检索结果-内蒙古大学图书馆

2024 IEEE International Conference on Big Data, BigData 2024

作者： Tran, Tan Chang, Lin-Ching Lum, Peter The Catholic University of America Department of Electrical Engineering and Computer Science WashingtonDC United States New Jersey Institute of Technology Department of Informatics NewarkNJ United States The Catholic University of America Department of Biomedical Engineering WashingtonDC United States

ISBN: (纸本)9798350362480

Post-stroke rehabilitation of upper extremity (UE) motor function is essential. Despite the widespread use of UE rehabilitation in clinical settings, assessing the success of these treatments is challenging. Methods for evaluating UE motor function include task performance under clinical supervision, patient self-reports, and data analysis from wearable devices equipped with accelerometers and gyroscopes. In prior research, we demonstrated that machine learning and deep learning models using data from a single wrist-worn accelerometer sensor could accurately differentiate between functional and non-functional UE movements in stroke patients. To overcome the limitations of single wrist-worn accelerometer sensors - challenges in capturing the full context of functional movements - this study presents a new deep learning (DL) based framework designed to classify functional and non-functional arm movements in videos captured from a conventional camera. The system is entirely automated and comprises two DL networks. The first network performs human pose estimation, extracting 2D pose key points of the paretic arm(s) and torso on 2-second sequences of the frames. The second network then uses these 2D pose landmarks to classify the movement sequence as functional or non-functional. This system offers two key benefits for rehabilitation. First, it can automatically generate initial annotations for video frames, significantly reducing the time needed for manual labeling. Second, analyzing the effectiveness of UE rehabilitation through third-person videos allows for objective outcome measurement of UE treatments in stroke survivors' home environments. © 2024 IEEE.

关键词： Accelerometers

来源：评论

学校读者我要写书评

暂无评论

A Logical Investigation of Stock Market Prediction and Analysis using Supervised Machine Learning Algorithm 1

A Logical Investigation of Stock Market Prediction and Analy...

引用

1st IEEE International Conference on Networking and Communications, ICNWC 2023

作者： Dhanalakshmi, R. Kumar, V. Vasantha Basha, Saleem Vijayaraghavan, N. Vellore Institute of Technology School of Computer Science and Engineering Chennai India Mazoon University College Computing and Informatics Department Muscat Oman KCG College of Technology Department of CSE Chennai India

ISBN: (纸本)9798350336009

In the Field of computer science, artificial intelligence (AI) is a broad field, which is concerned with structuring smart products and machines able to perform tasks which require the intellectual capability of humans. Artificial Intelligence is in need for Stock trading's future. Robotic advisers examine millions of pieces of data using artificial intelligence and carry out the analysis at the best possible price. Market forecasts are more precise and effective thanks to the analysts. For providing higher returns, the trading firms efficiently mitigate the risk using Artificial Intelligence Techniques. Our proposed Stock Market system is a Web Application which uses the Random Forest Algorithm. The Random Forest Algorithm comes under Supervised Learning. With the system, one can forecast the stock prices for the subsequent days. © 2023 IEEE.

关键词： Forecasting

来源：评论

学校读者我要写书评

暂无评论

Decentralized State Estimation and Opacity Verification Based on Partially Ordered Observation Sequences

arXiv

引用

arXiv 2025年

作者： Sun, Dajiang Hadjicostis, Christoforos N. Li, Zhiwu School of Electro-Mechanical Engineering Xidian University Xi’an710071 China Department of Electrical and Computer Engineering University of Cyprus Nicosia Cyprus Institute of Systems Engineering Macau University of Science and Technology Taipa999078 China

In this paper, we investigate state estimation and opacity verification problems within a decentralized observation architecture. Specifically, we consider a discrete event system whose behavior is recorded by a set of observation sites. These sites transmit the partially ordered sequences of observations that they record to a coordinator whenever a synchronization occurs. To properly analyze the system behavior from the coordinator’s viewpoint, we first introduce the notion of a Complete Synchronizing Sequence structure (CSS structure), which concisely captures the state evolution of each system state upon different information provided by the observation sites. Based on the CSS structure, we then construct corresponding current-state and initial-state estimators for offline state estimation at the coordinator. When used to verify state-isolation properties under this decentralized architecture, the use of CSS structure demonstrates a significant reduction in complexity compared with existing approaches in the literature. In particular, we discuss how to verify initial-state opacity at the coordinator, as well as a novel opacity notion, namely current-state-at-synchronization opacity. Copyright © 2025, The Authors. All rights reserved.

