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

3,156 篇 会议
173 册 图书
28 篇 期刊文献

馆藏范围

3,356 篇 电子文献
1 种 纸本馆藏

日期分布

学科分类号

2,251 篇 工学
- 2,046 篇 计算机科学与技术...
- 1,087 篇 软件工程
- 408 篇 信息与通信工程
- 359 篇 电气工程
- 249 篇 生物工程
- 176 篇 控制科学与工程
- 157 篇 生物医学工程（可授...
- 73 篇 光学工程
- 73 篇 化学工程与技术
- 70 篇 机械工程
- 63 篇 仪器科学与技术
- 61 篇 电子科学与技术（可...
- 60 篇 安全科学与工程
- 48 篇 交通运输工程
- 40 篇 动力工程及工程热...
- 38 篇 材料科学与工程（可...
719 篇 理学
- 378 篇 数学
- 273 篇 生物学
- 169 篇 物理学
- 148 篇 统计学（可授理学、...
- 104 篇 系统科学
- 70 篇 化学
467 篇 管理学
- 305 篇 图书情报与档案管...
- 206 篇 管理科学与工程(可...
- 99 篇 工商管理
300 篇 医学
- 273 篇 临床医学
- 125 篇 基础医学(可授医学...
- 70 篇 药学(可授医学、理...
- 53 篇 公共卫生与预防医...
64 篇 法学
- 57 篇 社会学
32 篇 经济学
31 篇 教育学
28 篇 农学
6 篇 艺术学
3 篇 军事学
1 篇 文学

主题

550 篇 data mining
550 篇 machine learning
291 篇 pattern recognit...
244 篇 deep learning
192 篇 feature extracti...
163 篇 support vector m...
156 篇 artificial intel...
127 篇 accuracy
123 篇 training
117 篇 machine learning...
100 篇 data models
93 篇 data mining and ...
75 篇 classification
73 篇 convolutional ne...
72 篇 neural networks
70 篇 predictive model...
69 篇 computational mo...
68 篇 big data
59 篇 sentiment analys...
55 篇 learning systems

机构

10 篇 chinese academy ...
9 篇 chitkara centre ...
8 篇 zhejiang univers...
7 篇 guangdong univer...
7 篇 centre of resear...
6 篇 central south un...
6 篇 unsw sydney
6 篇 college of compu...
5 篇 badji mokhtar un...
5 篇 indian institute...
5 篇 xi'an university...
5 篇 shanghai jiao to...
5 篇 indian statistic...
5 篇 southwest jiaoto...
5 篇 kobe university ...
5 篇 southern univers...
4 篇 columbia univers...
4 篇 chitkara univers...
4 篇 institute of big...
4 篇 centre for patte...

作者

12 篇 de-shuang huang
12 篇 venkatesh svetha
8 篇 petra perner
7 篇 phung dinh
7 篇 jacek m. zurada
7 篇 krasotkina olga
7 篇 mottl vadim
6 篇 zheng-guang wu
6 篇 ryszard tadeusie...
6 篇 marcin korytkows...
6 篇 hongyi li
6 篇 chaojie li
6 篇 biao luo
6 篇 lotfi a. zadeh
6 篇 kang-hyun jo
6 篇 leszek rutkowski
6 篇 long cheng
6 篇 zhongzhi shi
5 篇 zhang yan
5 篇 vitoantonio bevi...

语言

2,924 篇 英文
416 篇 其他
38 篇 中文

检索条件"任意字段=4th International Conference on Machine Learning and Data Mining in Pattern Recognition"

共 3357 条记录，以下是1171-1180 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：

Classification of Flower dataset using machine learning Models

Classification of Flower Dataset using Machine Learning Mode...

