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

145 篇 会议
1 册 图书
1 篇 期刊文献

馆藏范围

147 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

90 篇 工学
- 72 篇 计算机科学与技术...
- 64 篇 软件工程
- 37 篇 信息与通信工程
- 22 篇 生物工程
- 19 篇 生物医学工程（可授...
- 14 篇 电子科学与技术（可...
- 13 篇 电气工程
- 10 篇 控制科学与工程
- 8 篇 机械工程
- 8 篇 化学工程与技术
- 7 篇 动力工程及工程热...
- 5 篇 仪器科学与技术
- 4 篇 材料科学与工程（可...
- 4 篇 农业工程
- 4 篇 安全科学与工程
56 篇 理学
- 25 篇 数学
- 24 篇 生物学
- 18 篇 物理学
- 8 篇 化学
- 8 篇 系统科学
- 8 篇 统计学（可授理学、...
21 篇 医学
- 20 篇 临床医学
- 19 篇 基础医学(可授医学...
- 14 篇 药学(可授医学、理...
- 4 篇 公共卫生与预防医...
19 篇 管理学
- 11 篇 图书情报与档案管...
- 10 篇 管理科学与工程(可...
- 5 篇 工商管理
4 篇 农学
- 4 篇 作物学
3 篇 经济学
- 3 篇 应用经济学
2 篇 法学
1 篇 教育学
1 篇 军事学

主题

13 篇 machine learning
10 篇 signal processin...
8 篇 deep learning
7 篇 support vector m...
6 篇 electroencephalo...
4 篇 feature extracti...
4 篇 signal processin...
4 篇 machine learning...
4 篇 forecasting
3 篇 object detection
3 篇 websites
3 篇 decision trees
2 篇 covid-19
2 篇 dermatology
2 篇 signal detection
2 篇 task analysis
2 篇 anomaly detectio...
2 篇 neural networks
2 篇 learning algorit...
2 篇 robots

机构

2 篇 school of electr...
2 篇 ugam solutions c...
2 篇 school of comput...
2 篇 guangzhou power ...
2 篇 beijing world yo...
2 篇 tiangong univers...
2 篇 school of electr...
2 篇 school of busine...
2 篇 school of foreig...
2 篇 gitam university...
2 篇 nanjing universi...
2 篇 kiet group of in...
2 篇 department of el...
2 篇 school of econom...
1 篇 school of automa...
1 篇 school of comput...
1 篇 american interna...
1 篇 vit-ap universit...
1 篇 institute of sig...
1 篇 ait-cse chandiga...

作者

2 篇 pandey trilok na...
2 篇 trilok nath pand...
1 篇 tieran zheng
1 篇 zao feifei
1 篇 tyagi tanishqa
1 篇 lei wang
1 篇 aynur nahar urme
1 篇 singh priyank
1 篇 md. ariyan jahan...
1 篇 pangavhane madhu...
1 篇 zhang meifang
1 篇 chen hang
1 篇 junaid abdul man...
1 篇 saqib jahir
1 篇 sletzer concy ma...
1 篇 sathishkumar v.e...
1 篇 al juabir abdull...
1 篇 saroj kumar pani...
1 篇 mishra tasmin
1 篇 lima jose

语言

136 篇 英文
11 篇 其他
3 篇 中文

检索条件"任意字段=3rd International Conference on Signal Processing and Machine Learning, CONF-SPML 2023"

共 147 条记录，以下是61-70 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：

ADBIS: Anomaly Detection to Bolster IoT Security Using machine learning 3

ADBIS: Anomaly Detection to Bolster IoT Security Using Machi...

