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

4,918 篇 会议
4,164 篇 期刊文献
21 册 图书

馆藏范围

9,103 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

4,812 篇 工学
- 2,425 篇 计算机科学与技术...
- 1,928 篇 软件工程
- 1,054 篇 信息与通信工程
- 855 篇 电气工程
- 825 篇 电子科学与技术（可...
- 754 篇 控制科学与工程
- 504 篇 仪器科学与技术
- 462 篇 光学工程
- 418 篇 生物工程
- 387 篇 生物医学工程（可授...
- 382 篇 化学工程与技术
- 342 篇 材料科学与工程（可...
- 330 篇 动力工程及工程热...
- 316 篇 核科学与技术
- 310 篇 机械工程
- 278 篇 网络空间安全
- 211 篇 交通运输工程
- 209 篇 安全科学与工程
4,448 篇 理学
- 2,602 篇 物理学
- 1,069 篇 数学
- 634 篇 地球物理学
- 570 篇 生物学
- 527 篇 化学
- 371 篇 统计学（可授理学、...
1,012 篇 管理学
- 699 篇 管理科学与工程(可...
- 356 篇 图书情报与档案管...
- 337 篇 工商管理
545 篇 医学
- 427 篇 临床医学
- 365 篇 基础医学(可授医学...
- 266 篇 公共卫生与预防医...
208 篇 法学
160 篇 经济学
151 篇 农学
76 篇 教育学
26 篇 文学
6 篇 艺术学
5 篇 军事学
2 篇 哲学
1 篇 历史学

主题

387 篇 accuracy
306 篇 deep learning
275 篇 real-time system...
220 篇 machine learning
205 篇 feature extracti...
202 篇 internet of thin...
189 篇 hadron colliders
170 篇 computational mo...
152 篇 support vector m...
151 篇 predictive model...
149 篇 convolutional ne...
137 篇 training
125 篇 security
109 篇 artificial intel...
102 篇 optimization
98 篇 scalability
96 篇 technological in...
94 篇 reliability
91 篇 medical services
89 篇 neural networks

机构

597 篇 department of ph...
585 篇 physikalisches i...
562 篇 cavendish labora...
491 篇 oliver lodge lab...
431 篇 school of physic...
349 篇 faculty of scien...
329 篇 department of ph...
327 篇 department for p...
323 篇 department of ph...
321 篇 yerevan physics ...
313 篇 department of ph...
312 篇 kirchhoff-instit...
305 篇 department of ph...
297 篇 infn laboratori ...
296 篇 dept. of astrono...
290 篇 dept. of physics...
278 篇 dept. of physics...
271 篇 institute of phy...
270 篇 infn sezione di ...
262 篇 fakultät für phy...

作者

268 篇 a. gomes
258 篇 j. m. izen
256 篇 c. alexa
246 篇 g. bella
243 篇 j. strandberg
243 篇 d. calvet
243 篇 c. amelung
232 篇 s. chen
229 篇 g. spigo
226 篇 f. siegert
217 篇 hoshina k.
213 篇 haungs a.
212 篇 fürst p.
209 篇 y. tayalati
208 篇 r. ströhmer
203 篇 m. rijssenbeek
203 篇 s. jin
201 篇 g. gaudio
196 篇 m. gandelman
195 篇 t. gershon

语言

6,852 篇 英文
2,211 篇 其他
49 篇 中文
3 篇 德文
2 篇 斯洛文尼亚文
1 篇 法文
1 篇 日文

检索条件"机构=Be in Computer Engineering L D College of Engineering"

共 9103 条记录，以下是821-830 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：

Robust Sensor Fusion Algorithms for IoT

Robust Sensor Fusion Algorithms for IoT

引用

2024 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems, ICSES 2024

作者： Subramanian, Sakthitharan lokeshwari, Gandrakoti Jagadeesan, S. leela, S. Pratheep, V.G. Venkatasudhahar, Murugesan School of Computing Faculty of Engineering and Technology Srm Institute of Science and Technology Department of Computational Intelligence Tamil Nadu Chennai603203 India Geethanjali College of Engineering and Technology Department of Cybersecurity Telangana Hyderabad501301 India Department of Computer Science and Engineering Tamil Nadu Erode638052 India Sri Eshwar College of Engineering Department of Computer Science and Business Systems Tamil Nadu Coimbatore641202 India Kongu Engineering College Department of Mechatronics Engineering Tamil Nadu Perundurai638060 India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Mechanical Engineering Tamil Nadu Chennai600062 India

