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A Time Series Forecasting Model Based on Machine Learning 6

A Time Series Forecasting Model Based on Machine Learning

引用

6th ieee international conference on Power, Intelligent computing and systems, ICPICS 2024

作者： Cui, Xiangyu School of Ai and Advanced Computing Xi'an Jiaotong-Liverpool University Suzhou215018 China

ISBN: (纸本)9798350374315

Time series forecasting plays a critical role in domains such as finance, environmental science, and health management. Traditional methods like ARIMA and exponential smoothing often fall short in addressing the complexities of modern datasets, especially in modeling non-linear relationships and intricate dependencies. This study introduces the CLT model, an innovative ensemble approach that combines CatBoost, LightGBM, and Transformers to enhance forecasting accuracy and reliability. Employing a dataset of Bitcoin prices from 2014 to 2024, this research involves rigorous data preprocessing, sophisticated feature engineering, and systematic model selection. The CLT model significantly outperforms traditional and single-model approaches by effectively capturing complex dependencies and demonstrating remarkable stability across various validation folds. The results demonstrate that the CLT model outperforms traditional and single-model approaches by effectively capturing complex dependencies and showing remarkable stability across various validation folds. In terms of quantitative metrics, our model achieves an average Root Mean Square Error (RMSE) of 434.54 and an average Mean Absolute Error (MAE) of 374.9, underscoring its superior predictive capabilities. This research underscores the potential of integrating multiple advanced machine learning models to significantly improve the performance of time series forecasting, offering a scalable and efficient solution for high-stakes domains. © 2024 ieee.

关键词： Adversarial machine learning

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Investigation of Different Techniques on Digital Actual Frameworks Toward distributed Denial of Services Attack 15

Investigation of Different Techniques on Digital Actual Fram...

引用

15th international conference on computing Communication and Networking Technologies, ICCCNT 2024

作者： Mandale, Prashant Modi, Shabina Jadhav, Makarand Mohan Khawate, Swati Shailesh Borate, Vishal Kisan Mali, Yogesh Kisan Department of Information Technology Pune India Karmaveer Bhaurao Patil College of Engineering Satara India Dept of Electronics & Telecommunication Engg NBN SinhgadTechnical Institutes Campus Pune India Ajeenkya D Y Patil School of Engineering Dept Electronics and Telecommunication Pune India Dr D Y Patil College of Engineering & Innovation Department of Computer Engineering Talegaon Pune India G.H Raisoni College of Engineering and Management Department of Artificial Intelligence Wagholi Pune India

ISBN: (纸本)9798350370249

The arising development of the Web of Things (IoT) offers different adaptability in information move processes, specialized strategies, and capacity. distributed storage is used by limit of the populace to use their information for simple access. Then again, business utilization of distributed storage additionally expanding. The Cloud control is produced through a brought together organization. The arising development gives an approach to outsider access in normally associated networks. Digital actual frameworks(CPS) are applied in many computerized networks. The investigation of different difficulties in digital actual frameworks and their effect on information security are examined in different academic articles. The arising development of AI innovation offers solid advantages for digital actual frameworks security. The introduced concentrate on led far reaching research on difficulties in digital actual frameworks and planned an idea for future improvement. © 2024 ieee.

关键词： Cloud security

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A Resource Allocation Strategy to Enhance User Experience for IoT Devices in Multi-Access Edge computing 6

A Resource Allocation Strategy to Enhance User Experience fo...

引用

6th international conference on Intelligent computing in Data Sciences, ICDS 2024

作者： Myyara, Marouane Lagnfdi, Oussama Darif, Anouar Farchane, Abderrazak Applications and Information Technology Polydisciplinary Faculty Sultan Moulay Slimane University Laboratory of Innovation in Mathematics Beni Mellal Morocco

ISBN: (纸本)9798350351200

Multi-Access Edge computing (MEC) has become a key paradigm in contemporary network topologies due to the spread of Internet of Things (IoT) devices and the growing need for low-latency, high-performance applications. Reducing latency and enhancing performance, MEC places processing resources closer to end users. IoT applications have a variety of changing needs, therefore effectively allocating these few resources to meet those needs continues to be a major difficulty. This paper addresses this challenge by proposing a novel greedy resource allocation strategy designed to optimize the user ex-perience of IoT devices in MEC environments. Our approach aims to optimally distribute computational resources to meet the requirements of IoT applications while maintaining a high Quality of Experience (QoE). We propose a greedy algorithm that dynamically allocates resources based on real-Time user demands, server computational capabilities, processing delay, and application characteristics. Extensive simulations have demon-strated that our strategy significantly improves key performance indicators such as response time, task failure rate, average QoE, and computational resource utilization rate compared to traditional allocation methods. This research provides a scalable and effective solution to the challenges associated with IoT in MEC, paving the way for more responsive and reliable edge computing systems. © 2024 ieee.

关键词： Computation offloading

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Innovative Data Replication Techniques for Effective Latency Reduction in Mobile Edge computing and IoT 4

Innovative Data Replication Techniques for Effective Latency...

