检索结果-内蒙古大学图书馆

Joint Content Caching and Request Routing for User-Centric Many-Objective Metaverse Services

IEEE Transactions on Network science and engineering 2025年第3期12卷 1911-1925页

作者： Hu, Zhaoming Fang, Chao Wang, Zhuwei Chen, Jining Tseng, Shu-Ming Dong, Mianxiong Beijing University of Technology College of Computer Science Beijing100021 China Beijing University of Technology School of Information Science and Technology Beijing100021 China Guangxi Information Center Guangxi Key Laboratory of Digital Infrastructure Nanning530000 China National Taipei University of Technology Department of Electronic Engineering Taipei106 Taiwan Muroran Institute of Technology Department of Sciences and Informatics Muroran050-0071 Japan

Metaverse, as a revolutionary technology that changes the way of human interaction, brings new challenges to content delivery services due to the extensive data transmission and personalized service requirements. To ensure a personalized user experiences while improving the utilization of heterogeneous network resources, a user-centric many-objective metaverse content delivery framework is proposed to optimize content delivery through user attention awareness. This framework addresses two key subproblems in metaverse content delivery by investigating user-centric many-objective cooperative content caching and deep reinforcement learning (DRL)-based request routing. The user-centric many-objective cooperative content caching is proposed to dynamically combine three basic preference prediction results to predict user preferences and control network resource allocation, which can simultaneously optimize prediction precision, delay, offloaded traffic, and load balancing. In DRL-based request routing, the reward function is designed to enable the optimization of multiple objectives. The multi-objective DRL routing algorithm is employed to continuously observe network states and make adaptive routing decisions in response to user requests. In the simulation, a movie dataset is employed to simulate user requests and support user attention awareness. The results show that the proposed content delivery framework outperforms existing basic prediction algorithms and other content delivery algorithms on four evaluation indicators. © 2013 IEEE.

关键词： Deep reinforcement learning

来源：评论

学校读者我要写书评

暂无评论

Fingerprint Matching for Noisy and Distorted Patterns using a Siamese Network with ResNet50 and Multihead Attention

引用

IEEE Access 2025年 13卷 88047-88064页

作者： Sawhney, Tinny Sharma, Ashu Abrol, Pawanesh Lehana, Parveen Kumar Chaahat Yadav, Vaishali Scherer, Rafal Kulakov, Oleksii AlZubi, Ahmad Ali University of Jammu DSP Lab Department of Electronics India University of Jammu Department of Computer Science and IT India Chitkara University Institute of Engineering and Technology Chitkara University Department of Computer Science and Engineering Punjab India Manipal University Jaipur Jaipur India Czestochowa University of Technology Department of Artificial Intelligence Częstochowa Poland National Technical University of Ukraine "Igor Sikorsky Kyiv Polytechnic Institute" Faculty of Informatics and Software Engineering Kyiv Ukraine King Saud University Community College Department of Computer Science Riyadh Saudi Arabia

Dermatoglyphics, the study of unique ridge patterns on fingertips, plays a crucial role in fingerprint-based identification. However, skin conditions such as psoriasis, eczema, and verruca vulgaris can distort these patterns. This distortion creates challenges for fingerprint matching systems in forensic science and biometric authentication. This paper proposes a Siamese network with a modified ResNet50 architecture and multihead attention mechanisms to improve fingerprint matching under such distortions. The Siamese network enables the system to compare fingerprint pairs effectively, even under distortions. The modified ResNet50 architecture captures intricate ridge patterns, while the multihead attention mechanisms focus on critical fingerprint regions. Overall, the proposed approach enhances the system’s ability to learn discriminative features. As a result, it can effectively differentiate between matched and unmatched fingerprint pairs, even in the presence of moderate to extreme noise. The proposed system was trained on the Sokoto coventry fingerprint dataset (SOCOFing) and a custom dataset. Experimental results demonstrate high accuracy, with the system achieving 99.47% accuracy under low noise conditions and 90% under moderate noise. However, performance declined to 55.56% under extreme distortions. A comparative analysis highlighted the superiority of the proposed system over widely used minutiae-based methods. The latter exhibited a significant drop in matching scores, falling below 15% for highly distorted fingerprints. These findings underscore the limitations of traditional fingerprint recognition techniques and highlight the effectiveness of proposed approach in handling dermatoglyphic distortions. © 2013 IEEE.

