检索结果-内蒙古大学图书馆

2021 IEEE International Conference on Electronic Communications, Internet of Things and Big Data, ICEIB 2021

作者： Tsao, Yung-Chung Kuo, Yaw-Wen Wu, Chia-Chun Tsai, Yin-Te Hsu, Chih-Cheng National Chi Nan University Dep. of Electrical Engineering Taiwan National Quemoy University Dep. of Industrial Engineering and Management Taiwan Providence University Dep. of Computer Science and Communication Engineering Taiwan National Central University Dep. of Information Management Taiwan

ISBN: (纸本)9781665437554

The energy monitoring system is always a critical and popular issue for a long time, although many information systems have been built and dep.oyed into schools, factories, schools, commercial organizations, and governments. For many years, most of them have closed systems based on their business considerations, so add-on sensor types or heterogeneous hardware and software integrations are the bottlenecks. The study is carried out to integrate the internet of thing (IoT) and message queue telemetry transport (MQTT) to design a mechanism and to implement a prototyping clouding platform as the proposed system to develop the AC collectors with electricity loop current sensors connected to the proposed controller with Ameba 8195 AM in the study. The AC collectors are dep.oyed in labs and classrooms in the National Chi Nan University and classrooms in Xinhe Elementary School to validate the usability and feasibility of the proposed controllers and the prototyping web system. A distributed message-deliver is developed as a publisher based on MQTT Broker to implement a central message-collector as a subscriber to transmit all data into the proposed system. The analytical and visualized reports are created for users and administrators in the proposed system to offer useful and real-time information as feedback to be the proof of concept (PoC). © 2021 IEEE.

关键词： Internet of things

来源：评论

学校读者我要写书评

暂无评论

Biological age estimation from ECG using Artificial Intelligence approaches

Biological age estimation from ECG using Artificial Intellig...

引用

Biomedical engineering Conference (JIBEC), Jordanian International

作者： Jana Sleiman Mohamad Daher Mohamad Khalil Ahmad Diab Abdallah Kassem Computer and Communications Engineering Lebanese University Tripoli Lebanon Dep. of Computer Science/Dep. of Computer Science & Information Technology Faculty of Science/Faculty of Applied Sciences & Technology Tripoli Lebanon Electrical Engineering Lebanese University Tripoli Lebanon Biomedical Engineering Lebanese International University Beirut Lebanon ECCE Dept. Faculty Of Engineering Notre Dame University Zouk Mosbeh Lebanon

ISBN: (数字)9798331539542

ISBN: (纸本)9798331539559

This paper explores the application of Artificial Intelligence (AI) techniques in healthcare, specifically focusing on electrocardiogram (ECG) data analysis and biological age estimation. The study begins with an overview of ECG as a diagnostic tool and discusses the integration of Deep Learning (DL) models, particularly Convolutional Neural Networks (CNNs), in healthcare for various applications. Previous studies reviewed in this context consistently identify CNNs as the superior model for ECG analysis and prediction tasks. The primary research investigates the use of Machine Learning (ML) algorithms to estimate biological age from ECG data, employing metrics such as Mean Absolute Error (MAE) and variance to evaluate model performance. Subsequently, DL models are implemented and compared, demonstrating superior accuracy in estimating the biological age of new patients. While both ML and DL approaches were initially explored, this paper primarily focuses on deep learning methodologies due to their demonstrated superiority in healthcare applications. The findings highlight the efficacy of deep learning over traditional ML methods, underscoring its potential for enhancing diagnostic accuracy and personalized patient care. For instance, the most accurate deep learning model, the Convolutional Neural Network (CNN), achieved a Mean Absolute Error (MAE) of 1.49 and an R 2 score of 0.53, whereas the best-performing traditional ML model, Random Forest, achieved an MAE of 1.54 and an R 2 score of 0.55. These results clearly indicate the advantage of deep learning models in capturing complex patterns in ECG data, leading to more precise and reliable estimations of biological age.

关键词： Deep learning Maximum likelihood estimation Accuracy Data analysis Biological system modeling Medical services Electrocardiography Data models Convolutional neural networks Random forests

来源：评论

学校读者我要写书评

暂无评论

Feature Selection for Robust Spoofing Detection: A Chi-Square-based Machine Learning Approach

Feature Selection for Robust Spoofing Detection: A Chi-Squar...

