检索结果-内蒙古大学图书馆

Gasoline and Crude Oil Price Prediction using Multi-headed Variational Neighbour Search-tuned Recurrent Neural Networks

引用

Computational Economics 2025年 1-36页

作者： Kljajic, Maja Mizdrakovic, Vule Jovanovic, Luka Bacanin, Nebojsa Simic, Vladimir Pamucar, Dragan Zivkovic, Miodrag Business Economics Singidunum University Danijelova 32 Belgrade 11000 Serbia Department of Mathematics Saveetha School of Engineering SIMATS Thandalam Tamilnadu Chennai 602105 India Informatics and Computing Singidunum University Danijelova 32 Belgrade 11000 Serbia Sinergija University Raje Banjičića Bijeljina 76300 Bosnia and Herzegovina University of Belgrade Faculty of Transport and Traffic Engineering Vojvode Stepe 305 Belgrade 11010 Serbia Yuan Ze University College of Engineering Department of Industrial Engineering and Management Taoyuan City 320315 Taiwan Department of Computer Science and Engineering College of Informatics Korea University Seoul 02841 South Korea Department of Operations Research and Statistics Faculty of Organizational Sciences University of Belgrade Belgrade Serbia Department of Mechanics and Mathematics Western Caspian University Baku Azerbaijan İstinye University Faculty of Engineering and Natural Sciences Industrial Engineering Department İstanbul Turkey

The rapidly growing worldwide demand for energy and finite reservoirs of fossil fuels have intensified interest in energy projection research. Artificial intelligence, particularly in time series forecasting, holds significant promise for enhancing predictions of both cost and demand, offering numerous prospective applications over various domains. Numerous variables, ranging from socio-economic conditions to distribution, supply, and international policies, exert influence on global price fluctuations. Thus, crafting precise forecasts necessitates consideration of these multifaceted factors. Through an examination of existing literature, a discernible gap emerges in the quest for advancement in this domain. Consequently, this study proposes to delve into the perspective of employing multi-headed long short-term memory (LSTM) models for gasoline and crude oil price prediction, an area largely untouched by multi-headed approaches. Moreover, recognizing the computational demands of such models, this research emphasizes the development of lightweight methodologies, characterized by a modest neuron within each layer and trained over a limited epoch count. Given the pivotal role of hyper-parameter selection in algorithm performance, an adapted version of the variable neighbour search algorithm is introduced to aid in tuning the model’s architecture and training parameters. A comprehensive side-by-side comparison is undertaken utilizing gasoline and oil data sourced from diverse public repositories, employing a variety of contemporary optimizers. The resultant outcomes are subjected to strict statistical scrutiny to ascertain the robustness and significance of the findings. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Crude oil prices Gasoline prices Modified VNS Multi-headed long short-term memory models Time series prediction

来源：评论

学校读者我要写书评

暂无评论

Performance and Implementation Comparison of Knuth-Morris-Pratt and Boyer-Moore String Search Algorithms

Performance and Implementation Comparison of Knuth-Morris-Pr...

引用

Advanced Innovations in Smart Cities (ICAISC), International Conference on

作者： Taj Saleh Fatma Corut Ergin Malek Malkawi Reda Alhajj Department of Computer Engineering Marmara University Istanbul Turkey Department of Computer Engineering Istanbul Medipol University Istanbul Turkey Department of Computer Science University of Calgary Alberta Canada Department of Heath Informatics University of Southern Denmark Odense Denmark

ISBN: (数字)9798331506995

ISBN: (纸本)9798331507008

String search algorithms play an important role in many research areas such as data mining and bioinformatics. While there exist a number of algorithms that handles the topic, we are exploring the the Knuth-Morris-Pratt (KMP) and Boyer-Moore algorithms due to their efficiency and versatility. In this work, we compared the algorithms in terms of characteristics, performance and implementation details. We also tested both the algorithms with various patterns and texts that differs in size. We also analyzed the performance of the algorithms on 4 different processors to understand the technological advancements effects on their performance. Our findings suggest that the BM algorithm perform better with large texts and patterns, while the KMP algorithm is better suited for smaller ones. Also, while newer processor generally exhibit improved performance, the significance of these enhancements may vary. Thus, we should rather be looking specific architectural advancements within generations rather than focusing solely on the generational gap.

