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

1,313 篇 会议
11 篇 期刊文献
9 册 图书

馆藏范围

1,333 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

901 篇 工学
- 745 篇 计算机科学与技术...
- 487 篇 软件工程
- 302 篇 信息与通信工程
- 204 篇 电气工程
- 175 篇 生物工程
- 126 篇 生物医学工程（可授...
- 105 篇 光学工程
- 89 篇 控制科学与工程
- 80 篇 电子科学与技术（可...
- 58 篇 仪器科学与技术
- 46 篇 机械工程
- 35 篇 化学工程与技术
- 27 篇 安全科学与工程
- 26 篇 交通运输工程
- 17 篇 动力工程及工程热...
423 篇 理学
- 189 篇 生物学
- 186 篇 数学
- 149 篇 物理学
- 76 篇 统计学（可授理学、...
- 54 篇 系统科学
- 40 篇 化学
165 篇 管理学
- 104 篇 图书情报与档案管...
- 69 篇 管理科学与工程(可...
- 37 篇 工商管理
145 篇 医学
- 129 篇 临床医学
- 87 篇 基础医学(可授医学...
- 65 篇 药学(可授医学、理...
- 23 篇 公共卫生与预防医...
28 篇 教育学
- 28 篇 教育学
27 篇 法学
- 21 篇 社会学
16 篇 经济学
16 篇 农学
5 篇 军事学
1 篇 文学
1 篇 历史学

主题

211 篇 machine learning
132 篇 deep learning
78 篇 signal processin...
72 篇 accuracy
71 篇 feature extracti...
68 篇 signal processin...
61 篇 support vector m...
57 篇 machine learning...
46 篇 predictive model...
33 篇 neural networks
32 篇 convolutional ne...
30 篇 random forests
29 篇 computational mo...
29 篇 training
26 篇 natural language...
26 篇 prediction algor...
21 篇 technological in...
20 篇 real-time system...
19 篇 adversarial mach...
18 篇 image processing

机构

16 篇 chitkara centre ...
14 篇 centre of resear...
12 篇 shenyang ligong ...
6 篇 ai & ds vishwaka...
6 篇 school of inform...
5 篇 department of me...
5 篇 khulna universit...
5 篇 department of co...
4 篇 delhi technologi...
4 篇 heilongjiang uni...
4 篇 department of s&...
4 篇 beijing univ pos...
4 篇 it vishwakarma i...
4 篇 department of co...
4 篇 sri lanka instit...
4 篇 advanced telecom...
4 篇 shenyang ligong ...
4 篇 graduate school ...
3 篇 chitkara univers...
3 篇 karunya institut...

作者

6 篇 mehta shiva
5 篇 rasmita panigrah...
4 篇 neelamadhab padh...
4 篇 wang xin
3 篇 m. vigenesh
3 篇 baydeti nagaraju
3 篇 sinambela marzuk...
3 篇 santosh kumar sh...
3 篇 ganapati panda
3 篇 debendra muduli
3 篇 abujar sheikh
3 篇 a. sivanantham
3 篇 li yajie
3 篇 li aihua
3 篇 zhou feng
3 篇 islam md. sanzid...
2 篇 aira kharvel par...
2 篇 nokshangthemba
2 篇 maheswara rao v ...
2 篇 lee tzong-ru

语言

1,292 篇 英文
32 篇 其他
11 篇 中文

检索条件"任意字段=Proceedings of the 2019 2nd International Conference on Signal Processing and Machine Learning"

共 1333 条记录，以下是21-30 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：

Research on machine learning-Based Fault Diagnosis and Prediction Techniques for Power Transformers 2

Research on Machine Learning-Based Fault Diagnosis and Predi...

引用

2nd international conference on signal processing and Intelligent Computing, SPIC 2024

作者： Zhang, Shuangying Fu, Lidong Wang, Jiaxing State Grid Liaoning Electric Power Company Limited Economic Research Institute Shenyang China Shenyang Ligong University School of Information Science and Engineering Shenyang China

ISBN: (纸本)9798350368888

Due to the diversity and heterogeneity of electrical equipment, the operation monitoring systems for power transformers are undergoing diversified development. By utilizing machine learning methods, this study focuses on the prediction and diagnosis of heterogeneous terminal data from power transformers to address the challenges of online monitoring. This research is of significant importance for achieving the interconnection of all components in the power system and maintaining its operational stability. The study adopts machine learning techniques to comprehensively analyze the heterogeneous terminal data of power transformers, considering seven electrical energy parameters and four environmental parameters as the main input variables for the experimental analysis. A weighted least squares support vector machine algorithm based on chemical reaction optimization is proposed for parameter prediction in power transformers. Through a detailed comparative analysis during the simulation process, with a focus on evaluating regression algorithm performance metrics, the simulation results demonstrate that the proposed algorithm can rapidly and accurately predict parameter values for future time intervals. © 2024 IEEE.

