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

6,837 篇 会议
70 篇 期刊文献
44 册 图书

馆藏范围

6,952 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

3,943 篇 工学
- 3,644 篇 计算机科学与技术...
- 2,747 篇 软件工程
- 1,195 篇 信息与通信工程
- 706 篇 控制科学与工程
- 511 篇 电气工程
- 472 篇 生物工程
- 333 篇 网络空间安全
- 312 篇 生物医学工程（可授...
- 275 篇 电子科学与技术（可...
- 261 篇 动力工程及工程热...
- 216 篇 交通运输工程
- 216 篇 安全科学与工程
- 210 篇 机械工程
- 210 篇 仪器科学与技术
- 179 篇 光学工程
- 167 篇 化学工程与技术
- 166 篇 石油与天然气工程
1,718 篇 理学
- 1,019 篇 数学
- 492 篇 生物学
- 369 篇 统计学（可授理学、...
- 305 篇 物理学
- 255 篇 化学
- 194 篇 系统科学
1,361 篇 管理学
- 776 篇 图书情报与档案管...
- 744 篇 管理科学与工程(可...
- 353 篇 工商管理
339 篇 医学
- 270 篇 临床医学
- 261 篇 基础医学(可授医学...
- 169 篇 公共卫生与预防医...
- 164 篇 药学(可授医学、理...
195 篇 法学
129 篇 经济学
87 篇 农学
69 篇 教育学
19 篇 文学
8 篇 军事学
2 篇 艺术学

主题

311 篇 big data
294 篇 machine learning
211 篇 deep learning
210 篇 data mining
202 篇 data models
158 篇 cloud computing
139 篇 machine learning...
124 篇 training
119 篇 computational mo...
116 篇 feature extracti...
108 篇 signal processin...
102 篇 predictive model...
98 篇 internet of thin...
96 篇 signal processin...
94 篇 prediction algor...
88 篇 support vector m...
83 篇 data privacy
80 篇 artificial intel...
74 篇 sentiment analys...
72 篇 edge computing

机构

88 篇 maharishi school...
67 篇 centre of interd...
31 篇 department of ar...
28 篇 tianjin universi...
28 篇 college of compu...
27 篇 vishwakarma inst...
26 篇 department of co...
26 篇 department of el...
25 篇 chitkara univers...
25 篇 department of co...
22 篇 department of in...
20 篇 vivekananda glob...
18 篇 university of ch...
18 篇 vishwakarma inst...
17 篇 chitkara univers...
17 篇 lovely professio...
17 篇 department of sc...
16 篇 department of el...
15 篇 vishwakarma inst...
14 篇 key laboratory o...

作者

13 篇 ramakrishnan ram...
11 篇 dipannita mondal
10 篇 wang jing
10 篇 nitin n. sakhare
10 篇 mondal dipannita
10 篇 rajeswari d.
9 篇 jambi ratna raja...
8 篇 kumar ritesh
8 篇 akhilendra prata...
8 篇 xu xiaolong
8 篇 li zuoyong
7 篇 chen chen
7 篇 liu ying
7 篇 wang tao
7 篇 vaishali singh
7 篇 kodipalli ashwin...
7 篇 zhang jing
6 篇 zheng-guang wu
6 篇 bin wang
6 篇 ye jun

语言

6,664 篇 英文
279 篇 其他
62 篇 中文
1 篇 西班牙文
1 篇 葡萄牙文

检索条件"任意字段=2023 International Conference on Algorithms, Computing and Data Processing, ACDP 2023"

共 6952 条记录，以下是241-250 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：

Impact of Cloud computing on Agricultural Advancement using data Mining algorithms 2

Impact of Cloud Computing on Agricultural Advancement using ...

引用

2nd international conference on Automation, computing and Renewable Systems, ICACRS 2023

作者： Saravanan, S.K. Nisha, F. Robin Rohit, V. Lenin, J. Selvam, P.D. Rajmohan, M. Department of BigData and Network Security Saveetha School of Engineering SIMATS Tamil Nadu Chennai India School of Engineering and Technology Computer Science & Engineering CMR University Karnataka Bengaluru India Computer Science & Engineering School of Computer Science & Engineering and Information Science Presidency University Karnataka Bengaluru India Department: Computer Science & Engineering Alliance College of Engineering and Design Alliance University Karnataka Bengaluru India Department of Electronics and Communication Engineering Vel Tech High Tech Dr.Rangarajan Dr.Sakunthala Engineering College Tamil Nadu Chennai India Department of Electronics and Communication Engineering Hindustan Institute of Technology and Science Tamil Nadu Chennai India

