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检索条件"任意字段=Proceedings - 3rd International Conference on Distributed Computing Systems."

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3rd international conference on Advancements in Smart computing and Information Security, ASCIS 2024

3rd International Conference on Advancements in Smart Comput...

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3rd international conference on Advancements in Smart computing and Information Security, ASCIS 2024

ISBN: (纸本)9783031862953

The proceedings contain 28 papers. The special focus in this conference is on Advancements in Smart computing and Information Security. The topics include: Prediction of Anti-Cholinesterase Inhibitor for Alzheimer Disease Using Machine Learning Techniques;scrutinize Search Engine Optimization Strategies with Artificial Intelligence to Rank a Website;a Study on Machine Learning Algorithms: Forecasting Crop Prices;improving Student Stress Analysis: Novel Methods for Collecting, Preprocessing, and Fusing Features;extraction of Normal and Abnormal Region in Colposcopy Image to Support Cervical Cancer Clinical Decision;optimizing Pricing Strategies: A Comprehensive Framework Using Bayesian Inference and Game Theory;hybrid Ensemble Gradient Boosting Algorithm to Predict Diabetes Health Care Analytics;a Review on Machine Learning Algorithms for Real-Time Traffic Management;hyperparameter-Tuned Intention Mining for Mental Health Diagnosis Using Logistic Regression;machine Learning for Accessible and Precise Assessment in Smart Monitoring systems.an Efficient Model for Academic Performance Prediction of the University Students;comprehensive Comparative Study on Data Mining and Machine Learning Approaches for Fraud Detection in Financial Services;prediction of Stock Price and Detection of Stock Market Trends Using Adaptive Learning Techniques;asthma Prediction Using Fuzzification and Machine Learning Based Ensemble Classifier;detection and Analysis of Features of Optic Nerve Head Using Retinal Fundus Images of an Eye for a Priori Prediction of Glaucoma;a Hybrid Deep Learning Framework for Uncertain Supply Chains: An Optimization Approach;a Comprehensive Review of Machine Learning Techniques in Recommender System for E-Commerce Platform;Ethics of AI in the Educational Sector - Navigating the Moral Landscape;international Law and AI Interface.

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3rd international conference on Advancements in Smart computing and Information Security, ASCIS 2024

3rd International Conference on Advancements in Smart Comput...

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3rd international conference on Advancements in Smart computing and Information Security, ASCIS 2024

ISBN: (纸本)9783031863042

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A decentralized control approach to formation maneuvers in Multiple Unmanned Aerial Vehicles 3

A decentralized control approach to formation maneuvers in M...

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3rd international conference on Unmanned systems.(ICUS)

作者： Liu, Weiheng Zheng, Xin Luo, Yang Beijing Inst Technol Beijing Peoples R China HIWING Gen Aviat Equipment Co Ltd Beijing Peoples R China China Aerosp Sci & Technol Grp Beijing Peoples R China

ISBN: (纸本)9781728180250

This paper proposes a decentralized control approach to formation maneuver. The dynamic model of multiple unmanned aerial vehicles is established, and the position and velocity are regarded as coordination variables of formation. Through the second-order integral dynamics feedback linearization, the consistency coordination strategy is coupled with the dynamics input. The formation error and position error functions of multiple unmanned aerial vehicles are established, and two contradictions of formation maneuver are balanced by error parameters. Simulation results demonstrate the effectiveness and strategy of the proposed approach.

关键词： distributed control approach coordination control unmanned aerial vehicle dynamic model formation maneuver

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EMPATHY: Third international Workshop on Empowering People in Dealing with Internet of Things Ecosystems 3

EMPATHY: Third International Workshop on Empowering People i...

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3rd international Workshop on Empowering People in Dealing with Internet of Things Ecosystems. EMPATHY 2022

作者： Balducci, Fabrizio Breve, Bernardo Cena, Federica Mattioli, Andrea Rizvi, Mehdi University of Bari"A. Moro" Bari Italy University of Salerno Salerno Italy University of Turin Torino Italy CNR-ISTI Pisa Italy Politecnico di Milano Milano Italy

Preface of the third international Workshop on Empowering People in Dealing with Internet of Things Ecosystems. The EMPATHY project has been funded by the Italian Ministry of Education, Universities and Research (MIUR) through a three-year basis research projects of national interest (PRIN) in 2017. © 2022 Copyright for this paper by its authors.

关键词： Internet of things

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3rd international conference on Advancements in Smart computing and Information Security, ASCIS 2024

3rd International Conference on Advancements in Smart Comput...

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3rd international conference on Advancements in Smart computing and Information Security, ASCIS 2024

ISBN: (纸本)9783031863011

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3rd international conference on Advancements in Smart computing and Information Security, ASCIS 2024

3rd International Conference on Advancements in Smart Comput...

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3rd international conference on Advancements in Smart computing and Information Security, ASCIS 2024

ISBN: (纸本)9783031862892

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3rd international conference on Advancements in Smart computing and Information Security, ASCIS 2024

3rd International Conference on Advancements in Smart Comput...

