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

632 篇 期刊文献
538 篇 会议

馆藏范围

1,170 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

660 篇 理学
- 543 篇 物理学
- 65 篇 数学
- 38 篇 生物学
- 25 篇 统计学（可授理学、...
- 18 篇 化学
514 篇 工学
- 255 篇 计算机科学与技术...
- 200 篇 软件工程
- 179 篇 核科学与技术
- 99 篇 信息与通信工程
- 81 篇 控制科学与工程
- 69 篇 电气工程
- 54 篇 电子科学与技术（可...
- 40 篇 动力工程及工程热...
- 32 篇 生物医学工程（可授...
- 30 篇 光学工程
- 30 篇 生物工程
- 28 篇 机械工程
- 28 篇 网络空间安全
- 26 篇 安全科学与工程
- 23 篇 仪器科学与技术
- 21 篇 环境科学与工程（可...
- 18 篇 交通运输工程
- 17 篇 石油与天然气工程
- 15 篇 材料科学与工程（可...
93 篇 管理学
- 70 篇 管理科学与工程(可...
- 34 篇 图书情报与档案管...
- 30 篇 工商管理
46 篇 医学
- 40 篇 临床医学
- 36 篇 基础医学(可授医学...
- 24 篇 公共卫生与预防医...
16 篇 法学
11 篇 农学
10 篇 经济学
4 篇 教育学
1 篇 军事学

主题

170 篇 hadron colliders
78 篇 higgs bosons
57 篇 accuracy
49 篇 w & z bosons
43 篇 deep learning
39 篇 relativistic hea...
38 篇 top quark
38 篇 convolutional ne...
34 篇 feature extracti...
33 篇 supersymmetric m...
31 篇 machine learning
30 篇 hadron-hadron sc...
30 篇 quark & gluon je...
26 篇 particle product...
25 篇 training
23 篇 real-time system...
23 篇 extensions of hi...
22 篇 internet of thin...
22 篇 support vector m...
21 篇 hypothetical par...

机构

331 篇 faculty of scien...
331 篇 department for p...
327 篇 kirchhoff-instit...
318 篇 department of ph...
314 篇 department of ph...
304 篇 department of ph...
304 篇 department of ph...
303 篇 yerevan physics ...
299 篇 fakultät für phy...
297 篇 institute of phy...
296 篇 graduate school ...
286 篇 institute of phy...
280 篇 department of ph...
279 篇 instituto de fís...
277 篇 dipartimento di ...
248 篇 department of ph...
245 篇 nikhef national ...
242 篇 irfu cea univers...
228 篇 marian smoluchow...
228 篇 department of ph...

作者

266 篇 c. alexa
264 篇 j. m. izen
262 篇 g. bella
258 篇 j. strandberg
257 篇 d. calvet
257 篇 c. amelung
243 篇 n. orlando
235 篇 y. tayalati
232 篇 s. veneziano
232 篇 g. spigo
232 篇 v. chiarella
225 篇 f. siegert
220 篇 r. ströhmer
218 篇 f. deliot
218 篇 m. rijssenbeek
217 篇 y. abulaiti
216 篇 s. jin
216 篇 g. gaudio
216 篇 s. mohapatra
216 篇 i. van vulpen

语言

1,115 篇 英文
52 篇 其他
4 篇 中文

检索条件"机构=M.Tech Computer Science and Engineering"

共 1170 条记录，以下是401-410 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：

Leveraging Grey Wolf Optimizer for Reliable Data Transmission in Underwater Wireless Sensor Networks

Leveraging Grey Wolf Optimizer for Reliable Data Transmissio...

