检索结果-内蒙古大学图书馆

2024 IEEE International Conference on Modeling, Simulation and Intelligent Computing, MoSICom 2024

作者： Agrawal, Ansh Kaur, Kirandeep Kaur, Harkeerat Indian Institute of Technology Computer Science Engineering Jammu & Kashmir Jammu India Indian Institute of Technology Faculty Computer Science Engineering Jammu & Kashmir Jammu India

ISBN: (纸本)9798331533311

In recent years, the integration of Explainable Artificial Intelligence (XAI) in facial biometrics has gained momentum, driven by a growing need for transparency in automated decision-making systems. This paper explores the application of XAI in face detection and identification to bridge the gap between model accuracy and interpretability, aiming to address challenges unique to biometric systems, including bias mitigation, user trust, and ethical implications. Unlike traditional 'black-box' models, the proposed framework incorporates visual and feature-based explanations that highlight key facial regions contributing to identification outcomes, enabling more transparent model assessments. We evaluate the effectiveness of state-of-the-art XAI techniques, such as SHAP, LIME, and Grad-CAM, through rigorous testing on diverse datasets under varying conditions, including occlusions, lighting, and facial expressions. Additionally, this research introduces a novel hybrid interpretability metric tailored to biometric applications, assessing the quality of explanations based on fidelity, usability, and impact on user trust. Results demonstrate that the proposed XAI-enhanced model offers insights into decision processes without compromising accuracy, advancing the state of explainability in biometric systems. This work underscores the importance of explainability as a means to achieve ethical, reliable, and legally compliant AI systems in sensitive domains like facial biometrics. © 2024 IEEE.

关键词： Biometrics

来源：评论

学校读者我要写书评

暂无评论

Advanced Construction Materials Based on Concrete to Protect the Living Space from Non-Ionizing Radiation 1

引用

32nd International Conference on Organization and technology of Maintenance, OTO 2023

作者： Mandrić, Vanja Rupčić, Slavko Vinko, Davor Dominković, Ivica Faculty of Electrical Engineering Computer Science and Information Technology Osijek Osijek Croatia

ISBN: (数字)9783031514944

ISBN: (纸本)9783031514937

This paper presents measurements of the reflection and transmission coefficient of electromagnetic waves through concrete and two concrete-based composites: concrete with steel fibers and concrete with carbon fibers with the aim of enhancing the attenuation of electromagnetic waves passing through concrete and concrete composites. The frequency range for which the measurements were carried out extends from 30 MHz to 18 GHz and includes most of the existing stationary (and mobile) radiation EM sources in the environment in which the general population moves (FM, DVBT2, 2G - 5G mobile radio systems, stationary radar border area control systems). The measurement results show that as the thickness of the concrete increases, the transmission through concrete and concrete composites decreases (parameter S12 decreases). Furthermore, composites with carbon fibers partially reduce, while those with steel fibers significantly reduce, the transmission of EM waves through such composites. The reduction of parameter S12 by composites with steel is up to 52 dB (at a frequency of 2.9 GHz) for a block thickness of 100 mm. It is important to emphasize that in the case of composites with steel fibers, the influence of the fibers on the transmission parameters is more significant than the thickness of the sample. The reason for this is the increase in electrical conductivity of composites with steel fibers due to the increased proportion of conductive components in the concrete. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Steel fibers

来源：评论

学校读者我要写书评

暂无评论

Reset-Free Reinforcement Learning via Multi-State Recovery and Failure Prevention for Autonomous Robots

引用

Tsinghua science and technology 2024年第5期29卷 1481-1494页

作者： Xu Zhou Benlian Xu Zhengqiang Jiang Jun Li Brett Nener School of Mechanical Engineering Changshu Institute of TechnologyChangshu 215500China School of Automation Nanjing University of Science and TechnologyNanjing 210094China School of Electronic and Information Engineering Suzhou University of Science and TechnologySuzhou 215009China Faculty of Medicine and Health The University of SydneySydney 2006Australia Department of Electrical Electronic and Computer EngineeringThe University of Western AustraliaPerth 6009Australia

