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

65,368 篇 会议
597 册 图书
335 篇 期刊文献

馆藏范围

66,299 篇 电子文献
1 种 纸本馆藏

日期分布

学科分类号

39,061 篇 工学
- 35,706 篇 计算机科学与技术...
- 23,365 篇 软件工程
- 9,207 篇 信息与通信工程
- 5,480 篇 电气工程
- 5,261 篇 控制科学与工程
- 4,772 篇 生物工程
- 3,836 篇 生物医学工程（可授...
- 1,926 篇 网络空间安全
- 1,712 篇 安全科学与工程
- 1,466 篇 光学工程
- 1,413 篇 机械工程
- 1,385 篇 动力工程及工程热...
- 1,336 篇 电子科学与技术（可...
- 1,252 篇 仪器科学与技术
- 1,220 篇 交通运输工程
- 1,203 篇 化学工程与技术
14,268 篇 理学
- 8,059 篇 数学
- 5,262 篇 生物学
- 3,664 篇 统计学（可授理学、...
- 3,114 篇 系统科学
- 2,384 篇 物理学
- 1,293 篇 化学
9,125 篇 管理学
- 5,395 篇 管理科学与工程(可...
- 4,576 篇 图书情报与档案管...
- 2,255 篇 工商管理
4,536 篇 医学
- 3,733 篇 临床医学
- 3,238 篇 基础医学(可授医学...
- 2,071 篇 药学(可授医学、理...
- 1,808 篇 公共卫生与预防医...
1,914 篇 法学
- 1,661 篇 社会学
1,191 篇 农学
1,039 篇 经济学
770 篇 教育学
349 篇 文学
45 篇 艺术学
39 篇 军事学
5 篇 哲学
5 篇 历史学

主题

17,419 篇 machine learning
5,383 篇 machine learning...
4,691 篇 deep learning
4,145 篇 accuracy
4,006 篇 support vector m...
3,475 篇 computational mo...
3,330 篇 training
3,248 篇 predictive model...
2,957 篇 feature extracti...
2,054 篇 data models
1,987 篇 cloud computing
1,814 篇 real-time system...
1,731 篇 neural networks
1,605 篇 adversarial mach...
1,560 篇 prediction algor...
1,350 篇 artificial intel...
1,311 篇 data mining
1,295 篇 decision trees
1,228 篇 random forests
1,175 篇 internet of thin...

机构

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

作者

36 篇 anjali t.
26 篇 isman kurniawan
26 篇 de-shuang huang
24 篇 rakesh kumar
23 篇 kumar anil
22 篇 kumar ajay
22 篇 hui wang
21 篇 amandeep nagpal
21 篇 gill kanwarparta...
20 篇 qin zengchang
20 篇 sharif mhd saeed
19 篇 yu wang
19 篇 ajay kumar
19 篇 babu tina
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

语言

51,677 篇 英文
14,449 篇 其他
729 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 66300 条记录，以下是4671-4680 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：

Exploratory Data Analysis of Titanic Survival Prediction using machine learning Techniques 2

Exploratory Data Analysis of Titanic Survival Prediction usi...

引用

2nd international conference on Applied Artificial Intelligence and computing, ICAAIC 2023

作者： Gupta, Anshika Arora, Deepak Tiwari, Shivam Aset Lucknow Amity University Dept. of Computer Science & Engineering Uttar Pradesh India Gl Bajaj Institute Technology & Management Dept. of Computer Science & Engineering Greater Noida India

ISBN: (纸本)9781665456302

It is very important to find out the root causes of human tragedies in the past so that future crises can be avoided. The incident of 15 April 1912 is an example of a human tragedy in which around 1500 passengers and working staff lost their lives. Continuous research in today's time shows that if the proper statistical assessment was done then it would probably be possible that human devastation could be reduced. In today's era, many new and powerful technologies are available, with the help of which accurate statistical calculations can be done. In this research study, Titanic survivals have been studied based on machine learning techniques. In the study, out of the total entities, 891 entities have been used for training and 418 entities have been used for the test set and the comparative study of different machine learning algorithms gives importance to this research study. © 2023 IEEE.