关键词： Technological innovation Program processors Smart cities Focusing Data mining Bioinformatics Pattern matching

来源：评论

学校读者我要写书评

暂无评论

ChainSafari: A General and Efficient Blockchain Verifiable Query Scheme with Real-Time Synchronization 24th

ChainSafari: A General and Efficient Blockchain Verifiable ...

引用

24th International Conference on Algorithms and Architectures for Parallel Processing, ICA3PP 2024

作者： Xu, Yuwei Dai, Shengjiang Zeng, Junyu Cao, Jie He, Ran Xiang, Qiao School of Cyber Science and Engineering Southeast University Nanjing China Purple Mountain Laboratories for Network and Communication Security Nanjing China Engineering Research Center of Blockchain Application Supervision and Management Nanjing China Department of Electrical and Computer Engineering Queen’s University Kingston Canada School of Informatics Xiamen University Xiamen China

ISBN: (纸本)9789819615414

With the rapid advancement of blockchain technology, its utilization has become widespread across various domains, resulting in the accumulation of significant volumes of valuable data. This trend has created an increasing demand for efficient querying of blockchain data. However, the inherent structure of blockchain leads to low querying efficiency and limited support for complex queries. Therefore, there is a trend toward migrating data to off-chain storage for querying purposes. The untrustworthy nature of the off-chain environment necessitates data validation. However, existing validation schemes incur high costs and fail to ensure the timely updating of the off-chain data. To address the challenges above, we propose ChainSafari, a general, efficient, and real-time synchronized scheme for verifiable querying on the blockchain. In our scheme, we design a general data model for storing transactions of various blockchains and develop an efficient authenticated data structure (ADS) based on this data model, enabling efficient verifiable queries for multiple data types in parallel. Furthermore, we propose a multi-party synchronization detection and recovery protocol to ensure the consistency of on-chain and off-chain data. Finally, we implement a prototype system of ChainSafari and conduct extensive experiments. The experimental results demonstrate the superior query throughput and enhanced data validation efficiency of ChainSafari compared to existing schemes. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Structured Query Language

来源：评论

学校读者我要写书评

暂无评论

FedCSpc: A Cross-Silo Federated Learning System with Error-Bounded Lossy Parameter Compression

引用

IEEE Transactions on Parallel and Distributed Systems 2025年第7期36卷 1372-1386页

作者： Zhang, Zhaorui Di, Sheng Zhao, Kai Jin, Sian Tao, Dingwen Ji, Zhuoran Liu, Benben Alharthi, Khalid Ayed Cao, Jiannong Cappello, Franck The Hong Kong Polytechnic University Department of Computing Hong Kong Florida State University Department of Computer Science United States Temple University Department of Computer and Information Sciences United States Indiana University Bloomington Luddy School of Informatics Computing and Engineering United States Shandong University School of Cyber Science and Technology China The University of Hong Kong LSCM R&D Center Hong Kong University of Bisha Department of Computer Science College Of Computing And Information Technology Saudi Arabia Argonne National Laboratory United States

Cross-Silo federated learning is widely used for scaling deep neural network (DNN) training over data silos from different locations worldwide while guaranteeing data privacy. Communication has been identified as the main bottleneck when training large-scale models due to large-volume model parameters and gradient transmission across public networks with limited bandwidth. Most previous works focus on gradient compression, while limited work tries to compress parameters that can not be ignored and extremely affect communication performance during the training. To bridge this gap, we propose FedCSpc: an efficient cross-silo federated learning system with an XAI-driven adaptive parameter compression strategy for large-scale model training. Our work substantially differs from existing gradient compression techniques due to the distinct data features of gradient and parameter. The key contributions of this paper are fourfold. (1) Our designed FedCSpc proposes to compress the parameter during the training using the state-of-the-art error-bounded lossy compressor – SZ3. (2) We develop an adaptive compression error bound adjustment algorithm to guarantee the model accuracy effectively. (3) We exploit an efficient approach to utilize the idle CPU resources of clients to compress the parameters. (4) We perform a comprehensive evaluation with a wide range of models and benchmarks on a GPU cluster with 65 GPUs. Results show that FedCSpc can achieve the same model accuracy as FedAvg while reducing the data volume of parameters and gradients in communication by up to 7.39× and 288×, respectively. With 32 clients on a 4Gb size model, FedCSpc significantly outperforms FedAvg in wall-clock time in the emulated WAN environment (at the bandwidth of 1 Gbps or lower without loss of generality). © 1990-2012 IEEE.

