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

67,855 篇 会议
804 册 图书
452 篇 期刊文献
2 篇 学位论文
1 篇 科技报告

馆藏范围

69,113 篇 电子文献
3 种 纸本馆藏

日期分布

学科分类号

39,677 篇 工学
- 34,083 篇 计算机科学与技术...
- 28,134 篇 软件工程
- 8,367 篇 信息与通信工程
- 6,577 篇 控制科学与工程
- 6,149 篇 生物工程
- 4,733 篇 生物医学工程（可授...
- 4,648 篇 电气工程
- 2,823 篇 机械工程
- 2,217 篇 光学工程
- 2,112 篇 电子科学与技术（可...
- 2,056 篇 安全科学与工程
- 1,919 篇 动力工程及工程热...
- 1,743 篇 化学工程与技术
- 1,540 篇 交通运输工程
- 1,536 篇 仪器科学与技术
18,280 篇 理学
- 10,074 篇 数学
- 6,671 篇 生物学
- 4,386 篇 统计学（可授理学、...
- 3,610 篇 系统科学
- 3,414 篇 物理学
- 1,655 篇 化学
11,702 篇 管理学
- 6,962 篇 管理科学与工程(可...
- 5,566 篇 图书情报与档案管...
- 3,445 篇 工商管理
4,615 篇 医学
- 4,107 篇 临床医学
- 4,013 篇 基础医学(可授医学...
- 2,408 篇 药学(可授医学、理...
- 2,044 篇 公共卫生与预防医...
1,961 篇 法学
- 1,649 篇 社会学
1,476 篇 农学
- 1,436 篇 作物学
1,377 篇 教育学
1,314 篇 经济学
134 篇 文学
95 篇 军事学
21 篇 艺术学
1 篇 哲学
1 篇 历史学

主题

12,070 篇 machine learning
4,972 篇 accuracy
4,497 篇 machine learning...
4,201 篇 support vector m...
3,831 篇 deep learning
3,784 篇 real-time system...
3,275 篇 training
3,217 篇 feature extracti...
3,122 篇 predictive model...
2,285 篇 computational mo...
1,956 篇 data models
1,825 篇 neural networks
1,767 篇 learning systems
1,717 篇 convolutional ne...
1,479 篇 artificial intel...
1,449 篇 adversarial mach...
1,343 篇 prediction algor...
1,267 篇 internet of thin...
1,205 篇 random forests
1,102 篇 decision trees

机构

456 篇 chitkara univers...
87 篇 lovely professio...
68 篇 department of co...
54 篇 department of co...
47 篇 chitkara univers...
46 篇 school of comput...
46 篇 centre of resear...
42 篇 karnataka bangal...
41 篇 computer science...
38 篇 department of co...
36 篇 department of co...
34 篇 chennai
33 篇 chitkara univers...
32 篇 department of co...
31 篇 school of comput...
30 篇 cmr technical ca...
30 篇 department of ar...
29 篇 department of in...
29 篇 school of comput...
29 篇 department of me...

作者

44 篇 gill kanwarparta...
36 篇 rakesh kumar
28 篇 senthil pandi s.
26 篇 shiva mehta
26 篇 pareek piyush ku...
24 篇 amandeep nagpal
23 篇 lakshmanarao a.
20 篇 ramesh s.
20 篇 yu wang
20 篇 kumar ajay
20 篇 zheng-ou wang
19 篇 j. relin francis...
19 篇 wang lei
19 篇 kumar atul
18 篇 zhang tao
18 篇 anjali t.
17 篇 sharma neha
17 篇 deepak gerard
17 篇 ajay kumar
17 篇 swain debabrata

语言

63,020 篇 英文
5,946 篇 其他
357 篇 中文
10 篇 土耳其文
4 篇 西班牙文
2 篇 德文
2 篇 葡萄牙文
1 篇 法文
1 篇 俄文

检索条件"任意字段=International Conference on Advances in Machine Learning and Systems Engineering"

共 69114 条记录，以下是231-240 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：

Improved Fraud Detection in Banking systems through machine learning and Big Data Analytics with Management Key Components 3

Improved Fraud Detection in Banking Systems through Machine ...

