检索结果-内蒙古大学图书馆

5th international conference on Data science, Machine Learning and Applications, ICDSMLA 2023

作者： Mahender, Udutala Arivalagan, S. Sathiyasuntharam, V. Sudhakar, P. Annamalai University Tamilnadu Chidambaram India Department of Computer Science and Technology Annamalai University Tamilnadu Chidambaram India Department of Cyber Security CMR Engineering College Hyderabad India Department of Computer Science and Engineering Annamalai University Tamilnadu Chidambaram India

ISBN: (纸本)9789819780426

Major Depressive Disorder (MDD) is a significant global mental health concern predicted to become the leading mental health condition by 2030. It is characterised by persistent sadness, hopelessness, and a lack of interest in daily activities. The current diagnostic procedures for MDD are often unreliable, subjective, and prone to biases, emphasising the urgent need for accurate prediction methods. Predicting MDD is crucial for various reasons: it enables early detection, including identifying milder forms of depression, facilitating timely interventions and preventing the condition from worsening. Additionally, accurate prediction contributes to a deeper understanding of the disorder's neurobiological and psychological underpinnings, potentially leading to more effective treatments. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Electronic health record

来源：评论

学校读者我要写书评

暂无评论

Enhancing Effective Spectrum Utilization in 6G Cellular Networks via Sharing Algorithms 5th

Enhancing Effective Spectrum Utilization in 6G Cellular Netw...

引用

5th international conference on Data science, Machine Learning and Applications, ICDSMLA 2023

作者： kudari, Jayashree M. Garg, Aanchal Sridevi, S. Savita Department of Computer Science and Information Technology Jain University Karnataka Bangalore India Department of Electronic & Communication Engineering School of Engineering and Computing Dev Bhoomi Uttarakhand University Manduwala Dehradun Navgaon India School of Computer Science and Engineering Presidency University Karnataka Bengaluru India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India

ISBN: (纸本)9789819780426

The emerging 6G mobile network technology presents an opportunity to noticeably increase spectrum utilization in comparison to previous generations. This paper introduces a singular sharing algorithm to correctly utilize the spectrum in 6G mobile networks. This set of rules leverages device mastering techniques to dynamically calculate a charge primarily based at the average channel usage and bandwidth usage through distinct users. Moreover, it gives a dispensed mechanism to define the resource allocation method used in different communique situations. The proposed set of rules is powerful, reliable, and cozy, and it is able to efficaciously apply spectrum sharing amongst unique users to maximize spectral performance. Simulation results display that the proposed set of rules achieves giant improvements in spectral efficiency as compared to both a traditional unmarried-mobile method, and a present sharing algorithm proposed in the literature. Finally, experimental results are supplied, which demonstrate the effectiveness of the proposed set of rules in real-world check setups. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Wireless networks

来源：评论

学校读者我要写书评

暂无评论

Label-Enhanced Cross-Modal Hashing with Dual-Semantic Learning 25th

Label-Enhanced Cross-Modal Hashing with Dual-Semantic Learn...

引用

25th international conference on Web Information Systems Engineering, WISE 2024

作者： Fang, Ziye Teng, Luyao Zheng, Zefeng Zhang, Wei Teng, Shaohua School of Computer Science and Technology Guangdong University of Technology Guangzhou510006 China School of Information Engineering Guangzhou Panyu Polytechnic Guangzhou511483 China

ISBN: (纸本)9789819605668

Due to its excellent storage and retrieval performance, Cross-Modal Hashing (CMH) has garnered widespread attention in recent years. However, current methods still face two major limitations: 1) most methods fail to adequately address noise in the labels, 2) many methods neglect learning the correlations between labels. To address these issues, we propose Label-enhanced Cross-modal Hashing with Dual-semantic Learning (LCHDL) in this study. LCHDL mainly consists of two parts, i.e., DeNoise Label Semantics Learning (DNLSL) and Static Label Correlations Embedding (SLCE). DNLSL uses low-rank and sparse representation to reduce noise containing in the labels. SLCE employs a relaxation strategy to achieve static label correlations embedding. Experiments on two benchmark datasets demonstrate that the performance of LCHDL surpasses many state-of-the-art methods. The source code for LCHDL is publicly available at https://***/Fizzyf/LCHDL. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Contrastive Learning

来源：评论

学校读者我要写书评

暂无评论

Efficient Online Path Selection and Workload Allocation for In-Network Computing in MEC 18th

Efficient Online Path Selection and Workload Allocation for...

