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4971-4980 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：

A Principal Component Analysis Technique to Monitor the Hematomas Using a Wide Band Textile Monopole Antenna

A Principal Component Analysis Technique to Monitor the Hema...

引用

2024 international conference on Recent advances in Electrical, Electronics, Ubiquitous Communication, and Computational Intelligence, RAEEUCCI 2024

作者： Rizwan, Shaik Kumar, Kanaparthi V Phani SRM Institute of Science and Technology Diagnostics Therapeutics and Assistive Devices Laboratory Faculty of Engineering and Technology Department of Electronics and Communication Engineering Tamil Nadu Kattankulathur603203 India

ISBN: (纸本)9798350354539

The rapid advancements in wearable antenna tech-nologies have opened up new avenues for non-invasive healthcare applications. This work uses a principal component analysis technique to monitor the hematomas using a wide-band textile monopole (WTM) antenna. The WTM antenna operates at 3.13 GHz to 10.87 GHz, and its electrical size of λ0/5.38×λ0/5.1 ×λ0/90.9m3 shows that it is compact and easy to assemble in the microwave sensing system. This research proposes an unsupervised machine-learning framework that leverages the data collected from wearable antennas to identify the presence of hematomas. These WTM antennas continuously capture elec-tromagnetic signals from the fractured bone, allowing for real-time monitoring of subtle changes in the fractured bone and tissue characteristics. The reported findings demonstrate the feasibility and effectiveness of this innovative approach, providing a promising foundation for further research and development in the field of non-invasive monitoring of hematomas using wearable antennas. © 2024 IEEE.

关键词： Monopole antennas

来源：评论

学校读者我要写书评

暂无评论

machine learning Techniques for GDP Per Capita Prediction in Developing Countries: A Case Study of Sub-Saharan Africa

Machine Learning Techniques for GDP Per Capita Prediction in...

引用

international conference on Artificial Intelligence in Renewable Energetic systems, IC-AIRES 2024

作者： Zermane, Hanane Laboratory of Automation and Manufacturing Industrial Engineering Department Faculty of Technology Batna 2 University Batna Algeria

ISBN: (纸本)9783031803000

Solid biofuels are a crucial energy source for Sub-Saharan Africa, with a significant portion of the population relying on them for cooking and heating. The prediction of Gross Domestic Product (GDP) is a critical task for economic planning and policy making. Traditional statistical methods have been widely used for this purpose;however, the advent of machine learning techniques offers new opportunities for improving predictions. Several machine learning models are used to determine their effectiveness in predicting GDP per Capita. The models’ performance was assessed based on many metrics. Support Vector Regression achieved the lowest MAE of 0.81 and the highest R2 of 0.98. Ridge and Lasso Regression also performed well with an MAE of 0.89 and 0.88, respectively, and a high R2 of 0.98. In contrast, Decision Tree and Random Forest exhibited higher MAE values and lower R2, while XGBoost and TabNet provided intermediate performance metrics. Accurate GDP forecasts enable governments and organizations to make informed decisions. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Prediction models

来源：评论

学校读者我要写书评

暂无评论

Analysis of Brain Health Using machine learning and Artificial Intelligence Technology: Modern Drug Discovery Perspective 1

Analysis of Brain Health Using Machine Learning and Artifici...

引用

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

作者： Thirunagalingam, Arunkumar Usha, M.S. Geetha, S. Dhanamathi, A. Dhivya, S. Thendral, M.Tamil Business Intelligence and Reporting B. S. Abdur Rahman Crescent Institute of Science and Technology Department of CSE Chennai600048 India VS.B. ENGINEERING COLLEGE Department of Computer Science and Engineering Karur India Roever Engineering College Department of CSE Perambalur621220 India Sri Venkateswara College of Engineering Department of Computer Science and Engineering Sriperumbudur India Bannari Amman Institute of Technology Department of Information Technology Sathyamangalam India