引用

international conference on advances in Computing, Communication and Applied Informatics (ACCAI)

作者： Balaji, Kalaivani Saxena, Niti Behera, Nihar Ranjan Kumar, Kiran M. Prasad, H. K. Gedamkar, Pallavi Rahul MEASI Inst Management Chennai Tamil Nadu India Jagannath Int Management Sch Management & Commerce New Delhi India GBC Geneva Business Ctr Swiss Sch Business & Management SSBM Geneva Geneva Switzerland JAIN Seshadri Rd Bengaluru Karnataka India Jain Coll Bengaluru Karnataka India Dr Vishwanath Karad MIT WPU Pune Maharashtra India

ISBN: (纸本)9798350389432;9798350389449;9798350389456

The banking industry needs to set up strong detection systems to fight the continuing risk of fraud in order to keep people's trust in financial systems and keep their cash safe. Problems often arise with traditional rule-based detection systems when they are put up against complicated fraud plans. It is possible to find fake activities more easily now that machine learning and big data analytics are becoming more popular. In this research, a complete approach is introduced that makes it easier to spot fraud in banking systems. The system has algorithms for machine learning, important management parts, and big data analytics. using "big data" technologies to collect and examine a lot of data from a lot of different sources, such as external data streams, internal transaction records, and profiles of customers. Fraud detection systems get better at telling the difference by picking out key features from preprocessed data. Researching on a system that will constantly watch all incoming transfers and send alerts right away if any suspicious activity is seen. Because of this, it is necessary to set limits, create automatic systems for sending out warnings, and come up with ways to spot anomalies. The financial industry must make sure that the methods they use to find and stop fraud are legal and meet their compliance responsibilities.

关键词： Fraud Detection Banking systems machine learning Big Data Analytics Management Key Components Risk Management Financial Security

来源：评论

学校读者我要写书评

暂无评论

Investigation Emotion Dynamics in Bangla Social Media Text for Improved Emotion Detection

Investigation Emotion Dynamics in Bangla Social Media Text f...

引用

2024 IEEE international conference on Computing, Applications and systems, COMPAS 2024

作者： Ahmed, Mostak Sobhan, Abdus Hossen Sajib, Md. Sahadat Masud, Nasrullah Green University of Bangladesh Department of Computer Science and Engineering Dhaka Bangladesh Varendra University Department of Computer Science and Engineering Rajshahi Bangladesh Varendra University Department of Electrical and Electronic Engineering Rajshahi Bangladesh

ISBN: (纸本)9798331529765

Recognizing emotion from text is fundamental to machine learning and influences our understanding of human interaction. While English sentiment analysis has been well-researched, Bengali (Bangla) is still under-researched. Our study bridges this gap by investigating the emotional dynamics in Bengali social media posts using extensive user-generated content. We use a modified dataset defined by seven emotions: Happy, Surprise, Fearful, Angry, Neutral, Disgust, and Sad. Using machine learning techniques such as support vector classifiers, decision trees, logistic regression, and random forest, we found that the Random Forest model achieved the highest accuracy of 84%. This study advances our understanding of Bengali emotional expression and demonstrates its application in social media sentiment analysis, psychoanalysis, and communication technologies. By addressing the complexities of Bengali language and culture, our work contributes to a broader definition of emotion and lays the foundation for future research in different contexts. © 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

Smart Eating: A machine learning Approach to Personalized Calorie Consumption Prediction 15

Smart Eating: A Machine Learning Approach to Personalized Ca...

