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

6,816 篇 会议
15 篇 期刊文献
11 册 图书

馆藏范围

6,842 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

4,365 篇 工学
- 3,828 篇 计算机科学与技术...
- 3,203 篇 软件工程
- 881 篇 信息与通信工程
- 710 篇 生物工程
- 588 篇 控制科学与工程
- 562 篇 生物医学工程（可授...
- 499 篇 电气工程
- 304 篇 电子科学与技术（可...
- 290 篇 光学工程
- 284 篇 安全科学与工程
- 238 篇 动力工程及工程热...
- 223 篇 机械工程
- 215 篇 交通运输工程
- 210 篇 化学工程与技术
- 199 篇 仪器科学与技术
- 176 篇 网络空间安全
2,145 篇 理学
- 1,267 篇 数学
- 766 篇 生物学
- 655 篇 统计学（可授理学、...
- 399 篇 系统科学
- 331 篇 物理学
- 234 篇 化学
1,306 篇 管理学
- 715 篇 管理科学与工程(可...
- 650 篇 图书情报与档案管...
- 393 篇 工商管理
597 篇 医学
- 531 篇 临床医学
- 502 篇 基础医学(可授医学...
- 310 篇 药学(可授医学、理...
- 282 篇 公共卫生与预防医...
200 篇 法学
190 篇 农学
- 186 篇 作物学
173 篇 经济学
137 篇 教育学
11 篇 军事学
2 篇 文学

主题

999 篇 machine learning
668 篇 accuracy
630 篇 deep learning
393 篇 machine learning...
382 篇 support vector m...
359 篇 predictive model...
355 篇 data models
302 篇 training
300 篇 feature extracti...
246 篇 adversarial mach...
223 篇 computational mo...
208 篇 convolutional ne...
197 篇 data mining
179 篇 real-time system...
179 篇 prediction algor...
174 篇 forecasting
144 篇 decision trees
142 篇 random forests
132 篇 analytical model...
117 篇 classification a...

机构

131 篇 chitkara univers...
69 篇 maharishi school...
53 篇 centre of interd...
22 篇 department of co...
21 篇 uttaranchal inst...
21 篇 school of electr...
20 篇 department of el...
19 篇 maharishi school...
19 篇 department of ar...
18 篇 graphic era deem...
18 篇 computer science...
17 篇 college of techn...
17 篇 department of in...
16 篇 pune
15 篇 graphic era hill...
15 篇 computer science...
14 篇 chitkara univers...
14 篇 department of co...
14 篇 graphic era hill...
13 篇 vishwakarma inst...

作者

19 篇 gill kanwarparta...
17 篇 shiva mehta
17 篇 mehta shiva
16 篇 kanwarpartap sin...
14 篇 chauhan rahul
12 篇 vinay kukreja
11 篇 kukreja vinay
10 篇 singh jagendra
10 篇 rahul chauhan
8 篇 deepak upadhyay
8 篇 akhilendra prata...
8 篇 girija shankar s...
7 篇 deepak banerjee
7 篇 kumar manish
7 篇 babu tina
7 篇 anand vatsala
7 篇 sheifali gupta
7 篇 vatsala anand
7 篇 radha d.
6 篇 banerjee deepak

语言

5,556 篇 英文
1,283 篇 其他
91 篇 中文
3 篇 土耳其文

检索条件"任意字段=3rd International Conference on Machine Learning and Data Engineering, ICMLDE 2024"

共 6842 条记录，以下是321-330 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：

Proceedings of the 3rd international conference on Applied Artificial Intelligence and Computing, ICAAIC 2024

Proceedings of the 3rd International Conference on Applied A...

引用

3rd international conference on Applied Artificial Intelligence and Computing, ICAAIC 2024

ISBN: (纸本)9798350375190

The proceedings contain 308 papers. The topics discussed include: machine vision-driven semantic segmentation for autonomous navigation;face recognition based automated smart attendance using hybrid machine learning algorithms and computer vision;DDoS attacks detection in IoT networks using Naive Bayes and random forest;data analysis and visualization of master sample management and due date alert tool;recent developments in designing internet of things architectures;a study on hybrid electric vehicles (HEV) safety and industrial control network security;detection of cyber-attacks in network traffic using machine learning algorithm;smart grid protection with AI and cryptographic security;deep learning based cotton plant pest detection and fertilizer recommendation system;and interactive tactile book framework for visually challenged community.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Text Semantic Matching Model Based on Adversarial Training and Multi-angle Features 3

Text Semantic Matching Model Based on Adversarial Training a...

