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

708 篇 会议
64 篇 期刊文献
7 册 图书

馆藏范围

779 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

223 篇 工学
- 184 篇 计算机科学与技术...
- 143 篇 软件工程
- 61 篇 信息与通信工程
- 58 篇 控制科学与工程
- 36 篇 网络空间安全
- 35 篇 电气工程
- 24 篇 生物医学工程（可授...
- 23 篇 电子科学与技术（可...
- 21 篇 生物工程
- 21 篇 安全科学与工程
- 18 篇 光学工程
- 13 篇 交通运输工程
- 12 篇 机械工程
- 9 篇 仪器科学与技术
- 9 篇 动力工程及工程热...
- 8 篇 化学工程与技术
- 8 篇 环境科学与工程（可...
115 篇 理学
- 53 篇 数学
- 30 篇 生物学
- 28 篇 统计学（可授理学、...
- 26 篇 物理学
- 11 篇 化学
81 篇 管理学
- 65 篇 管理科学与工程(可...
- 27 篇 工商管理
- 25 篇 图书情报与档案管...
29 篇 医学
- 26 篇 临床医学
- 23 篇 基础医学(可授医学...
- 21 篇 公共卫生与预防医...
20 篇 法学
- 19 篇 社会学
8 篇 经济学
- 8 篇 应用经济学
7 篇 农学
2 篇 教育学
2 篇 文学

主题

179 篇 accuracy
96 篇 real-time system...
91 篇 deep learning
91 篇 convolutional ne...
74 篇 feature extracti...
73 篇 computational mo...
72 篇 predictive model...
70 篇 training
66 篇 machine learning
50 篇 support vector m...
50 篇 machine learning...
46 篇 data models
39 篇 security
37 篇 optimization
32 篇 random forests
31 篇 scalability
30 篇 internet of thin...
29 篇 measurement
28 篇 analytical model...
26 篇 prediction algor...

机构

59 篇 department of co...
45 篇 lovely professio...
32 篇 the islamic univ...
29 篇 department of cs...
24 篇 school of comput...
23 篇 mlr institute of...
19 篇 department of ai...
19 篇 nitte meenakshi ...
18 篇 sr university sc...
15 篇 department of co...
14 篇 department of co...
14 篇 the islamic univ...
14 篇 graphic era deem...
13 篇 school of comput...
13 篇 radiology techni...
12 篇 koneru lakshmaia...
12 篇 department of co...
11 篇 department of co...
10 篇 department of me...
9 篇 gla university d...

作者

12 篇 r. akhilesh redd...
11 篇 manjunatha
11 篇 reddy r. akhiles...
10 篇 piyush kumar par...
10 篇 jagendra singh
9 篇 pareek piyush ku...
9 篇 neeraj varshney
8 篇 shilpa choudhary
8 篇 hassan m. al-jaw...
8 篇 prakash kumar sa...
8 篇 pundru chandra s...
7 篇 varshney neeraj
7 篇 navdeep singh
7 篇 niranjanamurthy ...
7 篇 mohammed al-faro...
7 篇 m. misba
6 篇 sandeep kumar
6 篇 manish rai
6 篇 munish kumar
6 篇 vishnu kurnala

语言

704 篇 英文
75 篇 其他
1 篇 中文

检索条件"机构=Department of Computer Science and Engineering-AI&ML"

共 779 条记录，以下是431-440 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 Gastric Cancer Diagnosis Through Ensemble Learning for Medical Image Analysis

Enhancing Gastric Cancer Diagnosis Through Ensemble Learning...

引用

International Conference on Image Information Processing

作者： N V Sai Manoj M Rithani R S SyamDev Department of Computer Science and Engineering (AIE) Amrita School of Computing Amrita Vishwa Vidyapeetham Chennai India Department of AI & ML New Horizon College of Engineering Bangalore

