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

256 篇 会议
3 篇 期刊文献
2 册 图书

馆藏范围

261 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

196 篇 工学
- 183 篇 计算机科学与技术...
- 123 篇 软件工程
- 37 篇 信息与通信工程
- 35 篇 生物工程
- 25 篇 生物医学工程（可授...
- 19 篇 电气工程
- 15 篇 机械工程
- 8 篇 控制科学与工程
- 7 篇 光学工程
- 7 篇 化学工程与技术
- 7 篇 交通运输工程
- 5 篇 动力工程及工程热...
- 5 篇 电子科学与技术（可...
- 4 篇 仪器科学与技术
- 4 篇 土木工程
71 篇 理学
- 40 篇 数学
- 35 篇 生物学
- 18 篇 统计学（可授理学、...
- 14 篇 系统科学
- 11 篇 物理学
- 7 篇 化学
50 篇 管理学
- 34 篇 管理科学与工程(可...
- 23 篇 图书情报与档案管...
- 22 篇 工商管理
18 篇 医学
- 12 篇 基础医学(可授医学...
- 11 篇 临床医学
- 8 篇 药学(可授医学、理...
8 篇 法学
- 7 篇 社会学
7 篇 经济学
- 7 篇 应用经济学
5 篇 教育学
- 5 篇 教育学
3 篇 农学

主题

71 篇 machine learning
17 篇 deep learning
14 篇 data analytics
13 篇 machine learning...
10 篇 predictive model...
9 篇 big data
8 篇 support vector m...
8 篇 predictive analy...
8 篇 decision trees
8 篇 artificial intel...
7 篇 data mining
6 篇 classification
5 篇 covid-19
5 篇 real-time system...
5 篇 prediction algor...
5 篇 data visualizati...
5 篇 accuracy
5 篇 forecasting
4 篇 clustering
4 篇 self-supervised ...

机构

3 篇 university of du...
2 篇 ecloud system an...
2 篇 amity school of ...
2 篇 department of ar...
2 篇 aset amity unive...
2 篇 department of co...
2 篇 banasthali vidya...
2 篇 meghnad saha ins...
2 篇 department of el...
2 篇 department of co...
2 篇 bba in marketing...
2 篇 vardhaman colleg...
2 篇 shanghai jiao to...
2 篇 study group aust...
2 篇 zhejiang lab peo...
2 篇 college of manag...
2 篇 department of el...
2 篇 information syst...
2 篇 autonomous unive...
2 篇 college of infor...

作者

2 篇 fatima ruhi
2 篇 v. karthik
2 篇 j. sasidevi
2 篇 du siyu
2 篇 ma yong
2 篇 ferreira joao c.
2 篇 zhao yong
2 篇 zhang sichao
2 篇 tong jinyu
2 篇 chen yu
2 篇 draschner carste...
2 篇 wathiq mansoor
2 篇 m. a. jabbar
2 篇 kazi sabeena
2 篇 zhao shouwang
2 篇 galpin ixent
2 篇 suryanarayana g.
2 篇 pan haiyang
2 篇 lehmann jens
2 篇 shahbaz nadeem

语言

255 篇 英文
6 篇 其他
1 篇 中文

检索条件"任意字段=5th International Conference on Applied Machine Learning and Data Analytics, AMLDA 2022"

共 261 条记录，以下是181-190 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：

AI-Enhanced Unmanned Aerial Vehicles for Search and Rescue Operations

AI-Enhanced Unmanned Aerial Vehicles for Search and Rescue O...