引用

4th ieee international conference on ICT in Business Industry and Government, ICTBIG 2024

作者： Garg, Mohan Aswini, E. Gupta, Mohit Premalatha, K.P. Habbu, Shraddha Kuppuraj, T. Chitkara University Centre of Research Impact and Outcome Punjab Rajpura140417 India Prince Shri Venkateshwara Padmavathy Engineering College Department of Eee Chennai127 India Chitkara University Chitkara Centre for Research and Development Himachal Pradesh 174103 India School of Mangement - Pg Management Karnataka Bangalore India Vishwakarma Institute of Technology Pune India Karpagam Academy of Higher Education Department of Computer Applications Coimbatore641021 India

ISBN: (纸本)9798331518981

Applications like smart cities, driverless vehicles, healthcare, and industrial automation are made possible by the revolutionary grouping of the IoT and MEC. Problems with data management, especially with lowering latency for real-time applications, plague these systems. System efficiency, operational dependability, and user experience can all take a hit when data transmission delays occur. Copying and distributing data over numerous servers or edge nodes enables faster access to information, and novel data replication techniques are being developed to meet this. Because of resource limitations, unpredictable user requests, and changing network circumstances, traditional data replication approaches struggle in the distributed and dynamic settings of MEC and the Internet of Things (IoT). Intelligent data placement algorithms, network-aware replication, and demand-based dynamic replication are examples of advanced approaches. By decreasing latency and avoiding the need for repeated calls to the central cloud, these strategies guarantee that data is always available at the proper location and time. Particularly for latency-sensitive applications like augmented reality, autonomous vehicles, and telemedicine, this paper assesses these methods using simulations and case studies from real-world MEC and IoT deployments, showing that they can considerably decrease latency, improve system responsiveness, and increase data availability. Results show that MEC and IoT data management may be enhanced in terms of performance, scalability, and user happiness, which advances the state of the art. © 2024 ieee.

关键词： Mobile edge computing

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A Counterfactual Ultrasound Anti-Interference Self-Supervised Network for B-mode Ultrasound Tongue Extraction

A Counterfactual Ultrasound Anti-Interference Self-Supervise...

引用

2025 ieee international conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Jia, Yan Cheng, Yuqing Xu, Kele Dou, Yong Qiao, Peng He, Zhouyu National Key Laboratory of Parallel and Distributed Computing College of Computer Science and Technology National University of Defense Technology Changsha China College of Systems Engineering National University of Defense Technology Changsha China

ISBN: (纸本)9798350368741

B-mode ultrasound tongue imaging is a non-invasive and real-time method for visualizing vocal tract deformation. However, accurately extracting the tongue's surface contour remains a significant challenge due to the low signal-to-noise ratio (SNR) and prevalent speckle noise in ultrasound images. Traditional supervised learning models often require large labeled datasets, which are labor-intensive to produce and susceptible to noise interference. To address these limitations, we present a novel Counterfactual Ultrasound Anti-Interference Self-Supervised Network (CUAI-SSN), which integrates self-supervised learning (SSL) with counterfactual data augmentation, progressively disentangles confounding factors, ensuring that the model generalizes well across varied ultrasound conditions. Our approach leverages causal reasoning to decouple noise from relevant features, enabling the model to learn robust representations that focus on essential tongue structures. By generating counterfactual image-label pairs, our method introduces alternative, noise-independent scenarios that enhance model training. Furthermore, we introduce attention mechanisms to enhance the network's ability to capture fine-grained details even in noisy conditions. Extensive experiments on real ultrasound tongue images demonstrate that CUAI-SSN outperforms existing methods, setting a new benchmark for automated contour extraction in ultrasound tongue imaging. Our code is publicly available at https://***/inexhaustible419/CounterfactualultrasoundAI. © 2025 ieee.

关键词： contour extraction counterfactual inference self-supervised learning speech production ultrasound image

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Routing Drones Being Aware of Wind Conditions: a Case Study 17

Routing Drones Being Aware of Wind Conditions: a Case Study

引用

17th Annual ieee international conference on distributed computing in Sensor systems (ieee DCOSS)

作者： Palazzetti, Lorenzo Univ Florence Dept Comp Sci Florence Italy

ISBN: (纸本)9781665439299

In this paper, we investigate the manner in which energy consumption in drone deliveries is affected by windy environmental conditions. We know in fact that the energy consumption of the drone will depend on the static and dynamic parameters of the scenario where it moves and according to these it decides, during the mission, to detour from the originally planned path to take advantage of the wind changes. In order to validate this, we simulate possible deliveries among fixed destinations relying on a real data-set of recorded winds obtained from different weather stations in Corsica, France. For our analysis, we will mainly concentrate on the evaluation of the delivery scheme proposed in the literature, where completing a delivery means finding a cycle for the drone that is feasible, i.e., that can be completed with the available energy autonomy of the drone.

