## 2 Related Works
A model trained using TensorFlow in the object detection arena holds great potential for the early detection and prevention of violent incidents from the results obtained. The detection of violent speech is also made possible, thanks to data processing from audio channels processed using NLP techniques. This study has succeeded in developing a powerful tool contributing to the prevention of violent incidents, offering an effective, low-cost solution to the current security systems [8]. The work of [PERSON] et al. (2022) investigates the performance of various natural language processing models in automatically identifying and classifying interpersonal violence events in mental health electronic health records. This study, therefore, basically attempts to develop a model using NLP that will be able to automatically identify violent incidents from the clinical texts of patients under mental health services and then test this model on the available datasets. This was an application of the CRIS database, containing over 500,000 de-identified patient records from the South London and Maudsley NHS Trust. Text fragments from those data were manually labeled with tags such as the presence of violence, the type of violence--physical, sexual, and domestic violence--and the patient's status in the violent event of being a perpetrator, victim, or witness. Then, a test of the BioBERT model was conducted, which had been trained with these labels through the 10-fold cross-validation method. It is possible to calculate the precision, recall, and F1 score of the model. The outcome obtained was that the BioBERT model had a high correctness in detecting sexual violence types of up to 97%, while generally its performance was between 89 and 98% for the accuracy and recall scores in the detection and classification of violent events. These findings suggest that NLP models can be an effective tool for the automatic recognition and management of violent incidents in mental health services. However, in the study, it was stated that NLP methods can only detect recorded events and cannot detect unrecorded violent events, and it was emphasized that ethical and regulatory issues should be considered during the integration of such models into clinical decision support systems [16]. [PERSON] et al. have discussed some comparative approaches to the detection of offensive and insulting language in Hindi, Bengali, and English languages using natural language processing techniques in their study conducted in 2021. The purpose of this study was to automatically detect the offensive and insulting language generally encountered in social media content and to understand the relationship between these two phenomena. In this regard, the authors of the paper performed the detection of offensive language using support vector machine and deep learning models: BERT, ALBERT, and DistilBERT. The datasets used were from shared tasks, namely HASOC-detection of hate speech and insult content in Indo-European languages, and TRAC-2-detection of aggression and misogyny. In fact, the best performing classifiers developed during this study for different tasks achieved F-scores within the range of 0.70-0.80. Cross-annotating the two datasets with the use of the classifier, the researchers examined the overlap of the subcategories of offensive and insulting language. Indeed, the results showed that the categories of aggression and insult do overlap; aggression may or may not encompass insult, and vice versa. The study identified the development of approaches from different viewpoints as important for a better understanding and detection of such complex linguistic phenomena [17]. [PERSON] et al. (2022) developed a novel NLP algorithm for detecting partner violence in an emergency department setting. The main aim of the study was to develop a model capable of detecting cases of partner violence through unstructured clinical notes in EHRs and assess the effectiveness of this model.