检索结果-内蒙古大学图书馆

IEEE Transactions on Machine Learning in Communications and Networking 2025年 3卷 659-676页

作者： Dogan-Tusha, Seda Bouanani, Faissal El Qaraqe, Marwa Hamad Bin Khalifa University Qatar Foundation Division of Information and Computing Technology College of Science and Engineering Doha Qatar Mohammed V University Department of Computer Science Rabat10000 Morocco

Federated Learning (FL) has attracted the interest of researchers since it hinders inefficient resource utilization by developing a global learning model based on local model parameters (LMP). This study introduces a novel optimal stopping theory (OST) based online node selection scheme for low complex and multi-parameter FL procedure in IoT networks. Global model accuracy (GMA) in FL depends on the accuracy of the LMP received by the central entity (CE). It is therefore essential to choose trusty nodes to guarantee a certain level of global model accuracy without inducing additional system complexity. For this reason, the proposed technique in this study utilizes the secretary problem (SP) approach as an OST to perform node selection considering both received signal strength (RSS) and local model accuracy (LMA) of available nodes. By leveraging the SP, the proposed technique employs a stopping rule that maximizes the probability of selecting the node with the best quality, and thereby avoids testing all candidate nodes. To this end, this work provides a mathematical framework for maximizing the selection probability of the best node amongst candidate nodes. Specifically, the developed framework has been used to calculate the weighting coefficients of the RSS and LMA to define the node quality. Comprehensive analysis and simulation results illustrate that the OST based proposed technique outperforms state-of-the-art methods including the random node selection and the offline node selection (exhaustive search methods) in terms of GMA and computational complexity, respectively. © 2023 CCBY.

关键词： Federated learning

来源：评论

学校读者我要写书评

暂无评论

Precaution Chatbot for Epilepsy Patients using Natural Language Processing and Deep Learning Sequential Model 5

Precaution Chatbot for Epilepsy Patients using Natural Langu...

引用

5th International Conference on Innovative Data Communication Technologies and Application, ICIDCA 2024

作者： Kasthuri, E. Subbulakshmi, S. Sreedharan, Rajasree Department of Computer Science and Engineering Amrita School of Computing Amritapuri India Department of Computer Science and Applications Amrita School of Computing Amritapuri India Division of Nephrology Department of Pediatrics Medical College of Wisconsin MilwaukeeWI United States

Epilepsy is a brain disorder that can affect people of all ages which is one of the most common neurological disorders affecting around 50 million individuals. Healthcare Chatbots schedule appointments, respond to patient inquiries, and offer relevant information, freeing medical staff to handle life crises. This paper implements a chatbot for epilepsy patients to increase awareness about epilepsy. Patients may easily access this AI-based healthcare chatbot whenever they have queries regarding precautions to be taken by epilepsy patients. Chatbot uses a rich set of epilepsy-related datasets collected from trusted websites which are preprocessed with NLP techniques to prepare the data for model ***, LSTM, and RNN models are created and evaluated to produce answers to user queries related to possible precautions to avoid seizures, safety measures to be taken by patients with seizure history, etc. Evaluation of models shows that the RNN model generates more accurate answers with 94% accuracy. Finally, the epilepsy question answering Chatbot for precaution is concluded with the RNN model. © 2024 The Authors. Published by Elsevier B.V.

关键词： Deep neural networks

来源：评论

学校读者我要写书评

暂无评论

Insightful Clinical Assistance for Anemia Prediction with Data Analysis and Explainable AI 5

Insightful Clinical Assistance for Anemia Prediction with Da...