关键词： Distribution transformers

来源：评论

学校读者我要写书评

暂无评论

Optimizing Agricultural Productivity: Developing a Smart Crop Recommendation System Using machine learning 2

Optimizing Agricultural Productivity: Developing a Smart Cro...

引用

2nd international conference on Recent Trends in Microelectronics, Automation, Computing, and Communications Systems, ICMACC 2024

作者： Nokshangthemba Kumar, Saurabh Soloman, Shyam Sunder Jannu Baydeti, Nagaraju National Institute of Technology Nagaland Department of Computer Science and Engineering Nagaland Chumukedima797 103 India

ISBN: (纸本)9798350366570

Crop recommendation is the crucial aspect of modern agriculture, aiming to assist farmers in selecting the most suitable crops for their land and maximizing yield. In this study, the effectiveness of various preprocessing techniques, grid search optimization and the pipeline class are applied in conjunction with the different machine learning algorithm for the crop recommendation. The accuracy and efficiency of the recommendation process enhancement is aimed by applying techniques including data standardization, relative density normalization, and grid search optimization. Leveraging machine learning algorithms within a unified pipeline framework allows for systematic evaluation and comparison of models, ultimately providing farmers with reliable recommendations for crop selection. machine learning algorithms Decision Tree, K-Nearest Neighbor, Random Forest, Support Vector machine, Logistic Regression and Naïve Bayes were applied to predict and optimize agricultural productivity based on the dataset collected from Nagaland state. Among these algorithms, Random Forest outperformed the others, achieving an impressive accuracy of 99.54% in predicting agricultural productivity. © 2024 IEEE.

关键词： Smart agriculture

来源：评论

学校读者我要写书评

暂无评论

2024 IEEE 2nd international conference on Image processing and Computer Applications, ICIPCA 2024

2024 IEEE 2nd International Conference on Image Processing a...

引用

2nd IEEE international conference on Image processing and Computer Applications, ICIPCA 2024

ISBN: (纸本)9798350360240

The proceedings contain 376 papers. The topics discussed include: analysis of badminton motion trajectory algorithm based on neural network;intelligent insurance actuarial model under machine learning and data mining;research on real-time data transmission and signal processing system of CIM practical training teaching platform based on 5G network;design of systematic financial risk warning system based on integrated classification algorithm;development of a multi-objective optimization framework for submersible localization and search operations;improved thermal dome structure optimization design based on neural network algorithm;transmission line inspection image intelligent diagnosis system;and application of computer artificial intelligence infrared image processing technology in strength detection of building steel structure.

关键词：

来源：评论

学校读者我要写书评

暂无评论

proceedings of 2024 2nd international conference on signal processing and Intelligent Computing, SPIC 2024

Proceedings of 2024 2nd International Conference on Signal P...

引用

2nd international conference on signal processing and Intelligent Computing, SPIC 2024

ISBN: (纸本)9798350368888

The proceedings contain 241 papers. The topics discussed include: image processing based on inter-frame color feature matching for visible light positioning;research on short-term load forecasting method based on ANFIS-BPNN-LSSVM;multi-scale complex terrain wind power output prediction based on multi-feature refinement fusion;research on multi-feature triplet attention based sharpness-aware classification network;optimization and application of the search algorithm for the efficacy of airfield blockade by penetration submunitions;a deep learning approach for rectifying fisheye image;lightweight image deraining network based on dilated depthwise separable convolution and enhanced channel attention;study on the combination of S-growth curves based on ultra-wideband displacement ranging for predicting landslides;and multi-agent reinforcement learning based on cross task information sharing.

关键词：

来源：评论

学校读者我要写书评

暂无评论

THE 2nd CLARITY PREDICTION CHALLENGE: A machine learning CHALLENGE FOR HEARING AID INTELLIGIBILITY PREDICTION 49

THE 2ND CLARITY PREDICTION CHALLENGE: A MACHINE LEARNING CHA...

