Main Reference PaperA Model of Telecommunication Network Performance Anomaly Detection Based on Service Features Clustering, IEEE Access, March 2017 [Java]
  • K-Means clustering based multi-indicator anomaly detection method an ensemble clustering algorithm is proposed that applies clustering of service features of service providers with multi-dimensional physical scene characteristics.

+ Description
  • K-Means clustering based multi-indicator anomaly detection method an ensemble clustering algorithm is proposed that applies clustering of service features of service providers with multi-dimensional physical scene characteristics.

  • To perform classified anomaly detection for cells with different features and scene categories.

  • To improve accuracy of network performance anomaly detection.

+ Aim & Objectives
  • To perform classified anomaly detection for cells with different features and scene categories.

  • To improve accuracy of network performance anomaly detection.

  • A technique is contributed that improves the accuracy further..

+ Contribution
  • A technique is contributed that improves the accuracy further..

  • Java JDK 1.8, MySQL 5.5.40

  • Netbeans 8.0.1, & J2SE.

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

  • Netbeans 8.0.1, & J2SE.

  • M.E / M.Tech / MS / Ph.D.- Customized according to the client requirements.

+ Project Recommended For
  • M.E / M.Tech / MS / Ph.D.- Customized according to the client requirements.

  • No Readymade Projects-Depending on the complexity of the project and requirements.

+ Order To Delivery
  • No Readymade Projects-Depending on the complexity of the project and requirements.

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