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

262 篇 会议
3 册 图书

馆藏范围

265 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

185 篇 工学
- 171 篇 计算机科学与技术...
- 148 篇 软件工程
- 46 篇 信息与通信工程
- 35 篇 生物工程
- 24 篇 生物医学工程（可授...
- 21 篇 控制科学与工程
- 18 篇 安全科学与工程
- 14 篇 交通运输工程
- 12 篇 网络空间安全
- 10 篇 电气工程
- 8 篇 机械工程
- 8 篇 电子科学与技术（可...
- 7 篇 农业工程
95 篇 理学
- 57 篇 数学
- 37 篇 生物学
- 29 篇 系统科学
- 27 篇 统计学（可授理学、...
- 12 篇 物理学
71 篇 管理学
- 45 篇 图书情报与档案管...
- 43 篇 管理科学与工程(可...
- 28 篇 工商管理
33 篇 医学
- 24 篇 基础医学(可授医学...
- 24 篇 临床医学
- 17 篇 药学(可授医学、理...
- 11 篇 公共卫生与预防医...
- 9 篇 特种医学
12 篇 经济学
- 12 篇 应用经济学
12 篇 法学
- 11 篇 社会学
9 篇 教育学
- 9 篇 教育学
9 篇 农学
- 9 篇 作物学

主题

44 篇 machine learning
14 篇 deep learning
9 篇 machine learning...
9 篇 forecasting
8 篇 support vector m...
8 篇 predictive model...
8 篇 artificial intel...
7 篇 big data
7 篇 decision trees
6 篇 sentiment analys...
5 篇 random forest
5 篇 predictive analy...
5 篇 data analytics
5 篇 data science
4 篇 internet of thin...
4 篇 visual analytics
4 篇 data mining
4 篇 adversarial mach...
4 篇 computational mo...
4 篇 time series anal...

机构

6 篇 national univers...
6 篇 the islamic univ...
6 篇 altoosi universi...
5 篇 college of mlt a...
5 篇 medical technica...
2 篇 doctoral school ...
2 篇 valdosta state u...
2 篇 waterford instit...
2 篇 karnataka bangal...
2 篇 college of scien...
2 篇 al-zahraa univer...
2 篇 hitachi asia ltd...
2 篇 wuhan business u...
2 篇 bengaluru
2 篇 vardhaman colleg...
2 篇 international sc...
2 篇 computer science...
2 篇 autonomous unive...
2 篇 st michaels hosp...
2 篇 national school ...

作者

2 篇 chen fan
2 篇 chattopadhyay su...
2 篇 cornely pierre-r...
2 篇 sumit
2 篇 kumar narendra
2 篇 kumar aman
2 篇 m. a. jabbar
2 篇 tohazen
2 篇 sanju tiwari
2 篇 fernando ortiz-r...
2 篇 khademi april
2 篇 wang jin
1 篇 banerjee anasua
1 篇 karl w. jenkins
1 篇 mahammed nadir
1 篇 yunxia feng
1 篇 harutyun s. hovh...
1 篇 chang yuan
1 篇 nguyen ngoc-nhu ...
1 篇 usmani m. ahmer

语言

259 篇 英文
6 篇 其他
6 篇 中文

检索条件"任意字段=6th International Conference on Applied Machine Learning and Data Analytics, AMLDA 2023"

共 265 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：

Cost Analysis and Optimization of Virtual machine Allocation in the Cloud data Center 6

Cost Analysis and Optimization of Virtual Machine Allocation...

引用

6th international conference on Inventive Computation Technologies, ICICT 2023

作者： De, Utpal Chandra Satapathy, Rabinarayan Patra, Sudhansu Shekhar School of Computer Applications Kalinga Institute of Industrial Technology Bhubaneswar India Faculty of Emerging Technologies Sri Sri University Cuttack India

