检索结果-内蒙古大学图书馆

Joint fuzzy background and adaptive foreground model for moving target detection

Frontiers of computer science 2024年第2期18卷 93-106页

作者： Dawei ZHANG Peng WANG Yongfeng DONG Linhao LI Xin LI School of Artificial Intelligence Hebei University of TechnologyTianjin 300401China Lane Department of Computer Science and Electrical Engineering West Virginia UniversityMorgantownWV 26506-6109USA

Moving target detection is one of the most basic tasks in computer *** conventional wisdom,the problem is solved by iterative optimization under either Matrix Decomposition(MD)or Matrix Factorization(MF)*** utilizes foreground information to facilitate background *** uses noise-based weights to fine-tune the *** both noise and foreground information contribute to the recovery of the *** jointly exploit their advantages,inspired by two framework complementary characteristics,we propose to simultaneously exploit the advantages of these two optimizing approaches in a unified framework called Joint Matrix Decomposition and Factorization(JMDF).To improve background extraction,a fuzzy factorization is *** fuzzy membership of the background/foreground association is calculated during the factorization process to distinguish their contributions of both to background *** describe the spatio-temporal continuity of foreground more accurately,we propose to incorporate the first order temporal difference into the group sparsity constraint *** temporal constraint is adjusted *** foreground and the background are jointly estimated through an effective alternate optimization process,and the noise can be modeled with the specific probability *** experimental results of vast real videos illustrate the effectiveness of our *** with the current state-of-the-art technology,our method can usually form the clearer background and extract the more accurate ***-noise experiments show the noise robustness of our method.

关键词： matrixdecomposition matrix factorization generalized sparsity noise modeling

来源：评论

学校读者我要写书评

暂无评论

Ultrasensitive textile strain sensors redefine wearable silent speech interfaces with high machine learning efficiency

引用

npj Flexible Electronics 2024年第1期8卷 664-674页

作者： Chenyu Tang Muzi Xu Wentian Yi Zibo Zhang Edoardo Occhipinti Chaoqun Dong Dafydd Ravenscroft Sung-Min Jung Sanghyo Lee Shuo Gao Jong Min Kim Luigi Giuseppe Occhipinti Electrical Engineering Division Department of EngineeringUniversity of CambridgeCambridgeUK Department of Electronic and Electrical Engineering University College LondonLondonUK UKRI Centre for Doctoral Training in AI for Healthcare Department of ComputingImperial College LondonLondonUK School of Materials Science and Engineering Kumoh National Institute of Technology(KIT)GumiSouth Korea School of Instrumentation Science and Optoelectronic Engineering Beihang UniversityBeijingChina

This work introduces a silent speech interface(SSI),proposing a few-layer graphene(FLG)strain sensing mechanism based on thorough cracks and AI-based self-adaptation capabilities that overcome the limitations of state-of-the-art technologies by simultaneously achieving high accuracy,high computational efficiency,and fast decoding speed while maintaining excellent user *** demonstrate its application in a biocompatible textile-integrated ultrasensitive strain sensor embedded into a smart choker,which conforms to the user’s *** to the structure of ordered through cracks in the graphene-coated textile,the proposed strain gauge achieves a gauge factor of 317 with<5%strain,corresponding to a 420%improvement over existing textile strain sensors fabricated by printing and coating technologies reported to *** high sensitivity allows it to capture subtle throat movements,simplifying signal processing and enabling the use of a computationally efficient neural *** resulting neural network,based on a one-dimensional convolutional model,reduces computational load by 90%while maintaining a remarkable 95.25%accuracy in speech *** synergy in sensor design and neural network optimization offers a promising solution for practical,wearable SSI systems,paving the way for seamless,natural silent communication in diverse settings.

关键词： strain wear neural

来源：评论

学校读者我要写书评

暂无评论

Optimizing Energy Usage and Data Storage in Industrial IoT Settings Through Heuristic-Based Data-Rate Processors 2

Optimizing Energy Usage and Data Storage in Industrial IoT S...

