检索结果-内蒙古大学图书馆

GENERALIZED BAYES SURE FOR MULTIVARIATE NORMAL DISTRIBUTION UNDER BALANCED-QUADRATIC LOSS

Journal of Mathematical sciences (United States) 2024年第2期280卷 262-274页

作者： Karamikabir, Hamid Afshari, Mahmoud Karamikabir, Nasrin Kiaei, Seyed Faridoddin Department of Statistics Faculty of Intelligent Systems Engineering and Data Science Persian Gulf University Bushehr Iran Department of Mathematics Hamedan Branch Islamic Azad University Hamedan Iran School of Electrical and Computer Engineering College of Engineering University of Tehran Tehran Iran

One of the most important subjects in statistics is the theory of estimation. In this paper, we consider the generalized Bayes shrinkage estimator of the mean vector for multivariate normal distribution with the unknown covariance matrix under balanced quadratic loss. Also, we find the minimax and admissible estimator of the mean vector based on the generalized Bayes estimator. We obtain Stein’s unbiased risk estimator (SURE) threshold with a generalized Bayes shrinkage estimator and we find the wavelet shrinkage generalized Bayes SURE. Finally, we present a simulation study to test the validity of the generalized Bayes SURE and the concrete compressive strength dataset is given to test the efficiency of this estimator in denoising. © 2024, The Author(s), under exclusive licence to Springer Nature Switzerland AG.

关键词： Balanced-quadratic loss Generalized bayes estimator Shrinkage estimaton Shrinkage wavelet SURE threshold

来源：评论

学校读者我要写书评

暂无评论

Advanced Arabic Alphabet Sign Language Recognition Using Transfer Learning and Transformer Models

Advanced Arabic Alphabet Sign Language Recognition Using Tra...

引用

International Conference on computer and Applications (ICCA)

作者： Mazen Balat Rewaa Awaad Hend Adel Ahmed B. Zaky Salah A. Aly CS & IT Dept. Egypt-Japanese University of Science & Technology Alexandria Egypt Computer Science Faculty of Science Fayoum University Fayoum Egypt Faculty of Computing & Data Science Badya University Giza Egypt

ISBN: (数字)9798350367560

ISBN: (纸本)9798350367577

This paper presents an Arabic Alphabet Sign Language recognition approach, using deep learning methods in conjunction with transfer learning and transformer-based models. We study the performance of the different variants on two publicly available datasets, namely ArSL2018 and AASL. This task will make full use of state-of-the-art CNN architectures like ResNet50, MobileNetV2, and EfficientNetB7, and the latest transformer models such as Google ViT and Microsoft Swin Transformer. These pre-trained models have been fine-tuned on the above datasets in an attempt to capture some unique features of Arabic sign language motions. Experimental results present evidence that the suggested methodology can receive a high recognition accuracy, by up to 99.6% and 99.43% on ArSL2018 and AASL, respectively. That is far beyond the previously reported state-of-the-art approaches. This performance opens up even more avenues for communication that may be more accessible to Arabic-speaking deaf and hard-of-hearing, and thus encourages an inclusive society.

关键词： Sign language Accuracy Translation Computational modeling Transfer learning computer architecture Transformers Robustness Real-time systems Residual neural networks

来源：评论

学校读者我要写书评

暂无评论

A Conversational Brain-Artificial Intelligence Interface

arXiv

引用

arXiv 2024年

作者： Meunier, Anja Žák, Michal Robert Munz, Lucas Garkot, Sofiya Eder, Manuel Xu, Jiachen Grosse-Wentrup, Moritz Research Group Neuroinformatics Faculty of Computer Science University of Vienna Vienna Austria Doctoral School Computer Science Faculty of Computer Science University of Vienna Vienna Austria Vienna Cognitive Science Hub University of Vienna Vienna Austria Research Network Data Science University of Vienna Vienna Austria

We introduce Brain-Artificial Intelligence Interfaces (BAIs) as a new class of Brain-computer Interfaces (BCIs). Unlike conventional BCIs, which rely on intact cognitive capabilities, BAIs leverage the power of artificial intelligence to replace parts of the neuro-cognitive processing pipeline. BAIs allow users to accomplish complex tasks by providing high-level intentions, while a pre-trained AI agent determines low-level details. This approach enlarges the target audience of BCIs to individuals with cognitive impairments, a population often excluded from the benefits of conventional BCIs. We present the general concept of BAIs and illustrate the potential of this new approach with a Conversational BAI based on EEG. In particular, we show in an experiment with simulated phone conversations that the Conversational BAI enables complex communication without the need to generate language. Our work thus demonstrates, for the first time, the ability of a speech neuroprosthesis to enable fluent communication in realistic scenarios with non-invasive technologies. © 2024, CC BY.