关键词： Decentralized systems

来源：评论

学校读者我要写书评

暂无评论

Password Manager with Multi-Factor Authentication 1

Password Manager with Multi-Factor Authentication

引用

1st IEEE International Conference on Networking and Communications, ICNWC 2023

作者： Dhanalakshmi, R. Vijayaraghavan, N. Narasimhan, S. Basha, Saleem Vellore Institute of Technology School of Computer Science and Engineering Chennai India KCG College of Technology Department of Mathematics Chennai India Mazoon University College Computing and Informatics Department muscat Oman

ISBN: (纸本)9798350336009

Data breach is a serious issue as it leaks the personal information of more than billions of users and their privacy is compromised. More than 77% of organizations do not have a Cyber Security Incident Response plan. So, it is necessary to be informed of network security and ways to store and generate secured passwords. Having different and random passwords for ones digital accounts can exponentially increase the security of user's data. The goal of this project is to build a password manager which can securely store and encrypt passwords and other data. The multifactor authentication will provide increased security to validate the user into password management system. Multifactor authentication system includes physical security key and graphical password authentication. © 2023 IEEE.

关键词： Cryptography

来源：评论

学校读者我要写书评

暂无评论

Developing Engagement in the Learning Management System Supported by Learning Analytics

引用

computer Systems Science & engineering 2022年第7期42卷 335-350页

作者： Suraya Hamid Shahrul Nizam Ismail Muzaffar Hamzah Asad W.Malik Department of Information System Faculty of Computer Science and Information TechnologyUniversiti MalayaKuala LumpurMalaysia Faculty of Computing and Informatics Universiti Malaysia SabahSabahMalaysia Department of Computing School of Electrical Engineering and Computer Science(SEECS)National University of Sciences and Technology(NUST)IslamabadPakistan

Learning analytics is an emerging technique of analysing student par-ticipation and *** recent COVID-19 pandemic has signiﬁcantly increased the role of learning management systems(LMSs).LMSs previously only complemented face-to-face teaching,something which has not been possible between 2019 to *** date,the existing body of literature on LMSs has not analysed learning in the context of the pandemic,where an LMS serves as the only interface between students and ***,productive results will remain elusive if the key factors that contribute towards engaging students in learning are notﬁrst identiﬁ***,this study aimed to perform an exten-sive literature review with which to design and develop a student engagement model for holistic involvement in an *** required data was collected from an LMS that is currently utilised by a local Malaysian *** model was validated by a panel of experts as well as discussions with *** is our hope that the result of this study will help other institutions of higher learning determine factors of low engagement in their respective LMSs.

关键词： Engagement analysis learning analytics learning management system student engagement

来源：评论

学校读者我要写书评

暂无评论

Estimating the AGV load and a battery lifetime based on the current measurement and random forest application

Estimating the AGV load and a battery lifetime based on the ...

引用

2023 IEEE International Conference on Big Data, BigData 2023

作者： Hanzel, Krzysztof Grzechca, Damian Ziebinski, Adam Chruszczyk, Lukas Janus, Artur Silesian University of Technology Faculty of Automatic Control Electronics and Computer Science Department of Telecommunications and Teleinformatics Gliwice Poland Silesian University of Technology Faculty of Automatic Control Electronics and Computer Science Department of Electronics Electrical Engineering and Microelectronics Gliwice Poland Silesian University of Technology Faculty of Automatic Control Electronics and Computer Science Department of Distributed Systems and Informatics Devices Gliwice Poland Silesian University of Technology Student of Electrical EngineeringFaculty of Electrical Engineering Gliwice Poland

ISBN: (纸本)9798350324457

The paper describes the energy consumption from the battery based on the current measurements for various cases, i.e., speed (PWL adjustment) and loads. The main purpose of the research is to have additional and reliable information about power consumption and battery life estimation for autonomous guided vehicles (AGV). The authors propose a two-step algorithm. In the first step, a linear classifier was proposed. Then, the KNN classifier was tested;however, it did not give satisfactory results, so it was finally decided to use the random forest to estimate the load and PWL. The time domain current measurement is evaluated, and the beforementioned algorithms process the selected statistical measures. It has been proven that a two-step algorithm allows for achieving high accuracy. Based on the current observation, the paper is a good starting point for further investigation of the AGV because it is usually implemented in the AGV - so it does not require additional hardware. Moreover, it can lead to better energy management and increase battery lifetime. © 2023 IEEE.