引用

2nd IEEE International Conference for Women in Innovation, Technology and Entrepreneurship, ICWITE 2024

作者： Bharathi Kannan, B. Sriramulu, Srinivasan Gopal, Vidhya Karthick, M. Reddy, Yanna Nithin Natarajan, B. Galgotias University School of Computing Science and Engineering India Sona College of Technology Computer Science and Engineering Salem India K Ramakrishnan College of Engineering Department of Electronics and Communication Engineering Tamil Nadu Tiruchirappalli India Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Chennai India VIT University School of Computer Science and Engineering Tamilnadu Chennai India

ISBN: (纸本)9798350383287

The Internet of Things (IoT) is a fast-expanding field, with applications in areas as diverse as transportation, aviation, automation, electricity, and healthcare. Sensors, protocols, actuators, cloud services, and layers are just few of the components that make up an IoT architecture. The architecture of the Internet of Things is crucial in delivering the goods. The research community now provides dozens of IoT architectures. interoperability, security, privacy, dependability, energy limits, scalability, and a lack of shared standards are only some of the problems that have been recognized by the research community. The proposed IoT architecture should also consider issues of interoperability, scalability, dependability, and trust. By fixing these issues, this research work builds an IoT ecosystem that can accommodate a wide range of devices and services, guarantee smooth communication and scalability, and lay the groundwork for secure, dependable IoT offerings. In effective offloading and resulting interruptions in task execution might result from high latency or network outages. However, because of the importance of a select few delay-sensitive applications, it is important to guarantee their safety. Therefore, this research work aims to unload data at edge and cloud servers, Heuristic based Energy Efficient Data Rate Controller for Offloading has been developed for sensor networks. The bio-inspired NSGA-2 method is used to assess the performance of the suggested algorithm within the context of the problem's multi-objective function formulation. When compared to the current energy aware scheduling approach, the results show that the offloading method can decrease the amount of time and energy needed to run edge servers, maintain load balancing, and maximize resource utilization while still allowing applications that require delays to meet their deadlines. Finally, simulation results demonstrate the proposed approach outperforms 20% high efficiency compared to the

关键词： Internet of things

来源：评论

学校读者我要写书评

暂无评论

Secure information embedding in images with hybrid firefly algorithm

引用

Neural computing and Applications 2024年 1-14页

作者： Nokhwal, Sahil Chandrasekharan, Manoj Chaudhary, Ankit Department of Computer Science University of Memphis MemphisTN United States Department of Electrical and Computer Engineering University of Memphis MemphisTN United States School of Engineering Jawaharlal Nehru University Delhi New Delhi India

Various methods have been proposed to secure access to sensitive information over time, such as many cryptographic methods in use to facilitate secure communications on the internet. But other methods like steganography have been overlooked which may be more suitable in cases where the act of transmission of sensitive information itself should remain a secret. Multiple techniques that are commonly discussed for such scenarios suffer from low capacity and high distortion in the output signal. This research introduces a novel steganographic approach for concealing a confidential portable document format (PDF) document within a host image by employing the hybrid firefly algorithm (HFA) proposed to select the pixel arrangement. This algorithm combines two widely used optimization algorithms to improve their performance. The suggested methodology utilizes the HFA algorithm to conduct a search for optimal pixel placements in the spatial domain. The purpose of this search is to accomplish two main goals: increasing the host image’s capacity and reducing distortion. Moreover, the proposed approach intends to reduce the time required for the embedding procedure. The findings indicate a decrease in image distortion and an accelerated rate of convergence in the search process. The resultant embeddings exhibit robustness against steganalytic assaults, hence rendering the identification of the embedded data, a formidable undertaking. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Steganography