Reinforcement learning holds promise in enabling robotic tasks as it can learn optimal policies via trial and ***,the practical deployment of reinforcement learning usually requires human intervention to provide episodic resets when a failure *** manual resets are generally unavailable in autonomous robots,we propose a reset-free reinforcement learning algorithm based on multi-state recovery and failure prevention to avoid failure-induced *** multi-state recovery provides robots with the capability of recovering from failures by self-correcting its behavior in the problematic state and,more importantly,deciding which previous state is the best to return to for efficient *** failure prevention reduces potential failures by predicting and excluding possible unsafe actions in specific *** simulations and real-world experiments are used to validate our algorithm with the results showing a significant reduction in the number of resets and failures during the learning.

关键词： reinforcement learning manual reset multi-state recovery failure prediction

来源：评论

学校读者我要写书评

暂无评论

Obstacle Detection Using Arduino Board and Bluetooth Control 8th

Obstacle Detection Using Arduino Board and Bluetooth Control

引用

8th International Conference on Information and Communication technology for Competitive Strategies, ICTCS 2023

作者： Pavitha, N. Dardige, Rohit Patil, Vaibhav Pawar, Ameya Shah, Bhavesh Department of Computer Engineering Faculty Science and Technology Vishwakarma University Pune India

ISBN: (纸本)9789819994885

An intelligent robotic vehicle with an ultrasonic sensor that can avoid obstacles in its path is the research idea. This sensor recognizes obstructions, permitting the vehicle to perform activities like halting, turning right or left, and going in reverse. The essential objective is to make a robot vehicle that can work independently or under client direction. The vehicle responds to avoid collision when the ultrasonic sensor detects an obstruction. The vehicle's development incorporates an Arduino UNO microcontroller, ultrasonic sensor, engines, and other electric parts. The microcontroller, which serves as the vehicle's brain, receives information about objects in the vehicle's path from the ultrasonic sensor. After that, the microcontroller issues commands to a servo motor that is connected to the wheels. This ensures that the vehicle comes to a stop in order to avoid colliding with anything. Moreover, the vehicle's usefulness incorporates Bluetooth availability for control. Bluetooth allows users to control the vehicle from a distance and even use voice commands to do so. The joining of a Bluetooth module empowers these control choices, upgrading the vehicle's flexibility and connection prospects. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Bluetooth

来源：评论

学校读者我要写书评

暂无评论

Conditional Diffusion Models for Semantic 3D Brain MRI Synthesis

引用

IEEE Journal of Biomedical and Health Informatics 2024年第7期28卷 4084-4093页

作者： Dorjsembe, Zolnamar Pao, Hsing-Kuo Odonchimed, Sodtavilan Xiao, Furen National Taiwan University of Science and Technology Department of Computer Science and Information Engineering Taipei106 Taiwan University of Tokyo Faculty of Engineering Tokyo113-8654 Japan College of Medicine Institute of Medical Device and Imaging National Taiwan University Taipei100 Taiwan

Artificial intelligence (AI) in healthcare, especially in medical imaging, faces challenges due to data scarcity and privacy concerns. Addressing these, we introduce Med-DDPM, a diffusion model designed for 3D semantic brain MRI synthesis. This model effectively tackles data scarcity and privacy issues by integrating semantic conditioning. This involves the channel-wise concatenation of a conditioning image to the model input, enabling control in image generation. Med-DDPM demonstrates superior stability and performance compared to existing 3D brain imaging synthesis methods. It generates diverse, anatomically coherent images with high visual fidelity. In terms of dice score in the tumor segmentation task, Med-DDPM achieves 0.6207, close to the 0.6531 dice score of real images, and outperforms baseline models. Combined with real images, it further increases segmentation accuracy to 0.6675, showing the potential of the proposed method for data augmentation. This model represents the first use of a diffusion model in 3D semantic brain MRI synthesis, producing high-quality images. Its semantic conditioning feature also shows potential for image anonymization in biomedical imaging, addressing data and privacy issues. © 2013 IEEE.