ISBN: (纸本)9798350398496

IoT sensors and devices are being used in more and more applications every day. Cloud computing has been introduced in order to meet the storage and processing demands for time-sensitive applications on low-powered end devices that leverage IoT devices and sensors. Processing and analysis of IoT workloads are carried out by virtual machines (VMs) housed inside of actual computers. It is an NP-complete issue to allocate VMs inside a physical machine (PM). If a VM is unsatisfied with the provided service, the VM may decide to re-join the line as a feedback customer. Whenever the system is empty, the physical machine may take a working vacation rather than remaining in the system idle. Working vacations and vacation service times are both independent random variables with exponential distributions. An M/M/1 feedback queue in steady-state with balking, reneging, and recurrent working vacations (WV) have been applied in the VM allocation to the PMs. the matrix approach is used to determine the model's steady-state system length distributions. A population based meta-heuristic approach teaching learning based optimization (TLBO) technique is provided for cost optimization and performance features of the VM allocation. Numerical findings presented as tables are used to perform sensitivity analysis. © 2023 IEEE.

关键词： Sensitivity analysis

来源：评论

学校读者我要写书评

暂无评论

machine learning-based Depression Prediction using Social Media Feeds 6

Machine Learning-based Depression Prediction using Social Me...

引用

6th international conference on Inventive Computation Technologies, ICICT 2023

作者： Keerthiga, M. Abisha, D. Kalaiselvi, P. Shenbagalakshmi, S. Computer Science and Engineering National Engineering College Kovilpatti India

ISBN: (纸本)9798350398496

In today's environment, young people frequently use social media platforms to communicate emotions. they post about their feelings on social media, which can help us understand how they feel at the time. As a reaction to the critical need for early detection tools, this research study uses sentiment analysis techniques to examine user contributions to social networks to help detect potential depression at an early stage. the research describes different methods for predicting sadness from user posts. the dataset is vectorised using count vectoriser and TF-IDFvectorizer, and features like post sentiment is retrieved. In our project, the model is divided into training and test datasets and trained using the Naive Bayes, Support Vector machine, Decision Trees, Random Forest, and K-Nearest Neighbors machine learning techniques. the measures that are assessed are recall and accuracy. the Instagram API is applied to mine Instagram posts to create the dataset for the model. Each comment will undergo preprocessing;each word will be processed through a lexicon to determine if it is positive or negative. this research study presents a new feature vector for classifying the texts as positive or negative. Each comment generates a score value from the lexicon to signify the degree of positivity, negativity, and other factors. A CSV file containing around 6, 300 posts has been preprocessed. the distinctive characters and extraneous characters are eliminated using regular expressions. the data quality is then enhanced using stop words, Lemmatization, and tokenization. the best method for this approach yields an accuracy of 90.19% and a recall of 89.85% utilizing a decision tree model using a count vectorizer. © 2023 IEEE.

关键词： Sentiment analysis

来源：评论

学校读者我要写书评

暂无评论

Enhancing Forensic Analysis Using a machine learning-based Approach 6

Enhancing Forensic Analysis Using a Machine Learning-based A...

引用

6th international conference on Advanced Communication Technologies and Networking, CommNet 2023

作者： Benkerroum, Samira Chougdali, Khalid University Ibn Tofail Kenitra National School of Applied Sciences Morocco

ISBN: (纸本)9798350329391

In recent years, computers or digital devices contribute to the global spread of cyber threats and cyber crimes. these cyberattacks leave some artefacts on the storage of the target device, for this reason they require special treatment, and which will have to be the subject of various investigations in order to study its behavior and analyze and prevent it so that this never happen *** the continued development of digital forensic investigations for the recovery of evidence whether volatile or non-volatile, manual investigations are both time-intensive and laborious. the proposed solution is to use a method to automate manual forensic investigation tasks (forensic analysis) to reduce human effort and improve time *** paper presents a summary of the digital forensic investigation process, we discuss existing ML solutions to automate the analysis ***, the paper proposes an approach based on machine learning where the binary classification was performed using the algorithms K-Nearest Neighbors, Naive Bayes, Random Forest, Support Vector machine, Decision Tree, Logistic Regression, Gradient Boosted Tree, Multi-Layer Perceptron, using CIC-MalMem-2022 dataset to identify *** algorithms' respective performances were contrasted. the performance metrics Precision, F1-score, Accuracy, Recall, and Area Under the Curve were used to assess the outcomes. Consequently, the Random Forest and Gradient Boosted Tree algorithms demonstrated superior performance, achieving a remarkable accuracy level of 99.98% in the detection of malware through memory scans. the Logistic Regression algorithm exhibited the least favorable performance in analyzing malware using memory data, achieving an accuracy rate of 95.75%. According to the results obtained, many algorithms used have obtained very satisfactory results. © 2023 IEEE.

