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

2,603 篇 会议
179 册 图书
30 篇 期刊文献

馆藏范围

2,811 篇 电子文献
1 种 纸本馆藏

日期分布

学科分类号

1,873 篇 工学
- 1,691 篇 计算机科学与技术...
- 833 篇 软件工程
- 322 篇 信息与通信工程
- 274 篇 电气工程
- 191 篇 生物工程
- 170 篇 控制科学与工程
- 153 篇 生物医学工程（可授...
- 67 篇 安全科学与工程
- 65 篇 光学工程
- 64 篇 化学工程与技术
- 57 篇 机械工程
- 50 篇 交通运输工程
- 44 篇 仪器科学与技术
- 38 篇 电子科学与技术（可...
- 33 篇 网络空间安全
554 篇 理学
- 293 篇 数学
- 222 篇 生物学
- 113 篇 统计学（可授理学、...
- 104 篇 物理学
- 80 篇 系统科学
- 55 篇 化学
398 篇 管理学
- 268 篇 图书情报与档案管...
- 183 篇 管理科学与工程(可...
- 63 篇 工商管理
260 篇 医学
- 201 篇 临床医学
- 121 篇 基础医学(可授医学...
- 70 篇 药学(可授医学、理...
- 33 篇 公共卫生与预防医...
47 篇 法学
- 42 篇 社会学
39 篇 教育学
- 35 篇 教育学
29 篇 经济学
27 篇 农学
7 篇 文学
5 篇 艺术学
2 篇 军事学

主题

483 篇 machine learning
482 篇 data mining
272 篇 pattern recognit...
160 篇 feature extracti...
152 篇 deep learning
148 篇 artificial intel...
112 篇 support vector m...
106 篇 machine learning...
94 篇 data mining and ...
85 篇 training
76 篇 data models
74 篇 accuracy
59 篇 computer imaging...
56 篇 learning systems
55 篇 neural networks
47 篇 information syst...
47 篇 convolutional ne...
46 篇 computer applica...
45 篇 predictive model...
44 篇 big data

机构

15 篇 national univers...
15 篇 the islamic univ...
15 篇 fujian provincia...
13 篇 medical technica...
13 篇 school of inform...
13 篇 altoosi universi...
11 篇 guangdong univer...
10 篇 indian statistic...
10 篇 school of manage...
10 篇 department of bu...
9 篇 institute of env...
9 篇 department of to...
8 篇 xi'an university...
8 篇 al-zahraa univer...
8 篇 college of mecha...
8 篇 chinese academy ...
7 篇 school of inform...
7 篇 zhejiang univers...
7 篇 institute of aut...
7 篇 unsw sydney

作者

17 篇 chang kuo-chi
12 篇 de-shuang huang
10 篇 chu kai-chun
10 篇 mottl vadim
9 篇 chang fu-hsiang
9 篇 wang hsiao-chuan
8 篇 zheng-guang wu
8 篇 hongyi li
8 篇 chaojie li
8 篇 biao luo
8 篇 petra perner
8 篇 long cheng
7 篇 yuhui shi
7 篇 krasotkina olga
7 篇 alexander gelbuk...
7 篇 sung tien-wen
7 篇 ying tan
7 篇 lin yuh-chung
6 篇 ryszard tadeusie...
6 篇 marcin korytkows...

语言

2,724 篇 英文
73 篇 其他
34 篇 中文
1 篇 土耳其文

检索条件"任意字段=6th International Conference on Machine Learning and Data Mining in Pattern Recognition"

共 2812 条记录，以下是411-420 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：

Domestic Solid Waste Prediction with an Enhanced LSTM with SigmoReLU and RAdam Optimizer 6th

Domestic Solid Waste Prediction with an Enhanced LSTM with S...

