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

4,137 篇 会议
38 册 图书
6 篇 期刊文献

馆藏范围

4,181 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

2,738 篇 工学
- 2,465 篇 计算机科学与技术...
- 2,042 篇 软件工程
- 616 篇 生物工程
- 587 篇 信息与通信工程
- 362 篇 生物医学工程（可授...
- 266 篇 控制科学与工程
- 216 篇 电气工程
- 211 篇 光学工程
- 148 篇 安全科学与工程
- 136 篇 机械工程
- 124 篇 电子科学与技术（可...
- 118 篇 化学工程与技术
- 107 篇 交通运输工程
- 91 篇 动力工程及工程热...
1,369 篇 理学
- 675 篇 数学
- 646 篇 生物学
- 332 篇 统计学（可授理学、...
- 307 篇 物理学
- 197 篇 系统科学
- 127 篇 化学
720 篇 管理学
- 393 篇 管理科学与工程(可...
- 388 篇 图书情报与档案管...
- 237 篇 工商管理
340 篇 医学
- 315 篇 临床医学
- 308 篇 基础医学(可授医学...
- 221 篇 药学(可授医学、理...
- 125 篇 公共卫生与预防医...
159 篇 法学
- 138 篇 社会学
157 篇 教育学
- 155 篇 教育学
141 篇 农学
- 140 篇 作物学
89 篇 经济学
9 篇 军事学
5 篇 文学

主题

526 篇 machine learning
243 篇 deep learning
117 篇 support vector m...
99 篇 adversarial mach...
92 篇 accuracy
92 篇 machine learning...
83 篇 forecasting
75 篇 convolutional ne...
70 篇 predictive model...
69 篇 feature extracti...
58 篇 artificial intel...
53 篇 students
51 篇 learning systems
48 篇 decision trees
48 篇 training
47 篇 reinforcement le...
47 篇 computational mo...
47 篇 computer vision
46 篇 internet of thin...
41 篇 neural networks

机构

46 篇 chitkara univers...
15 篇 graphic era hill...
10 篇 college of manag...
9 篇 graphic era deem...
8 篇 computer science...
8 篇 beijing universi...
7 篇 school of comput...
7 篇 school of comput...
7 篇 school of comput...
7 篇 department of in...
6 篇 università di ca...
6 篇 infn sezione di ...
6 篇 north-west unive...
6 篇 computer science...
6 篇 lovely professio...
6 篇 in2p3 lpc campus...
6 篇 kkr and ksr inst...
6 篇 institut univers...
6 篇 national centre ...
6 篇 greater bay area...

作者

18 篇 kukreja vinay
15 篇 mehta shiva
10 篇 william p.
8 篇 sharma vikrant
7 篇 mahamudul hasan
7 篇 shiva mehta
6 篇 chen a.
6 篇 benoit d.m.
6 篇 ferrara g.
6 篇 ardid m.
6 篇 calvo d.
6 篇 gagliardini s.
6 篇 eddymaoui a.
6 篇 kulikovskiy v.
6 篇 labalme m.
6 篇 gal t.
6 篇 bozza c.
6 篇 khatun a.
6 篇 kalekin o.
6 篇 huang f.

语言

3,930 篇 英文
250 篇 其他
34 篇 中文

检索条件"任意字段=Proceedings of the 2023 4th International Conference on Machine Learning and Computer Application"

共 4181 条记录，以下是1221-1230 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：

Sentiment Polarity Classification for Khmer 18

Sentiment Polarity Classification for Khmer

引用

18th international Joint Symposium on Artificial Intelligence and Natural Language Processing (ISAI-NLP) / 4th international conference on Artificial Intelligence and Internet of things (AIoT)

作者： Khim, Sokheng thu, Ye Kyaw Sam, Sethserey Cambodia Acad Digital Technol Phnom Penh Cambodia Natl Elect & Comp Technol Ctr Pathum Thani Thailand

ISBN: (纸本)9798350371215

Sentiment polarity classification is a natural language processing (NLP) technique that determines whether a given piece of text has a positive, negative, or neutral sentiment. this article investigates the application of a well-known shallow neural network classifier, FastText, to the Khmer language, which is a low-resource language. To our knowledge, there isn't a freely available polarity corpus for Khmer. thus, one of the primary objectives of this research is to develop a human-annotated benchmark corpus tailored for sentiment analysis in this language. Our findings indicate that the FastText model achieves a precision and recall of 0.740 using our modest-sized Khmer polarity corpus, which consists of 9,000 training sentences and 1,000 testing sentences. Among traditional machine learning methods, the support vector machine (SVM) outperforms the rest.