关键词： Bandwidth compression

来源：评论

学校读者我要写书评

暂无评论

Cyber Risk Identification and Classification-Based Load Forecasting Tool for Pandemic Situations

引用

IET Cyber-Physical Systems: Theory and Applications 2025年第1期10卷

作者： Shivran, Kuldeep Singh Swire-Thompson, Kyle Saxena, Neetesh Das, Sarasij Department of Electrical Engineering Indian Institute of Science Bangalore India School of Computer Science and Informatics Cardiff University Cardiff United Kingdom

Smart grid operators use load forecasting algorithms to predict energy load for the reliable and economical operation of the electricity grid. COVID-19 pandemic-like situations (PLS) can significantly impact energy load demand due to uncertainties in factors such as regulatory orders, pandemic severity and human behavioural patterns. Additionally, in a smart grid, cyberattacks can manipulate forecasted load data, leading to suboptimal decisions, economic losses and potential blackouts. Forecasting load during these situations is challenging for traditional load forecasting tools, as they struggle to identify cyberattacks amidst uncertain load demand, where cyberattacks may mimic pandemic-like load patterns. Traditional forecasting methods do not incorporate factors related to pandemics and cyberattacks. Recent studies have focused on forecasting by considering factors such as COVID-19 cases, social distancing, weather, and temperature but fail to account for the impact of regulatory orders and pandemic severity. They also lack the ability to differentiate between normal and anomalous forecasts and classify the type of attack in anomalous data. This paper presents a tool for short-term load forecasting, anomaly detection and cyberattack classification for pandemic-like situations (PLS). The proposed short-term load forecasting algorithm uses a weighted moving average and an adjustment factor incorporating regulatory orders and pandemic severity, making it computationally efficient and deterministic. Additionally, the proposed anomaly detection and cyberattack classification algorithm provides robust options for detecting anomalies and classifying various types of cyberattacks. The proposed tool has been evaluated using K-Fold cross-validation to improve generalisability and reduce overfitting. The mean squared error (MSE) was used to measure prediction accuracy and detect discrepancies. It has been analysed and tested on real-load data from the State Load Dispatch Ce

关键词： Mean square error

来源：评论

学校读者我要写书评

暂无评论

DataJudge: Cross-Chain Data Consistency Verification Based on Extended Merkle Hash Tree 24th

DataJudge: Cross-Chain Data Consistency Verification Based o...

引用

24th International Conference on Algorithms and Architectures for Parallel Processing, ICA3PP 2024

作者： Xu, Yuwei Zeng, Junyu Cao, Jie Dai, Shengjiang Xiang, Qiao Cheng, Guang School of Cyber Science and Engineering Southeast University Nanjing China Purple Mountain Laboratories for Network and Communication Security Nanjing China Engineering Research Center of Blockchain Application Supervision and Management Nanjing China Department of Electrical and Computer Engineering Queen’s University Kingston Canada School of Informatics Xiamen University Xiamen China

