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Autor Mühlethaler, Paul |
Documentos disponibles escritos por este autor (3)
Crear una solicitud de compra Refinar búsquedaFirst International Conference, MLN 2018, Paris, France, November 27–29, 2018, Revised Selected Papers / Renault, Éric ; Mühlethaler, Paul ; Boumerdassi, Selma
TÃtulo : First International Conference, MLN 2018, Paris, France, November 27–29, 2018, Revised Selected Papers Tipo de documento: documento electrónico Autores: Renault, Éric, ; Mühlethaler, Paul, ; Boumerdassi, Selma, Mención de edición: 1 ed. Editorial: [s.l.] : Springer Fecha de publicación: 2019 Número de páginas: XIII, 388 p. 208 ilustraciones, 156 ilustraciones en color. ISBN/ISSN/DL: 978-3-030-19945-6 Nota general: Libro disponible en la plataforma SpringerLink. Descarga y lectura en formatos PDF, HTML y ePub. Descarga completa o por capítulos. Idioma : Inglés (eng) Palabras clave: Procesamiento de datos Inteligencia artificial Red de computadoras Computadoras Propósitos especiales Software de la aplicacion MinerÃa de datos y descubrimiento de conocimientos Redes de comunicación informática Sistemas de propósito especial y basados ​​en aplicaciones Aplicaciones informáticas y de sistemas de información Clasificación: 6.312 Resumen: Este libro constituye las actas minuciosamente arbitradas de la Primera Conferencia Internacional sobre Aprendizaje Automático para Redes, MLN 2018, celebrada en ParÃs, Francia, en noviembre de 2018. Los 22 artÃculos completos revisados ​​incluidos en el volumen fueron cuidadosamente revisados ​​y seleccionados entre 48 presentaciones. Presentan nuevas tendencias en los siguientes temas: Aprendizaje profundo y por refuerzo; Reconocimiento y clasificación de patrones para redes; Aprendizaje automático para optimización de corte de red, sistema 5G, predicción del comportamiento del usuario, multimedia, IoT, seguridad y protección; Optimización y nuevos métodos innovadores de aprendizaje automático; Análisis de rendimiento de algoritmos de aprendizaje automático; Evaluaciones experimentales de aprendizaje automático; MinerÃa de datos en redes heterogéneas; Algoritmos de aprendizaje automático distribuidos y descentralizados; Comunicaciones inteligentes basadas en la nube, asignación de recursos, comunicaciones ecológicas/conscientes de la energÃa, redes definidas por software, redes cooperativas, sistemas de posicionamiento y navegación, comunicaciones inalámbricas, redes de sensores inalámbricos, redes de sensores submarinos. Nota de contenido: Learning Concave-Convex Profiles of Data Transport Over Dedicated Connections -- Towards Analyzing C-ITS Security Data -- Towards a Statistical Approach for User Classification in Twitter -- RILNET: A Reinforcement Learning Based Load Balancing Approach for Datacenter Networks -- Building a Wide-Area File Transfer Performance Predictor: An Empirical Study -- Advanced Hybrid Technique in Detecting Cloud Web Application's Attacks -- Machine-Learned Classifiers for Protocol Selection on a Shared Network -- Common Structures in Resource Management as Driver for Reinforcement Learning: a Survey and Research Tracks -- Inverse Kinematics Using Arduino and Unity for People with Motor Skill Limitations -- Delmu: A Deep Learning Approach to Maximizing the Utility of Virtualised Millimetre-Wave Backhauls -- Malware Detection System Based on an In-depth Analysis of the Portable Executable Headers -- DNS Traffic Forecasting Using Deep Neural Networks -- Energy-Based Connected Dominating Set for Data Aggregation for Intelligent Wireless Sensor Networks -- Touchless Recognition of Hand Gesture Digits and English Characters Using Convolutional Neural Networks -- LSTM Recurrent Neural Network for Anomaly Detection in Cellular Mobile Networks -- Towards a Better