引用

6th international conference on Soft Computing and data mining (SCDM)

作者： Fadhel, Abdulrahman Sharaf Mohammed Ghazali, Rozaida Tomari, Mohd Razali Md Hassim, Yana Mazwin Mohmad Hassan, Abdullahi Abdi Abubakar Ismail, Lokman Hakim Univ Tun Hussein Onn Malaysia Fac Comp Sci & Informat Technol Batu Pahat Kagawa Malaysia Univ Tun Hussein Onn Malaysia Fac Elect & Elect Engn Batu Pahat Kagawa Malaysia Univ Tun Hussein Onn Malaysia Fac Civil Engn & Built Environm Batu Pahat Kagawa Malaysia

ISBN: (纸本)9783031669644;9783031669651

A novel approach is presented to address the prediction challenge in domestic solid waste generation through the application of machine learning techniques. To overcome the limitations inherent in capturing intricate temporal patterns faced by conventional Long Short-Term Memory (LSTM) models designed for time series forecasting, an enhanced variant, termed e-LSTM, is introduced. this model incorporates crucial enhancements to rectify standard LSTM shortcomings. Introducing a hybrid activation function, SigmoRelu, bolsters the model's capacity to grasp complex time series patterns. Furthermore, the RAdam optimizer is employed to optimize the learning process and improve convergence. Dropout layers are seamlessly integrated within the LSTM architecture to counter overfitting, ensuring robust generalization to novel data. A series of comprehensive experiments is conducted to compare the performance of the e-LSTM model against standard LSTM and GRU models, showcasing its noteworthy advancements. Notably, the e-LSTM model demonstrates superior predictive accuracy in forecasting waste generation compared to standard LSTM and GRU models. In essence, the proposed e-LSTM model represents a significant stride in domestic solid waste prediction, effectively mitigating the limitations of traditional LSTM models. the synergistic integration of SigmoRelu activation, RAdam optimization, and dropout mechanisms results in a resilient and accurate predictive framework. Empirical results affirm the model's superiority, establishing it as a valuable tool for waste management applications and decision-making processes.

关键词： Domestic Solid Waste Time series forecasting Hybrid activation function LSTM RADAM machine learning

来源：评论

学校读者我要写书评

暂无评论

Boosting Out-of-Distribution Detection with Sample Weighting 6th

Boosting Out-of-Distribution Detection with Sample Weighting

引用

6th Chinese conference on pattern recognition and Computer Vision (PRCV)

作者： Ke, Ao Chen, Wenlong Feng, Chuanwen Xie, Xike Univ Sci & Technol China Suzhou Inst Adv Res Data Darkness Lab MIRACLE Ctr Hefei Peoples R China

ISBN: (纸本)9789819984343;9789819984350

To enhance the reliability of machine learning models in the open world, there is considerable interest in detecting out-of-distribution (OOD) inputs that lie far away from the training distribution. Existing distance-based methods have shown promising performance in OOD detection. these methods generally follow a common approach of capturing the distances between training samples and each test sample in the feature space. this can be understood as encoding the distance information to assign sample weights, which are then used to calculate a weighted distance score to determine if the input is OOD. However, these methods often adopt a coarse-grained weighting approach, where only a small fraction of the training samples are considered and given weights. Consequently, they fail to fully leverage the complete distance information of the training data, leading to occasional difficulties in effectively distinguishing OOD samples. In this paper, we propose a novel approach to encode the complete distance information of the training data by assigning a weight to each sample based on its distance from the test sample, with the weights decaying as the distance increases. Furthermore, we introduce a weighted distance-based method for OOD detection. We demonstrate the superiority of our method over most existing supervised OOD detectors. Particularly, on a hard OOD detection task involving CIFAR-100 vs. CIFAR-10, our method achieves a reduction of 1.82% in the average FPR95 compared to the current best method KNN.

关键词： Out-of-distribution detection center dot Contrastive training

来源：评论

学校读者我要写书评

暂无评论

the Performance Analysis of Several machine learning Methods in Predicting Diabetes

The Performance Analysis of Several Machine Learning Methods...