引用

Communication, Computing and Signal Processing (IICCCS), IEEE International Conference on

作者： G Umamahesh Senthurya S Poonam Khatarkar Zaid Alsalami Jaspal Kaur P.S. Ramesh Department of Computer Science & Engineering Aditya Engineering College India Prince Shri Venkateshwara Padmavathy Engineering College Chennai India Department of Electrical & Electronics Engineering IES College of Technology Bhopal M.P. India Department Of Computers Techniques Engineering College Of Technical Engineering The Islamic University Najaf Iraq Department Of Computers Techniques Engineering College Of Technical Engineering The Islamic University Of Al Diwaniyah Al Diwaniyah Iraq Department of Computer Application Chandigarh Engineering College Chandigarh Group of Colleges Mohali Punjab India Department of CSE Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai India

ISBN: (数字)9798350390759

ISBN: (纸本)9798350390766

This research investigates the application of the Grey Wolf Optimizer (GWO) to enhance reliable data transmission in Underwater Wireless Sensor Networks (UWSNs). UWSNs are critical for various applications, including environmental monitoring and underwater exploration, but face challenges such as high propagation delays, limited bandwidth, and energy constraints. The GWO algorithm, inspired by the social hierarchy and hunting behavior of grey wolves, is employed to optimize routing paths, minimize energy consumption, and improve packet delivery ratio. The methodology includes simulating UWSNs with different network configurations using synthetic and real-world datasets, and implementing the GWO algorithm in Python within the NS-3 simulation framework. Comparative analysis with traditional routing protocols, AODV and DSR, demonstrates that the GWO-based routing algorithm significantly reduces energy consumption, increases packet delivery ratio, lowers latency, and extends network lifetime. These results underscore the effectiveness of GWO in enhancing UWSN performance. The study concludes that GWO offers a robust and adaptable solution for reliable data transmission in UWSNs, with potential for further enhancements through hybrid optimization techniques and real-world validations. This research provides a comprehensive understanding of GWO's application in UWSNs, contributing to advancements in underwater communication technologies.

关键词： Wireless communication Wireless sensor networks Underwater communication Energy consumption Signal processing algorithms Routing Throughput Routing protocols Robustness Data communication

来源：评论

学校读者我要写书评

暂无评论

Harnessing the Power of AI With Deep Learning: Innovations and Implications

Harnessing the Power of AI With Deep Learning: Innovations a...

引用

Computational, Communication and Information technology (ICCCIT), International Conference on

作者： Talluri Upender C. Sathiyamoorthy Udayakumar K D. Kirubakaran Purushothaman V Kiruba Thangam Raja Department of CSE CMR College of Engineering and Technology Medchal Hyderabad Telangana Department of Commerce Saveetha College of Liberal Arts and Sciences Saveetha Institute of Medical and Technical Sciences (SIMATS) Chennai Tamil Nadu India Department of CSE Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Tamil Nadu India Department of Computer Science and Engineering R.M.K. Engineering College Chennai Tamil Nadu India Department of Electronics and Communication Engineering Chennai Institute of Technology Chennai Tamil Nadu India Department of Software and Systems Engineering School of Computer Science Engineering and Information Systems (SCORE) VIT University Vellore Tamil Nadu India

ISBN: (数字)9798331512965

ISBN: (纸本)9798331512972

The rapid advancements in artificial intelligence (AI) and deep learning have revolutionized various sectors, enabling unprecedented levels of innovation and efficiency. This paper delves into the transformative impact of deep learning across multiple domains, highlighting key innovations and their broader implications. By examining cutting-edge applications in healthcare, finance, autonomous systems, and natural language processing, the study elucidates how deep learning models, such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs), are pushing the boundaries of what's possible. moreover, the paper explores the integration of deep learning with emerging technologies such as the Internet of Things (IoT), edge computing, and quantum computing, underscoring the synergistic potential of these combinations. The role of deep learning in predictive analytics and personalized solutions is discussed, showcasing its ability to enhance decision-making processes and improve user experiences. Furthermore, the paper discusses the ethical considerations, challenges, and future prospects associated with the widespread adoption of deep learning technologies. The insights provided aim to foster a deeper understanding of how harnessing AI with deep learning can drive progress while addressing critical societal and technological issues.

关键词： Deep learning technological innovation Ethics Recurrent neural networks Autonomous systems Finance medical services Safety Internet of Things Artificial intelligence

来源：评论

学校读者我要写书评

暂无评论

Energy-Efficient Clustering in WSNs Using multi-Objective Genetic Algorithms

Energy-Efficient Clustering in WSNs Using Multi-Objective Ge...