关键词： AGV load estimation machine learning Prediction of energy consumption random forest

来源：评论

学校读者我要写书评

暂无评论

RAFA-Net: Region Attention Network for Food Items and Agricultural Stress Recognition

IEEE Transactions on AgriFood Electronics

引用

IEEE Transactions on AgriFood Electronics 2025年第1期3卷 121-133页

作者： Bera, Asish Krejcar, Ondrej Bhattacharjee, Debotosh Birla Institute of Technology and Science Department of Computer Science and Information Systems Pilani333031 India University of Hradec Kralove Center for Basic and Applied Science Faculty of Informatics and Management Hradec Kralove50003 Czech Republic Skoda Auto University Mlada Boleslav29301 Czech Republic Universiti Teknologi Malaysia Malaysia Japan International Institute of Technology Kuala Lumpur81310 Malaysia Jadavpur University Department of Computer Science and Engineering Kolkata700032 India

Deep convolutional neural networks (CNNs) have facilitated remarkable success in recognizing various food items and agricultural stress. A decent performance boost has been witnessed in solving the agro-food challenges by mining and analyzing region-based partial feature descriptors. Also, computationally expensive ensemble learning schemes fusing multiple CNNs have been studied in earlier works. This work proposes a region attention scheme for modeling long-range dependencies by building a correlation among different regions within an input image. The attention method enhances feature representation by learning the usefulness of context information from complementary regions. Spatial pyramidal pooling and average pooling pairs aggregate partial descriptors into a holistic representation. Both pooling methods establish spatial and channelwise relationships without incurring extra parameters. A context gating scheme is applied to refine the descriptiveness of weighted attentional features, which is relevant for classification. The proposed region attention network for food items and agricultural stress recognition method, dubbed RAFA-Net, has been experimented on three public food datasets, and has achieved state-of-the-art performances with distinct margins. The highest top-1 accuracy of RAFA-Net is 91.69%, 91.56%, and 96.97% on the UECFood-100, UECFood-256, and MAFood-121 datasets, respectively. In addition, better accuracies have been achieved on two benchmark agricultural stress datasets. The best top-1 accuracies on the Insect Pest (IP-102) and PlantDoc-27 plant disease datasets are 92.36%, and 85.54%, respectively;implying RAFA-Net's generalization capability. © 2023 IEEE.

关键词： Plant diseases

来源：评论

学校读者我要写书评

暂无评论

Training a Lightweight CNN Model for Fine-Grained Sewer Pipe Cracks Classification Based on Knowledge Distillation 5

Training a Lightweight CNN Model for Fine-Grained Sewer Pipe...

引用

5th International Conference on Pattern Recognition and Artificial Intelligence, PRAI 2022

作者： Ma, Beier Zuo, Xin Shen, Jifeng Shu, Xin Huang, Shucheng Li, Yuanhao Jiangsu University of Science and Technology School of Computer Science and Engineering Zhenjiang China Jiangsu University School of Electronic and Informatics Engineering Zhenjiang China

ISBN: (数字)9781665499163

ISBN: (纸本)9781665499163

Current sewage pipeline inspection researches focus on locating defects, with only a few studies addressing the crack fine-grained classification task. However, in engineering applications, such as Pipeline Assessment Certification Program (PACP), subcategory coding of cacks is required. Therefore, this paper studies the fine-grained classification of pipeline cracks. Algorithms based on deep learning have been widely used in the field of sewage pipeline inspection, but these models have a large number of parameters and are computationally time-consuming. Edge or embedded devices with weak GPUs cannot accept the time and storage costs of these large models. To solve the above problems, this paper introduces knowledge distillation (KD) which is one of the mainstream model compression methods. We select the large-scale network Resnet50 as the teacher model and design a lightweight network Combined MobileNetV2 & Inception (CMI) for crack detection as the student model. High-temperature distillation allows the student model to obtain dark knowledge from the pre-trained, well-performing teacher model, which is combined with ground truth (GT) for training. Experiments show that KD is effective. By adjusting the KD hyperparameters, the classification accuracy of the student model is improved by 1.41%, and the F1-score is improved by 1.12%. This demonstrates the robustness of knowledge distillation to improve the performance of small models, laying the foundation for future deployment on multi-sensor automatic detection robots. © 2022 IEEE.