引用

Electrical, Energy, and Artificial Intelligence (EICEEAI), International engineering Conference on

作者： Qais Al-Na'amneh Mohammad Aljaidi Hasan Gharaibeh Ahmad Nasayreh Rabia Emhamed Al Mamlook Sattam Almatarneh Dalia Alzu'bi Abla Suliman Husien Dep. of Cyber Security Applied Science Private University Amman Jordan Dep. of Computer Science Zarqa University Zarqa Jordan Dep. Computer Science Yarmouk University Irbid Jordan Dep. of Business Administration Trine University Indiana USA Department of Industrial Engineering University of Zawia Tripoli Libia Dep. of Data Science and Artificial Intelligence Zarqa University Zarqa Jordan Dep. Information and Systems Engineering Concordia University Montreal Canada

ISBN: (数字)9798350373363

ISBN: (纸本)9798350373370

Nowadays, the Internet of Things (IoT) system is vulnerable to spoofing attacks that can easily where attackers can easily pose as a legal entity of the network. A “spoofing attack” refers to a type of cyber-attack when an attacker purposefully impersonates or masquerades as someone or something else to deceive the target or obtain unauthorized access to systems, information, or resources. In such attacks, the attacker alters their name, IP address, or other attributes to fool the victim into thinking they are engaging with a legitimate entity. Spoofing attacks can take place via a variety of channels, including, ARP and DNS spoofing. Therefore. Spoofing attacks can have serious consequences. We proposed a new approach based on three machine learning models LightGBM, Gradient Boost, and XGBoost to classify attacks on spoofing, we used Chi-square to select the best features to get the highest performance, and we demonstrated that the results using Chi-square achieved higher results than without Chi-square and improved the result with a rate three percent of accuracy. In terms of results, LightGBM outperformed other models by achieving 89%, 91 %, 87 %, and 89 % for accuracy, precision, recall, and f1-score, respectively. This shows the potential and efficiency of Chi-square to achieve the best performance by selecting the best features, thus providing a secure system and identifying cyber-attacks on systems.

关键词： Electric potential Accuracy Law Machine learning Learning (artificial intelligence) Feature extraction Internet of Things

来源：评论

学校读者我要写书评

暂无评论

Comparison of Milk Quality Grades Using the Logistic Model Tree Algorithm

Comparison of Milk Quality Grades Using the Logistic Model T...

引用

Cybernetics and Innovations (ICCI), International Conference on

作者： Phanintorn Suaprae Thanakrit Janchidfah Brijesh Bhatt Dep. Of Business Information Technology Management Phibulsongkram Rajabhat University Phitsanulok Thailand Dep. Of Digital Media Technology Muban Chombueng Rajabhat University Ratchaburi Thailand Dep. Of Computer Engineering Faculty of Technology Dharmsinh Desai University India

ISBN: (数字)9798331533267

ISBN: (纸本)9798331533274

This study aims to track and categorize milk quality grades using the Logistic Model Tree (LMT) algorithm, based on the analysis of 1,059 milk samples. The focus of the research is to evaluate key factors such as pH value, taste, smell, fat content, concentration, and color, which play a crucial role in determining the overall quality of milk. The goal is to create an efficient and standardized process for sorting cow's milk, which can save significant time and resources in the dairy industry. To identify the most suitable algorithm for this task, the research team initially tested 12 different algorithms, assessing their performance in predicting milk quality. Among these, the Logistic Model Tree (LMT) emerged as the most effective model, achieving an impressive accuracy rate of 99.76%. The LMT algorithm, known for combining the advantages of decision trees and logistic regression, demonstrated its ability to classify milk quality grades with high precision. The results of this study indicate that the LMT algorithm can be a valuable tool for automating the milk quality grading process. By leveraging key attributes such as pH, fat content, and color, it is possible to efficiently sort and grade milk, reducing the need for manual inspection and improving overall productivity in the dairy industry. This method offers a significant improvement over traditional quality assessment techniques and has the potential to be widely adopted for milk sorting and quality control applications.

关键词： Analytical models Dairy products Accuracy Standardization Prediction algorithms Data models Classification algorithms Quality assessment Logistics Sorting

来源：评论

学校读者我要写书评

暂无评论

COVID-19 detection from Xray and CT scans using transfer learning

COVID-19 detection from Xray and CT scans using transfer lea...

引用

2021 International Conference of Women in Data science at Taif University, WiDSTaif 2021

作者： Berrimi, Mohamed Hamdi, Skander Yahia Cherif, Raoudha Moussaoui, Abdelouahab Oussalah, Mourad Chabane, Mafaza Dep. of Computer Science University of Ferhat Abbes Setif I Setif Algeria Dep. of Computer Science and Engineering University of Oulu Oulu Finland

ISBN: (纸本)9781665449489

Since the novel coronavirus SARS-CoV-2 outbreak, intensive research has been conducted to find suitable tools for diagnosis and identifying infected people in order to take appropriate action. Chest imaging plays a significant role in this phase where CT and Xrays scans have proven to be effective in detecting COVID-19 within the lungs. In this research, we propose deep learning models using Transfer learning to detect COVID-19. Both X-ray and CT scans were considered to evaluate the proposed methods. © 2021 IEEE.