关键词： Brain computer interface

来源：评论

学校读者我要写书评

暂无评论

DISCO: Internal Evaluation of Density-Based Clustering

arXiv

引用

arXiv 2025年

作者： Beer, Anna Krieger, Lena Weber, Pascal Ritzert, Martin Assent, Ira Plant, Claudia Faculty of Computer Science University of Vienna Vienna Austria IAS-8: Data Analytics and Machine Learning Forschungszentrum Jülich Jülich Germany UniVie Doctoral School Computer Science University of Vienna Vienna Austria Data Science @ Uni Vienna University of Vienna Vienna Austria Institute of Computer Science and Campus Institute Data Science University of Göttingen Göttingen Germany Department of Computer Science Aarhus University Aarhus Denmark

In density-based clustering, clusters are areas of high object density separated by lower object density areas. This notion supports arbitrarily shaped clusters and automatic detection of noise points that do not belong to any cluster. However, it is challenging to adequately evaluate the quality of density-based clustering results. Even though some existing cluster validity indices (CVIs) target arbitrarily shaped clusters, none of them captures the quality of the labeled noise. In this paper, we propose DISCO, a Density-based Internal Score for Clustering Outcomes, which is the first CVI that also evaluates the quality of noise labels. DISCO reliably evaluates density-based clusters of arbitrary shape by assessing compactness and separation. It also introduces a direct assessment of noise labels for any given clustering. Our experiments show that DISCO evaluates density-based clusterings more consistently than its competitors. It is additionally the first method to evaluate the complete labeling of density-based clustering methods, including noise labels. © 2025, CC BY.

关键词： Cluster analysis

来源：评论

学校读者我要写书评

暂无评论

Empowering Real-Time Insights Through LLM, LangChain, and SAP HANA Integration 6th

Empowering Real-Time Insights Through LLM, LangChain, and SA...

引用

6th International Conference on Recent Innovations in Computing, ICRIC 2023

作者： Easin Arafat, Md. Asuah, Georgina Saha, Sourav Orosz, Tamas Data Science and Engineering Department Faculty of Informatics Eötvös Loránd University Pázmány Péter Str. 1/A Budapest1117 Hungary Department of Computer Science and Engineering United International University United City Madani Avenue Dhaka1212 Bangladesh

ISBN: (纸本)9789819734412

Real-time integration within the SAP HANA (High-Performance Analytics Appliance) environment through open-source components signifies a substantial financial investment in information technology (IT) projects. The prevalence of budget overruns, delays in implementation, and imprecise forecasts remain significant factors contributing to project failures. This has led many companies to adopt the usage of real-time insights in planning and decision-making facilities. By keeping the fundamental components of SAP, businesses can upgrade their information systems without having to completely reconfigure them, which proves to be cost-effective. This study initiates an exploration of the intricate integration of large language model (LLM), LangChain, and SAP HANA, underlining the intrinsic value of real-time insights and their multi-faceted applications. The study's main objective is to provide individuals with a road map for utilizing these essential components, customized to their enterprise's specific phase of development. This study highlights how important this integration is and provides a strong push in the direction of operational effectiveness and long-term expansion. The study equally outlines the challenges and considerations of such components in operational effectiveness and long-term expansion. The integrated framework offers the following key benefits, linguistic precision, seamless language-technology integration, and robust analytics infrastructure. As the integrated framework develops, it becomes a revolutionary instrument that changes how real-time insights and decision-making are conducted across a variety of industries. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Budget control

来源：评论

学校读者我要写书评

暂无评论

Cloud-Based Oxygen Management System with Seamless HTTP Protocol Integration 4

Cloud-Based Oxygen Management System with Seamless HTTP Prot...

引用

4th International Conference on Innovative Practices in Technology and Management, ICIPTM 2024

作者： Hemalatha, S. Ramasamy, Jayabharathi Selvi, K. Anandan, P. Meenakshi, R. Maniraj, S.P. Tagore Engineering College Department of Artificial Intelligence and Data Science Tamil Nadu Chennai India Rmk College of Engineering and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India S. A. Engineering College Faculty of Management Studies Tamil Nadu Chennai India Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Electronics and Communication Engineering Tamil Nadu Chennai India Chennai Institute of Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Srm Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350307757

