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

4,129 篇 会议
6 篇 期刊文献
3 册 图书

馆藏范围

4,138 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

2,733 篇 工学
- 2,463 篇 计算机科学与技术...
- 2,042 篇 软件工程
- 616 篇 生物工程
- 586 篇 信息与通信工程
- 362 篇 生物医学工程（可授...
- 266 篇 控制科学与工程
- 214 篇 电气工程
- 211 篇 光学工程
- 147 篇 安全科学与工程
- 136 篇 机械工程
- 123 篇 电子科学与技术（可...
- 118 篇 化学工程与技术
- 105 篇 交通运输工程
- 91 篇 动力工程及工程热...
1,368 篇 理学
- 674 篇 数学
- 646 篇 生物学
- 332 篇 统计学（可授理学、...
- 306 篇 物理学
- 197 篇 系统科学
- 127 篇 化学
720 篇 管理学
- 393 篇 管理科学与工程(可...
- 388 篇 图书情报与档案管...
- 237 篇 工商管理
340 篇 医学
- 315 篇 临床医学
- 308 篇 基础医学(可授医学...
- 221 篇 药学(可授医学、理...
- 125 篇 公共卫生与预防医...
159 篇 法学
- 138 篇 社会学
156 篇 教育学
- 154 篇 教育学
141 篇 农学
- 140 篇 作物学
89 篇 经济学
9 篇 军事学
5 篇 文学

主题

509 篇 machine learning
240 篇 deep learning
117 篇 support vector m...
99 篇 adversarial mach...
92 篇 accuracy
92 篇 machine learning...
83 篇 forecasting
74 篇 convolutional ne...
70 篇 predictive model...
69 篇 feature extracti...
53 篇 students
51 篇 learning systems
48 篇 decision trees
48 篇 training
47 篇 reinforcement le...
47 篇 computational mo...
47 篇 computer vision
46 篇 internet of thin...
43 篇 artificial intel...
41 篇 sentiment analys...

机构

46 篇 chitkara univers...
15 篇 graphic era hill...
10 篇 college of manag...
9 篇 graphic era deem...
8 篇 computer science...
7 篇 school of comput...
7 篇 school of comput...
7 篇 school of comput...
7 篇 department of in...
6 篇 università di ca...
6 篇 infn sezione di ...
6 篇 north-west unive...
6 篇 computer science...
6 篇 lovely professio...
6 篇 in2p3 lpc campus...
6 篇 kkr and ksr inst...
6 篇 institut univers...
6 篇 national centre ...
6 篇 greater bay area...
6 篇 school of nursin...

作者

18 篇 kukreja vinay
15 篇 mehta shiva
10 篇 william p.
8 篇 sharma vikrant
7 篇 mahamudul hasan
7 篇 shiva mehta
6 篇 chen a.
6 篇 benoit d.m.
6 篇 ferrara g.
6 篇 ardid m.
6 篇 calvo d.
6 篇 gagliardini s.
6 篇 eddymaoui a.
6 篇 kulikovskiy v.
6 篇 labalme m.
6 篇 gal t.
6 篇 bozza c.
6 篇 khatun a.
6 篇 kalekin o.
6 篇 huang f.

语言

3,995 篇 英文
142 篇 其他
30 篇 中文

检索条件"任意字段=Proceedings of the 2023 4th International Conference on Machine Learning and Computer Application"

共 4138 条记录，以下是271-280 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：

Design of Quantum machine learning Course for a computer Science Program 4

Design of Quantum Machine Learning Course for a Computer Sci...

引用

4th IEEE international conference on Quantum Computing and Engineering (QCE) / IEEE Quantum Week / 2nd IEEE Quantum Science and Engineering Education conference (QSEEC)

作者： Kumar, Sathish Adeniyi, Temitope Alomari, Ahmad Ganguly, Santanu Cleveland State Univ Cleveland OH 44115 USA Photon Inc London England

ISBN: (纸本)9798350343236

In this work, we present the design and plan of Quantum machine learning (QML) course in a computer science (CS) University program at senior undergraduate level / first year graduate level. Based on our survey, there is a lack of detailed design and assessment plan for the delivery of QML course. In this paper we have presented the QML course design with week by week details of QML concepts and hands on activities that are covered in the course. We also present how this QML course can be assessed from CS program learning outcomes perspective.