关键词： Decision trees

来源：评论

学校读者我要写书评

暂无评论

Digital Twinning in Precision Agriculture: Fabrication of a Close-Range Photogrammetry and Microclimate IoT-Enabled data Acquisition System 6

Digital Twinning in Precision Agriculture: Fabrication of a ...

引用

6th international conference on applied Computational Intelligence in Information Systems, ACIIS 2023

作者： Chavez Baliguat, Dennis Ivan Alagon, Francis Jann A. Aleluya, Earl Ryan M. Haim, Stephen H. Salaan, Carl John O. Mindanao State University Iligan Institute of Technology Department of Computer Engineering and Mechatronics Iligan City9200 Philippines Mindanao State University Iligan Institute of Technology Department of Electrical Engineering Iligan City9200 Philippines

ISBN: (纸本)9798350317428

Volatility in the agriculture sector is increasing due to the market and especially the climate conditions. In precision agriculture, resource optimization is key for cost reduction, emission control, and efficiency. However, this relies on large data volumes. With insufficient data, mathematical and machine learning models can fail. this issue is particularly pronounced in developing countries like the Philippines, where agricultural data infrastructure is often lacking. this deficiency can lead to poor decision-making, inefficient resource allocation, and negative impacts on agricultural production and economy. With IoT, massive amounts of data can be aggregated to create Systems of Digital Twins, particularly for Precision Agriculture. there are multiple designs and approaches for Digital Twinning in Precision Agriculture. this paper presents the fabrication of a photogrammetric crop pseudo-volume data acquisition system in a hydroponic setup. Moreover, a realtime microclimate and nutrient availability data gathering system that showed current values and historical data was created. Results showed that the low-cost close-range photogrammetry image capture system's execution, the microclimate gathering system's performance, and the dashboard's renditions were reliable. the photogrammetric pseudo-volumes were linearly related to the ground truth weight of the crops with an R2 of 0.87. Additionally, the Pearson correlation between the actual weight and pseudo-volume resulted in a value of 0.93 which signifies a strong positive correlation. therefore, the photogrammetric pseudo-volumes can be reliably and remotely used to predict the current fresh weight of the crops. To forward this research, more data from multiple cropping cycles is needed for robust machine learning applications. © 2023 IEEE.

关键词： Internet of things

来源：评论

学校读者我要写书评

暂无评论

Chimp Optimization Algorithm Based Feature Selection with machine learning for Medical data Classification 6

Chimp Optimization Algorithm Based Feature Selection with Ma...

引用

6th Iraqi international conference on Engineering Technology and its Applications, IICETA 2023

作者： Abosinnee, Ali S. Saeed Issa, Samer Alkhayyat, Ahmed Latef Naser, Zamen Jaffer, Zain Ramadan, Ghazi Mohamad Altoosi University College Najaf Iraq Al Rafidain University College Department of Computer Science Bagdad Iraq The Islamic University Najaf Iraq National University of Science and Technology Dhi Qar Iraq Al-Farahidi University Medical Technical College Baghdad Iraq Ahl Al Bayt University College of MLT Karbala Iraq

ISBN: (纸本)9798350303339

data mining is discovering interesting knowledge, namely associations, patterns, changes, significant structures, and anomalies, from enormous quantities of data stored in data warehouses, databases, or other data repositories. Association is the discovery of association correlations or relationships amongst a set of items. Associate rule mining is a method for identifying frequent patterns, correlations, associations, or causal structures in data sets found in numerous relational databases, transactional databases, and other data repositories. the algorithm performed is a fundamental process for mining association rules called a priori. In the healthcare field, association rules are useful as they offer the possibility to build important knowledge bases, conduct intelligent diagnoses, and extract invaluable information automatically and quickly. this study develops a machine learning based Association Rule Mining and Classification for Healthcare data Management System (MLARMC-HDMS). the presented MLARMC-HDMS technique performs both classification and ARM processes. Initially, the MLARMC-HDMS technique employs the chimp optimization algorithm-based feature selection (COAFS) technique for attribute selection. Next, stochastic gradient descent with a multilayer perceptron (SGD-MLP) model is applied for the classification process. Moreover, the Apriori algorithm is used to determine the relationship between the attributes. To illustrate the enhanced performance of the presented MLARMC-HDMS model, a detailed experimental validation is performed on a benchmark medical dataset. the experimental values indicated the enhancements of the MLARMC-HDMS model over other methods with maximum accuracy of 99.75%. © 2023 IEEE.