ISBN: (纸本)9798350340235

Incorporating Internet of Things (IoT) algorithms, Ensemble Learning, and Explainable AI (XAI) into the Reinforcement Learning framework provides a comprehensive solution. The framework's dynamic paradigm for precision agriculture increases output while decreasing resource consumption. The system adapts to its environment in real-time based on what it has learned via its previous experiences, thanks to Reinforcement Learning. By incorporating IoT algorithms, agricultural machinery may more easily gather and exchange data with cloud servers, improving precision and flexibility. By merging many models to provide reliable results, Ensemble Learning allows us to significantly enhance our predictive abilities. The usage of XAI, which provides context and justification for computations, has the potential to increase public confidence in AI-powered agricultural systems. It demystifies decision-making and arms participants with relevant information. In order to take use of the scalability and processing capacity of cloud resources, agricultural algorithms may now be readily installed and controlled with the help of this framework. data mining algorithms have the potential to change agriculture by fostering sustainable practices and addressing challenges of global food security, as this research elucidates. © 2023 IEEE.

关键词： Agricultural Innovation Cloud computing data Mining algorithms Ensemble Learning Explainable AI (XAI) Internet of Things (IoT) Precision Agriculture Reinforcement Learning Resource Optimization Sustainable Practices

来源：评论

学校读者我要写书评

暂无评论

A Hybrid Approach to Maze Generation algorithms

A Hybrid Approach to Maze Generation Algorithms

引用

2023 international conference on algorithms, High Performance computing, and Artificial Intelligence, AHPCAI 2023

作者： Zhao, Guangxin Gu, Hao Cai, Emma School of Data Science the Chinese University of HongKong Shenzhen518172 China Beijing Chaoyang RCF Dongba School Beijing100028 China The Harker School San Jose95129 United States

ISBN: (纸本)9781510671881

Maze generation algorithms play a significant role in video games with mazes. Most of the existing methods focus on improving the performance of nonuniform maze creation algorithms. However, generating different mazes with equal probability through uniform algorithms is crucial to increase the user’s gaming experience. In this paper, we develop a hybrid of two uniform maze generation algorithms, Aldous-Broder and Wilson, by alternating between the two at some critical running phase to reduce the execution time. We tested several turning points and analyzed the results. The experimental data shows that the performance is better compared to either algorithm by itself when the percentage of visited cells reaches a specific value. © 2023 SPIE. All rights reserved.

关键词： Artificial intelligence

来源：评论

学校读者我要写书评

暂无评论

Comprehensive Study of algorithms for the Analysis of algorithms 2

Comprehensive Study of Algorithms for the Analysis of Algori...

引用

2nd IEEE international conference on Futuristic Technologies, INCOFT 2023

作者： Dhamne, Nidhi Thakare, Arnav Gutte, Vitthal Bhatt, Arya Deshmukh, Aryaa Dr. Vishwanath Karad's Mit World Peace University Department of Computer Engineering and Technology Pune India

ISBN: (纸本)9798350308846

At the heart of modern computing, lies algorithms, which enable the processing of vast datasets and complex analytical tasks. Having a comprehensive understanding of these concepts is paramount in computer science, finding application in software development, system optimization, and computational theory. We discuss 5 key techniques of analysis in this paper. The 5 key analysis techniques - asymptotic, symbolic, recurrence, data flow and amortized are explored theoretically and graphically. Some well-known algorithms and their implementation are presented under their respective techniques. Asymptotic Analysis examines the Big O notation and its growth rates of algorithms. Amortized analysis addresses the average-case performance of algorithms and presents a method to understand how costly operations are balanced by more efficient ones. Symbolic analysis involves reasoning about program behavior symbolically rather than thorough execution. data Flow analysis, the final technique explored elucidates how this techniques aids in identifying variable dependencies, dead code, and security risks, making it invaluable for software analysis and optimization. These techniques and their usages and applications are discussed with the aim of facilitating new researchers. This comprehensive study will help the new and expert researchers to provide the wider understanding of analysis techniques. This study is helpful in furthering study of analysis of algorithms. © 2023 IEEE.

关键词： algorithms Amortized Analysis Asymptotic data Flow Recurrence Symbolic

来源：评论

学校读者我要写书评

暂无评论

data Classification with Different Sorting Techniques using Machine Learning Algorithm on FPGA 14

Data Classification with Different Sorting Techniques using ...

引用

14th international conference on computing Communication and Networking Technologies, ICCCNT 2023

ISBN: (纸本)9798350335095

In this modern world, the biggest challenge faced by the generation is classification and analyzing a large amount of data. There are many algorithms that are present today as a tool for us to treat and analyze this bulk, complex and varied data set. This is where machine learning comes into play, which is a type of AI which uses training models in order to train itself and provide the desired output. The main aim of training computers is to compute complex data, do classification and present the desired results on the basis of the inputs provided. In this paper, we have implemented a classification algorithm namely, KNN using two different sorting techniques and compared them on the grounds of hardware utilization and speed on FPGA using HDL. © 2023 IEEE.