引用

49th IEEE international conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Barker, Jon Akeroyd, Michael A. Bailey, Will Cox, Trevor J. Culling, John F. Firth, Jennifer Graetzer, Simone Naylor, Graham Univ Sheffield Dept Comp Sci Sheffield S Yorkshire England Univ Nottingham Sch Med Nottingham England Univ Salford Acoust Res Ctr Salford Lancs England Cardiff Univ Sch Psychol Cardiff Wales

ISBN: (纸本)9798350344868;9798350344851

This paper reports on the design and outcomes of the 2nd Clarity Prediction Challenge (CPC2) for predicting the intelligibility of hearing aid processed signals heard by individuals with a hearing impairment. The challenge was designed to promote new approaches for estimating the intelligibility of hearing aid signals that can be used in future hearing aid algorithm development. It extends an earlier round (CPC1, 2022) in a number of critical directions, including a larger dataset coming from new speech intelligibility listening experiments, a greater degree of variability in the test materials, and a design that requires prediction systems to generalise to unseen algorithms and listeners. This paper provides a full description of the new publicly available CPC2 dataset, the CPC2 challenge design, and the baseline systems. The challenge attracted 12 systems from 9 research teams. The systems are reviewed, their performance is analysed and conclusions are presented, with reference to the progress made since the earlier CPC1 challenge. In particular, it is seen how reference-free, non-intrusive systems based on pre-trained large acoustic models can perform well in this context.

关键词： speech-in-noise speech intelligibility hearing aids hearing loss machine learning

来源：评论

学校读者我要写书评

暂无评论

Research on signal processing Methods for Gas Ultrasonic Flowmeters 2

Research on Signal Processing Methods for Gas Ultrasonic Flo...

引用

2nd international conference on machine learning, Cloud Computing, and Intelligent Mining, MLCCIM 2023

作者： Wan, Yong China Coal Technology and Engineering Group Chongqing Research Institute Chongqing400039 China State Key Laboratory of Gas Disaster Monitoring and Emergency Technology Chongqing400039 China

ISBN: (纸本)9798350328530

On the basis of the difficulty in determining the feature point in the signal processing of gas ultrasonic flowmeter,a variable threshold based zero-crossing detection signal processing method is proposed to determine a feature wave of the signal accurately. Thus the propagation time of ultrasonic signal is calculated,and the gas flow rate is measured *** method is realized real time in a signal processing system with DSP and FPGA as dualcore for the gas ultrasonic *** judging the 'step', the error caused by noise interference is eliminated,and the stability of the measurement results is enhanced. With piecewise linear calibration,the non-linear error is corrected,and the measurement accuracy of the system is improved. The experimental results of gas flow calibration verify the effectiveness of the method and the system. © 2023 IEEE.

关键词： Flow of gases

来源：评论

学校读者我要写书评

暂无评论

Hate Speech Detection in Telugu Language Using Transformer Models and machine learning 2

Hate Speech Detection in Telugu Language Using Transformer M...

引用

2nd international conference on Recent Trends in Microelectronics, Automation, Computing, and Communications Systems, ICMACC 2024

作者： Rakhil, M.L. Sirohi, Aman Sriviswa, R. Sachin Kumar, S. Amrita School of Artificial Intelligence Amrita Vishwa Vidyapeetham Coimbatore India

ISBN: (纸本)9798350366570

Hate speech in the social media raises a fundamental concern of social integration and quality individual life. This paper aims at presenting the key importance of hate speech detection in the Telugu language which is a Dravidian language and officially used in the Indian states. Through the use of various transformer models such as BERT, DistilBERT, indicBERT, LaBSE, and MuRIL, the proposed solution offers acceptable accuracy, especially with the MuRIL imprinting an Fl-score of 0.79. The combination of ensemble embedding with machine learning classifiers escalates the performance and exhibits a better position to advance content moderation for only languages in the mainstream multilingual online platforms. This research not only addresses a relevant problem but also helps demonstrate the possibilities of using NLP developments to deal with online hate speech, thus creating less dangerous digital landscapes. The results also underscore the need to maintain the development process and update the further strategies to identify the hate speech persistently due to new tendencies in the interaction within social media platforms. © 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

Momentum Study Based on CRITIC Evaluation Method and machine learning 2

Momentum Study Based on CRITIC Evaluation Method and Machine...

