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

68,123 篇 会议
807 册 图书
446 篇 期刊文献
1 篇 学位论文

馆藏范围

69,376 篇 电子文献
3 种 纸本馆藏

日期分布

学科分类号

39,661 篇 工学
- 34,070 篇 计算机科学与技术...
- 28,121 篇 软件工程
- 8,365 篇 信息与通信工程
- 6,574 篇 控制科学与工程
- 6,146 篇 生物工程
- 4,737 篇 生物医学工程（可授...
- 4,645 篇 电气工程
- 2,823 篇 机械工程
- 2,215 篇 光学工程
- 2,110 篇 电子科学与技术（可...
- 2,055 篇 安全科学与工程
- 1,915 篇 动力工程及工程热...
- 1,740 篇 化学工程与技术
- 1,538 篇 交通运输工程
- 1,533 篇 仪器科学与技术
18,270 篇 理学
- 10,069 篇 数学
- 6,666 篇 生物学
- 4,383 篇 统计学（可授理学、...
- 3,614 篇 系统科学
- 3,413 篇 物理学
- 1,653 篇 化学
11,702 篇 管理学
- 6,961 篇 管理科学与工程(可...
- 5,566 篇 图书情报与档案管...
- 3,443 篇 工商管理
4,615 篇 医学
- 4,106 篇 临床医学
- 4,011 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...
1,963 篇 法学
- 1,651 篇 社会学
1,474 篇 农学
- 1,435 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

12,140 篇 machine learning
5,094 篇 accuracy
4,545 篇 machine learning...
4,232 篇 support vector m...
3,907 篇 real-time system...
3,873 篇 deep learning
3,311 篇 training
3,252 篇 feature extracti...
3,188 篇 predictive model...
2,324 篇 computational mo...
2,009 篇 data models
1,831 篇 neural networks
1,770 篇 learning systems
1,746 篇 convolutional ne...
1,501 篇 artificial intel...
1,439 篇 adversarial mach...
1,358 篇 prediction algor...
1,291 篇 internet of thin...
1,239 篇 random forests
1,102 篇 decision trees

机构

456 篇 chitkara univers...
87 篇 lovely professio...
68 篇 department of co...
55 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...
39 篇 department of co...
36 篇 department of co...
35 篇 department of co...
34 篇 chennai
33 篇 chitkara univers...
32 篇 department of me...
31 篇 department of ar...
31 篇 school of comput...
30 篇 department of in...
30 篇 cmr technical ca...
29 篇 school of comput...

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

56,589 篇 英文
12,642 篇 其他
428 篇 中文
8 篇 土耳其文
4 篇 西班牙文
2 篇 德文
1 篇 法文
1 篇 葡萄牙文
1 篇 俄文

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

共 69377 条记录，以下是671-680 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：

Enhancing Academic Feedback systems with Deep learning and Ensemble Methods 1

Enhancing Academic Feedback Systems with Deep Learning and E...

引用

1st international conference on advances in Computer Science, Electrical, Electronics, and Communication Technologies, CE2CT 2025

作者： Sharma, Hemant Joshi, Hiren Gujarat University Dept. Computer Science Ahmedabad India

ISBN: (纸本)9798331518578

This paper attempts to improve the academic feedback systems via complex deep learning and ensemble methods. Leveraging the NIRF Rankings 2022 dataset that provides radical performance metrics of Indian academic institutions, the project would attempt to develop trustworthy models to monitor and facilitate improvement in the academic feedback system. The techniques used in data preparation prevent missing values, outliers, and categorical data in using advanced machine learning models. In order to construct the model and capture fine-grained patterns linked to academic achievement, 1D Convolutional Neural Networks (CNNs) along with Multi-Layer Perceptrons (MLPs), both individually and in combination, are employed extrapolated from the National Institutional Ranking Framework to obtain data. Precision, accuracy, recall, along with the F1-score will be used to evaluate these models' performance. By the time these findings are finished, they should be able to provide practical advice on how to improve academic evaluations' effectiveness and advance educational institutions' more flexible feedback systems. © 2025 IEEE.

关键词： Contrastive learning

来源：评论

学校读者我要写书评

暂无评论

Advanced machine learning Models for Electrical Fault Detection and Classification in Transmission Lines 3

Advanced Machine Learning Models for Electrical Fault Detect...

