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

64,565 篇 会议
597 册 图书
326 篇 期刊文献

馆藏范围

65,487 篇 电子文献
1 种 纸本馆藏

日期分布

学科分类号

38,377 篇 工学
- 35,097 篇 计算机科学与技术...
- 23,067 篇 软件工程
- 8,726 篇 信息与通信工程
- 5,271 篇 电气工程
- 4,761 篇 生物工程
- 4,757 篇 控制科学与工程
- 3,813 篇 生物医学工程（可授...
- 1,632 篇 网络空间安全
- 1,578 篇 安全科学与工程
- 1,451 篇 光学工程
- 1,326 篇 机械工程
- 1,256 篇 动力工程及工程热...
- 1,242 篇 电子科学与技术（可...
- 1,192 篇 仪器科学与技术
- 1,187 篇 化学工程与技术
14,082 篇 理学
- 7,955 篇 数学
- 5,257 篇 生物学
- 3,617 篇 统计学（可授理学、...
- 3,113 篇 系统科学
- 2,334 篇 物理学
- 1,213 篇 化学
8,772 篇 管理学
- 5,070 篇 管理科学与工程(可...
- 4,398 篇 图书情报与档案管...
- 2,233 篇 工商管理
4,424 篇 医学
- 3,697 篇 临床医学
- 3,227 篇 基础医学(可授医学...
- 2,071 篇 药学(可授医学、理...
- 1,730 篇 公共卫生与预防医...
1,854 篇 法学
- 1,604 篇 社会学
1,181 篇 农学
- 1,154 篇 作物学
991 篇 经济学
733 篇 教育学
338 篇 文学
45 篇 艺术学
39 篇 军事学
5 篇 哲学
5 篇 历史学

主题

17,119 篇 machine learning
5,347 篇 machine learning...
4,566 篇 deep learning
4,067 篇 accuracy
3,975 篇 support vector m...
3,446 篇 computational mo...
3,311 篇 training
3,214 篇 predictive model...
2,928 篇 feature extracti...
2,026 篇 data models
1,949 篇 cloud computing
1,764 篇 real-time system...
1,713 篇 neural networks
1,600 篇 adversarial mach...
1,551 篇 prediction algor...
1,311 篇 artificial intel...
1,302 篇 data mining
1,282 篇 decision trees
1,198 篇 random forests
1,150 篇 internet of thin...

机构

203 篇 chitkara univers...
134 篇 school of comput...
80 篇 karnataka bangal...
78 篇 school of comput...
66 篇 department of co...
48 篇 chennai
48 篇 lovely professio...
44 篇 univ chinese aca...
44 篇 centre of interd...
42 篇 school of comput...
40 篇 graphic era deem...
37 篇 graphic era deem...
35 篇 shenyang institu...
31 篇 department of co...
30 篇 department of in...
30 篇 faculty of compu...
29 篇 department of co...
29 篇 shanghai jiao to...
29 篇 vishwakarma inst...
28 篇 computer science...

作者

36 篇 anjali t.
28 篇 de-shuang huang
26 篇 isman kurniawan
24 篇 rakesh kumar
23 篇 kumar anil
22 篇 hui wang
21 篇 amandeep nagpal
21 篇 kumar ajay
21 篇 gill kanwarparta...
20 篇 qin zengchang
20 篇 sharif mhd saeed
19 篇 yu wang
19 篇 ajay kumar
18 篇 qomariyah nunung...
18 篇 pareek piyush ku...
17 篇 zohreh doborjeh
16 篇 schuurmans dale
16 篇 andersson karl
16 篇 kumar manish
15 篇 shiva mehta

语言

55,297 篇 英文
10,017 篇 其他
635 篇 中文
3 篇 法文
2 篇 葡萄牙文
2 篇 俄文
1 篇 西班牙文
1 篇 土耳其文

检索条件"任意字段=International Conference on Machine Learning and Computing"

共 65488 条记录，以下是4681-4690 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：

Heart Attack Prediction using machine learning Techniques 4

Heart Attack Prediction using Machine Learning Techniques

引用

4th IEEE international conference on Advance computing and Innovative Technologies in Engineering, ICACITE 2024