关键词： Learning algorithms

来源：评论

学校读者我要写书评

暂无评论

Enhancing decision-making for breast cancer through advanced machine learning and data analytics 5th

Enhancing decision-making for breast cancer through advanced...

引用

5th international conference on Advances in AI for Biomedical Instrumentation, Electronics and computing, ICABEC 2023

作者： Aggarwal, Sahil Aggarwal, Priyanshu Chauhan, Satyam Dheivanai, R. Pahwa, Ritu Dronacharya COE Gurugram India

ISBN: (纸本)9781032642987

Advancements in machine learning and data analytics have transformed the landscape of biomedical decision-making, offering innovative solutions to address the complexities of diagnosing, prognosing, and planning treatments for breast cancer. Breast cancer is a significant health concern, necessitates precise and timely decision-making in diagnosis, prognosis, and treatment. This research endeavours to advance decision-making in breast cancer care through the application of cutting-edge machine learning and data analytics techniques. The context of this research is the urgent need for improved breast cancer management. Current approaches, while invaluable, face inherent complexities in handling diverse data sources and tailoring treatments to individual patients. Advanced machine learning and data analytics offer the potential to mitigate these challenges. This paper provides a comprehensive examination of the application of machine learning and data analytics in the realm of breast cancer. We begin by delving into the sources of biomedical data and their pre-processing, subsequently exploring a range of machine learning algorithms and feature engineering methods. Our primary objectives are to develop highly accurate diagnostic models for breast cancer, to predict disease progression through advanced prognosis models. This research also underscores the importance of model interpretability and ethical considerations, promoting transparency and equity in the application of artificial intelligence in clinical practice. Our findings reveal the potential for advanced machine learning and data analytics to significantly enhance decision-making in breast cancer care. © 2024 The Author(s).

关键词： Decision making

来源：评论

学校读者我要写书评

暂无评论

Improving Deep Learning Performance Using Sampling Techniques for IoT Imbalanced data 18

Improving Deep Learning Performance Using Sampling Technique...

引用

18th international conference on Future Networks and Communications (FNC) / 20th international conference on Mobile Systems and Pervasive computing (MobiSPC) / 13th international conference on Sustainable Energy Information Technology (SEIT)

作者： El Hariri, Ayyoub Mouiti, Mohamed Habibi, Omar Lazaar, Mohamed Mohammed V Univ Rabat ENSIAS Rabat Morocco

In intrusion detection systems, imbalanced data is a problem since the large majority of data points are considered normal, while the minority of data points are considered anomalies, in which there is an unequal distribution of data among different classes, and many deep learning algorithms might not perform well in this situation because there is an imbalance between the classes, to solve this problem, several approaches come from different fields and have their own strengths and weaknesses, such as using feature engineering and Deep Convolutional Neural Networks (DCNNs). Other than that, it may be difficult to find the optimal for a given problem when using this approach. Additionally, depending on the size of the dataset, it may take a long time to search through all the possible combinations of features to identify the most optimal. This paper presents and implements different data balancing techniques such as Synthetic Minority Oversampling Technique (SMOTE), Adaptive Synthetic (ADASYN), and Random Under Sampling (RUS), analyzes the results of the generated models trained and evaluated and discusses the differences between each approach, on the IoT intrusion dataset 2020 (IoTID20), to improve security in the internet of things environment by using three advanced deep learning algorithms: Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN), and CNN-LSTM. The experimental results of our findings were evaluated in terms of accuracy, precision, recall, Area Under the Curve (AUC), f1-score and sensitivity, and compared to several other state-of-the-art studies, they were outstanding and competent, achieved an accuracy, precision and AUC of 99.49%, 99.49% and 99.93% respectively. (c) 2023 The Authors. Published by Elsevier B.V.

关键词： Internet of things Deep Learning Balancing Techniques Oversampling Under-Sampling

来源：评论

学校读者我要写书评

暂无评论

Real-time data processing and Optimization Research of Edge computing in Industrial Automation 4

Real-time Data Processing and Optimization Research of Edge ...

引用

4th international conference on Industrial IoT, Big data and Supply Chain, IIoTBDSC 2023

作者： Liu, Xin Zhang, Yue Zhang, Yefeng Sun, Yingxue State Grid Information and Telecommunication Co. Ltd. Beijing Branch Beijing100031 China

ISBN: (纸本)9798350341690

At present, the manufacturing industry is in the process of intelligent transformation and upgrading. With the rapid expansion of the connection scale of manufacturing terminals, the amount of data generated by network edge devices such as intelligent terminals is also growing rapidly, this puts forward higher requirements for the industrial transmission broadband, the new manufacturing mode has higher requirements for the real-time performance of data processing, and the existing centralized cloud computing processing mode is difficult to solve these problems efficiently. In order to meet the demand of real-time supervision and quality supervision in the field of industrial automation, it is necessary to process and analyze the class of industrial automation material production test data, explore the data processing technology based on edge computing, establish the cloud collaborative data processing method, the factory production inspection data identification, correlation and preliminary analysis, and upgrade the automation equipment. So as to improve the efficiency of industrial Internet data collection and processing, and relieve the operating pressure of industrial Internet automation equipment. © 2023 IEEE.