引用

Artificial Intelligence and Speech Technology (AIST), international conference on

作者： Tina Gupta Puja Arora Ritu Rani Garima Jaiswal Poonam Bansal Amita Dev Computer Science Engineering Indira Gandhi Delhi Technical University for Women Delhi India Center of Excellence - Artificial Intelligence Indira Gandhi Delhi Technical University for Women Delhi India Computer Science Engineering Bennet University Noida Artificial Intelligence and Data Sciences Indira Gandhi Delhi Technical University for Women Delhi India

Modern day machine learning aims to categorize data based on developed models and predict future outcomes according to these models. Today machine learning finds its application in various fields such as facial recognition, speech recognition, medical diagnosis for example predicting potential heart failure, sentiment analysis, product recommendations etc. this paper proposes 3 classification models to efficiently predict the Iris flower species. the proposed model uses Exploratory data Analysis (EDA) to analyse and pre-process the dataset and the prediction is performed by the three classification models namely- "Logistic Regression, Support Vector machine (SVM) and K-Nearest Neighbours (KNN)". All the proposed models are tested on Iris dataset and achieved maximum accuracy of 96.43, 98.21 and 94.64 percent respectively. this paper provides a thorough analysis of the various supervised machine learning models that are suitable for predicting the species of Iris flower based on the various attributes like sepal width, sepal length, petal width and petal length.

关键词： Analytical models Sentiment analysis data analysis Support vector machine classification machine learning Speech recognition Predictive models

来源：评论

学校读者我要写书评

暂无评论

machine learning-Based Research on Reserve Prediction of Natural-Gas-Hydrates 23

Machine Learning-Based Research on Reserve Prediction of Nat...

引用

Proceedings of the 4th international conference on Artificial Intelligence and Computer Engineering

作者： Sumin Ye Shuxian Mai Wenbo Niu Tao Yang Lei Ye Kun Zhang Huimin Tang Chenglong Li Boyang Peng Qiannan Yu College of Energy and Power Engineering Guangdong University of Petrochemical Technology China School of mechanical sctence and Engineering Northeast Petroleum University China and College of Energy and Power Engineering Guangdong University of Petrochemical Technology China Hainan Branch CNOOC (China) Co. Ltd. China Exploration and Development Research institute Daqing Oilfield Co. Ltd. China and Heilongjiang Key Laboratory of Reservoir Physics and Fluid Mechanics in Porous Medium China Geological Research Institute No. 4 Oil Production Plant of Daqing Oilfield Co. Ltd. China

ISBN: (纸本)9798400708831

Currently, there are challenges in reserve prediction of natural-gas-hydrates, including difficulties in obtaining parameters, limited calculation accuracy, and complex processes. machine learning is a data-driven predictive method with strong generalization capabilities, capable of abstracting and inducing knowledge and rules from basic data witch has made significant advancements in various fields and has been preliminarily applied in fields of earth sciences and energy exploration and development. Various machine learning algorithms for determining the parameters of gas hydrate reserves were introduced in this paper. the support vector machine algorithm was employed to calculate effective area and thickness, while neural network algorithm was introduced to calculate porosity, saturation, and decomposition coefficient for the determination of parameters of natural-gas-hydrates reserves in Shenhu area of China's offshore, and then the feasibility and accuracy of the method was verified. Compared to evaluation results based on trial mining or numerical simulation, the machine learning-based reserve prediction method demonstrates high predictive accuracy, clear processes, and simple structure, making it highly valuable for future research on storage and transportation of natural-gas-hydrates and provides foundation for future industrial applications.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Predictive Student Performance Analysis Using machine learning and Student Assistance System 4th

Predictive Student Performance Analysis Using Machine Learni...

引用

4th international conference on Communications and Cyber-Physical Engineering, ICCCE 2021

作者： Sai Charan, N. Ali Hussain, Md. Vineela, P. Vamsi Adi Tilak, M. Chandu Siva Shankar, T. Department of ECM Koneru Lakshamiah Education Foundation Vaddeswaram India

