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检索条件"任意字段=25th International Conference on Intelligent Data Engineering and Automated Learning"

共 3597 条记录，以下是291-300 订阅

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A data Science Solution to Integrate Weather data for Energy Consumption Analysis 25

A Data Science Solution to Integrate Weather Data for Energy...

引用

25th IEEE international conference on Information Reuse and Integration for data Science (IEEE IRI)

作者： Mai, thanh Huy Daniel Leung, Carson K. Lu, Junyi Giesbrecht, Nathaniel Hnylycia, Owen A. Univ Manitoba Dept Comp Sci Winnipeg MB Canada

ISBN: (纸本)9798350351194;9798350351187

Energy efficiency and identification of consistent energy consumption patterns are crucial for reliability of a modern power grid. In this paper, we present a data science solution that integrates weather data with historical energy consumption data for energy consumption analysis. Consequently, the solution predicts temporal energy consumption patterns via techniques like frequent pattern mining, traditional machine learning, and deep learning. Our solution integrates meteorological and environmental conditions over time series, analyzes them, forecasts energy consumption, and examines how weather conditions affect energy usage variation. Evaluation results on a real-world dataset show that our solution identifies several distinct frequent patterns with frequent pattern mining, and it reveals a significant relationship between irradiance and energy consumption, as well as a positive correlation between temperature and energy usage. Moreover, our solution predicts and compares energy consumption for a specific year using linear regression, decision tree, random forest, and gradient boosting models with daily weather data. Additionally, we applied a long short-term memory (LSTM) model to analyze energy consumption as time-series data, uncovering patterns based on given time steps. these results demonstrate the practicality of our data science solution for energy consumption analysis.

关键词： information reuse information integration data science energy weather meteorological data environmental data binning machine learning random forest gradient boosting deep learning long short-term memory (LSTM) data mining frequent pattern mining

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Application of Blockchain Technology in Student learning data Security and Privacy Protection 6

Application of Blockchain Technology in Student Learning Dat...

引用

6th international conference on Artificial Intelligence and Advanced Manufacturing, AIAM 2024

作者： Liu, Yan Office of Development Planning Shenyang University Shenyang110044 China

ISBN: (纸本)9781837242672

the rapid digital transformation across various sectors has intensified the need for secure, automated management systems to handle sensitive data with integrity and privacy. Traditional systems often struggle to securely manage and authenticate large volumes of data, leaving it vulnerable to unauthorized access and breaches. this paper presents an intelligent information management system based on blockchain technology, designed to enhance data security, integrity, and automated access control through decentralized storage, cryptographic algorithms, and smart contracts. the proposed system leverages blockchain’s decentralized ledger to offer a tamper-resistant, distributed storage solution, minimizing risks of data tampering and enhancing transparency. Smart contracts add automation to access control and data verification processes, reinforcing system robustness and reducing the potential for human error. this paper’s contributions include developing a blockchain-based intelligent data management framework, evaluating its efficacy in real-world scenarios, and demonstrating its advantages over traditional centralized approaches in terms of security, privacy, and operational automation. this study provides a foundational approach to enhance the security and autonomy of digital information systems, enabling more resilient data management infrastructures. © the Institution of engineering & Technology 2024.

关键词： data integrity

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Vessel behaviour classification from AIS without geographical biases 25

Vessel behaviour classification from AIS without geographica...

引用

IEEE 25th international conference on intelligent Transportation Systems (ITSC)

作者： Sturgis, Raphael Emiya, Valentin Couetoux, Basile Garreau, Pierre Aix Marseille Univ Univ Toulon LIS CNRS Marseille France Searoutes SAS Marseille France

ISBN: (数字)9781665468800

ISBN: (纸本)9781665468800

the automatic detection of vessel behaviours from Automatic Identification System (AIS) data is a challenging aspect of designing intelligent systems and aiding maritime situational awareness. the development of such systems remains limited to some activities like fishing, and by geographical biases that prevent systems to generalise to other areas than that used for training. To contribute to these questions, we investigate how to treat raw data or engineered features so that they do not convey such biases at training time and we propose methods for point-wise behaviour detection in the context of container vessels with four target behaviours. Several systems are studied, with raw data or engineered features as inputs, followed by shallow or deep learning classifiers. While good performances are obtained by several of them, we observe that a decision tree classifier with engineered features outperforms an LSTM in areas where no labelled data is available for training.

关键词： Training Veins Containers Transformers data models Trajectory Decision trees

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Reduced- and Full-order Concurrent learning Extended State Observers for Fully Adaptive Anti-disturbance Surge Speed Tracking Control of Autonomous Surface Vehicles 25

Reduced- and Full-order Concurrent Learning Extended State O...