引用

18th international conference on Wireless Artificial Intelligent Computing Systems and Applications, WASA 2024

作者： Ouyang, Sheng Zhang, Fanlong Mai, Junyu Chai, Yuan Chen, Quan Tao, Yongchao School of Computer Science and Technology Guangdong University of Technology Guangzhou China Shenzhen Academy of Aerospace Technology Shenzhen China

ISBN: (纸本)9783031714696

In this paper, we study the joint optimization problem of computation path selection and workload allocation for in-network computing at the edge. The existing works, which only consider the end-to-end latency, ignore the operational cost of the servers and the dynamic tasks arrived in an online manner. Thus, we investigate the first online scheduling problem of path selection and workload allocation for in-network computing. Such a problem is modeled as a mixed integer programming problem, which tries to jointly minimize the server operating cost and end-to-end latency. Then, a dynamic 3D-map is constructed to take both the operation cost and end-to-end latency into account. Based on the constructed 3D-map, a cooperative learning based algorithm with ant colony optimization is proposed. Finally, the extensive simulation demonstrates the proposed algorithm shows good robustness and outperforms the state-of-the-art algorithms. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Mixed-integer linear programming

来源：评论

学校读者我要写书评

暂无评论

A Dynamic Traffic Management System for Itinerary Optimization 11

A Dynamic Traffic Management System for Itinerary Optimizati...

引用

11th international conference on Vehicle technology and Intelligent Transport Systems, VEHITS 2025

作者： Monzón Catalán, Iván Tomás López, Vicente R. García Fernández, Luís A. Computer Science and Engineering Department Universitat Jaume I Campus del Riu Sec S/N Castelló Spain

ISBN: (纸本)9789897587450

Freight transport is a fundamental pillar of the European economy, accounting for more than 9 % of the European Union’s Gross Domestic Product (GDP). Seaports are critical nodes in this logistics chain, handling more than 67.8% of freight transport in tonne-kilometres. This article presents an intelligent traffic management system designed specifically for road access to the Port of Rotterdam, the largest and busiest port in Europe, with the aim of optimising the flow of vehicles, improving operational efficiency and reducing CO2 emissions. Copyright © 2025 by SCITEPRESS - science and technology Publications, Lda.

关键词： Freight transportation

来源：评论

学校读者我要写书评

暂无评论

Distribution and Segmentation Methods of Graph States Based on Quantum Networks 27

Distribution and Segmentation Methods of Graph States Based ...

引用

27th international conference on Advanced Communications technology, ICACT 2025

作者： Chen, Haomin Zhu, Tianze Yu, Guanting Lin, Yongzhe Jiang, Xiaofeng Department of Automation University of Science and Technology of China Hefei China

ISBN: (纸本)9791188428137

In recent years, quantum communication and quantum computing have become strategic technologies of great importance to countries around the world, with profound implications for individuals, businesses, and even nations. Among these, graph states are a very important class of multi-particle entangled states, playing a crucial role in fields such as quantum secret sharing, measurement-based quantum computing, and quantum metrology. However, the distribution of graph states faces the following challenges: existing quantum communication networks designed for single-qubit transmission cannot effectively accomplish graph state distribution tasks, and current methods for graph state distribution lack consideration of node resources, making it difficult to complete graph state distribution in some cases. We conduct research on cross-node graph states distribution methods in quantum communication networks under limited resource constraints. To address the issue that current quantum communication networks cannot handle graph states and other multi-particle entangled states, a graph state distribution method is proposed. This method combines local operations and classical communication to meet the distribution requirements for graph states, which are multi-particle entangled states. Additionally, to solve the problem of limited quantum resources at individual nodes, we proposed a graph states segmentation method. This method divides a complete graph state into subgraphs for distribution and subsequently integrates them, enabling graph state distribution services in a broader range of resource scenarios. Copyright 2025 Global IT Research Institute (GIRI). All rights reserved.

关键词： Quantum computers

来源：评论

学校读者我要写书评

暂无评论

Spatio-Temporal Spiking Attention Recurrent Neural Network for EEG-Based Motor Imagery Classification

Spatio-Temporal Spiking Attention Recurrent Neural Network f...

引用

2025 international conference on Electronics, Information, and Communication, ICEIC 2025

作者： Lim, Jin-Gyo Kim, Seong-Eun Seoul National University of Science and Technology Dept. Defence AI Convergence Engineering Seoul Korea Republic of Seoul National University of Science and Technology Dept. Applied Artificial Intelligence Dept. Defence AI Convergence Engineering Seoul Korea Republic of

ISBN: (纸本)9798331510756

Motor Imagery (MI), a primary research domain of brain-computer interface (BCI) studies, focuses on the recording and analysis of brain activity related to imagined movements, primarily using electroencephalography (EEG). Recent advance-ments in deep learning have significantly enhanced EEG-based MI classification. New challenges, raised with increasing interest in developing low-power neuromorphic devices, brought the demand for spiking neural networks (SNNs), which promise efficient MI classification with reduced energy requirements. However, the application of SNNs to EEG-based MI classification remains relatively unexplored. In this study, we propose a novel approach that integrates spatio-temporal spike encoding with an attention recurrent neural network (ARNN), referred to as spiking ARNN (SARNN). This architecture is designed to capture both the spatial dependencies across EEG channels and the temporal dynamics within the EEG signals, enabling more robust feature extraction and classification. Our method achieved an accuracy of 78.46% on the benchmark BCI Competition IV 2a dataset, demonstrating substantial improvements in MI classification performance and emphasizing the potential of SNNs in BCI applications. © 2025 IEEE.

