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检索条件"任意字段=2023 International Conference on Algorithms, Computing and Data Processing, ACDP 2023"

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An LSTM-Based Outlier Detection Approach for IoT Sensor data in Hierarchical Edge computing 31

An LSTM-Based Outlier Detection Approach for IoT Sensor Data...

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31st international conference on Software, Telecommunications and Computer Networks, SoftCOM 2023

作者： Bibi, Somia Titouna, Chafiq Titouna, Faiza Nait-Abdesselam, Farid University of Batna 2 Department of Computer Science Algeria University Gustave Eiffel LIGM ESIEE Paris France University of Missouri Kansas City United States

ISBN: (纸本)9798350301076

Outlier detection in sensor data has recently gained significant recognition, particularly with the proliferation of wireless sensor networks (WSNs) and the Internet of Things (IoT). Several challenges face outlier detection in WSNs and IoTs, including sensor nodes' limited energy and processing capabilities and high communication costs. This paper presents a novel deep learning-based outlier detection method for IoT sensor data in hierarchical edge computing. First, we proposed a hierarchical edge computing framework to save energy, provide load balance, and low latency data processing at sensor nodes. Then, we designed an outlier detection algorithm that resides on each edge server. The proposed algorithm consists of two modules: A predictor model and an outlier detector. The predictor module uses Long Short-Term Memory networks (LSTM) to forecast the subsequent data measurements of sensor nodes. The predicted values are then passed to an outlier detector module, which decides whether a data measurement is an outlier. The proposed method is evaluated on twelve different datasets. The simulation results provided by our proposal are promising in terms of a set of metrics. © 2023 University of Split, FESB.

关键词： Long short-term memory

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Advances in federal learning technology research 3

Advances in federal learning technology research

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3rd international conference on Machine Learning and Computer Application, ICMLCA 2022

作者： Jin, Houji Department of Computer Changshu Institute of Technology Changshu China

ISBN: (纸本)9781510664814

Along with the boom in data rights and the increasing number of responses to protect data privacy, scholars have begun to study the "data isolation" phenomenon while ensuring data privacy and security. Users upload source data to a high computing power cloud server for centralised training required by traditional machine learning algorithms, which leads to uncontrolled data theft and sensitive data leakage problems. By removing data barriers and collaborating on modelling utilizing data from several clients, federated learning has become a successful remedy for this issue. In this presentation, the history of federated learning is discussed, federated learning's precise defining categories is clarified, and its technological evolution is examined. How well training frameworks for federated learning, including the FedAvg, MOON, and Fedprox algorithms perform is assessed too. Finally, the current problems of federated learning algorithms and the future directions of development are also illustrated objectively. © 2023 SPIE.

关键词： Sensitive data

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international conference on Artificial Intelligence for Innovations in Healthcare Industries, ICAIIHI 2023

International Conference on Artificial Intelligence for Inno...

引用

1st international conference on Artificial Intelligence for Innovations in Healthcare Industries, ICAIIHI 2023

ISBN: (纸本)9798350330915

The proceedings contain 194 papers. The topics discussed include: health interpretation of COVID-19 patients using artificial intelligence;application of multi-sensor based audio wearable device in sleep analysis, wellness tracking and prediction;cloud computing based POC diagnostic device: rapid infectious disease testing by data analytics;isolation forest anomaly detection in vital sign monitoring for healthcare;computer vision algorithms for surgical assistance in cloud-based telemedicine;graph convolutional networks for disease network analysis in healthcare;patient monitoring and data analytics with fog computing in healthcare IoT;and enhancing operational efficiency in healthcare with AI-powered management.

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Parallel computing Approach for Preprocessing AIS data in Transshipment Feature Extraction

Parallel Computing Approach for Preprocessing AIS Data in Tr...