引用

15th international conference on advances in Computing, Control, and Telecommunication Technologies, ACT 2024

作者： Ramesh, K. Madasu, Satya Dinesh Bolla, Rajeev Department of Electrical and Electronics Engineering Bapatla Engineering College Andhra Pradesh Bapatla India

ISBN: (纸本)9798331300579

In an era characterized by increased awareness of environmental concerns and the importance of energy conservation, the accurate prediction of individual energy consumption is a critical endeavour. This journal paper presents a comprehensive approach to forecast the energy usage of individuals by harnessing the power of machine learning algorithms. The study covers data collection, pre-processing, model selection, training, evaluation, deployment, and the interpretation of results. The primary aim is to empower individuals and utility companies with invaluable insights into predicting and optimizing energy usage, thereby reducing environmental impact and promoting energy efficiency. © Grenze Scientific Society, 2024.

关键词： Adversarial machine learning

来源：评论

学校读者我要写书评

暂无评论

A Comparative Analysis of Asthma Outcome Prediction with Improved Accuracy using Linear Discriminant Classifier and Narrow Neural Network Classifier 3

A Comparative Analysis of Asthma Outcome Prediction with Imp...

引用

3rd international conference on Electronics and Renewable systems, ICEARS 2025

作者： Viswanathan, C. Sivakumar, Vidhya Lakshmi Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Biomedical Engineering Chennai India Saveetha School of Engineering Chennai India

ISBN: (纸本)9798331509675

There are significant regional and national variations in the prevalence of childhood asthma and related risk factors. This study uses data from a prospective study with 202 children, both with and without asthma, to build machine learning models to predict childhood asthma. Narrow neural network classifiers and linear discriminant are used in the analysis. For this study, Kaggle datasets were used. The normal and abnormal values of (n=20), Excel datasets are used for the machine learning algorithm. MATLAB 2021 is used to obtain the machine learning algorithm for the normal and abnormal data sets. The linear discriminant and narrow neural network machine learning algorithm are used for the normal and abnormal datasets. The statistical analysis was done by using the latest version of IBM SPSS 22. There was a statistically significant difference in machine learning algorithms (p=0.001, p © 2025 IEEE.

关键词： Contrastive learning

来源：评论

学校读者我要写书评

暂无评论

Advancing Battery Management systems: A Hybrid Approach Combining Unscented Kalman Filter and machine learning for Accurate State of Charge Estimation 4

Advancing Battery Management Systems: A Hybrid Approach Comb...

引用

4th international conference on Robotics, Electrical and Signal Processing Techniques, ICREST 2025

作者： Hossain, Ismail Anonto, Hasanur Zaman Omi, Mehrab Islam Shufian, Abu Emon, Mukter Hossain Basak, Rinku American International University-Bangladesh Dept. of Electrical and Electronic Engineering Dhaka Bangladesh Lamar University Dept. of Electrical Engineering TX United States

ISBN: (纸本)9798331530938

As electric vehicle adoption and renewable energy integration rises globally;advanced battery monitoring is ever more essential. Precise determinations of State of Charge (SOC) and capacity are pivotal for optimizing performance over time, securing endurance, and maximizing efficiency. However, traditional approaches struggle with the nonlinear behavior and fading holding exhibited by modern battery arrays. This analysis explores merging machine learning methods, particularly Random Forest regression, with conventional techniques such as the UKF (Unscented Kalman Filter) and KF (Kalman Filter) for enhanced State of Charge and capacity projection. Using a sophisticated simulated Li-ion battery model comprising real-world factors like thermal effects and charging-discharging cycles, the examination contrasts the executions of the UKF and KF against the machine learning model. The results demonstrate that the machine learning-based approach, when combined with UKF and KF, significantly enhances State of Charge and capacity forecast precision. Namely, the hybrid strategy improves State of Charge estimation exactness to within 0.5% of real values and accomplishes a 12% advancement in capacity estimation precision contrasted with conventional techniques. These discoveries demonstrate the potential of joining machine learning with customary sifting strategies to fundamentally improve the exactness, dependability, and productivity of battery administration frameworks, guaranteeing preferable vitality use and RMSE of 68% expanding the operational lifetime of battery-subordinate advances. © 2025 IEEE.