作者： Mall, Shubham Abes Engineering College Dept.: Electrical & Electronics Engineering Uttar Pradesh Ghaziabad India

ISBN: (纸本)9798350360165

Heart is vital organ in the circulatory system, which functions as a muscular pump responsible for circulating blood throughout the body and plays crucial role in sustaining life. Nowadays heart disease is increasing as per WHO, nearly 30 million people die every year. The increased incidence of heart attacks in younger people highlights the need for an efficient detection system that can recognize early warning signs and prevent future events. An easy-to-use and trustworthy predictive method for determining the risk of heart disease is required due to the impracticality of frequent and costly diagnostic procedures like ECGs for the general population. To meet this requirement, we suggest developing a program that uses basic indications like age, gender, cholesterol, chest pain type, blood pressure and pulse rate to forecast a person's risk of heart disease. By utilizing neural networks' established precision and dependability in machine learning methods, this suggested system seeks to offer a useful and approachable instrument for estimating the likelihood of heart-related illnesses. © 2024 IEEE.

关键词： Blood pressure

来源：评论

学校读者我要写书评

暂无评论

Simulation and Analysis of Heart Disease Predictor Using machine learning Techniques 2

Simulation and Analysis of Heart Disease Predictor Using Mac...

引用

2nd international conference on Distributed computing and Optimization Techniques, ICDCOT 2024

作者： Queyam, Abdullah Bin Dixit, Ashutosh Kumar, Indubhushan Gupta, Sandeep School of Automation Banasthali Vidyapith Rajasthan India Department of Electrical Engineering Uttarakhand Dehradun India Saharsa College of Engineering Department of Electrical Engineering Bihar Saharsa India

ISBN: (纸本)9798350382952

Heart disease is easier to treat and has a lower fatality rate when detected early. Doctor visits, electrocardiograms, cardiac MRIs, and several medical procedures, such as stress tests for people, are all traditional means of predicting heart disease. This manual detection often takes a long time and requires much professional input during patient visits. The heart transmits to the patient all minor symptoms that the patient can only make sense of based on knowledge throughout the usual course of an individual's conduct. In this paper, an evaluation of various machine learning algorithms, such as Logistic Regression, K-Nearest Neighbor, Random Forest Classifier, Decision Tree, and Naive Bayes, has been conducted to predict heart disease. Their performance is assessed based on accuracy, recall, precision, and F1-score metrics. The simulation results have been obtained using Python programming software. The simulation's results show that, out of all the classification algorithms taken into consideration, the random forest technique had the best accuracy, coming in at 88%. © 2024 IEEE.

关键词： Forecasting

来源：评论

学校读者我要写书评

暂无评论

Investigation of Hand Gesture Recognition and Script Generation Models 2

Investigation of Hand Gesture Recognition and Script Generat...

引用

2nd international conference on Inventive computing and Informatics (ICICI)

作者： Udhayakumar, M. Issac, Titus Terance, J. Sebastian Karunya Inst Technol & Sci Comp Sci & Engn Coimbatore Tamil Nadu India Karunya Inst Technol & Sci Artificial Intelligence & Machine Learning Coimbatore Tamil Nadu India

ISBN: (纸本)9798350373301;9798350373295

The communication between differently challenged persons especially deaf and mute persons to a common man is challenging. The research work proposes to investigate various deep learning models to enhance communication between the deaf and persons who may not know sign language. The work uses computer vision technology for real-time hand gesture identification and natural language processing to produce coherent and understandable words This article investigates the feasibility of applying deep learning techniques like convolutional neural networks, xgboost and random forest to recognize hand gestures in real-time. The models are trained on a dataset of hand gestures and are investigated to identify hand gestures in real-time. The model can recognize a wide range of hand motions, from simple ones like pointing and waving to complex ones involving several fingers. The system creates sentences that are consistent with the hand gesture's intended meaning once it has been identified. Based on established rules and grammar suggestions, the model tries to generate appropriate sentences while taking the context of the conversation into account. The proposed work would be highly beneficial to researchers and target audiences who have trouble expressing themselves in a quick, efficient way to communicate.