引用

pattern recognition and Artificial Intelligence (PRAI), international conference on

作者： Yuanyuan Wu Zhuomin Xia Huizhou Liu Mengxing Huang Yu Zhang College of Information and Communication Engineering Hainan University Haikou P.R. China College of Computer Science and Technology Hainan University Haikou P.R. China

Diabetes is a chronic disease characterized by hyperglycemia. Early screening of diabetes patients will help reduce the incidence of diabetes. Accurately modeling the early symptoms of diabetes to reduce the incidence of diabetes has become an urgent problem. therefore, it is a valuable research direction to mine and study the information of diabetes patients, predict the occurrence of noninfectious diseases, and assist doctors to make correct diagnoses. the precision of different data mining techniques has been compared in this research, employing machine learning techniques to predict the incidence of diabetes. the University of California, Irvine’s open-source dataset has been used as the main database for experiment. Different machine learning classifiers such as random forest, neural network and support vector machine have been employed in this experiment. this research gives a technology review of machine learning algorithms that can be used for diabetes prediction through classification. the experimental results indicate that random forest performs better than other machine learning methods.

关键词：

来源：评论

学校读者我要写书评

暂无评论

ISM 2022 - 4th international conference on Industry 4.0 and Smart Manufacturing

ISM 2022 - 4th International Conference on Industry 4.0 and ...

引用

4th international conference on Industry 4.0 and Smart Manufacturing, ISM 2022

the proceedings contain 199 papers. the topics discussed include: infrastructure network support and leapfrogging Africa to Industry 4.0: the case of Tanzania;comparison of energy-use efficiency for lettuce plantation under nutrient film technique and deep-water culture hydroponic systems;an edge-cloud based reference architecture to support cognitive solutions in process industry;logistics 4.0 in intermodal freight transport;analysis of sustainable concrete obtained from the by-products of an industrial process and recycled aggregates from construction and demolition waste;intelligent concrete surface cracks detection using computer vision, pattern recognition, and artificial neural networks;spatial change recognition model using artificial intelligence to remote sensing;smart trip prediction model for metro traffic control using data mining techniques;encryption and generation of images for privacy-preserving machine learning in smart manufacturing;and using analytical and data-driven methods to develop a soft-sensor for flow rate monitoring in tube extrusion.

关键词：

来源：评论

学校读者我要写书评

暂无评论

On the use of synthetic images in deep learning for defect recognition in industrial infrastructures 23

On the use of synthetic images in deep learning for defect r...

引用

6th international conference on machine Vision and Applications, ICMVA 2023

作者： Mailhe, Clement Ammar, Amine Chinesta, Francisco CNRS@CREATE Ltd. Create Tower 1 Create Way #08-01 Singapore Singapore LAMPA Arts et Métiers Institute of Technology HESAM Université 2 boulevard du Ronceray AngersF-49035 France ESI Chair PIMM Arts et Métiers Institute of Technology 151 Boulevard de l Hôpital ParisF-75013 France

ISBN: (纸本)9781450399531

the use of synthetic images in deep learning for object detection applications is recognized as a key technological lever in reducing time and cost constraints associated with data-driven processes. In this work, the applicability of training an instance recognition algorithm on a synthetic database in an industrial context is assessed based on the detection of dents in pipes. Photo-realistic artificial images are procedurally generated using a rendering software and used for the training of the YOLOv5 object recognition algorithm. Its prediction effectiveness is assessed on a small test set in different configurations to identify improvement steps towards the reliable use of artificial data in computer-vision. © 2023 ACM.

关键词： Remote sensing

来源：评论

学校读者我要写书评

暂无评论

Research on vegetable automated operation decision-making based on data mining methods using random forest, LSTM-Seq2Seq, and NSGA-II 6

Research on vegetable automated operation decision-making ba...