关键词： Feature Selection

来源：评论

学校读者我要写书评

暂无评论

LSTM-XGBoost: An Ensemble Model for Blood Demand Distribution Forecasting – A Case Study in Zakho City, Kurdistan Region, Iraq

引用

Operations Research Forum 2025年第1期6卷 1-22页

作者： Zebari, Rizgar R. Zebari, Gheyath M. Al-zebari, Adel Mohammed, Marwan Aziz Department of Computer Science College of Science Knowledge University Erbil 44001 Iraq Department of Information Technology Technical College of Informatics Akre University for Applied Sciences Kurdistan Akre Iraq Department of Computer Engineering College of Engineering Knowledge University Erbil 44001 Iraq

A safe and adequate blood supply is essential for healthcare systems to function effectively. Accurately forecasting blood demand plays a key role in efficient inventory management and resource allocation. Traditional forecasting methods, like moving averages and ARIMA models, often fall short due to the complexity of factors influencing blood demand. This study introduces an innovative hybrid ensemble model that combines Long Short-Term Memory (LSTM) networks with XGBoost, harnessing their combined strengths to enhance forecasting accuracy. By analyzing blood donation data from the Zakho Blood Bank (January 1, 2015—July 22, 2022), the model outperforms individual LSTM and XGBoost models, excelling in metrics such as Mean Square Error (MSE) and Mean Absolute Error (MAE). These findings underscore the potential of advanced machine learning techniques to improve healthcare supply chain management and ensure the timely availability of critical blood supplies. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.

关键词： Blood banking Blood demand forecasting Healthcare supply chain LSTM network Machine learning XGBoost

来源：评论

学校读者我要写书评

暂无评论

Bidirectional LSTM-Based Energy Consumption Forecasting:Advancing AI-Driven Cloud Integration for Cognitive City Energy Management

引用

Computers, Materials & Continua 2025年第5期83卷 2907-2926页

作者： Sheik Mohideen Shah Meganathan Selvamani Mahesh Thyluru Ramakrishna Surbhi Bhatia Khan Shakila Basheer Wajdan Al Malwi Mohammad Tabrez Quasim Department of Computer Science and Engineering Srinivasa Ramanujan CentreSASTRA Deemed UniversityKumbakonam612001India Department of Computer Science and Engineering Faculty of Engineering and TechnologyJAIN(Deemed-to-be University)Bangalore562112India School of Science Engineering and EnvironmentUniversity of SalfordManchesterM54WTUK University Centre for Research and Development Chandigarh UniversityMohali140413PunjabIndia Centre for Research Impact and Outcome Chitkara University Institute of Engineering and TechnologyChitkara UniversityRajpura140401PunjabIndia Department of Information Systems College ofComputer and Information SciencePrincessNourah bintAbdulrahmanUniversityP.O.Box 84428Riyadh11671Saudi Arabia College of Computer Science Informatics and Computer Systems Department King Khalid UniversityAbha61421Saudi Arabia Department of Computer Science and Artificial Intelligence College of Computing and Information TechnologyUniversity of BishaBisha61922Saudi Arabia

Efficient energy management is a cornerstone of advancing cognitive cities,where AI,IoT,and cloud computing seamlessly integrate to meet escalating global energy *** this context,the ability to forecast electricity consumption with precision is vital,particularly in residential settings where usage patterns are highly variable and *** study presents an innovative approach to energy consumption forecasting using a bidirectional Long Short-Term Memory(LSTM)*** a dataset containing over twomillionmultivariate,time-series observations collected froma single household over nearly four years,ourmodel addresses the limitations of traditional time-series forecasting methods,which often struggle with temporal dependencies and non-linear *** bidirectional LSTM architecture processes data in both forward and backward directions,capturing past and future contexts at each time step,whereas existing unidirectional LSTMs consider only a single temporal *** design,combined with dropout regularization,leads to a 20.6%reduction in RMSE and an 18.8%improvement in MAE over conventional unidirectional LSTMs,demonstrating a substantial enhancement in prediction accuracy and *** to existing models—including SVM,Random Forest,MLP,ANN,and CNN—the proposed model achieves the lowest MAE of 0.0831 and RMSE of 0.2213 during testing,significantly outperforming these *** results highlight the model’s superior ability to navigate the complexities of energy usage patterns,reinforcing its potential application in AI-driven IoT and cloud-enabled energy management systems for cognitive *** integrating advanced machine learning techniqueswith IoT and cloud infrastructure,this research contributes to the development of intelligent,sustainable urban environments.

