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

775 篇 会议
10 册 图书
2 篇 期刊文献

馆藏范围

787 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

522 篇 工学
- 503 篇 计算机科学与技术...
- 331 篇 软件工程
- 149 篇 信息与通信工程
- 114 篇 控制科学与工程
- 93 篇 生物工程
- 89 篇 网络空间安全
- 85 篇 石油与天然气工程
- 52 篇 生物医学工程（可授...
- 41 篇 电气工程
- 25 篇 光学工程
- 23 篇 农业工程
- 18 篇 化学工程与技术
- 16 篇 机械工程
- 16 篇 安全科学与工程
293 篇 理学
- 203 篇 数学
- 101 篇 生物学
- 63 篇 统计学（可授理学、...
- 48 篇 系统科学
- 38 篇 物理学
- 17 篇 化学
182 篇 管理学
- 133 篇 管理科学与工程(可...
- 56 篇 图书情报与档案管...
- 33 篇 工商管理
55 篇 医学
- 55 篇 基础医学(可授医学...
- 55 篇 临床医学
- 39 篇 药学(可授医学、理...
- 20 篇 公共卫生与预防医...
26 篇 法学
- 20 篇 社会学
25 篇 农学
- 25 篇 作物学
15 篇 经济学
- 15 篇 应用经济学
8 篇 教育学

主题

145 篇 machine learning
69 篇 deep learning
38 篇 machine learning...
36 篇 support vector m...
30 篇 adversarial mach...
23 篇 computational mo...
20 篇 training
19 篇 forecasting
16 篇 predictive model...
16 篇 prediction algor...
13 篇 internet of thin...
13 篇 sentiment analys...
13 篇 artificial intel...
12 篇 object detection
12 篇 convolutional ne...
11 篇 neural networks
11 篇 feature extracti...
10 篇 face recognition
10 篇 decision trees
9 篇 random forest

机构

17 篇 chitkara univers...
6 篇 pimpri chinchwad...
6 篇 univ johannesbur...
5 篇 pimpri chinchwad...
5 篇 faculty of infor...
5 篇 key lab complex ...
5 篇 baoji univ arts ...
4 篇 dept. of compute...
4 篇 department of co...
4 篇 qiannan normal u...
4 篇 department of co...
4 篇 univ witwatersra...
4 篇 department of co...
4 篇 karpagam academy...
4 篇 computer science...
3 篇 university of sa...
3 篇 amrita school of...
3 篇 department of cs...
3 篇 department of el...
3 篇 easwari engineer...

作者

7 篇 reza ahmed wasif
5 篇 s abhishek
5 篇 goyal s.b.
5 篇 jennifer sanjeda...
4 篇 anjali t.
4 篇 abhishek s.
4 篇 van zyl terence ...
3 篇 andersson karl
3 篇 chohan jasgurpre...
3 篇 swain debabrata
3 篇 singh balbir
3 篇 sha akhbar
3 篇 pawar rajendra
3 篇 shamim mahbub ha...
3 篇 shanmugasundar g...
3 篇 kukreja vinay
3 篇 tawfik hissam
3 篇 akhbar sha
3 篇 zhou jincheng
3 篇 kalita kanak

语言

759 篇 英文
28 篇 其他

检索条件"任意字段=7th International Conference on Machine Learning and Soft Computing, ICMLSC 2023"

共 787 条记录，以下是111-120 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：

Human Brain Stroke Prediction using machine learning Methods with Synthetic Minority Oversampling Approach 7

Human Brain Stroke Prediction using Machine Learning Methods...

引用

7th international Women in Data Science conference at Prince Sultan University (WiDS-PSU)

作者： Mujahid, Muhammad Ayesha, Noor Azar, Ahmad Taher Saba, Tanzila Haider, Zeeshan Prince Sultan Univ Artificial Intelligence & Data Analyt AIDA Lab CCIS Riyadh Saudi Arabia Zhengzhou Univ Sch Clin Med Zhengzhou 450001 Henan Peoples R China Prince Sultan Univ Coll Comp & Informat Sci Riyadh Saudi Arabia Prince Sultan Univ Automated Syst & Soft Comp Lab ASSCL Riyadh 11586 Saudi Arabia Benha Univ Fac Comp & Artificial Intelligence Banha Egypt