引用

Smart Systems for applications in Electrical sciences (ICSSES), International Conference on

作者： P. Sivaprakash Haritha K Kondalarao Punati D. Shyamala Devi R. R. Ramya m. Perarasi Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Avadi Chennai India Department of AIADS St Joseph's College Of Engineering Semmancheri Chennai Department of Electronics and Communication Engineering Hyderabad Institute of Technology and Management Telangana India Department of Information Technology Annai College of Engineering and Technology Kumbakonam India Department of Information Technology University College of Engineering Nagercoil India Department of Electronics and Communication Engineering R.M.K. Engineering College Tamil Nadu India

ISBN: (数字)9798331507671

ISBN: (纸本)9798331507688

This research proposes a multiobjective Genetic Algorithm (mOGA) for energy efficient clustering in Wireless Sensor Networks (WSN). Clustering is a key tactic for improving network scalability and energy efficiency in WSNs since it lowers communication overhead and lengthens network lifetime. The proposed mOGA addresses this by optimizing several goals at once, such as improving network longevity, decreasing energy usage, and guaranteeing consistent energy distribution among sensor nodes. Under the guidance of a Pareto-based methodology, the algorithm evolves possible solutions using genetic operators including crossover, mutation, and selection in order to find a set of ideal trade-offs. The effectiveness of the proposed strategy is demonstrated by simulation results showing significant improvements in energy efficiency and network lifetime compared to conventional clustering methods. In large-scale WSNs, this method offers a reliable and expandable option for energy-efficient clustering. A mOGA was used to achieve a 93.5% efficiency.

关键词： Wireless sensor networks Scalability Heuristic algorithms Taxonomy Clustering algorithms Smart systems Energy efficiency Real-time systems Reliability Genetic algorithms

来源：评论

学校读者我要写书评

暂无评论

A Secure IoT Based Wireless Sensor Network for an Efficient modified User Authentication and Key Agreement Scheme

A Secure IoT Based Wireless Sensor Network for an Efficient ...

引用

Ambient Intelligence, Knowledge Informatics and Industrial Electronics (AIKIIE), International Conference on

作者： S. Sudha mercy C Prabhu V Revathi K Suresh Kumar Anitha G Department of Computer Science& Design R.M.K Engineering College Chennai India Department of ECE Nandha Engineering College Erode India Department of IT Vel tech Multi Tech Dr.Rangarajan & Dr. Sakunthala Engineering College Chennai India Department of MBA Panimalar Engineering College Chennai India Department of ECE Saveetha School of Engineering SIMATS Chennai India

medical and transportation systems are only two examples of the many domains in which the IoT has found success. To meet the growing demand for continuous health surveillance that is affordable, noninvasive, and long-term, smart healthcare platforms have emerged as the most efficient and useful alternatives. To improve the reliability of IoT devices in smart cities, this study first establishes a cloud-assisted dependability evaluation approach, as reliability examination for IoT gadgets is a crucial part of data source authentication. In addition, a non-interactive zero-knowledge argument-based anonymous verification and key agreement system is developed to address the issue of user privacy being compromised during the process of verification. The proposed method may safeguard sensitive information collected by Internet of Things gadgets in smart cities. The experimental modeling shows that the proposed method outperforms competing approaches by a significant margin. Lastly, the suggested strategy is demonstrated to be more practical and secure than the previous schemes by comparison of effectiveness.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Optimizing Energy Efficiency in Electric Vehicles Through Fuzzy Logic and Neural Network Algorithms

Optimizing Energy Efficiency in Electric Vehicles Through Fu...

引用

Distributed Computing and Optimization techniques (ICDCOT), International Conference on

作者： m Devi Siva Satya Sreedhar P S. Geetha Priya m. Amutha D. Kanagajothi R.Kalaivani Sri Research Scholar Bharath Institute of Higher Education and Research Chennai Tamil Nadu India Department of Information Technology Seshadri Rao Gudlavalleru Engineering College Gudlavalleru Andhra Pradesh India Department of computer science and Engineering R. M. D. Engineering College Kavaraipettai Chennai India Department of ECE Sona College of Technology Salem Tamil Nadu India Department of Mathematics Vel Tech Rangarajan Dr. Sagunthala R& D Institute of Science and Technology Avadi Chennai India Department of computer science Arunai Engineering College Tiruvannamalai Tamil Nadu India