关键词： Quantum machine learning Quantum computing Course Assessment Course Design QML Workflow

来源：评论

学校读者我要写书评

暂无评论

4th Crowd Science Workshop - CANDLE: Collaboration of Humans and learning Algorithms for Data Labeling 23

4th Crowd Science Workshop - CANDLE: Collaboration of Humans...

引用

16th international conference on Web Search and Data Mining

作者： Ustalov, Dmitry Savage, Saiph van Berkel, Niels Liu, Yang Toloka Belgrade Serbia Northeastern Univ Boston MA USA Aalborg Univ Aalborg Denmark Univ Calif Santa Cruz Santa Cruz CA USA

ISBN: (纸本)9781450394079

Crowdsourcing has been used to produce impactful and large-scale datasets for machine learning and Artificial Intelligence (AI), such as ImageNET, SuperGLUE, etc. Since the rise of crowdsourcing in early 2000s, the AI community has been studying its computational, system design, and data-centric aspects at various angles. We welcome the studies on developing and enhancing of crowdworker-centric tools, that offer task matching, requester assessment, instruction validation, among other topics. We are also interested in exploring methods that leverage the integration of crowdworkers to improve the recognition and performance of the machine learning models. thus, we invite studies that focus on shipping active learning techniques, methods for joint learning from noisy data and from crowds, novel approaches for crowd-computer interaction, repetitive task automation, and role separation between humans and machines. Moreover, we invite works on designing and applying such techniques in various domains, including e-commerce and medicine.

关键词： crowdsourcing data labeling human-in-the-loop human-machine collaboration fairness and inclusion

来源：评论

学校读者我要写书评

暂无评论

Implementing IoT and Deep learning-Based Educational Systems for Assessing Practical learning with Cloud Computing applications 4

Implementing IoT and Deep Learning-Based Educational Systems...

引用

4th international conference on Technological Advancements in Computational Sciences, ICTACS 2024

作者： thakur, Hardeo Kumar Bafna, Minal Singh, Jagendra Naresh, Usikela Biswas, Susmi Kiran, Saggurthi School of Computer Science Engineering and Technology Bennett University Greater Noida India Agrawal Pg College Department of Physics Jaipur India Rangareddy India Haldia Institute of Management Makaut Department of Management Science West Bengal India Hyderabad India

ISBN: (纸本)9798350387490

In this study, the practical application of technologies related to IoT and machine learning in the robotics education context is examined based on the use of a robot with two proximity sensors. the trial system was designed to leverage the real-time sensor data and monitor and optimize student interactions with the robot in the course of performing such tasks as object manipulation and palletization. the sensor data is used to critically track the accuracy of student actions and help mentors provide feedback on the issue. the machine learning models used in the study, which are Recurrent Neural Network (RNN), K-Nearest Neighbors (KNN), Support Vector machine (SVM), and Long Short-Term Memory (LSTM), are applied to analyze the sensor data and predict and classify the student's actions so that corrective measures can be taken in time. the RNN model has the highest accuracy and efficiency for the task, as it reaches the level of 96.7% compared to 80% for KNN, 71% for SVM, and 75% for LSTM. this advantage is explained by the capability to model temporal dependencies or otherwise capture with its memory the sequences present in the sensor data. the data is transmitted securely and efficiently to the cloud platforms and back due to the implementation of the IoT, which also helps to deploy the model more effectively. Overall, it is concluded that the integration of IoT and machine learning is beneficial and transformative in educational contexts as it provides students with personalized real-time feedback. In this way, the level of proficiency of the students in dealing with robotic and IoT-based systems is significantly enhanced, preparing them to successfully address the future challenges of industry and academia in the sphere. © 2024 IEEE.

关键词： Proximity sensors

来源：评论

学校读者我要写书评

暂无评论

the application of machine learning in Android Malware Detection 4

The Application of Machine Learning in Android Malware Detec...

引用

4th international conference on Neural Networks, Information and Communication Engineering, NNICE 2024

作者： Xie, Wei Zhang, Xueying Rdfz Chaoyang Branch School Beijing China

ISBN: (纸本)9798350394375

the android system had been a popular system among mobile phone users, there are lots of applications based on android system, which contain malware and good software. Malware is very harmful to the computer because it can embed rubbish advertisements, applications and viruses in order to disrupt the functioning of your mobile phone or steal your personal information. Network information security has gained its significance on the internet which is related to our safety directly. the traditional method to detect malware is to use anti-virus software to check the mobile phone. However, with the rapid uploading and increase in the number of software, some malware cannot be detected well and also the anti-virus software may embed some advertisements or download bundle software (usually hard to uninstall) automatically by itself. With development, machine learning 's calculation efficiency has been improved significantly and it has been used in many regions. In this paper, we select several typical machine-learning algorithms to build the malware detection model. Considering the unbalanced data, we made an unweighted and weighted model simultaneously. As a result, the weighted random forest model has the best effect and its accuracy is 98.94% © 2024 IEEE.