引用

Innovative Trends in Information Technology (ICITIIT), international conference on

作者： K Rashida Farsath K Jitha V.K Mohammed Marwan A Muhammed Ali Jouhar K.P Muhammed Farseen K.A Musrifa Department of Computer Science & Engineering MEA Engineering College

ISBN: (数字)9798350386813

ISBN: (纸本)9798350386820

this paper introduces a cutting-edge AI-empowered Unmanned Aerial Vehicle (UAV) system, enriched with state-of-the-art sensor technology, advanced image recognition algorithms, and autonomous navigation capabilities. the system represents a transformative approach to search and rescue operations, offering unparalleled precision and rapid response times. Our methodology encompasses multifaceted data collection techniques, including surveys, interviews, data mining, Internet of things (IoT) sensors, and sophisticated video analytics. machine learning and deep learning models are then applied to process and analyze this data, enabling real-time image recognition for precise target identification. the system’s AI-driven autonomous navigation algorithms optimize mission planning, resulting in significantly reduced response times and heightened mission success rates. Extensive real-world tests and simulations validate the exceptional performance of the proposed AI-empowered UAV system. these tests underscore its capacity to expedite emergency response efforts in dynamic and challenging environments. In parallel, this paper addresses critical ethical considerations, em-emphasizing responsible data handling practices, and robust security measures to ensure the system’s integrity in sensitive contexts. As exemplified through a compelling case study of successful rescue operations, this technology represents a groundbreaking advancement in the field. By bridging the gap between cutting- edge technology and life-saving applications, it holds the potential to redefine the landscape of search and rescue missions, ushering in an era of heightened efficiency, precision, and impact.

关键词： Image recognition machine learning algorithms Heuristic algorithms Autonomous aerial vehicles Emergency services Real-time systems Time factors

来源：评论

学校读者我要写书评

暂无评论

Cassava Syndrome Scan a Pioneering Deep learning System for Accurate Cassava Leaf Disease Classification 1

引用

6th international conference on applied machine learning and data analytics, amlda 2023

作者： Rahat, Irfan Sadiq Ghosh, Hritwik Ramesh, Janjhyam Venkata Naga Kiran, Ajmeera Verma, Poonam VIT-AP University Andhra Pradesh Amaravati India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram Andhra Pradesh Guntur522502 India Department of Computer Science and Engineering MLR Institute of Technology Dundigal Telangana Hyderabad500043 India School of Computing Graphic Era Hill University Dehradun R/S Graphic Era Deemed to be University Dehradun248002 India

ISBN: (数字)9783031554865

ISBN: (纸本)9783031554858

Cassava serves as a crucial source of carbohydrates for a vast population globally, playing a significant role in food security. the crop, however, faces threats from a variety of leaf diseases, leading to substantial reduction in yield. Prompt and precise identification of these diseases is essential for implementing effective countermeasures and maintaining adequate food supply. Recently, deep learning methodologies have demonstrated remarkable capabilities in image-based recognition and classification, including the diagnosis of plant diseases. the objective of this research is to construct a dependable cassava leaf disease identification system utilizing deep learning approaches, with an emphasis on achieving exceptional accuracy and dependability. We assess the performance of multiple cutting-edge deep learning architectures, such as ResNet50, DenseNet121, EfficientNet-B0, NasNetLarge, and EfficientNet-V2, in the context of cassava leaf disease classification. the dataset is comprised of nearly 15,000 test images, accompanied by image_id and label data in *** and sample_*** files. data preprocessing, including resizing, normalization, and augmentation, is conducted before partitioning the data into training, validation, and test sets. Our findings indicate that the EfficientNet-V2 model surpasses its counterparts in performance, attaining the highest validation accuracy of 0.7480 and a comparatively low validation loss of 0.7488. We further explore several strategies to enhance model performance, such as optimizing hyperparameters, employing data augmentation, utilizing early stopping, implementing learning rate scheduling, applying transfer learning, and adopting ensemble techniques. the resulting classifier offers potential benefits to farmers and agricultural specialists in making informed decisions regarding cassava leaf disease management, ultimately leading to improved crop yields and food security. © the Author(s), under exclusive licens

关键词： Food supply

来源：评论

学校读者我要写书评

暂无评论

A Novel Deep Ensemble learning Framework for Classifying Imbalanced data Stream 5th

A Novel Deep Ensemble Learning Framework for Classifying Imb...

