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

64,565 篇 会议
597 册 图书
326 篇 期刊文献

馆藏范围

65,487 篇 电子文献
1 种 纸本馆藏

日期分布

学科分类号

38,377 篇 工学
- 35,097 篇 计算机科学与技术...
- 23,067 篇 软件工程
- 8,726 篇 信息与通信工程
- 5,271 篇 电气工程
- 4,761 篇 生物工程
- 4,757 篇 控制科学与工程
- 3,813 篇 生物医学工程（可授...
- 1,632 篇 网络空间安全
- 1,578 篇 安全科学与工程
- 1,451 篇 光学工程
- 1,326 篇 机械工程
- 1,256 篇 动力工程及工程热...
- 1,242 篇 电子科学与技术（可...
- 1,192 篇 仪器科学与技术
- 1,187 篇 化学工程与技术
14,082 篇 理学
- 7,955 篇 数学
- 5,257 篇 生物学
- 3,617 篇 统计学（可授理学、...
- 3,113 篇 系统科学
- 2,334 篇 物理学
- 1,213 篇 化学
8,772 篇 管理学
- 5,070 篇 管理科学与工程(可...
- 4,398 篇 图书情报与档案管...
- 2,233 篇 工商管理
4,424 篇 医学
- 3,697 篇 临床医学
- 3,227 篇 基础医学(可授医学...
- 2,071 篇 药学(可授医学、理...
- 1,730 篇 公共卫生与预防医...
1,854 篇 法学
- 1,604 篇 社会学
1,181 篇 农学
- 1,154 篇 作物学
991 篇 经济学
733 篇 教育学
338 篇 文学
45 篇 艺术学
39 篇 军事学
5 篇 哲学
5 篇 历史学

主题

17,119 篇 machine learning
5,347 篇 machine learning...
4,566 篇 deep learning
4,067 篇 accuracy
3,975 篇 support vector m...
3,446 篇 computational mo...
3,311 篇 training
3,214 篇 predictive model...
2,928 篇 feature extracti...
2,026 篇 data models
1,949 篇 cloud computing
1,764 篇 real-time system...
1,713 篇 neural networks
1,600 篇 adversarial mach...
1,551 篇 prediction algor...
1,311 篇 artificial intel...
1,302 篇 data mining
1,282 篇 decision trees
1,198 篇 random forests
1,150 篇 internet of thin...

机构

203 篇 chitkara univers...
134 篇 school of comput...
80 篇 karnataka bangal...
78 篇 school of comput...
66 篇 department of co...
48 篇 chennai
48 篇 lovely professio...
44 篇 univ chinese aca...
44 篇 centre of interd...
42 篇 school of comput...
40 篇 graphic era deem...
37 篇 graphic era deem...
35 篇 shenyang institu...
31 篇 department of co...
30 篇 department of in...
30 篇 faculty of compu...
29 篇 department of co...
29 篇 shanghai jiao to...
29 篇 vishwakarma inst...
28 篇 computer science...

作者

36 篇 anjali t.
28 篇 de-shuang huang
26 篇 isman kurniawan
24 篇 rakesh kumar
23 篇 kumar anil
22 篇 hui wang
21 篇 amandeep nagpal
21 篇 kumar ajay
21 篇 gill kanwarparta...
20 篇 qin zengchang
20 篇 sharif mhd saeed
19 篇 yu wang
19 篇 ajay kumar
18 篇 qomariyah nunung...
18 篇 pareek piyush ku...
17 篇 zohreh doborjeh
16 篇 schuurmans dale
16 篇 andersson karl
16 篇 kumar manish
15 篇 shiva mehta

语言

55,297 篇 英文
10,017 篇 其他
635 篇 中文
3 篇 法文
2 篇 葡萄牙文
2 篇 俄文
1 篇 西班牙文
1 篇 土耳其文

检索条件"任意字段=International Conference on Machine Learning and Computing"

共 65488 条记录，以下是4631-4640 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：

Fake Social Media Detection using machine learning 15

Fake Social Media Detection using Machine Learning

引用

15th international conference on computing Communication and Networking Technologies, ICCCNT 2024