Compromise Between Shallow and Deep CNN for Binary Classification Problems of Unstructured Data -- Reinforcement Learning Based Routing Protocols Analysis for Mobile Ad-Hoc Networks -- Deep Neural Ranking for Crowdsourced Geopolitical Event Forecasting -- The Comment of BBS: How Investor Sentiment Affects a Share Market of China -- A Hybrid Neural Network Approach for Lung Cancer Classification with Gene Expression Dataset and Prior Biological Knowledge -- Plant Leaf Disease Detection and Classification Using Particle Swarm Optimization -- A Game Theory Approach for Intrusion Prevention Systems -- WSN Heterogeneous Architecture Platform for IoT -- An IoT Framework for Detecting Movement Within Indoor Environments -- A Hybrid Architecture for Cooperative UAV and USV Swarm Vehicles -- Detecting Suspicious Transactions in Smart Living Spaces -- Intelligent ERP Based Multi Agent Systems and Cloud Computing. Tipo de medio : Computadora Summary : This book constitutes the thoroughly refereed proceedings of the First International Conference on Machine Learning for Networking, MLN 2018, held in Paris, France, in November 2018. The 22 revised full papers included in the volume were carefully reviewed and selected from 48 submissions. They present new trends in the following topics: Deep and reinforcement learning; Pattern recognition and classification for networks; Machine learning for network slicing optimization, 5G system, user behavior prediction, multimedia, IoT, security and protection; Optimization and new innovative machine learning methods; Performance analysis of machine learning algorithms; Experimental evaluations of machine learning; Data mining in heterogeneous networks; Distributed and decentralized machine learning algorithms; Intelligent cloud-support communications, resource allocation, energy-aware/green communications, software defined networks, cooperative networks, positioning and navigation systems, wireless communications, wireless sensor networks, underwater sensor networks. Enlace de acceso : https://link-springer-com.biblioproxy.umanizales.edu.co/referencework/10.1007/97 [...] First International Conference, MLN 2018, Paris, France, November 27–29, 2018, Revised Selected Papers [documento electrónico] / Renault, Éric, ; Mühlethaler, Paul, ; Boumerdassi, Selma, . - 1 ed. . - [s.l.] : Springer, 2019 . - XIII, 388 p. 208 ilustraciones, 156 ilustraciones en color.
ISBN : 978-3-030-19945-6
Libro disponible en la plataforma SpringerLink. Descarga y lectura en formatos PDF, HTML y ePub. Descarga completa o por capítulos.
Idioma : Inglés (eng)
Palabras clave: Procesamiento de datos Inteligencia artificial Red de computadoras Computadoras Propósitos especiales Software de la aplicacion MinerÃa de datos y descubrimiento de conocimientos Redes de comunicación informática Sistemas de propósito especial y basados ​​en aplicaciones Aplicaciones informáticas y de sistemas de información Clasificación: 6.312 Resumen: Este libro constituye las actas minuciosamente arbitradas de la Primera Conferencia Internacional sobre Aprendizaje Automático para Redes, MLN 2018, celebrada en ParÃs, Francia, en noviembre de 2018. Los 22 artÃculos completos revisados ​​incluidos en el volumen fueron cuidadosamente revisados ​​y seleccionados entre 48 presentaciones. Presentan nuevas tendencias en los siguientes temas: Aprendizaje profundo y por refuerzo; Reconocimiento y clasificación de patrones para redes; Aprendizaje automático para optimización de corte de red, sistema 5G, predicción del comportamiento del usuario, multimedia, IoT, seguridad y protección; Optimización y nuevos métodos innovadores de aprendizaje automático; Análisis de rendimiento de algoritmos de aprendizaje automático; Evaluaciones experimentales de aprendizaje automático; MinerÃa de datos