来源：评论

学校读者我要写书评

暂无评论

Zero Trust Networks: Evolution and Application from Concept to Practice

引用

computers, Materials & Continua 2025年第2期82卷 1593-1613页

作者： Yongjun Ren Zhiming Wang Pradip Kumar Sharma Fayez Alqahtani Amr Tolba Jin Wang School of Computer Science School of Cyber Science and EngineeringEngineering Research Center of Digital ForensicsMinistry of EducationNanjing University of Information Science and TechnologyNanjing210044China Department of Computing Science University of AberdeenAberdeenAB243FXUK Software Engineering Department College of Computer and Information SciencesKing Saud UniversityRiyadh12372Saudi Arabia Computer Science Department Community CollegeKing Saud UniversityRiyadh11437Saudi Arabia Sanya Institute of Hunan University of Science and Technology Sanya572024China

In the context of an increasingly severe cybersecurity landscape and the growing complexity of offensive and defen-sive techniques,Zero Trust Networks(ZTN)have emerged as a widely recognized *** Trust not only addresses the shortcomings of traditional perimeter security models but also consistently follows the fundamental principle of“never trust,always verify.”Initially proposed by John Cortez in 2010 and subsequently promoted by Google,the Zero Trust model has become a key approach to addressing the ever-growing security threats in complex network *** paper systematically compares the current mainstream cybersecurity models,thoroughly explores the advantages and limitations of the Zero Trust model,and provides an in-depth review of its components and key ***,it analyzes the latest research achievements in the application of Zero Trust technology across various fields,including network security,6G networks,the Internet of Things(IoT),and cloud computing,in the context of specific use *** paper also discusses the innovative contributions of the Zero Trust model in these fields,the challenges it faces,and proposes corresponding solutions and future research directions.

关键词： Zero trust cybersecurity software-defined perimeter micro-segmentation internet of things

来源：评论

学校读者我要写书评

暂无评论

LiDUT-Depth: A Lightweight Self-supervised Depth Estimation Model Featuring Dynamic Upsampling and Triplet Loss Optimization 27th

LiDUT-Depth: A Lightweight Self-supervised Depth Estimation ...

引用

27th International Conference on Pattern Recognition, ICPR 2024

作者： Jiang, Hao Fang, Zhijun Shao, Xuan Jiang, Xiaoyan Hwang, Jenq-Neng School of Computer Science and Technology Donghua University Shanghai China School of Electronic and Electrical Engineering Shanghai University of Engineering Science Shanghai China Department of Electrical and Computer Engineering University of Washington SeattleWA United States

ISBN: (纸本)9783031784439

Lightweight yet reliable depth estimation models that can deployed on edge devices are crucial for the practical application of fields such as autonomous driving, robot navigation, and augmented reality. However, previous research often fails to balance accuracy and complexity well. Existing lightweight models still have relatively high error rates in specific scenarios, which makes them unsuitable for industrial applications. Hence, we propose LiDUT-Depth, a lightweight architecture for self-supervised monocular depth estimation that is enhanced through dynamic upsampling and triplet loss optimization, which achieves competitive results with smaller model sizes and lower computational complexity. Specifically, an efficient dynamic upsampling module (EDU Module) is employed to preserve detailed local features, thereby obtaining more accurate depth estimation results. In addition, an improved semantic-aware triplet loss (SaTri Loss) is integrated into the training loss, significantly improving depth estimation accuracy in gradient-rich regions. Experiments show that our architecture achieves a considerably enhanced accuracy compared with previous works with a much lower model size. Our codes and models are available here. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Augmented reality

来源：评论

学校读者我要写书评

暂无评论

Congruent Feature Selection Method to Improve the Efficacy of Machine Learning-Based Classification in Medical Image Processing

