# SMS Spam Detection System Based on Deep Learning Architectures for Turkish and English Messages
## 3 Proposed Model  
It is considered that deep learning-based methods that are able to easily tolerate increases in the current amount of calculations and data will be more successful in SMS spam detection. It can be easily projected that deep neural networks are going to expedite this improvement further, along with the novel learning algorithms and architectures that are currently being developed. Today, deep learning algorithms such as CNN, LSTM, and RNN are being used for solutions to the problems encountered in blocking SMS spam in mobile phones. Our approach is described in Section 3. In this section, the deep learning-based model we propose in order to detect spam e-mails in a dataset consisting of Turkish and English text messages is explained. A deep learning algorithm was implemented following the classification process in order to identify those messages regarded as spam. We wanted to benefit from the power of deep neural networks while identifying SMS spam, as per the classification problem. In Figure 1, the proposed approach is presented. Overfitting is one of the most significant problems to overcome in the proposed hybrid model, as in all deep learning architectures. Overfitting occurs when a model gives high rates of performance for a training set but attains a low rate of performance on a new dataset other than the training set [44]. Various methods have been developed in order to prevent this problem. The first one among these methods is limiting the number of iterations. As the number of iterations increases in the learning phase, the model learns the training dataset with zero error, to the degree that it memorizes the dataset. The presence of noise in the input dataset leads to noise also being included in the model as real data [45]. Another method is using a part of the training data as the validation data and seeing whether the error rate is reduced in the validation data as the error rate is reduced in the training data and ensuring the early termination of the training [46]. The number of neurons and the activation function to be used directly lead to an over-fitting problem being encountered by the model. A classification process was conducted by using the proposed model and messages in the testing phase. A \"10-fold cross-validation\" method was used for all algorithms. The dataset was divided into 10 in the method of classification with 10-fold cross-validation. One of the parts was used as the test dataset, while the other nine were used as the training dataset. The aim here was to identify the best classifier for this dataset. Finally, the performances of the classifiers were analyzed, and the classifier with the highest performance was selected, in accordance with the evaluation criteria. The operations conducted on the hybrid model proposed in this study are explained step by step below. Step 1: The first step in the approach is data cleaning. In this step, redundant expressions are removed from the textual data. The special characters and punctuations within the dataset are removed using the \"string\" library in Python. Moreover, the \"Stop Words\" library from the \"Natural Language Toolkit (NLTK)--Corpus\" module is used with the aim of removing the stop words in Turkish such as \"ile, baz, ayrca, ve, belki, etc.\" from the dataset. The relevant library also involves stop words in the Turkish language. The expressions to be removed in this step are as follows:  
* Removal of redundant punctuation marks. * Transformation of all words to lowercase. * Removing the most common words bearing no significance in understanding a text in a language, such as \"the\", \"is\", and \"a\". Step 2: Data cleaning is followed by the text word embedding step. The purpose of this step is to transform the textual messages into a structure that is understandable by deep learning algorithms due to the fact that deep learning models only accept numerical data. Normalization is also performed in this step. Two different dictionaries are used for the normalization process in the study.