ISBN: (纸本)9798331543617

Sensor fusion is a critical component of the IoT ecosystem, as it combines a variety of data streams from multiple sensors to enhance the accuracy, reliability, and real-time decision-making. This research concentrates on the development of robust sensor fusion algorithms that are capable of effectively managing the heterogeneity, noise, and uncertainty that are inherent in data from Internet of Things (IoT) sensors. Our primary focus is on the ability of these algorithmic methodologies to manage data from multi-modal sensors. We investigate algorithms that are based on deep learning, Bayesian inference, and Kalman filtering. The proposed solutions aim to improve the performance of systems in sectors that are influenced by the Internet of Things (IoT), including healthcare, smart cities, industrial automation, and autonomous cars. This investigation investigates the scalability, energy efficiency, and computational burden of the Internet of Things (IoT) in the context of limited resources. Through simulations and practical applications, we demonstrate the enhancement of data precision, defect tolerance, and overall system dependability by these robust sensor fusion techniques. © 2024 IEEE.

关键词： Kalman filters

来源：评论

学校读者我要写书评

暂无评论

Applying Random Forest for IoT Systems in Industrial Environments 15

Applying Random Forest for IoT Systems in Industrial Environ...

引用

15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： dangore, Monika Bhaturkar, deepali Bhale, Kanchan M. Jadhav, Hemlata Makarand Borate, Vishal Kisan Mali, Yogesh Kisan Marathwada Mitramandal & Institute of Technology Department of Cse India Departmrnt of Information Technology Pune India Marathwada Mitra Mandal's College of Engineering Dept Electronics and Telecommunication Pune India Dr D Y Patil College of Engineering & Innovation Department of Computer Engineering Talegaon Pune India G.H Raisoni Institute of Engineering and Management Department of Artificial Intelligence Wagholi Pune India

ISBN: (纸本)9798350370249

There has been a total transformation in industrial environments as a result of the implementation of intelligent Internet of Things (IoT) technologies, which have enabled better resource allocation, predictive maintenance, and realtime monitoring. despite this, it is still challenging to guarantee the accuracy and dependability of these systems, particularly in industrial settings that are both complex and constantly evolving. despite the fact that previous research has studied the potential of machine learning in IoT applications, there is a lack of research on how to properly incorporate Random Forest in order to tackle the difficulties that are specific to industrial IoT systems. developing a novel strategy that makes use of Random Forest's characteristics to improve prediction accuracy in industrial IoT installations, boost adaptability, and minimize latency. Using Random Forest, a powerful machine learning technique that is known for its durability and flexibility, this work addresses the need for greater intelligence in IoT systems that are used in industrial settings. For the purpose of predictive analytics, the suggested technique includes the collection of data in real time from sensors on industrial devices, as well as the use of feature engineering and the utilization of a Random Forest model. When dealing with complex and ever-changing industrial data, the model will perform better than more conventional approaches. For example, it will perform better than other approaches. Consequently, the IoT system has become more adaptable and can swiftly adjust to new industrial conditions. Additionally, the accuracy of forecasts has substantially improved as a result of this. The findings will demonstrate that use of Random Forest for intelligent IoT systems in manufacturing facilities is successful. © 2024 IEEE.

关键词： Predictive maintenance

来源：评论

学校读者我要写书评

暂无评论

Cloud-Connected Wearable devices for lone Worker Protection With Wireless Sensor Networks 4

Cloud-Connected Wearable Devices for Lone Worker Protection ...

引用

4th International Conference on Sustainable Expert Systems, ICSES 2024

作者： Mohankumar, N. Manivannan, K.K. Babu, A. Ramesh Sampoornam, M. Maria Kovarasan, Rajesh Kambattan Srinivasan, C. Nagpur Campus Maharashtra Pune India Kcg College of Technology Department of Mechatronics Engineering Tamil Nadu Chennai India School of Computer Applications Vidya Vikas Institute of Engineering and Technology Karnataka Mysore India J.J College of Engineering and Technology Department of Information Technology Tamil Nadu Tiruchirappalli India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798331540364

Various sectors continue to prioritize lone worker safety, which requires creative ways to provide quick support and monitoring. To safeguard lone workers, this paper introduces a new method that makes use of cloud-connected wearable devices that are coupled with Wireless Sensor Networks (WSNs). The system design relies on sensors embedded in wearable devices that can monitor a range of environmental elements as well as the vital signs of workers. A central cloud platform receives data in real-time via wireless communication from these devices and processes and analyzes it. The system may detect possible dangers or crises using smart techniques and immediately notify those who are supposed to react or the proper authorities. Cloud computing also makes it easy to store, analyze, and monitor large amounts of data over time, which is great for doing proactive risk assessments and improving safety standards. It demonstrates solution's capacity to improve lone worker safety, reduce reaction times, and mitigate hazards via simulations and actual deployment scenarios, efficacy and dependability. This growing body of knowledge on how to protect lone workers in various workplaces by using the power of IoT and cloud computing. © 2024 IEEE.