关键词： Kalman filters

来源：评论

学校读者我要写书评

暂无评论

Sustainable machine learning for Real-Time DDoS Attack Detection and Mitigation in Industry 4.0 CPS

Sustainable Machine Learning for Real-Time DDoS Attack Detec...

引用

2024 IEEE international conference on Recent advances in Science and engineering Technology, ICRASET 2024

作者： Sahu, Devika Pandey, Ranu Sahu, Narendra Chahar, Mridula Shukla, Shikha Tiwari, Rovin Govt Engineering College Department of Computer Science and Engineering Raipur India Shri Rawatpura Sarkar University Department of Computer Science and Engineering Raipur India Rgpv Department of Aiml Bhopal India Rgpv Department of Ece Bhopal India

ISBN: (纸本)9798350388602

Industry 4.0, often known as the 'Fourth Industrial Revolution,' has remodelled factories into 'Sustainable Cyber-Physical Production systems' (CPPSs) that connect workers, tools, and completed products. Benefits from digitization include introducing customer-centric, transparent, and adaptable production methods. However, it has also created new attack vectors, with malicious actors taking advantage of network security gaps and focusing on IoT devices. The intricate nature of Distributed Denial-of-Service (DDoS) assaults endangers production lines, corporate operations, and workers in today's dynamic industrial context. To address these issues sustainably, this study employs machine learning (ML) to identify network anomalies and construct data-driven models for DDoS attack detection in Industry 4.0 CPPSs. Unlike previous methods, these use actual data collected from a semiconductor manufacturing facility's network. During training, 45 aspects of network flow are extracted in both directions, and various labeled datasets are curated. Eleven distinct forms of monitoring strategies for supervised, unsupervised, and semi-supervised machine learning settings are examined in detail in this paper. The simulations' accuracy indicates the efficacy of supervised algorithms, which are demonstrated to work with both loose and stringent supervision for detection. The Decision Tree model, for instance, achieves an outstanding 0.999 Accuracy and a truly remarkable 0.001% False Positive Rate. In the face of increasing cyber threats, this long-term, sustainable strategy to cybersecurity in Industry 4.0 strengthens CPPSs' resilience and security. © 2024 IEEE.

关键词： Adaptive control systems

来源：评论

学校读者我要写书评

暂无评论

Navigating Futures: A Paper on Comprehensive Review of Career Guidance Portal 15

Navigating Futures: A Paper on Comprehensive Review of Caree...

引用

15th international conference on advances in Computing, Control, and Telecommunication Technologies, ACT 2024

作者： Karnewar, Bhavesh Purankar, Shreyash Borkar, Prem Jagtap, Siddhant Shirbhate, Dhiraj Government College of Engineering Yavatmal India

ISBN: (纸本)9798331300579

Career Guidance is a very critical process that helps individual in making informed decisions about their educational and professional *** career guidance systems were dependent on career exploration resources, standardized assessments and human ***, the expanding multiplicity of the workforce and the dynamic nature of the job market, underscored the need for more personalized and advanced career guidance *** advent of Intelligent Career Guidance System (ICGS), uses Artificial Intelligence (AI) and machine learning (ML) which solves this *** advance algorithms and data analytics used by the ICGS, helps in the identification of correlations and patterns among the huge datasets of individual profiles and career *** Intelligent Career Guidance systems (ICGS) equipped with the power of Artificial Intelligence (AI) and machine learning (ML) provides more personalized and customized recommendations lined up with an individual's unique profile of skills, interests and *** thorough review delves into the current condition of ICGS, explaining their various functionalities, components and potential *** also crucially examines the limitations and challenges of ICGS, laying the foundation for future research endeavors. © Grenze Scientific Society, 2024.

关键词： Adversarial machine learning

来源：评论

学校读者我要写书评

暂无评论

Bi-level Cargo Volume Prediction Method Based on machine learning Approach 4

Bi-level Cargo Volume Prediction Method Based on Machine Lea...