关键词： Semantics

来源：评论

学校读者我要写书评

暂无评论

Harmful Brain Activity Classification Using Ensemble Deep Learning 66

Harmful Brain Activity Classification Using Ensemble Deep Le...

引用

66th International Symposium ELMAR, ELMAR 2024

作者： Habijan, Marija Sojo, Robert Tolic, Ivana Hartmann Galic, Irena Faculty of Electrical Engineering Computer Science and Information Technology Osijek Osijek Croatia

ISBN: (纸本)9798350375411

Electroencephalography (EEG) is a crucial tool for monitoring electrical brain activity and diagnosing neurological conditions. Manual analysis of EEG signals is time-consuming and prone to variability, necessitating automated methods for accurate and efficient interpretation. This paper presents an ensemble learning approach for the automated classification of harmful brain activity from EEG data. The model combines the strengths of ResNet1d GRU, EfficientNetB0, and EfficientNetB1 to predict brain activity from spectrograms and raw EEG signals. By leveraging the unique capabilities of each model and employing a weighted averaging scheme, the ensemble achieves a competitive Kullback-Liebler divergence score of 0.3 on the testing dataset. This approach demonstrates the potential of ensemble learning to improve the accuracy and robustness of automated EEG signal classification, offering a valuable tool for early detection and diagnosis of neurological conditions. © 2024 IEEE.

关键词： Contrastive Learning

来源：评论

学校读者我要写书评

暂无评论

A Study on DDoS Attacks Detection on IoT Devices Using Machine Learning for Microcontrollers

A Study on DDoS Attacks Detection on IoT Devices Using Machi...

引用

2024 IEEE International Conference on Consumer Electronics, ICCE 2024

作者： Mwaura, Jedidah Araki, Shunsuke Kakizaki, Ken'Ichi Kyushu Institute of Technology Graduate School of Computer Science and Systems Engineering FuKuoka Japan Kyushu Institute of Technology Faculty of Computer Science and Systems Engineering Fukuoka Japan

ISBN: (纸本)9798350324136

In recent years, there has been a proliferation of Internet of Things (IoT) devices, and so has been the attacks on them. In this paper we will propose a methodology to detect Distributed Denial of Service (DDoS) attacks on IoT devices using Machine Learning for Microcontrollers. We will discuss a model which we made for Arduino Nano 33 BLE Sense using Machine Learning for Microcontrollers. Additionally, we will discuss results of our proposal in detecting DDoS attacks on IoT devices. Lastly, we will describe the feasibility of our model on IoT devices. © 2024 IEEE.

关键词： DDoS IoT Machine Learning TinyML

来源：评论

学校读者我要写书评

暂无评论

The Optimum Switching Bilateral of Various Mix Noise Filters to reduce medical X-Ray Images 12

The Optimum Switching Bilateral of Various Mix Noise Filters...

引用

12th International Electrical engineering Congress, iEECON 2024

作者： Langampol, Kriengkri Prayote, Akara Lee, Wilaiporn Srisomboon, Kanabadee Rajamangala University Of Technology Phra Nakhon Faculty Of Engineering Department Of Computer Engineering Thailand King Mongkut's University Of Technology North Bangkok Faculty Of Applied Science Department Of Computer And Information Science Thailand King Mongkut's University Of Technology North Bangkok Faculty Of Engineering Department Of Electrical And Computer Engineering Thailand

ISBN: (纸本)9798350383591

In the past few years, image processing has been widely adopted for symptom diagnosis of medical application. To achieve accurate analysis, the medical applications require high quality image for applying to the symptom diagnosis applications. However, the medical image quality may drop due to the imperfection of devices and the environment. Noise is one of the most critical factors that reduces the image quality where the noise may form by combining two types of noises. In this paper, we considered four well-known mixed noise filters including TF, BM3D, NCSR and OSBF filters. Since the OSBF filter is the alternative version of SBF filter that requires to determine the optimal weights, therefore we first determine the optimal weights for the medical image under the combination of Gaussian noise and salt-and-pepper noise. Then, we evaluate the filtering performance under three metrics including PSNR, SSIM and filtering time. As a result, OSBF filter achieves the best filtering performance since it can reduce the noise effect, preserve image details and consumes the short filtering time. On the other hand, the filtering performance of the TF filter suffers from the combining of salt-and-pepper noise. © 2024 IEEE.