引用

3rd international conference on Cloud Computing, Big data Application and Software engineering, CBASE 2024

作者： Huang, Xiaofan Beihang University Beijing China

ISBN: (纸本)9798331506582

Text semantic matching is a fundamental task in natural language processing, widely applied in areas such as information retrieval, question answering systems, and recommendation systems. Traditional matching methods primarily focus on shallow features, but recent advances in deep learning have significantly propelled the development of text matching techniques. In light of the challenges faced by existing models, this paper proposes a text semantic matching model based on adversarial training and multi-angle features (MATM). This model adopts a siamese network framework, integrating various neural network structures. It leverages the WoBERT pre-trained model to extract deep contextual semantic information and combines the strengths of BiLSTM and CNN in feature extraction. Additionally, the model introduces an attention mechanism to merge multi-angle semantic features and employs adversarial training to generate adversarial samples, enhancing the model's generalization ability. Experiments on two Chinese text semantic matching datasets, BQ and BDZD, demonstrate that the model achieves accuracy and F1-Score of 85.93% and 85.77% on BQ, 85.38% and 86.72% on BDZD, respectively, showcasing substantial competitiveness. © 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

A Hybrid Slime Mould Meta Heuristic Algorithm and machine learning Technique for Intrusion Detection System 3

A Hybrid Slime Mould Meta Heuristic Algorithm and Machine Le...

引用

3rd IEEE international conference on Intelligent Techniques in Control, Optimization and Signal Processing, INCOS 2024

作者： Umarani, V. Swetha, K. Computer Science and Engineering Saveetha Engineering College Thandalam TamilNadu Chennai India

ISBN: (纸本)9798350361186

Network anomaly prediction is a crucial aspect of network security, as it helps identify unusual or potentially malicious activities within a computer network. There are several approaches and techniques used for network anomaly prediction, and machine learning (ML) is often employed due to its ability to detect patterns and anomalies in large datasets. In this research, network anomaly detection is carried out by three stages. In first stage, data set from IEEE data port is collected and LRC, RFC models are trained for prediction. In second stage, dimension reduction algorithm PCA is used for preprocessing and ML models are trained for prediction. In third stage, the hybrid PCA +SMA is proposed along with ML models for prediction. From this analysis, the hybrid PCA+SMA+ML provide better performance (accuracy 96) compared with previous three stages. Finally, the hybrid PCA+SMA+RFC is selected for final deployment. © 2024 IEEE.

关键词： machine learning

来源：评论

学校读者我要写书评

暂无评论

Leveraging machine learning for Predicting Vehicle Fuel Efficiency 3

Leveraging Machine Learning for Predicting Vehicle Fuel Effi...

引用

3rd international conference for Advancement in Technology, ICONAT 2024

作者： Maheshwari, Rishit Nagersheth, Mahir Tandel, Karan Vyas, Soham Pandit Deendayal Energy University Department of Computer Science Gujarat India

ISBN: (纸本)9798350354171

Amid rising environmental concerns and escalating fuel costs, enhancing vehicle fuel efficiency is a critical focus in automotive engineering. This study applies advanced ML (machine learning) approaches to predict vehicle fuel efficiency, specifically targeting the 'comb08' variable in a comprehensive vehicle dataset. Six predictive models are evaluated: Linear Regression, Decision Trees, and Random Forest among them. Rigorous data preprocessing ensures data quality and consistency, involving normalizing features, handling missing values, and encoding categorical variables. After preprocessing, models are trained and validated to assess their predictive accuracy and robustness. The performance varies significantly across models, with the Decision Tree model standing out as the most accurate and robust, achieving a low root mean square error (RMSE) of ' 2.752455 ' in fuel efficiency predictions. These findings enhance our understanding of the factors influencing fuel efficiency and provide essential insights for developing more energy-efficient vehicles. The implications extend beyond academia, impacting automotive design and informing environmental policies. By showcasing the potential of machine learning, this research underscores its pivotal role in advancing fuel economy standards and promoting sustainability within the automotive industry. It highlights the importance of leveraging advanced analytical techniques to address critical challenges in modern transportation systems, contributing to more sustainable and cost-effective automotive solutions. © 2024 IEEE.