作者： Thangamani, R. Vimaladevi, M. Mohammed Rifath, J. Harini, G. Hari, S. Kongu Engineering College Department of Artificial Intelligence Tamil Nadu India Kongu Engineering College Artificial Intelligence And Machine Learning Perundurai India

ISBN: (纸本)9798350370249

Social media platforms such as Facebook, Twitter, Instagram and others have become an integral part of our lives, facilitating global connectivity. But the proliferation of fake profiles on these platforms poses serious risks, including the spread of false information, phishing attacks and identity theft. This research project focuses on identifying fake Twitter profiles using machine learning techniques to distinguish them from genuine accounts. In the dataset used in this study, Twitter profiles are classified into genuine accounts (TFP and E13) and fake accounts (INT, TWT and FSF).. The main objective is to evaluate the effectiveness of the ensemble learning technique Random Forest algorithm in classifying social media profiles. In-depth discussions include algorithm architecture, hyperparameters, and selection of critical features such as follower and friend counts, status changes, and other relevant features. Additionally, alternative classifiers including neural networks, LSTM, XG Boost and others are briefly discussed in the summary. The Random Forest algorithm has a commendable 95.54% accuracy after rigorous training. This high accuracy underlines its ability to effectively distinguish between real and fake Twitter profiles. By assigning a value of 0 to genuine profiles and 1 to fake profiles, the algorithm provides a practical solution to detect and mitigate fake accounts, thereby solving the cyber security issues associated with social media. This research contributes to ongoing efforts to create a safer online environment by identifying and removing fake profiles. © 2024 IEEE.

关键词： Fake News LSTM Profiles ROC Social Media

来源：评论

学校读者我要写书评

暂无评论

Online Job Portal Application using machine learning 15

Online Job Portal Application using Machine Learning

引用

15th international conference on Advances in computing, Control, and Telecommunication Technologies, ACT 2024

作者： Subhashini, S.J. Angelina, J. Jane Rubel Phaneesh, Musini Kiran, Chirumavilla Sai Chowdary, Gonuguntla Mohitha Renuka, Chittapu SRM Madurai College for Engineering and Technology Department of CSE TamilNadu Sivagangai India Kalasalingam academy of research and education Department of CSE Tamil Nadu Virudhunagar India

ISBN: (纸本)9798331300579

In the rapidly evolving job market, an efficient and personalized job portal plays a pivotal role in connecting job seekers with suitable opportunities. This project aims to develop a comprehensive job portal application by integrating various machine learning algorithms, enhancing user experience and job matching accuracy. The key components include Natural Language Processing (NLP), K-Nearest Neighbors (KNN), skill matching, job recommendation, resume parsing, and Decision Tree-based decision-making. This project aims to create a dynamic and intelligent job portal that not only streamlines the job search process but also provides a personalized and efficient experience for both job seekers and employers. The amalgamation of NLP, K-Nearest Neighbors, skill matching, recommendation systems, resume parsing, and decision trees forms a robust framework for enhancing the functionality and activeness of the job portal application. © Grenze Scientific Society, 2024.

关键词： ALGORITHMS CONCLUSION INTRODUCTION LITERATURE SURVEY USES

来源：评论

学校读者我要写书评

暂无评论

RaiDen: An IOT-based automated and integrated system for improvement of plant growth

RaiDen: An IOT-based automated and integrated system for imp...

引用

2024 international conference on Advances in computing, Communication, Electrical, and Smart Systems, iCACCESS 2024

作者： Aurpita, Sumyea Nazifa Momo, Anika Tahsin Rahman, Mohammed Ashikur Computer Science and Engineering University of Liberal Arts Bangladesh Dhaka Bangladesh

ISBN: (纸本)9798350350289

Utilizing Internet of Things (IoT) technology, the proposed system is an automated system that establishes connectivity among various hardware components to enhance plant growth. The system is aimed for its use in agriculture, to make the process more sustainable and productive. This research focuses on controlling the pH level of water to improve plant growth. In Bangladesh, about 2,200 millimeters of rainfall every year on average. Nevertheless, a significant challenge arises from the polluted atmosphere resulting from rapid urbanization, where harmful gases such as sulfur dioxide, carbon and nitrogen dioxide combine with rainwater, leading to the formation of acidic precipitation. The system is to develop an automated, integrated system that utilizes acidic(low pH) rainwater to maintain pH between 5-6, enhancing plant growth. Furthermore, after data collection through IoT devices and analysis via machine learning algorithms like linear regression, random forest regression, and gradient boosting regression so the system can autonomously adjust the water pH and watered plants. © 2024 IEEE.