引用

4th international conference on machine learning and Intelligent systems engineering (MLISE)

作者： Zhang, Zhixiang Univ Wollongong Sch Comp & Informat Technol Wollongong NSW Australia

ISBN: (数字)9798350375077

ISBN: (纸本)9798350375084;9798350375077

In recent years, with the rapid development of the e-commerce industry, logistics has become pivotal, and the sorting platform, as the core of logistics and distribution, has long been characterized by problems such as large fluctuations in task demand and complex personnel scheduling. This paper seeks to accurately predict the cargo volume of the sorting center through machine learning methods including exponential smoothing and Gaussian process regression. The dataset used in this paper is from the 14th MathorCup Mathematics Application Challenge 2024. time series and three times exponential smoothing methods are used to predict the daily cargo volume, the prediction accuracy reaches 89.7%, and the RMSE value is 1.836, the MAE value is 1.213. The prediction result is better than the traditional ARIMA model. In addition, the noise caused by many uncertainties and the problems caused by changes in transportation routes were considered. To solve this problem, a Gaussian process regression prediction is established, which is characterized by time series and daily cargo volume, and the prediction for the next 30 days is relatively accurate.

关键词： Data analysis ARIMA machine learning Route planning Time series

来源：评论

学校读者我要写书评

暂无评论

machine learning for Early Detection of Diabetic Retinopathy: Leveraging DenseNet CNNs 3

Machine Learning for Early Detection of Diabetic Retinopathy...

引用

international conference on advances in Computing, Communication and Applied Informatics (ACCAI)

作者： Pravallika, Paruchuri Begum, Shaik Shabana Sree, Pedasingu Sai Naidu, Thota Hitesh Rao, M. V. L. N. Raja Seshadri Rao Gudlavalleru Engn Coll Dept Informat Technol Gudlavalleru Andhra Pradesh India

ISBN: (纸本)9798350389432;9798350389449;9798350389456

The research overcomes the limitations of existing diabetic retinopathy detection systems by proposing an innovative system based on DenseNet CNNs. Existing systems have limitations, such as manual examination, which can result in inaccuracies and time-consuming procedures. The proposed system improves efficiency and accuracy using the cutting-edge machine learning technique DenseNet CNNs. Comprehensive datasets, preprocessing, DenseNet architectural modification, and training processes are utilized. In comparison to the existing system, the results show increased performance in accuracy (98%), sensitivity (98.7%), specificity (96%), and F1-Score (93%). The system's limitations include low accuracy (97%) and specificity (94.5%). The proposed system's autonomous feature extraction with DenseNet CNNs eliminates the need for user intervention while enhancing adaptability. These enhancements not only improve diagnosis accuracy but also give practical benefits by automating activities and lowering the workload of healthcare workers. The development has the potential to transform diabetic retinopathy identification, allowing for more rapid treatments and better patient care in clinical settings.

关键词： machine learning Diabetic Retinopathy DenseNet CNNs Early Detection Classification

来源：评论

学校读者我要写书评

暂无评论

CropSmart-A Crop Prediction Application Using machine learning 12th

CropSmart-A Crop Prediction Application Using Machine Learni...

引用

12th international conference on Frontiers of Intelligent Computing: Theory and Applications, FICTA 2024

作者： Madugula, Vidya Perumandla, Anshu Likhitha, Singari Gupta, Manu Dholvan, Mohan Department of Electronics and Computer Engineering Sreenidhi Institute of Science and Technology Hyderabad India

ISBN: (纸本)9789819601424

India is a country whose primary sources of income are agriculture and farming. Varied soil in the nation enables farmers to grow a wide range of crops all year round. The agricultural industry has been the focus of in-depth investigation in the past several years due to recent technological advances, including machine learning and smart computing. The proposed work aims to develop a web application for crop selection supporting farmers and improving agricultural productivity. Various supervised machine learning classifiers are used for crop prediction based on environmental factors and soil conditions. Performance metrics are included in the evaluation of several machine learning models. The results attained show that the bagging classifier achieved better results for crop prediction with an accuracy of 97%. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Smart agriculture

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 << < 20 21 22 23 24 25 26 27 28 29 > >>

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：