ISBN: (纸本)9789811679841

In this, we mainly focus on the analysis of the student’s performance in academics based not only on external exams, but also taking into consideration the internal marks of each and every student. this work is useful for the users either for just getting an idea on the performance of the student or in situations where the institutions are unable to conduct the examinations and they wanted to promote the students based on their previous performance. We segregate and calculate the performance of students using data (Kaunang and Rotikan, Students academic performance prediction using data mining. IEEE, pp 18–10, 2018 [1]). We then predict the performance of the students who are going to pass and fail based on previous result and also the predicted marks of a student using a new algorithm namely Student Predictive Analysis algorithm (SPA algorithm) with the help of already existing algorithms SVM and linear regression algorithms. these two algorithms are the supervised machine learning algorithms. the support vector classifier is take from the SVM algorithm in order to classify the students as Pass/Fail. In this project, we mainly focus on the analysis of the student’s performance and then predict the results through them using training data and then test data of academics not only external exams, but also the overall academic performance of each and every student. In addition to that we have added some more features like student faculty interaction for clarifying doubts or getting faculty assistance, placement prediction and giving suggestions to the to get placed in the companies, OTP login validation and facilitate faculty to give overall feedback on students. © 2022, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Classification (of information)

来源：评论

学校读者我要写书评

暂无评论

Degradation Characteristics Analysis and Fault Prediction of Switching Power Supply Based on data mining 21

Degradation Characteristics Analysis and Fault Prediction of...

引用

4th international conference on machine learning and machine Intelligence, MLMI 2021

作者： Shi, Linlin Yu, Pengfei He, Shilie Zhou, Zhenwei Meng, Linghui Liu, Junbin The Fifth Electronics Research Institute of Ministry of Industry and Information Technology China

ISBN: (纸本)9781450384247

Fault prediction and health monitoring of DC-DC switching power supply plays an important role in the safe and reliable operation of power electronic equipment. In this paper, a long-term high temperature degradation test was carried out for DC-DC power supply, and the characteristic parameters related to device health, such as input current, input voltage, output current and output voltage, were collected during the test. through data mining technology, we carry out data preprocessing, feature analysis, health index modeling and fault prediction analysis on the samples collected in the power supply test, so as to study the degradation and fault predictor of the power supply from the real power supply test degradation data. the research results of this paper have important engineering significance for the monitoring and health prediction of power supply. © 2021 ACM.

关键词： Health

来源：评论

学校读者我要写书评

暂无评论

Voice Sentiment Analysis System Using Librosa

Voice Sentiment Analysis System Using Librosa

引用

Ubiquitous Computing and Intelligent Information Systems (ICUIS), international conference on

作者： Ashok Reddy Kandula Rayala Nathaniel thota Bhanu Vissamsetti Sai Vignesh Meduri Leela Naga Sai Dept of AI&DS Seshadri Rao Gudlavalleru Engineering College Gudlvalleru India

ISBN: (数字)9798331529635

ISBN: (纸本)9798331529642

the Voice Sentiment Analysis System is an innovative solution that can analyze the emotional content of spoken audio data in real-time. By using advanced machine learning algorithms and signal processing techniques, the system can accurately detect and categorize the emotions expressed by speakers. this provides valuable insights into the sentiment conveyed in conversations, speeches, or customer interactions. By processing streaming audio data, the system allows for immediate feedback and actionable insights in various applications. In business settings, it can be used to analyze customer service calls or feedback recordings in real-time to assess satisfaction levels, identify areas for improvement, and prioritize follow-up actions. Integration with voice-enabled assistants enhances user interactions by interpreting emotional cues and personalizing responses. In healthcare, the system can assist in monitoring patients' emotional well-being during therapy sessions or remote consultations. this enables healthcare professionals to assess progress, intervene when necessary, and customize treatment plans effectively. With its easy-to-use interface and strong performance, the Voice Sentiment Analysis System offers a versatile solution for understanding and interpreting human emotions through voice communication channels

关键词： Sentiment analysis Emotion recognition Speech analysis Customer services machine learning Feature extraction Ubiquitous computing Real-time systems Monitoring Spectrogram

来源：评论

学校读者我要写书评

暂无评论

A Review of Advancements in Driver Emotion Detection: Deep learning Approaches and dataset Analysis

A Review of Advancements in Driver Emotion Detection: Deep L...