引用

IEEE 25th international conference on intelligent Transportation Systems (ITSC)

作者： Cui, Kai Li, Huijuan Jing, Yun Liu, Lu Wang, Dan Dalian Maritime Univ Sch Marine Elect Engn Dalian 116026 Peoples R China

ISBN: (数字)9781665468800

ISBN: (纸本)9781665468800

this paper considers surge speed tracking control of an autonomous surface vehicle (ASV) model subject to fully unknown internal dynamic, external disturbance, and unknown control input gain. A fully adaptive anti-disturbance control method is proposed for ASV without using any model parameters. Specifically, reduced- and full-order data-driven concurrent learning extended state observers (CLESOs) by utilizing real-time and historical data are designed to estimate the unknown model parameters of the ASV and ensure the convergence of the estimation without requiring persistent excitation. then, an anti-disturbance surge speed tracking control law is designed. the stability of the data-driven CLESO and surge speed tracking control law are analyzed by using input-state stability (ISS) theory. Simulation results validate the effectiveness of the proposed data-driven CLESO for surge speed tracking of the autonomous surface vehicle with fully unknown dynamic model.

关键词： Adaptation models Uncertainty Simulation Observers Stability analysis Real-time systems Surges

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Enhancing efficiency in recurrent reinforcement learning for automated data-driven investment 28th

Enhancing efficiency in recurrent reinforcement learning for...

引用

28th international conference on Knowledge Based and intelligent information and engineering Systems, KES 2024

作者： Witkowski, Tomasz University of Economics Katowice Poland

In this study, we introduce a novel approach for training Recurrent Reinforcement learning (RRL) agents for automated, data-driven investment tasks. the primary goal of this paper is to provide a Proof of Concept for the approach. Our method has demonstrated the ability to achieve Sharpe Ratio (SR) values comparable to those of classical online RRL while accelerating the training process by a factor of 24.6. this training method is model-agnostic and input-agnostic, enhancing its flexibility with complex model architectures. However, a significant limitation is the assumption that the agent's actions influence rewards but do not affect the rest of the environment. this assumption may hinder generalisation to some non-financial tasks. this study advances the field of RRL by improving efficiency and facilitating more complex experimental schemes. © 2024 the Authors.

关键词： Deep reinforcement learning

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Domain Adversarial SegFormer 25

Domain Adversarial SegFormer

引用

25th international conference on Digital Image Computing: Techniques and Applications, DICTA 2024

作者： Bergemann, Moritz Singha, Tanmay Pham, Duc-Son Krishna, Aneesh School of Electrical Engineering Computing and Mathematical Sciences Curtin University PerthWA Australia

ISBN: (纸本)9798350379037

Unsupervised domain adaptation (UDA) is a pivotal challenge in machine learning, aiming to transfer knowledge from a labeled source domain to an unlabeled target domain. Despite significant advancements, many UDA methods struggle with real-world applications due to domain discrepancies and the lack of labeled data in the target domain. In this paper, we propose a novel UDA framework that leverages domain-adversarial training and efficient semantic segmentation models to bridge this gap. Our approach involves two key innovations. First, we introduce a domain-adversarial training method specifically designed for segmentation transformers, uncovering new complexities and flexibilities inherent in transformer architectures. Second, we develop an efficient semantic segmentation model tailored for scenarios with limited computational resources and training data, employing an auto-labeling-like Pseudo-Teacher mechanism. Extensive experiments demonstrate that our model achieves a mean Intersection over Union (mIoU) of 61.18% on the challenging GTA → CS, representing 91.92% of oracle performance, while significantly reducing computational requirements. these results highlight the potential of our framework to improve the performance in UDA, making it a viable solution for practical applications. © 2024 IEEE.

关键词： Adversarial machine learning

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Mobility-Aware Computation Offloading and Blockchain-based Handover in Vehicular Edge Computing Networks 25

Mobility-Aware Computation Offloading and Blockchain-based H...

引用

IEEE 25th international conference on intelligent Transportation Systems (ITSC)

作者： Lang, Ping Tian, Daxin Duan, Xuting Zhou, Jianshan Beihang Univ Beijing Adv Innovat Ctr Big Data & Brain Comp Sch Transportat Sci & Engn Beijing Key Lab Cooperat Vehicle Infrastruct Syst Beijing 100191 Peoples R China

ISBN: (数字)9781665468800

ISBN: (纸本)9781665468800

As a novel computing paradigm, vehicular edge computing (VEC) provides additional computing capacity to connected automated vehicles by deploying resources of computing and storage on base stations or roadside units. Vehicles migrate their tasks to VEC servers for execution through computation offloading (CO) to improve processing efficiency. However, the high-speed movement of vehicles causes handover among multiple VEC servers while raising the security issue of data sharing. In this paper, we design a mobility-aware CO and blockchain-based handover architecture to reduce the latency and improve the security of vehicular CO. A CO decision problem with models of mobility, CO, and blockchain-based handover is proposed to optimize the offloading decision of vehicles. Further, we transform this optimization into a Markov decision process (MDP) and construct a multi-agent deep reinforcement learning (MADRL) algorithm to solve it. the effectiveness and performance of the proposed method are verified by simulations.

