检索结果-内蒙古大学图书馆

作者： Pundir, Sumit Obaidat, Mohammad S. Wazid, Mohammad Das, Ashok Kumar Singh, Devesh Pratap Rodrigues, Joel J. P. C. Department of Computer Science and Engineering Graphic Era Deemed to be University Dehradun 248 002 India College of Computing and Informatics University of Sharjah UAE with King Abdullah II School of Information Technology The University of Jordan Amman 11942 Jordan University of Science and Technology Beijing Beijing 100083 China Center for Security Theory and Algorithmic Research International Institute of Information Technology Hyderabad 500 032 India Federal University of Piauí (UFPI) PI Teresina 64049-550 Brazil Instituto de Telecomunicações Covilhã 6201-001 Portugal

Internet of Things (IoT) is one of the fastest-growing technologies. With the deployment of massive and faster mobile networks, almost every daily-use item is connected to the Internet. IoT-enabled industrial multimedia environment is used for the collection and analysis of different types of multimedia data (i.e., images, videos, audios, etc.). This multimedia data is generated by various types of smart devices like drones, robots, smart controller, smart surveillance system which are deployed for the industrial monitoring and control. The multimedia data is generated in the enormous amount which can be considered as the big data. This data is further utilized in various types of business needs for example, chances of fire accidents in the industrial plant, overall machine health, etc., which can be predicted through the application of big data analytics. Therefore, IoT-enabled industrial multimedia environment is very helpful to the concerned authorities as they come to know the important information in advance. However, all the smart devices are connected and controlled through the Internet. It further causes severe threats to the communication happens in an IoT-enabled industrial multimedia environment. It is vulnerable to various types of attacks such as replay, man-in-the-middle, impersonation, secret information leakage, sensitive information modification, and malware injection (i.e., mirai). Therefore, it is important to prevent the communication of such an environment against the different types of possible attacks. These days, the attacks performed by botnets (i.e., malware attacks such as mirai and reaper) have drawn attention to the researchers. Under the influence of such attacks, the communication of IoT-enabled industrial multimedia environment is disrupted. Moreover, the attackers may also control the smart devices remotely and can change their functionalities. Hence, we need some robust mechanism to detect the presence of the malware attacks in such

关键词： Internet of things

来源：评论

学校读者我要写书评

暂无评论

Data Driven Traffic Density Monitoring: A UAV and ResNet50 Deep Learning Framework 21

Data Driven Traffic Density Monitoring: A UAV and ResNet50 D...

引用

21st IEEE International Colloquium on Signal Processing and Its Applications, CSPA 2025

作者： Rubab, Saddaf Dahri, Fida Hussain Mustafa Abro, Ghulam E Tahir, Nooritawati Md Javed, Muhammad University of Sharjah College of Computing and Informatics Department of Computer Engineering Sharjah 27272 United Arab Emirates Southeast University School of Computer Science and Engineering Nanjing China King Fahd University of Petroleum and Minerals (KFUPM) Interdisciplinary Research Centre for Aviation and Space Exploration Saudi Arabia College of Engineering Universiti Teknologi Mara School of Electrical Engineering Selangor Shah Alam Malaysia International Information Technology University Department of Cybersecurity Alamty Kazakhstan Donghua University College of Computer Science Department of Computer Science and Technology Shanghai 200022 China

ISBN: (纸本)9798331522193

Urban traffic congestion necessitates innovative monitoring and control solutions. This study leverages UAV footage and deep learning techniques to classify and analyze traffic density levels autonomously. By employing a pre-trained ResN et50 model enhanced with additional layers, the research optimizes traffic density classification using UAV-captured data. A custom dataset of traffic scenarios in Dammam, Saudi Arabia, was developed, incorporating diverse conditions through advanced normalization and data augmentation techniques to improve resilience and robustness. The modified ResN et50 model demonstrated high classification accuracy across varied traffic scenarios, validating its reliability and scalability for real-world applications. Key findings include the superior adaptability of UAV-based monitoring for real-time urban traffic assessment and the enhanced classification precision achieved through model optimization. This research underscores the potential of integrating UAV technology with deep learning to advance urban traffic management and foster more efficient urban planning strategies. © 2025 IEEE.