ISBN: (纸本)9798350395839;9798350395846

the brain is considered to be the principal anatomical component of the human body. A stroke is a medical condition defined by cerebral injury that occurs as a consequence of blood vessel rupture within the brain. Symptoms might become apparent when there is an interruption in the brain circulation of blood and other vital chemicals. Stroke is widely acknowledged as a significant medical disorder because to its elevated mortality rates, enduring cognitive impairments, and concomitant comorbidities. the timely recognition of the numerous prodromal manifestations linked to a stroke has the capacity to attenuate its magnitude. the primary objective of this study is to utilize machine learning methodologies in order to predict the likelihood of an early-stage occurrence of a cerebral stroke. In order to assess the efficacy of the algorithm, a dataset for stroke prediction was acquired via the Kaggle platform. Various preprocessing techniques are applied to normalize and clean the dataset for stroke prediction. this research utilized many classification models including Random Forest, Ada Boost, LightGBM,, Decision Tree (DT), Logistic Regression (LR),and Support vector machine (SVM) to perform classification tasks. the Random Forest classifier has a higher level of classification accuracy in comparison to alternative machine learning classifiers, obtaining an impressive accuracy rate of 99.45%. the results of the investigation revealed that machine learning approaches demonstrated a higher level of performance in comparison to alternative methods.

关键词： Brain stroke Prediction Random Forest Stroke data SMOTE Health Risks

来源：评论

学校读者我要写书评

暂无评论

Research on Online Consumer Demand Ranking and Content Prediction Based on Kano Model 24

Research on Online Consumer Demand Ranking and Content Predi...

引用

8th international conference on machine learning and soft computing, icmlsc 2024

作者： Zhao, Xiaogang Liu, Yuerui Shen, Hai Dang, Yiwei Hou, Jun Pu, Zitong Comparison of Chinese and Foreign Enterprises Strategy Institute Xi'an International Studies University China Business School Xi'an International Studies University China

ISBN: (纸本)9798400716546

Aiming at the shortcomings of high cost, low efficiency and coarse granularity of traditional consumer demand sequencing methods in practical applications, this paper proposes an online consumer demand ranking and content prediction method based on Kano model. First, this method combines SnowNLP sentiment analysis and consumer attention to classify the demand attributes. then, the probability of topic words under different demands is obtained by LDA topic model. Finally, the ARIMA model is introduced to predict the probability of topic words and sentiment values under different demands in the future, which can accurately and efficiently recognize the rapidly changing consumer demands. Example calculations show that this method can classify and sort consumer demands in a more fine-grained way, which provides a reference for consumer demand prediction research of enterprises and solves the problem of priority level of enterprise marketing resource allocation. © 2024 ACM.

关键词： Sentiment analysis

来源：评论

学校读者我要写书评

暂无评论

A Comparative Study of Text Classification using Selective machine learning Algorithms 7

A Comparative Study of Text Classification using Selective M...

引用

7th international conference on Intelligent computing and Control Systems, ICICCS 2023

作者： Philip, Jeethu Veerasekharreddy, B. Harshini, M. Haritha, I.V.S.L. Patil, Shruti Shareef, Sk.Khaja MLR Institute of Technology Department of Information Technology Hyderabad India

ISBN: (纸本)9798350397253

the process of categorizing documents into specific groups or categories is known as text classification (TC). this study focuses on implementing the Support Vector machine (SVM) model for analyzing English text documents. Two separate experiments were conducted to test the efficacy of selected classifiers. the experimental results, based on a set of 1033 text documents, demonstrated that the SVM classifier achieved the best performance when the feature set size was small, while NB outperformed the other classifiers. Our findings showed that a classification rate of over 90% could be achieved by using 4000 more features. © 2023 IEEE.

关键词： Support vector machines

来源：评论

学校读者我要写书评

暂无评论

machine learning Based CH Selection for Energy Efficient Routing in WSN 7

Machine Learning Based CH Selection for Energy Efficient Rou...

引用

2023 7th international conference On computing, Communication, Control And Automation, ICCUBEA 2023

作者： Madkar, Shankar Pardeshi, Sanjay Kumbhar, M.S. Shivaji University Maharashtra Kolhapur India Government Women's Polytechnic Maharashtra Tasgaon India Rajarambapu Institute of Technology Dept. of Etc Sangli Maharashtra Rajaramnagar India