引用

3rd IEEE international conference on Applied Electromagnetics, signal processing, and Communication, AESPC 2023

作者： Vidhya, N. Bhuavneswari, P.T.V. Madras Institute of Technology Anna University Department of Electronics Engineering Chennai India

ISBN: (纸本)9798350358742

The Internet of Things (IoT) represents a transformative technology with profound implications for our daily lives. However, the rapid proliferation of IoT devices has brought about heightened concerns regarding security vulnerabilities. To address these challenges, this research focuses on the application of machine learning (ML) algorithms to detect anomalies in the CTU-IoT dataset, a prominent IoT-based home automation dataset. The methodology entails extracting comma-separated values (.csv) files from packet capture (pcap) data, followed by the removal of outliers using a simple imputer technique. The Isolation Forest algorithm is then utilized to label dataset entries as anomalies or not, with these labels incorporated into the dataset. Given the class imbalance issue that arises, oversampling techniques such as SMOTE (Synthetic Minority Oversampling Technique) and Borderline-SMOTE are employed to balance the dataset. The study encompasses three datasets: the original unbalanced dataset, the SMOTE balanced dataset, and the Borderline-SMOTE dataset. A selection of ML algorithms, including SVM, Naive Bayes, Decision Tree, and Random Forest is applied to these datasets, and their performance is assessed using key metrics like accuracy, precision, recall, F1-score and computation time. The findings reveal that the Decision Tree algorithm outperforms the other three algorithms based on the performance metrics, showcasing its suitability for anomaly detection in the Borderline- SMOTE balanced dataset. This research contributes to strengthening the security and safety of IoT applications. © 2023 IEEE.

关键词： Decision trees

来源：评论

学校读者我要写书评

暂无评论

Graph neural networks for protein-protein interactions 3

Graph neural networks for protein-protein interactions

引用

3rd international conference on Advanced Algorithms and signal Image processing, AASIP 2023

作者： Chang, Sheng Wang, Yifan Zhang, Xinhong Zhang, Fan School of Computer and Information Engineering Henan University Kaifeng475004 China School of Software Henan University Kaifeng475004 China

ISBN: (纸本)9781510668522

Protein-protein interaction (PPI) is the process by which two or more protein molecules form a protein complex through non-covalent bonds. The interactions between protein and the protein complexes formed by these interactions are the primary complements of various essential cellular functions and carry out almost all vital life activities. Most PPI research today focuses on the use of machine learning methods, of which the use of graph neural networks (GNN) for prediction is a current hot direction. This paper highlights the main advances in the application of GNN to proteinprotein interactions. The first part reviews protein sequence-based methods for PPI prediction. And the second part focuses on structure-based graph neural network PPI prediction methods. Finally, we discuss the shortcomings of GNNs in this area and future directions in PPI prediction. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： machine learning

来源：评论

学校读者我要写书评

暂无评论

Intelligent Software Bug Prediction: An Empirical Approach 3

Intelligent Software Bug Prediction: An Empirical Approach

引用

3rd international conference on Robotics, Electrical and signal processing Techniques, ICREST 2023

作者： Jahangir, Md. Ariyan Tajwar, Md. Abtahi Marma, Waythin Islam, Md. Siyamul American International Univeristy-Bangladesh Department of Computer Science Dhaka Bangladesh

ISBN: (纸本)9798350346435

Despite the fact that, much research has been conducted to improve accuracy in software bug prediction through different machine learning (ML) classifiers, not concentrated on the performance evaluation on the applicability of ML algorithms to detect software bugs. This inadequacy is focused on this paper. In this research, we conducted software bug prediction comparison on six different ML algorithms. Moreover, we adopted Code Based Metrics (CBM) to predict software defect through sequential neural network (DL) model and compared it with generic models. The performance of different models has been evaluated and compared based on PROMISE dataset provided by NASA. Results have shown that ML Techniques and DL Approaches have similar bug prediction capabilities where Decision Tree technique performing the worst and Support Vector machine gave the best results. Also, thoughtful feature selection provides noticeable difference compared to no feature selection during model construction. © 2023 IEEE.

关键词： Forecasting

来源：评论

学校读者我要写书评

暂无评论

Information Recommendation of English Oral Teaching Resource Library Based on Collaborative Filtering and Support Vector machine 3

Information Recommendation of English Oral Teaching Resource...