引用

5th International Conference on Innovative Data Communication Technologies and Application, ICIDCA 2024

作者： Kasthuri, E. Subbulakshmi, S. Sreedharan, Rajasree Department of Computer Science and Engineering Department of Computer Science and Applications Amrita School of Computing Amritapuri India Division of Nephrology Department of Pediatrics Medical College of Wisconsin MilwaukeeWI United States

Anemia among young people has positive correlation with susceptibility and impede cognitive *** per WHO 2019 statistics global prevalence rate of anemia is 33 percent. This work aims to predict anemia with inferences to identify the root cause of anemia. Prediction is done with machine learning algorithms with proper pre-processing, data normalization, and class imbalance approaches. Explainable AI, a transparent model, is used to enhance the prediction which enables to draw inferences. LGM Boost learning model prediction has a 91% accuracy and explainable AI framework predictions of most contributing attribute is accurate when compared to other models. This approach helps healthcare professionals make informed decisions on the most optimal treatment for anemia. © 2024 The Authors. Published by Elsevier B.V.

关键词： Forecasting

来源：评论

学校读者我要写书评

暂无评论

On Error-Based Step Size Control for Discontinuous Galerkin Methods for Compressible Fluid Dynamics

引用

应用数学与计算数学学报 2025年第1期7卷 3-39页

作者： Hendrik Ranocha Andrew R.Winters Hugo Guillermo Castro Lisandro Dalcin Michael Schlottke-Lakemper Gregor J.Gassner Matteo Parsani Applied Mathematics University of HamburgBundesstr.5520146 HamburgGermany Department of Mathematics Applied Mathematics Linköping UniversityLinköpingSweden Extreme Computing Research Center(ECRC) Computer Electrical and Mathematical Science and Engineering Division(CEMSE)King Abdullah University of Science and Technology(KAUST)Thuwal 23955-6900Saudi Arabia Applied and Computational Mathematics RWTH Aachen UniversityAachenGermany High-Performance Computing Center Stuttgart University of StuttgartStuttgartGermany Department of Mathematics and Computer Science University of CologneCologneGermany Center for Data and Simulation Science University of CologneCologneGermany Extreme Computing Research Center(ECRC) Computer Electrical and Mathematical Science and Engineering Division(CEMSE)King Abdullah University of Science and Technology(KAUST)Thuwal 23955-6900Saudi Arabia Physical Science and Engineering Division(PSE) King Abdullah University of Science and Technology(KAUST)Thuwal 23955-6900Saudi Arabia

We study a temporal step size control of explicit Runge-Kutta(RK)methods for com-pressible computational fluid dynamics(CFD),including the Navier-Stokes equations and hyperbolic systems of conservation laws such as the Euler *** demonstrate that error-based approaches are convenient in a wide range of applications and compare them to more classical step size control based on a Courant-Friedrichs-Lewy(CFL)*** numerical examples show that the error-based step size control is easy to use,robust,and efficient,e.g.,for(initial)transient periods,complex geometries,nonlinear shock captur-ing approaches,and schemes that use nonlinear entropy *** demonstrate these properties for problems ranging from well-understood academic test cases to industrially relevant large-scale computations with two disjoint code bases,the open source Julia pack-ages *** with *** and the C/Fortran code SSDC based on PETSc.

关键词： Explicit Runge-Kutta(RK)methods Step size control Compressible fluid dynamics Adaptivity in space and time Shock capturing

来源：评论

学校读者我要写书评

暂无评论

An FPGA-based neuromorphic vision system accelerator 2

An FPGA-based neuromorphic vision system accelerator

引用

Artificial Intelligence for Security and Defence Applications II 2024

作者： Ali, Teymoor Rainey, James Lau, Sook Yen Gheorghiu, Elena Maier, Patrick Appiah, Kofi Bhowmik, Deepayan School of Computing Newcastle University Newcastle upon Tyne United Kingdom Division of Psychology University of Stirling Stirling United Kingdom Division of Computing Science and Mathematics University of Stirling Stirling United Kingdom Department of Computer Science University of York York United Kingdom