引用

3rd international conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Deepika, Dugganapalli Charan, Maridinapalli Madhu Nossam, Sri Chakradhar Manitha, P.V. Department of Electrical and Electronics Engineering Amrita School of Engineering Amrita Vishwa Vidyapeetham Bengaluru India Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (纸本)9798331527549

With the fast development of power systems, efficient methods for fault detection and classification are needed to maintain the stability, safety and efficiency of the systems. In particular, this paper investigates advanced machine learning techniques for fault detection and classification in transmission lines using supervised learning algorithms, including Support Vector machines (SVM), Decision Trees, Random Forests, Naïve Bayes and Linear Discriminant Analysis (LDA). A dataset consisting of three phase currents and voltages from MATLAB simulations of normal and faulty system operations are used to study the problem. These models classify faults to be single line to ground, line to line and the three phase faults. Performance evaluation is done by metrics such as accuracy, precision, recall, F1 score, and AUC with respect to ROC analysis. Decision Trees are of high reliability and have good computational efficiency for fault detection among the models. The results presented in this research point to the importance of machine learning in reducing fault diagnosis circuit in order to achieve a more intelligent and resilient power transmission systems. © 2025 IEEE.

关键词： Power transmission lines

来源：评论

学校读者我要写书评

暂无评论

Introduction to advances in Information, Intelligence, systems and Applications 14th

Introduction to Advances in Information, Intelligence, Syste...

引用

14th international conference on Information, Intelligence, systems, and Applications, IISA 2023

作者： Tsihrintzis, George A. Virvou, Maria Bourbakis, Nikolaos G. Jain, Lakhmi C. Department of Informatics University of Piraeus Piraeus185 34 Greece Department of Electrical and Computer Engineering Wright State University DaytonOH45435-0001 United States KES International Shoreham-by-Sea United Kingdom

ISBN: (纸本)9783031674259

Information is widely available and accessible, but frequently leads to information overload and overexposure and the effort for coding, storing, hiding, securing, transmitting, and retrieving it may be excessive. Intelligence, inspired by biological and other paradigms, is required to manage information and extract knowledge from it. Information and Multimedia systems, with an increasing level of Intelligence, are being developed that incorporate these advances. As a result, new Technologies, Protocols, and novel Applications are emerging. In turn, the novel applications lead to the collection of new information and the "Information → Intelligence → systems → Applications" circle is repeated at ever higher levels. This book includes extended versions of selected research works that were presented at the 14th international conference on Information, Intelligence, systems, and Applications (IISA2023), Volos, Greece, July 10–12, 2024 (https://***/iisa2023) along with additional relevant papers by invited authors. These works cover a broad spectrum of topics from both novelties in theory and advanced methodologies as well as applications in the Energy, Agriculture, Medicine, Forensics, Healthcare, Education, Tourism, Inspection, Traffic and Software engineering. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Adversarial machine learning

来源：评论

学校读者我要写书评

暂无评论

advances in Crime Identification: A machine learning Perspective

Advances in Crime Identification: A Machine Learning Perspec...

引用

2024 international conference on Communication, Computer Sciences and engineering, IC3SE 2024

作者： Rachana, N. Norton Stanley, S.A. Sandeep, J. Sreeja, C.S. Department Of Computer Science Bangalore India

ISBN: (数字)9798350366846

ISBN: (纸本)9798350366846

Crime profoundly impacts individuals, communities, and families. Technological advancements have provided perpetrators with new opportunities for criminal activities. The primary objective of the police department is to resolve crimes, ensuring justice for the victims. Additionally, preventing such incidents is crucial for creating a safer world. The landscape of criminal justice has undergone a significant shift with the integration of machine learning techniques, unlocking unparalleled potential for accuracy and efficiency. This study thoroughly examines the concept of various applications of machine learning in crime detection, prediction, and prevention. We examine the evolution of these technologies, from early developments to state- of-the-art methodologies, conducting a thorough analysis of their strengths, limitations, and ethical considerations. Moreover, the paper sheds light on crimes discussed in academic circles, serving as a repository for scholars and researchers. This facilitates informed discussions and guides future research endeavours. © 2024 IEEE.

关键词： machine learning

来源：评论

学校读者我要写书评

暂无评论

Enhanced Lung Cancer Detection using Support Vector machine Algorithms 5

Enhanced Lung Cancer Detection using Support Vector Machine ...

