Titlebook: Machine Learning for Networking; Second IFIP TC 6 Int Selma Boumerdassi,éric Renault,Paul Mühlethaler Conference proceedings 2020 IFIP Inte

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Machine Learning Methods for Connection RTT and Loss Rate Estimation Using MPI Measurements Under Rem; instead, we fuse them to retain their design diversity. Overall, the results show that accurate estimates can be generated at low loss rates but become inaccurate at loss rates 10% and higher, thereby illustrating both their strengths and limitations.

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Algorithm Selection and Model Evaluation in Application Design Using Machine Learning,y AI examine just as in numerous mechanical settings. This article audits various systems that can be utilized for every one of these three subtasks and talks about the primary focal points and drawbacks of every method with references to theoretical and observational investigations. Further, sugges

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Revealing User Behavior by Analyzing DNS Traffic,es. Finding human patterns in the data could be of high interest to researchers that analyze the human behavior regarding Internet’s usage. The procedure was able to detect some trends and patterns in the data that are discussed along with proper evaluation measures for further comparison.

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Emotion-Based Adaptive Learning Systems,ery learner based upon the learner’s personality, interests and performance. This research presents a novel approach of adaptive learning by presenting an emotion-based adaptive learning system where the emotion and psychological traits of the learner is considered to provide learning materials that

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0302-9743 tware de ned networks,cooperative networks, positioning and navigation systems, wireless communications,wireless sensor networks, underwater sensor networks.978-3-030-45777-8978-3-030-45778-5Series ISSN 0302-9743 Series E-ISSN 1611-3349

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Diogo Ferreira,Carlos Senna,Paulo Salvador,Luís Cortes?o,Cristina Pires,Rui Pedro,Susana Sargento