ISBN: (纸本)9781510681200

Rapid reaction to a specific event is a critical feature for an embedded computer vision system to ensure reliable and secure interaction with the environment in resource-limited real-time applications. This requires high-level scene understanding with ultra-fast processing capabilities and the ability to operate at extremely low power. Existing vision systems, which rely on traditional computation techniques, including deep learning-based approaches, are limited by the compute capabilities due to large power dissipation and slow off-chip memory access. These challenges are evident in environments with constrained power, bandwidth and hardware resources, such as in the applications of drones and robot navigation in expansive areas. A new NEuromorphic Vision System (NEVIS) is proposed to address the limitations of existing computer vision systems for many resource-limited real-time applications. NEVIS mimics the efficiency of the human visual system by encoding visual signals into spikes, which are processed by neurons with synaptic connections. The potential of NEVIS is explored through an FPGA-based accelerator implementation on a Xilinx Kria board that achieved 40× speed up compared to a Raspberry Pi 4B CPU. This work informs the future potential of NEVIS in embedded computer vision system development. © 2024 SPIE.

关键词： Photons

来源：评论

学校读者我要写书评

暂无评论

A Comparative Market Research and Trend Analysis of Volatility in Decentralized and Regulated Markets 3

A Comparative Market Research and Trend Analysis of Volatili...

引用

3rd IEEE International Conference on Intelligent Techniques in Control, Optimization and Signal Processing, INCOS 2024

作者： Mittal, Anamika Hariharasitaraman, S. Subramanian, R. Raja School of Computing Science and Engineering Vit Bhopal University Division of Cyber Security and Digital Forensics Madhya Pradesh India School of Computing Kalasalingam Academy of Research and Education Department of Computer Science and Engineering Tamil Nadu India

ISBN: (纸本)9798350361186

Decentralized markets like Bitcoin and Ethereum, which utilize blockchain technology, offer advantages such as increased transparency, lower transaction costs, and quicker settlement times when compared to regulated markets. However, these markets are also known for their higher volatility. On the other hand, traditional regulated markets such as stock and commodity exchanges have also been subject to volatility due to macroeconomic factors such as inflation, geopolitical tensions, and policy changes. Therefore, it is crucial to compare and analyze the volatility of both decentralized and regulated markets to understand their behaviour and potential risks. This paper aims to conduct comparative market research and trend analysis of volatility in decentralized and regulated markets, investigating the contributing factors such as market size, liquidity, regulations, and the impact of market events like economic downturns or regulatory changes. The study will also examine historical trends, explore the correlation between the volatility of these markets, assess the potential impact of market volatility on investors and traders, and analyze how different trading strategies and investor behaviours can affect market volatility. The research could provide valuable insights into the behaviour of decentralized and regulated markets, which could be useful for investors, traders, policymakers, and other stakeholders. The paper will conclude with a discussion of the findings and their implications for traders and investors in both types of markets. © 2024 IEEE.

关键词： Blockchain

来源：评论

学校读者我要写书评

暂无评论

FedDAD: Federated Domain Adaptation for Object Detection

引用

IEEE Access 2023年 11卷 51320-51330页

作者： Lu, Peggy Joy Jui, Chia-Yung Chuang, Jen-Hui National Yang Ming Chiao Tung University Department of Computer Science Hsinchu30010 Taiwan National Center for High-Performance Computing Data Science and Technology Division Taichung40763 Taiwan

Training an object detection model often requires numerous annotated images on a centralized host, which may violate user privacy and data confidentiality. Federated learning (FL) resolves this issue by allowing multiple clients, e.g., cameras, to collaboratively train a model while protecting user privacy. However, models trained with FL may fail to be generalized for new target domain due to domain shift when the data between source and target domains are statistically different. In this work, we formulate a real-world object detection problem as a source-free multi-domain adaptation problem in FL architecture. Moreover, we propose an adaptive FL algorithm, called FedDAD (Federated Domain Adaptive Detector), which aggregates models with dynamic attention targeting the unsupervised domain on server, and utilize instance-level alignment to alleviate the effects of scene variation on clients. Experimental results show that FedDAD improves the average precision (AP) by up to 10.05% and 19.15% compared to the popular FedAvg for specific object classes in the KAIST and MI3 datasets, respectively. © 2013 IEEE.

关键词： Image annotation

来源：评论

学校读者我要写书评

暂无评论

Prediction of miRNA family based on class-incremental learning

Prediction of miRNA family based on class-incremental learni...

