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

共 6952 条记录，以下是341-350 订阅

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Exploring the Potential Benefits of Machine Learning for Optimizing Real-Time data Analysis 3

Exploring the Potential Benefits of Machine Learning for Opt...

引用

3rd international conference on Smart Generation computing, Communication and Networking, SMART GENCON 2023

作者： Kumar, Ravindra Sakhare, Nitin N. Thangarasu, N. Senthilkumar, K.K. Wawage, Pawan S. Rengarajan, A. Vivekananda Global University Department of Civil Engineering Jaipur India Vishwakarma Institute of Information Technology Department of Computer Engineering Pune India Karpagam Academy of Higher Education Department of Computer Science Coimbatore India Prince Shri Venkateshwara Padmavathy Engineering College Department of Electronics and Communication Engineering Chennai India Vishwakarma Institute of Information Technology Department of Information Technology Pune India Department of Computer Science and Information Technology Karnataka Bangalore India

ISBN: (纸本)9798350319125

This studies paper examines the capability blessings of implementing device getting to know algorithms to optimize real-time information evaluation. The subjects mentioned consist of techniques for predicting traits in actual-time information, the ability of gadget studying algorithms to discover anomalies and outliers in datasets, and how using records mining and herbal language processing (NLP) can improve the accuracy of real-time facts insights. It also evaluates the demanding situations that can arise while applying machine gaining knowledge of algorithms to actual-time statistics, along with scalability problems and overfitting of records. In end, the authors argue that system studying has notable potential to enhance the accuracy and timing of actual-time facts analysis, and that they provide steerage on choosing and enforcing appropriate gadget getting to know algorithms. © 2023 IEEE.

关键词： Machine learning

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An empirical review of Machine Learning models for Energy Optimizations in IoT Networks 14

An empirical review of Machine Learning models for Energy Op...

引用

14th international conference on computing Communication and Networking Technologies, ICCCNT 2023

作者： Charde, Pallavi Lonkar, Bhupesh B. Faculty of Engineering and Technology Department of Artificial Intelligence and Data Science Wardha Sawangi India Datta Meghe Institute of Engineering Technology & Research Department of Information Technology Wardha Sawangi India

ISBN: (纸本)9798350335095

The Internet of Things (IoT) has transformed how users interact with their devices and enabled the accumulation of enormous quantities of data from various sources. There has been an increase in energy consumption due to the expanding use of IoT devices, resulting in environmental concerns and higher operational costs. The use of Machine Learning (ML) algorithms to optimize energy consumption in IoT networks is widespread for different scenarios. This paper empirically analyzes the most widely used machine learning models for energy optimization in IoT networks. The researchers evaluate the efficacy of various ML models regarding energy efficiency, precision, and scalability. The review includes a summary of the most recent research in this field and a comparison of different algorithms, including Decision Trees, Random Forests, Support Vector Machines, Neural Networks, and Deep Learning methods. In addition, this paper discusses the difficulties and limitations of ML-based energy optimization in IoT networks, including data acquisition and processing, model selection, and scalability levels. Finally, this paper presents recommendations for future research and prospective machine-learning applications in energy optimization for IoT networks. This review emphasizes the potential of machine learning (ML) models to reduce energy consumption in IoT networks. It provides researchers and practitioners with insight into this field under real-time scenarios. © 2023 IEEE.

关键词： Internet of things

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Economical Electricity Supplement for Mobile Collectors in Large-Scale Multitask WSNs 21

Economical Electricity Supplement for Mobile Collectors in L...

引用

21st IEEE international Symposium on Parallel and Distributed processing with Applications, 13th IEEE international conference on Big data and Cloud computing, 16th IEEE international conference on Social computing and Networking and 13th international conference on Sustainable computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2023

作者： Zhai, Xin Xu, Lijie Zhang, Jialei Wang, Kun Xu, Jia Xu, Bei Nanjing University of Posts and Telecommunications Jiangsu Key Laboratory of Big Data Security and Intelligent Processing Nanjing210023 China

ISBN: (纸本)9798350329223

For large-scale multitask wireless sensor networks (LSM-WSNs), the traditional data collection mode could suffer low energy-efficiency on data transmission, since the large-scale multitask scenarios could result in much higher packet collision probability, especially for harsh environments. Mobile data collection is an efficient data acquisition way to prolong network lifetime for LSM-WSNs. However, the mobile collectors could suffer electricity shortage problem, since the limited battery capacity of any mobile collector could not afford the energy consumption of its long-distance movement and massive data collection in large-scale multitask scenarios. Deploying wireless chargers to supplement the energy of mobile collectors is a feasible solution to electricity shortage problem, but will incur extra charger deployment cost. In this paper, we focus on the problem that how to optimize such charger deployment cost, which is NP-hard. By transforming it into minimum-cost submodular cover problem, we devise an efficient approximation algorithm with a provable approximation ratio. The extensive simulation results reveal that our solution always outperforms the other solutions under whatever configurations. © 2023 IEEE.