关键词： sentiment analysis polarity classification traditional machine learning FastText low-resource NLP

来源：评论

学校读者我要写书评

暂无评论

Pneumonia Detection Using Federated learning 24

Pneumonia Detection Using Federated Learning

引用

24th international conference on Control Systems and computer Science, CSCS 2023

作者： Farkas, Camelia-Raluca Ciobanu, Radu-Ioan Dobre, Ciprian University Politehnica of Bucharest Faculty of Automatic Control and Computers Romania National Institute for Research and Development in Informatics Bucharest Romania

ISBN: (纸本)9798350313390

Pneumonia is a major cause of illness and death among older adults, and hospitalization rates for pneumonia in patients aged 65 and older have been increasing. However, detecting pneumonia automatically is challenging because it requires large amounts of data from many patients, making traditional machine learning solutions impractical in real life, despite the good results they can achieve. To address this issue, our paper focuses on using federated learning, a new method for applying machine learning techniques in a distributed environment while maintaining patients' privacy and reducing communication and storage overhead at the processing servers. We propose a federated learning system that can analyze X-rays of patients with and without pneumonia, present our approach, and demonstrate promising results. © 2023 IEEE.

关键词： machine learning

来源：评论

学校读者我要写书评

暂无评论

An In-Depth Analysis of Deep learning and machine learning Methods for Identifying Rice Leaf Diseases 4

An In-Depth Analysis of Deep Learning and Machine Learning M...

引用

4th international conference on Smart Electronics and Communication, ICOSEC 2023

作者： Praveena, Murala Dubisetty, Vidyanadha Babu Varaprasad, Kurapati Veeranjaneya Rama, M. Vadana, Pittala Susmitha Sai, thota Siva Ratna Vignan's Institute of Technology and Science Dept. of Cse Telangana India Qis College of Engineering and Technology Dept. of Cse Ongole523002 India Dept. of Cse Telangana India Qis College of Engineering and Technology Dept. of It Ongole523002 India Dept. of Cse Andhra Pradesh Mothadaka India Dept. of Cse Telangana India

ISBN: (纸本)9798350300888

By training models on massive datasets of rice leaf photos and differentiating between healthy and diseased leaves, machine learning and deep learning approaches are employed to identify rice leaf disease. To correctly identify and categorize rice leaf diseases, these models extract pertinent information from the pictures and use classification methods like Support Vector machines or Convolutional Neural Networks. the measures adopted to assess the efficacy of the predictive models are accuracy, precision, recall, and F1 score. However, a variety of rice leaf problems can harm the crop. It will result in low-quality harvests and also have an impact on the crop's growth, and it's challenging. without the aid of a specialist, enables farmers to identify illnesses in rice crops at the appropriate time. therefore, using an automated technique to find rice leaf disease is highly beneficial. Farmers should take safeguards early on to prevent harm to their crops. By automating the identification and classification process, machine learning (ML) and Deep learning (DL) approaches give researchers useful tools for detecting rice leaf disease. With the use of these approaches, massive datasets of rice leaf pictures may be analyzed, facilitating the creation of precise and effective disease detection models. these methods can help researchers better understand rice leaf diseases, aid in early identification, and assist in the creation of efficient disease management procedures. © 2023 IEEE.

关键词： Support vector machines

来源：评论

学校读者我要写书评

暂无评论

Early Detection and Effective Treatment for ADHD using machine learning for Sri Lankan Children 5

Early Detection and Effective Treatment for ADHD using Machi...

引用

5th international conference on Advancements in Computing, ICAC 2023

作者： Kumarasingha, R.M.I.S. Dissanayake, D.M.C.L.B. Pathirathne, P.S.R. Ranathunga, K.M.M.Y.S. Rajapakshe, Samantha Kalapuge, Vindhya Sri Lanka Institute of Information Technology Department of Software Engineering Malabe Sri Lanka

ISBN: (纸本)9798350358131

Attention Deficit Hyperactivity Disorder (ADHD) is a prevalent neurodevelopmental disorder that significantly impacts children's behavior, attention, and ability to learn. In Sri Lanka, there is limited awareness, resources, and accessibility to therapy services for children with ADHD. Early identification of ADHD is crucial for effective treatment, but traditional approaches in Sri Lanka have limitations in accurately identifying the disorder and providing adequate interventions. To address these challenges, this research emphasizes the development of a comprehensive mobile application utilizing game-based features, machine learning, image processing, motion detection, and speech recognition technology to enhance the early detection and treatment of ADHD in Sri Lankan children. the primary objective of this research is to accurately identify ADHD symptoms in children and offer treatments through a mobile application. the application offers interactive activities designed to address specific ADHD symptoms, supporting children's cognitive development, improving attention, concentration, memorizing, the ability to follow directions, and the ability to respond to a command. this research adopts a multidisciplinary approach, integrating principles from psychology, computer science, and machine learning. the findings inform the development of more accessible and effective interventions for children with ADHD in Sri Lanka and similar settings, ultimately improving the quality of life for affected children. © 2023 IEEE.