关键词： Deep Learning Algorithms ResNet50 Model Traffic Density Classification UAV Imagery Urban Traffic

来源：评论

学校读者我要写书评

暂无评论

Using clinical entity recognition for curating an interface terminology to aid fast skimming of EHRs

Using clinical entity recognition for curating an interface ...

引用

IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Navya Martin Kollapally Mahshad Koohi H. Dehkordi Yehoshua Perl James Geller Fadi P. Deek Hao Liu Vipina K. Keloth Gai Elhanan Andrew J. Einstein Shuxin Zhou Department of Computer Science Kean University USA Department of Computer Science New Jersey Institue of Technology Newark USA Department of DataScience New Jersey Institue of Technology Newark USA Department of Informatics New Jeresy Institue of Technology Newark USA School of Computing Montclair State University USA Section of Biomedical Informatics and DataScience Yale University New Haven USA School of Medicine University of Nevada Nevada USA Dept. of Medicine Columbia Medical Center New York USA

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Highlighting of Electronic Health Records (EHRs) involves marking essential content of EHR notes, corresponding to concepts of a clinical terminology. However, employing the best clinical terminology (SNOMED CT) for highlighting EHRs, captures only a portion of their crucial content. In this paper, we describe the curation of a Cardiology Interface Terminology (CIT) dedicated to the application of highlighting EHRs of cardiology patients. We utilize a Clinical-Named Entity Recognition (Clinical NER) approach for extracting phrases, of higher granularity than SNOMED CT concepts, from EHRs, for enriching CIT. For this purpose, we train a neural network model with BIOE-tagged (Beginning, Inside, End, and Outside) cardiology entities. Transfer Learning can be used to facilitate the curation of an interface terminology for highlighting EHRs for other specialties e.g. Nephrology. Large-scale highlighting enables overworked physicians and other healthcare providers to fast skim the dense volume of EHRs they regularly read. Secondary research and EHRs interoperability are other applications that can be supported by highlighting.

关键词： Training Nephrology Terminology Biological system modeling Transfer learning Neural networks Medical services Cardiology Electronic medical records Interoperability

来源：评论

学校读者我要写书评

暂无评论

Multi-agent Traffic Light Controls with SUMO

Multi-agent Traffic Light Controls with SUMO

引用

International Conference on Business and Industrial Research (ICBIR)

作者： Somnuk Phon-Amnuaisuk Soon-Jiann Tan Yok-Hoe Yap Florence Chiao-Mei Choong Pikul Vejjanugraha Kok-Chin Khor Keng-Hoong Ng School of Computing and Informatics Universiti Teknologi Brunei Gadong Brunei Faculty of Engineering Universiti Teknologi Brunei Gadong Brunei School of Engineering and Physical Sciences Heriot-Watt University Malaysia Campus Malaysia Department of International College Thai-Nichi Institute of Technology Thailand LKC Faculty of Engineering and Science Universiti Tunku Abdul Rahman Malaysia School of Computing Asia Pacific University of Technology and Innovation Malaysia

This study focuses on the optimization of Traffic Light System (TLS) control through the use of adaptive agents. The performance of adaptive cycle TLS was compared with fixed cycle TLS. Two different adaptive cycle TLS agents were investigated, reactive and Deep Q-Network (DQN) agents. The reactive agent adjusted its control signals based on traffic measures such as queue length, while the DQN agent employed a deep reinforcement learning algorithm to determine its control signals. Two sets of simulations were conducted to evaluate the performance of both approaches. The results showed that the adaptive cycle TLS was more effective in reducing waiting times and increasing traffic throughput than the fixed cycle TLS. Among the adaptive agents, the reactive agent outperformed the DQN agent, due to the difficulty in learning an optimal policy in the traffic control domain, which has a non-stationary and complex nature. This preliminary study showed the potential benefits of using adaptive agents for traffic light control, and further studies in various areas such as employing more advanced TLS control methods, expanding the scale of the study, and applying real-demand in the simulation, could be carried out in future work.