引用

5th international conference on Information and Communication Technology for Intelligent Systems, ICTIS 2021

作者： Arya, Monika Hanumat Sastry, G. Bhilai Institute of Technology Chhattisgarh Durg India University of Petroleum and Energy Studies Dehradun India

ISBN: (纸本)9789811639449

machine learning has emerged as one of the most indispensable fields of this era in mining stream data. the stream data have the tendency to change their characteristics over time. Mining imbalanced stream is a research demanding subfield of this area. In imbalanced data, one of the target classes has much less instances than other class. the imbalanced data may differ either in their ratio between majority and minority class or dimension or the number of classes. the performance of the classifier is affected by variety of imbalanced data set used for training the classifier. Because, the learning of classifier is different for different types of data sets. Imbalanced data can result due to rare events which can have negative impact on society. Most traditional data mining algorithm misclassifies the minority class of the imbalance data sets or considers it as noise. therefore, the decision is biased toward majority class and hence reduces the accuracy and overall performance of the algorithm. the algorithms for classifying imbalanced data sets thus demand high adaptability to changes in the majority and minority class ratios. the performance of traditional machine learning algorithms is enhanced by applying ensemble method and deep learning approaches. Purpose: this paper proposes a framework based on deep ensemble learning for classifying imbalanced stream of data. Methodology: the deep ensemble methods ensemble multiple base learners. While deep learning approach is applied to improve the performance by extracting lower-level features and feeding them forward for the next layer to identify higher level features. Results: In this method, the effect of highly imbalanced classes is reduced by uniting the ensemble method with deep learning. the accuracy of the classifier is improved in terms of not only accuracy but also categorical accuracy. Conclusion: In addition to accuracy, other performance measures like categorical prediction accuracy, training accuracy and pr

关键词： Deep learning

来源：评论

学校读者我要写书评

暂无评论

3rd international Workshop on New Approaches for Multidimensional Signal Processing, NAMSP 2022

3rd International Workshop on New Approaches for Multidimens...

引用

3rd international Workshop on New Approaches for Multidimensional Signal Processing, NAMSP 2022

ISBN: (纸本)9789811978418

the proceedings contain 23 papers. the special focus in this conference is on New Approaches for Multidimensional Signal Processing. the topics include: Development of IoT Indoor Monitoring System for Independent Elderly;improving the Process of Evaluating User Stories Using the Paraconsistent Annotated Evidential Logic Eτ;neural Network Algorithm applied in Electrical Engineering Automation;Truss Structure Optimization Design Based on FE-PSO-SQP Algorithm;practice System of Ant Colony Optimization Algorithm in Business Administration;literature Review of Smart Contracts Using Blockchain Technology;a Comprehensive Study of 5th Generation Scheduling Algorithms;a Comparative Analysis of Homogeneous and Heterogeneous Protocols to Maximize the Lifetime of Wireless Sensor Network for Precision Agriculture;the Integration Development and Upgrading Path of Industry 4.0 Architecture Industrial Engineering Network Driven by Big data;video Tracing of Moving Objects by Fusing three-Term Decompositions;SAAS Application Prospect Analysis in Hrm and Methods to Upgrade the Contemporary System;All Digital Phase Locked Loop (ADPLL) and Its Blocks—A Comprehensive Knowledge;on Realization of Smart Logistic Warehouse Management with Internet of things;development and Design of Intelligent Traction System for Bulk Cargo Terminal;deep learning Approaches for Classroom Audio Classification Using Mel Spectrograms;Tensor Spectral Pyramid for Color Video Sequences Representation, Based on 3D FO-AHKLT;electromyography Signal Acquisition, Processing, Optimization and Its Applications;research on the Radar Signal Classification Method Based on the Deep Faith Network Model;measuring machine Intelligence Using Black-Box-Based Universal Intelligence Metrics;COVID Detection Using ECG Image Reports: A Survey;disease Detection Techniques in Plants: Transition from Manual to Automation.