关键词： Recurrent neural networks

来源：评论

学校读者我要写书评

暂无评论

Analysis of Energy Consumption Using Support Vector Regression 3rd

Analysis of Energy Consumption Using Support Vector Regress...

引用

3rd international conference on Emerging Electronics and automation, E2A 2023

作者： Pavan Kumar, V. Likhith Reddy, G. Mahadev, H. Harish Kumar, V. Krishna Prasad, K. Computer Science and Engineering B V Raju Institute of Technology Telangana Narsapur India

ISBN: (纸本)9789819730896

Energy consumption analysis is essential for maximizing energy use and improving energy efficiency across a range of applications. In this study, we suggest a unique method for analyzing energy use based on real-time data from an energy meter. The information gathered comprises the voltage, current, power, and overall energy consumption’s root mean square (RMS) values. We use the Support Vector Regression (SVR) model, a potent machine learning method known for its ability to handle non-linear connections and high-dimensional data, to reliably forecast future energy usage. A popular interactive computing tool called Jupyter Notebook has the SVR model incorporated, making experimentation and analysis simple. Our proposed methodology proves its effectiveness in anticipating future energy use through testing and evaluation. The SVR-based prediction model’s findings offer useful insights into trends in energy consumption, empowering users to make deft choices that would optimize energy use, cut down on waste, and enhance overall energy sustainability. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Support vector regression

来源：评论

学校读者我要写书评

暂无评论

RF Challenge: The Data-Driven Radio Frequency Signal Separation Challenge

IEEE Open Journal of the Communications Society

引用

IEEE Open Journal of the Communications Society 2025年 6卷 4083-4100页

作者： Lancho, Alejandro Weiss, Amir Lee, Gary C. F. Jayashankar, Tejas Kurien, Binoy G. Polyanskiy, Yury Wornell, Gregory W. Universidad Carlos III de Madrid Signal Theory and Communications Leganés28911 Spain Gregorio Marañón Health Research Institute Madrid28007 Spain Bar-Ilan University Ramat Gan5290002 Israel Institute for Infocomm Research Communications and Networks Fusionopolis 138632 Singapore Massachusetts Institute of Technology Electrical Engineering and Computer Science Department CambridgeMA02139 United States MIT Lincoln Laboratory LexingtonMA02421 United States

We address the critical problem of interference rejection in radio-frequency (RF) signals using a data-driven approach that leverages deep-learning methods. A primary contribution of this paper is the introduction of the RF Challenge, which is a publicly available, diverse RF signal dataset for data-driven analyses of RF signal problems. Specifically, we adopt a simplified signal model for developing and analyzing interference rejection algorithms. For this signal model, we introduce a set of carefully chosen deep learning architectures, incorporating key domain-informed modifications alongside traditional benchmark solutions to establish baseline performance metrics for this intricate, ubiquitous problem. Through extensive simulations involving eight different signal mixture types, we demonstrate the superior performance (in some cases, by two orders of magnitude) of architectures such as UNet and WaveNet over traditional methods like matched filtering and linear minimum mean square error estimation. Our findings suggest that the data-driven approach can yield scalable solutions, in the sense that the same architectures may be similarly trained and deployed for different types of signals. Moreover, these findings further corroborate the promising potential of deep learning algorithms for enhancing communication systems, particularly via interference mitigation. This work also includes results from an open competition based on the RF Challenge, hosted at the 2024 IEEE international conference on Acoustics, Speech, and Signal Processing (ICASSP'24). © 2020 IEEE.

关键词： Matched filters

来源：评论

学校读者我要写书评

暂无评论

A Comprehensive Study of Convolutional Neural Networks for Medical Image Segmentation 5th

A Comprehensive Study of Convolutional Neural Networks for M...

引用

5th international conference on Data science, Machine Learning and Applications, ICDSMLA 2023

作者： Nautiyal, Kapish Augustine, Jacob Savita Rengarajan, A. Department of Electronic & Communication Engineering School of Engineering and Computing Dev Bhoomi Uttarakhand University Navgaon Manduwala Dehradun India School of Computer Science and Engineering Presidency University Karnataka Bengaluru India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India Karnataka Bangalore India

ISBN: (纸本)9789819780426

It affords an overview latest, the modern-day inside the field of modern-day clinical picture segmentation, after which highlights the gain of trendy CNNs in it. The paper then describes today's CNNs’ fundamentals, architectures, and education techniques. It similarly affords one-of-a-kind CNN fashions used for clinical image segmentation. It presents a comprehensive analysis of new research addressing modern-day CNNs’ utility for this assignment. In the end, the paper affords steering for destiny research guidelines in scientific photo segmentation, the use of CNNs, and an outlook for the ability of today’s this era. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Convolutional neural 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：