关键词： Automotive industry

来源：评论

学校读者我要写书评

暂无评论

AutoML Framework for Physical Activities Recognition 3

AutoML Framework for Physical Activities Recognition

引用

3rd international conference on Artificial Intelligence For Internet of Things, AIIoT 2024

作者： Kovalevsky, Vladislav Delhibabu, Radhakrishnan Zhukova, Nataly St. Petersburg Federal Research Center The Russian Academy of Sciences St. Petersburg Russia Vellore Institute of Technology School of Computer Science and Engineering Vellore India

ISBN: (纸本)9798350372120

The rise of measuring, computing, and storage capabilities in modern information systems has led to vast amounts of data in various fields of human activities. Various algorithms for machine learning have been developed to get valuable insights from this raw data. Tuning the hyperparameters of each algorithm is necessary. Finding the right algorithm and manually tuning its hyperparameters for a given task can be very time-consuming. The methods for automating this search are within the scope of AutoML (automated machine learning). The wide use of wearable devices such as smartwatches and fitness trackers enables the gathering of various information about the physical activities of the wearer. This information, in turn, can be used to produce useful insights, such as recognizing activities being performed. AutoML can automate the process of building models for this task, but the performance of the resulting models can be very different depending on the AutoML library that performed the task. In this work, we develop an AutoML framework that unites several commonly used AutoML libraries and enables the use of common constraints and metrics. The developed framework is then used to build models for the task of human activity recognition by processing a dataset containing information collected from smart devices worn by users. © 2024 IEEE.

关键词： machine learning

来源：评论

学校读者我要写书评

暂无评论

Design and Application of Sports Management Decision Support System based on B/S Model 3

Design and Application of Sports Management Decision Support...

引用

3rd international conference on data Analytics, Computing and Artificial Intelligence, ICDACAI 2024

作者： Zhang, Qinqin Gansu Institute of Mechanical & Electrical Engineering Gansu Tianshui741001 China

ISBN: (纸本)9798331519032

Due to complex data and changing environments, sports managers face many challenges when making decisions. In order to provide scientific and accurate decision-making support, a sports management decision-making support system based on the B/S model came into being. This research adopts the method and process of system design to develop the system, and uses data analysis and mining techniques to conduct in-depth analysis of the data, including statistical analysis, data modeling, and machine learning methods, to identify patterns, trends, and relevance in the data, and design and implement specific decision-making models and algorithms, based on historical data and domain knowledge, used to predict competition results, evaluate athlete status, and formulate training plans. After experimental testing, the response time range of the system is 0.4-1s. It provides sports managers with powerful tools and resources to improve the efficiency and accuracy of decision-making. © 2024 IEEE.

关键词： data accuracy

来源：评论

学校读者我要写书评

暂无评论

From Manual to machine: How AI is Redefining Web Scraping for Superior Efficiency: A Literature Review 3

From Manual to Machine: How AI is Redefining Web Scraping fo...

引用

3rd international conference on Communication, Control, and Intelligent Systems, CCIS 2024

作者： Ayuso, Enrique Dumfeh Brogya, Maame Serwah Kumar Ahlawat, Vinay Sain, Mangal Dongseo University Dept. of Computer Engineering Busan Korea Republic of Gla University Dept. of Electronics & Communication Mathura India

ISBN: (纸本)9798331528201

Web Scraping involves the use of bots for the purpose of extracting data from the online web. To extract such data, the web scraper must conduct at least 3 different steps, i.e., collect the necessary links from the web, extract data using the provided links, and save the information in a neatly organized CSV file or database. However, traditional methods to accomplish these steps have faced many difficulties, such as anti-scraping measures and website changes. To tackle these difficulties and to accomplish the steps in a more rapid yet advanced way, Artificial Intelligence (AI) can be utilized. As of mid-2024, the rapid development and improvement of AI has resulted in its integration into many businesses and educational institutions. The current state of AI is remarkably advanced that it can be integrated in about any field and sector available. In this paper, the researcher defined the term Web Scraping, Artificial Intelligence, Natural Language Processing, and machine learning, explained the use of AI in Web Scraping including its importance and potential, and the understanding of ethical web scraping practices. This literature review indicated that the integrated use of AI improves the performance of Web Scraping compared to traditional methods. © 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

Fraud Detection in Financial Transactions using machine learning - A Comparative Study 3

Fraud Detection in Financial Transactions using Machine Lear...