关键词： machine learning

来源：评论

学校读者我要写书评

暂无评论

Survey on Visual Speech Recognition using Deep learning Techniques 4

Survey on Visual Speech Recognition using Deep Learning Tech...

引用

4th international conference on Communication Systems, Computing and IT applications, CSCITA 2023

作者： Chand, Ritika Jain, Pushpit Mathur, Abhinav Raj, Shiwansh Kanikar, Prashasti Department of Computer Engineering SVKM's NMIMS Mukesh Patel School of Technology Management and Engineering Mumbai India

ISBN: (纸本)9781665459877

Lip Reading has evolved and from where it began to help deaf people has slowly turned into a service where in the Digital Entertainment industry has started utilizing it. With the recent rise of AI, automated technologies have touched the boundaries of Lip Reading as well. Various Algorithms have been devised using Neural Network Methodologies. We observe that a lot of the algorithms reviewed, have been exploring various techniques whether it be a variation from detecting lip features to the text generation process *** the amount of research done in the field, one can always look out towards a better & optimized lip detection. the study emphasizes more towards looking at the utilization of the machine learning & Deep learning technologies and thus provides a vivid view at the bigger picture of the interpolation of AI in the Visual based Lip Reading domain. © 2023 IEEE.

关键词： Deep learning

来源：评论

学校读者我要写书评

暂无评论

Investigating the Effectiveness of machine learning Techniques for Real Estate Price Prediction 4

Investigating the Effectiveness of Machine Learning Techniqu...

引用

4th international conference on Smart Electronics and Communication, ICOSEC 2023

作者： Singh, Baljap Singh, Jaspreet Kumar, Anubhav Gupta, Shikha Chandigarh University Computer Science and Engineering Mohali India

ISBN: (纸本)9798350300888

In the real-estate industry, the capacity to extract significant information from raw data makes it extremely valuable for predicting home values, housing attributes, and much more. the cost of housing varies from day to day and is frequently increased arbitrarily rather than using calculations. the housing market and homeowners are routinely impacted by changes in property values. A literature study is done to evaluate the most important factors and reliable models for predicting housing values. the results of this analysis confirmed the use of Support Vector Regression, Linear Regression, and Artificial Neural Networks as the most effective models when compared to others. Furthermore, our data imply that geographical and real estate agents are important determinants in forecasting property values. this research will be very helpful in determining the most important variables for estimating housing costs and the best machine learning model to employ when doing this type of research, especially for academics and housing developers. © 2023 IEEE.

关键词： Housing

来源：评论

学校读者我要写书评

暂无评论

Analysis of machine learning Algorithms for Classification of Osteosarcoma 4

Analysis of Machine Learning Algorithms for Classification o...

引用

4th IEEE international conference on Mobile Networks and Wireless Communications, ICMNWC 2024

作者： Deepa, B.G. Ayshwarya, A. Lokesh, C.K. Pooja, N.G. Christ University Department of Computer Science Bangalore India Kristu Jayanti College Department of Computer Science Bangalore India School of Computer Science and Application Reva University Bangalore India

ISBN: (数字)9798331528348

ISBN: (纸本)9798350352931

the exploration of histopathological osteosarcoma data is a profitable technique to observe the pathological textures of osteosarcoma. Osteosarcoma classification is complex and inefficient due to inter-class similarities and variations. In this research, we employ two ML methods to classify OST: RF alias Random Forest and SVM alias Support Vector machine. Sensitivity, Accuracy, F1-score, AUC, Kappa, and Specificity are the performance metrics used in the evaluation of ML models. Based on these classifiers, osteosarcoma can be classified as three categories which are explained in this work. the overall accuracy of the RF and SVM are 94.70% and 93.2% respectively. RF algorithm outperforms SVM algorithm with specificity 98.5%, F1 score 93.60%, sensitivity 92.30%, overall accuracy 94.70%, AUC 0.99 and Kappa 0.92. © 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

machine learning Methods for the Timely Identification of Autism Spectrum Disorder in Toddlers 4

Machine Learning Methods for the Timely Identification of Au...