ISBN: (纸本)9798350304268

In wireless sensor networks (WSNs), energy efficiency is a crucial consideration due to the limited battery power of sensor nodes. this paper proposes a machine learning-based energy-efficient routing protocol (ML-EERP) that takes into account traffic awareness. the protocol incorporates initial clustering using the K-medoid algorithm and utilizes machine learning techniques for cluster head selection based on factors such as distance, traffic, and energy levels. the protocol's experimental analysis focuses on evaluating the effectiveness of selecting the most suitable candidate nodes as cluster heads and assessing their impact on energy efficiency. Comparative analysis is performed by comparing the results of the proposed machine learning-based protocol with the traditional LEACH protocol. the performance evaluation of the proposed protocol includes metrics such as network lifetime, throughput, packet delivery ratio, and energy consumption analysis. the experimental results demonstrate significant improvements in energy efficiency compared to the LEACH protocol. this highlights the effectiveness of using machine learning techniques in the selection of cluster heads, leading to enhanced energy efficiency and improved network performance. By employing a machine learning-based approach, the proposed protocol offers a promising solution for achieving energy efficiency in WSNs. It leverages the power of machine learning algorithms to adaptively select cluster heads based on various parameters, resulting in improved energy utilization and extended network lifespan. © 2023 IEEE.

关键词： cross-lingual Deep learning (DL) hi-lingual mono-lingual Natural Language Processing (NLP) Sentiment Analysis (SA)

来源：评论

学校读者我要写书评

暂无评论

Multiple Disease Prediction Using machine learning Algorithm 7

Multiple Disease Prediction Using Machine Learning Algorithm

引用

2023 7th international conference On computing, Communication, Control And Automation, ICCUBEA 2023

作者： Chaudhari, Sahil Deo, Prathamesh Deshmukh, Purvaja Deshpande, Aman Shelke, Priya Chitre, Abhijit Viit Department of Information Technology Pune India Viit Department of E&tc Pune India

ISBN: (纸本)9798350304268

Using machine learning techniques like Logistic Regression, the Support Vector machine i.e. (SVM) classifiers, the Random Forest classifiers i.e. (RFC), the Decision Tree classifiers i.e. (DTC), and K-Nearest Neighbor (KNN), as well as basic metrics like heart rate, blood pressure, cholesterol, and pulse rate, the goal of this project is to forecast the occurrence of various diseases like diabetes, heart disease, and Parkinson's disease. the most accurate calculation is used to train the dataset, while Python pickling and streamlit are used to record the model behavior. By entering pertinent disease-related information, the initiative seeks to determine the risk factors for the diseases and provide users a prognosis of whether they have the condition or not. this program can assist people in keeping an eye on their health and taking the necessary actions to prolong © 2023 IEEE.

关键词： andSVM decision tree diabetes heart disease KNN logistic regression Parkinson's pickle random forest streamlit

来源：评论

学校读者我要写书评

暂无评论

Unleashing the Potential of Convolutional Neural Networks for Automated Depression Detection Using Audio Modality 7

Unleashing the Potential of Convolutional Neural Networks fo...

引用

2023 7th international conference On computing, Communication, Control And Automation, ICCUBEA 2023

作者： Srinivasan, Jothiga Vishnu, Anala Jeyendra Sri Greeshma, M. Pragna, Nerella Venkata Polavarapu, Rithvik School of Electronics Engineering Vellore Institute of Technology Vellore India

ISBN: (纸本)9798350304268

the conventional method for diagnosing depression involves conducting clinical interviews, wherein mental health professionals scrutinize patients' responses to evaluate their psychological state. In the realm of healthcare, tasks that rely on speech often necessitate the derivation of acoustic features that are manually engineered, such as pitch, prosody, loudness, speech rate, and energy. these features are then integrated with machine learning algorithms. Recent developments in deep learning have demonstrated significant achievements in automated learning for signal processing duties, such as the analysis of images, videos, and audio. the present study introduces a new methodology that utilizes Convolutional Neural Networks (CNNs) to identify depression through the analysis of audio signals. By utilizing CNN-based models, we aim to uncover hidden patterns and signals in the audio data for enhanced depression detection accuracy and reliability. © 2023 IEEE.

关键词： audio modality automatic learning convolutional neural networks depression diagnosis mental health assessment signal processing speech processing

来源：评论

学校读者我要写书评

暂无评论

Proceedings - 7th international conference on computing Methodologies and Communication, ICCMC 2023

Proceedings - 7th International Conference on Computing Meth...

引用

7th international conference on computing Methodologies and Communication, ICCMC 2023

ISBN: (纸本)9781665464086

the proceedings contain 280 papers. the topics discussed include: a review on adoption of green cloud computing;modified firefly algorithm for optimal test case selection;prognosis of breast cancer cells using image processing;predicting the driver variants and mutations in lung cancer genome using transcriptional regulation network;a extensive survey on sign language recognition methods;enhanced image-based histopathology lung cancer detection;non-invasive detection of coronary artery disease symptoms using Cardiocare application;a review on various image-recognition types and their efficiency;performance analysis of machine learning algorithm for the credit risk analysis in the banking sector;a comparative study on job recommendation using classification algorithms;a real time system to analyze patient's health condition using second layer computing;and identification of voting patterns using clustering methodology.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Weapon Detection from Surveillance Footage in Real-Time Using Deep learning 7

Weapon Detection from Surveillance Footage in Real-Time Usin...