ISBN: (纸本)9798331518578

The creation and discovery of pharmaceuticals may be considered the most important translational science activity that improves human invulnerability and happiness. In the pharmaceutical sector, strategies to reduce costs and speed up the development of new drugs have sparked a rigorous and fascinating brainstorming session. The use of classified big data in conjunction with remarkably improved computer power and cloud storage has enabled the application of artificial intelligence (AI), particularly the deep-learning (DL) component, in all domains. In healthy individuals, ML evaluation of neuroimaging data may accurately predict ordered age;illness and mental impairment have been linked to departures from normal brain maturation. Convolutional neural networks (CNN), a DL-based predictive display technique, were applied to both pre-handled and raw T1-weighted X-ray data in order to further evaluate the eligibility of 'brain-predicted age' as a biomarker of individual contrasts in the brain ageing process. Brain-predicted age is a very accurate, remarkably stable, and genetically valid trait that exhibits assurance as a biomarker of brain maturation. Additionally, age may be accurately predicted from raw T1-MRI data, which greatly reduces the amount of time needed to compute fresh data and accelerates the process of delivering immediate data on brain health in clinical settings. © 2025 IEEE.

关键词： Electronic health record

来源：评论

学校读者我要写书评

暂无评论

System for Predicting the Diabetes using machine learning Techniques 2

System for Predicting the Diabetes using Machine Learning Te...

引用

2nd international conference on Electronics and Renewable systems, ICEARS 2023

作者： Alagarsamy, Saravanan Muthuvel, P. Kumar, K. V. Sudheer Reddy, M. Madhusudhan Naidu, M. Mallikarjuna Kumar, M. Venkat Kalasalingam Academy of Research and Education Department of Computer Science and Engineering Tamilnadu Anand Nagar India

ISBN: (纸本)9798350346640

Diabetes needs to be managed constantly because it is a chronic and complicated medical illness. The importance of creating cutting-edge solutions to aid in the management of this condition is growing as diabetes prevalence rises. This study suggests using machine learning to manage healthcare costs and anticipate the onset of diabetes. To be more precise, we suggest combining supervised and unsupervised learning techniques to deliver patient-specific real-time forecasts and track the development of the disease. Using publicly accessible diabetes datasets, we will assess the performance of the suggested approach and contrast the findings with current techniques. In addition, we'll talk about how applying machine learning approaches could help with diabetes management. The goal of this paper is to present the potential of machine learning for enhancing diabetes treatment and to offer a framework for further investigation in this field. © 2023 IEEE.

关键词： Predictive analytics

来源：评论

学校读者我要写书评

暂无评论

AntiMicrobial Resistance Prediction Using Advanced Image Encoding Techniques 1

AntiMicrobial Resistance Prediction Using Advanced Image Enc...

引用

1st international conference on Sustainable Energy Technologies and Computational Intelligence, SETCOM 2025

作者： Ghadekar, Premanand Jaggi, Arjun Swain, Debabrata Ghadekar, Aryan Vishwakarma Institute of Technology Department of Information Technology Pune India HCLTech CA United States Pandit Deendayal Energy University Department of Computer Science and Engineering Gandhinagar India Vishwakarma Institute of Technology Department of Computer Engineering Pune India

ISBN: (纸本)9798331520540

Antimicrobial resistance (AMR) is one of the significant problems in the health sector and requires resources to create and develop accurate forecasting systems capable of recognizing resistant microbes and their treatment approaches. The traditional machine learning models used in AMR prediction usually suffer from poor genomic sequences feature extraction and finding it hard to solve big biological problems with big data. K-mer based methods or other simpler classifiers have been able to provide certain accuracy, but these have strived when dealing with many genomic strains and variants. To address these shortcomings, deep learning models are able to learn data representation at various abstraction levels which understanding complex data of fundamental and high dimensional patterns. In this work we provide a new deep learning approach exploiting CNNs on FCGR images of microbial genomes. The sequence is represented as frequency chaos game representation making it better for the enn structural application as resnet and densenet where features are deeply embedded in classifiers. When it comes to string and image based methods it is clear that they both have their own benefits and shortcomings. String based analysis is a faster and more accurate way to analyze naked sequence data conversion, but it may not be as informative as visual presentation. Our study verified the hypothesis that it is feasible to encode genomic data for further FCGR, so that the data might be utilized as an input for machine learning and deep learning methods. The model is effective with substantial improvements with the accuracy of over 90% in test dataset. This represents a significant improvement in accuracy score with better systems for predicting antibiotic resistance among many types of microbes. © 2025 IEEE.