引用

computer Modeling in engineering & sciences 2025年第1期142卷 357-384页

作者： Mohd Anjum Naoufel Kraiem Hong Min Ashit Kumar Dutta Yousef Ibrahim Daradkeh Department of Computer Engineering Aligarh Muslim UniversityAligarh202002India College of Computer Science King Khalid UniversityAbha61413Saudi Arabia School of Computing Gachon UniversitySeongnam13120Republic of Korea Department of Computer Science and Information Systems College of Applied SciencesAlMaarefa UniversityRiyadh13713Saudi Arabia Department of Computer Engineering and Information College of Engineering in Wadi AlddawasirPrince Sattam bin Abdulaziz UniversityAl-Kharj16273Saudi Arabia

Machine learning(ML)is increasingly applied for medical image processing with appropriate learning *** applications include analyzing images of various organs,such as the brain,lung,eye,etc.,to identify specific flaws/diseases for *** primary concern of ML applications is the precise selection of flexible image features for pattern detection and region *** of the extracted image features are irrelevant and lead to an increase in computation ***,this article uses an analytical learning paradigm to design a Congruent Feature Selection Method to select the most relevant image *** process trains the learning paradigm using similarity and correlation-based features over different textural intensities and pixel *** similarity between the pixels over the various distribution patterns with high indexes is recommended for disease ***,the correlation based on intensity and distribution is analyzed to improve the feature selection ***,the more congruent pixels are sorted in the descending order of the selection,which identifies better regions than the ***,the learning paradigm is trained using intensity and region-based similarity to maximize the chances of ***,the probability of feature selection,regardless of the textures and medical image patterns,is *** process enhances the performance of ML applications for different medical image *** proposed method improves the accuracy,precision,and training rate by 13.19%,10.69%,and 11.06%,respectively,compared to other models for the selected *** mean error and selection time is also reduced by 12.56%and 13.56%,respectively,compared to the same models and dataset.

关键词： computer vision feature selection machine learning region detection texture analysis image classification medical images

来源：评论

学校读者我要写书评

暂无评论

Explaining Parkinson's Disease Detection by Ensemble Classifiers with Local Interpretable Features 8th

Explaining Parkinson's Disease Detection by Ensemble Classif...

引用

8th IFIP TC 12 International Conference on computer, Communication and Signal Processing, ICCCSP 2024

作者： Karn, Alok Kumar Dixit, Aniket Pati, Peeta Basa Mahalingam, Anand Department of Computer Science and Engineering Amrita School of Computing Bengaluru Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (纸本)9783031736162

Parkinson's disease (PD) is a neurodegenerative condition and a progressive nervous system disorder condition, marked by its characteristic symptoms such as tremors, muscle stiffness, and slow, imprecise body movements. Typically affecting the middle-aged and elderly, the most crucial challenge in the treatment of PD is posed in the form of its early diagnosis. While there is no cure for the disease, early intervention is found to have alleviated the symptoms and to have also improved the overall quality of life, potentially slowing down the disease’s progression. With the rapid advancements in Artificial Intelligence (AI) and Machine Learning (ML), almost every industry seems to be benefiting from its incorporation and the field of biomedical sciences is no different. AI-ML techniques have seemed to be specifically beneficial in classification-based biomedical problems. This research is built upon this very same idea. The application of 14 ML algorithms is studied on a dataset that consists of vocal characteristics of several individuals, both the healthy and the PD patients, to classify based on a patient’s speech whether or not, they have Parkinson’s. To improve the overall performance of the algorithms, SMOTE and PCA pre-processing techniques have also been applied to deal with the dataset’s shortcomings. The study observes that Random Forest, Gaussian Process, Extra Trees, and CatBoost are the 4 best performing algorithms for the task, providing an accuracy of 96.61%. The paper concludes with the deployment of an Explainable AI (XAI) LIME module on the best-performing algorithms to provide transparency and interpretability to the decision-making process of these AI systems. © IFIP International Federation for Information Processing 2025.

关键词： Neurodegenerative diseases

来源：评论

学校读者我要写书评

暂无评论

Deep Convolutional Neural Networks for Automated Screening and Diagnosis of Ophthalmic Diseases 3

Deep Convolutional Neural Networks for Automated Screening a...