关键词：

来源：评论

学校读者我要写书评

暂无评论

A Brief Survey on Image Denoising based Feature Extraction and Classification Models for Oral Cancer Detection

A Brief Survey on Image Denoising based Feature Extraction a...

引用

Sustainable computing and Data Communication Systems (ICSCDS), International Conference on

作者： P.Sandhya Krisha Subba Rao Peram Department of IT and Computer Applications School of computing and informatics Vignan’s Foundation for science Technology & Research Deemed to be university vadlamudi Guntur Andhra Pradesh India Department of IT Vignan’s Nirula Institute of Technology and Science for Women Peda Palakaluru Andhra Pradesh

A subset of oral cancer is called Oropharyngeal Carcinoma (OPC). As opposed to other forms of oral cancer, this one seems to be fatal more often. Those with oropharyngeal cancer also have damage to their tonsils, the back of their tongues, and the vallecula. The oral cavity is frequently attacked by malignant tumours, and oral cancer is one of the most common types. Understanding the precise nature of the oral cancer at an early stage allows for more precise treatment planning and more accurate prediction of the patient’s response to therapy. Diagnosing oral cancer from histological pictures is a significant obstacle. Visual characteristics of oral cancers are typically gleaned from epithelial layer architecture and the presence of keratin pearls. Salivary glands, tonsils, and the jawline, face, and mouth are all affected by the tumour. In this research, a brief survey is presented on image denoising models, feature extraction models for accurate oral cancer detection. A deep learning algorithm and cutting-edge technology make it possible to discover and classify things at an early stage. Multiple filtering strategies are used here to get rid of the speckle noise in the CT image. Oropharyngeal carcinoma is often detected with CT scans. In this research, a deep learning systems are analyzed for application in the classification of oral pictures into normal and pathological categories. By employing morphological operations, users can separate out the cancerous areas for accurate detection of oral cancer.

关键词： Deep learning Visualization Computed tomography Mouth Speckle Feature extraction Cancer detection

来源：评论

学校读者我要写书评

暂无评论

Communication and Control Co-Design in 6G: Sequential Decision-Making with LLMs

引用

IEEE Network 2024年

作者： Chen, Xianfu Wu, Celimuge Shen, Yi Ji, Yusheng Yoshinaga, Tsutomu Ni, Qiang Zarakovitis, Charilaos C. Zhang, Honggang Shenzhen CyberAray Network Technology Co. Ltd China Chinese Academy of Sciences Shanghai Advanced Research Institute China Meta-Networking Research Center China The University of Electro-Communications Graduate School of Informatics and Engineering Tokyo Japan National Institute of Informatics Information Systems Architecture Research Division Tokyo Japan Lancaster University School of Computing and Communications Ukraine AXON Logic Greece City University of Macau Faculty of Data Science Macau China China

This article investigates a control system within the context of sixth-generation wireless networks. The remote control performance optimization confronts the technical challenges that arise from the intricate interactions between communication and control sub-systems, asking for a co-design. Considering the system dynamics, we formulate the sequential co-design decision-makings of communication and control over a discrete time horizon as a Markov decision process, for which a practical offline learning framework is proposed. Our proposed framework integrates large language models into the elements of reinforcement learning. We present a case study on the age of semantics-aware communication and control co-design to showcase the potential of our proposed learning framework. Furthermore, we discuss the open issues remaining to make our offline learning framework feasible for real-world implementations and highlight the research directions for future explorations. © 1986-2012 IEEE.