引用

Innovative Research in Applied Science, Engineering and Technology (IRASET),conference on

作者： Mohammed Shukur Alfaras Oğuz Karan Electrical and Computer Engineering Altinbas University Istanbul Turkey

ISBN: (数字)9798350309508

ISBN: (纸本)9798350309515

this review paper delves into the rapidly evolving field of driver emotion detection, with a specific focus on the contributions of deep learning methodologies and the diverse datasets that facilitate this research. Facial emotion detection and recognition is a highly dynamic and challenging field in machine learning (ML) and Artificial Intelligence (AI). It has gained attraction for several decades, but it is extremely challenging due to the intrinsic complexities associated with understanding and interpreting human emotions. Understanding and responding to a driver's emotions is increasingly important when autonomous or assisted driving becomes common. To ensure safety, comfort, and optimal interaction between the driver and the car's systems, predicting the emotional state of the driver is essential, also of great significance in practical applications. We give a comprehensive survey of the state-of-the-art driver emotion recognition works that can effectively make use of the recent deep-learning approaches to identify complex emotional cues. Moreover, we explain a variety of datasets that play a vital role in flourishing this field along with the analysis of their effect, like AffectNet, CK+, and EMOTIC. Via this survey we try to investigate the challenges authors are faced with involving this field, e.g., concerns about data privacy, real-time processing demands, and the need for interdisciplinary collaboration. More importantly, the potential of these technologies to improve the driving experience and road safety has been highlighted. We hope this survey can benefit researchers and practitioners to have more insights and provide directions for advancing drivers’ emotions.

关键词： Deep learning Surveys Emotion recognition Protocols Reviews Road safety Safety

来源：评论

学校读者我要写书评

暂无评论

Wood Species recognition Using machine learning 4

Wood Species Recognition Using Machine Learning

引用

4th international conference on Microelectronics, Signals and Systems, ICMSS 2021

作者： Neethu, S. Baby Syla, L. A P J Abdul Kalam Technical University Department of Computer Applications CET Trivandrum India

ISBN: (纸本)9781665448857

Although a computer vision-based automated wood species detection system is not extensively employed today, it is in high demand in a range of industries. Because each species of wood has distinct features and traits that determine how it is used and implemented. Human experts have traditionally been used nearly entirely in the identifying process, which is based on a variety of features of wood, such as texture, structure, and colour. Even competent scientists have difficulties distinguishing between different species of wood found in nature. the most difficult aspect of wood recognition is that some wood species have a texture that is identical to that of others, hence feature extraction is the most important step in the process. So in this work, two statistical feature extraction techniques are used 1) Local Binary pattern (LBP) 2) Gray Level Co-Occurrence Matrix (GLCM). Features extracted from both methods are combined to see whether they increase classification accuracy or not. Multi-SVM is used for classification. For this study, a new dataset is created which includes the three most common tree species in Indian territory. this study found that integrating LBP and GLCM features enhanced classification accuracy. Proposed system produces a result of 97.2%, which is better than individual approaches. © 2021 IEEE.

关键词： Local binary pattern

来源：评论

学校读者我要写书评

暂无评论

Android Malware Defense: Leveraging machine learning Models

Android Malware Defense: Leveraging Machine Learning Models

引用

Advancement in Electronics & Communication Engineering (AECE), international conference on

作者： Asif Iqubal Happy Happy Subodh Kant Tiwari USICT Guru Gobind Singh Indraprastha University New Delhi India Dept. of Computer Science & Engg. The NorthCap University Gurugram Haryana India

ISBN: (数字)9798350364729

ISBN: (纸本)9798350364736

the rapid increase in the number of Android devices has made it a major target for malware attacks that put many users around the globe at risk of security breaches. As technology advances and malware becomes more complex and diverse at a pace conventional methods relying solely on recognizing specific patterns are becoming less effective in safeguarding against these threats. this study delves into the use of machine learning (ML) models as a line of defense against Android Malware. We delve into advancements in ML techniques specifically tailored for detecting Android malware, highlighting their advantages and constraints. We are looking into how different models such as Logistic Regression (LR) Support Vector machine (SVM) Decision Trees (DT) k. Nearest Neighbor (k. NN) Naive Bayes (NB) Neural Networks (NN) Random Forest (RF) Gradient Boosting machine (GBM) AdaBoost (AdaB) Bagging Classifier (BC) and XGBoost (XGB) can accurately identify malicious applications with high precision and low rates of false positives using the " TUNADROMD " dataset. the models were thoroughly evaluated based on accuracy, precision, recall, F1 score, and ROC AUC criteria. the outcomes reveal that methods such as k-NN, NN, and RF achieved a flawless score of 1 across all measures signifying their exceptional accuracy in recognizing malware without any incorrect identifications. the results highlight the promise of ML in transforming Android security and laying groundwork for creating more dynamic defenses against malware threats.