关键词： machine learning

来源：评论

学校读者我要写书评

暂无评论

Detection of Oil Spill Events at Sea Using machine learning 5

Detection of Oil Spill Events at Sea Using Machine Learning

引用

5th international conference on Inventive Research in computing Applications, ICIRCA 2023

作者： Sandhiya, S. Ramya, R. Sukitha, M. Vikhashni, S. Sri Ramakrishna Engineering College Department of Electronics and Communication Engineering Coimbatore India

ISBN: (纸本)9798350321425

Oil spill detection performed by using machine learning (ML) involves building and training a model to identify and classify oil spill images from satellite or drone imagery. Synthetic Aperture Radar (SAR) is a technology that has been extensively used in oil spill detection. It is to detect and monitor tea-based oil pollution by airplanes and satellites furnished with SAR. It is necessary to see and spot oil spills in seas, oceans, rivers, and lakes. The ML model can be trained by images labeled with clean water and water with oil spills gathered from satellite and drone images with high resolution. In this proposed method, AIS data of ships were utilized to predict the oil spill events at sea before it happens. With the parameters like container details, type of oil, dispersion rate, skim rate, etc. The probability of an oil spill is detected by training a machine learning model. The software used for implementing the idea is Spyder IDE and algorithms like Linear Regression, Support Vector machine (SVM), and Decision Tree were employed here. The major challenge in oil spill detection is accuracy and time complexity. © 2023 IEEE.

关键词： Decision trees

来源：评论

学校读者我要写书评

暂无评论

Sleep Apnea Detection System Using machine learning on Resource-Constrained Devices 17

Sleep Apnea Detection System Using Machine Learning on Resou...

引用

17th Annual IEEE international Systems conference (SysCon)

作者： Mallick, Sayani Gawali, Shubhangi Onime, Clement Goveas, Neena BITS Pilani EEE Dept Goa Campus Sancoale Goa India BITS Pilani CSIS Dept Goa Campus Sancoale Goa India Abdus Salaam Int Ctr Theoret Phys Trieste Italy

ISBN: (纸本)9781665439947

Sleep Apnea is a condition in which a person has pauses in breathing or very low breathing episodes during sleep. It is a condition that could prove life-threatening if not monitored and treated. A medical diagnosis of Sleep Apnea involves overnight recording of body signals, monitoring by a medical professional, use of hospital based equipment and data analysis for detection of anomalies. During the past decade, the measurement and analysis of human body signals using machine learning techniques on embedded devices have started to transform healthcare applications. The use of cost effective micro-controllers can ensure that health monitoring is available and accessible to all. In this paper, we show that machine learning models deployed on microcontrollers can successfully analyze ECG signals in real-time for Sleep Apnea detection. We have created TinyML models using TensorFlow Lite which we have deployed on cost effective and resource constrained devices like the Raspberry Pi Pico and ESP32. Our setup has given results comparable to more advanced and expensive devices for the detection of Sleep Apnea using ECG signals.

关键词： ECG Embedded Device Signal Processing TinyML TensorFlow Lite machine learning Edge Impulse Sleep Apnea Artificial Neural Networks Edge computing

来源：评论

学校读者我要写书评

暂无评论

Privacy-Preserving Detection of DDoS Attacks in IoT Using Federated learning Techniques

Privacy-Preserving Detection of DDoS Attacks in IoT Using Fe...

引用

2024 IEEE international conference on Big Data and machine learning, ICBDML 2024

作者： Bhatia, Kunal Bhattacharya, Saurav Sharma, Ishu Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Microsoft Bothell United States

ISBN: (纸本)9798350374100

Internet of Things devices are widely employed in various industries, cities, and institutions as with low-cost investment various benefits can be obtained. The computing power, bandwidth, memory storage and battery are low in these devices and lightweight routing protocols are used for communication. Distributed Denial Service of Attack takes benefit of Internet of Things architecture and targets vulnerable devices to take control of the device. These hacked devices can be further used by attackers to send massive data packets to the victim's device. Generally, data servers and high computing devices are targeted by attackers through Internet of Things devices. Artificial intelligence-based cybersecurity is extensively employed in various scenarios but as there is a constraint of resources in the Internet of Things, the same approach is not feasible. Further, artificial intelligence-based solutions are required to deal with network traffic-related data, and organisations are reluctant to share the data. This research work targets to provide a solution for the early detection of distributed denial of service attacks using a federated learning model by preserving the information of the users. The client and server in the proposed approach are trained using Identically and Non-identically distributed approaches. The performance evaluation of both approaches is performed using metrics like accuracy, loss, precision and recall for the detection of cyberattack by model aggregation. © 2024 IEEE.