ISBN: (数字)9798350371406

ISBN: (纸本)9798350371413

Even with the present state of medical imaging technology, accurately and quickly detecting stomach cancer is still a major difficulty. Even though many detection techniques have advanced, current research noticeably lacks a cohesive strategy that integrates multiple models for reliable detection. In response, this research uses stacking ensembling methods to present a unique paradigm for stomach cancer diagnosis. Our goal is to increase the accuracy of stomach cancer diagnosis by using the power of many sophisticated semantic segmentation models, such as U-Net, DeepLab, and SegNet, in order to overcome the current constraints. We comprehensively examine the effectiveness of the various models and the ensemble technique via a rigorous review of important performance measures, such as accuracy (Ensemble: 97.8%, U-Net: 95.8%, DeepLab: 93.3%, SegNet: 92.5%), F1 score, ROC-AUC, precision, recall, and IoU. According to our findings, the ensemble model—which is enabled by a Random Forest Classifier—performs better than the individual models in identifying stomach cancer. By offering a complete and accurate method for the accurate and dependable identification of stomach cancer, this research contributes significantly to the area of medical image analysis and facilitates early diagnosis and better patient outcomes.

关键词： Deep learning Solid modeling Stomach Analytical models Image analysis Semantic segmentation Stacking

来源：评论

学校读者我要写书评

暂无评论

A Machine Learning Approach for District-Wise Classification of Employability Rate in Southern Indian States

A Machine Learning Approach for District-Wise Classification...

引用

Innovative Computing, Intelligent Communication and Smart Electrical Systems (ICSES), International Conference on

作者： Shaahid Ahmed N K Selvakumar Dilshad Begum M L. SaiRamesh Computer Science With Specializtion in AI&ML Vellore Institute of Technology (Chennai Campus) Chennai India Department of Computer Applications NIT Trichy India Department of Computer Science Justice Basheer Ahmed Sayeed College For Women Chennai India Department of CSE St.Joseph’s Institute of Technology Chennai India

Prediction and classification of data regarding the economic sustainability status of various districts of Southern Indian States / UT viz., Tamil Nadu, Kerala, Karnataka, Undivided Andhra Pradesh and Pondicherry. Prediction of sustainable employability of population using parameters such as Literacy and Higher Education using Machine Learning approach is the basis of this research work. Since Independence the Government of India has periodically introduced several schemes that aim at generating continuous and sustainable employment to the unemployed youth of large segment of underprivileged communities. Machine learning is an approach where computers are trained to make accurate predictions with previous knowledge and experiences. This project uses Machine learning algorithms to suggest future implementation for the employability of a candidate of a particular district in the recruitment process to sphere head the growth and development of the individual and *** analysis is a key for any endeavour. The work given in this paperdiscussed about the approaches of machine learning techniques for predict the employability status of every district of the Southern Indian states. The proposed work considered the data from the census of year 2011, Government of India for conducting the studies of the unemployment. Making betterments in schemes for various districts of different states and Union Territory based on their present employability status is necessary for the overall credibility and successful implementation.

关键词：

来源：评论

学校读者我要写书评

暂无评论

A Hybrid Model for Disaster Damage Detection Using Satellite Images

A Hybrid Model for Disaster Damage Detection Using Satellite...

引用

International Conference on Image Information Processing

作者： Penta Aravinda Swamy M Rithani R S SyamDev Department of Computer Science and Engineering (AIE) Amrita School of Computing Amrita Vishwa Vidyapeetham Chennai India Department of AI & ML New Horizon College of Engineering Bangalore

ISBN: (数字)9798350371406

ISBN: (纸本)9798350371413

Efficient and accurate assessment of disaster-induced damage is paramount for effective disaster response and relief operations. Traditional methodologies reliant on manual analysis are often impractical for large-scale disaster areas. The challenges inherent in disaster damage assessment, such as varying environmental conditions, noise in satellite imagery, and the need for rapid response, are confronted through a multistep approach. We propose a novel approach leveraging the combined capabilities of EfficientNet and HRNet architectures for disaster damage detection in dual-temporal satellite imagery. By harnessing the unique strengths of these models, we aim to improve building localization and multi-level change detection. Extensive experimentation on a comprehensive building damage assessment dataset (xBD) showcases that our approach achieves state-of-the-art results. Based on our experiments, it was found that this method demonstrated the capability to detect disaster damage with an accuracy rate of 98.2%. This method could be applied to many real-world applications, including Emergency Response Planning and Management, Environmental Monitoring and Conservation, and Agricultural Damage Assessment, so that it could be applied in many different contexts.