关键词： Wireless sensor networks

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Feature Map for Quantum data in Classification 1

Feature Map for Quantum Data in Classification

引用

1st international conference on Quantum Communications, Networking, and computing, QCNC 2024

作者： Kwon, Hyeokjea Lee, Hojun Bae, Joonwoo School of Electrical Engineering Korea Adv. Inst. of Sci. and Tech. Yuseong-gu Korea Republic of

ISBN: (纸本)9798350366778

The kernel trick in supervised learning signifies transformations of an inner product by a feature map, which then restructures training data in a larger Hilbert space according to an endowed inner product. A quantum feature map corresponds to an instance with a Hilbert space of quantum states by fueling quantum resources to machine learning algorithms. In this work, we point out that the quantum state space is specific such that a measurement postulate characterizes an inner product and that manipulation of quantum states prepared from classical data cannot enhance the distinguishability of data points. We present a feature map for quantum data as a probabilistic manipulation of quantum states to improve supervised learning algorithms. © 2024 IEEE.

关键词： Self-supervised learning

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On Overcoming HPC Challenges of Trillion-Scale Real-World Graph datasets

On Overcoming HPC Challenges of Trillion-Scale Real-World Gr...

引用

2023 IEEE international conference on Big data, Bigdata 2023

作者： Koohi Esfahani, Mohsen Boldi, Paolo Vandierendonck, Hans Kilpatrick, Peter Vigna, Sebastiano Queen's University Belfast United Kingdom University of Sistan Iran Università Degli Studi di Milano Italy

ISBN: (纸本)9798350324457

Progress in High-Performance computing in general, and High-Performance Graph processing in particular, is highly dependent on the availability of publicly-accessible, relevant, and realistic data sets. To ensure continuation of this progress, we (i) investigate and optimize the process of generating large sequence similarity graphs as an HPC challenge and (ii) demonstrate this process in creating MS-BioGraphs, a new family of publicly available real-world edge-weighted graph datasets with up to 2.5 trillion edges, that is, 6.6 times greater than the largest graph published recently. The largest graph is created by matching (i.e., all-toall similarity aligning) 1.7 billion protein sequences. The MSBioGraphs family includes also seven subgraphs with different sizes and direction types. We describe two main challenges we faced in generating large graph datasets and our solutions, that are, (i) optimizing data structures and algorithms for this multi-step process and (ii) WebGraph parallel compression technique. The datasets are available online on https://***/ DIPSA/MS-BioGraphs. © 2023 IEEE.

关键词： Big data Management and processing Biological Networks Graph algorithms Graph datasets High-Performance computing Sequence Similarity Graph

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Fast Robust Principle Component Analysis Using Gauss-Newton Iterations 48

Fast Robust Principle Component Analysis Using Gauss-Newton ...

引用

48th IEEE international conference on Acoustics, Speech and Signal processing, ICASSP 2023

作者： Chettleburgh, William Huang, Zhishen Yan, Ming Michigan State University Department of Mathematics MI48824 United States Amazon WA98107 United States The Chinese University of HongKong School of Data Science Shenzhen China

ISBN: (纸本)9781728163277

Robust Principal Component Analysis (RPCA) is an optimization problem that decomposes a data matrix into a low-rank and a sparse matrix. However, solving this problem using alternating procedures requires sequentially computing singular value decompositions (SVDs) of large matrices, which is computationally expensive. In this work, we propose a computation protocol that leverages Gauss-Newton iterations to speed up the sequential computation of SVDs and accelerate the entire RPCA process. Our method is validated on synthetic and video data, benchmarked against established RPCA algorithms, and analyzed for stability with respect to hyperparameters. Our proposed protocol can also be applied to problems that require repeated computation of the proximal of functions that solely depend on singular values of the input matrix. © 2023 IEEE.

关键词： Singular value decomposition

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Research on Enterprise Marketing Resource Allocation Strategy Based on data Mining Algorithm 3

Research on Enterprise Marketing Resource Allocation Strateg...