The introduction of electric vehicles (EVs) has represented a significant step forward in the development of environmentally sustainable transportation. The study addresses the critical need to enhance energy management systems in electric vehicles (EVs) by overcoming the limitations of traditional algorithms. Existing systems, which rely on rule-based controllers and basic predictive models, have poor flexibility and precision when it comes to dynamically optimizing energy efficiency. To revolutionize energy optimization in EVs, the proposed approach uses a hybrid model that combines fuzzy logic and neural networks. It tackles limits through a comprehensive analysis, the design of a fuzzy logic-based controller, and the development of a neural network model trained on historical data. TensorFlow-implemented simulation results in Python validate the reliability of the model by closely matching predicted energy usage in a range of driving scenarios. Real-world experiments with an instrumented EV prototype highlight the proposed system's advantages even further, giving noteworthy results—92% energy efficiency, 25 mile range extension, and 95% predicted accuracy. These findings support the system's ability to greatly improve energy efficiency and range extension in electric vehicles, therefore transforming sustainable transportation.

关键词：

来源：评论

学校读者我要写书评

暂无评论

medium Access Control Scheduling Policy Performance Evaluation in Wireless Networks

Medium Access Control Scheduling Policy Performance Evaluati...

引用

Artificial Intelligence and Knowledge Discovery in Concurrent engineering (ICECONF), International Conference on

作者： N. Banupriya P. Jyothi K malathy R Sureshkumar D Dhanush G Sajiv Department of Computer Science and Engineering R.M.K. Engineering College Chennai Departmpnt of Management Studies Saveetha Engineering college Chennai Department of Master of Computer Applications S.A Engineering College Chennai Department of Electronics and Communication Engineering Vel Tech Multitech Dr. Rangarajan Dr. Sakunthala Engineering College Chennai Department of Electronics and Communication Engineering R.MK. Engineering College Chennai Department of Electronics and Communication Engineering Saveetha School of Engineering Chennai

According to the implementation model, mAC scheduling techniques may be broken down into two distinct categories centralized and distributed scheduling. These categories apply to wireless networks. Both centralized and distributed schedulers have the ability to assure a dependable throughput capacity region, also known as achieving throughput-guaranteed, by establishing mAC scheduling criteria in a reasonable manner. However, it has been shown that some throughput-guaranteed scheduling systems that are already in use are unable to further provide constrained end-to-end mean latency, as well as the explanation for these phenomena has not been well investigated. Both throughput and latency are regarded as critically significant in operational communication networks. In this study, we build on this concept by thoroughly examining the current mAC scheduling algorithms from the perspectives of throughput and latency, and by comparing their respective performances using theoretical analysis and simulation tests. The goal of this investigation is to provide a conceptual basis for the development of mAC routing technique in next-generation cellular networks.

关键词： Cellular networks Scheduling algorithms Wireless networks Throughput Routing media Access Protocol Knowledge discovery

来源：评论

学校读者我要写书评

暂无评论

A Federated Learning Paradigm for Early Alzheimer's Detection using mRI Imaging

A Federated Learning Paradigm for Early Alzheimer's Detectio...

引用

Electronics and Sustainable Communication Systems (ICESC), 2020 International Conference on

作者： Janjhyam Venkata Naga Ramesh P. Poonkuzhali S. Rama Krishna P. Ranjidha A. farithkhan A. Periya Nayaki Department of CSE Graphic Era Hill University & Graphic Era Deemed to be University Dehradun India Department of ECE R.M.D. Engineering College Kavaraipettai Department of Computer Science & Engineering GSoT GITAM(Deemed-to-be-University) Hyderabad Department of AI &DS Panimalar Engineering College Department of ECE Vel tech rangarajan Dr.sagunthala r&d institute of science and technology Chennai Department of IT Velammal College of Engineering and Technology Madurai