关键词： Disasters Buildings Noise Organizations Real-time systems Satellite images Planning

来源：评论

学校读者我要写书评

暂无评论

Smart Happy Device with IoT : For those who suffer anxiety, stress, and depression 12

Smart Happy Device with IoT : For those who suffer anxiety, ...

引用

12th IEEE Region 10 Humanitarian Technology Conference, R10-HTC 2024

作者： Yakub, Fitri Azizan, Azizul Dhamanti, Inge Khudzari, Ahmad Zahran Md. Govindan, Niranjana Muljono, Muljono UTM Malaysia-Japan International Institute of Technology Electronic System Engineering Department Kuala Lumpur Malaysia Universiti Teknologi Malaysia Cyberphysical Research Group Embedded AI & ML Science Lab Faculty of Artificial Intelligence Kuala Lumpur Malaysia Universitas Airlangga Faculty of Public Health Department of Health Policy and Administration Surabaya Indonesia UTM Faculty of Engineering Department of Biosciences & Medical Engineering Johor Bahru Malaysia SRMIST Faculty of Engineering & Technology Department of Computing Technologies Chennai India Universitas Dian Nuswantoro Faculty of Computer Science Semarang Indonesia

ISBN: (纸本)9798350340204

Due to the rising cases of Covid-19 in the world especially in Malaysia, the impact had been hit hard for many people. Due to this all people must be isolated in their home. It has affected the well-being and working life of millions of people across the world. Due to this disease, the mental health among people has increased significantly for the past 2 years. According to MoH, out of 983 Malaysian students, about 20.4%, 6.6%, and 2.8% are experienced minimal to moderate, marked to severe, and most extreme levels of anxiety, respectively. The main stressors are the remote online teaching, and uncertainty about the future regarding study and career which can affect mental health. Thus, the main objective of this project is to develop a product that may provide different medication methods to its users by helping people who suffer anxiety, stress, and depression. We used aromatherapy, positive quotes, and music therapy in order to release stress. The user can also check his or her pulse rate by only touching the fingerprint through the sensor. The microcontrollers used in this project are Arduino Uno and Arduino Mega to build the project circuit. The experimental results can improve body health, mind and spirit which enhances both physical and emotional health. © 2024 IEEE.

关键词： COVID-19

来源：评论

学校读者我要写书评

暂无评论

Implementing Machine Learning Techniques for the Anticipation of Maintenance Requirements in Naval Assets

Implementing Machine Learning Techniques for the Anticipatio...

引用

International Conference on Computational and Characterization Techniques in engineering & sciences (CCTES)

作者： Vanaparthi S R Krishna Jayamma Rairala K. Raghuvardhan R. Senthamil Selvan D M Abhinay Kanth Department of CSE Sai Spurthi Institute of Technology B. Gangaram India Department of CSE(AI&ML) Anurag Engineering College Anathagiri India Department of Electronics and Communication Engineering Annamacharya Institute of Technology and Sciences Tirupati India Department of CSE Madanapalle Institute of Technology & Science Madanapalle India

ISBN: (数字)9798350364699

ISBN: (纸本)9798350364705

Implementation of Machine Learning Techniques for Forecasting Maintenance Requirements of Naval Assets Automation plays a crucial role in the shipping industry's pursuit of having the fewest operating personnel feasible. Efficient control at several phases, including shipbuilding and navigation, is crucial for the effective application of automation. Autonomous shipping, which requires careful oversight, is now the industry's top priority. Though there are several established control and automation systems in use today, Artificial Intelligence Schemes (aiS) are quickly becoming the preferred method of operation in the maritime sector due to their many benefits. Fuzzy logic systems combined with Artificial Immune Systems (aiS) are showing promising results. Researchers in the future may be able to utilize the widespread usage of aiS in maritime applications as a benchmark. This study aims to examine the various machine learning algorithms utilized in various maritime applications and conduct thorough research on Automatic Identification Systems (aiS). With the right tools, the transportation industry may become fully automated.

关键词： Automation Machine learning algorithms Transportation industry Machine learning Organizations Seaports Software Maintenance Personnel Forecasting

来源：评论

学校读者我要写书评

暂无评论

A Machine Learning Model for the Early Prediction of Cardiovascular Disease in Patients

A Machine Learning Model for the Early Prediction of Cardiov...