关键词： Cloud platforms

来源：评论

学校读者我要写书评

暂无评论

drug discovery with Machine learning: Target Identification using Random Forest

Drug Discovery with Machine Learning: Target Identification ...

引用

2024 International Conference on Communication, computer Sciences and engineering, IC3SE 2024

作者： Choudhari, Pragati Rawat, Ruchira Al-Fatlawy, Ramy Riad Shrivastava, Anurag Yadavk, Kanchan Pratap, Arun School Of Computer Science Engg. & Applications D Y Patil International University Pune Akurdi India Graphic Era Deemed To Be University Department Of Computer Science & Engineering Dehradun India College Of Technical Engineering The Islamic University Department Of Computers Techniques Engineering Najaf Iraq Saveetha School Of Engineering Saveetha Institute Of Medical And Technical Sciences Tamilnadu Chennai India Gla University Department Of Electrical Engineering Mathura India Lloyd Institute Of Engineering & Technology Greater Noida India

ISBN: (纸本)9798350366846

The abstract presents a see into our inquiry about on-target distinguishing proof in sedate disclosure utilizing Random Forest, displaying the transformative effect of machine learning. leveraging different natural datasets, we utilized Random Forest nearby other calculations to foresee potential medicate targets. Our comprehensive assessment measurements, counting accuracy, precision, recall, and F1 score, emphasize the prevalent execution of Random Forest in comparison to decision Trees, Support Vector Machines, and k-Nearest Neighbors. The results demonstrate a precision of 85%, accuracy of 86%, review of 84%, and an F1 score of 85% for Irregular Forest, certifying its viability in precisely recognizing promising sedate targets. The study contributes to the advancing scene of machine learning applications in biomedical research, emphasizing the potential of gathering learning procedures, especially Irregular Woodland, to streamline and improve the target identification stage of sedate improvement. This research adjusts with broader patterns in healthcare and pharmaceutical examinations, highlighting the flexibility of machine learning in tending to complex challenges and quickening the disclosure of novel restorative arrangements. © 2024 IEEE.

关键词： decision trees

来源：评论

学校读者我要写书评

暂无评论

Optimization Techniques for Reconfigurable Intelligent Surfaces in 6G 5

Optimization Techniques for Reconfigurable Intelligent Surfa...

引用

5th International Conference on Electronics and Sustainable Communication Systems, ICESC 2024

作者： dheshmuk, Mallikarjun Kumbalavati, S.B. Basaveshwar Engineering College Dept. of Electronics and Communication Bagalkote India B.L.D.E.A's C.E. T Vijayapura Affiliated to Visvesvaraya Technological University Belagavi590018 India Basaveshwar Engineering College Bagalkote Affiliated to Visvesvaraya Technological University Dept. of Electronics and Communication Engineering Belagavi590018 India

ISBN: (纸本)9798350379945

The new age digital services and applications, deeper penetration of smartphones, billions of devices getting associated to internet has increased the hunger for high-speed data. This demand will keep exploding, rise exponentially as the days progresses ahead. In order to satisfy the soaring data traffic demands, communication has evolved all the way from 1G to 5G i.e. from first generation to fifth generation. After a span of few years, there will be a migration to sixth generation (6G). Several innovative techniques are expected to be part of 6G, though the specifications are not yet laid out. In this paper, several research areas of 6G are discussed like artificial intelligence, Reconfigurable Intelligent Surfaces (RIS), Quantum communication, channel estimation. Reconfigurable intelligent surfaces have a very prominent role to play in 6G. A survey of most recent works found in literature is done and the challenges involved in deploying RIS, future research outlook in RIS has been discussed. © 2024 IEEE.

关键词： Channel estimation

来源：评论

学校读者我要写书评

暂无评论

Enhancing Alzheimer's disease diagnosis Using Multi-Relation Graph Convolutional Networks and Structural MRI data 5

Enhancing Alzheimer's Disease Diagnosis Using Multi-Relation...