引用

3rd international signal processing, Communications and Engineering Management conference, ISPCEM 2023

作者： Chen, Hang Zhang, Meifang He, Weiying School of Information Engineering Guangzhou Vocational College of Technology and Business Guangdong Guangzhou511442 China School of Foreign Languages Guangzhou Institute of Science and Technology Guangdong Guangzhou510540 China School of Computer Science and Engineering Guangzhou Institute of Science and Technology Guangdong Guangzhou510540 China

ISBN: (纸本)9798350307023

In order to improve the sharing and real-time access scheduling capabilities of English oral teaching resource library information, a collaborative filtering and support vector machine based English oral teaching resource library information recommendation model is proposed. Using the keyword graph model and text semantic feature detection method to construct an information allocation network structure model for English oral teaching resource library, using collaborative filtering method to achieve keyword feature detection and interference information filtering for English oral teaching resources, and combining support machine classification recognition method to extract semantic keywords from text pairs, Introducing syntax parsing tree and pre trained language model to establish semantic information tree learning in the information recommendation process of English oral teaching resource library, constructing a text graph model based on extracted keywords, fully utilizing the semantic information tree of the text to pair the recommendation information between learning nodes and neighboring nodes, and inputting the generated text sequence into the recommendation sequence vector table, Combining collaborative filtering method and support vector machine algorithm to achieve information recommendation for English oral teaching resource library. The simulation results show that this method has good feature matching ability for recommending information in the English oral teaching resource library, with an Fl of 87.3 % on datasets such as CNSE. It has strong filtering ability for mixed information in the context of English oral resources, and improves the ability to accurately recommend information. © 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

3rd international conference on machine learning, Image processing, Network Security and Data Sciences, MIND 2021

3rd International Conference on Machine Learning, Image Proc...

引用

3rd international conference on machine learning, Image processing, Network Security and Data Sciences, MIND 2021

ISBN: (纸本)9789811958670

The proceedings contain 68 papers. The special focus in this conference is on machine learning, Image processing, Network Security and Data Sciences. The topics include: Analyzing Wearable Data for Diagnosing COVID-19 Using machine learning Model;comparative Analysis of Classification Methods to Predict Diabetes Mellitus on Noisy Data;a Robust Secure Access Entrance Method Based on Multi Model Biometric Credentials Iris and Finger Print;region Classification for Air Quality Estimation Using Deep learning and machine learning Approach;neuroevolution-Based Earthquake Intensity Classification for Onsite Earthquake Early Warning;detection of Credit Card Fraud by Applying Genetic Algorithm and Particle Swarm Optimization;traditional Indian Textile Designs Classification Using Transfer learning;classification of Electrocardiogram signal Using Hybrid Deep learning Techniques;automated Detection of Type 2 Diabetes with Imbalanced and machine learning Methods;fault Diagnosis in Wind Turbine Blades Using machine learning Techniques;real-Time Detection of Vehicles on South Asian Roads;stock Market Prediction Using Ensemble learning and Sentimental Analysis;Multiple Feature-Based Tomato Plant Leaf Disease Classification Using SVM Classifier;a Methodological Review of Time Series Forecasting with Deep learning Model: A Case Study on Electricity Load and Price Prediction;unexpected Alliance of Cardiovascular Diseases and Artificial Intelligence in Health Care;a Novel Smartphone-Based Human Activity Recognition Using Deep learning in Health care;An Enhanced Deep learning Approach for Smartphone-Based Human Activity Recognition in IoHT;classification of Indoor–Outdoor Scene Using Deep learning Techniques;prediction of the Reference Evapotranspiration Data from Raipur Weather Station in Chhattisgarh using Decision Tree-Based machine learning Techniques;deep Transfer learning and Intelligent Item Packing in Retail Management;preface.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Classification and Prediction of Incipient Faults in Transformer Oil by Supervised machine learning using Decision Tree 3

Classification and Prediction of Incipient Faults in Transfo...