关键词： Stochastic systems

来源：评论

学校读者我要写书评

暂无评论

Deep learning for Pneumonia Diagnosis Using CXR Images 6

Deep Learning for Pneumonia Diagnosis Using CXR Images

引用

6th international conference of Women in data Science at Prince Sultan University, WiDS-PSU 2023

作者： Ijaz, Ayesha Akbar, Shahzad Alghofaily, Bayan Hassan, Syed Ale Saba, Tanzila Riphah International University Department of Computing Pakistan Prince Sultan University AIDA Lab CCIS Riyadh Saudi Arabia

ISBN: (纸本)9781665477239

Pneumonia is a life-threatening infection that affects the lungs caused by bacterial or virus attacks. It fills the air sacs of the lungs with pus or fluid. the factors that cause pneumonia are cystic fibrosis, asthma, sickle cell disease, diabetes, heart failure, and a weak immune system. Moreover, pneumonia is usually diagnosed by an expert radiologist by examining CXR images, however, it is a difficult and time-consuming task when images are unclear. In addition, after recent advancements in image processing, automatic detection of pneumonia is now achievable;nevertheless, existing research still faces overfitting and efficiency issues. this research proposed the Convolutional Neural Network (CNN) based approach for classifying normal and pneumonia-affected images. A publicly available dataset named Chest X-ray images comprised of 5,856 CXR images, has been utilized for training and testing the methodology. the proposed model consists of four stages: resizing, pre-processing, data augmentation, and classification of the images. Initially, images are resized into 448∗448 pixels' resolution and then pre-processed to remove noise from images. After that, various data augmentation approaches were applied to solve the overfitting problem. Additionally, a modified CNN model is used for the classification process. the experimental performance of the model has shown 97.5% of accuracy, 98.3% of recall, 97.4% of precision, and 98.9% of F1 score. Hence, the proposed framework can serve as an efficient application in pneumonia diagnosis and assist doctors in decision-making. © 2023 IEEE.

关键词： Diagnosis

来源：评论

学校读者我要写书评

暂无评论

Smart Contract Vulnerability Detection Based on machine learning 6

Smart Contract Vulnerability Detection Based on Machine Lear...

引用

6th international conference on Electronic Engineering and Informatics, EEI 2024

作者： Wang, Deguang Shan, Mengtao Tong, Ning School of Software Dalian Jiaotong University Dalian116028 China

ISBN: (纸本)9798350353594

Smart contracts are among the most important applications of blockchain technology and are vulnerable to network attacks, leading to significant financial losses. thus, smart contract vulnerability detection has become an important research field. Currently, there are problems with limited detection types and low detection efficiency in smart contract vulnerabilities. It is particularly crucial to achieve high detection efficiency and cover a wide range of vulnerability types in smart contract vulnerabilities. this paper builds a dataset named SC-4, which consists of over 3000 data of four common vulnerabilities in smart contracts: Reentrancy, Transaction Order Dependence, Unchecked-Send, and Unhandled-Exceptions. We propose a smart contract vulnerability detection model based on an improved gated recurrent unit (GRU) and random forest (RF) fusion algorithm. the experimental results show that the proposed algorithm can accurately identify four types of smart contract vulnerabilities, and the accuracy can reach 98.47%. the number of vulnerability types detected has also increased compared to that in previous research. © 2024 IEEE.

关键词： Smart contract

来源：评论

学校读者我要写书评

暂无评论

Meta-learning Guided Few-Shot learning Method for Gearbox Fault Diagnosis Under Limited data Conditions 6th

Meta-Learning Guided Few-Shot Learning Method for Gearbox Fa...