关键词： Wind Energy consumption Routing Sensor systems Batteries distributed computing Drones

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An Orderly Regulation Method of Grid Energy Storage Capacity Under distributed New Energy Access 4

An Orderly Regulation Method of Grid Energy Storage Capacity...

引用

4th international conference on Energy Engineering and Power systems, EEPS 2024

作者： Xiong, Xiaocui Chen, Songling Zhang, Xiaoming Zhang, Mingnian State Grid Huanggang Electric Power Supply Company Hubei Huanggang438000 China

ISBN: (纸本)9798350366914

The method of regulating energy storage capacity in the power grid mainly uses the normal distribution to generate the optimal solution for ordered regulation, which is easily affected by the convergence speed of energy storage load, resulting in poor regulation of energy storage capacity. Therefore, it is necessary to consider distributed new energy access and design a new method for orderly regulation of energy storage capacity in the power grid. We have designed a decision-making process for the orderly regulation of energy storage in the power grid and considered the integration of distributed new energy to generate an orderly regulation strategy for energy storage in the power grid, thereby achieving disorderly regulation of energy storage in the power grid. The experimental results show that the electricity shortage rate, leveling cost, and excess electricity index are low, while the renewable energy capacity index is high. This proves that the designed orderly regulation method for power grid energy storage capacity has a good regulation effect and certain economic value, which helps to improve the comprehensive energy storage efficiency of the power grid and reduce power supply and distribution losses. © 2024 ieee.

关键词： Charge storage

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Overview of distributed Task Scheduling 12

Overview of Distributed Task Scheduling

引用

12th international conference of Information and Communication Technology, ICTech 2023

作者： Guo, Ruifeng Wang, Hongliang Li, Youkang Xiu, Pengfei Shenyang Institute of Computing Technology Chinese Academy of Sciences University of Chinese Academy of Sciences Shenyang China

ISBN: (纸本)9798350332919

Under the background of big data how to more efficiently solve the problem of concurrency has always been a hot research problem. In this case, the distributed scheduling technology produces many new ideas and new structures. In this context, this paper introduces the task architecture and three scheduling techniques in big data environment. © 2023 ieee.

关键词： Big data

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Cross Network Layer Cognitive Service Orchestration in Edge computing systems 8

Cross Network Layer Cognitive Service Orchestration in Edge ...

引用

8th ieee international conference on Edge computing and Communications, EDGE 2024

作者： Rafiee, Milad Taherkordi, Amir Alay, Ozgu University of Oslo Department of Informatics Oslo Norway Karlstad University Computer Science Department Sweden

ISBN: (纸本)9798350368499

The edge architecture introduces challenges due to the presence of heterogeneous constrained devices and the dynamic nature of the network. Dynamic service orchestration is essential for efficient utilization of network resources in this context. Service orchestration plays a critical role in 5G networks in automating end-to-end service deployment and operations, and 5G network slicing. However, existing approaches on service orchestration primarily focus on resource availability, lacking a comprehensive understanding of network conditions-cross network layer orchestration. As a result, providing efficient service orchestration for delay-sensitive services remains a significant research area. To address this gap, this paper proposes a cognitive service orchestration framework that leverages not only application-level resource demands, but also the real-time status of the network infrastructure. This cognitive framework incorporates Reinforcement Learning, enabling it to dynamically interact with the network environment and continuously update policies for intelligent decision-making in complex 5G networks. The efficacy of the proposed framework is evaluated using an object detection application in smart cities. The evaluation results show that the proposed framework achieves a significant reduction in latency as compared to OpenELB, with a remarkable 58% decrease, which highlights its efficiency and effectiveness in meeting the requirements of delay-sensitive services. © 2024 ieee.

关键词： 5G mobile communication systems

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New Measurement Method of distributed Power Generation 4

New Measurement Method of Distributed Power Generation

引用

4th ieee international conference on Power, Intelligent computing and systems, ICPICS 2022

作者： Kang, Yu Zhao, Xiang Gao, Wei Shi, Boyu Wang, Jiaying Jin, Dai State Grid Fushun Electric Power Supply Company Liaoning Province Fushun China

ISBN: (数字)9781665467735

ISBN: (纸本)9781665467735

With the rapid growth of load and the gradual implementation of the power market, the traditional centralized power generation gradually exposes the disadvantages such as large investment, long construction cycle, inflexible adjustment and high accident rate. To solve these problems, distributed power generation has received wide attention. Based on the situation of distributed power generation user in Liaoning, the cause of why distributed power generation accuracy is low is found out, the influence factors of power generation accuracy are demonstrated and the measurement method of low voltage distributed photovoltaic power generation are further analyzed. The main reasons of distributed power generation accuracy are the a long time of troubleshooting cycle on field, the data transmission rate is slow, and the marketing system file lags behind. Aiming at these problems, the marketing system 'triple' regulatory function is added, the relay communication technology is applied, and file key link 'three synchronous' control mechanism is established to make a new measurement method. After a lot of practical application, the photovoltaic power generation accuracy is successfully improved, thus the distributed power generation accuracy is greatly improved. © 2022 ieee.

关键词： Optical fibers

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