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

共 6957 条记录，以下是4611-4620 订阅

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Benchmarking Onboard Science data Retrieval algorithms on the Snapdragon Platform

Benchmarking Onboard Science Data Retrieval Algorithms on th...

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IEEE conference on Aerospace

作者： Jack Lightholder Benjamin Donitz Julie Castillo-Rogez Douglas Sheldon Jet Propulsion Laboratory California Institute of Technology Pasadena CA

The coming decade of planetary missions will have stringent data requirements. Optimizing limited bandwidth is a major motivation for the use of onboard science data analysis algorithms for deep space missions. Under the traditional mission operations paradigm, all data is sent to Earth before follow-on activities are planned by ground operators. This operational model creates challenges for time and bandwidth constrained mission events. The Near Earth Asteroid Scout (NEAScout) mission is a prime example of a mission with these challenges. NEAScout is an interplanetary CubeSat manifested on Artemis-1. The mission will use a solar sail to navigate to a Near Earth asteroid and perform flyby reconnaissance imaging. Vehicle size, configuration, trajectory and distance from Earth all impose bandwidth constraints. To minimize these limitations, the Project decided to move image processing capabilities onboard the spacecraft. This onboard science software enables image calibration, image coaddition, image subtraction, data compression, downsampling and cropping all onboard the spacecraft. The goal is to reduce the data downlink, without compromising science contents. NEAScout utilizes the SPHINX computing platform, whose processing performance is akin to the RAD750, but tailored for CubeSat resources. Although sufficient to enable these sorts of algorithms, the SPHINX takes multiple minutes per image, which diminishes the prospect of using image content for rapid response reasoning. This effort ported, characterized and benchmarked the performance of the NEAScout science software on the Snapdragon platform, a next-generation higher performing processor. Between $10\mathrm{x}$ and $200\mathrm{x}$ processing time speedup was achieved, depending on the image processing algorithm, with a $50\mathrm{x}$ speedup for a notional image processing pipeline. This work highlights new performance baselines for onboard science data processing algorithms which missions could expect

关键词： Earth Space vehicles data analysis Image processing Software algorithms Bandwidth Software

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Deep Learning for Scoliosis Diagnosis: Methods and databases 4th

Deep Learning for Scoliosis Diagnosis: Methods and Databas...

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4th international conference on Numerical Computations: Theory and algorithms, NUMTA 2023

作者： Bottino, Lorella Settino, Marzia Promenzio, Luigi Cannataro, Mario Data Analytics Research Center Department of Medical and Surgical Sciences University Magna Graecia of Catanzaro Catanzaro88100 Italy Chief of Pediatric Orthopaedics Department Villa Serena for Children Catanzaro88100 Italy

ISBN: (纸本)9783031812460

The advent of data-driven science and artificial intelligence (AI) has provided a deeper knowledge about data that has driven the clinical research to unprecedented change. AI has shown great potential in the scoliosis diagnosis for which the current widely adopted standard of evaluation is the manual measurement on the X-ray radiographs of the Cobb angle to quantify the magnitude of spinal deformities in scoliosis. The reliability of the Cobb angle measurement mainly depends on the subjective experience of the operators and it is time-consuming. Machine learning (ML) and Deep Learning (DL) methods can help surgeons to avoid misjudgment about scoliosis screening, diagnosis and classification by providing a powerful solution for saving time and effort in the Cobb angle measurement. The contribution of this work is twofold. Primarily it aims to provide an overview of the main ML approaches, with special focus on DL, that can be used in the spine field to make physicians and researchers aware of the benefits of the ML approaches for scoliosis diagnosis and treatment with respect to the traditional methods. Furthermore, because the reliability of the all ML approaches depends strongly on the training data and often it is difficult to obtain a large amount of representative data, we survey the main databases containing spinal data with the purposes of providing the stakeholders with a knowledge that can substantially help them to choose the best dataset in the spine field for training their ML models. Furthermore, with this work we intend to lay the foundations for development of a ML-based tool for supporting the physicians in the diagnosis, classification, screening and prognosis prediction of scoliosis. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： X ray radiography

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A Preliminary Study on University Computer Basic Curriculum Reform Aimed at Cultivating Computational Thinking from the Perspective of Smart Education 18th

A Preliminary Study on University Computer Basic Curriculum ...