关键词： AntiMicrobial Resistance Prediction CARD Frequency Chaos Game Representation microbes NCBI

来源：评论

学校读者我要写书评

暂无评论

Exploring Drug Recommendations Through Sentiment Analysis: A machine learning Perspective

Exploring Drug Recommendations Through Sentiment Analysis: A...

引用

2024 international conference on Electrical and Computer engineering Researches, ICECER 2024

作者： Qureshi, Imran Hashil Alsiyabi, Zahra Nasser Mohammad, Burhanuddin Shadab, S.G.M. Irfan, Mohammed University of Technology and Applied Science College of Computing and Information Sciences AlMusanna Oman

ISBN: (纸本)9798331539733

Current drug recommendation systems often rely on clinical trials and general guidelines, which may not fully account for individual patient experiences. This study addresses the gap by leveraging sentiment analysis of patient reviews to enhance drug recommendations. The primary objective is to develop a machine learning-based drug recommendation system that incorporates sentiment from drug reviews, improving the personalization of drug recommendations. Using a dataset from UCI ML repository with over 215,000 instances, we apply sentiment analysis and various feature extraction techniques, including TF-IDF and Word2Vec, alongside machine learning classifiers to predict drug efficacy for conditions such as acne, hypertension, and depression. By integrating sentiment-based insights, this approach aims to provide more tailored recommendations for individual patients. Our results show that models like Linear SVC (TF-IDF) and Random Forest (manual features) outperform traditional methods, achieving up to 93% accuracy. These findings demonstrate the potential of sentiment analysis to enhance the relevance and accuracy of drug recommendations. This research contributes to the growing field of personalized healthcare by introducing a data-driven framework that captures patient sentiment, offering more accurate and individualized medication guidance. © 2024 IEEE.

关键词： BoW Classifier Drug Recommender ML

来源：评论

学校读者我要写书评

暂无评论

machine learning Forecasting Approaches for Evapotranspiration: A Comparative Analysis 4th

Machine Learning Forecasting Approaches for Evapotranspirati...

引用

4th international conference on Electronic engineering and Renewable Energy systems, ICEERE 2024

作者： Mokhtari, Hassan Benzaouia, Mohammed Hajji, Bekkay Ayadi, Nabil Chaabane, Khalid Faculty of Sciences Mohamed First University Oujda Morocco Renewable Energy Embedded System and Data Processing Laboratory National School of Applied Sciences Mohamed First University Oujda Morocco

ISBN: (纸本)9789819606436

Accurate prediction of reference evapotranspiration (ET0) is essential for effective management across various sectors, particularly in agriculture and irrigation. This paper presents a comparative analysis of three prominent machine-learning techniques, Support Vector machines (SVM), Random Forest (RF), and K-Nearest Neighbors (KNN), utilizing a comprehensive dataset of meteorological variables. The study focuses on the eastern region of Morocco, specifically Oujda, where water resource management is of critical importance. Through rigorous experimentation and evaluation, our findings reveal SVM’s superior accuracy (R2 = 0.9981) in ET0 prediction when compared to RF and KNN. This superior performance underscores SVM's potential for precise ET0 forecasting, facilitating informed decision-making in water resource management. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

来源：评论

学校读者我要写书评

暂无评论

Breast Cancer Classification using machine learning Algorithm

Breast Cancer Classification using Machine Learning Algorith...