ISBN: (纸本)9789819615414

Consortium blockchains, with their decentralized characteristics, have found widespread applications across various industries. However, this also forms isolated data islands. These data islands have become obstacles to data sharing between industries, and the issue of cross-chain data consistency verification remains unresolved. Current mainstream cross-chain technologies solve asset transfer issues through complex transaction steps, thus failing to meet the need for efficient data transmission. Existing research has proposed solutions for cross-chain data consistency verification, but two major issues remain. Firstly, these schemes do not consider data changes or support data dynamic operations. Secondly, these schemes involve a significant amount of complex computations, leading to slow efficiency. To address the above issues, we propose DataJudge, a cross-chain data consistency verification scheme based on an extended Merkle hash tree. This scheme not only verifies cross-chain data consistency but also effectively fills the gap in existing schemes regarding dynamic operations. Additionally, we modify the paillier homomorphic encryption. To improve efficiency, we do not introduce many complex computations. Furthermore, the extended Merkle hash tree structure enhances efficiency. Finally, we implement a prototype system and evaluate it. The results demonstrate that DataJudge can effectively verify cross-chain data consistency and outperform existing schemes such as DCIV and BeDCV. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Data consistency

来源：评论

学校读者我要写书评

暂无评论

Big social data analysis for quality of MOOC courses: the moderating role of practical examples

引用

Quality and Quantity 2025年 1-33页

作者： Nilashi, Mehrbakhsh Abumalloh, Rabab Ali Zibarzani, Masoumeh Centre for Business Informatics and Industrial Management (CBIIM) UCSI University No. 1 Jalan Menara Gading UCSI Heights Cheras Kuala Lumpur 56000 Malaysia Department of Computer Science and Engineering Qatar University Doha 2713 Qatar Department of Management Faculty of Social Sciences and Economics Alzahra University Tehran Iran

Big social data analysis has played an important role in understanding learners’ preferences in Massive Open Online Courses (MOOCs). This study investigates learners’ satisfaction with MOOCs using big social data analysis and a large-scale survey. The learners’ online reviews are analyzed using a hybrid method based on machine learning techniques and Partial Least Squares Structural Equation Modeling. We adopted a text mining approach, Latent Dirichlet Allocation (LDA), which is widely used in topic modeling. We utilized a large dataset of 6004 online reviews obtained from the learners of Udemy’s online courses. The results from the analysis identified nine topics as aspects of the quality of online courses, namely Course Accessibility, Course Cost, Course Material Quality, Course Organization, Course Resource Availability, Course Usefulness, Instructor Explanation, Instructor Knowledge, and Practical Examples in the Course. The results of the analysis were used to construct a research model, which was evaluated using a large-scale survey (N = 464). The results of the PLS-SEM confirmed the significant impacts of the proposed research factors, except for course organization, on learners’ satisfaction. The results also revealed that the practical examples in the course moderate the relationships between course usefulness, instructor explanation, instructor knowledge, and learners’ satisfaction. The findings of this research contribute to both the theoretical and methodological aspects of discovering and analyzing the influential factors for learners' satisfaction with MOOCs, thereby offering valuable insights for enhancing the overall quality of education in digital learning environments. © The Author(s), under exclusive licence to Springer Nature B.V. 2025.

关键词： Big social data Education quality Learners' satisfaction Learners’ online reviews Online courses Soft computing

来源：评论

学校读者我要写书评

暂无评论

Respiratory Anomaly and Disease Detection using Multi-Level Temporal Convolutional Networks

引用

IEEE Journal of Biomedical and Health informatics 2025年 PP卷 PP页

作者： Le, Kim-Ngoc T. Byun, Gyurin Raza, Syed M. Le, Duc-Tai Choo, Hyunseung The Department of AI Systems Engineering Sungkyunkwan University Suwon Korea Republic of The James Watt School of Engineering University of Glasgow Glasgow United Kingdom The Department of Electrical and Computer Engineering Sungkyunkwan University Suwon Korea Republic of The Department of AI Systems Engineering Department of Electrical and Computer Engineering College of Computing and Informatics Sungkyunkwan University Suwon Korea Republic of