关键词： Real-time hand gesture recognition Sentence generation Deaf communication Non-verbal communication Deep learning Convolutional neural networks Recurrent neural networks Natural language processing

来源：评论

学校读者我要写书评

暂无评论

Examine the Incorporation of Integration of Imaging Data for Orthodontic Prediction 6

Examine the Incorporation of Integration of Imaging Data for...

引用

6th IEEE international conference on Emerging Smart computing and Informatics, ESCI 2024

作者： Ramyaalakshmi, A. Poonguzhali, S. Vels Institute of Science Technology and Advanced Studies India

ISBN: (纸本)9798350306613

This study examines the crucial but final decision about tooth extraction during orthodontic therapy. The oral health and general wellbeing of individuals are significantly impacted by this choice. In the past, orthodontists' knowledge and clinical assessments were used to make these decisions. However, the development of machine learning (ML) and artificial intelligence (AI) offers a chance to improve the accuracy and predictability of these choices. With the goal of revolutionising orthodontic practise, this study will create and evaluate an AI-based orthodontist prediction model. An informed and accurate approach is required due to the significant effects that tooth extraction decisions have on orthodontic treatment. By developing an AI-based orthodontist prediction model, our research initiative aims to close this gap. This model can be used as a guide for people interested in pursuing careers in orthodontics, in addition to helping the current orthodontic practise by offering insightful information on tooth extraction choices. There usher in a new era of oral healthcare by embracing technology breakthroughs in orthodontics, which will result in better patient results and well-informed decision-making. © 2024 IEEE.

关键词： Artificial Neural Networks Decision Tree K-Nearest Neighbour machine learning and Orthodontic

来源：评论

学校读者我要写书评

暂无评论

An Analysis of the Performance of Lightweight Gradient Boosting and Random Forest Methods for Producing Effective Nutritional Diet Recommendations 15

An Analysis of the Performance of Lightweight Gradient Boost...

引用

15th international conference on computing Communication and Networking Technologies, ICCCNT 2024

作者： Kumar, D. Mahesh Gnanajeyaraman, R. Dept of Applied Machine Learning Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India

ISBN: (纸本)9798350370249

The aim of the research is to recommend Efficient Nutrition and diet to patients using Light weighted gradient Boosting compared over Ensemble Random Forest to improve accuracy. In order to provide patients with food and nutrition recommendations, models were created that make use of a simple gradient boosting technique and Ensemble Random Forest. The suggested system is implemented with the help of a python programming tool. By using the Gpower 3.1 software (g power setting parameters alpha:0.05 and G power =0.8). Light Weighted gradient boosting is used for Group 1 and Naive Bayes with 10 sample sizes is used for Group 2. The outcome of the patients is monitoring the health conditions. The recommended light weighted gradient boosting algorithm classifier accuracy level is confirmed with 92.45%. Gradient Boosting Algorithm and Random Forest classifier’s processing time is also calculated. The recommended light weighted gradient boosting algorithm classifier accuracy level is confirmed with 92.45% with a significance of 0.001 (p ©2024 IEEE.

关键词： Diet Food Light Weighted Gradient Boosting machine learning Nutrition Random Forest Vitamins

来源：评论

学校读者我要写书评

暂无评论

Fire-Flyer AI-HPC: A Cost-Effective Software-Hardware Co-Design for Deep learning 24

Fire-Flyer AI-HPC: A Cost-Effective Software-Hardware Co-Des...

引用

2024 international conference for High Performance computing, Networking, Storage and Analysis

作者： An, Wei Bi, Xiao Chen, Guanting Chen, Shanhuang Deng, Chengqi Ding, Honghui Dong, Kai Du, Qiushi Gao, Wenjun Guan, Kang Guo, Jianzhong Guo, Yonggiang Fu, Zhe He, Ying Huang, Panpan Li, Jiashi Liang, Wenfeng Liu, Xiaodong Liu, Xin Liu, Yiyuan Liu, Yuxuan Lu, Shanghao Lu, Xuan Nie, Xiaotao Pei, Tian Qiu, Junjie Qu, Hui Ren, Zehui Sha, Zhangli Su, Xuecheng Sun, Xiaowen Tan, Yixuan Tang, Minghui Wang, Shiyu Wang, Yaohui Wang, Yongji Xie, Ziwei Xiong, Yiliang Xu, Yanhong Ye, Shengfeng Yu, Shuiping Zha, Yukun Zhang, Liyue Zhang, Haowei Zhang, Mingchuan Zhang, Wentao Zhang, Yichao Zhao, Chenggang Zhao, Yao Zhou, Shangyan Zhou, Shunfeng Zou, Yuheng DeepSeek AI Beijing Peoples R China