关键词：

来源：评论

学校读者我要写书评

暂无评论

the Values Encoded in machine learning Research 22

The Values Encoded in Machine Learning Research

引用

5th ACM conference on Fairness, Accountability, and Transparency, FAccT 2022

作者： Birhane, Abeba Kalluri, Pratyusha Card, Dallas Agnew, William Dotan, Ravit Bao, Michelle Mozilla Foundation & University College Dublin School of Computer Science Dublin Ireland Stanford University Computer Science Department Palo Alto United States University of Michigan School of Information Ann Arbor United States University of Washington United States University of Pittsburg Center for Philosophy of Science Pittsburgh United States

ISBN: (纸本)9781450393522

machine learning currently exerts an outsized influence on the world, increasingly affecting institutional practices and impacted communities. It is therefore critical that we question vague conceptions of the field as value-neutral or universally beneficial, and investigate what specific values the field is advancing. In this paper, we first introduce a method and annotation scheme for studying the values encoded in documents such as research papers. Applying the scheme, we analyze 100 highly cited machine learning papers published at premier machine learning conferences, ICML and NeurIPS. We annotate key features of papers which reveal their values: their justification for their choice of project, which attributes of their project they uplift, their consideration of potential negative consequences, and their institutional affiliations and funding sources. We find that few of the papers justify how their project connects to a societal need (15%) and far fewer discuss negative potential (1%). through line-by-line content analysis, we identify 59 values that are uplifted in ML research, and, of these, we find that the papers most frequently justify and assess themselves based on Performance, Generalization, Quantitative evidence, Efficiency, Building on past work, and Novelty. We present extensive textual evidence and identify key themes in the definitions and operationalization of these values. Notably, we find systematic textual evidence that these top values are being defined and applied with assumptions and implications generally supporting the centralization of power. Finally, we find increasingly close ties between these highly cited papers and tech companies and elite universities. © 2022 Owner/Author.

关键词： machine learning

来源：评论

学校读者我要写书评

暂无评论

Validation of AI models for ITCZ Detection from Climate data 5

Validation of AI models for ITCZ Detection from Climate Data

引用

5th international conference on data Science and Information Technology, DSIT 2022

作者： Serra, J. Quezada, R. Fortes, S. Tellez, N. Allaico, A. Landaverde, E. Kumar, Y. Li, J.J. Morreale, P. School of Computer Science and Technology Kean University UnionNJ07083 United States

ISBN: (数字)9781665498685

ISBN: (纸本)9781665498685

the Climate Change topic itself and associated life adjustments became a top global problem of the XXI century. It requires immediate attention and long-term solutions. Physical tracing of weather changes, performed without Artificial Intelligence (AI), is not capable of detection in a timely manner and correctly classifying dangerous situations, which is crucial for making rapid decisions and taking immediate actions on the ground. AI and machine learning (ML) systems are currently dominating solutions in Computer Science research fields and the issues of their testing and validation remain a critical open problem due to their uncertain outcomes. We use test automation to validate and assure the quality of various AI systems for one kind of weather prediction. the subject of our study is Inter-Tropical Convergence Zones (ITCZs). ITCZs play an important role in the global circulation system and even small changes in their patterns can cause severe droughts or flooding as well as other disasters like hurricanes. Global warming is causing more ITCZ scenarios as shown in weather data, which makes physical detection infeasible. Our research initially discovered that ITCZ detection based on a physical model alone could misclassify some unexpected situations when the double bands occur at different places with similar intensity or at the same places with different intensities. We then designed experiments to train AI models to detect ITCZs with test automation to collect results. We further trained several AI models with focus on VGG-16, VGG-19, Xception and MobileNETV2 models, collected and compared their results through test automation. Our exhaustive trials eventually achieved a 96.8% accuracy, which might be the best AI model to detect ITCZs with test automation and without human intervention. these results show that test automation can contribute to the selection of optimum AI models. © 2022 IEEE.

关键词： Global warming

来源：评论

学校读者我要写书评

暂无评论

data-Driven Approach: Predicting Critical Braking Temperature in Train Wheels Using Fiber Bragg Grating Sensors

Data-Driven Approach: Predicting Critical Braking Temperatur...