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18th international conference on Computer Science and Education, ICCSE 2023

作者： Yu, Niefang Liu, Yiwen Peng, Xiaoning Li, Xiaomei Lu, Youmin School of Computer and Artificial Intelligence Huaihua University Hunan Huaihua418000 China Key Laboratory of Wuling-Mountain Health Big Data Intelligent Processing and Application in Hunan Province Universities Hunan Huaihua418000 China Key Laboratory of Intelligent Control Technology for Wuling-Mountain Ecological Agriculture in Hunan Province Hunan Huaihua418000 China

ISBN: (纸本)9789819707904

With the rapid advancement of information technology such as artificial intelligence, cloud computing, big data, virtual reality, etc., in the context of the deep integration of information technology and education and teaching, smart classroom has set off a new round of revolution. Based on this background, this paper discuss how to cultivate students’ computational thinking in a smart education environment, and explore the cultivation of computational thinking in basic university computer courses from three aspects: before class, during class, and after class in smart classrooms. In the background of smart education, only by comprehensively cultivating students’ computational thinking that we can achieve more results in the future. Thereby improving students’ ability to comprehensively apply computer technology. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Artificial intelligence

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Classifying the Country of Origin of Traffic Transactions Based on Computer Raw dataset data processing

Classifying the Country of Origin of Traffic Transactions Ba...

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Computers, Information processing and Advanced Education (CIPAE), 2020 international conference on

作者： Yichun Wang College of Design and Innovation Tongji University Shanghai China

Accurate network traffic classification is the key to achieve controllable and manageable Internet. This paper analyzes and compares the application of algorithm in the classification of network traffic source country. The experimental results show that logistic regression is the most efficient. SVM and neural networks take longer, probably because their algorithms are more complex. Optimize the neural network time consumption by setting the appropriate batch size. The time consumption of the three machine learning algorithm models on Precision is about 92%. In terms of Recall and F1 scores, the three machine learning algorithms mainly concentrate between 89% and 90%. There is no significant difference between the algorithm models. SVM was superior in precision (92%), recall (89%) and F1 score (90%).

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Trusted computing data Transaction Scheme based on Blockchain

Trusted Computing Data Transaction Scheme based on Blockchai...

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international conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)

作者： Fanchang Fanchang Amit Yadav Asif Khan Sami Azam Dakshina Yadav Department of Software Engineering Chengdu Neusoft University Chengdu China College of Engineering IT and Environment Charles Darwin University NT Australia Department of Computer Application Integral University Lucknow India College of Engineering IT and Environment Charles Darwin University Darwin Australia Department of Industry Tourism and Trade Line Plant Industry Darwin Australia

A blockchain technology based on encryption algorithm is proposed to address the issue of data theft and leakage in data transactions. Use blockchain to record transaction information and confirm the rights of data assets and sources; Use trusted computing and encryption algorithms to ensure data transmission security; After the data provider and demander complete the trusted calculation, the encrypted data results are returned. This scheme can perform data calculations without exposing the data to the data requester, and ensure transmission security through trusted encryption.

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data Deduplication Using Python

Data Deduplication Using Python

引用

international conference on computing Communication Control and Automation (ICCUBEA)

作者： Harshada Deshingkar Preeti Desai Shrinidhi Deshmane Aarya Deshmukh Tanuja Desai Sankhya Desai Ganesh Ubale DESH Vishwakarma Institute of Technology Pune Maharashtra INDIA Vishwakarma Institute of Technology Pune Maharashtra INDIA

This paper aims to develop a data deduplication technique for optimizing storage utilization in enterprise environments and for general users. data deduplication is a process that identifies and eliminates duplicate data from storage systems, resulting in reduced requirements and lower costs. The project involves working on duplicate files from the folders present in storage systems and processing them to reduce the redundancy of data. The results of the project will provide valuable insights on usage of data deduplication technique to delete duplicate files. This method can be used by users to optimize their storage utilization and reduce their storage costs. Additionally, the project will contribute to the field of data storage and management by providing a way to reduce the constant storage of duplicate files.

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Group Personalized Federated Learning

Group Personalized Federated Learning

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international conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Zhe Liu Yue Hui Fuchun Peng Meta AI Menlo Park CA USA

Federated learning (FL) can help promote data privacy by training a shared model in a de-centralized manner on the physical devices of clients. In the presence of heterogeneous distributions of local data, personalized FL strategy is introduced to mitigate the potential client drift. In this paper, we present the group personalization approach for applications of FL in which there exist inherent partitions over clients that are significantly distinct. In our approach, the global FL model is fine-tuned through another FL training process over each homogeneous group of clients, after which each group-specific FL model is further adapted and personalized per client. The proposed method can be well interpreted from a Bayesian hierarchical modeling perspective. With experiments on two real-world datasets for language modeling task, we demonstrate this approach can achieve superior personalization performance than other FL counterparts.