关键词： Medical imaging

来源：评论

学校读者我要写书评

暂无评论

FOCUSED ACTIVE LEARNING FOR HISTOPATHOLOGICAL IMAGE CLASSIFICATION

arXiv

引用

arXiv 2024年

作者： Schmidt, Arne Morales-Álvarez, Pablo Cooper, Lee A.D. Newberg, Lee A. Enquobahrie, Andinet Katsaggelos, Aggelos K. Molina, Rafael Dep. of Computer Science and AI University of Granada Granada Spain Dep. of Statistics and Operations Research University of Granada Granada Spain Dep. of Pathology Northwestern University Chicago United States Kitware Inc Carrboro United States Dep. of Electrical Computer Engineering Northwestern University Evanston United States

Active Learning (AL) has the potential to solve a major problem of digital pathology: the efficient acquisition of labeled data for machine learning algorithms. However, existing AL methods often struggle in realistic settings with artifacts, ambiguities, and class imbalances, as commonly seen in the medical field. The lack of precise uncertainty estimations leads to the acquisition of images with a low informative value. To address these challenges, we propose Focused Active Learning (FocAL), which combines a Bayesian Neural Network with Out-of-Distribution detection to estimate different uncertainties for the acquisition function. Specifically, the weighted epistemic uncertainty accounts for the class imbalance, aleatoric uncertainty for ambiguous images, and an OoD score for artifacts. We perform extensive experiments to validate our method on MNIST and the real-world Panda dataset for the classification of prostate cancer. The results confirm that other AL methods are’distracted’ by ambiguities and artifacts which harm the performance. FocAL effectively focuses on the most informative images, avoiding ambiguities and artifacts during acquisition. For both experiments, FocAL outperforms existing AL approaches, reaching a Cohen’s kappa of 0.764 with only 0.69% of the labeled Panda data. © 2024, CC BY.

关键词： Image classification

来源：评论

学校读者我要写书评

暂无评论

EEG-Based Stress Assessment During Robot Assisted Surgery. Comparison of Statistical Methods with Machine Learning

EEG-Based Stress Assessment During Robot Assisted Surgery. C...

引用

Metrology for Extended Reality, Artificial Intelligence and Neural engineering (MetroXRAINE), IEEE International Conference on

作者： Pasquale Arpaia Irene Del Chicca Matteo De Luca Ludovica Gargiulo Giovanna Mastrati Nicola Moccaldi Elisa Morganti Marco Nalin Mauro Picciafuoco Francesca Pierro Filippo Rigoni Riccardo Rigoni Dep. of Electrical Engineering and Information Technology (DIETI) University of Naples Federico II Naples Italy ab medica S.p.A. Cerro Maggiore MI Italy Dep. of Electronics Information and Bioengineering (DEIB) Polytechnic University of Milan Milan Italy Dep. of Computer Science (D-INFK) ETH Zurich Zurich Switzerland

ISBN: (数字)9798350378009

ISBN: (纸本)9798350378016

This study proposes a adaptive pipeline (feature extraction, feature selection, and classification) for the processing of electroencephalographic (EEG) signals to assess Mental Workload (MWL) during training sessions with a surgical robot. Unlike previous studies in this field, EEG signals were acquired by a dry eight-electrode device to maximize usability and comfort for the users. Fourteen medical students were EEG monitored while performing four different tasks on a simulation platform incorporated into the Da Vinci System. The proposed pipeline was compared with typical machine-learning approaches based on handcraft feature extraction approaches. The results confirmed the effectiveness of the proposed solution as a further contribution to EEG-based monitoring of robotic surgery for adaptive training.

关键词： Training Medical robotics Pipelines Surgery Machine learning Feature extraction Electroencephalography Biomedical monitoring Usability Monitoring

来源：评论

学校读者我要写书评

暂无评论

Enhancements to the 5G-NR 2-Step RACH: an Unsourced Multiple Access Perspective

Enhancements to the 5G-NR 2-Step RACH: an Unsourced Multiple...