关键词： Computational modeling Simulation Vehicular ad hoc networks Computer architecture Transforms Handover Servers

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intelligent Sorting Method for Smartphones Based on Agents 6

Intelligent Sorting Method for Smartphones Based on Agents

引用

6th international conference on Robotics and Computer Vision, ICRCV 2024

作者： Hu, Xuejun Li, Jie Zheng, Hangbin Zhang, Gaohua School of Mechanical Engineering Donghua University Shanghai China School of Mechanical Engineering Zhejiang University Hangzhou China

ISBN: (纸本)9798331527426

automated disassembly of discarded smartphones is a critical step in electronic waste processing, yet existing automated systems often lack adaptability when handling various types of discarded smartphones. To address the issue of poor adaptability in sorting processes, this study proposes an agent-based sorting method for discarded smartphones. this method leverages large language model(LLM) for natural language understanding and generation, enhancing user-friendliness. It integrates information from knowledge graphs and historical sorting records to build a comprehensive knowledge base. By combining intelligent agents that track historical data and utilize external knowledge bases, the proposed method improves adaptability for sorting multiple phone types. this allows the system to dynamically formulate sorting strategies based on the specific characteristics of discarded smartphones, significantly enhancing the adaptability of disassembly lines. Furthermore, the mobile phone classification module draws inspiration from facial recognition model architectures. A deep learning-based classification method for discarded smartphones is proposed, achieving highly adaptable classification functionality for various phone types. Experimental results demonstrate that the proposed classification algorithm maintains high accuracy in recognizing phone models not included in the training set. © 2024 IEEE.

关键词： Smartphones

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HEADS: Hybrid Ensemble Anomaly Detection System for Internet-of-things Networks 25th

HEADS: Hybrid Ensemble Anomaly Detection System for Internet...

引用

25th international conference on engineering Applications of Neural Networks (EANN)

作者： Ahmad, Zeeshan Petrovski, Andrei Arifeen, Murshedul Khan, Adnan Shahid Shah, Syed Aziz Robert Gordon Univ Natl Subsea Ctr Aberdeen Scotland Univ Malaysia Sarawak Fac CS & IT Kota Samarahan Sarawak Malaysia Coventry Univ Res Ctr Intelligent Healthcare Coventry W Midlands England

ISBN: (纸本)9783031624940;9783031624957

the rapid expansion of Internet-of-things (IoT) devices has revolutionized connectivity, facilitating the exchange of extensive data within IoT networks via the traditional internet. However, this innovation has also increased security concerns due to the presence of sensitive nature of data exchanged within IoT networks. To address these concerns, network-based anomaly detection systems play a crucial role in ensuring the security of IoT networks through continuous network traffic monitoring. However, despite significant efforts from researchers, these detection systems still suffer from lower accuracy in detecting new anomalies and often generate high false alarms. To this end, this study proposes an efficient Hybrid Ensemble learning-based Anomaly Detection System (HEADS) to secure an IoT network from all types of anomalies. the proposed solution is based on a novel hybrid approach to improve the voting strategy for ensemble learning. the ensemble prediction is assisted by a Random Forest-based model obtained through the best F1 score for each label through dataset subset selection. the efficiency of HEADS is evaluated using the publicly available CICIoT2023 dataset. the evaluation results demonstrate an F1 score of 99.75% and a false alarm rate of 0.038%. these observations signify an average 4% improvement in the F1 score while a reduction of 0.7% in the false alarm rate comparing other anomaly detection-based strategies.

关键词： Anomaly Detection System Ensemble-based learning Gradient Boosting Machine Internet-of-things Machine learning

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Support Vector Based Anomaly Detection in Federated learning 25th

Support Vector Based Anomaly Detection in Federated Learning

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25th international conference on engineering Applications of Neural Networks (EANN)

作者： Frasson, Massimo Malchiodi, Dario Univ Milan Dept Comp Sci Via Celoria 18 I-20133 Milan Italy

ISBN: (纸本)9783031624940;9783031624957

Anomaly detection plays a crucial role in various domains. However, traditional centralized approaches often encounter challenges related to data privacy. In this context, Federated learning emerges as a promising solution. this work introduces two algorithms that leverage Support Vector Machines for anomaly detection in a federated setting. In comparison with the Neural Networks typically used in this field, these algorithms emerge as potential alternatives, as they can operate with small datasets and incur lower computational costs. the algorithms are tested in various configurations, yielding promising initial results Specifically, we attain comparable results to the centralized counterpart when the distributed system simulates a centralized setting. A trade-off emerges between split bias and client fraction, indicating that higher client fractions are necessary for optimal performance in scenarios with high bias.

关键词： Anomaly Detection Federated learning SVM

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