关键词： Edge computing

来源：评论

学校读者我要写书评

暂无评论

A power fusion data cleaning method based on exponential moving average and cosine similarity algorithms 10

A power fusion data cleaning method based on exponential mov...

引用

IEEE 10th international conference on Edge computing and Scalable Cloud (IEEE EdgeCom) / 11th IEEE international conference on Cyber Security and Cloud computing (IEEE CSCloud)

作者： Yang, Hongshan Zhang, Guoqing Su, Yun Guo, Naiwang Transwarp Technol Shanghai Peoples R China State Grid Shanghai Elect Power Co Elect Power Res Inst Shanghai Peoples R China

ISBN: (纸本)9798350377149;9798350377132

With the rapid development of the distribution Internet of Things, massive heterogeneous data is continuously generated from the production, transmission and consumption end. Power big data is the key basis to support the business analysis of smart grid. However, the current power big data has the characteristics of fast update speed, high data complexity, low value density and strong time series. This has higher requirements on the speed of data processing and the guarantee of data quality. To solve the above problems, this paper proposes a power fusion data cleaning method based on exponential moving average and cosine similarity algorithm. The exponential moving average algorithm uses a sliding time window to repair and complete missing data, which greatly improves the data repair speed under the power data discontinuous missing mode. The cosine similarity algorithm comprehensively considers the power consumption law of the same attribute users, and can ensure the data repair accuracy in the continuous missing mode of power data. Finally, the test results on real data sets show that the fusion of the two methods can repair large-scale power default data more quickly and efficiently, and greatly improve the robustness of the cleaning method to adapt to different power data missing scenarios.

关键词： Exponential moving average Cosine similarity Power big data data cleaning data recovery

来源：评论

学校读者我要写书评

暂无评论

A Pedestrian Detection Method Based on Small Sample data Set

A Pedestrian Detection Method Based on Small Sample Data Set

引用

2023 IEEE international conference on Image processing and Computer Applications, ICIPCA 2023

作者： Xu, Zan Pan, Sichen Ma, Xiao Guilin University of Electronic Technology Guilin China Zhongnan University of Economics and Law Wuhan China Guangdong University of Technology Guangzhou China

The current popular pedestrian detection methods are mainly based on deep learning algorithms, including Faster R-CNN, YOLO, SSD and other algorithms. In this paper, the INRIA data set is used to improve the accuracy ... 详细信息

ISBN: (纸本)9798350314670

关键词： Deep learning

来源：评论

学校读者我要写书评

暂无评论

Moving State Estimation by CNN from Long Time data of Smartphone Sensors Sussex-Huawei Locomotion Challenge 2023

Moving State Estimation by CNN from Long Time Data of Smartp...

引用

ACM international Joint conference on Pervasive and Ubiquitous computing / ACM international Symposium on Wearable computing (UbiComp/ISWC)

作者： Kawakatsu, Masaki Hyugaji, Takumi Toyoshima, Yugo Tokyo Denki Univ Sch Syst Design & Technol Tokyo Japan

ISBN: (纸本)9798400702006

This paper presents the ideas utilized by Team Ds at the 2023 Sussex-Huawei Locomotion-Transportation (SHL) recognition challenge. The 2023 SHL Challenge involves classifying eight modes of locomotion and transportation using data acquired by smartphone motion and GPS sensors. The data were obtained from three users, User1 provided the training set, and parts of the data acquired by User2 and User3 were used as the validation and test sets. Long-period data (up to 110 s) were used to estimate the modes of locomotion and transportation for a sample in the main processing. We have created a model to estimate the modes of locomotion and transportation using User1's accelerometer, gyro sensor, magnetic sensor, and GPS location data. The neural network model comprised four sections, i.e., two types of a one-dimensional convolutional neural networks (1D-CNNs) with different filter size using raw waveform data for nine sensors, one 2D-CNN using the spectrograms of accelerometer and gyro sensor, and two fully-connected layers of 22 statistical data computed from the sensors. In a post-processing step, a model was constructed to output the labels from the SoftMax output obtained from the previous model, and this output was used as the final estimation. The developed model was evaluated experimentally, and the F1-score obtained on the validation data was 0.952.

关键词： CNN Neural network Long-term data Multi-input model

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 << < 21 22 23 24 25 26 27 28 29 30 > >>

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：