引用

2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Liu, Yajun Qiu, Lulu Li, Aimin Fei, Rong Li, Junhuai Wu, Fang-Xiang Xi'an University of Technology Shaanxi Key Laboratory for Network Computing and Security Technology Xi'an China Division of Biomedical Engineering Department of Mechanical Engineering Department of Computer Science Saskatoon Canada

ISBN: (纸本)9798350386226

With the development of deep sequencing, recent studies indicate that a miRNA precursor can generate multiple miRNA isoforms (isomiRs). The family prediction of canonical miRNAs and isomiRs could provide a basis for miRNA functional research. In this study, we propose a novel method for family identification of canonical miRNA and isomiRs based on incremental learning. First, a benchmark dataset is constructed by processing data based on miRNA sequences and their family annotation. Moreover, sequence embedding and RNN are used for capturing essential features and inherent dependencies within a sequence. Finally, incremental learning is applied to accommodate the continuous influx of miRNA sequencing data, enabling RNN to stay relevant and effective over time. Comparative experiments and ablation studies illustrate the effectiveness of our model, which can help to comprehensively understand miRNA's function. © 2024 IEEE.

关键词： Contrastive Learning

来源：评论

学校读者我要写书评

暂无评论

Classification of Breast Cancer using a Novel Neural Network-based Architecture 14

Classification of Breast Cancer using a Novel Neural Network...

引用

14th International Conference on computing Communication and Networking Technologies, ICCCNT 2023

作者： Mishra, Priti Khare, Sangita Singh, Tripty Nair, Rekha R Sharma, Mansi Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India Dayananda Sagar University School of Engineering Department of Computer Applications Bengaluru India Cloud Computing Sw Research Section Future Computing Research Division Korea Republic of

ISBN: (纸本)9798350335095

Breast cancer is currently one of the deadliest types of cancer, and the death rate has considerably grown as a result of a lack of knowledge about the disease, its symptoms, and prevention techniques. Therefore, early detection at a nearly stage is crucial and vital in order to limit the progression of cancer. Malignant and benign breast cancer are the two further subtypes. In order to categorise the different forms of breast cancer, an automated system with logistic regression and neural network is proposed. The classification of the breast cancer data utilises the DNN with various levels of processing. The Digital Database for Screening Mammography (DDMS) dataset was used in the proposed study on the Kaggle platform. The dataset was divided into various train-test splits. On the basis of accuracy, ROC AUC cuve the system's performance is evaluated. The outcome demonstrates that proposed-2 outperforms in a comparable sense with a training accuracy of 98.99% and testing accuracy of 98.83%. © 2023 IEEE.

关键词： Diseases

来源：评论

学校读者我要写书评

暂无评论

A Graph Data Model-based Micro-Provenance Approach for Multi-level Provenance Exploration in End-to-End Climate Workflows

A Graph Data Model-based Micro-Provenance Approach for Multi...

引用

2023 IEEE International Conference on Big Data, BigData 2023

作者： Fiore, Sandro Rampazzo, Mattia Elia, Donatello Sacco, Ludovica Antonio, Fabrizio Nassisi, Paola University of Trento Department of Information Engineering and Computer Science Trento Italy Cmcc Foundation Advanced Scientific Computing Division Lecce Italy

ISBN: (纸本)9798350324457

Open science is a vital part in the current and future research agenda worldwide. In order to meet Open science goals, it is of paramount importance to fully support the research process, which includes also properly addressing provenance and reproducibility of scientific experiments. Indeed, provenance and reproducibility are two key requirements for analytics workflows in Open science contexts. Handling provenance at different levels of granularity and during the entire experiment lifecycle becomes key to properly and flexibly managing lineage information related to large-scale experiments as well as enabling reproducibility scenarios. To this end, this work introduces the micro-provenance concept, and it provides an in-depth description of its design, implementation and exploitation in the context of a multi-model climate analytics workflow. © 2023 IEEE.

关键词： climate domain graph data model micro-provenance Open science reproducibility

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：