Intelligent Distributed Computing XII

著者名：Del Ser, Javier (EDT)/Osaba, Eneko (EDT)/Bilbao, Miren Nekane (EDT)/Sanchez-Medina, Javier J. (EDT)/Vecchio, Massimo (EDT)/Yang, Xin-She (EDT)

Springer（2018/09/14発売）

• 言語：ENG
• ISBN：9783319996257
• eISBN：9783319996264

Description

This book gathers a wealth of research contributions on recent advances in intelligent and distributed computing, and which present both architectural and algorithmic findings in these fields. A major focus is placed on new techniques and applications for evolutionary computation, swarm intelligence, multi-agent systems, multi-criteria optimization and Deep/Shallow machine learning models, all of which are approached as technological drivers to enable autonomous reasoning and decision-making in complex distributed environments. Part of the book is also devoted to new scheduling and resource allocation methods for distributed computing systems. The book represents the peer-reviewed proceedings of the 12th International Symposium on Intelligent Distributed Computing (IDC 2018), which was held in Bilbao, Spain, from October 15 to 17, 2018.

Table of Contents

Part I: Main Track.- Long distance in-links for ranking enhancement.- Concept Tracking and Adaptation for Drifting Data Streams under Extreme Verification Latency.- Adversarial Sample Crafting for Time Series Classification with Elastic Similarity Measures.- Slot Co-allocation Optimization in Distributed Computing with Heterogeneous Resources.- About Designing an Observer Pattern-Based Architecture for a Multi-Objective Metaheuristic Optimization Framework.- Scalable Inference of Gene Regulatory Networks with the Spark Distributed Computing Platform.- Finding Best Compiler Options for Critical Software Using Parallel Algorithms.- Drift Detection over Non-stationary Data Streams using Evolving Spiking Neural Networks.- Part II: Energy.- A Hybrid Ensemble of Heterogeneous Regressors for Wind Speed Estimation in Wind Farms.- Bio-inspired approximation to MPPT under real irradiation conditions.- Part III: Industry.- Decision Making in Industry 4.0 Scenarios supported by Imbalanced Data Classification.