关键词： Markov processes

来源：评论

学校读者我要写书评

暂无评论

Machine Learning based Voltage Regulation Technique using Smart Rotating Magnetic Inverter

Machine Learning based Voltage Regulation Technique using Sm...

引用

2022 IEEE International Conference on Electrical, Computer, and Energy Technologies, ICECET 2022

作者： Hussain, Muhammad Majid Siddique, Muhammad Javed, Waqas Razaq, Abdul Akhter, Muhammad Naveed Malik, Farhan Hameed University of South Wales Department of Electrical and Electronic Engineering Cardiff United Kingdom NFC Institute of Engineering and Technology Department of Electrical Engineering Multan Pakistan Glasgow Caledonian University School of Computing Engineering and Built Enviornment Glasgow United Kingdom Abertay University School of Design and Informatics Dundee United Kingdom Rachna Colllege of Engineering and Technology Department of Electrical Engineering Gujranwala Pakistan Abu Dhabi Polytechnic Department of Electromechnical Engineering Technology Abu Dhabi United Arab Emirates

ISBN: (数字)9781665470872

ISBN: (纸本)9781665470872

In this paper, a control methodology for power distribution line voltage regulations is presented. The salient features of this rotating magnetic inverter (RMI) include novel architecture with reduced electronic components and simple circuitry rather than conventional inverters, making it very economical and easy to implement. A new adaptive control based on a fuzzy logic approach is discussed in this paper, enabling the inverter to work more efficiently for dynamic voltage regulation. This proposed control method is best to mitigate the effect of voltage sags and swells in power distribution lines. On dynamic load variations, the system becomes unstable, and output contains voltage harmonics. Moreover, the RMI is a novel contribution from the authors as it has a unique structure for rotation of magnetic fields. The claim about the performance of the proposed RMI is tested and verified through MATLAB simulation. © 2022 IEEE.

关键词： Dynamic loads

来源：评论

学校读者我要写书评

暂无评论

mini-ELSA: using Machine Learning to improve space efficiency in Edge Lightweight Searchable Attribute-based encryption for Industry 4.0

arXiv

引用

arXiv 2022年

作者： Aljabri, Jawhara Michala, Anna Lito Singer, Jeremy Vourganas, Ioannis School of Computing Science University of Glasgow United Kingdom Faculty of Computers and Information Technology University of Tabuk Saudi Arabia School of Design and Informatics Abertay University United Kingdom

In previous work a novel Edge Lightweight Searchable Attribute-based encryption (ELSA) method was proposed to support Industry 4.0 and specifically Industrial Internet of Things applications. In this paper, we aim to improve ELSA by minimising the lookup table size and summarising the data records by integrating Machine Learning (ML) methods suitable for execution at the edge. This integration will eliminate records of unnecessary data by evaluating added value to further processing. Thus, resulting in the minimization of both the lookup table size, the cloud storage and the network traffic taking full advantage of the edge architecture benefits. We demonstrate our mini-ELSA expanded method on a well-known power plant dataset. Our results demonstrate a reduction of storage requirements by 21% while improving execution time by 1.27x. Copyright © 2022, The Authors. All rights reserved.

关键词： Cloud computing architecture

来源：评论

学校读者我要写书评

暂无评论

Ensemble Based Machine Learning Model for Heart Disease Prediction 3

Ensemble Based Machine Learning Model for Heart Disease Pred...