引用

3rd international conference on Smart Generation computing, Communication and Networking, SMART GENCON 2023

作者： Zhang, Zifeng Shijiazhuang University of Applied Technology Hebei Shijiazhuang050081 China

ISBN: (纸本)9798350319125

In the enterprise, marketing can better make customers prefer this enterprise, and in this process, marketing resources are very important, due to the limited marketing resources, so reasonable allocation is an indispensable part. Ordinary resource allocation strategies do not better meet marketing expectations. analyze marketing resources, and indicators are divided according to resource allocation requirements to reduce resource allocation in the interfering factor. Then, artificial intelligence will form a resource allocation plan for the results of marketing resources for enterprises, and carry out the resource allocation results Comprehensive analysis. the data mining algorithm has an accurate effect on the resource allocation of enterprise marketing The rationality of resource allocation is better than that of ordinary resource allocation strategies. © 2023 IEEE.

关键词： artificial intelligence Corporate Marketing data mining algorithms Marketing distribution

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ECG Signal Denoising by Cross-Bispectrum data processing

ECG Signal Denoising by Cross-Bispectrum Data Processing

引用

international Scientific and Technical conference on Integrated Computer Technologies in Mechanical Engineering - Synergetic Engineering (ICTM)

作者： Viunytskyi, Oleh Totsky, Alexander Natl Aerosp Univ KhAI Chkalov St 17 UA-61070 Kharkiv Ukraine

ISBN: (纸本)9783031614149;9783031614156

The paper is devoted to the issues of digital data processing in ECG signal application. The benefits and disadvantages of common methods dedicated to extraction useful signal component in interference environment have been compared and analyzed. A novel technique for filtering the ECG signal and extraction useful signal component based on cross-bispectrum signal processing strategy is proposed and studied. New algorithm for ECG signal processing has been developed and studied. The proposed algorithms have shown their efficiency and high quality of ECG extraction in the interference environment.

关键词： ECG signal processing bispectrum noise suppression

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Review on distribution network damage prediction and assessment under typhoon disasters 18

Review on distribution network damage prediction and assessm...

引用

18th international conference on Future Networks and Communications (FNC) / 20th international conference on Mobile Systems and Pervasive computing (MobiSPC) / 13th international conference on Sustainable Energy Information Technology (SEIT)

作者： Wei, Ruizeng Wang, Lei He, Huan Guangdong Power Grid Co Ltd Elect Power Res Ititute Guangzhou Guangdong Peoples R China

How to deal with damage to the power grid caused by natural disasters has become one of the main research focus of disaster prevention and mitigation. This paper collates two main approaches to distribution network damage prediction and assessment under typhoon disasters: based on physical model-driven and data model-driven. Research based on physical models is divided into three parts by considering real-time wind speed correction, meteorological forecast information, and non-meteorological information. The field of data-driven models includes single-algorithm models and multi-algorithm models. These methods are known for their high accuracy, but have a strong dependence on data. Finally, the paper explores the potential benefits of integrating physical and data model-driven approaches, as well as implementing additional defensive measures based on predictive results.

关键词： Distribution network Real-time wind speed prediction data-driven Typhoon disasters Machine learning algorithms

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Design of Airborne Intelligent computing System Oriented to Intelligent Task processing 26

Design of Airborne Intelligent Computing System Oriented to ...

引用

26th ACIS international Winter conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed computing, SNPD-Winter 2023

作者： Bai, Linting Wen, Pengcheng Xie, Jianchun Li, Yunxi Lin, Rongchao School of Computer Science Northwestern Polytechnical University Xi'an Aeronautics Computing Technique Research Institute Avic Xi'an China Xi'an Aeronautics Computing Technique Research Institute Avic Aviation Key Laboratory of Science and Technology on Airborne and Missile-borne Computer Xi'an China Xi'an Aeronautics Computing Technique Research Institute Avic Aeronautics Data and Sensor Business Unit Avic Xi'an China

ISBN: (纸本)9798350345865

The rapid development of artificial intelligence technology promotes the gradual evolution of the 'three integration' of mechanization, informatization and intelligence. The mass data characteristics of airborne mission informatization put forward higher demands on the storage, transmission and management capabilities of airborne data. The characteristics of high complexity, strong confrontation, strong real-time, and uncertainty of airborne intelligent tasks pose more challenges to the intelligent processing of airborne data. This paper analyzes the data characteristics and mission characteristics in the process of airborne intelligence missions, and proposes an airborne intelligent computing architecture for airborne data processing of intelligent missions. The composition and function of each technical layer are expounded, and several problems of airborne intelligent computing technology are analyzed. Finally, the development trend of airborne intelligent technology development is prospected. © 2023 IEEE.

关键词： Digital storage

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