引用

3rd international conference on Artificial Intelligence and signal processing, AISP 2023

作者： Raj, Raymon Antony Sarathkumar, D. Venkatachary, Sampath Kumar Andrews, Leo John Baptist Kongu Engineering College Department of Electrical and Electronics Engineering Tamilnadu Erode district638 060 India Grant Thornton Botswana Plot Acument Park Fair Grounds Gaborone50370 Botswana Botho University Faculty of Engineering and Technology Department of Information Technology Gaborone Botswana

ISBN: (纸本)9798350320749

Transformer condition evaluation is a crucial technique for displaying a transformer's health index. Key gases (KG), which dissolve in oil and become inseparable, are developed because of the different nascent flaws formed in the transformer enclosure. The exact defect that caused the gases to evolve may be found using the composition of KG present in the transformer. Five KG, including hydrogen H2, methane (CH4), ethane (C2 H2), ethylene C2 H4), and acetylene (C2 H2), are employed in this study as an input to anticipate the real incipient defect. In the inquiry that follows, classification is carried out using a Decision Tree (DT) and the data is trained using a Supervised machine learning (SML). The dataset is made up of 180 different input KG compositions and defects that are trained, identified, and predicted using a predictive model. 16 more datasets with KG composition are utilized for prediction. TPR versus FNR, PPV vs FDR, AUC, parallel coordinates plot, confusion matrix, and lowest classification error are used to assess the statistical correctness. The accuracy of the projected data using DT and optimized DT is 62.9%, however the accuracy of the optimized DT with principal component analysis (PCA) is only 55%. Therefore, the suggested DT fault classification approach may be utilized to accurately forecast the onset of faults. © 2023 IEEE.

关键词： Decision trees

来源：评论

学校读者我要写书评

暂无评论

ConvoWaste: An Automatic Waste Segregation machine Using Deep learning 3

ConvoWaste: An Automatic Waste Segregation Machine Using Dee...

引用

3rd international conference on Robotics, Electrical and signal processing Techniques, ICREST 2023

作者： Nafiz, Md. Shahariar Das, Shuvra Smaran Morol, Md. Kishor Al Juabir, Abdullah Nandi, Dip Department of Electrical and Electronic Engineering Bangladesh Department of Computer Science Bangladesh

ISBN: (纸本)9798350346435

Nowadays, proper urban waste management is one the biggest concerns for maintaining a green and clean environment. An automatic waste segregation system can be a viable solution to improve the sustainability of the country and to boost up the circular economy. This paper proposes a machine to segregate the waste into the different parts with the help of smart object detection algorithm using ConvoWaste in the field of Deep Convolutional Neural Network (DCNN), and image processing technique. In this paper, the deep learning and image processing techniques are applied to classify the waste precisely and the detected waste is placed inside the corresponding bins with the help of a servo motor-based system. This machine has the provision to notify the responsible authority regarding the waste level of the bins and the time to trash out the bins filled with garbage by using the ultrasonic sensors placed in each bin and the dual-band GSM-based communication technology. The entire system is controlled remotely through an android app in order to dump the separated waste in a desired place by its automation properties. The use of this system can aid the process of recycling resources that were initially destined to become waste, utilizing natural resources and turning these resources back into the usable products. Thus, the system helps to fulfill the criteria of circular economy through the resource optimization and extraction. Finally, the system is made to provide the services at a low cost with higher accuracy level in terms of the technological advancement in the field of Artificial Intelligence (AI). We have got 98% accuracy for our ConvoWaste deep learning model. © 2023 IEEE.

关键词： Object detection

来源：评论

学校读者我要写书评

暂无评论

Enhancing Interactive Storytelling: A Computational Approach to Autonomously Generating Role-Playing Game Scripts with Natural Language processing 3

Enhancing Interactive Storytelling: A Computational Approach...

引用

3rd international signal processing, Communications and Engineering Management conference, ISPCEM 2023

作者： Zhong, Qijun Beijing World Youth Academy Beijing China

ISBN: (纸本)9798350307023

In the realm of role-playing games (RPGs), the crafting of rich and detailed narratives is crucial, with legendary games like Dungeons & Dragons (D&D) setting the bar for creativity and player-driven storytelling. This research explores the innovative intersection of artificial intelligence and RPGs by developing a method for the automated generation of RPG scripts, harnessing the capabilities of advanced natural language processing (NLP). Our study presents an elaborate NLP framework, utilizing a comprehensive collection of D&D source materials, to methodically extract key character features (such as attributes, races, and classes) using machine learning techniques. This framework is further enhanced by integrating sentiment analysis and a sophisticated bi-directional recurrent neural network equipped with a multi-head attention mechanism. This combination is instrumental in accurately modeling narrative elements like event sequences, causal relationships, and thematic links. The culmination of this process is the automated generation of diverse and engaging RPG scripts, each characterized by distinct character dynamics, immersive settings, and compelling story arcs. The effectiveness and impact of these generated scripts were evaluated through a user study that gathered both qualitative and quantitative data. The findings demonstrate our system's proficiency in creating narratives that are not only contextually diverse but also resonate deeply with RPG conventions, indicating a significant advancement in interactive storytelling. This research not only reinforces the role of AI in augmenting game design but also underscores the broader potential of AI in creative content generation, marking a new era in the synergy between computational technology and artistic narrative creation. © 2023 IEEE.

