## 2 Materials and Methods
### Module Development
#### 2.2.3 The Learning Algorithm
\begin{table}
\begin{tabular}{p{85.4 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**References** & **Machine Learning Algorithms** & **Dataset** & **Approaches** \\ \hline
[24] & Multilayer perceptron & Medical data set (diabetes, glass, ionosphere, iris) & Explored the tradeoff between energy consumption and accuracy for different hyperparameter configurations of a popular machine learning framework \\ \hline
[25] & Neural network & ImageNet & This approach leverages existing efficient network building blocks and focuses on exploiting hardware characteristics and adapting computational resources to accommodate target latency and/or power constraints. \\ \hline
[20] & Neural Networks & Not stated & HyperPower, a framework that enables efficient Bayesian optimization and random search in the context of power- and memory-constrained \\ \hline
[26] & Multilayer perceptron & Not stated & Tool for measuring the energy consumption of JVM programs using a bytecode level model of energy cost. \\ \hline
[16] & Convolutional neural network & Poker-Hand Forest Cover type & The goal is to provide useful guidelines to the machine learning community, giving them the fundamental knowledge to use and build specific energy estimation methods for machine learning algorithms. \\ \hline Proposed work & Recurrent neural network (LSTM) & Traffic data set & Exploring and optimizing the hyperparameters of machine learning algorithms employed in cellular traffic prediction. \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison with related work. ## References

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