In this study, an efficient system to improve the oxygen supply in hospitals and other healthcare institutions is proposed. It uses Internet of things (IoT) devices and cloud based data transmission and processing system. The controller unit collects the oxygen sensor data for the analysis and uses HTTP protocols to send and receive data with cloud computing in HTTP website server. The cloud is data storage, processes and real-time monitoring, analysis and control system. It has a user interface with viewed administrators and medical professionals. The interface provides real-time data regarding oxygen supply, consumption patterns, and possible concerns. Authorized workers can access and manage the system via the display, allowing for timely adjustments to oxygen supply settings, optimizations to refill schedules, and responses to emergency circumstances. It's adaptable, flexible, and resilient, which are benefits. There will be less need to worry about oxygen shortages and interruptions, and healthcare institutions of all sizes can benefit from this. The cloud's remote access and control capabilities provide continuous functioning and rapid reaction times. It's an effective solution for hospitals and other healthcare facilities that need to control their oxygen supplies. With its cloud-based design, the solution increases productivity, improves patient safety, and simplifies oxygen management. © 2024 IEEE.

关键词： HTTP

来源：评论

学校读者我要写书评

暂无评论

DRA-Net: densely residual attention based low-light image enhancement 14

DRA-Net: densely residual attention based low-light image en...

引用

14th International Conference on Graphics and Image Processing, ICGIP 2022

作者： Ul Rehman, Sami Hawbani, Ammar Wang, Xingfu Hamza, Muhammad Zhao, Liang H. Alsamhi, Saeed Al-Qatf, Majjed Computer Science and Technology University of Science and Technology of China JinZhai Anhui Hefei230026 China Shenyang Aerospace University China Insight Center for Data Analytics University of Galway Galway Ireland Faculty of Engineering Ibb University Ibb70270 Yemen

ISBN: (纸本)9781510666313

The visual quality of nighttime photographs diminishes greatly due to low contrast and high noise. We need a robust image enhancement methodology to improve such low-light images close to standard daylight images. Due to deteriorated conditions of uneven light and noise, this image enhancement problem becomes ill-posed. Our paper has proposed a Densely Residual Attention Network (DRANet), an end-to-end attention base densely residual network. The architecture of DRANet consists of sub-modules convolution block (CB) and densely residual feature - convolutional block attention module (DRF-CBAM). DFR-CBAM also has sub-modules, deep residual feature block (DRFB), and convolutional block attention module (CBAM). Using the most recent results from attention and deep residual-based convolution networks in number of computer vision problems, we have used DRFB to enhance the features in-depth by using its dense and residual skip connections. Similarly, features in both spatial and channel axis have been extracted by using a lightweight CBAM attention module. Contrast, luminosity, and noise of the enhanced images have been balanced by additionally implementing a color balancing function at the end of the proposed network. Furthermore, we have used a combination of LLab, LSSIM and LMAE loss functions to make the proposed network stable and recover both contextual and local details while training. MAE, PSNR, SSIM, MS-SSIM, FSIM, Cosine Similarity, and deltaE2000 have been used as referenced while NIQE as nonreferenced based image quality assessment (IQA) metric. Experiment results showed that our proposed methodology is very effective with higher referenced and lower non-referenced image IQA metrics values. Furthermore, the effectiveness of our method has also demonstrated by the visual and perceptual quality of enhanced images. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Image enhancement

来源：评论

学校读者我要写书评

暂无评论

Enhanced Image Captioning Using Features Concatenation and Efficient Pre-Trained Word Embedding

引用

computer Systems science & Engineering 2023年第9期46卷 3637-3652页

作者： Samar Elbedwehy T.Medhat Taher Hamza Mohammed F.Alrahmawy Department of Data Science Faculty of Artificial IntelligenceKafrelsheikh UniversityKafrelsheikh33511Egypt Department of Electrical Engineering Faculty of EngineeringKafrelsheikh UniversityKafrelsheikh33511Egypt Department of Computer Science Faculty of Computer and Information ScienceMansoura UniversityMansoura35516Egypt

One of the issues in computer Vision is the automatic development of descriptions for images,sometimes known as image *** Learning techniques have made significant progress in this *** typical architecture of image captioning systems consists mainly of an image feature extractor subsystem followed by a caption generation lingual *** paper aims to find optimized models for these two *** the image feature extraction subsystem,the research tested eight different concatenations of pairs of vision models to get among them the most expressive extracted feature vector of the *** the caption generation lingual subsystem,this paper tested three different pre-trained language embedding models:Glove(Global Vectors for Word Representation),BERT(Bidirectional Encoder Representations from Transformers),and TaCL(Token-aware Contrastive Learning),to select from them the most accurate pre-trained language embedding *** experiments showed that building an image captioning system that uses a concatenation of the two Transformer based models SWIN(Shiftedwindow)and PVT(PyramidVision Transformer)as an image feature extractor,combined with the TaCL language embedding model is the best result among the other combinations.