关键词： Deep learning bidirectional LSTM energy consumption forecasting time-series analysis predictive modeling machine learning in energy management

来源：评论

学校读者我要写书评

暂无评论

Awake at the Wheel: Enhancing Automotive Safety Through EEG-Based Fatigue Detection 27th

Awake at the Wheel: Enhancing Automotive Safety Through EEG-...

引用

27th International Conference on Pattern Recognition, ICPR 2024

作者： Siddhad, Gourav Dey, Sayantan Roy, Partha Pratim Iwamura, Masakazu Department of Computer Science and Engineering Indian Institute of Technology Roorkee Uttarakhand Roorkee247667 India Department of Core Informatics Graduate School of Informatics Osaka Metropolitan University Osaka Sakai599-8531 Japan

ISBN: (纸本)9783031781940

Driver fatigue detection is increasingly recognized as critical for enhancing road safety. This study introduces a method for detecting driver fatigue using the SEED-VIG dataset, a well-established benchmark in EEG-based vigilance analysis. By employing advanced pattern recognition technologies, including machine learning and deep neural networks, EEG signals are meticulously analyzed to discern patterns indicative of fatigue. This methodology combines feature extraction with a classification framework to improve the accuracy of fatigue detection. The proposed NLMDA-Net reached an impressive accuracy of 83.71% in detecting fatigue from EEG signals by incorporating two novel attention modules designed specifically for EEG signals, the channel and depth attention modules. NLMDA-Net effectively integrate features from multiple dimensions, resulting in improved classification performance. This success stems from integrating temporal convolutions and attention mechanisms, which effectively interpret EEG data. Designed to capture both temporal and spatial characteristics of EEG signals, deep learning classifiers have proven superior to traditional methods. The results of this study reveal a substantial enhancement in detection rates over existing models, highlighting the efficacy of the proposed approach for practical applications. The implications of this research are profound, extending beyond academic realms to inform the development of more sophisticated driver assistance systems. Incorporating this fatigue detection algorithm into these systems could significantly reduce fatigue-related incidents on the road, thus fostering safer driving conditions. This paper provides an exhaustive analysis of the dataset, methods employed, results obtained, and the potential real-world applications of the findings, aiming to contribute significantly to advancements in automotive safety. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Deep neural networks

来源：评论

学校读者我要写书评

暂无评论

Insights into seasonal temperature shifts on a robust composite-substrate autotrophic denitrification reactor for treating municipal secondary effluent

引用

Frontiers of Environmental science and engineering 2025年第6期19卷 1-16页

作者： Zhang, Chuanyi Zhu, Zhiping Yuan, Limei Yang, Daxin Xu, Qiang Ge, Sijie School of Environment Science and Spatial Informatics China University of Mining and Technology Xuzhou221116 China Dan F. Smith Department of Chemical & Biomolecular Engineering Lamar University BeaumontTX77710 United States

Sulfur autotrophic denitrification (SAD) is a promising biological nitrogen removal technology without CO2 emissions. However, the impact of seasonal temperature variations on SAD performance, especially in the treatment of actual municipal secondary effluent, remains unclear. To address this issue, a composite substrate SAD reactor (i.e., SPSAD), where element S0 and pyrite (v:v/1:3) were uniformly mixed in hollow plastic balls that served as the filler, was developed in this paper. The performance of the SPSAD reactor was comprehensively evaluated under 190 d of seasonal variation. The results indicated that the nitrate removal loading (NRL), nitrate removal efficiency (NRE), and PO43−-P removal rate decreased from 0.060 kg NO3−-N /(m3·d), 93.2% and 67.9% to 0.032 kg NO3−-N /(m3·d), 49.9% and 30.2%, respectively, when the temperature decreased from 35 °C to 9 °C. The SPSAD reactor was effective at performing denitrification under temperature variations. Additionally, the ratio of ΔSO42− to ΔNO3−-N gradually decreased from 6.48 to 5.34 as the temperature decreased, revealing a shift in the predominant electron donor for denitrification from S0 to pyrite. Microbial analysis revealed that the average abundance of Proteobacteria was 53.11%, making it the dominant phylum in the reactor. Thiobacillus was significantly enriched as the predominant genus responsible for denitrification, with its abundance decreasing from 33.2% at Stage I (25–35 °C) to 26.4% at Stage III (9–11 °C). The feasibility and advantages of NO3−-N and PO43−-P removal via the SPSAD reactor were discussed and demonstrated in practical applications. This study provides stakeholders with scientific support for the deep treatment of municipal secondary effluent in cold areas. © Higher Education Press 2025.

