scholarly journals Modelling children ever born using performance evaluation metrics: A dataset

Data in Brief ◽  
2021 ◽  
pp. 107077
Author(s):  
Jecinta U Ibeji ◽  
Temesgen Zewotir ◽  
Delia North ◽  
Lateef Amusa
Keyword(s):  
Performance Evaluation ◽  
Evaluation Metrics ◽  
Children Ever Born

Performance Evaluation Metrics

1998 ◽  
pp. 17-21
Author(s):  
Albert N. Link ◽  
John T. Scott
Keyword(s):  
Performance Evaluation ◽  
Evaluation Metrics

scholarly journals Predicting the Enrollment and Dropout of Students in the Post-Graduation Degree using Machine Learning Classifier

2019 ◽  
Vol 8 (11) ◽  
pp. 3083-3088 ◽  
Keyword(s):  
Performance Evaluation ◽  
Naive Bayes ◽  
Confusion Matrix ◽  
Dropout Rate ◽  
Naïve Bayes ◽  
Random Tree ◽  
Drop Out ◽  
Classification Model ◽  
Evaluation Metrics ◽  
Tree Classifier

Nowadays, In Bangladesh, the dropout rate at post-graduation level or incompletion of the post-graduation degree is considered as a serious problem in the education sector. This work can be used to support for identifying the specific individuals as well as the institutional factors which may next lead to the enrollment or drop out at the post-graduation degree. The real dataset is used to accomplish this work. Here, seven classification algorithms namely Naïve Bayes, Multilayer Perceptron, Logistic, Locally Weighted Learning (LWL), Random Forest, Random Tree, and Part are applied in this context. A confusion matrix is calculated for each classification model. Then, we computed all the seven performance evaluation metrics (accuracy, sensitivity, precision, specificity, F1 score, FPR, and FNR). Each classifier's performances are analyzed and measured from the computed performance evaluation metrics. Naïve Bayes, LWL, and Part classifier perform better than all other working classifiers attaining 86.36% accuracy and on the contrary, Random Tree classifier performs worst achieving 74.24% accuracy. After further analyzing of the result based on performance evaluation metrics, it is observed that LWL classifier performed best in this context among all the classifiers.

scholarly journals Performance Analysis of Hyperledger Fabric Platforms

2018 ◽  
Vol 2018 ◽  
pp. 1-14 ◽  
Author(s):  
Qassim Nasir ◽  
Ilham A. Qasse ◽  
Manar Abu Talib ◽  
Ali Bou Nassif
Keyword(s):  
Performance Evaluation ◽  
Performance Analysis ◽  
Open Source ◽  
Execution Time ◽  
Database Systems ◽  
Performance Level ◽  
Evaluation Metrics ◽  
Key Technology ◽  
High Workload ◽  
Time Latency

Blockchain is a key technology that has the potential to decentralize the way we store, share, and manage information and data. One of the more recent blockchain platforms that has emerged is Hyperledger Fabric, an open source, permissioned blockchain that was introduced by IBM, first as Hyperledger Fabric v0.6, and then more recently, in 2017, IBM released Hyperledger Fabric v1.0. Although there are many blockchain platforms, there is no clear methodology for evaluating and assessing the different blockchain platforms in terms of their various aspects, such as performance, security, and scalability. In addition, the new version of Hyperledger Fabric was never evaluated against any other blockchain platform. In this paper, we will first conduct a performance analysis of the two versions of Hyperledger Fabric, v0.6 and v1.0. The performance evaluation of the two platforms will be assessed in terms of execution time, latency, and throughput, by varying the workload in each platform up to 10,000 transactions. Second, we will analyze the scalability of the two platforms by varying the number of nodes up to 20 nodes in each platform. Overall, the performance analysis results across all evaluation metrics, scalability, throughput, execution time, and latency, demonstrate that Hyperledger Fabric v1.0 consistently outperforms Hyperledger Fabric v0.6. However, Hyperledger Fabric v1.0 platform performance did not reach the performance level in current traditional database systems under high workload scenarios.

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