ISBN: (数字)9798350352917

ISBN: (纸本)9798350352924;9798350352917

The rapid progress in Deep learning (DL) and Large Language Models (LLMs) has exponentially increased demands of computational power and bandwidth. This, combined with the high costs of faster computing chips and interconnects, has significantly inflated High Performance computing (HPC) construction costs. To address these challenges, we introduce the Fire-Flyer AI-HPC architecture, a synergistic hardware-software co-design framework and its best practices. For DL training, we deployed the Fire-Flyer 2 with 10,000 PCIe A100 GPUs, achieved performance approximating the DGX-A100 while reducing costs by half and energy consumption by 40%. We specifically engineered HFReduce to accelerate allreduce communication and implemented numerous measures to keep our Computation-Storage Integrated Network congestion-free. Through our software stack, including HaiScale, 3FS, and HAI-Platform, we achieved substantial scalability by overlapping computation and communication. Our system-oriented experience from DL training provides valuable insights to drive future advancements in AI-HPC.

关键词： High Performance computing Cost-Effective All-Reduce Best Practices Deep learning machine learning Large Language Models Artificial Intelligence Infrastructure

来源：评论

学校读者我要写书评

暂无评论

Performance Evaluation of Solar Photovoltaic Generation Forecasting using machine learning Algorithms 3

Performance Evaluation of Solar Photovoltaic Generation Fore...

引用

3rd international conference on Power, Control and computing Technologies, ICPC2T 2024

作者： Abdul Salam, Shereen Siddhara Petra, M.I. Azad, Abul K. Sulthan, Sheik Mohammed Rai, Veena Universiti Brunei Darussalam Faculty of Integrated Technologies Bandar Seri Begawan Brunei Universiti Teknologi Brunei Faculty of Engineering Bandar Seri Begawan Brunei

ISBN: (纸本)9798350349207

In recent years, there has been a rapid growth in the utilization of electricity generated from renewable energy sources. Solar energy stands out as a promising solution to the need for a sustainable and easily accessible power source. The integration of intermittent solar PV (photovoltaic) systems into the electricity grid can have a substantial impact on its stability. The implementation of dependable and accurate power forecasting is of utmost importance to enhance grid stability, optimize energy management, and maximize the economic advantages of photovoltaic (PV) power generation. To study the prediction capabilities, three distinct machine learning models, namely Support Vector machine (SVM), Random Forest (RF), and eXtreme Gradient Boost (XGBoost), were selected and subjected to refining and testing. The chosen models are briefly described. The photovoltaic (PV) power is computed by utilizing weather data for a span of one year, and subsequent generation forecasting is conducted based on the computed PV power. The evaluation of the performance of the chosen tools is conducted by assessing metrics such as Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), Normalized Root Mean Squared Error (NRMSE), and R-squared (R2) values. The findings of this study not only contribute to the improvement of the integration of solar energy into the power grid, but also offer useful insights into the selection of machine learning techniques for accurate and efficient forecasting of solar photovoltaic (PV) generation. © 2024 IEEE.

关键词： Support vector machines

来源：评论

学校读者我要写书评

暂无评论

Smart Aquaculture: machine learning-Based Water Quality Prediction in Kadammakudy's Pokkali Fields, Ernakulam

Smart Aquaculture: Machine Learning-Based Water Quality Pred...