引用

Advances in Computational Intelligence and Communication (ICACIC), International Conference on

作者： Abhishek Hutashan Vishal Bhagat Manminder Singh Department of Computer Science and Engineering Sant Longowal Institute of Engineering and Technology Sangrur Department of AI &ML (CSE) B V Raju Institute of Technology Hyderabad India

Cardiovascular disease is a major health concern worldwide, with an estimated 17.9 million deaths each year. It encompasses a range of conditions affecting the heart and blood vessels, including coronary heart disease, stroke, and heart failure. These diseases are often associated with a range of modifiable and non-modifiable risk factors, including high blood pressure, high cholesterol, smoking, physical inactivity, obesity, diabetes, and genetic predisposition. Data analysis and machine learning have emerged as powerful tools for predicting and preventing cardiovascular disease. By analysing large and complex datasets of medical records, researchers can identify patterns and risk factors associated with cardiovascular disease. Machine learning models can help identify important features in the data that are predictive of heart disease outcomes, and can be used to develop accurate and reliable predictive models. Moreover, data analysis can help identify disparities and biases in cardiovascular disease outcomes across different populations and regions. This research aims to develop a machine-learning model for predicting cardiovascular disease risk using benchmark datasets. Various imputation techniques, including Mean, Median, Most Frequent, KNNI, and the proposed risk prediction model, are compared in terms of accuracy and efficiency. The CatBoost classification algorithm is used for developing the proposed model, achieving an accuracy of 91% for the Hungarian dataset. This research provides valuable insights into the use of machine learning and data analysis for predicting cardiovascular disease and improving healthcare outcomes.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Breast Cancer Prediction using Feature Selection and Classification with XGBoost

Breast Cancer Prediction using Feature Selection and Classif...

引用

Recent Trends in Electronics and Communication (ICRTEC), International Conference on

作者： Raye Haarika Tina Babu Rekha R Nair Rajesh T M Department of Computer Science and Engineering (AI & ML) School of Engineering Dayananda Sagar University Bengaluru India Department of Computer Science and Engineering School of Engineering Dayananda Sagar University Bengaluru India Department of Computer Applications School of Engineering Dayananda Sagar University Bengaluru India

As a result of high population growth, medical research and early sickness identification has become a responsibility. As the inhabitants grows, the likelihood of succumbing to breast cancer rises. A illness detection system that's automated reduces the danger of mortality, provides dependable, efficient, and rapid reaction, and assists doctors in disease diagnosis. to reinforce the predictive efficacy of current models and To estimate the risk of breast cancer, proposed model used Extreme Gradient Boosting (XGBoost) and compared it to Logistic Regression (LR), The Random Forest (RF), and Support Vector Machines (SVM). In this work, target picking the most weighted features using the XGBoost method and training the model with those features was performed. This approach yielded the identical accuracy as training the model with all features, however training the model with all features takes longer. The suggested approach has produced findings that are comparable and may help radiologists provide opinions

关键词： Training Support vector machines Costs Sociology Predictive models Breast cancer Reliability

来源：评论

学校读者我要写书评

暂无评论

AES Efficient Encryption of Multimedia Data using Threads for 5G System

AES Efficient Encryption of Multimedia Data using Threads fo...

引用

Machine Learning and Autonomous Systems (ICmlAS), International Conference on

作者： Dr. Govinda Rajulu. G Dr. L. Sharmila Mr. D. Venkatesan Nathi Kranthi Kumar Jarabhala Venkateswaramma Chelumala Nirosha Department of Computer Science and Design Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Avadi Chennai Tamil Nadu India Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Tamil Nadu India Dept. of Artificial Intelligence and Machine Learning St. Martin's Engineering College Hyderabad India Deptarment of CSE(AI & ML) St. Martin's Engineering College Hyderabad India Department of CSE(AI&ML) St. martin's engineering college Secunderabad Department of EEE St. Martin's Engineering College Dhulapally Secunderabad Telangana

ISBN: (数字)9798331505745

ISBN: (纸本)9798331505752

As technology advances, encryption methods must adapt to protect sensitive information. Organizations must stay informed about emerging threats and regularly update their protocols to stay ahead. Investing in advanced encryption technologies can mitigate potential breaches. Encryption protects intellectual property, multimedia data, and personal data from cyber threats, a crucial consideration for businesses and individuals in the digital age. This work introduces four AES-based threads for encrypting and decoding multimedia data in 5G. Both sequential and parallel methods have been used for data security. Furthermore, time analyses for sequential and parallel processing algorithms have been conducted using images of various sizes and formats. The results indicate that the encryption process will be completed faster and will provide network-wide data safety in parallel method.