关键词： Denitrification

来源：评论

学校读者我要写书评

暂无评论

Autonomous Exploration System of Electrical Stimulus Condition for Maturating in vitro Skeletal Muscle Tissue with Bayesian Optimization

引用

IEEJ Transactions on Sensors and Micromachines 2025年第3期145卷 33-40页

作者： Miyata, Daiki Kasahara, Keitaro Taguchi, Yujin Tokuoka, Yuta Yamada, Takahiro G. Kurashina, Yuta Funahashi, Akira Onoe, Hiroaki Department of Mechanical Engineering Faculty of Science and Technology Keio University 3-14-1 Hiyoshi Kohoku-ku Kanagawa Yokohama223-0061 Japan Department of Biosciences and Informatics Faculty of Science and Technology Keio University 3-14-1 Hiyoshi Kohoku-ku Kanagawa Yokohama223-0061 Japan Division of Advanced Mechanical Systems Engineering Institute of Engineering Tokyo University of Agriculture and Technology 2-24-16 Nakacho Koganei Tokyo184-8588 Japan

This paper describes an autonomous exploration system to optimize electrical stimulation parameters for the maturation of in vitro skeletal muscle tissues with Bayesian optimization. We established a cyclic experimental procedures, where (i) C2C12 myoblasts were cultured under multiple electrical stimulus conditions in parallel to promote the maturation, (ii) the cell maturation levels were scored by microscopic image analysis with immunofluorescence staining, and (iii) each maturation score with electrical stimulus condition were then applied to Bayesian optimization to suggest new experimental stimulus conditions for the next cycle of the C2C12 culture to expect higher maturation scores. We performed this experimental cycle repeatedly and derived the trend of electrical stimulus conditions to obtain high maturation scores. The C2C12 myoblasts cultured under the derived condition were also verified by the increase of myogenic gene expression. We believe that our proposed method could reveal optimal application method for tissue maturation, and would contribute to regenerative medicine or other medical technologies using tissue culture. © 2025 The Institute of Electrical Engineers of Japan.

关键词： Regenerative medicine

来源：评论

学校读者我要写书评

暂无评论

IDCE:Integrated Data Compression and Encryption for Enhanced Security and Efficiency

引用

Computer Modeling in engineering & sciences 2025年第4期143卷 1029-1048页

作者： Muhammad Usama Arshad Aziz Suliman A.Alsuhibany Imtiaz Hassan Farrukh Yuldashev Department of Cyber Security Pakistan Navy Engineering CollegeNational University of Sciences and TechnologyKarachi75350Pakistan Department of Computer Science Main CampusIqra UniversityKarachi75500Pakistan Department of Computer Science College of ComputerQassim UniversityBuraydah51452Saudi Arabia Department of Informatics and Its Teaching Methods Tashkent State Pedagogical UniversityTashkent100170Uzbekistan

Data compression plays a vital role in datamanagement and information theory by reducing ***,it lacks built-in security features such as secret keys or password-based access control,leaving sensitive data vulnerable to unauthorized access and *** the exponential growth of digital data,robust security measures are *** encryption,a widely used approach,ensures data confidentiality by making it unreadable and unalterable through secret key *** their individual benefits,both require significant computational ***,performing them separately for the same data increases complexity and processing *** the need for integrated approaches that balance compression ratios and security levels,this research proposes an integrated data compression and encryption algorithm,named IDCE,for enhanced security and *** on 128-bit block sizes and a 256-bit secret key *** combines Huffman coding for compression and a Tent map for ***,an iterative Arnold cat map further enhances cryptographic confusion *** analysis validates the effectiveness of the proposed algorithm,showcasing competitive performance in terms of compression ratio,security,and overall efficiency when compared to prior algorithms in the field.