关键词： Support vector machines Logistic regression Accuracy Artificial neural networks Feature extraction Malware pattern recognition Security Random forests Classification tree analysis

来源：评论

学校读者我要写书评

暂无评论

Detection of Attacks in Smart Healthcare deploying machine learning Algorithms*

Detection of Attacks in Smart Healthcare deploying Machine L...

引用

Emerging Technology (INCET), international conference for

作者： Anshika Sharma Himanshi Babbar Amit Kumar Vats Chitkara University Institute of Engineering and Technology Chitkara University Rajpura Punjab India ABES Institute of Technology Ghaziabad Uttar Pradesh India

the Internet of things (IoT) is a sort of network that uses a set of protocols and data-sensing tools to link anything to the Internet. this type of network allows for data exchange as well as smart recognition, surveillance, deployment, tracking, and maintenance. the healthcare business is rapidly digitizing, and the IoT is a key factor in this. It has already had an impact on medical networks and will continue to do so soon. this paper highlights privacy concerns as well as security attacks, which have rapidly increased in recent years in the healthcare industry. An architecture is proposed for detecting IoT attacks in smart health-care systems using machine learning (ML) techniques. this study used the UNSW-NB15 dataset to simultaneously classify Random Forest (RF), Naive Bayes (NB) and K-Nearest Neighbour (KNN) classifiers for detecting the attacks (Generic, Fuzzers, Exploits, Analysis, Denial of Service (DoS), Reconnaissance, Backdoor, Worms and Shellcode) in the healthcare industry. In addition to developing the essential smart and effective online system, this research increased knowledge of privacy and security in the smart healthcare sector. Furthermore, performance measures like accuracy, precision, recall, and F1-score have been calculated using the existing ML techniques based on the mentioned UNSW-NB15 dataset. the results indicate that when comparing the mentioned ML techniques, the KNN model has the highest accuracy value, or 90%, while RF and NB provide 89% and 75% accuracy values, respectively.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Student behavior based on information technology and machine learning 2021

Student behavior based on information technology and machine...

引用

4th international conference on Information Systems and Computer Aided Education, ICISCAE 2021

作者： Su, Yanfang University of Electronic Science and Technology of China Sichuan Chengdu China

ISBN: (纸本)9781450390255

With the development of science and technology, people can extract behavioral information from the chaotic footprints of life. this behavioral information can help to analyze a person's life trajectory and behavioral rules, and can serve to analyze a person's life to facilitate this group of people. Conducting behavioral analysis on student groups can help understand students' behavioral habits, and characteristics, which has important practical significance for improving the efficiency of education management. this article mainly studies student behavior (SB) analysis based on information technology and machine learning, using data mining technology to mine SB information, using appropriate visualization techniques to display the mining results in a suitable form, and using machine learning technology to show that the SB information is qualified. this paper studies and analyzes SB, and analyzes the three indicators of student consumption pattern, life patterns, and studies effort, based on the campus all-in-one card and student account login data. this paper studies the average monthly consumption and consumption frequency of students, analyzes the consumption behavior of students, and conducts cluster analysis on the three variables of student attendance index, performance index and course pass rate to study the degree of student's study efforts. the results of the study show that the analysis of consumer behavior can enable school administrators to provide targeted assistance to students in need, and to control high-spending students. For example, type 1 students are students with stable low consumption, accounting for 15.45%, and their consumption frequency is high but the amount of consumption is not high;type 2 students are low consumption and unstable people, accounting for 7.91%, their consumption frequency is not high, and their consumption is mostly off-campus, the amount of consumption is not high;the third type of students is the group with the largest p

关键词： Students

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336页 << < 114 115 116 117 118 119 120 121 122 123 > >>

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：