en redes heterogéneas; Algoritmos de aprendizaje automático distribuidos y descentralizados; Comunicaciones inteligentes basadas en la nube, asignación de recursos, comunicaciones ecológicas/conscientes de la energÃa, redes definidas por software, redes cooperativas, sistemas de posicionamiento y navegación, comunicaciones inalámbricas, redes de sensores inalámbricos, redes de sensores submarinos. Nota de contenido: Learning Concave-Convex Profiles of Data Transport Over Dedicated Connections -- Towards Analyzing C-ITS Security Data -- Towards a Statistical Approach for User Classification in Twitter -- RILNET: A Reinforcement Learning Based Load Balancing Approach for Datacenter Networks -- Building a Wide-Area File Transfer Performance Predictor: An Empirical Study -- Advanced Hybrid Technique in Detecting Cloud Web Application's Attacks -- Machine-Learned Classifiers for Protocol Selection on a Shared Network -- Common Structures in Resource Management as Driver for Reinforcement Learning: a Survey and Research Tracks -- Inverse Kinematics Using Arduino and Unity for People with Motor Skill Limitations -- Delmu: A Deep Learning Approach to Maximizing the Utility of Virtualised Millimetre-Wave Backhauls -- Malware Detection System Based on an In-depth Analysis of the Portable Executable Headers -- DNS Traffic Forecasting Using Deep Neural Networks -- Energy-Based Connected Dominating Set for Data Aggregation for Intelligent Wireless Sensor Networks -- Touchless Recognition of Hand Gesture Digits and English Characters Using Convolutional Neural Networks -- LSTM Recurrent Neural Network for Anomaly Detection in Cellular Mobile Networks -- Towards a Better Compromise Between Shallow and Deep CNN for Binary Classification Problems of Unstructured Data -- Reinforcement Learning Based Routing Protocols Analysis for Mobile Ad-Hoc Networks -- Deep Neural Ranking for Crowdsourced Geopolitical Event Forecasting -- The Comment of BBS: How Investor Sentiment Affects a Share Market of China -- A Hybrid Neural Network Approach for Lung Cancer Classification with Gene Expression Dataset and Prior Biological Knowledge -- Plant Leaf Disease Detection and Classification Using Particle Swarm Optimization -- A Game Theory Approach for Intrusion Prevention Systems -- WSN Heterogeneous Architecture Platform for IoT -- An IoT Framework for Detecting Movement Within Indoor Environments -- A Hybrid Architecture for Cooperative UAV and USV Swarm Vehicles -- Detecting Suspicious Transactions in Smart Living Spaces -- Intelligent ERP Based Multi Agent Systems and Cloud Computing. Tipo de medio : Computadora Summary : This book constitutes the thoroughly refereed proceedings of the First International Conference on Machine Learning for Networking, MLN 2018, held in Paris, France, in November 2018. The 22 revised full papers included in the volume were carefully reviewed and selected from 48 submissions. They present new trends in the following topics: Deep and reinforcement learning; Pattern recognition and classification for networks; Machine learning for network slicing optimization, 5G system, user behavior prediction, multimedia, IoT, security and protection; Optimization and new innovative machine learning methods; Performance analysis of machine learning algorithms; Experimental evaluations of machine learning; Data mining in heterogeneous networks; Distributed and decentralized machine learning algorithms; Intelligent cloud-support communications, resource allocation, energy-aware/green communications, software defined networks, cooperative networks, positioning and navigation systems, wireless communications, wireless sensor networks, underwater sensor networks. Enlace de acceso : https://link-springer-com.biblioproxy.umanizales.edu.co/referencework/10.1007/97 [...]