引用

2023 7th international conference On computing, Communication, Control And Automation, ICCUBEA 2023

作者： Jadhav, Vedantika Deshmukh, Rutuja Gupta, Palak Ghogale, Sharvi Bodireddy, Mahalakshmi Pimpri Chinchwad College of Engineering Computer Department Pune India

ISBN: (纸本)9798350304268

Weapon violence is a serious threat nowadays due to the rise in technology and criminal intelligence. Manual surveillance is a very tedious task because these activities are atypical in comparison with everyday activities. By introducing machine learning techniques to detect such activities, we can minimise the risk of human errors and prevent details from going unnoticed. Current systems fail when it comes to efficiency of the system and ease of use. We have implemented various models like CNN, YOLOV7, YOLOV8 and VGG for weapon detection and identified their strengths and limitations. A complete system for detection of weapons using these models and raising an alarm has been implemented here to overcome the problems of manual surveillance. Results are presented in a table focusing on metrics like precision and recall as these metrics prove to be more insightful and reliable as compared to accuracy for object detection. © 2023 IEEE.

关键词： AATpI CNN deep learning gun detection object detection YOLO

来源：评论

学校读者我要写书评

暂无评论

A Prediction and Classification Process for DDoS Attacks Using machine learning 7

A Prediction and Classification Process for DDoS Attacks Usi...

引用

2023 7th international conference On computing, Communication, Control And Automation, ICCUBEA 2023

作者： Salunke, Mangesh D. Rathod, Vijay U. Mali, Yogesh Kisan Tambe, Ravindra S. Dange, Anup A. Kothavle, Suhas Ramdas Marathwada Mitra Mandal's Institute of Technology Lohgaon Pune India G H Raisoni College of Engineering and Management Wagholi Pune India Dr.Vithalrao Vikhe Patil College of Engineering Ahmednagar India

ISBN: (纸本)9798350304268

the popularity of computer networks in our daily life has grown recently as a result of the extensive use of the Internet. Due to server flaws, hackers can access computers using not only well-known attack types but also newer, more advanced, and harder to spot ones. One of the maximum popular defenses is the Intrusion Detection System (IDS), which uses machine learning techniques to train on a pre-collected dataset to defend computers from them. In this paper, the authors examine the present practice of Distributed Denial of Service (DDoS) attacks. DDoS attacks make use of restrictions that are applicable to any arrangement asset, such as the website's framework for an authorized organization. However, it is crucial to use the most recent dataset in order to identify the most recent DDoS activities. the authors employed an old-fashioned KDD dataset. In this research, a machine learning technique was used to identify and predict the sorts of DDoS attacks. the K Nearest Neighbor, Random Forest, and Decision Tree are three machine-learning-based IDSs approaches that we propose in this paper. © 2023 IEEE.

关键词： Classification D-DoS Attacks KNN Prediction Random Forest

来源：评论

学校读者我要写书评

暂无评论

machine learning based Underwater Mine Detection 7

Machine Learning based Underwater Mine Detection

引用

7th international conference on Intelligent computing and Control Systems, ICICCS 2023

作者： Ram, M Sitha Navyatha, Palavalli Sai Ashitha, Ranga Laxmi Aparna Kumar, Senapathi Anand Jagadeesh Lakireddy Bali Reddy College of Engineering Department of Computer Science and Engineering Mylavaram India

ISBN: (纸本)9798350397253

Underwater mining of minerals and rocks is a highly challenging task before the discovery of SONAR (Sound Navigation and Ranging) system. Lately, the mine detection process was performed by the divers trained in the disposal of hazardous ordnance, marine mammals, video cameras mounted on mine-neutralization trucks, and laser systems. which leads to risk and loss to the marine life. SONAR system is capable of capturing Scan-side sonar images, but the model's accuracy is a concern. So Naval defense system need to use a much more accurate system as mines can be easily mistaken as rock, to obtain accurate results we will be working on the dataset of frequencies. Recently, this prediction system was constructed using many machine learning methodologies. this research study proposes to apply XGBoost algorithm to develop a prediction system to predict whether the object is rock or mine. Here, the accuracy of the proposed model is compared with the accuracy of the existing models. © 2023 IEEE.

关键词： Support vector machines

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79页 << < 8 9 10 11 12 13 14 15 16 17 > >>

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：