关键词： Protocols 5G mobile communication Multithreading Multimedia systems Instruction sets Organizations Streaming media Encryption Protection Videos

来源：评论

学校读者我要写书评

暂无评论

A Novel Hybrid Approach Integrating Machine Learning and ANN for Stroke Risk Assessment

A Novel Hybrid Approach Integrating Machine Learning and ANN...

引用

Ubiquitous Computing and Intelligent Information Systems (ICUIS), International Conference on

作者： Nagagopiraju Vullam S.V.V.D. Jagadeesh K. Siva Pavani Maddala Janakidevi A. Lakshmanarao Y S N Chandreswari Department of CSE Chalapathi Institute of Engineering and Technology Guntur A.P India Department of AI&DS LakiReddy Bali Reddy College of Engineering Mylavaram Department of AI&ML Aditya University Surampalem India Department of CSE SRKR Engineering College Bhimavaram A.P India Department of IT Aditya University Surampalem India Department of MCA Bonam Venkata Chalamayya institute of technology and science Batlapalem

ISBN: (数字)9798331529635

ISBN: (纸本)9798331529642

Brain stroke is a life-threatening condition that needs to be detected early and correct precision to improve patient wellness. This paper presents a method for predicting stroke using ml and ANN using a publicly available data from Kaggle. The dataset has class imbalance. This can be solved using SMOTE and ADASYN to guarantee a balanced class distribution. After preprocessing, six traditional ml models including Logistic Regression, DTC, Random Forest, KNN, SVM and Gradient Boosting were used to predict the risk of stroke. To further increase the accuracy of the forecast. Later, ensemble models also applied to further enhance performance. Finally, ANN was applied to capture complex patterns in the data. The results shown that the ensemble model significantly improves the prediction performance compared to the individual ml models. Later a hybrid ml and DL ANN is proposed. The proposed hybrid model combining ml best model and ANN technique improved the accuracy in predicting stroke and provide a more reliable system for early detection of stroke. It is observed that the hybrid method will outperform both individual ml models and ensemble models.

关键词： Support vector machines Patient monitoring Accuracy Predictive models Nearest neighbor methods Ubiquitous computing Real-time systems Risk management Reliability Random forests

来源：评论

学校读者我要写书评

暂无评论

Advances in Big Data and Data Mining: Techniques and Applications in Data Fusion for Enhanced Insights and Decision-Making

Advances in Big Data and Data Mining: Techniques and Applica...

引用

Computational, Communication and Information Technology (ICCCIT), International Conference on

作者： Himanshu Rai Goyal Anurag Shrivastava Amandeep Nagpal R. Akhilesh Reddy Kanchan Yadav Revathi V Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai Tamilnadu India Lovely Professional University Phagwara India Department of CSE-AI&ML MLR Institute of Technology Hyderabad Telangana India Department of Electrical Engineering GLA University Mathura India Department of Applied Sciences New Horizon College of Engineering Bangalore India

ISBN: (数字)9798331512965

ISBN: (纸本)9798331512972

The advancement of big data has accelerated over the years to change how companies utilize data to drive decisions. And so, this paper aims to look at big data and data mining in different perspectives while highlighting the importance of data fusion in decision making in the current society. Many times, data fusion combines different data sources to provide better and broader results. Clustering of raw data, anomaly detection and machine learning are discussed as well as their use in healthcare, financial management and smart cities. This paper also presents such issues as data heterogeneity, data quality assurance, as well as scalability issues. Lastly, tomorrow's possibilities, such as artificial intelligence and quantum computing, are presented as possible enhancements of the data fusion effectiveness are mentioned due to the expected marked advancement in real-time and adaptive decisionmaking systems.

关键词： Technological innovation Adaptive systems Smart cities Scalability Soft sensors Data integration Medical services Machine learning Big Data Real-time systems

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78页 << < 40 41 42 43 44 45 46 47 48 49 > >>

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：