TÃtulo : Second IFIP TC 6 International Conference, MLN 2019, Paris, France, December 3–5, 2019, Revised Selected Papers Tipo de documento: documento electrónico Autores: Boumerdassi, Selma, ; Renault, Éric, ; Mühlethaler, Paul, Mención de edición: 1 ed. Editorial: [s.l.] : Springer Fecha de publicación: 2020 Número de páginas: XIII, 486 p. 267 ilustraciones, 183 ilustraciones en color. ISBN/ISSN/DL: 978-3-030-45778-5 Nota general: Libro disponible en la plataforma SpringerLink. Descarga y lectura en formatos PDF, HTML y ePub. Descarga completa o por capítulos. Idioma : Inglés (eng) Palabras clave: Procesamiento de datos IngenierÃa Informática Red de computadoras Software de la aplicacion Protección de datos MinerÃa de datos y descubrimiento de conocimientos IngenierÃa Informática y Redes Aplicaciones informáticas y de sistemas de información Seguridad de datos e información Clasificación: 6.312 Resumen: Este libro constituye las actas minuciosamente arbitradas de la Segunda Conferencia Internacional sobre Aprendizaje Automático para Redes, MLN 2019, celebrada en ParÃs, Francia, en diciembre de 2019. Los 26 artÃculos completos revisados ​​incluidos en el volumen fueron cuidadosamente revisados ​​y seleccionados entre 75 presentaciones. Presentan y discuten nuevas tendencias en aprendizaje profundo y de refuerzo, reconocimiento de patrones y clasificación para redes, aprendizaje automático para optimización de corte de redes, sistema 5G, predicción del comportamiento del usuario, multimedia, IoT, seguridad y protección, optimización y nuevos métodos innovadores de aprendizaje automático, análisis del rendimiento de las máquinas. algoritmos de aprendizaje, evaluaciones experimentales de aprendizaje automático, minerÃa de datos en redes heterogéneas, algoritmos de aprendizaje automático distribuidos y descentralizados, comunicaciones inteligentes con soporte en la nube, asignación de recursos, comunicaciones conscientes de la energÃa, redes definidas por software, redes cooperativas, sistemas de posicionamiento y navegación, comunicaciones inalámbricas, Redes de sensores inalámbricos, Redes de sensores submarinos. Nota de contenido: Network Anomaly Detection using Federated Deep Autoencoding Gaussian Mixture Model -- Towards a Hierarchical Deep Learning Approach for Intrusion Detection -- Network Trafic Classifi cation using Machine Learning for Software Defined Networks -- A Comprehensive Analysis of Accuracies of Machine Learning Algorithms for Network Intrusion Detection -- Q-routing: from the algorithm to the routing protocol -- Language Model Co-occurrence Linking for Interleaved Activity Discovery -- Achieving Proportional Fairness in WiFi Networks via Bandit Convex Optimization -- Denoising Adversarial Autoencoder for Obfuscated Tra c Detection and Recovery -- Root Cause Analysis of Reduced Accessibility in 4G Networks -- Space-time pattern extraction in alarm logs for network diagnosis -- Machine Learning Methods for Connection RTT and Loss Rate Estimation Using MPI Measurements Under Random Losses -- Algorithm Selection and Model Evaluation in Application Design using Machine Learning -- GAMPAL: Anomaly Detection forInternet Backbone Tra c by Flow Prediction with LSTM-RNN -- Revealing User Behavior by Analyzing DNS Tra c -- A new approach to determine the optimal number of clusters based on the Gap statistic -- MLP4NIDS: an e cient MLP-based Network Intrusion Detection for CICIDS2017 dataset -- Random Forests with a Steepend Gini-Index Split Function and Feature Coherence Injection -- Emotion-based Adaptive Learning Systems -- Machine learning methods for anomaly detection in IoT networks, with illustrations -- DeepRoute: Herding Elephant and Mice Flows with Reinforcement Learning -- Arguments Against using the 1998 DARPA Dataset for Cloud IDS Design and Evaluation and Some Alternative -- Estimation of the Hidden Message Length in Steganography: A Deep Learning Approach -- An Adaptive Deep Learning Algorithm Based Autoencoder for Interference Channels -- A Learning Approach for Road Tra c Optimization in Urban Environments -- CSI based Indoor localization using Ensemble Neural Networks -- Bayesian Classi ersin Intrusion Detection Systems -- A Novel Approach towards Analysis of Attacker Behavior in DDoS Attacks -- Jason-RS, a Collaboration between Agents and an IoT Platform -- Scream to Survive(S2S): Intelligent System to Life-Saving in Disasters Relief -- Association Rules Algorithms for Data Mining Process Based on Multi Agent System -- Internet of Things: Security Between Challenges and Attacks -- Socially and biologically inspired computing for self-organizing communications networks. . Tipo de medio : Computadora Summary : This book constitutes the thoroughly refereed proceedings of the Second International Conference on Machine Learning for Networking, MLN 2019, held in Paris, France, in December 2019. The 26 revised full papers included in the volume were carefully reviewed and selected from 75 submissions. They present and discuss new trends in deep and reinforcement learning, patternrecognition and classi cation for networks, machine learning for network slicingoptimization, 5G system, user behavior prediction, multimedia, IoT, securityand protection, optimization and new innovative machine learning methods, performanceanalysis of machine learning algorithms, experimental evaluations ofmachine learning, data mining in heterogeneous networks, distributed and decentralizedmachine learning algorithms, intelligent cloud-support communications,ressource allocation, energy-aware communications, software de ned networks,cooperative networks, positioning and navigation systems, wireless communications,wireless sensor networks, underwater sensor networks. Enlace de acceso : https://link-springer-com.biblioproxy.umanizales.edu.co/referencework/10.1007/97 [...] Second IFIP TC 6 International Conference, MLN 2019, Paris, France, December 3–5, 2019, Revised Selected Papers [documento electrónico] / Boumerdassi, Selma, ; Renault, Éric, ; Mühlethaler, Paul, . - 1 ed. . - [s.l.] : Springer, 2020 . - XIII, 486 p. 267 ilustraciones, 183 ilustraciones en color.