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3rd international conference on Advancements in Smart computing and Information Security, ASCIS 2024

ISBN: (纸本)9783031862984

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distributed Optimisation of Perfect Preventive Maintenance and Component Replacement Schedules Using SPEA2

Distributed Optimisation of Perfect Preventive Maintenance a...

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3rd international conference on Intelligent computing and Optimization, ICO 2020

作者： Ikechukwu, Anthony O. Nggada, Shawulu H. Quenum, José G. Namibia University of Science and Technology Windhoek Namibia Higher Colleges of Technologies Ras Al Khaimah Women’s Campus Ras Al Khaimah United Arab Emirates

ISBN: (纸本)9783030681531

The upsurge of technological opportunities has brought about the speedy growth of the industry’s processes and machineries. The increased size and complexity of these systems. followed by high dependence on them, have necessitated the need to intensify the maintenance processes, requiring more effective maintenance scheduling approach to minimise the number of failure occurrences;which could be realised through a well-articulated perfect preventive maintenance with component replacement (PPMR) schedules from infancy stage through to completion stage. Then, using Strength Pareto Evolutionary Algorithm 2 (SPEA2), we devise a multi-objective optimisation approach that uses dependability and cost as objective functions. Typically, due to large scale problems, SPEA2 implementation on a single node proves to be computationally challenging, taking up to 1 h 20 min to run an instance of SPEA2 PPMR scheduling optimisation, the search time, the quality of the solution and its accuracy suffer much. We address this limitation by proposing a distributed architecture based on MapReduce, which we superimpose on SPEA2. The evaluation of our approach in a case study presents the following results: (1) that our approach offers an effective optimisation modelling mechanism for PPMR;(2) that the distributed implementation tremendously improves the performance by 79% computational time of the optimisation among 4 nodes (1 master-node and 3 worker-nodes) and the quality and accuracy of the solution without introducing much overhead. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： MapReduce

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distributed Deep Learning System for Efficient Face Recognition in Surveillance System 21

Distributed Deep Learning System for Efficient Face Recognit...

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proceedings of the 3rd international conference on Advanced Information Science and System

作者： Jinjin Liu Zhifeng Chen Xiaonan Li Tongxin Wei School of Computer Science Zhongyuan University of Technology China and State Key Laboratory of Mathematical Engineering and Advanced Computing China State Key Laboratory of Mathematical Engineering and Advanced Computing China School of Computer Science Zhongyuan University of Technology China School of Cyberspace Security Zhengzhou University China

ISBN: (纸本)9781450385862

In view of the bandwidth consumption caused by data stream transmission in video analysis system and the demand for accurate online real-time analysis of massive data, this paper proposes a deep learning model framework for face recognition employed in the embedded system. Through data collaboration, the cloud could build a more complex data set with a small amount of uploaded data gathered by the end devices. And the framework collaboration makes sure that the fully-trained cloud model directly download or distillate knowledge to the end devices. Experiments show that the deep model not only realizes the real-time response and the accurate response of the cloud system, but also greatly reduces the bandwidth consumption caused by sample data transmission in the model training process.

关键词： Deep learning

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SEQUENTIALLY UTILITY MAXIMIZING PATH PLANNING USING A distributed POOL ARCHITECTURE 47

SEQUENTIALLY UTILITY MAXIMIZING PATH PLANNING USING A DISTRI...

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ASME international Design Engineering Technical conferences / 41st Annual Computers and Information in Engineering conference (IDETC-CIE) / 47th Design Automation conference (DAC)

作者： Mollan, Calahan Pandey, Vijitashwa Slon, Christopher Gorsich, David Oakland Univ Ind & Syst Engn Dept Rochester MI 48309 USA US Army GVSC 6501 E 11 Mile Rd Warren MI 48397 USA

ISBN: (纸本)9780791885390

Operation of ground vehicles in remote unstructured terrains is a challenging task. While information on terrain parameters such as elevation, slope, and soil content and its use in tasks such as path planning and vehicle simulation is immensely useful, it requires detailed analyses that are computationally expensive and time-consuming. At the same time, higher-level decisions made without a full understanding of the terrain properties and vehicle capabilities are bound to be suboptimal, possibly even leading to mission failure. In this paper, we approach the problem from a decision-making standpoint to address both the aspects described above. The approach divides a given terrain into smaller cells and progressively finds the path to the goal. At each step, the operator's utility between competing immediate terrain cells is compared so that the vehicle chooses the best next step considering detailed vehicle simulation. To account for the increased computational effort, a pool-based distributed computing architecture is employed to speed-up pathfinding and provide resilience towards failure of processors and communication links. The pool architecture also admits heterogeneous, geographically distributed processors and storage locations. Concurrently, the multiattribute utility formulation takes into account the tradeoff between different attributes and the uncertainty in the vehicle performance. The proposed method can be used for medium and long-range navigation in unstructured environments for efficient rough path planning or re-planning based on changing mission requirements and dynamically arriving terrain data. The method is demonstrated on data acquired from USGS sources.

关键词： Path planning Vehicles Simulation Failure Navigation Surface roughness Decision making Soil Storage Uncertainty Tradeoffs Resilience

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