An automated analysis of respiratory sounds using Deep Learning (DL) plays a pivotal role in the early detection of lung diseases. However, current DL methods often examine the spatial and temporal characteristics of respiratory sounds in isolation, which inherently limit their potential. This study proposes a novel DL framework that captures spatial features through convolution operations and exploits the spatiotemporal correlations of these features using temporal convolution networks. The proposed framework incorporates MultiLevel Temporal Convolutional Networks (ML-TCN) to considerably enhance the model accuracy in detecting anomaly breathing cycles and respiratory recordings from lung sound audio. Moreover, a transfer learning technique is also employed to extract semantic features efficiently from limited and imbalanced data in this domain. Thorough experiments on the well-known ICBHI 2017 challenge dataset show that the proposed framework outperforms state-of-the-art methods in both binary and multi-class classification tasks for respiratory anomaly and disease detection. In particular, improvements of up to 2.29% and 2.27% in terms of the Score metric, average sensitivity and specificity, are demonstrated in binary and multi-class anomaly breathing cycle detection tasks, respectively. In respiratory recording classification tasks, the classification accuracy is improved by 2.69% for healthy-unhealthy binary classification and 1.47% for healthy, chronic, and non-chronic diagnosis These results highlight the marked advantage of the ML-TCN over existing techniques, showcasing its potential to drive future innovations in respiratory healthcare technology. © 2025 IEEE.

关键词： Lung cancer

来源：评论

学校读者我要写书评

暂无评论

Simultaneous Communications and Sensing with Hybrid Reconfigurable Intelligent Surfaces

Simultaneous Communications and Sensing with Hybrid Reconfig...

引用

European Conference on Antennas and Propagation, EuCAP

作者： Ioannis Gavras George C. Alexandropoulos Department of Informatics and Telecommunications National and Kapodistrian University of Athens Greece Department of Electrical and Computer Engineering University of Illinois Chicago USA

ISBN: (数字)9788831299107

ISBN: (纸本)9798350366327

Hybrid Reconfigurable Intelligent Surfaces (HRISs) constitute a new paradigm of truly smart metasurfaces with the additional features of signal reception and processing, which have been primarily considered for channel estimation and self-reconfiguration. In this paper, leveraging the simultaneous tunable reflection and signal absorption functionality of HRIS elements, we present a novel framework for the joint design of transmit beamforming and the HRIS parameters with the goal to maximize downlink communications, while simultaneously illuminating an area of interest for guaranteed localization coverage performance. Our simulation results verify the effectiveness of the proposed scheme and showcase the interplay of the various system parameters on the achievable Integrated Sensing and Communications (ISAC) performance.

关键词： Location awareness Simulation Human-robot interaction Europe Reconfigurable intelligent surfaces Integrated sensing and communication Metasurfaces Reflection Reliability Terahertz communications

来源：评论

学校读者我要写书评

暂无评论

Increasing Reliability in Hybrid RF-Optical Networks: A Cross-Band Modulation Design

Increasing Reliability in Hybrid RF-Optical Networks: A Cros...

引用

IEEE Conference on Wireless Communications and Networking

作者： Thrassos K. Oikonomou Sotiris A. Tegos Panagiotis D. Diamantoulakis Department of Electrical and Computer Engineering Aristotle University of Thessaloniki Thessaloniki Greece Department of Applied Informatics University of Macedonia Thessaloniki Greece

ISBN: (数字)9798350368369

ISBN: (纸本)9798350368376

This paper presents a novel hybrid cross-band radio frequency (RF)-optical system that utilizes coherent RF detection with $M$ -QAM modulation and optical intensity modulation and direct detection (IM/DD) to fully utilize the diversity gains of cross-band architectures. By performing joint signal processing in the RF and optical domains, the proposed system achieves significant improvements in reliability, spectral efficiency, and data rates compared to conventional approaches. A key feature of the system is an optimized linear mapping between the RF and optical signals, which ensures seamless signal integration and improved performance. To evaluate the system, we derive the theoretical mutual information (MI) and a tight closed-form approximation for the symbol error probability (SEP) of the proposed modulation scheme. Numerical results validate the theoretical analysis and show that the proposed scheme significantly outperforms state-of-the-art cross-band modulations in terms of MI and SEP. The proposed scheme, with its low-complexity design and superior performance, is a promising candidate for next-generation wireless networks.

关键词： Radio frequency Error probability Spectral efficiency Wireless networks Symbols Reliability theory Optical receivers Adaptive optics Optical signal processing Mutual information

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：