ISBN : 978-3-030-45778-5
Libro disponible en la plataforma SpringerLink. Descarga y lectura en formatos PDF, HTML y ePub. Descarga completa o por capítulos.
Idioma : Inglés (eng)
Palabras clave: Procesamiento de datos IngenierÃa Informática Red de computadoras Software de la aplicacion Protección de datos MinerÃa de datos y descubrimiento de conocimientos IngenierÃa Informática y Redes Aplicaciones informáticas y de sistemas de información Seguridad de datos e información Clasificación: 6.312 Resumen: Este libro constituye las actas minuciosamente arbitradas de la Segunda Conferencia Internacional sobre Aprendizaje Automático para Redes, MLN 2019, celebrada en ParÃs, Francia, en diciembre de 2019. Los 26 artÃculos completos revisados ​​incluidos en el volumen fueron cuidadosamente revisados ​​y seleccionados entre 75 presentaciones. Presentan y discuten nuevas tendencias en aprendizaje profundo y de refuerzo, reconocimiento de patrones y clasificación para redes, aprendizaje automático para optimización de corte de redes, sistema 5G, predicción del comportamiento del usuario, multimedia, IoT, seguridad y protección, optimización y nuevos métodos innovadores de aprendizaje automático, análisis del rendimiento de las máquinas. algoritmos de aprendizaje, evaluaciones experimentales de aprendizaje automático, minerÃa de datos en redes heterogéneas, algoritmos de aprendizaje automático distribuidos y descentralizados, comunicaciones inteligentes con soporte en la nube, asignación de recursos, comunicaciones conscientes de la energÃa, redes definidas por software, redes cooperativas, sistemas de posicionamiento y navegación, comunicaciones inalámbricas, Redes de sensores inalámbricos, Redes de sensores submarinos. Nota de contenido: Network Anomaly Detection using Federated Deep Autoencoding Gaussian Mixture Model -- Towards a Hierarchical Deep Learning Approach for Intrusion Detection -- Network Trafic Classifi cation using Machine Learning for Software Defined Networks -- A Comprehensive Analysis of Accuracies of Machine Learning Algorithms for Network Intrusion Detection -- Q-routing: from the algorithm to the routing protocol -- Language Model Co-occurrence Linking for Interleaved Activity Discovery -- Achieving Proportional Fairness in WiFi Networks via Bandit Convex Optimization -- Denoising Adversarial Autoencoder for Obfuscated Tra c Detection and Recovery -- Root Cause Analysis of Reduced Accessibility in 4G Networks -- Space-time pattern extraction in alarm logs for network diagnosis -- Machine Learning Methods for Connection RTT and Loss Rate Estimation Using MPI Measurements Under Random Losses -- Algorithm Selection and Model Evaluation in Application Design using Machine Learning -- GAMPAL: Anomaly Detection forInternet Backbone Tra c by Flow Prediction with LSTM-RNN -- Revealing User Behavior by Analyzing DNS Tra c -- A new approach to determine the optimal number of clusters based on the Gap statistic -- MLP4NIDS: an e cient MLP-based Network Intrusion Detection for CICIDS2017 dataset -- Random Forests with a Steepend Gini-Index Split Function and Feature Coherence Injection -- Emotion-based Adaptive Learning Systems -- Machine learning methods for anomaly detection in IoT networks, with illustrations -- DeepRoute: Herding Elephant and Mice Flows with Reinforcement Learning -- Arguments Against using the 1998 DARPA Dataset for Cloud IDS Design and Evaluation and Some Alternative -- Estimation of the Hidden Message Length in Steganography: A Deep Learning Approach -- An Adaptive Deep Learning Algorithm Based Autoencoder for Interference Channels -- A Learning Approach for Road Tra c Optimization in Urban Environments -- CSI based Indoor localization using Ensemble Neural Networks -- Bayesian