usage of compiler warnings has long been suspected as a way to decrease bugs and increase code quality. However, empirical evidence that supports this hypothesis is rare. In this study, we present evidence from a study of 127 open source C++ projects. We categorize their usage of compiler warnings into five groups based on which warning flags are being used, and analyse the relationship between compiler warnings and five quality metrics (bugs, critical issues, vulnerabilities, code smells, and technical debt) using Bayesian analysis. We conclude that, in general, compiler warnings indeed correlate with, and potentially cause, higher code quality, with the clearest impact being on the number of critical issues in a project. Using stricter warning flags expectantly correlates with higher code quality in our study objects. However, there are substantial differences between projects, which we attribute to the project's individual development culture. That is, while warnings matter, other factors such as quality culture, are likely to be even more important to source code quality. © 2024 ACM.

关键词： Quality control

来源：评论

学校读者我要写书评

暂无评论

Neural Style Transfer: A Comparative Study 15

Neural Style Transfer: A Comparative Study

引用

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

作者： Suryamritha, M. Balaji, Varshini Kannan, Srinidhi Singh, Tripty Sharma, Mansi Amrita School of Computing Amrita Vishwa Vidyapeetham Dept. of Computer Science and Engineering Bangalore India University Dept. of Computer Science and Engineering India

ISBN: (纸本)9798350370249

Neural Style Transfer (NST) has emerged as a powerful technique for artistic image synthesis by fusing the base image with style source. In this study, we present a comparative analysis of NST using popular convolutional neural network (CNN) architectures, using pre-trained models such as VGG19, DenseNet121, ResNet50, and MobileNetV2. The methodology involves leveraging the deep features extracted by the models to balance content and style in synthesized images. We delve into the specifics of the model's layer utilization, loss function definition, and TensorFlow implementation details. Experimental results demonstrate the model's ability to produce visually compelling images that seamlessly merge content and style characteristics. Densenet121 has demonstrated superior performance compared to the other models in the evaluation. This work not only contributes to the field of NST but also offers a user-friendly TensorFlow framework, fostering further exploration and advancements in the realm of computer vision and image processing. © 2024 IEEE.

关键词： Artistic image synthesis CNN DenseNet121 image processing MobileNetV2 Neural Style Transfer ResNet50 TensorFlow VGG-19

来源：评论

学校读者我要写书评

暂无评论

Accidental Prone Area Detection in Dhaka Metropolitan City Using Machine Learning Models 5

Accidental Prone Area Detection in Dhaka Metropolitan City U...

引用

5th IEEE International Conference on Recent Trends in computer science and Technology, ICRTCST 2024

作者： Jerin, Foyjunnesa Akhi, Amatul Bushra Tasin, Mobin Mohtadi Sultana, Naznin Akbar, Md. Atef Sharmin, Shayla Daffodil International University Dept. of Computer Science and Engineering Dhaka Bangladesh North South University Dept. of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9798350351378

Dhaka, known for its status as one of the world's most densely populated areas, grapples with numerous challenges associated with urbanization and safety. This study identifies accident-prone zones within the Dhaka Metropolitan City by analyzing data collected from 2019 to 2023. Ten specific locations were investigated using various machine learning models: Decision Tree Classifier, Gradient Boosting Classifier, Random Forest, Gaussian Naive Bayes, and Logistic Regression. Evaluation metrics, including precision, recall, accuracy, F1-score, and standard deviation, were utilized, with the Random Forest classifier exhibiting the highest accuracy at 94.39%. The analysis encompassed both monthly and annual accident data, revealing trends and probabilities associated with different incident types. The study's implications suggest potential advancements in accident investigation by incorporating advanced machine learning techniques, integrating diverse datasets, and expanding the geographical coverage to encompass the entire metropolitan area. This comprehensive approach aims to bolster accident prevention measures and enhance urban safety. Overall, this research contributes to a deeper understanding of accident-prone regions and proposes strategies for targeted interventions to improve safety within metropolitan areas. © 2024 IEEE.

关键词： Accidents

来源：评论

学校读者我要写书评

暂无评论

Assessing Landslide Susceptibility in Active Tectonic Zones using Convolutional Neural Networks (CNN)

Assessing Landslide Susceptibility in Active Tectonic Zones ...

引用

2024 International Conference on System, Computation, Automation and Networking, ICSCAN 2024

作者： Dhinakaran, K. Iyyappan, V. Divya, E. Pavithra, S. Lakshmi, R Poornima Ponnusamy, R. Chennai Institute of Technology Dept. of Computer Science and Engineering Tamil Nadu India Chennai Institute of Technology Dept. of Computer Science and Engineering India

ISBN: (纸本)9798331510022

Assessing landslide susceptibility is vital in mitigating natural disasters, particularly in zones with active tectonics. This paper will discuss a novel approach based on a CNN architecture in a U -Net designed to assess landslide susceptibility in these zones of active tectonism. The model thus takes advantage of spatial data sources from remote sensing and GIS and multi-coupled geospatial factors such as slope, elevation, rainfall, and tectonic activity in a multivariate framework for predicting landslide-prone areas. The U-Net CNN architecture is used as it captures the local and global spatial patterns and results in very precise susceptibility maps. The selected historical data about landslides was then fed into the trained and validated model to predict potential landslide occurrence zones, indicating high accuracy in susceptibility mapping. The CNN-based U-Net approach suggests a higher order of precision and spatial resolution than the conventional machine learning approach. This work portrays the capabilities of deep learning approaches in developing landslide risk assessment with much-improved precision. It thus offers an essential tool for disaster risk management along with urban planning for tectonically active regions. © 2024 IEEE.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

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 << < 2 3 4 5 6 7 8 9 10 11 > >>

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：