引用

5th International Conference on IoT Based Control Networks and Intelligent Systems, ICICNIS 2024

作者： Kanna, P. Rajesh Gunasundari, C. Senthamarai, M. Pandiaraja, P. Nithin, P. Chitra, K. Bannari Amman Institute of Technology Department of Computer Science and Engineering Tamil Nadu Erode India SRM Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu Trichy India Nandha Engineering College Department of Artificial Intelligence and Data Science Tamil Nadu Erode India Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Bannari Amman Institute of Technology Department of Artificial Intelligence and Machine Learning Tamil Nadu Erode India Kongu Engineering College Department of Computer Applications Tamil Nadu Erode India

ISBN: (纸本)9798331518097

Alzheimer's disease (Ad) has substantial obstacles to early detection, which frequently leads to therapy delays. In this article a unique method that uses structural MRI data and Multi-Relation Graph Convolutional Networks (MR-GCN) to improve Ad diagnostic accuracy. Which can capture the nuanced interactions that characterize neurodegenerative disorders by representing the intricate relationships between various brain regions as a multi-relational graph. This strategy provides a comprehensive picture of brain function and pathology through the combination of relational data and structural MRI features. Using a large dataset, the resusts are evaluated and contrasted in MR-GCN method with other machine-learning techniques and single-relation graph models. The findings demonstrate that this method outperforms current models in terms of classification accuracy and resilience, achieving higher sensitivity and specificity in distinguishing between individuals in good health and various stages of Ad. Then for further exploration the interpretability of the model is identified in critical brain regions that support the diagnosing process. This work provides a promising framework for the integration of sophisticated graph-based medical imaging tools, which could lead to more sophisticated clinical applications in the diagnosis and monitoring of neurodegenerative diseases in addition to improving our understanding of how Ad progresses. © 2024 IEEE.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

Application of deep Neural Networks for Weed detection and Classification 2

Application of Deep Neural Networks for Weed Detection and C...

引用

2nd International Conference on Applied Artificial Intelligence and Computing, ICAAIC 2023

作者： Ganesan, R. Manjula, G. Chandan, Madhavarapu Thamarai, I. Rajavelu, S. Bhatt, Upendra Mohan Sarkar, Swagata Vel Tech RangarajanDr.Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu Chennai600062 India Rmk College of Engineering and Technology Department of Computer Science and Engineering Tamil Nadu Thiruvallur601206 India Koneru Lakshmaiah Education Foundation Department of Computer Science Engineering Andhra Pradesh Vaddeswaram522302 India Panimalar Engineering College Department of Computer Science and Engineering Chennai city campus Tamil Nadu 600029 India Department of Physics Tamil Nadu Dindigul624622 India Graphic Era Deemed to Be University Department of Computer Science and Engineering Uttarakhand Dehradun248002 India Sri Sairam Engineering College Department OfArtificial Intelligence and Data Science Tamil Nadu Chennai600044 India

ISBN: (纸本)9781665456302

Weeds compete for natural resources both in forest areas, harming the development of native vegetation, and in agricultural areas, affecting crop quality. The need then arises to classify these species, so that mechanical or chemical methods can be applied appropriately to contain the pests. This research presents the application and comparison of machine learning techniques, with the aim of automating the classification of images for agricultural challenges, such as the detection of defective seeds, and weeds and the category between these and native vegetation, while finally, the architecture of a convolutional neural network is presented. As a differential, the network's self-learning ability stands out, as images are captured in less than ideal conditions at varying heights and lighting levels in most cases. This research is expected to provide important information on artificial intelligence techniques that can be used in the classification of weed images, a factor that will contribute to the forestry and agricultural sector. © 2023 IEEE.

关键词： Classification (of information)

来源：评论

学校读者我要写书评

暂无评论

Robust Heart Rate Monitoring System: Contactless Approach using Fast Fourier Transform 15

Robust Heart Rate Monitoring System: Contactless Approach us...

引用

15th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2024

作者： Sujatha, E. divya, M. Nandhini, I. Raju, d. Naveen Chennai India Chennai India Department of Electronics and Communication Engineering School of Electrical and Communication Vel Tech Rangarajan R&D Institute of Science and Technology Avadi Chennai India Department of Computer Science and Engineering R.M.K. Engineering College Kavaraipettai Chennai India

ISBN: (纸本)9798331300579

Today, to be alive in this world we need to have all our body parts working in good condition and since technology is improving faster day by day everything has become automatic and AI enabled. Measuring the heart-rate (HR) of people has multiple applications in telemedicine, Internet-of-Things (IoT), sports, security, etc. However, sometimes it becomes difficult to use a classic method for measuring HR and it does not support scalability. This project is about giving a solution that works live and gives the HR of a person using web camera. Face detection is combined with object tracking is used to produce a set of frames, which are sampled in the later stages of the pipeline for color variations. The average of the color, in a region of interest (ROI) is chosen on the face, representing a signal which corresponds to the heart rate. Signal processing techniques are used to extract the frequency from the signals obtained. The algorithm used is FFT (Fast Fourier Transform) to make image transformation between frequency and special domain. This method is precise and efficient. And using this system another method is also enabled in which the video of a person is uploaded and the heart rate is found through video. By utilizing object tracking combined with face detection, this method reduces the processing power needed and allows better scaling. The accuracy is 98% compared to the real time existing systems. © Grenze Scientific Society, 2024.