ISBN: (数字)9798350379945

ISBN: (纸本)9798350379952

Alzheimer's disease is a progressive neurovascular disorder that results in loss of cognitive function and memory impairment. Detection of this infection earlier can help reduce the threat of this infection to a minimum, by providing required intervention and management. In this contribution, we present a novel model of Hybrid Xception-GhostNet Classification (HXGC) in combination with federated learning for classifying the progression of Alzheimer's disease (AD) across mRI images in the Alzheimer's mRI Preprocessed Dataset (AmP Dataset). The HXGC architecture, which combines the excellent feature extraction of Xception with the lightweight GhostNet, exhibits strong and lightweight features. Collaborative model training using federated learning across multiple healthcare institutions and kept patient data privacy The HXGC model was trained locally on each center's portion of the dataset, and these locally trained models were then combined into a global model using federated averaging on the central server. The HXGC model achieved a top accuracy of 97.5% on the training set, 97.0% on the validation set, and 97.5% on the test set, which significantly exceeded other models in both accuracy rates and time cost. At a glance, the study demonstrates the potential of advanced neural network architectures and federated learning for early detection and diagnosis of Alzheimer's disease.

关键词： Training Accuracy Federated learning magnetic resonance imaging Neural networks medical services Feature extraction Data models Servers Alzheimer's disease

来源：评论

学校读者我要写书评

暂无评论

Enhancing the Efficacy of machine Learning-Based Intrusion Detection Systems on an Imbalanced and Contemporary Dataset

Enhancing the Efficacy of Machine Learning-Based Intrusion D...

引用

Advanced Computing technologies (ICoACT), International Conference on

作者： A. Kiruthikkumar N. Uma maheswari D. David Neels Ponkumar A. Niyas Ahamed S. Nazrin Salma R. Saravanakumar Department of Master of Computer Application Karpagam Academy of Higher Education Coimbatore Tamil Nadu India Department of Electronics and Communication Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Tamil Nadu India Department of Electronics Engineering (VLSI Technology) R.M.K. Engineering College Thiruvallur Tamil Nadu India General Engineering R.M.K. College of Engineering and Technology Thiruvallur Tamil Nadu India Iconix Software Solution Tirunelveli Tamil Nadu India

ISBN: (数字)9798331509859

ISBN: (纸本)9798331509866

As the Internet has increased in popularity, more computers are networked and becoming part of our daily lives. Common and less evident server vulnerabilities may be used by hackers to access systems and conduct complicated assaults. An Intrusion Detection System (IDs), a popular computer security tool, uses machine learning on a previously acquired dataset. The datasets were generated in distinct networks across brief periods and may not contain the latest information. They're bloated and can't hold enough data to survive an attack. modern intrusion detection systems are less effective with inconsistent and outdated information for rare attack paths. This paper presents five machine-learning intrusion detection solutions. K Nearest Neighbor (KNN), Decision Tree (DT), Gradient Boosting (GB), Adaboost (AB), and Random Forest (RF) are used. The latest security dataset CSE-CIC-IDS2018 replaces earlier, more often-used datasets to improve Intrusion Detection Systems (IDS). An unbalanced dataset was selected. The imbalance ratio is reduced using the Synthetic minority Oversampling technique (SmOTE), a synthetic data model. The approach increases attack type-specific system performance while lowering false alarms and missing incursions. minor classes increase their data amounts to reach the average data size. Experimental results demonstrate that the recommended strategy greatly enhances uncommon intrusion detection.

关键词： Radio frequency Adaptation models Runtime Intrusion detection Nearest neighbor methods Feature extraction Boosting Decision trees Random forests Testing

来源：评论

学校读者我要写书评

暂无评论

Transforming Yard management for Optimizing Efficiency through IoT and AI Integration

Transforming Yard Management for Optimizing Efficiency throu...

引用

International Conference on I-SmAC (IoT in Social, mobile, Analytics and Cloud) (I-SmAC)

作者： S. Kolangiammal S. Prabha P. Sivalakshmi Narayanasamy P S. Kalaichelvi S. Sujatha Department of Electronics and Communication Engineering SRM Institute of Science and Technology Kattankulathur Chennai Tamil Nadu India Department of Computer Science Jawaharlal Nehru College for Women ( Arts & Science ) Ulundurpet Kallakuruchi Tamil Nadu India Department of Electronics and Communication Engineering R.M.K. College of Engineering and Technology Chennai Tamil Nadu India Department of Mathematics Vel Tech Rangarajan Sagunthala R&D Institute of Science and Technology Chennai Tamil Nadu India PG and Research Department of Computer Science and Applications Vivekanandha College of Arts and Sciences for Women (Autonomous) Elayampalayam Tiruchengode Tamil Nadu India Department of Biomedical Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India

