Research Article
An Optimized Hyperparameter of Convolutional Neural Network Algorithm for Bug Severity Prediction in Alzheimer’s-Based IoT System
| Abbreviation | Full form |
| Adadelta | Adaptive delta | Adagrad | Adaptive gradient | Adam | Adaptive moment estimation | AdaMax | Adaptive max-pooling | AdaBoost | Adaptive boosting | ACO | Ant colony optimizer | AUC | Area under cover | BTS | Bug tracking system | CMT | Class membership information of a term | CNN | Convolution neural network | CNRFB | Convolutional neural network and ransom forest with boosting classifier | DL | Deep learning | J48 | Decision tree | XGBoost | Extreme gradient boosting | GA | Genetic algorithm | GWO | Gray wolf optimizer | HHO | Harris Hawk optimization | IoT | Internet of Things | KNN | K-nearest neighbor | LF | Levy flight | LSTM | Long short term memory | LMT | Logistic model trees | ML | Machine learning | MIoT | Medical in internet of things | NB | Naïve Bayes | MRIs | Magnetic resonance images | NLP | Natural language preprocessing | NLTK | Natural language toolkit | OS | Operating system | PSO | Particle swarm optimization | RLU | Rectified linear unit | RNG | Relative neighbor graph | RF | Random forest | SMOTE | Synthetic minority oversampling technique | SVM | Support vector machine | WSM | Weighted sum method |
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