引用

6th international conference on Information Science, Electrical, and Automation Engineering, ISEAE 2024

作者： Zhou, Kai Liu, Zhaofeng Ai, Weixi Yang, Qiteng International College Chongqing University of Posts and Telecommunications Chongqing400065 China College of Science Hebei Agricultural University Hebei Baoding071001 China

ISBN: (纸本)9781510682924

Vegetables, characterized by short shelf life and easily perishable features, pose operational challenges for fresh food supermarkets in replenishing and pricing vegetable products within limited sales space. this study proposes an automated decision-making framework based on machine learning, data mining, and intelligent optimization algorithms, using a random forest model for regression prediction and an LSTM-based Seq2Seq model for time series prediction to address these challenges. the random forest model is trained using historical wholesale price data for regression prediction, with an average root mean square error (RMSE) of 0.7046 on the test set. In the time series prediction based on historical sales data, an LSTM-based Seq2Seq model is used, resulting in an average RMSE of 0.9631 on the test set. Subsequently, the random forest model is trained to predict actual sales volume based on the relationship between sales volume, wholesale price, and sales price, combining it with a multi-objective optimization model. the NSGA-II intelligent optimization algorithm is employed to solve the model. Ultimately, we determined various vegetables' sales prices and replenishment quantities by maximizing profits and minimizing supply surplus losses. the method proposed in this study helps improve supermarkets' operational efficiency and profits, providing new insights and methods for fresh food management decision-making. © 2024 SPIE.

关键词： Vegetables

来源：评论

学校读者我要写书评

暂无评论

A Comparative Study for Prediction of Hematopoietic Stem Cell Transplantation-Related Mortality 6th

A Comparative Study for Prediction of Hematopoietic Stem Cel...

引用

6th international conference on Innovative Computing and Communication, ICICC 2023

作者： Hanselia, Rishabh Choubey, Dilip Kumar Department of Computer Science and Engineering Indian Institute of Information Technology Bhagalpur Bihar India

ISBN: (纸本)9789819933143

Multipotent hematopoietic stem cells are transplanted during hematopoietic stem cell therapy (HSCT) in order to replicate inside of a patient and produce more healthy blood cells. these cells are often taken from bone marrow, peripheral blood, or umbilical cord blood. Patients with particular blood or bone marrow malignancies, like multiple myeloma or leukemia, are those who have the procedure the most frequently. though a lifesaving procedure, it comes with its risk;hence, to minimize the risk, prediction of survivability and the factors affecting it is crucial. In this study, the authors have done an extensive and rigorous analysis of various works done in predicting the mortality of patients undergoing hematopoietic stem cell transplantation that involves the use of machine learning and data mining techniques and have compared them. this study gives an overview of the available machine learning and data mining techniques in improving risk prediction for patients undergoing HSCT that provides an alternative to traditional risk scores and indexes. though these techniques already outperform the in-use risk scores and indexes, they can be still improved upon by the use of a specialized and large amount of data. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023.

关键词： machine learning

来源：评论

学校读者我要写书评

暂无评论

An Improved Local Weighted Mean-Based k-Nearest Neighbor Classifier 6

An Improved Local Weighted Mean-Based k-Nearest Neighbor Cla...

引用

6th IEEE international conference on Automation, Electronics and Electrical Engineering, AUTEEE 2023

作者： Liu, Fangfei He, Chenqing Chang, Zhiming Shandong University Weihai China Shenzhen University Shenzhen China

ISBN: (纸本)9798350305623

the article presents a Local Weighted Mean-Based k-Nearest Neighbor (LWMKNN) classifier, which improves the performance of traditional k-nearest neighbor (KNN) and k-nearest neighbor methods based on local means vector. the LWMKNN algorithm incorporates local weighted based on the distance between samples, which enables it to better capture the complex structure of the data. the article provides a detailed analysis of the LWMKNN algorithm, including its underlying principles and mathematical formulations. this provides a clear understanding of how the algorithm works and why it is superior to other KNN methods. the experimental results show that LWMKNN outperforms both KNN and LMKNN on several data sets, demonstrating its effectiveness and versatility. In conclusion, the LWMKNN classifier offers a powerful and flexible tool for machine learning practitioners. By incorporating local weighted, LWMKNN is able to better capture the complex structure of data and outperform traditional KNN methods. © 2023 IEEE.