关键词： Salt and pepper noise

来源：评论

学校读者我要写书评

暂无评论

Integrating Decision Trees with Cloud Computing for Predicting Cognitive Decline in Alzheimer's Patients 2

Integrating Decision Trees with Cloud Computing for Predicti...

引用

2nd IEEE International Conference on Integrated Circuits and Communication Systems, ICICACS 2024

作者： Meenakshi, K. Ravinder, Bathini Pasha, Mohammed Rehaman Thinakaran, K. Jayanayudu, Dabbara Maniraj, S.P. SRM Institute of Science and Technology Faculty of Engineering and Technology Department of Computer Science and Engineering Tamil Nadu Vadapalani India Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Andhra Pradesh Guntur India Malla Reddy Institute of Engineering and Technology Department of Computer Science and Engineering Telangana Hyderabad India Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Saveetha School of Engineering Tamil Nadu Chennai India Siddartha Institute of Science and Technology Department of Electronics and Communication Engineering Andhra Pradesh Puttur India SRM Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350317558

The proposed research work suggests a combined technique based on decision trees and cloud computing for predicting cognitive decline in Alzheimer's patients. medical records and data from wearable devices are among the many sources that perform preprocessing and feature engineering. Decision tree models can make the most efficient use of computing resources. Decision tree topologies are tested for performance criteria, including accuracy and F1 score. The system's predictive powers are rather encouraging since it frequently achieves accuracy rates over 90%. However, limitations in data quality and the intricacy of decision tree interpretation provide challenges. The prediction of cognitive deterioration in Alzheimer's patients may be made more scalable and interpretable by integrating decision trees with cloud computing. This method has promise for early identification and intervention despite its limitations. To further improve prediction accuracy and clinical value, future research should increase data quality and make it easier to understand. © 2024 IEEE.

关键词： Alzheimer's disease Cloud computing Decision trees Early detection Healthcare

来源：评论

学校读者我要写书评

暂无评论

Aggregation-induced emission biomarkers for early detection of orthopaedic implant failure

Aggregate

引用

Aggregate 2024年第6期5卷 93-108页

作者： Javad Tavakoli Qi Hu Joanne L.Tipper Youhong Tang Department of Biomedical Engineering School of EngineeringRMIT UniversityMelbourneVictoriaAustralia School of Biomedical Engineering Faculty of Engineering and ITUniversity of Technology SydneySydneyNew South WalesAustralia Institute for NanoScale Science and Technology Medical Device Research InstituteCollege of Science and EngineeringFlinders UniversityAdelaideSouth AustraliaAustralia

In recent years,the substantial increase in total joint replacements for treating degenerative joint disease has heightened concerns regarding implant loosening and *** is especially critical as more young patients are undergoing both initial and subsequent joint replacement *** complications often necessitate additional revision ***,current clinical practices lack effective methods for the early detection of implant failure,and there is a noticeable absence of strategies utilizing molecular markers to identify post-surgery implant *** article critically assesses the potential of aggregation-induced emission(AIE)biomarkers in detecting molecular markers relevant to implant *** begins by outlining the pathogenesis of implant loosening and identifying pertinent molecular *** study then delves into how AIE luminogens(AIEgens)can play a crucial role in detecting processes such as osteogenesis and ***,it discusses the utilization of AIEgens in detecting key molecular markers,including TNF-α,osteocalcin,and urinary N-terminal *** prospect of AIE biomarkers for the early detection of bone loss and implant failure presents a promising avenue for enhancing our understanding of skeletal health and improving clinical outcomes through timely intervention and personalized treatment *** research and development in this area are crucial for translating AIE-based technologies into practical tools for optimizing bone health management.

关键词： aggregation-induced emission biomarkers early detection implant failure orthopaedic implants osteolysis

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：