关键词： Image captioning word embedding concatenation transformer

来源：评论

学校读者我要写书评

暂无评论

Robust graph‐based localization for industrial Internet of things in the presence of flipping ambiguities

引用

CAAI Transactions on Intelligence Technology 2023年第4期8卷 1140-1149页

作者： Mian Imtiaz ul Haq Ruhul Amin Khalil Muhannad Almutiry Ahmad Sawalmeh Tanveer Ahmad Nasir Saeed Faculty of Electrical Engineering Swedish College of Engineering and TechnologyWah CanttPakistan Department of Electrical Engineering Faculty of Electrical and Computer EngineeringUniversity of Engineering and TechnologyPeshawarPakistan Department of Electrical Engineering Remote Sensing UnitNorthern Border UniversityArarSaudi Arabia Data Science and Artificial Intelligence Department‐College of Science and Information Technology Irbid National UniversityIrbadJordan Innovation Education and Research Center for On‐Device AI Software(Bk21) Department of Computer Science and EngineeringChungnam National UniversityDaejeonRepublic of Korea Department of Electrical and Communication Engineering United Arab Emirates University(UAEU)Al AinUnited Arab Emirates

Localisation of machines in harsh Industrial Internet of Things(IIoT)environment is necessary for various ***,a novel localisation algorithm is proposed for noisy range measurements in IIoT *** position of an unknown machine device in the network is estimated using the relative distances between blind machines(BMs)and anchor machines(AMs).Moreover,a more practical and challenging scenario with the erroneous position of AM is considered,which brings additional uncertainty to the final position ***,the AMs selection algorithm for the localisation of BMs in the IIoT network is *** those AMs will participate in the localisation process,which increases the accuracy of the final location ***,the closed‐form expression of the proposed greedy successive anchorization process is derived,which prevents possible local convergence,reduces computation,and achieves Cramér‐Rao lower bound accuracy for white Gaussian measurement *** results are compared with the state‐of‐the‐art and verified through numerous simulations.

关键词： Cramér‐Rao lower bound greedy successive anchorization industrial internet of things localization

来源：评论

学校读者我要写书评

暂无评论

Task Offloading and Resource Allocation in IoT Based Mobile Edge Computing Using Deep Learning

引用

computers, Materials & Continua 2023年第8期76卷 1463-1477页

作者： Ily s Abdullaev Natalia Prodanova KAruna Bhaskar ELaxmi Lydia Seifedine Kadry Jungeun Kim Dean of the Faculty of Economics Department of Management and MarketingFaculty of EconomicsUrgench State UniversityUrganch220100Uzbekistan Basic Department Financial Control Analysis and Audit of Moscow Main Control DepartmentPlekhanov Russian University of EconomicsMoscow117997Russia Department of Computer Science and Engineering KL Deemed to UniversityVaddeswaramGunturAndhra PradeshIndia Department of Computer Science and Engineering GMR Institute of TechnologyAndhra PradeshRajamIndia Department of Applied Data Science Noroff University CollegeKristiansandNorway Artificial Intelligence Research Center(AIRC) Ajman UniversityAjman346United Arab Emirates Department of Electrical and Computer Engineering Lebanese American UniversityByblosLebanon Department of Software Kongju National UniversityCheonan31080Korea

Recently,computation offloading has become an effective method for overcoming the constraint of a mobile device(MD)using computationintensivemobile and offloading delay-sensitive application tasks to the remote cloud-based data *** city benefitted from offloading to edge *** a mobile edge computing(MEC)network in multiple *** comprise N MDs and many access points,in which everyMDhasM independent real-time *** study designs a new Task Offloading and Resource Allocation in IoT-based MEC using Deep Learning with Seagull Optimization(TORA-DLSGO)*** proposed TORA-DLSGO technique addresses the resource management issue in the MEC server,which enables an optimum offloading decision to minimize the system *** addition,an objective function is derived based on minimizing energy consumption subject to the latency requirements and restricted *** TORA-DLSGO technique uses the deep belief network(DBN)model for optimum offloading ***,the SGO algorithm is used for the parameter tuning of the DBN *** simulation results exemplify that the TORA-DLSGO technique outperformed the existing model in reducing client overhead in the MEC systems with a maximum reward of 0.8967.

关键词： Mobile edge computing seagull optimization deep belief network resource management parameter tuning

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：