关键词： Training Adaptation models data privacy Federated learning Signal processing algorithms Signal processing data models

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Resource-Coupling-Oriented Business Process Modeling and Optimal Sequencing

Resource-Coupling-Oriented Business Process Modeling and Opt...

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international conference on Wireless Communications, Networking and Applications (WCNA 2022)

作者： Han, Ke Li, Kunlin Cao, Zhiyong Wu, Wendou Cai, Xiaobo College of Electrical and Mechanical Kunming Metallurgy College Kunming China Laboratory of Intelligent Organic Tea Garden Construction in Universities of Yunnan Province Kunming China Big Data College of Yunnan Agricultural University Kunming China

ISBN: (纸本)9789819939503

In this paper, key theoretical approaches such as modeling and identification of business process-oriented coupling structure problems are studied from the perspective of resource allocation efficiency of computation, network and storage that can be provided by terminal sensing devices and edge servers of edge computing. For the DSM-based business process general matrix model, the business process coupling structure is identified and studied by applying relevant algorithms, and the activity ordering method of the multidimensional asynchronous random perturbation particle swarm optimization algorithm (MSPSO) is proposed in combination with the inertial weight concave function decreasing strategy to study how to efficiently allocate the resources of the edge server to dynamically perform the near-optimal process ordering for all applications and to make all The average completion time of business activities and the average energy consumption of both mobile terminals and edge servers are approximated to be optimal. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Edge computing

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Stacking Ensemble Model for Celestial Object Classification: Galaxies, Stars and Quasars

Stacking Ensemble Model for Celestial Object Classification:...

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international conference on Image Information processing

作者： S Sudharson R Annamalai Avuthu Avinash Reddy P Varsha Department of Computer Science and Engineering School of Computer Science and Engineering Vellore Institute of Technology Chennai India Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Chennai India Department of Computer Science and Engineering (AIE) Amrita School of Computing Amrita Vishwa Vidyapeetham Chennai India

ISBN: (数字)9798350371406

ISBN: (纸本)9798350371413

In the field of astronomy, it is essential to classify celestial objects like stars, galaxies, and quasars based on their spectral characteristics. This spectral data provides valuable information about various properties, such as the elements present, temperature, density, and magnetic field. To tackle this classification task, we investigate the application of different classification and ensemble algorithms. The proposed approach uses a variety of machine learning classifiers, including logistic regression, support vector machines, k-nearest neighbors, decision trees, random forests, and XGBoost. These classifiers are combined to create a stacking classifier, which is then evaluated on its accuracy, precision, recall, F1 score, and support. The Stacking classifier demonstrates the highest accuracy, reaching an impressive 99.99% on the training data. Train Logloss is 0.011. The precision, recall, and f1 score values (all 1.00) indicate a robust classification capability a cross all classes of celestial objects. This outstanding accuracy means that it effectively identifies almost all celestial objects in the training data-set. Consequently, the Stacking model serves as a highly dependable and precise tool for recognizing galaxies, stars, and quasars based on their spectral characteristics.

关键词： Logistic regression Analytical models Machine learning algorithms Stacking Stars Training data data models

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Big data Mining of Maritime Shipping Flows Based on Dependency Relationships

Big Data Mining of Maritime Shipping Flows Based on Dependen...

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6th international conference on Intelligent computing and Signal processing (ICSP)

作者： Yude Fu Xiang Li Xiongyi Lu Mengjun Yu Qizi Huangpeng Xiaojun Duan College of Science National University of Defense Technology Changsha China National Research Center of Parallel Computer Engineering and Technology Beijing China

ISBN: (数字)9798350376548

ISBN: (纸本)9798350376555

This research utilizes higher-order dependency relationships for the modeling of higher-order dependency networks from voluminous maritime flow data and introduces higher-order dependency mining to uncover critical port information in the China-Southeast Asia region as well as the coopetition relationships between ports. Analysis of maritime data from the years 2018 and 2023 indicates that global ports maintain stability and competitiveness through their evolution, with Singapore serving as the central hub connecting China and Southeast Asia. Subsequent analysis employing dependency paths indicates that in recent years, the dependency relationships among ports between China and Southeast Asia have achieved higher orders and greater complexity.

关键词： Asia Seaports Strategic planning Signal processing data models Stability analysis Trajectory data mining Optimization Logistics

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