引用

Communication, Computing and Industry 4.0 (C2I4), international conference on

作者： Sumith Sigtia H N Gayathri Vidhan Mehta Deepa N Sneha Sharma Preeta Sharan Dept. of AI & ML The Oxford College of Engineering Bangalore India Dept. of Chemistry The Oxford College of Engineering Bangalore India Dept. of Electronics and Communication Dayananda Sagar University Bangalore India Dean R&D ECE The Oxford College of Engineering Bangalore India

ISBN: (数字)9798350379884

ISBN: (纸本)9798350379891

this research presents a novel approach to predicting the critical temperature at which brakes must be applied to train wheels, combining real-time data collection through Fiber Bragg Grating (FBG) sensors with advanced machine learning models. While previous studies have either focused on theoretical models or utilized real-time sensor data without predictive capabilities, our approach uniquely integrates both sensor data and predictive modeling to improve accuracy. Conducted at the Oxford College of Engineering, our experiment used a scaled-down train model to measure key parameters such as speed, weight, and temperature during braking. Unlike traditional models, our study employed machine learning algorithms, including Linear Regression, Decision Tree, and Random Forest, to forecast the critical braking temperature. the Random Forest model significantly outperformed other models, achieving a perfect R 2 score of 1.00 and a Mean Squared Error (MSE) of 0.00. Our integrated approach not only enhances the precision of braking temperature predictions but also offers a robust framework for predictive maintenance, improving the operational safety and efficiency of rail systems. this research marks a significant step forward in real-time monitoring and predictive analytics for rail transport systems, providing a superior method for detecting and responding to critical thermal conditions.

关键词： Temperature sensors Temperature measurement Rails Wheels Predictive models Real-time systems data models Sensors Safety Random forests

来源：评论

学校读者我要写书评

暂无评论

Artificial Intelligence in Business Operations in one of the country in South East Asia: Exploring Applications, Challenges, Limitations, and Future Research Directions

Artificial Intelligence in Business Operations in one of the...

引用

data Intelligence and Cognitive Informatics (ICDICI), international conference on

作者： Jolou Vincent M. Jala Everly A. Nacalaban Nenon Roy A. Sandinao Joshua T. Gaid Randy Joy M. Ventayen Bachelor of Science in Entrepreneurship City College of El Salvador Cagayan de Oro City Philippines Bachelor of Science in Business Administration Bukidnon State University Malitbog Bukidnon Philippines Bachelor of Science in Business Administration Bukidnon State University Bukidnon Philippines Department of Math and Science University of Science and Technology of Southern Philippines - Claveria Claveria Philippines Business Administration Department Pangasinan State University Lingayen Pangasinan Philippines

ISBN: (数字)9798350389609

ISBN: (纸本)9798350389616

the advent, growth, advancement and development of artificial intelligence (AI) has transformed businesses and organizations around the globe, as it contributes to innovation and operational efficiency. It utilizes big data analytics as well as machine learning, which drives strategic decision-making. However, even with the amplified discussion as well as the availability of Artificial Intelligence techniques and tools, there is still a lack of knowledge on how it affects business *** study explores the future of Artificial Intelligence in Business Operations in the Philippines. More specifically, the study explored how Artificial Intelligence can be applied in operations management, supply chain management, sales and marketing, training and development, accounting and finance, customer service, strategic planning and quality controlIn order to address this objective, the proponents conducted a critical analysis of 153 Scopus indexed publications in the body of literature to examine Artificial Intelligence in the context of business *** Intelligence has a surmountable potential in business organizations and employee adjustments in lieu to this technology necessitates to adopt in these changes. Due to the preceding concerns about the integration of Artificial Intelligence within business operations, the study provided exploratory discussion around its use, challenges, limitations and suggested areas requiring future studies.

关键词： Training Technological innovation Supply chain management Government machine learning Strategic planning Artificial intelligence Organizational aspects Standards Business

来源：评论

学校读者我要写书评

暂无评论

Assessing Digital Competence through Teacher Training in Early Education Teachers 4th

Assessing Digital Competence Through Teacher Training in Ear...