关键词： machine learning

来源：评论

学校读者我要写书评

暂无评论

Employing a machine learning-Based Approach for Enhanced Big Data Analysis in Supply Chain Management

Employing a Machine Learning-Based Approach for Enhanced Big...

引用

2024 international conference on Advances in Data Engineering and Intelligent computing Systems, ADICS 2024

作者： Pradeepa, M. Sumathi, V. Ponnusamy, Sivakumar Suneetha, G. Singh, Gurjeet Kruthika, P. School Of Computer Science Engineering And Information Systems Vellore Institute Of Technology Tamil Nadu Vellore632 014 India Sri Sai Ram Engineering College Department Of Mathematics West Tambaram Tamil Nadu Chennai44 India Ksr College Of Engineering Department Of Computer Science And Engineering Namakkal TamilNadu Tiruchengode India Koneru Lakshmaiah Education Foundation Department Of Cse Andhra Pradesh Vaddeswaram India Ies College Of Technology Ies University Bhopal Department Of Mechanical Engineering Madhya Pradesh India Sona College Of Technology Department Of Information Technology Tamil Nadu Salem India

ISBN: (纸本)9798350364828

This study uses machine learning models as potent analytical tools to look into the issue of inventory cost and profit prediction in the automobile industry. Throughout a ten-year dataset from 2012 to 2023, the study carefully analyses several cost components and profit data, illuminating the complex relationships between these elements. To provide insights into inventory dynamics, the Convolutional Neural Network (CNN), k-nearest Neighbours (KNN), and Support Vector machine (SVM) models are rigorously trained and tested. An impressive feat is achieved by the CNN model, which achieves a phenomenal accuracy of 98.7%, followed by KNN at 96.7% and SVM at 93.4%. Additional proof of the models' propensity to make accurate predictions comes from their precision, recall, F1 score, and confusion matrices. The research provides data-driven insights that players in the dynamic vehicle industry can use to improve inventory management procedures and financial results. This work serves as an example of how machine learning may fundamentally alter how people make decisions in this difficult domain. © 2024 IEEE.

关键词： Forecasting

来源：评论

学校读者我要写书评

暂无评论

Augmented Speech Emotion Recognition Using machine learning

Augmented Speech Emotion Recognition Using Machine Learning

引用

2024 international conference on Electrical, Electronics and computing Technologies, ICEECT 2024

作者： Suba Renjani, N. Supriya, P. Amrita School of Engineering Department of Electrical and Electronics Engineering Coimbatore India

ISBN: (数字)9798350378092

ISBN: (纸本)9798350378092

Speech emotion recognition (SER) is a technology used to analyze emotions from spoken language. These systems aim to classify a speaker's emotional state by analyzing subtle vocal characteristics like pitch, tone, and rhythm. However, background noise and distractions can significantly reduce the accuracy of emotion detection by SER systems. The Wiener filter acts as a digital noise-canceling tool, specifically targeting speech signals corrupted by environmental noise. Mel- frequency cepstral coefficients (MFCCs) enhance the speech features. The work revolves on increasing the feature count followed by exercising feature selection using the filter method. Select- K best provides the stage for the application of four chosen machine learning (ML) models namely SVM, Random Forest, Decision Tree, and KNN. Random Forest outperforms other models in its performance. The work is an elementary analysis on ML to explore the need of other high end DL architectures like CNN, RNN and LSTM for SER. Work is in progress to port the most accurate algorithm to an embedded platform like Raspberry Pi for integration with IoT. © 2024 IEEE.

关键词： Wiener filtering

来源：评论

学校读者我要写书评

暂无评论

Multidisease Prediction and Causality Analysis with Hospital Management System 3

Multidisease Prediction and Causality Analysis with Hospital...

