# A Review on Deep-Learning-Based Cyberbullying Detection
## 7 Deep Learning Frameworks
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Frameworks** & **Strengths** & **Limitations** & **Supported** & **Used in** \\  & & & **DL Algorithms** & **Cyberbullying** \\ \hline \multirow{4}{*}{DyNet} & \(\bullet\) & Optimized C++ backend and lightweight & \multirow{4}{*}{\(\bullet\)} & Does not support model parallelism, although it supports execution on a single CPU or GPU. & \multirow{4}{*}{RNNs [176]} & No Works Found \\  & \(\bullet\) & Optimized C++ backend and lightweight & & \\  & representation. & & & \\  & It is faster than other static & & & \\  & declaration toolkits and significantly faster than & & single CPU or GPU. & \\  & Chainer. & & & \\ \hline \multirow{4}{*}{MXNet} & \(\bullet\) & Portable and lightweight framework supported in mobile devices. & \multirow{4}{*}{\(\bullet\)} & \multirow{4}{*}{RNNs [176]} & \multirow{4}{*}{No Works Found} \\  & \(\bullet\) & Faster computational scalability with multiple CPUs and GPUs. & & \\  & Supports Python, Julia, C++, JavaScript, R, Go, Matlab, Perl, Scala, and Wolfram. & & & \\  & Supports ONNX format & & & \\  & such as Microsoft CNTK, for transforming models & & & \\  & between MXNet, Caffe, CNTK, and other DL & & & \\  & & & & \\  & & & & \\  & & & & \\  & & & & \\ \hline \multirow{4}{*}{Lasagne} & \(\bullet\) & Easy to understand, use, and expand. & & & \\  & Does not hide the abstractions behind Theano and directly processes. & & & \\  & Supports many & \(\bullet\) & Reliability and validity is not well established. & \multirow{4}{*}{\(\bullet\)} & \multirow{4}{*}{\(\bullet\)} \\  & optimization methods such as Network momentum, ADAM, and RMSprop. & & & \\  & & & & \\  & & & & \\  & & & & \\  & & & & \\  & & & & \\  & & & & \\  & & & & \\  & & & & \\ \hline \multirow{4}{*}{H\({}_{2}\)O} & \(\bullet\) & For Big Data processing and analytics, it is an optimized framework. & & & \\  & It provides a generic set of ML algorithms that & & & \\  & leverage Hadoop/Spark engines for large-scale dataset processing. & & & \\  & Supports ONNX format & & & \\  & such as Microsoft CNTK, for transforming models & & & \\  & between MXNet, Caffe, CNTK, and other DL & & & \\  & models. & & & \\ \hline \multirow{4}{*}{Google JAX} & \(\bullet\) & Ability to accelerate computations on hardware accelerators such as GPUs and TPUs. & \(\bullet\) & Smaller user community compared to other deep learning frameworks. & \multirow{4}{*}{\(\bullet\)} & \multirow{4}{*}{\(\bullet\)} \\  & Easy implementation of complex mathematical models & & & \\  & Relatively simple and intuitive API & & & \\  & Support for distributed computing. & & & \\  & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 7: _Cont.__Applicability of Different DL Frameworks_  
We observed different DL frameworks to pinpoint their suitability of using in cyberbully detection and prediction problems. Some frameworks are flexible and easy to implement while others are quick to implement in any deep-learning-based prototypes. In some cases, a few frameworks are fast to deploy different machine learning models, while other frameworks have quick processing speed. In order to define layers, network types (CNNs, RNNs), and standard model designs for image or natural language processing, these frameworks provide easy accessibility to their respective libraries. We find that several studies used TensorFlow [14, 129, 182, 183] in their experiments, because the framework is flexible, easy to use and suits natural language processing tasks. A rich collection of libraries make Tensorflow easily usable for the researchers in the field of natural language processing and in cyberbullying detection. The TensorFlow interface is not difficult to understand and use and the framework does not make the platform complicated for the beginners. However, Keras is also a popular framework [20, 25, 171, 184] for cyberbullying detection since the framework is built to provide a simple interface for quick prototyping by building active neural networks that can work upon TensorFlow.