引用

5th international conference on IoT Based Control Networks and Intelligent systems, ICICNIS 2024

作者： Rani, Ritu Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (数字)9798331518097

ISBN: (纸本)9798331518097

Still one of the biggest causes of mortality linked to cancer worldwide is lung cancer. Improving treatment efficacy and survival rates requires early identification. Recent advances in ML and AI provide considerable potential to improve lung cancer diagnosis. The aim of this work is for improved lung cancer detection accuracy or efficiency by use of Support Vector machine (SVM) methods. Effective but expensive and prone to mistakes are conventional methods for diagnosis include biopsy specimen's chest X-rays, and CT scans. Usually requiring much manual learning, these methods increase human error. Applying ML techniques, especially SVMs allows a streamlined and enhanced diagnostic process, therefore maybe generating more accurate and quick diagnosis. This work summarizes results of several ML and artificial intelligence approaches applied for lung cancer diagnosis, stressing the advances attained by means of reconstructed independent component analysis, sparse filter options, pretreatment and extraction of characteristics, transfer learning, and optimization techniques including Particle Swarm Optimization (PSO). Deep learning, hybrid models, and computational image processing approaches taken together have greatly boosted the subject by proving superior detection accuracy and resilience. This work aims to contribute to early identification and treatment by means of a precise and efficient SVM-based lung cancer prediction system, thereby improving patient outcomes. Future studies should keep investigating and enhancing these methods to raise their flexibility, dependability, and use in clinical settings. © 2024 IEEE.

关键词： Lung cancer

来源：评论

学校读者我要写书评

暂无评论

Quantum Ensemble learning with Quantum Support Vector machine 7

Quantum Ensemble Learning with Quantum Support Vector Machin...

引用

7th international conference on Advanced Algorithms and Control engineering, ICAACE 2024

作者： Yu, Zhiqian Chen, Zhaoyun Xue, Cheng Anhui University AHU-IAI Ai Joint Laboratory Anhui Hefei China Hefei Comprehensive National Science Center Institute Of Artificial Intelligence Anhui Hefei China

ISBN: (纸本)9798350361445

Ensemble learning, a technique that combines multiple base models to enhance performance and reduce variance, is a well-established approach in machine learning. In recent years, quantum machine learning has emerged as a field aiming to accelerate classical machine learning through quantum algorithms. In this paper, we introduce a quantum ensemble learning framework, with a particular focus on utilizing the parallel amplitude amplification (parallel AA) technique when employing a quantum support vector machine (QSVM) as the base learner. This approach reduces complexity compared to classical ensemble techniques, making it a promising advancement in the field of quantum machine learning. It demonstrates significant improvements in time complexity and maintains competitive accuracy when compared to classical bagging, including support vector machine (SVM) and least squares support vector machine (LS-SVM). © 2024 IEEE.

关键词： learning systems

来源：评论

学校读者我要写书评

暂无评论

Emotion Speech Recognition in the Cloud 2

Emotion Speech Recognition in the Cloud

引用

2nd international conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2024

作者： Ganesh, Devalla Bhaskar Prasanth, Balisetty Chowdary, Manne Teena Avala, Varsha Sree Gangashetty, Suryakanth V Koneru Lakshmaiah Education Foundation Computer Science and Engineering Vaddeswaram India

ISBN: (纸本)9798350327533

Emotion speech recognition in the cloud has emerged as a transformative technology with the potential to revolutionize various sectors, induding customer service, mental health, and human-computer interaction. This abstract provides an overview of the key concepts and approaches involved in the development and deployment of emotion speech recognition systems in doud-based environments. Emotion speech recognition leverages advances in machine learning, natural language processing, and cloud computing to detect and analyze emotional cues in spoken language. By identifying emotional states, such as happiness, sadness, anger, and more, these systems offer valuable insights for businesses and healthcare providers, enabling them to improve customer experiences and mental health services. © 2024 IEEE.

关键词： Cloud computing Deep learning models Emotion recognition Natural language processing Speech analysis

来源：评论

学校读者我要写书评

暂无评论

Shower vs. Bath: A Comparative Study of Water Usage and Environmental Impact

Shower vs. Bath: A Comparative Study of Water Usage and Envi...