引用

conference of the-Efficiency-and-Performance-Engineering-Network (TEPEN) / 6th international conference on Maintenance Engineering (IncoME)

作者： Zhang, Ming Wang, Duo Xu, Yuchun Aston Univ Coll Engn & Phys Sci Birmingham B4 7ET W Midlands England Tsinghua Univ Dept Automat Beijing 100084 Peoples R China

ISBN: (纸本)9783030990756;9783030990749

Recently, intelligent fault diagnosis technology based on deep learning has been extensively researched and applied in large industrial equipment system for ensuring safe and stable production. However, these deep models only effective when enough data for each observed failure category are available in the training durations. Otherwise, the performance of these models will notably decrease. As the critical component in large machinery, the gearbox often changes the speed and load along with the production demand in the practical application, which caused few data samples to be collected at certain conditions. this phenomenon introduces the few-shot fault diagnosis, and its goal is to identify the fault types with extremely limited data samples. To address this problem, a Meta-learning guided Few-shot Fault Diagnosis method, named MFFD, is proposed for gearbox fault diagnosis under limited data conditions. the results verify the effectiveness of our MFFD method at one-shot and five-shot fault diagnosis tasks under different speed and load conditions.

关键词： Meta-learning Few-shot learning Fault diagnosis Gearbox Limited data samples

来源：评论

学校读者我要写书评

暂无评论

Wearable Sensor Based Cloud data analytics Using Federated learning Integrated with Classification by Deep learning Technique 1

引用

6th international conference on Innovative Computing and Communication, ICICC 2023

作者： Munnangi, Ashok Kumar Rajeyyagari, Sivaram Sekaran, Ramesh Jikkiriya, Nashreen Begum Ramachandran, Manikandan Department of Information Technology Velagapudi Ramakrishna Siddhartha Engineering College Andhra Pradesh Vijayawada520007 India Department of Computer Science College of Computing and Information Technology Shaqra University Shaqra Saudi Arabia Karnataka Bangalore562112 India Department of Artificial Intelligence and Machine Learning Malla Reddy University Telangana Hyderabad500100 India School of Computing SASTRA Deemed University Tamil Nadu Thanjavur India

ISBN: (数字)9789819933150

ISBN: (纸本)9789819933143

Wearable sensors and person to person communication stages assume a key part in giving another strategy to gather patient information for productive medical care observing. In any case, persistent patient observing utilizing wearable sensors produces a lot of medical care information. Deep learning (DL) is a potential method for big data analytics since it can be utilized to quickly classify as well as analyse this plethora of data. this research proposes novel method in wearable sensor based cloud data analysis by federated learning as well as classification using DL. Input is collected by cloud architecture based wearable sensor and processed for noise removal as well as normalization. Cloud data is secured based on federated learning and classified using U-Net convolutional auto encoder radial neural networks. Experimental analysis has been carried out in terms of accuracy, precision, recall, F-1 score, RMSE and NSE for different wearable sensor dataset. Proposed method attained accuracy 98%, precision 85%, recall 75%, F-1 score 66%, RMSE 55% and NSE 55%. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023.

关键词： Classification (of information)

来源：评论

学校读者我要写书评

暂无评论

Human Resources Analysis Based on machine learning 6

Human Resources Analysis Based on Machine Learning

引用

6th international conference on Information Technologies and Electrical Engineering, ICITEE 2023

作者： Guo, Ning School of Public Administration Guangxi University Nanning China

ISBN: (纸本)9798400708299

this study is grounded in a human resource dataset, examining the correlation between employee performance and various factors, encompassing age, and educational background. the dataset comprises 13 dimensions, and meticulous data cleaning and processing were applied to rectify missing and incongruous data issues. Subsequent to the preparatory steps, data mining techniques were employed to scrutinize the influence of age and education on employee value. the paper then details the methodology for establishing and optimizing the model, utilizing the Particle Swarm Optimization (PSO) algorithm and BP00 neural network. Validation of the model's efficacy follows, with results indicating its robust capability to predict employee value accurately. these findings offer valuable insights for enhancing human resources management practices. the paper concludes by summarizing research outcomes and underscored the pivotal role of a judicious performance assessment system in human resources management. Future research directions are also delineated. © 2023 ACM.

关键词： Decision support 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27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：