关键词： Fast Fourier transforms

来源：评论

学校读者我要写书评

暂无评论

Enhancing Predictive Maintenance in Industrial IoT through Machine learning Models 4

Enhancing Predictive Maintenance in Industrial IoT through M...

引用

4th IEEE Asian Conference on Innovation in Technology, ASIANCON 2024

作者： Rani, A. Jaya Mabel Prithivirajan, V. Shiney, S Arumai Babaiah, Ch. Swamy, Hemanth Sivagamidevi, G. Saveetha University Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Saveetha School of Engineering Tamil Nadu Chennai India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Ece Tamil Nadu Chennai India S A Engineering College Department of Computer Science and Engineering Tamil Nadu Chennai India Mlr Institute of Technology Department of Electronics and Communication Engineering Dundigal Hyderabad India Motorola Solutions India Dhanalakshmi College of Engineering Department of Ai & Ds Tamil Nadu Chennai India

ISBN: (纸本)9798350354218

Significant production losses and increased maintenance costs can arise from unplanned downtime in industrial settings caused by machine faults. In order to identify issues early and prevent significant breakdowns, predictive maintenance approaches make use of data collected from Internet of Things (IoT)-enabled devices installed in operating equipment. Here, we present a predictive maintenance system that employs machine learning methods, namely AdaBoost, to categorize various types of machine pauses in real-time, with a focus on knitting machines. Six different types of machine stops were classified using machine learning: idle stop, lycra stop, full roll stop, gate stop, feeder stop, and needle stop. We did this by preprocessing and inputting machine learning model data, which included machine speeds and steps. After a rigorous training and optimization process that combines cross-validation and hyperparameter tuning, the model achieves an impressive accuracy rate of 92% on the test dataset. These results highlight how our suggested solution might help the textile sector become more productive and efficient overall by precisely detecting machine stoppage and enabling timely maintenance interventions. © 2024 IEEE.

关键词： Predictive maintenance

来源：评论

学校读者我要写书评

暂无评论

SVM-based Predictive Modeling of drowsiness in Hospital Staff for Occupational Safety Solution via IoT Infrastructure 2

SVM-based Predictive Modeling of Drowsiness in Hospital Staf...

引用

2nd International Conference on computer, Communication and Control, IC4 2024

作者： Ganesh, B. Jai Vijayan, P. Vaidehi, V. Murugan, S. Meenakshi, R. Rajmohan, M. R.M.D. Engineering College Department of Electronics and Communication Engineering Tamil Nadu Chennai India Saveetha Engineering College Department of Artificial Intelligence and Data Science Tamil Nadu Chennai India Vinayaka Mission's Kirupananda Variyar Arts and Science College Department of Computer Science Tamil Nadu Salem India Saveetha Institute of Medical and Technical Sciences Saveetha University Saveetha School of Engineering Department of Biomedical Engineering Tamil Nadu Chennai India Chennai Institute of Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Hindustan Institute of Technology and Science Department of Electronics and Communication Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350387933

This research proposes an efficient approach to improve hospital worker safety on the job by detecting and preventing drowsiness. The system incorporates data from hospital-deployed Internet of Things (IoT) infrastructure and using Support Vector Machines (SVM) as its primary predictive modeling tool. data pertaining to ambient factors, work habits, and physiological indications of employees are collected in real-time by the IoT infrastructure. To identify patterns linked to sleepiness, the SVM model is trained on an extensive dataset. The next step is to implement the model in a healthcare setting so that it can detect signs of sleepiness and act accordingly in a timely manner. With the help of the IoT the predictive model can be dynamically adjusted and monitored in real-time, making it useful in a wide range of situations. The recommended approach is to enhance workplace safety by reducing the likelihood of accidents caused by sleepiness among healthcare workers. The SVM-based prediction model can correctly detect and forecast cases of sleepiness, the research subjected it to extensive testing and validation. This system's adoption helps with the creation of data-driven, proactive methods to improve hospital occupational safety. © 2024 IEEE.

关键词： Internet of things

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500页 << < 79 80 81 82 83 84 85 86 87 88 > >>

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：