Main Reference PaperUsing Machine Learning to Detect ‘Multiple-Account’ Cheating and Analyze the Influence of Student and Problem Features, IEEE Transactions on learning technologies, 2017
  • The proposed method exploits the random forest classification model for identifying the Copying Answers using Multiple Existences Online (CAMEO) phenomenon. It relies only on the student, problem features, and submission rather than the IP.

+ Description
  • The proposed method exploits the random forest classification model for identifying the Copying Answers using Multiple Existences Online (CAMEO) phenomenon. It relies only on the student, problem features, and submission rather than the IP.

  • To identify the multiple-account cheating based on machine learning

  • To accomplish greater sensitivity and specificity levels

+ Aim & Objectives
  • To identify the multiple-account cheating based on machine learning

  • To accomplish greater sensitivity and specificity levels

  • The integration of the feature scaling technique with the proposed method assists in mitigating the complexity issue of submission.

+ Contribution
  • The integration of the feature scaling technique with the proposed method assists in mitigating the complexity issue of submission.

  • Operating system: Ubuntu / Windows

  • Java/Python/R

+ Software Tools & Technologies
  • Operating system: Ubuntu / Windows

  • Java/Python/R

  • 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-project delivery Depending on the complexity of the project and requirements.

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

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