关键词： Speech recognition

来源：评论

学校读者我要写书评

暂无评论

A Systematic Review of IoT Malware Detection using machine learning 10

A Systematic Review of IoT Malware Detection using Machine L...

引用

17th INDIACom;2023 10th international conference on Computing for Sustainable Global Development, INDIACom 2023

作者： Sharma, Badal Kumar, Rajendra Kumar, Anuj Chhabra, Megha Chaturvedi, Saumya School of Engineering and Technology Sharda University Department of Computer Science and Engineering Greater Noida India

ISBN: (纸本)9789380544472

the Internet of things (IoT) is spreading quickly across the globe, yet its security is insufficient. the results are encouraging for using machine learning to identify malware on these frequently unattended devices. Among all infections found in mobile networks, the Internet of things was the target of more than 30% of them. Such detection in a single IoT node has been done yet, though, because IoT nodes often have insufficient processing. this study explores the feasibility of malware detection in a single IoT device by scaling machine learning methods to allow a single IoT device to perform near-real- time network traffic anomaly detection and label packets as malware or benign. Using one of the machine learning techniques, the suggested software for categorizing data points from the IoT-23 dataset can be implemented on an ESP32 device. When completely deployed, this could imply that IoT devices would be able to decide for themselves whether a network connection is a part of a malware attack or a regular connection. Interconnected devices and terminals are used as a lucrative target by cybercriminals, especially those who have the backing of the government or other national state actors. Vector malware application is a frequent form of assault. Using the operational code sequence or functional code of the device, the learning things and learning technique seeks out harmful online applications on the Internet. this paper uses deep learning approach to convert the Opcodes into vector space and categorize them into dangerous and malicious applications. this paper present evidence for the efficiency of our proposed approach against malware detection and garbage Code insertion assaults. © 2023 Bharati Vidyapeeth, New Delhi.

关键词： Internet of things

来源：评论

学校读者我要写书评

暂无评论

Lane Detection in Autonomous Vehicles Using Unsupervised machine learning and Light Detection and Ranging 4th

Lane Detection in Autonomous Vehicles Using Unsupervised Mac...

引用

4th international conference on Advanced Network Technologies and Intelligent Computing-ANTIC-Annual

作者： Singh, Siddharth Sharma, Shruti Singh, Adarsh Dwivedi, Rajendra Kumar Madan Mohan Malaviya Univ Technol Dept ITCA Gorakhpur Uttar Pradesh India

ISBN: (纸本)9783031837890;9783031837906

the high growth in the number of automobiles has raised the demand of enhanced driver and passenger safety measures. the goal being effective and efficient application of evolving technology to address the concerns arising with partially or fully autonomous vehicles. Accurate lane positioning and lane change detection is an integral part of Advanced Driver Assistance System which helps in safer trajectory planning. the engineering marvel of Light Detection and Ranging sensors corrects global localization error and provides centimeter-level accuracy but finds its use in restricted domain due to its computational complexity and cost implied. Lane detection is a technique for automatically road markers to ensure that cars stay in their assigned lane and do not collide with vehicles in other lanes. As an alternative, machine learning enabled vision-based lane change detection is highly regarded to provide lane-level localization maintaining high level accuracy. the algorithm design provides polynomial fitting with LiDAR data that helps model perform well in bad lighting conditions and help detect curved lane marking. the technology is well adopted based on the real-world testing scenarios which affirms its real time robustness with average of 89% lane detection accuracy.

关键词： Advanced Driver Assistance System (ADAS) Dedicated Road Infrastructure for Vehicle Safety in Europe (DRIVE) Light Detection and Ranging (LiDAR) Density-Based Spatial Clustering of applications with Noise (DBSCAN)

来源：评论

学校读者我要写书评

暂无评论

CT Image-Based Lung Cancer Detection Using a CNN-Based Model 4

CT Image-Based Lung Cancer Detection Using a CNN-Based Model

引用

4th international conference on Data Analytics for Business and Industry, ICDABI 2023

作者： Prity, Afrina Jannat Firoz Mridha, M. Abdullah-Al-Jubair, Md. American International University Department of Computer Science & Engineering Bangladesh