关键词： Internet of things

来源：评论

学校读者我要写书评

暂无评论

AICCC 2023 - 2023 6th Artificial Intelligence and Cloud computing conference

AICCC 2023 - 2023 6th Artificial Intelligence and Cloud Comp...

引用

6th Artificial Intelligence and Cloud computing conference, AICCC 2023

ISBN: (纸本)9798400716225

The proceedings contain 37 papers. The topics discuss include: weakly-supervised motion in-betweening learning via pose priors;conventional machine learning approach for waste classification;the effect of data augmentation using SMOTE: diabetes prediction by machine learning techniques;enhancing regression tree predictions with terminal-node anomaly detection;a machine learning based novel approach of predicting international roughness index(IRI) from traffic characteristics using random forest regression;CLEB: a continual learning energy bidding framework for an energy market bidding application;artificial intelligence aspect of transportation analysis using large scale systems;a comparative analysis of symbolic and deep learning based RDFS materialization;and sparse fully convolutional network for video-based point cloud compression color enhancement.

关键词：

来源：评论

学校读者我要写书评

暂无评论

N-Gram Based HASSANIYA Dialect Classification 6

N-Gram Based HASSANIYA Dialect Classification

引用

6th international conference on Intelligent computing in Data Sciences, ICDS 2024

作者： El Arby Chrif, Mohamed El Moustapha Saleck, Fatimetou Mohamed Nanne, Mohamedade Farouk Beqqali, Omar El Mahmoud, El Benany Mohamed University Sidi Mohamed Ben Abdallah LISAC Laboratory Department of Computer Science Fez Morocco Nouakchott University CSIDS Department of Mathematics and Computer Science Nouakchott Mauritania

ISBN: (纸本)9798350351200

This paper focuses on the HASSANIYA dialect, which consists of the same Arabic letters and is widely used locally as a Mauritania dialect. Despite other local dialects, it has also been studied in terms of sentiment analysis. Yet, it still needs further studies to understand how word roots could be extracted and provide clear directions for future research in the relevant axis of Natural Language Processing (NLP), as text classification by machine learning techniques is largely influenced by the affixes of words, which could change the meaning of words. In this work, we present a novel approach that aims to study the effect of the N-gram method on a HASSANIYA dataset containing around two thousand comments collected from the Facebook platform and evaluate how it influenced the text classification results after preprocessing them. Additionally, three machine learning techniques, such as Support Vector machine (SVM), Random Forests (RF), and Logistic Regression (LR), were applied for evaluation. The experiments give satisfactory results, confirming that the unigram method performs better than the bigram method for all algorithms. © 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

Control Strategy for Energy Internet Edge computing System Based on Multi-Agent Reinforcement learning 5

Control Strategy for Energy Internet Edge Computing System B...

引用

5th international conference on Applied machine learning, ICAML 2023

作者： Dai, Hao Jin, Yue Wang, Xihuai Shanghai Maritime University Shanghai201306 China

ISBN: (纸本)9798350341416

With the popularity of the Internet of Things (IoT), more and more devices with sensors are being deployed in real environments, producing massive amounts of data with high analytical value. This has also pushed human society into the era of big data. Edge computing (EC) is a new computing method that can avoid transmission delays and network congestion. However, as the number of edge computing tasks increases, the energy supply of edge data centers has become a huge challenge. Although edge computing can improve computing response speed, the popularity of IoT also inevitably results in a surge in energy consumption of edge data centers. Utilizing the energy internet to supply energy to edge data centers is a feasible direction, but the renewable energy capacity and energy consumption of edge data centers have strong uncertainty and randomness. In the face of this challenge, a multi-agent reinforcement learning control strategy is proposed. The controller maximizes the use of renewable energy for edge data centers through optimal control, while also ensuring that edge data centers operate with maximum working power as much as possible during data processing. In addition, the controller can also ensure stable bus frequency deviation in environments with random disturbances. Finally, the feasibility of the proposed method is verified through numerical simulation. © 2023 IEEE.

关键词： Edge computing

来源：评论

学校读者我要写书评

暂无评论

Enhancing Marketing Strategies Through Big Data-Driven Customer Journey Mapping: An Analysis Using machine learning Algorithms

Enhancing Marketing Strategies Through Big Data-Driven Custo...