引用

2023 international conference on Smart-Green Technology in Electrical and Information Systems, ICSGTEIS 2023

作者： Setiawan, Widyadi Linawati, Linawati Made Oka Widyantara, I. Made Wiharta, Dewa Mertasana, Putu Arya Andriati Asri, Sri Udayana University Doctoral Program of Engineering Science Denpasar Indonesia Udayana University Electrical Engineering Departement Denpasar Indonesia Politeknik Negeri Bali Electrical Engineering Departement Denpasar Indonesia

ISBN: (纸本)9798350382822

AIS (Automatic Identification System) data is an important data source in the shipping and logistics industry to track and monitor ships in real-time. Extraction of transshipment features from AIS data is essential in understanding the pattern and characteristics of transshipment activity at a particular port or place. This study discusses an efficient AIS data preprocessing framework to prepare the transshipment feature extraction. The preprocessing steps include data collection, data cleansing, trajectory extraction, trajectory cleaning, and overlapping trajectory extraction. One of the crucial approaches used in this study is the application of parallel computing. Parallel computing allows us to execute independent data processing tasks at the same time simultaneously. In AIS data preprocessing, the parallel approach allows us to clean, populate, and process data from many vessels in less time. To test the applicability, we created a final comparison for different types of ships based on its AIS data for Indonesia from November 2022 to April 2023. Using parallel computing to process AIS data makes data processing more efficient, especially for large and complex AIS data. This efficiency accelerates data preparation before transshipment feature extraction. The results show that the framework can accelerate runtime and improve trajectory quality. © 2023 IEEE.

关键词： Feature extraction

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Empowering Precision in Financial News: A Revolution in Editorial Classification through Cutting-Edge Natural Language processing 7

Empowering Precision in Financial News: A Revolution in Edit...

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7th international conference on Natural Language processing and Information Retrieval (NLPIR)

作者： Khunti, Shravan Kureshi, Faizah Ankola, Rahulkumar Kumar, Prikshit Chandigarh Univ Ajitgarh India Vellore Inst Technol Chennai Tamil Nadu India JC Bose Univ Sci & Technol YMCA Faridabad India

ISBN: (纸本)9798400709227

Amidst the continuous stream of diverse data on the Bloomberg terminal, distinguishing editorial news articles from regular articles is critical to aid its users in tailoring their news experience and further analyzing the impact of news on global financial markets. In this paper, we propose various Artificial Intelligence and Neural Networks models regarding developing an editorial classifier that generalizes well across various news sources. The training set comprises articles published by news sources from the US. We compare the performance of these models using the Aggregate F1-measure and Binary Classification Performance Metric as evaluation metrics to account for the presence of class imbalance in our data. Further, we gauged our models by comparing their performance on a Zero-Shot dataset which comprised 1805 news articles published by Metro Winnipeg, a Canadian news source.

关键词： Natural Language processing (NLP) Editorial Classification Financial News Analysis Machine Learning in Finance Text Classification algorithms Precision in Content Curation data Stream Analysis Bloomberg Terminal Integration User Experience in News Consumption

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An Energy-Efficient Metaheuristic for Offloading Security-Critical Tasks in Mobile Edge computing with Integrated DPUs 3

An Energy-Efficient Metaheuristic for Offloading Security-Cr...

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3rd IEEE international conference on Intelligent Technology and Embedded Systems, ICITES 2023

作者： Hong, Shaoying Zhu, Jiayin Yuan, Yiqi Sun, Jin School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing210094 China International Joint Audit Institute Nanjing Audit Technology Nanjing211815 China

ISBN: (纸本)9798350343755

In mobile edge computing (MEC) paradigm, for the security-critical computation tasks offloaded from the mobile device, the MEC server needs to decrypt the encrypted task data before task execution, leading to heavy computation load on the CPUs of the MEC server. The integration of data processing unit (DPU) into the MEC server can release CPUs from data decrpytion for the purpose of reducing computation and energy overheads. This paper studies a task offloading problem in a MEC system with integrated DPUs, which aims at reducing the total energy consumption under the deadline constraint on task completion time. We formulate the studied problem as an combinatorial optimization model and propose a group mapping-based cuckoo search (GMCS) metaheuristic algorithm to explore high-quality energy-efficient solutions to the formulated model. The proposed GMCS introduces a group mapping operator and a greedy-based task offloading strategy for converting cuckoo individuals into offloading solutions and evaluating the objective values of the converted solutions, respectively. We provide a theoretical analysis to justify that the mapping operator can improve the diversity of the converted solutions and in turn the metaheuristic’s searching capability. We create various testing instances for a MEC system equipped with DPUs to demonstrate the proposed GMCS produces high-quality solutions to the studied offloading problem, with reduced energy consumption compared with baseline algorithms. © 2023 IEEE.

关键词： Mobile edge computing

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Large-Scale data processing for Information Retrieval Applications 23

Large-Scale Data Processing for Information Retrieval Applic...