Classi ersin Intrusion Detection Systems -- A Novel Approach towards Analysis of Attacker Behavior in DDoS Attacks -- Jason-RS, a Collaboration between Agents and an IoT Platform -- Scream to Survive(S2S): Intelligent System to Life-Saving in Disasters Relief -- Association Rules Algorithms for Data Mining Process Based on Multi Agent System -- Internet of Things: Security Between Challenges and Attacks -- Socially and biologically inspired computing for self-organizing communications networks. . Tipo de medio : Computadora Summary : This book constitutes the thoroughly refereed proceedings of the Second International Conference on Machine Learning for Networking, MLN 2019, held in Paris, France, in December 2019. The 26 revised full papers included in the volume were carefully reviewed and selected from 75 submissions. They present and discuss new trends in deep and reinforcement learning, patternrecognition and classi cation for networks, machine learning for network slicingoptimization, 5G system, user behavior prediction, multimedia, IoT, securityand protection, optimization and new innovative machine learning methods, performanceanalysis of machine learning algorithms, experimental evaluations ofmachine learning, data mining in heterogeneous networks, distributed and decentralizedmachine learning algorithms, intelligent cloud-support communications,ressource allocation, energy-aware communications, software de ned networks,cooperative networks, positioning and navigation systems, wireless communications,wireless sensor networks, underwater sensor networks. Enlace de acceso : https://link-springer-com.biblioproxy.umanizales.edu.co/referencework/10.1007/97 [...]
TÃtulo : Third International Conference, MLN 2020, Paris, France, November 24–26, 2020, Revised Selected Papers Tipo de documento: documento electrónico Autores: Renault, Éric, ; Boumerdassi, Selma, ; Mühlethaler, Paul, Mención de edición: 1 ed. Editorial: [s.l.] : Springer Fecha de publicación: 2021 Número de páginas: XIII, 375 p. 165 ilustraciones, 148 ilustraciones en color. ISBN/ISSN/DL: 978-3-030-70866-5 Nota general: Libro disponible en la plataforma SpringerLink. Descarga y lectura en formatos PDF, HTML y ePub. Descarga completa o por capítulos. Idioma : Inglés (eng) Palabras clave: Procesamiento de datos Ordenadores Aprendizaje automático Red de computadoras Sistemas informáticos Computadoras Propósitos especiales MinerÃa de datos y descubrimiento de conocimientos Entornos informáticos Redes de comunicación informática Implementación de sistema informático Sistemas de propósito especial y basados ​​en aplicaciones Clasificación: 6.312 Resumen: Este libro constituye las actas arbitradas exhaustivamente de la Segunda Conferencia Internacional sobre Aprendizaje Automático para Redes, MLN 2019, celebrada en ParÃs, Francia, en diciembre de 2019. Los 26 artÃculos completos revisados ​​incluidos en el volumen fueron cuidadosamente revisados ​​y seleccionados de 75 presentaciones. Presentan y discuten nuevas tendencias en aprendizaje profundo y de refuerzo, reconocimiento de patrones y clasificación para redes, aprendizaje automático para optimización de segmentación de red, sistema 5G, predicción del comportamiento del usuario, multimedia, IoT, seguridad y protección, optimización y nuevos métodos innovadores de aprendizaje automático, análisis de rendimiento de algoritmos de aprendizaje automático, evaluaciones experimentales de aprendizaje automático, minerÃa de datos en redes heterogéneas, algoritmos de aprendizaje automático distribuidos y descentralizados, comunicaciones inteligentes con soporte en la nube, asignación de recursos, comunicaciones conscientes de la energÃa, redes definidas por software, redes cooperativas, sistemas