引用

2nd IEEE international conference on Image processing and Computer Applications, ICIPCA 2024

作者： Zhou, Jinghua Zhang, Jingruo Beijing Jiaotong University Beijing China

ISBN: (纸本)9798350360240

This paper mainly focuses on the evaluation and prediction of sports competition results, and uses the methods of feature extraction, CRITIC weighted analysis and machine learning algorithm to discuss and analyze. First, after data preprocessing, the dimension of column data is reduced by feature extraction and the evaluation index is obtained. These metrics are then weighted using the CRITIC method to obtain a measure of quantified performance. Secondly, the correlation analysis model is introduced to prove the existence of momentum. Finally, by comparing the performance of XGBoost, Extra Trees and Random Forest algorithm in training and prediction, it is found that XGBoost algorithm has the best performance in AUC of test set, reaching 95.05%. The XGBoost parameters were further optimized by Bayesian optimization, and the signal was reconstructed by combining wavelet analysis to improve the accuracy and robustness of the model. © 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

Efficient Cardiovascular Health Monitoring System for Heart Disease Detection Based on machine learning Technique 2

Efficient Cardiovascular Health Monitoring System for Heart ...

引用

2nd international conference on Recent Trends in Microelectronics, Automation, Computing, and Communications Systems, ICMACC 2024

作者： Kumar, Kottu Santosh Sahoo, Ramesh Chandra Rout, Saroja Kumar Mohammad, Manzoor Jit, Biswa Ranjan Reddy, S. Eswar Kar, Bhabesh Ranjan Department of Information Technology Hyderabad India Manav Rachna International Institute of Research and Studies Faridabad India Hyderabad India School of Professional Studies DRIEMS UNIVERSITY Odisha Cuttack India Bhubaneswar India Cast and Crew LLC Hyderabad India

ISBN: (纸本)9798350366570

Heart diseases are a global leading cause of death, affecting nations universally. Early detection and machine learning assistance can mitigate mortality despite medical complexities. Hence, timely diagnosis is crucial to prevent heart attacks. This study aims to identify a machine-learning method that strikes a balance between computational efficiency and prediction accuracy when it comes to heart disease prognosis. This work proposes a model integrating various methods for effective heart disease prediction. Employing efficient data collection, pre-processing, and transformation, our model uses an ensemble approach that includes Six classification machine learning algorithms- SVM, DT, RF, LR, KNN, ANN, and PCA. The study identifies significant heart disease features, predicting the highest risk through machine learning. Experimental outcomes indicate prediction accuracy obtained by SVM, DT, RF, LR, KNN, ANN, and PCA. as a feature selection process are 91%, 90%, 86%, 91%, 88%, and 91%. Collaboration is vital for enhancing algorithm precision in clinical settings. The application of machine learning to cardiovascular health has the potential to transform continuous management, personalized treatment, and early diagnosis. Robust clinical tools for better patient outcomes and public health are ensured by addressing issues like data privacy and model interpretability and by fostering interdisciplinary collaboration. © 2024 IEEE.

关键词： Patient treatment

来源：评论

学校读者我要写书评

暂无评论

Overview of machine learning for Bioengineering EEG signal processing 2

Overview of Machine Learning for Bioengineering EEG Signal P...

引用

2nd international conference on Intelligent Cyber Physical Systems and Internet of Things (ICoICI)

作者： Dhande, Siddhi Kamble, Aahash Gundewar, Swapnil Babu, Naathi Naresh Kumar, Praveen Datta Meghe Inst Higher Educ & Res Dept Comp Sci & Med Engn Wardha India Datta Meghe Inst Higher Educ & Res DU Dept Artificial Intelligence & Data Sci Wardha Maharashtra India Datta Meghe Inst Higher Educ & Res Dept Artificial Intelligence & Machine Learning Wardha India

ISBN: (纸本)9798331540661;9798331540678

Electroencephalography (EEG) is a well-established method in neuroscience and bioengineering that offers valuable information about brain function. Recent technological advancements have led to more complex EEG recordings, which necessitate more advanced analysis techniques. machine learning has emerged as an asset in EEG signal processing, enabling novel approaches to comprehension and utilization of brain activity. This article provides an in-depth analysis of the intersection of machine learning and EEG signal processing in bioengineering applications. This text presents a comprehensive overview of EEG data analysis techniques, focusing on the key steps of acquisition, preprocessing, and feature extraction. It highlights the challenges and strategies used to extract valuable information from raw EEG recordings. Additionally, it surveys various machine learning algorithms, including classic and modern deep learning methods, demonstrating their effectiveness in analyzing EEG signals and opening new frontiers in the field of bioengineering. This paper explores the growing connection between machine learning and EEG signal processing, examining how they work together to enhance healthcare, neurotechnology, and our knowledge of the brain. By studying existing research and cutting-edge developments, this study intends to focus on this synergistic relationship and its importance in these fields.

关键词： EEG signal processing machine learning algorithms Bioengineering applications Neurotechnology

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134页 << < 1 2 3 4 5 6 7 8 9 10 > >>

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：