引用

2024 IEEE Conference on Standards for Communications and Networking, CSCN 2024

作者： Agostini, Patrick Chamberland, Jean-Francois Clazzer, Federico Dommel, Johannes Liva, Gianluigi Munari, Andrea Narayanan, Krishna Polyanskiy, Yury Stanczak, Slawomir Utkovski, Zoran Fraunhofer Heinrich-Hertz-Institut Berlin Germany Texas A&M University College Station Dep. of Electrical and Computer Engineering TX United States Wessling Germany Massachusetts Institute of Technology Laboratory for Information and Decision Systems CambridgeMA United States

ISBN: (纸本)9798331507428

The ever increasing demand for massive connectivity, combined with the specific characteristics of machinegenerated wireless traffic, is pushing the existing solutions for device registration and data transfer beyond their operational limits. In this paper, we target specifically the 3GPP 5G New Radio random access channel, proposing some enhancements inspired by recent research results in the field of unsourced multiple access (UMAC). Aiming to enable massive grant-free connectivity for large machine-type and Internet of things terminals, we focus on modifications of the two-step random access procedures originally introduced in 3GPP Release 16. By means of simulations, we show that remarkable gains in both energy and spectral efficiency can be attained while retaining a strong resemblance to the legacy protocol. © 2024 IEEE.

关键词： 5G mobile communication systems

来源：评论

学校读者我要写书评

暂无评论

AbotalebNet: A Novel Deep Learning Architecture for Time Series Forecasting with Convolutional and LSTM Layers Enhanced by Multi-Head Attention

AbotalebNet: A Novel Deep Learning Architecture for Time Ser...

引用

Trends in Quantum Computing and Emerging Business Technologies (TQCEBT), International Conference on

作者： Mostafa Abotaleb Tatiana Makarovskikh Maad M. Mijwil Indu Bala Kamal Kant Hiran Ruchi Doshi Dep. of System Programming South Ural State University Chelyabinsk Russia Computer Techniques Engineering Department Baghdad College of Economic Sciences University Baghdad Iraq Electrical and Electronics Engineering Lovely Professional University Punjab India Dep. of Computer Science Sir Padampat Singhania University Udaipur India Dep. of Computer Science and Engineering Universidad Azteca Chalco Mexico

ISBN: (数字)9798350384277

ISBN: (纸本)9798350384284

This paper introduces AbotalebNet, a novel deep learning architecture optimized for time series forecasting, with a particular focus on the complexities of COVID-19 data. AbotalebNet’s architecture is mathematically represented as:\begin{equation*}\hat Y = {\text{Attention}}(\operatorname{LSTM} ({\text{CNN}}(X)))\tag{1}\end{equation*}Here, X represents the input time series data, and Ŷ is the forecasted output. AbotalebNet integrates the feature extraction capabilities of Convolutional Neural Networks (CNNs) with the sequential data processing power of Long Short-Term Memory (LSTM) networks, further enhanced by a Multi-Head Attention *** address overfitting, a key challenge in deep learning, the model incorporates regularization strategies, dropout mechanisms, and batch normalization:\begin{equation*}\mathop {\min }\limits_\Theta ( Loss (X,\hat Y;\Theta ) + \lambda R(\Theta ))\tag{2}\end{equation*}In this equation, Θ denotes the model parameters, R(Θ) represents regularization terms, and λ is the regularization coefficient. These additions aid in preventing the model from overfitting to the training data, ensuring robust performance on unseen *** evaluation on COVID-19 time series data demonstrates AbotalebNet’s enhanced predictive accuracy over traditional models, solidifying its potential for advanced non-linear time series analysis.

关键词： COVID-19 Analytical models Solid modeling Time series analysis Training data Predictive models Data models

来源：评论

学校读者我要写书评

暂无评论

Impact of PTB Message Usage on Path MTU Discovery for QUIC

Impact of PTB Message Usage on Path MTU Discovery for QUIC

引用

Conference on Local computer Networks (LCN)

作者： Timo Völker Michael Tüxen Dep. of Electrical Engineering and Computer Science FH Münster University of Applied Sciences Steinfurt Germany

ISBN: (数字)9798350388008

ISBN: (纸本)9798350388015

A data sender in an IP-based network is only capable of efficiently using a network path if it knows the packet size limit of the path, i.e., the Path Maximum Transmission Unit (PMTU). An IP router that cannot forward and must drop an oversized packet should notify the sender with a Packet Too Big (PTB) message, which contains the maximum packet size it can forward. A transport protocol supporting a discovery to determine the PMTU may use PTB messages. However, for the transport protocol QUIC (e.g., used by HTTP/3), we identified only a few implementations that determine the PMTU and none of them process PTB messages. In this paper, we present results from simulations that show how much a QUIC sender using PMTU discovery benefits from processing PTB messages, which includes a result that outlines the benefit of determining the PMTU in general. Further, we analyze the risk of using PTB messages and describe a way to protect against potential attacks with forged PTB messages.

关键词： Transport protocols Routing protocols IP networks

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：