引用

4th international conference on applied Technologies, ICAT 2022

作者： Guallichico, Geovanna Ocaña, Mauro Tejada, Cristhian Bautista, Cuauhtémoc Universidad de Las Fuerzas Armadas ESPE Sangolquí Ecuador Departamento de Pedagogía Facultad de Filosofía Letras y Ciencias de la Educación Universidad Técnica de Manabí Portoviejo Ecuador Ayllu Academia Caledon Canada Agile Thought Ciudad de México Mexico

ISBN: (纸本)9783031249778

Training programs on digital competences for teachers are often carried out without a thorough diagnosis, so it is difficult to accurately identify which digital competences need to be developed in teachers. Best-case scenario, questionnaires are proposed as the only diagnostic tool, and the answers are often based on the subjectivity of respondents which inevitably led to contradictions as revealed by the literature. In this study, a machine learning algorithm has been used to determine the teachers’ needs in terms of detecting which and how much they need to improve their digital competences. 181 teachers took a 4-week-virtual course containing two modules (Content Creation and School Management) to assess specific teacher competences. Results showed that the type of question used in the evaluations influences the academic performance of the participants. data were analyzed based on 1) three types of questions (matching, multiple choice and true/false) and 2) scores obtained by the participants at the end of the course. Firstly, matching questions reflect a greater distribution in the results, which indicates that this type of question may be more challenging, followed by the multiple-choice questions. While true or false questions should not be included as they leave too much room for chance. Secondly, performance level reflects that demographic information (e.g. age, type of institution, and level at which they teach) is associated with final performance of digital competences. this type of analytics can provide a much more detailed overview in terms of detecting strengths and weaknesses in the development of digital competences of teachers. © 2023, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： E-learning

来源：评论

学校读者我要写书评

暂无评论

machine learning Adoption in Blockchain-Based Smart Applications

Machine Learning Adoption in Blockchain-Based Smart Applicat...

引用

international conference on Contemporary Computing and Informatics (IC3I)

作者： Vishal Suthar Vipul Bansal Ch.Srinivasa Reddy José Luis Arias Gonzáles Devendra Singh Devesh Pratap Singh Department of Computer Engineering Government Polytechnic College Mandore Jodhpur Rajasthan India Department of Information Technology Kurukshetra University Haryana India Department of IT Vignan's Institute of Information Technology Duvvada Departamento of Business Pontifical Catholic University of Peru Peru Uttaranchal Institute of Technology Uttaranchal University Dehradun Uttarakhand India Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun Uttarakhand India

the development of blockchain technology (BT) in recent years has made it a distinctive, revolutionary, and popular innovation. Information security and confidentiality are prioritised by the decentralised database in BT. Additionally, the consensus process in it ensures the validity and security of the data. However, it brings up fresh security concerns including majority assault and the double expenditures. data analytics using cryptocurrency sensitive data are needed to address the aforementioned problems. these dataset' analytics highlight the value of recently developed techniques such as machine learning (ML). ML uses a reasonable quantity of data to generate accurate predictions. In ML, data exchange and dependability are essential to enhancing the precision of outcomes. Results from the fusion of these two technologies (ML and BT) may be quite exact. In this research, we give a thorough investigation into the use of machine learning (ML) to strengthen the security of BT-based intelligent systems. the assaults on a blockchain-based network may be analysed using a variety of classic machine learning (ML) approaches, including Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), Clustering, Bagging, and Support Vector machines (SVM) (LSTM).We also discuss how the two technologies may be used together in a number of advanced areas, including smart urban, the national grid, medicine, and autonomous aerial vehicles (UAVs). the difficulties and concerns facing future research are then examined. Finally, a study based with a thorough analysis is offered.

关键词： Support vector machines Technological innovation Privacy data analysis Smart buildings machine learning Robustness

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 << < 15 16 17 18 19 20 21 22 23 24 > >>

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：