引用

3rd International Conference on Communications, Information, Electronic and Energy Systems, CIEES 2022

作者： Ambrews, Ashley Bryan Gubin Moung, Ervin Farzamnia, Ali Yahya, Farashazillah Omatu, Sigeru Angeline, Lorita Universiti Malaysia Sabah Faculty of Computing And Informatics Kota Kinabalu Sabah Malaysia Universiti Malaysia Sabah Faculty of Engineering Kota Kinabalu Sabah Malaysia Education Research Centre Higashi-Hiroshima739-0046 Japan Taylor's University School of Computer Science Faculty of Innovation And Technology Selangor Malaysia

ISBN: (数字)9781665491495

ISBN: (纸本)9781665491495

The World Health Organization reports that more than 10 million people worldwide die each year due to heart disease. In this project, an ensemble-based machine learning (ML) model is proposed for the prediction of heart disease. The proposed method was evaluated on two datasets: UCI heart disease and Framingham heart study. Various ML such as K-Nearest Neighbors, Naïve Bayes, Decision Tree, Support Vector Machine (SVM), Logistic Regression (LR) and Random Forest (RF) were carried out to predict the outcome of heart disease. Additionally, the Stacking and Voting ensemble approaches are also employed for heart disease prediction. All ML classifiers evaluated on both datasets have their performance metrics analyzed and compared. The results of all ML classifiers assessed on the Framingham dataset is inferior than that of the UCI heart disease dataset. For individual ML classifier, SVM achieved the highest accuracy for UCI heart disease with 91.09%, whereas LR achieved the highest accuracy for the Framingham heart study with 85.38%. However, it should be noted that RF trained on the UCI dataset is the best ML for prioritizing Sensitivity (92.52%). Finally, with the exception of Sensitivity, the Voting approach is strongly suggested because it outperforms the other classifiers noticeably where it achieved Accuracy (91.96%), F1 (91.69%), Sensitivity (91.72%), Specificity (90.77%), and Precision (92.40%) on UCI dataset. © 2022 IEEE.

关键词： Forecasting

来源：评论

学校读者我要写书评

暂无评论

Reliable Indoor Localization in Multi-Building Environments: Leveraging Environment-Invariant and Position-Related Features

引用

IEEE Internet of Things Journal 2025年

作者： Long, Wenhan Wen, Xinlong Liu, Hao Li, Mengyao Li, Songquan Wu, Yonghui Chen, Fuxiang Liu, Lu Zhu, Rongbo Huazhong Agricultural University College of Informatics Wuhan430070 China Ministry of Education Engineering Research Center of Intelligent Technology for Agriculture Wuhan430070 China Hubei Engineering Technology Research Center of Agricultural Big Data Wuhan430070 China University of Leicester School of Computing and Mathematical Sciences Leicester United Kingdom University of Exeter Department of Computer Science Exeter United Kingdom

Received Signal Strength Indicator (RSSI)-based indoor localization offers a cost-effective solution for autonomous mobile robot navigation in 3D indoor environments, including cross-floor and multi-building structures. However, localization accuracy is fundamentally constrained by the low sampling density and unstable measurement of RSSI data. So far, existing methods neglect cross-environment RSSI coherence (e.g., repeated signal patterns in geometrically similar areas), resulting in unreliable fingerprint databases. What’s more, most approaches fail to model the spatial hierarchy of buildings, floors, and coordinates, which leads to lower accuracy in indoor positioning model predictions. To address these issues, we propose EP-3DLoc, a novel 3D indoor localization framework that combines an Environment-Invariant feature-based Data Completion (EIC) method with a Position-Related feature-based Localization (PRL) method. The EIC enhances data quality by filling in sparse RSSI data using environment-invariant features, which are recurring RSSI patterns found in similar environmental structures. The PRL module combines multi-scale RSSI signal processing (raw data and image-like data) with a multi-task network that analyzes location relationships, enhancing localization accuracy in 3D environments. Experimental results on public datasets (TUT2018, UTSIndoorLoc, and UJIIndoorLoc) have demonstrated that EP-3DLoc achieves state-of-the-art performance on indoor localization in multi-building environments. Further testing on the self-constructed dataset HZAUIndoorLoc have revealed that EP-3DLoc not only outperforms existing methods in localization accuracy but also maintains low energy consumption and strong resistance to interference. © 2014 IEEE.

关键词： Floors

来源：评论

学校读者我要写书评

暂无评论

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：

请选择收藏分类：

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

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：