de posicionamiento y navegación, comunicaciones inalámbricas, redes de sensores inalámbricos, redes de sensores submarinos. Nota de contenido: Better anomaly detection for access attacks using deep bidirectional LSTMs -- Using Machine Learning to Quantify the Robustness of Network Controllability -- Configuration faults detection in IP Virtual Private Networks based on machine learning -- Improving Android malware detection through dimensionality reduction techniques -- A Regret Minimization Approach to Frameless Irregular Repetition Slotted Aloha -- Mobility based Genetic algorithm for Heterogeneous wireless networks -- Geographical Information based Clustering Algorithm for Internet of Vehicles -- Active Probing for Improved Machine-Learned Recognition of Network Traffic -- A Dynamic Time Warping and Deep Neural Network Ensemble for Online Signature Verification -- Performance evaluation of some Machine Learning algorithms for Security Intrusion Detection -- Three Quantum Machine Learning Approaches for Mobile User Indoor-Outdoor Detection -- Learning resource allocation algorithms for cellular networks -- Enhanced Pub/Sub Communications for Massive IoT Traffic with SARSA Reinforcement Learning -- Deep Learning-Aided Spatial Multiplexing with Index Modulation -- A Self-Gated Activation Function SINSIG Based on the Sine Trigonometric for Neural Network Models -- Spectral Analysis for Automatic Speech Recognition and Enhancement -- Road sign Identification with Convolutional Neural Network using TensorFlow -- A Semi-Automated Approach for Identification of Trends in Android Ransomware Literature -- Towards Machine Learning in Distributed Array DBMS: Networking Considerations -- Deep Learning Environment Perception and Self-Tracking for Autonomous and Connected Vehicles -- Remote Sensing Scene Classification Based on Effective Feature Learning by Deep Residual Networks -- Identifying Device Types for Anomaly Detection in IoT -- A novel heuristic optimization algorithm for solving the Delay-Constrained Least-Cost problem -- Terms Extraction from Clustered Web Search Results. . Tipo de medio : Computadora Summary : This book constitutes the thoroughly refereed proceedings of the Second International Conference on Machine Learning for Networking, MLN 2019, held in Paris, France, in December 2019. The 26 revised full papers included in the volume were carefully reviewed and selected from 75 submissions. They present and discuss new trends in deep and reinforcement learning, pattern recognition and classification for networks, machine learning for network slicing optimization, 5G system, user behavior prediction, multimedia, IoT, security and protection, optimization and new innovative machine learning methods, performance analysis of machine learning algorithms, experimental evaluations of machine learning, data mining in heterogeneous networks, distributed and decentralized machine learning algorithms, intelligent cloud-support communications, ressource allocation, energy-aware communications, software de ned networks, cooperative networks, positioning and navigation systems, wireless communications, wireless sensor networks, underwater sensor networks. Enlace de acceso : https://link-springer-com.biblioproxy.umanizales.edu.co/referencework/10.1007/97 [...] Third International Conference, MLN 2020, Paris, France, November 24–26, 2020, Revised Selected Papers [documento electrónico] / Renault, Éric, ; Boumerdassi, Selma, ; Mühlethaler, Paul, . - 1 ed. . - [s.l.] : Springer, 2021 . - XIII, 375 p. 165 ilustraciones, 148 ilustraciones en color.
ISBN : 978-3-030-70866-5
Libro disponible en la plataforma SpringerLink. Descarga y lectura en formatos PDF, HTML y ePub. Descarga completa o por capítulos.