关键词： k-Nearest neighbor rule Local Mean-Based k-Nearest Neighbor Classifier local weighted mean vector Local Weighted Mean-Based k-Nearest Neighbor Classifier pattern classification

来源：评论

学校读者我要写书评

暂无评论

A Survey on Oil Spill Detection using SAR images in machine learning 6

A Survey on Oil Spill Detection using SAR images in Machine ...

引用

6th Parul international conference on Engineering and Technology, PICET 2024

作者： Vekariya, Daxa Vaghasiya, Mihirkumar Tomar, Yuvrajsinh Laad, Priyanka Parmar, Khyati Department of CSE PIET Parul University Vadodara India

ISBN: (纸本)9798350369748

Oil spills pose grave threats to marine ecosystems, wildlife, and coastal communities, necessitating prompt detection and precise classification for effective mitigation. Leveraging remote sensing satellites, we can monitor vast areas from space to identify oil spills swiftly. this study delves into utilizing the You Only Look Once (YOLO) model, a deep learning algorithm renowned for real-time object detection, for oil spill detection using satellite imagery. the YOLO model employs a neural network architecture to predict bounding boxes and class probabilities simultaneously, enhancing efficiency. By segmenting satellite images into grid cells, the YOLO model adeptly discerns oil spills from other marine features. through its prowess in pattern recognition and feature extraction, the YOLO model showcases remarkable accuracy and performance in detecting oil spills in satellite imagery. this study provides an in-depth analysis of the oil spill detec- tion process using remote sensing satellites and the YOLO model. It discusses the workflow, technical considerations, and challenges associated with applying the YOLO algorithm to identify oil spills. the evaluation of the YOLO model includes metrics such as recall, precision and average precision, showcasing its effectiveness in detecting oil spills with a high degree of accuracy. © 2024 IEEE.

关键词： Satellite imagery

来源：评论

学校读者我要写书评

暂无评论

machine learning Techniques for Detection and Classification of Crop (Solanum lycopersicum) Plant Diseases Due to Pests: A Review 6th

Machine Learning Techniques for Detection and Classification...

引用

6th international conference on Recent Innovations in Computing, ICRIC 2023

作者： Singh, Tejinder Deep Bharti, Ramesh Jagannath University Rajasthan Jaipur India

ISBN: (纸本)9789819728381

Agriculture supplies all people with food, even in situations of high population growth. Early detection of plant diseases is encouraged as it is essential to make sure entire population has a supply of food. However, it can be difficult to predict diseases when crops are still developing. the main aim of this paper is to discuss various used machine learning techniques to identify different types of pests in crop (Solanum lycopersicum) plant. Artificial intelligence is a subset of machine learning and has gained attention recently due to autonomous learning benefits and data pre-processing. Additionally, it has evolved into a centre for research on agricultural plant protection, including identifying plant diseases and assessing pest ranges. ML in tomato (Solanum lycopersicum) leaf disease recognition can lessen negative effects of artificially choosing disease spot attributes, improves the features extraction, and fasten development of new technologies. this paper describes recent advances in the field of ML-based pest classification. Using ML and imaging technologies, we describe current developments and difficulties in pest identification. then, the research gaps and proposed methodology has been discussed which help scientists to investigate who are trying to recognise plant illnesses and insect pests. Additionally, we stated some of the current issues and challenges that require simultaneous resolution. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Crops

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282页 << < 38 39 40 41 42 43 44 45 46 47 > >>

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：