ISBN: (数字)9798350376425

ISBN: (纸本)9798350376432

This research study explores how yard management methods can be transformed using Artificial Intelligence (AI) algorithms and Internet of Things (IoT) technology. Problems with manual procedures, lack of transparency, and inefficient use of resources are common with older yard management systems. To improve operational efficiency, cost-effectiveness, and flexibility, this research proposes a new way that uses IoT sensors to collect data in real-time and AI algorithms to make predictions and decisions. IoT sensor deployment, data processing infrastructure, and optimization approaches powered by AI make up the three main components of the integrated system. Data on the location, condition, and environmental factors of assets is continually gathered via the strategic placement of IoT sensors across yard facilities. AI algorithms that can predict outcomes, spot trends, and maximize resource use in ever-changing settings are subsequently employed to process and analyze this data. Yard throughput, inventory correctness, labor productivity, and overall operational performance were all improved in various industrial settings, as shown in case studies and empirical data, demonstrating the advantages of this integrated technique. For complicated decision-making, supplementary data sources like traffic patterns or weather predictions should be incorporated, and decentralized architectures for resilient and scalable IoT deployments should be investigated. To revolutionize supply chain operations, enhance efficiency, and encourage innovation in logistics methods.

关键词： Productivity technological innovation Supply chains Weather forecasting Companies Transforms Prediction algorithms Internet of Things Artificial intelligence Optimization

来源：评论

学校读者我要写书评

暂无评论

IoT and Random Forest-Based Solutions for Blood Gas Quality Assurance in Clinical Laboratories

IoT and Random Forest-Based Solutions for Blood Gas Quality ...

引用

Sustainable Computing and Smart Systems (ICSCSS), International Conference on

作者： K. Sangeethalakshmi Arshadh Ariff mohamed Abuthahir Riazulhameed G. Arunachalam D. muthukumaran D.C. Joy Winnie Wise C. Srinivasan Department of Electronics and Communication Engineering R.M.K. College of Engineering and Technology Chennai Tamil Nadu India Blackstraw Technologies Pvt Ltd Chennai Tamil Nadu India Department of Electronics and Communication Engineering Gnanamani College of Technology (Autonomous) Namakkal Tamil Nadu India Department of Electronics and Communication Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai Tamil Nadu India Department of Computer Science and Engineering Rajalakshmi Institute of Technology Chennai Tamil Nadu India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India

ISBN: (数字)9798350379990

ISBN: (纸本)9798350391558

Integrating Internet of Things (IoT) technologies and Random Forest algorithms in clinical laboratories may improve blood gas analysis quality. Patients' respiratory and metabolic state may be determined through blood gas analysis. Analyses must be accurate and precise for patient care. manual and periodic examinations in clinical labs might be time-consuming and cause problems. IoT and Random Forest-based technologies handle blood gas analysis quality assurance issues in this paper. Diagnostic lab equipment like blood gas analyzers uses IoT sensors and devices to monitor and gather data. This data is put into a Random Forest algorithm, which provides intelligent quality control. Due to its past training, the Random Forest algorithm can detect real-time deviations from predicted values, trends, or patterns. It predicts, classifies, and warns of data abnormalities using ensemble learning. Real-time monitoring and early warning may greatly minimize misdiagnosis and increase patient safety. Increased automation, less human error, enhanced data analysis, and the capacity to detect and avoid patient care difficulties are all benefits of IoT and Random Forest-based blood gas quality assurance systems. It enables easy connection with laboratory information systems, producing a complete quality control infrastructure. Improving real-time blood gas analysis systems' anomaly detection capabilities may include tracking sensors for accuracy, making data transfer more efficient, and honing machine learning models.

关键词： Accuracy Quality assurance Laboratories Quality control Forestry Real-time systems Safety

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117页 << < 37 38 39 40 41 42 43 44 45 46 > >>

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：