ISBN: (纸本)9798350369786

One of the most prevalent cancers worldwide is lung cancer which contributes to cancer-related death. Smoking is mostly responsible for lung cancer. Existing lung cancer detection methods involve using numerous medical imaging procedures like X-ray, CT scan, and MRI, combined with machine learning algorithms for accurate diagnosis. Some of the current research on lung cancer detection has limitations in terms of accuracy, sensitivity, and specificity. More research is required to enhance precise detection. In this study, we used the "IQ-Oth/NCCD - Lung Cancer Dataset"sourced from Kaggle and proposed an XceptionNet based Convolutional Neural Network (CNN) architecture for the precise detection of lung cancer using CT images. the proposed Xception model achieved a higher accuracy of 99.64% and a lower loss compared to existing models and showed that it could be used to better accurately diagnose lung cancer from CT scans. We contrasted our model with other baseline models. © 2023 IEEE.

关键词： Lung cancer

来源：评论

学校读者我要写书评

暂无评论

Hybrid Deep learning Techniques for Glaucoma detection 4

Hybrid Deep Learning Techniques for Glaucoma detection

引用

4th international conference on Electronics and Sustainable Communication Systems, ICESC 2023

作者： Priyanka, C. Pooja, S. Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350300093

In today's world, glaucoma ranks high among the ocular conditions that often results in visual loss. there is currently no test that can reliably and specifically diagnose glaucoma on its own. Nonetheless, it has been speculated in earlier research if anatomical features of the optical whim-whams might predict glaucomatous damage. this research provides the public with a dataset that includes health data and fundus prints from the same case's both eyes. Furthermore, this study has performed segmentations of the optical slice and cup, as well as patient labelling based on the analysis of clinical data. To distinguish between those who are healthy and those who have glaucoma, a neural network was tested on the dataset. ResNet-50 is used to classify various cases using both the linked data from each case's two eyes as well as data from each eye individually. the results provide the required steps to further research on fast glaucoma diagnosis predicated on parallel screening among both eyes for a single subject. © 2023 IEEE.

关键词： Diagnosis

来源：评论

学校读者我要写书评

暂无评论

Classification and Segmentation of Leaf Images based on Deep learning for Peanut Plant Disease Detection 4

Classification and Segmentation of Leaf Images based on Deep...

引用

4th international conference on Electronics and Sustainable Communication Systems, ICESC 2023

作者： Ebenezer, V. Daniel, Arsha Anna Getzi Jeba Lillipushpam, P. Edwin, E.Bijolin Roshni thanka, M. Devassy, Rosebel Karunya Institute of Technology and Science Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350300093

Peanut is a key food commodity, and diseases of its leaves can have a negative impact on the quantity and quality of the product. the seed has high levels of potassium, magnesium, calcium, riboflavin, niacin, folic acid, vitamin E, resveratrol, and amino acids. It also contains 36% to 54% oil, 16% to 36% protein, and 10% to 20% carbs. Early, late, and rusty leaf spots, among other frequent illnesses, are recognized by this equipment. Techniques for image augmentation have been used, such as twisting, rotating, and scaling. the variant employs a Multi Class Convolutional neural network with 5 output which include Normal Leaf, Images Without Leaf and the images with the leaf been infected by the diseases. © 2023 IEEE.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

Soldier Behavior Analysis with AR-Based machine Vision and Big Data Deep learning 8

Soldier Behavior Analysis with AR-Based Machine Vision and B...

引用

8th international conference on Communication, Image and Signal Processing, CCISP 2023

作者： Wang, Jiang Wang, Hanning Zeng, Chuangzhan Cai, Chenjianong Wang, Xin Troops 61932 of PLA Beijing100081 China

ISBN: (纸本)9798350305838

To recognize the soldier behaviors according to the videos or images with Big Data analysis and Augmented Reality (AR) devices, the images was processed with intelligent techniques, and then used as the training samples and testing samples with deep learning neural network. In order to extend the intelligent application to the network, the deep learning model was encoded into JavaScript model, and could be utilized as a web application version via browser, WeChat and APP. the ResNet model was improved with the TensorFlow model, and the system framework of soldier behavior analysis was optimized with the PSO algorithm. In the experiments, the noises of the soldier images were filtered with the Laplasse high-pass filter. the behaviors of single soldier from the single-soldier image and multiple soldiers from the multiple-soldiers image were recognized with the core functions of the TensorFlow model and the skeleton models. Experimental results show that this intelligent approach is novel and useful to analyze the soldier behavior with the samples form the network. Furthermore, the web model could be optimized and fasten to improve the performance of this approach. © 2023 IEEE.

关键词： computer vision

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419页 << < 119 120 121 122 123 124 125 126 127 128 > >>

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：