引用

2024 international conference on Advancement in Renewable Energy and Intelligent Systems, AREIS 2024

作者： Kp, Shibu Perumal Sankar, S. Toc H Institute of Science & Technology Dept. of Ece Arakkunnam Kerala Emakulam India

ISBN: (纸本)9798350387230

Climate change and pollution pose profound threats to aquatic ecosystems, imperiling the long-term viability of aquaculture. To mitigate these impacts, we developed a novel Real-Time Prediction Model integrating Edge computing and Explainable Artificial Intelligence (XAI) specifically designed for farmer accessibility in Kadamakkudy's Pokkalifields, Ernakulam. Leveraging an ensemble of predictive algorithms, our model accurately forecasts critical water quality parameters. Despite limited daily measurements, timely interventions are enabled, safeguarding both ecological balance and agricultural profitability. By ensuring interpretability and openness, XAI enables farmers to make data-driven, well-informed decisions that balance environmental and economic sustainability. © 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

Android Malware Detection System using machine learning 2024

Android Malware Detection System using Machine Learning

引用

16th international conference on Contemporary computing, IC3 2024

作者： Kaur, Amanpreet Lal, Sangeeta Goel, Shruti Pandey, Mrinal Agarwal, Astha Department of Computer Science and Engineering & IT Jaypee Institute of Information Technology India School of computing and mathematics Keele University Newcastle-under-Lyme United Kingdom

ISBN: (纸本)9798400709722

Detecting Android malware is imperative for safeguarding user privacy, securing data, and preserving device performance. Consequently, numerous studies have underscored the complexities associated with Android malware detection, prompting a multidimensional approach to tackle these challenges effectively. This research leverages machine learning techniques, emphasizing feature extraction, classification algorithms, and both supervised and unsupervised learning methodologies. The exploration begins with in-depth Exploratory Data Analysis (EDA) to gain insights into the dataset, paving the way for informed decision-making. Principal Component Analysis (PCA) is employed for dimensionality reduction, a pivotal step in handling the multivariate nature of the data. The integration of API calls, clustering, and anomaly detection further enriches the model's capability to discern between benign and malicious applications. Crucially, the study delves into the intricacies of sampling, evaluation, and the Confusion Matrix to quantify the model's performance accurately. The utilization of diverse classification algorithms, including Support Vector machines (SVM), Multi-Layer Perceptrons (MLP), Random Forest, GaussianNB, Decision Tree, and Logistic Regression, underscores the comprehensive nature of the approach. These algorithms collectively contribute to a robust and versatile Android malware detection model capable of adapting to varying threat scenarios. The dataset employed for training and evaluation is sourced from Kaggle, encompassing 29,999 Android applications categorized as benign or malicious based on permissions sought. Current detection methods, deemed resource-intensive and exhaustive, face the challenge of keeping pace with the relentless evolution of new malware strains. This research seeks to address this gap by proposing a sophisticated, machine learning-driven model that not only enhances accuracy but also demonstrates efficiency and adaptability in the face

关键词： Android malware

来源：评论

学校读者我要写书评

暂无评论

Detecting DDoS attacks in Software Define Networking: A machine learning Based Approach

Detecting DDoS attacks in Software Define Networking: A Mach...

引用

2023 international conference on Next-Generation computing, IoT and machine learning, NCIM 2023

作者： Bala, Shimul Ahsan, Sk. Md. Masudul Khulna University of Engineering & Technology Department of CSE Khulna Bangladesh

ISBN: (纸本)9798350316001

Software-Defined Networking (SDN) is a type of network strategy that enables the separation of a network's control plane and data plane, thereby allowing centralized management and control of network traffic. Although SDN has many remarkable advancements, it encounters several security concerns like DDoS attack. The Distributed Denial-of-Service (DDoS) attack is a fast-growing threat that endangers the future of internet technology. In this paper, some machine learning classifiers namely support vector machine (SVM) using linear based kernel and radial basis function (RBF) based kernel, random forest (RF), decision tree (DT), logistic regression (LR), and k-nearest neighbors (KNN) are evaluated to identify and analyze DDoS assaults within an SDN environment. A comparative performance study according to the results from the experiments is described to determine the best suitable techniques for DDoS detection. The results show that some classifiers reached up to 100% accuracy and the performance is validated using ROC-AUC curve. By utilizing proper classifiers derived from machine learning and taking the benefits of SDN, the controller can be safeguarded against DDoS attacks. © 2023 IEEE.

关键词： Denial-of-service attack

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 << < 465 466 467 468 469 470 471 472 473 474 > >>

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：