引用

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

作者： Roy, Abhro Kumar Gayen, Subham Renukadevi, P. Chennai India Chennai India

ISBN: (纸本)9798350375190

In healthcare domain, the demand for efficient diagnosis and hospital management systems has increased unprecedentedly. This research study proposes a novel Symptom Checker integrated with a Multidisease Hospital Management System, leveraging state-of-the-art machine learning (ML) algorithms such as Random Forest Classifier and XGB Regression. Unlike existing systems, this advanced framework outperforms conventional methods by offering increased accuracy and agility in diagnosing a multitude of medical conditions. The proposed system operates as a comprehensive tool, integrating patient symptoms with ML algorithms to accurately identify the diseases. Through the analysis of symptoms given as input by patients, the Random Forest Classifier preforms well in classifying a broad spectrum of diseases, demonstrating high precision in analysing complex symptom patterns. Furthermore, the XGB Regression model enhances the system's predictive capabilities, enabling the estimation of disease progression and facilitating proactive treatment strategies. Moreover, the proposed approach extends beyond symptom analysis. It encompasses a robust hospital management component that streamlines administrative tasks, optimizes resource allocation, and enhances patient care. © 2024 IEEE.

关键词： machine learning

来源：评论

学校读者我要写书评

暂无评论

Security Monitoring for VANETs Using machine learning 7

Security Monitoring for VANETs Using Machine Learning

引用

7th international conference on Information and Computer Technologies, ICICT 2024

作者： Pape, Nathan Mansour, Christopher Mercyhurst University Computing And Information Science Department EriePA16546 United States

ISBN: (纸本)9798350385625

As Vehicular Ad Hoc Networks (VANETs) and the volume of vehicles on the streets continue to rise, so does the security threats to devices within these networks. A VANET is a system that is comprised of moving or stationary vehicles connected by a wireless network. They are crucial in enhancing safety and comfort for drivers in vehicular settings. To function optimally, vehicles within VANETs must interact and communicate transmitting crucial and vital information. Ensuring the legitimacy and safety of these messages, and preventing malicious activity, becomes essential. This can be achieved by monitoring the messages in real time in order to detect any malicious activity. In this study, we propose a monitoring system that harnesses the power of machine learning to identify any malicious activity emanating from vehicles in VANETs. © 2024 IEEE.

关键词： Vehicular ad hoc networks

来源：评论

学校读者我要写书评

暂无评论

Silent Skies: machine learning for Aircraft Noise Reduction Strategies

Silent Skies: Machine Learning for Aircraft Noise Reduction ...

引用

2024 IEEE international conference on computing, Semiconductor, Mechatronics, Intelligent Systems and Communications, COSMIC 2024

作者： Singh, Nageshwar Pawar, Kunal Afnan, Syed Nashath Hurthivk Hegde, Ramakrishna N Kannadaguli, Prashanth Srinivas Institute of Technology Dept. Aeronautical Engineering Mangalore India Dhaarini Academy of Technical Education Senior Data Science Trainer Bengaluru India

ISBN: (纸本)9798331517892

Aircraft noise pollution poses a significant challenge to communities near airports, impacting their health, well-being, and overall quality of life. Traditional noise mitigation approaches have predominantly focused on physical modifications to aircraft design and flight procedures. However, the emergence of machine learning (ML) techniques offers promising avenues for innovative noise reduction strategies. This paper introduces 'Silent Skies,' a groundbreaking framework utilizing machine learning algorithms to address aircraft noise. Silent Skies integrates diverse data sources, including aircraft telemetry, weather conditions, airport infrastructure, and community feedback, to train predictive models for accurately estimating noise levels. These models inform decision-making processes, optimizing flight paths, engine settings, and airport operations to minimize noise emissions. Key components of the Silent Skies framework include data preprocessing techniques for handling heterogeneous and noisy data, feature engineering methods for extracting relevant information from raw data sources, and ensemble learning approaches to enhance prediction accuracy. Additionally, the framework incorporates real-time monitoring and feedback mechanisms to continually refine noise reduction strategies in response to evolving conditions. To demonstrate the effectiveness of Silent Skies, we present case studies conducted at major airports, showcasing significant reductions in noise pollution while maintaining operational efficiency. These case studies underscore the framework's adaptability across diverse airport configurations, airspace constraints, and environmental contexts. In summary, Silent Skies represents a significant advancement in aircraft noise mitigation, offering scalable and adaptive solutions that prioritize community well-being without compromising aviation safety or efficiency. Through the integration of machine learning techniques, airports can transition towards q

关键词： Noise abatement

来源：评论

学校读者我要写书评

暂无评论

Graph-Based machine learning Approaches for Fraud Detection in Financial Networks 7

Graph-Based Machine Learning Approaches for Fraud Detection ...