引用

4th international conference on Emerging Systems and Intelligent Computing, ESIC 2024

作者： Jain, Sweta Tripathy, Hrudaya Kumar Shri Ramdeobaba College of Engineering and Management Department of Computer Science and Engineering Nagpur India Kalinga Institute of Industrial Technology Deemed to Be University School of Computer Engineering Odisha Bhubaneswar751024 India

ISBN: (纸本)9798350349856

Autism Spectrum Disorder (ASD) is an evolving ailment that upsets the nervous system, by means of a widespread range of symptoms and severity levels. Early ASD identification is challenging and there is no treatment, early detection and intervention are vital for improving outcomes in individuals with ASD. this research, explored the application of machine learning (ML) techniques, specifically Support Vector machines (SVM), Random Forest (RF), and Gradient Boosting (GB), for detection of ASD using a dataset focused on screening toddlers. the dataset, obtained from Kaggle in the 2018 release, encompasses demographic information, medical history, and screening test results for toddlers. the presented system employs leading edge algorithms to analyze the dataset and build predictive models for ASD detection. SVM is preferred due to its capability to lever multifaceted associations in high-dimensional data, while RF excels in handling diverse feature sets and capturing ***, known for its ensemble learning capabilities, is also employed to boost predictive performance. the assessment of these models is accomplished via bench mark estimation metrics in ML. this approach aims at an effective model for ASD detection in children based on the characteristics of the dataset. Additionally, feature importance analysis is conducted to gain insights into the factors contributing to ASD prediction. the implementation results reveals that SVM algorithm outperforms in the proposed ASD diagnosis. Early identification using ML techniques could assist healthcare professionals and caretakers in timely intervention, ultimately improving the prolonged effects for persons with ASD. © 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

Predicting Customer Satisfaction in Brazil E-commerce: A Comparative Study of machine learning Techniques 4

Predicting Customer Satisfaction in Brazil E-commerce: A Com...

引用

4th IEEE international conference on Computing, Communication, and Intelligent Systems, ICCCIS 2023

作者： Pandey, Trilok Nath Vasudev, Anirudh Sagayanathan, Dennis Anjan, Govind Arshad, Danish Patra, Sudhansu Shekhar School of Computer Science and Engineering Vellore Institute of Technology Chennai India School of Computer Applications KIIT Deemed to Be University Bhubaneswar India

ISBN: (纸本)9798350306118

Customers' propensity for brand loyalty, recurrent business, and positive word-of-mouth are directly influenced by the degree to which their needs are met. E-commerce happens to be one of the biggest online industries in the world which till date is not completely understood from a customer's standpoint. Brazil being a developing country is the 12th largest market for e-commerce in the world and accounts for 30 % of e-commerce in Latin America and remains largely unexplored. In this study, we used machine learning (ML) techniques such as LSTM, XGBClassifier, Logistic Regression, Neural Network, and Random Forest to predict customer satisfaction using a retail dataset from Brazil. We contrasted the efficacy of these models using a variety of evaluation criteria, such as precision, recall, and Fl-score. the LSTM model had the highest accuracy with a score of 0.89, followed by the XGBClassifier with 0.83, Naive Bayes with 0.73, Logistic Regression with 0.74, Support Vector Classifier with 0.74, and Random Forest with 0.78. the superior performance of the LSTM model over other models suggests that it has superior predictive abilities. © 2023 IEEE.

关键词： Customer satisfaction

来源：评论

学校读者我要写书评

暂无评论

4th international conference on Intelligent Engineering and Management, ICIEM 2023

4th International Conference on Intelligent Engineering and ...

引用

4th international conference on Intelligent Engineering and Management, ICIEM 2023

ISBN: (纸本)9798350341126

the proceedings contain 68 papers. the topics discussed include: app based implementation of modern agriculture utilities for farmers;application of intelligent computing (IC) in human resources (HR) analytics: towards better tomorrow;artificial intelligence based chatbot for promoting equality in high school advising;assessment of human activity recognition based on impact of feature extraction prediction accuracy;biometric and RFID passive tag based student identification system for secure attendance management;blockchain and federated learning enabled smart traffic management system for smart cities;climatic temperature forecasting with regression approach;covid-19 detection on X-ray image using deep learning;critical review on applications of self-healing concrete in reinforced concrete structures;decipherable for artificial intelligence in Medicare: a review;dementia identification using machine learning algorithms: comparative analysis;and design and implementation of an easy-to-use tracking device for logistic applications.

关键词：

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414页 << < 24 25 26 27 28 29 30 31 32 33 > >>

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：