引用

2024 international conference on Knowledge engineering and Communication systems, ICKECS 2024

作者： George, Angelina Ashik, F. Jose, Alphonsa Kumar, T.M. Mohan Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science & Engineering Bengaluru India Amrita Vishwa Vidyapeetham Amrita School of Engineering Department of Chemistry Bengaluru India

ISBN: (纸本)9798350359688

This study investigates the usage of water in urban areas, with particular attention to location, age, water quality, and bathing habits. We examined the data using machine learning, more especially a RandomForestClassifier. After accounting for several variables, the study's accuracy rate in predicting the types of baths was 83.63% without SMOTE and reached 90.51% after SMOTE analysis. Our knowledge of urban water consumption is improved by this research, which also shows how flexible machine learning is in identifying intricate links within datasets. The results of this study can help with resource management, urban planning, and environmental initiatives. © 2024 IEEE.

关键词： Bath Type Preferences Dataset Analysis machine learning Urban Water Consumption

来源：评论

学校读者我要写书评

暂无评论

Smart Energy Management in Buildings with machine learning Techniques

Smart Energy Management in Buildings with Machine Learning T...

引用

2024 international conference on Innovative Computing, Intelligent Communication and Smart Electrical systems, ICSES 2024

作者： Swetha, R Ramya Balamurthi, B. Latha, R. Golla, Suribabu Babu, S.B. G. Tilak Krishna, R.V.V. Institute of Aeronautical Engineering Department of Civil Engineering Dundigal Hyderabad India A.K. T Memorial College of Engineering and Technology Department of Civil Engineering Kallakurichi606213 India Faculty of Architecture and Planning Department of Architecture Tamilnadu Thaniavur613403 India Wet Centre Civil Engineering Department SRKR Marg Andhra Pradesh China Amiram534204 India Aditya University Department of Ece Andhra Pradesh Surampalem533437 India

ISBN: (纸本)9798331543617

The use of machine learning techniques for developing intelligent energy management systems for buildings. Because of the growing emphasis placed on energy efficiency and sustainability, optimizing the amount of energy that is consumed in buildings has become an extremely important task. Tominimize energy waste and reduce operational expenses, smart energy management systems offer a viable alternative. These systems leverage advanced data analytics and automation to achieve their goals. Throughout the course of this research project, we investigate the possibility of incorporating machine learning algorithms into building energy management systems. These algorithms include neural networks, decision trees, and reinforcement learning. These algorithms examine past data on energy usage, weather trends, occupancy schedules, and building attributes to estimate energy demand, optimize HVAC (heating, ventilation, and air conditioning) operations, and intelligently schedule tasks that use a significant amount of energy. We demonstrate the efficacy of machine learning approaches through case studies and performance evaluations. These techniques are utilized to improve energy efficiency, reduce carbon emissions, and enhance occupant comfort in buildings. The findings of this study contribute to the development of intelligent energy management methods, which in turn paves the way for buildings that are more sustainable and kind to the environment. © 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-Based Crop Recommendation System for Sustainable Agriculture 4

Machine Learning-Based Crop Recommendation System for Sustai...

引用

4th international conference on Sentiment Analysis and Deep learning, ICSADL 2025

作者： Sriharsha, Kantheti Chandan Joshi, Kotapuri Rahul Reddy, Vummadi Ravi Sankar Chowdary, Vadlapudi Kalyan Hanuman Hajarathaiah, Koduru Rajak, Shaik School of Computer Science and Engineering VIT-AP University Amaravati India Amrita Vishwa Vidyapeetham Department of Electronics and Communication Engineering Amaravati India

ISBN: (纸本)9798331523923

This research paper addresses the critical challenges in agricultural crop recommendation systems through machine learning approaches. Current systems face significant limitations including limited integration of real-time environmental data, inconsistent prediction accuracy across diverse geographical regions, difficulty in handling multiple conflicting parameters simultaneously, and lack of interpretability in recommendation decisions. Recent techniques like Random Forest, Support Vector machines, and Deep Neural Networks have attempted to address these issues but with varying degrees of success. This paper proposes an enhanced crop recommendation system that leverages multiple machine learning algorithms to overcome these limitations. The system implements real-time soil and weather parameter integration, while providing interpretable recommendations to farmers. Our approach achieves 99.54% accuracy in crop recommendations, significantly improving upon existing systems. The proposed model serves as a comprehensive solution for sustainable agriculture, contributing to improved crop yield and farmer decision-making processes. The system's effectiveness is validated across different agricultural zones, demonstrating its practical applicability and scalability in diverse farming conditions. © 2025 IEEE.

关键词： Adversarial machine learning

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 << < 64 65 66 67 68 69 70 71 72 73 > >>

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：