引用

7th international conference on Contemporary computing and Informatics, IC3I 2024

作者： Kotiyal, Arnav Hussein, Layth Deepak, A. Rana, Aryan Manjunatha Dixit, Krishna Kant Reddy, R. Akhilesh Graphic Era Deemed to be University Department of Computer Science & Engineering Dehradun India The Islamic University College of technical engineering Department of computers Techniques engineering Najaf Iraq Saveetha Institute of Medical and Technical Sciences Saveetha University Saveetha School of Engineering Tamilnadu Chennai India Amity University Amity School of Engineering and Technology Uttar Pradesh Greater Noida India New Horizon College of Engineering Department of Mechanical Engineering Bangalore India Gla University Department of Electrical Engineering Mathura India Mlr Institute of Technology Department of CSE-AI & Ml Telangana Hyderabad India

ISBN: (纸本)9798350350067

Fraud detection in financial networks presents a significant challenge due to the complexity and volume of transactions. Traditional detection methods often struggle with scalability and accuracy when faced with evolving fraudulent schemes. In this paper, we explore the application of graph-based machine learning techniques to enhance fraud detection in financial networks. By representing financial transactions as graph structures, we capture the relationships between entities, allowing for the identification of suspicious patterns that may indicate fraudulent behavior. We examine several graph-based algorithms, including Graph Neural Networks (GNNs) and Random Walk-based approaches, and compare their performance with conventional machine learning methods. The use of graph representations facilitates the extraction of features such as transaction flows and network centrality, which are critical in identifying anomalies. Furthermore, we propose an enhanced detection model that integrates community detection techniques with supervised learning for improved accuracy. Our experimental results, conducted on a real-world financial dataset, demonstrate that graph-based models outperform traditional models in detecting fraud with higher precision and recall. The study highlights the potential of these techniques to provide scalable and robust solutions for real-time fraud detection, contributing to a more secure financial ecosystem. © 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

Optimizing Crop Yield through A Data-Driven Approach with IoT Data Analytics and machine learning Algorithms 15

Optimizing Crop Yield through A Data-Driven Approach with Io...

引用

15th international conference on computing Communication and Networking Technologies, ICCCNT 2024

作者： Moeed, Syed Abdul Jinukala, Akshaya Kolipaka, Bhavana Kalvala, Aakanksha Kusuma, Vijay Kumar Kakatiya Institute of Technology and Science Dept of Computer Science and Engineering Warangal India

ISBN: (纸本)9798350370249

Agriculture has long been the backbone of the Indian economy, defining the country's social and cultural milieu. Some of the most common difficulties that farmers face includes selecting suitable crops for their area and utilizing the necessary fertilizers, which can lead to a drop in production. To address these challenges, precision agriculture is employed. Precision agriculture is a smart and modern farming methodology that uses IoT-based research to provide information on soil qualities, types of soil, and crop production statistics. This assists farmers in choosing the optimum crops to plant and provides fertilizer recommendations based on specific location characteristics, also predicting the likelihood of plant diseases. In this project, we introduce a recommendation system by applying machine learning models such as Support Vector machine (SVM), Logistic Regression, Decision Tree, Random Forest, and Naïve Bayes on a crop dataset, which is entirely IoT-based. The system recommends the best crop for the location's particular parameters with the highest accuracy. The system achieved an accuracy of 99% for crop recommendation and 92% for plant disease prediction. This research utilizes soil nutrients and pH data as inputs to create a website that predicts the most suitable crops for a particular soil type and suggests appropriate fertilizers. Additionally, the system predicts plant diseases and provides information about the diseases, including suggested cures. Thousands of farmers will be able to access the system through a Flask-based web interface. © 2024 IEEE.

关键词： Crop Recommendation Data Analytics (DA) Fertilizer Recommendation Internet of Things (IoT) machine learning (ML) Plant disease prediction Precision Agriculture ResNet

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 << < 460 461 462 463 464 465 466 467 468 469 > >>

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：