引用

3rd International Conference for Advancement in Technology, ICONAT 2024

作者： Babu, Tina Naik, Pramod Kumar Nair, Rekha R Janhavi, P.G. Alliance University Alliance School of Advanced Computing Department of Computer Science and Engineering Bengaluru India Dayananda Sagar University Department of Computer Science Engineering Bengaluru India

ISBN: (纸本)9798350354171

This project focuses on developing a deep learning algorithm to classify eye diseases from images. The dataset comprises images depicting various eye conditions, including glaucoma, cataracts, normal eyes, and diabetic retinopathy. We adopt a comprehensive approach by utilizing pre-trained architectures such as ResNet-18, GoogleNet, AlexNet, VGG19, and ResNet-50 as the backbone of our models, fine-tuning each on our dataset. During the training process, we employed data augmentation techniques to enhance the dataset, improving the models' generalization ability across diverse eye conditions. The evaluation of the models involves a combination of cross-entropy loss and accuracy metrics to assess performance. Notably, the individually trained models exhibited varying accuracies, with ResNet-18 achieving the highest accuracy of 92.7% on the validation set. Additionally, we conducted an analysis of model performance using metrics such as precision, recall, and F1-score, further enhancing our understanding of the models' capabilities. The visualization of results is facilitated through the presentation of confusion matrices, providing insights into the models' classification behavior across different eye diseases. This study opens up a promising new path for improving ophthalmology screening and diagnosis by demonstrating the prospective benefits of deep learning in automating the classification of eye diseases. © 2024 IEEE.

关键词： Ophthalmology

来源：评论

学校读者我要写书评

暂无评论

Signal Conducting System with Effective Optimization Using Deep Learning for Schizophrenia Classification

引用

computer Systems science & engineering 2023年第5期45卷 1869-1886页

作者： V.Divya S.Sendil Kumar V.Gokula Krishnan Manoj Kumar School of Electrical and Electronics Engineering Sathyabama Institute of Science and TechnologyChennaiTamilnaduIndia Department of Electrical and Electronics Engineering S A Engineering CollegeChennaiTamilnaduIndia Department of Computer Science and Information Technology CVR College of EngineeringMangalpallyHyderabadTelanganaIndia School of Computer Science University of Petroleum and Energy StudiesDehradunIndia

Signal processing based research was adopted with Electroencephalogram(EEG)for predicting the abnormality and cerebral *** proposed research work is intended to provide an automatic diagnostic system to determine the EEG signal in order to classify the brain function which shows whether a person is affected with schizophrenia or *** detection and intervention are vital for better ***,the diagnosis of schizophrenia still depends on clinical observation to *** reliable biomarkers,schizophrenia is difficult to detect in its early phase and hence we have proposed this *** this work,the EEG signal series are divided into non-linear feature mining,classification and validation,and t-test integrated feature selection *** this work,19-channel EEG signals are utilized from schizophrenia class and normal ***,the datasets initially execute the splitting process based on raw 19-channel EEG into 6250 sample point’s *** this process,1142 features of normal and schizophrenia class patterns can be *** other hand,157 features from each EEG patterns are utilized based on Non-linear feature extraction process where 14 principal features can be identified in terms of considering the essential *** last,the Deep Learning(DL)technique incorporated with an effective optimization technique is adopted for classification process called a Deep Convolutional Neural Network(DCNN)with mayfly optimization *** proposed technique is implemented into the platform of MATLAB in order to obtain better results and is analyzed based on the performance analysis framework such as accuracy,Signal to Noise Ratio(SNR),Mean Square Error,Normalized Mean Square Error(NMSE)and *** comparison,the proposed technique is proved to a better technique than other existing techniques.

关键词： Deep learning optimization algorithm signal conducting system schizophrenia convolutional neural network mayfly optimization algorithm

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：