Idioma : Inglés (eng)
Palabras clave: Procesamiento de datos Ordenadores Aprendizaje automático Red de computadoras Sistemas informáticos Computadoras Propósitos especiales MinerÃa de datos y descubrimiento de conocimientos Entornos informáticos Redes de comunicación informática Implementación de sistema informático Sistemas de propósito especial y basados ​​en aplicaciones Clasificación: 6.312 Resumen: Este libro constituye las actas arbitradas exhaustivamente de la Segunda Conferencia Internacional sobre Aprendizaje Automático para Redes, MLN 2019, celebrada en ParÃs, Francia, en diciembre de 2019. Los 26 artÃculos completos revisados ​​incluidos en el volumen fueron cuidadosamente revisados ​​y seleccionados de 75 presentaciones. Presentan y discuten nuevas tendencias en aprendizaje profundo y de refuerzo, reconocimiento de patrones y clasificación para redes, aprendizaje automático para optimización de segmentación de red, sistema 5G, predicción del comportamiento del usuario, multimedia, IoT, seguridad y protección, optimización y nuevos métodos innovadores de aprendizaje automático, análisis de rendimiento de algoritmos de aprendizaje automático, evaluaciones experimentales de aprendizaje automático, minerÃa de datos en redes heterogéneas, algoritmos de aprendizaje automático distribuidos y descentralizados, comunicaciones inteligentes con soporte en la nube, asignación de recursos, comunicaciones conscientes de la energÃa, redes definidas por software, redes cooperativas, sistemas de posicionamiento y navegación, comunicaciones inalámbricas, redes de sensores inalámbricos, redes de sensores submarinos. Nota de contenido: Better anomaly detection for access attacks using deep bidirectional LSTMs -- Using Machine Learning to Quantify the Robustness of Network Controllability -- Configuration faults detection in IP Virtual Private Networks based on machine learning -- Improving Android malware detection through dimensionality reduction techniques -- A Regret Minimization Approach to Frameless Irregular Repetition Slotted Aloha -- Mobility based Genetic algorithm for Heterogeneous wireless networks -- Geographical Information based Clustering Algorithm for Internet of Vehicles -- Active Probing for Improved Machine-Learned Recognition of Network Traffic -- A Dynamic Time Warping and Deep Neural Network Ensemble for Online Signature Verification -- Performance evaluation of some Machine Learning algorithms for Security Intrusion Detection -- Three Quantum Machine Learning Approaches for Mobile User Indoor-Outdoor Detection -- Learning resource allocation algorithms for cellular networks -- Enhanced Pub/Sub Communications for Massive IoT Traffic with SARSA Reinforcement Learning -- Deep Learning-Aided Spatial Multiplexing with Index Modulation -- A Self-Gated Activation Function SINSIG Based on the Sine Trigonometric for Neural Network Models -- Spectral Analysis for Automatic Speech Recognition and Enhancement -- Road sign Identification with Convolutional Neural Network using TensorFlow -- A Semi-Automated Approach for Identification of Trends in Android Ransomware Literature -- Towards Machine Learning in Distributed Array DBMS: Networking Considerations -- Deep Learning Environment Perception and Self-Tracking for Autonomous and Connected Vehicles -- Remote Sensing Scene Classification Based on Effective Feature Learning by Deep Residual Networks -- Identifying Device Types for Anomaly Detection in IoT -- A novel heuristic optimization algorithm for solving the Delay-Constrained Least-Cost problem -- Terms Extraction from Clustered Web Search Results. . Tipo de medio : Computadora Summary : This book constitutes the thoroughly refereed proceedings of the Second International Conference on Machine Learning for Networking, MLN 2019, held in Paris, France, in December 2019. The 26 revised full papers included in the volume were carefully reviewed and selected from 75 submissions. They present and discuss new trends in deep and reinforcement learning, pattern recognition and classification for networks, machine learning for network slicing optimization, 5G system, user behavior prediction, multimedia, IoT, security and protection, optimization and new innovative machine learning methods, performance analysis of machine learning algorithms, experimental evaluations of machine learning, data mining in heterogeneous networks, distributed and decentralized machine learning algorithms, intelligent cloud-support communications, ressource allocation, energy-aware communications, software de ned networks, cooperative networks, positioning and navigation systems, wireless communications, wireless sensor networks, underwater sensor networks. Enlace de acceso : https://link-springer-com.biblioproxy.umanizales.edu.co/referencework/10.1007/97 [...]