引用

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

作者： Anu, D. Vidhya, K. Nitte Meenakshi Institute of Technology Artificial Intelligence & Data Science Bangalore India East West Institute of Technology Information Science & Engg Bangalore India

ISBN: (纸本)9798350359688

Around the globe in this era breast cancer is the most common disease among women, previously detection of breast cancer was a long and tedious process. In the proposed system the patient's data set will be collected. The collected data set will be manipulated and cleaned using feature extraction process [1]. The data set attributes consist of the nuclei measurements information. Nuclei measurements include radius, texture, perimeter etc. Also, the data set has the information whether the women breast does have the cancer or not by means of already calculated data which is indicated as benign or malignant. Benign are the non-cancerous tumor cell and that does not invade the neighboring cell, but malignant are the cancercausing tumor and it invades the neighbor cell after the manipulation and feature extraction of the data, it is further processed by machine learning algorithms under neural network. The dataset will be split into dependent and independent data. Dependent data are the data expect id and diagnosis, where the independent are diagnosis data [2]. The split data is 75% of training data and 25 % is testing data. Then scale the data (feature scaling) to bring the training data to a particular range of 0 to 1. The logistic regression is a part of machine learning algorithm which is a subset of random forest classifier, confusion matrix, SVM classifier, KNN classifiers are made used for both training and testing the data set. With the result the accuracy will be calculated [3]. The highest accuracy method will be chosen and the result data and original data will be compare to find the accurate result that whether the women does have the cancer or not. © 2024 IEEE.

关键词： Lung cancer

来源：评论

学校读者我要写书评

暂无评论

KJSIT learning Feedback System Development and Implementation 6

KJSIT Learning Feedback System Development and Implementatio...

引用

6th IEEE international conference on advances in Science and Technology, ICAST 2023

作者： Gala, Rushabh Toradmalle, Dhanashree Loladia, Ayesha Machagar, Akshata Mhamane, Apurva K J Somaiya Institute of Technology Computer Engineering Mumbai India

ISBN: (纸本)9798350359817

The 'KJSIT learning Feedback System' is a user-centric platform developed at the K. J. Somaiya Institute of Information Technology (KJSIT) to enhance the feedback and review system among students, faculty, and administrators. It has a user-friendly interface for registration, login, and feedback submission, and it also ensures data security and privacy. Empowering students to share insights and concerns about their education and allowing educators to make data-driven improvements, the system aligns with contemporary educational needs and trends. The methodology prioritizes user-centric environment, agile development, data-driven decision-making reports, and professional development integration. According to us, the system's future scope includes AI and machine learning integration, and enhanced user dashboards to further enhance learning system, and data visualization methods, emphasizing continuous improvement and innovation in the quality of education within KJSIT. © 2023 IEEE.

关键词： Students

来源：评论

学校读者我要写书评

暂无评论

Transforming Diabetes Care with Predictive machine learning Techniques 4

Transforming Diabetes Care with Predictive Machine Learning ...

引用

4th international conference on Power, Energy, Control and Transmission systems, ICPECTS 2024

作者： Mariammal, G. Jasmine, R. Megiba Siva Rama Lingham, N. Poongothai, E. Prema, S. Sai Charan, S. Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Chennai India Ponjesly College of Engineering Nagercoil India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Chennai India Srm Institute of Science and Technology Department of Computational Intelligence Chennai India

ISBN: (纸本)9798331508845

Diabetes mellitus is a chronic metabolic disorder characterized by elevated blood sugar levels, affecting millions of individuals worldwide. Early detection and management of diabetes are crucial in preventing complications and improving patient outcomes. In recent years, machine learning techniques have shown promise in predicting diabetes risk based on various factors such as demographic information, medical history, and lifestyle habits. Verticaling through a not-too-well designed case study - This paper is an extensive examination and comparison of different ML techniques for diabetes prediction. It goes through the methodologies, datasets, features, performance metrics and challenges of current models that are based on machine learning. In addition, understand strengths and weaknesses of all the algorithms- SVM, Decision Trees, Random Forests, Logistic regression etc. Here the logistic regression algorithm is used to develop a predictive model for diabetes based on demographic and clinical features. After preprocessing the data, including handling missing values and scaling numerical features, trained a logistic regression model using a portion of the dataset. The model achieved a high accuracy of 90% on the testing set, indicating its effectiveness in distinguishing be- tween diabetic and non-diabetic individuals. © 2024 IEEE.

关键词： Logistic regression

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 << < 491 492 493 494 495 496 497 498 499 500 > >>

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：