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46th international ACM SIGIR conference on Research and Development in Information Retrieval (SIGIR)

作者： Khandel, Pooya Univ Amsterdam Amsterdam Netherlands

ISBN: (纸本)9781450394086

Developing Information Retrieval (IR) applications such as search engines and recommendation systems require training of models that are growing in complexity and size with immense collections of data that contain multiple dimensions (documents/items text, user profiles, and interactions). Much of the research in IR concentrates on improving the performance of ranking models;however, given the high training time and high computational resources required to improve the performance by designing new models, it is crucial to address efficiency aspects of the design and deployment of IR applications at large-scale. In my thesis, I aim to improve the training efficiency of IR applications and speed up the development phase of new models, by applying dataset distillation approaches to reduce the dataset size while preserving the ranking quality and employing efficient High-Performance computing (HPC) solutions to increase the processing speed.

关键词： distillation efficiency information retrieval large-scale processing

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Flatness Loss for Image Dehazing

Flatness Loss for Image Dehazing

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2023 international conference on algorithms, High Performance computing, and Artificial Intelligence, AHPCAI 2023

作者： Zhang, Chenyu Ye, Qihong Chen, Hongming Wang, Xiaoshuang School of Information Engineering Zhejiang Ocean University Zhoushan China Key Laboratory of Oceanographic Big Data Mining Application of Zhejiang Province China

ISBN: (纸本)9781510671881

In recent years, significant progress has been made in image dehazing, but most dehazing convolutional neural networks only learn from hazy images to the corresponding feature maps of clean images, ignoring the details of the images. In this paper, a new flatness loss function is proposed for single-image dehazing, thereby improving the overall effect of dehazing results. This loss function allows the model to focus on the texture features of hazy images and clean images, and supervises edge information by reducing the flatness difference between clean pixels and blurred pixels. The experiments on the benchmark dataset using the flatness loss function on the single-image dehazing model show that this method can effectively improve the quantitative performance of the model. © 2023 SPIE. All rights reserved.

关键词： Pixels

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Research on data Transmission Encryption Algorithm of a Wireless Sensor Network in Cloud Storage 13th

Research on Data Transmission Encryption Algorithm of a Wire...

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13th international conference on Frontier computing, FC 2023

作者： Zhou, Xianchun School of Information and Intelligence Engineering Sanya University Hainan Analysis and Application Engineering Research Center of Software Gene Virus Hainan Sanya572022 China

ISBN: (纸本)9789819992980

The role of encryption algorithms in wireless sensor network data is very important, but there is a problem with insecure data transmission. The ordinary transmission method cannot problem of multi-dimensional, and the security is low. Therefore, this paper proposes a a network data transmission model. First, according to the wireless transmission plan, wireless transmission data classification, according to the wireless transmission standard for transmission data selection. Standardize the processing of data transmitted wirelessly. Then, according to the classification of wireless transmission data, a collection of wireless transmission data is formed, and the wireless transmission data is iteratively analyzed. MATLAB simulation shows that under the condition that the wireless transmission plan is unchanged, the cloud storage method is effective for the security of transmitted data. The warning time is better than the standard wireless transmission method. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2024.

关键词： Cloud storage

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Simulation of Urban Carbon Emission Model Based on Big data Fusion Machine Learning Algorithm 23

Simulation of Urban Carbon Emission Model Based on Big Data ...

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4th international conference on computing, Networks and Internet of Things, CNIOT 2023

作者： Li, Zhuo Deng, Guo-Qun Hunan Automotive Engineering Vocational College 79 Wisdom Road Yunlong Demonstration Zone Hunan Province Zhuzhou City China

ISBN: (纸本)9798400700705

According to the characteristics of large throughput and strong effectiveness of carbon emissions information in urban transportation, people can manage this type of information more effectively. People have studied a method of processing, providing and service realization of carbon emissions data in urban transportation that adopts cloud computing service mode and can be expanded by using the information technology structure of big data platform, and implemented a simulation prototype system. This paper analyzes the massive urban traffic carbon emissions information through the distributed file data processing of the big data platform. The algorithm has higher efficiency and strong execution stability, and has more obvious advantages compared with the conventional methods. The experimental results show that this model effectively improves the effect of urban carbon emission data processing. © 2023 ACM.

关键词： Big data

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