关键词： Chaotic maps security data compression data encryption integrated compression and encryption

来源：评论

学校读者我要写书评

暂无评论

ADMM for Energy-Efficient Computation Offloading in Marine Mobile Edge Computing Networks 20th

ADMM for Energy-Efficient Computation Offloading in Marine...

引用

20th IFIP WG 10.3 International Conference on Network and Parallel Computing, NPC 2024

作者： He, Ang Lu, Zili Qin, Baolin Pan, Heng Si, Xueming Dai, Yueyue Huang, Xiaoyan Zhang, Yan Frontier Information Technology Research Institute Zhongyuan University of Technology Zhengzhou China School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan China School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu China Department of Informatics University of Oslo Oslo Norway

ISBN: (纸本)9789819628636

Marine mobile edge computing (MMEC) networks play a crucial role in collecting and analyzing oceanic data. However, the limited energy storage capacity and inconvenient power delivery of underwater sensor nodes (USNs) and surface sink nodes (SSNs) necessitate energy-saving operations. Moreover, the exhaustion of energy in individual USN results in a diminished lifespan of the underwater sensor networks, thereby significantly affecting the accuracy and reliability of oceanic monitoring data. In this paper, we propose a computation offloading scheme that utilizes collaborative underwater sensor data transmission via acoustic and radio frequency links. The main objective is to minimize the energy consumption of unmanned aerial vehicle (UAV)-aided MMEC networks through the joint optimization of USN clustering, SSN computation offloading decisions, and UAV trajectory planning. To address this joint problem, we design a new clustering algorithm followed by the alternating direction method of multipliers (ADMM) optimization algorithm sequentially. Numerical results show that, compared to no clustering and no computation offloading methods, the total energy consumption for the proposed method is reduced by 23.3% and 10.1%, respectively. © IFIP International Federation for Information Processing 2025.

关键词： Energy efficiency

来源：评论

学校读者我要写书评

暂无评论

SAR Images Change Detection Based on Attention Mechanism-Convolutional Wavelet Neural Network

引用

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2025年 18卷 12133-12149页

作者： Jiahui, E. Wang, Lu Zhao, Chunhui Mathiopoulos, P. Takis Ohtsuki, Tomoaki Adachi, Fumiyuki Harbin Engineering University College of Information and Communication Engineering Harbin150001 China Ministry of Industry and Information Technology Key Laboratory of Advanced Marine Communication and Information Technology Harbin150001 China National and Kapodistrian University of Athens Department of Informatics and Telecommunications Greece Keio University Department of Information and Computer Science Kanagawa223852 Japan Tohoku University International Research Institute of Disaster Science Sendai Japan

As Synthetic Aperture Radar (SAR) image change detection can continuously acquire target information under all weather conditions, it has been used in the past for various applications. However, it is very challenging to use SAR images for small area change detection due to the presence of noise and the imbalance between classes. To deal with these problems this paper proposes a SAR images change detection scheme which is based upon an Attention Mechanism and Convolutional Wavelet Neural Network (AMCW-NN). Firstly, employing Multi-Scale Superpixel Reconstructed Difference Image (MSRDI) effectively enhances the edge information of the images. Secondly, a twostage Weighted Center Constrained Fuzzy C-Means (WCCFCM) clustering algorithm is proposed. To constrain incorrect migration in the subsequent clustering phase, the algorithm uses a two-stage clustering algorithm. During the first stage weights in the clustering process to improve the accuracy of the resulting clustering centers are introduced while during the second stage, these refined cluster centers can be constrained. Thirdly, a Convolutional Squeeze-and-Excitation (SE) Attention Mechanism Wavelet Neural Network (CSWNN) is proposed which more accurately distinguishes uncertain pixels and thus reduces redundant features. The final Denoising Diffusion Probabilistic Model (DDPM) is introduced into the CSWNN to expand pseudo-labeled samples. Comparing this algorithm with seven sets of contrast experiments on six real SAR datasets shows that the accuracy of detecting changes in small regions is 2%-9% better than the classical algorithm. © 2008-2012 IEEE.

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：