引用

3rd IEEE Delhi Section Flagship conference, DELCON 2024

作者： Jayanthkumar, Avati Sathwik, Mayakuntla Sai Reddy, Shagam Srinivasa Kumar, Donadula Mounish Samir, Chougule Saish Patni, Jagdish Chandra Bahadure, Nilesh Kumar, Rohit Alliance School of Advanced Computing Alliance University Dept. of Computer Science Bengaluru India School of Technology Gsfc University Department of Computer Science and Engineering Vadodara India

ISBN: (纸本)9798331518592

data analysis techniques using machine learning approaches are the most prominent when we are considering the online fraud, it may be a credit card fraud or any other online fraud in the banking system. Using machine learning techniques, we can easily manage the information of fake transactions by extensive data analysis of time, location and amount in the real time. This paper gives the comprehensive study of the changing scene of the fraud discovery in monetary exchanges, investigating the most recent patterns, advances, and techniques utilized by financial institutions and other administrative bodies. An extensive review of various machine learning techniques has been done to find out the effective solution to protect the online fraud. © 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

Extensive data Preprocessing and Exploratory data Analysis for House Price Prediction using machine learning and Deep learning with an Introduction to AutoML Technologies 3

Extensive Data Preprocessing and Exploratory Data Analysis f...

引用

3rd IEEE World conference on Applied Intelligence and Computing, AIC 2024

作者： Ved, Aditya Ravindra Gupta, Archana K.J.S.C.E Svu Department of Computer Engineering Mumbai India

ISBN: (纸本)9798350384598

Houses contribute to one of the most essential needs of every human and house prices play a very important role in everyone's life. House Price prediction has been one of the crucial topics in the real estate industry. This paper focuses on data preprocessing techniques which are carried out using dataset containing the house prices of the Ames City in United States. It covers the entire journey right from data collection to data Preprocessing and data Analysis to Prediction using various Models. In this paper, machine learning, Deep learning and Auto ML techniques are applied to study historical property transactions in Ames to find useful models for both house buyers and sellers. The Models we test as a part of this paper are Random Forest Regression, Ridge Regression, Lasso Regression, (SVR) Support Vector Regression, XGBoost (which is an optimized distributed gradient boosting library) and ANN (Artificial Neural Networks). While ANN and Automated machine learning (AutoML) represent the forefront of machine learning, our exploration demonstrates that classical ML models remain indispensable across various domains. Our findings also demonstrate that the effectiveness of any machine learning model is highly dependent on comprehensive and rigorous data preprocessing. © 2024 IEEE.

关键词： Support vector regression

来源：评论

学校读者我要写书评

暂无评论

Hybrid Approach for Automated Discrimination between COVID-19 and Similar Respiratory Diseases 13

Hybrid Approach for Automated Discrimination between COVID-1...

引用

13th Iranian/3rd international machine Vision and Image Processing conference (MVIP)

作者： Rad, Houman Bahrami Kargari, Mehrdad Alavi, Meysam Kharazmy, Amir Behnam Tarbiat Modares Dept Informat Technol Tehran Iran Shahid Beheshti Univ Med Sci Dept Internal Med Tehran Iran

ISBN: (纸本)9798350350494;9798350350500

One of the main challenges in accurately diagnosing COVID-19 is its clinical manifestations, which are similar to some respiratory diseases such as viral and bacterial pneumonia, and even influenza during cold seasons. These similarities may lead to misdiagnosis and periodically threaten public health. In order to achieve an accurate and rapid diagnosis of COVID-19 and differentiate it from other respiratory diseases with similar features, this research aims to provide a comprehensive system. This system utilizes artificial intelligence and machine learning methods to analyze lung CT scan images to distinguish COVID-19 from other lung diseases. To train this system, data from lung CT scan images of patients from Imam Hussein Hospital in Tehran were used. The data includes three categories of patients: COVID-19 patients, patients with lung pneumonia, and patients with other respiratory conditions. In this study, two main approaches to patient classification were conducted using artificial intelligence and machine learning methods. The results show that deep learning models and hybrid models achieved acceptable performance with accuracies of 98.98% and 99.79%, respectively.

关键词： COVID-19 Deep learning Hybrid model CT Scan Images

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 << < 29 30 31 32 33 34 35 36 37 38 > >>

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：