关键词： computer vision

来源：评论

学校读者我要写书评

暂无评论

Predictive Analytics for Maximizing the Photovoltaic System Performance

Predictive Analytics for Maximizing the Photovoltaic System ...

引用

IEEE Conference Photovoltaic Specialists (PVSC)

作者： Andreas Livera Demetris Marangis Georgios Tziolis Vasiliki Paraskeva George Makrides George E. Georghiou Department of Electrical and Computer Engineering PV Technology Laboratory University of Cyprus Nicosia Cyprus PHAETHON Centre of Excellence for Intelligent Efficient and Sustainable Energy Solutions Nicosia Cyprus

ISBN: (数字)9781665464260

ISBN: (纸本)9781665475822

To ensure the smooth Terawatt (TW) photovoltaic (PV) transition and secure the TWh power generation over the service lifetime, enhanced digitalization, and automation approaches for effective operation and maintenance (O&M) of PV systems are required. This highlights the urgent need for corrective and predictive analytics to detect and predict possible anomalies in PV systems and to effectively manage and optimally schedule the field O&M activities. To fulfill this need, a failure detection algorithm along with a predictive maintenance model, that leverages statistical and machine learning (ML) principles, are proposed to accurately detect failures and to predict potential fault issues before they occur. The developed algorithm and the model were benchmarked using historical field data from a PV power plant in Greece. The obtained results showed the effectiveness of the algorithm for accurate failure detection and the capability of the predictive model for generating predictive maintenance alarms. The integration of corrective and predictive analytics into a monitoring software solution can help PV plant operators to schedule the field O&M activities, by providing valuable fault insights and actionable maintenance recommendations.

关键词： Photovoltaic systems Schedules Machine learning algorithms Software algorithms Predictive models Prediction algorithms Software Maintenance Predictive analytics Predictive maintenance

来源：评论

学校读者我要写书评

暂无评论

Analysis of Virtual Reality Based on the Internet of Things on Human Psychology ‘Internet of Thoughts’ (IoThs) for Rich Content Extraction Applied Natural Language Processing and Deep Learning 1

引用

20th International Conference on Smart Living and Public Health, ICOST 2023

作者： Mah, Pascal Muam Skalna, Iwona Pelech-Pilichowski, Tomasz Derlecki, Tomasz Nasr, Mahmoud Munyeshuri, Eric Amuzang, Gilly Njoh Itoe, Micheal Blake Somaah Tah, Ning Frida Department of Technical Informatics and Telecommunications AGH University of Science and Technology Krakow Poland Department of Business Informatics and Management Engineering AGH University of Science and Technology Krakow Poland Institute of Computer Science AGH University of Science and Technology Krakow Poland Department of Economics and Management Industry AGH University of Science and Technology Krakow Poland Department of Biomedical Engineering AGH University of Science and Technology Krakow Poland Department of Engineering Mathematics and Physics Faculty of Engineering and Technology Future University Cairo Egypt Department of Production Engineering and Logistics Enterprise Silesian University of Technology Gliwice Poland Department of Logistics Management Silesian University of Technology Gliwice Poland

ISBN: (数字)9783031439506

ISBN: (纸本)9783031439490

In the past decade, a lot of challenges to access, assess, and to acquire the needed technological opportunities to teach computers what naturally comes from the human brain and to understand how we naturally react when we rely on technology. The ability to document human thoughts, reactions and behavior to computers has led to the coming of NLP, AI, Dl, & ML. Aim to understand the influence of IoT on humans with the use of DL to achieve content correctness and accuracy with virtual technology. Studies show that the way we think, react, and do the things we think "Internet of thoughts" reflect our personality. The way we think determines the way we react and the way we do things are based on how we think. technology advancement has reinforced a lot of changes in humans which makes humans vulnerable to personal content exposure misappropriation due to the continuously changing nature of humanity and language. The study uses NLP, DL and behavior-oriented drive and influential function and results show that IoT based on VR influences human psychology "Internet of Thoughts". © 2023, The Author(s).

关键词： Internet of things

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：