Main Reference PaperMobile Big Data Analytics Using Deep Learning and Apache Spark, IEEE Transactions on BigData, 2016 [Java/Hadoop].
  • The aim of this work is to speed up the decision making process within large-scale mobile systems. This work is done by parallelizing the learning of deep models to a high-performance cluster. The deep model is performed by slicing the mobile big data into many partitions. Each partition is contained in a resilient distributed dataset and distributed to the worker nodes of Spark.

+ Description
  • The aim of this work is to speed up the decision making process within large-scale mobile systems. This work is done by parallelizing the learning of deep models to a high-performance cluster. The deep model is performed by slicing the mobile big data into many partitions. Each partition is contained in a resilient distributed dataset and distributed to the worker nodes of Spark.

  • To decrease the computational time of learning algorithms.

  • To achieve the time-efficient big data analytics.

+ Aim & Objectives
  • To decrease the computational time of learning algorithms.

  • To achieve the time-efficient big data analytics.

  • A technique is contributed to improve the learning process in large-scale activity recognition system.

+ Contribution
  • A technique is contributed to improve the learning process in large-scale activity recognition system.

  • Java JDK 1.8, Hadoop 1.2.1, MySQL 5.5.40.

  • Netbeans 8.0.1, J2SE, Hadoop,  Apache Spark.

+ Software Tools & Technologies
  • Java JDK 1.8, Hadoop 1.2.1, MySQL 5.5.40.

  • Netbeans 8.0.1, J2SE, Hadoop,  Apache Spark.

  • B.E / B.Tech / M.E / M.Tech

+ Project Recommended For
  • B.E / B.Tech / M.E / M.Tech

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