引用

2024 international conference on Emerging Innovations and Advanced computing, INNOCOMP 2024

作者： Singh, Chandra Pratap Yousuf, Rizwan Chandigarh University Department of Mathematics Punjab Mohali India

ISBN: (纸本)9798350376470

Big data analytics is becoming more important in marketing today. This tool helps companies understand market trends so that they can develop targeted campaigns. In this assignment, we are required to examine how the application of big data analytics can help marketers understand the behavior of their customers and develop an effective marketing campaign. This research paper contains a detailed analysis of consumer interactions on digital platforms using advanced machine learning algorithms like Gradient Boosting machines and Random Forests. The author employed these models to derive insights from large data sets. The goal is to understand how the use of large amounts of data in tracking customer behavior can improve marketing strategies. The research of this study included an in-depth examination of both the organized and disorganized consumer information data. By utilizing predictive models, the research can accurately forecast customer behavior. The GBM model showcased remarkable performance in foretelling customers' purchase behavior, realizing a phenomenal accuracy rate of 94.39%. Furthermore, by using K-means clustering to group consumers based on their behavior, marketers could develop better marketing strategies. This article provides a lot of useful information about digital marketing and the use of vast amounts of data to analyze it. This statement means that companies give great importance to data analytics to make better marketing decisions. This research serves as a foundational element for subsequent investigations and provides an avenue for further scrutiny regarding the integration of additional data and the enhancement of machine learning algorithms. Over time, this will facilitate a more precise comprehension of the preferences and conduct of our clientele. This study underscores the significance of big data in contemporary marketing strategies, given the escalating digitization of the world. © 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

machine learning Approaches for the Early Diagnosis of Autism Spectrum Disorder: A Systematic Framework 15th

Machine Learning Approaches for the Early Diagnosis of Autis...

引用

15th international conference on Soft computing and Pattern Recognition, SoCPaR 2023 and 14th World Congress on Nature and Biologically Inspired computing, NaBIC 2023

作者： Narayanan, Neethu Remya, K.R. Varghese, Bindiya M. Kerala Kochi India

ISBN: (纸本)9783031810824

This study uses machine learning to address the urgent need for early detection and intervention in autism spectrum disorder (ASD). Autism Spectrum Disorder (ASD), a disorder of neural development affecting cognition and social interaction, necessitates customized interventions. Our research uses various screening assessments in early infancy as datasets to train for neural network algorithms, with the objective of identifying patterns associated with ASD. The suggested methodology utilizes predictive machine learning techniques to assess the ASD spectrum, accounting for its diverse pitches. Utilizing sophisticated screening instruments and an innovative methodology, we seek to establish a dependable system for the early detection of ASD. Ethical considerations, data privacy, and algorithmic openness are essential. The studies seek to foster collaboration among experts in neurodevelopment, psychological science, and data science to build precise tools for earlier identification of ASD. The use of machine learning, an effective tool, enhances the knowledge of healthcare workers. The research results underscore technology's capacity to transform initial measures, leading to enhanced outcomes for those on the autistic spectrum. It provides this assistance to enhance the dialogue at the gathering, thereby promoting interaction at the nexus of healthcare and digital technology. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Neurons

来源：评论

学校读者我要写书评

暂无评论

A Strategic Review on Offensive Data Identification over Social Networking Environment Using machine learning Techniques

A Strategic Review on Offensive Data Identification over Soc...

引用

2023 international conference on Circuit Power and computing Technologies, ICCPCT 2023

作者： Uma Maheswari, V. Priya, R. Chennai India Chennai India

ISBN: (纸本)9798350333244

The social networking environment has evolved globally and dramatically in recent years. Peoples in the world needs a social networking platform to quickly communicate and share their thoughts, views, and ideas with others all over the world, but there are many defects that are happening because of using these communities. A novel learning scheme is required to resolve the issue of posting offensive data on social media. In the previous research, many of the standard machine learning models were enforced for the automatic determination of bullying on social media. However, the models have solved some of the mandatory features that can be used to detect or classify a comment as online harassment that negatively impacts others. This is also known as 'online bullying.' The social network environment is a best online tool for everyone, but the devastating effects of such unpleasant information posting issues bring the platform down. This study attempts to provide an overview of various tactics employed in past research, with a focus on identifying or detecting online bullying through the utilization of information from online social networking sites. Finally, a detailed discussion about the open issues for future research in the area of social networking sites based on identifying or classifying offensive content in the user's post using advanced machine learning techniques was organized to provide complete stability from false negative situations for both users and the social networking environment. © 2023 IEEE.

关键词： Decision trees

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 << < 464 465 466 467 468 469 470 471 472 473 > >>

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：