Welcome to
2019 International Conference on Machine Learning, Big Data and Business Intelligence (MLBDBI2019) provide a premier forum for the presentation of new advances and research results in the fields of machine learning, big data and business intelligence. The conference will bring together leading researchers, social workers and scientists in the domain of interest from around the world. Please click on the call for papers for the specific conference for further details.
Topics of interest for submission include, but are not limited to:
1.Machine Learning | 1.机器学习 |
Deep and Reinforcement learning Pattern recognition and classification for networks Machine learning for network slicing optimization Machine learning for 5G system Machine learning for user behavior prediction New innovative machine learning methods Optimization of machine learning methods Performance analysis of machine learning algorithms Experimental evaluations of machine learning Data mining in heterogeneous networks Machine learning for multimedia Machine learning for Internet of Things Machine learning for security and protection Distributed and decentralized machine learning algorithms | 深入和强化学习 网络的模式识别和分类 用于网络切片优化的机器学习 机器学习5G系统 用于用户行为预测的机器学习 新的创新机器学习方法 优化机器学习方法 机器学习算法的性能分析 机器学习的实验评估 异构网络中的数据挖掘 机器学习多媒体 物联网机器学习 机器学习的安全和保护 分布式和分散式机器学习算法 |
2.Big Data | 2.大数据 |
Big Data Analytics Data Science Models and Approaches Algorithms for Big Data Big Data Search and Information Retrieval Techniques Big Data Acquisition, Integration, Cleaning, and Best Practices Big Data and Deep Learning Scalable Computing Models, Theories, and Algorithms In-Memory Systems and Platforms for Big Data Analytics Big Data and High Performance Computing Cyber-Infrastructure for Big Data Performance Evaluation Reports for Big Data Systems Storage Systems (including file systems, NoSQL, and RDBMS) Resource Management Approaches for Big Data Systems Many-Core Computing and Accelerators Big Data Applications for Internet of Things Mobile Applications of Big Data Big Data Applications for Smart City Data Streaming Applications Fault Tolerance and Reliability Scalability of Big Data Systems Big Data Privacy and Security Big Data Archival and Preservation Visual Analytics Algorithms and Foundations Graph and Context Models for Visualization Big Data Transformation, and Presentation | 大数据分析 数据科学模型和方法 大数据的算法 大数据搜索和信息检索技术 大数据采集,集成,清洁和最佳实践 大数据和深度学习 可扩展的计算模型,理论和算法 用于大数据分析的内存系统和平台 大数据和高性能计算 大数据的网络基础设施 大数据系统的绩效评估报告 存储系统(包括文件系统,NoSQL和RDBMS) 大数据系统的资源管理方法 多核计算和加速器 物联网大数据应用 大数据的移动应用 智能城市的大数据应用 数据流应用程序 容错性和可靠性 大数据系统的可扩展性 大数据隐私和安全 大数据存档和保存 可视化分析算法和基础 可视化的图形和上下文模型 大数据转换和演示 |
3.Business Intelligence | 3.商务智能 |
Intelligent Computing Methodologies and Applications Evolutionary Computing and Learning Swarm Intelligence and Optimization Signal Processing and Pattern Recognition Image Processing and Information Security Virtual Reality and Human-Computer Interaction Business Intelligence and Multimedia Technology Healthcare Informatics Theory and Methods Natural Language Processing and Computingal Linguistics Intelligent Computing in Robotics Intelligent Control and Automation Intelligent Data Fusion Intelligent Agent and Web Applications Intelligent Fault Diagnosis Intelligent cloud-support communications Intelligent ressource allocation Intelligent energy-aware/green communications Intelligent software defined networks Intelligent positioning and navigation systems Intelligent wireless communications Intelligent wireless sensor networks | 智能计算方法和应用 进化计算和学习 群体智能与优化 信号处理和模式识别 图像处理与信息安全 虚拟现实与人机交互 商业智能和多媒体技术 医疗保健信息学理论与方法 自然语言处理与计算语言学 机器人智能计算 智能控制与自动化 智能数据融合 智能代理和Web应用程序 智能故障诊断 智能云支持通信 智能资源分配 智能能源感知/绿色通信 智能软件定义网络 智能定位和导航系统 智能无线通信 智能无线传感器网络 |
4. Other related topics | 4.其他相关主题 |