关键词： Sentiment analysis

来源：评论

学校读者我要写书评

暂无评论

Detection of Parkinson's cognitive impairment patients based on motor and non-motor symptoms 3

Detection of Parkinson's cognitive impairment patients based...

引用

3rd international conference on Advanced Algorithms and signal Image processing, AASIP 2023

作者： Shi, Zhan Chen, Xi Qi, Hongbin Li, Zeyang Jiang, Hu Beijing Institute of Technology Mechanical and Electrical College Beijing100010 China

ISBN: (纸本)9781510668522

Gait measurement is an objective analysis method that can detect abnormal gait, facilitate early disease identification, and support doctors in formulating rehabilitation treatment plans. In recent years, cognitive scales and biomarkers have been widely used to detect Parkinson's cognitive impairment (PD-MCI). However, a lack of objective and reliable detection methods for PD-MCI exists. This study aims to identify patients with Parkinson's cognitive impairment utilizing two approaches: gait and executive function. Non-contact measurement was employed to obtain the gait signal, while the clock drawing test was utilized to obtain the executive function score of the patients. Ten PD-MCI patients and 10 ordinary PD were tested, and the kinematics data of the subjects during walking were recorded. Using patients' gait parameters and executive function scores as data characteristics, machine learning methods were utilized to classify and identify patients. The most suitable machine learning method for PD-MCI patient detection was discovered utilizing this approach. The results indicate that the XGBoost algorithm can identify and classify PD-MCI patients with more than 90% accuracy, providing effective support for doctors in identifying Parkinson's cognitive impairment early and customizing the most suitable rehabilitation treatment plan. These findings have significant research significance. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： machine learning

来源：评论

学校读者我要写书评

暂无评论

Unravelling the Mind's Emotions: A Review of EEG-Based Emotion Detection Using machine learning and Deep learning 3rd

Unravelling the Mind's Emotions: A Review of EEG-Based Emoti...

引用

3rd international conference on Intelligent Computing Techniques for Smart Energy Systems, ICTSES 2023

作者： Vishwakarma, Aishwarya Sakalle, Vikas LNCT University Bhopal India

ISBN: (纸本)9789819784288

Emotions are complex psychological states that play a vital role. They influence our thoughts, behaviors, and interactions with others. While humans are experts at recognizing and understanding emotions in others, developing machines that can do the same remains challenging. Electroencephalography (EEG) is a non-invasive brain imaging technique that measures the brain's electrical activity. EEG signals are correlated with different emotional states, making them a promising source of information for emotion detection. machine learning and deep learning are powerful artificial intelligence techniques that have revolutionized many fields, including emotion detection. machine learning algorithms can be trained on EEG data to learn patterns associated with different emotions. Deep learning models can learn more complex patterns and achieve higher accuracy in emotion detection tasks. This review paper provides a comprehensive overview of EEG-based emotion detection using machine learning and deep learning approaches. We discuss the different EEG signal processing and feature extraction techniques commonly used in EEG-based emotion detection. We also review the different machine learning and deep learning algorithms used for EEG-based emotion detection. We then present a state-of-the-art review of the recent advances in EEG-based emotion detection. We discuss the challenges and limitations of existing EEG-based emotion detection models. Finally, we discuss the future directions of EEG-based emotion detection research. We highlight the potential applications of EEG-based emotion detection models in various fields, such as healthcare, education, and human–computer interaction. This review paper will be valuable for researchers and practitioners interested in EEG-based emotion detection. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Electroencephalography

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15页 << < 3 4 5 6 7 8 9 10 11 12 > >>

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：