## 2 Related Works
The main objective of the study was to examine how technologies such as AI, machine learning (ML), deep learning (DL), big data, and the Internet of Things (IoT) can be effectively used in the detection and prevention of such violence. These researchers reviewed various algorithms and applications regarding the performance and capability of these technologies for the large-scale prediction and prevention of violent behavior. They also explored how strategies developed based on the WHO's violence prevention strategy, 'INSPIRE', can be amalgamated with AI and mHealth solutions. The study found the application of AI-based crime prediction models particularly helpful in tracing and preventing violent crimes committed over the internet. The authors have claimed that hate crimes can be successfully detected using different ML algorithms like SVM, KNN, CNN, and GRU with a high degree of accuracy. It has also been underlined that the chatbot applications, for example LAW-U, are of relevance in victim support, both legally and in health matters. The obtained results show that AI might be an innovative, powerful tool in combating gender-based violence. This study brings in knowledge on how AI technologies could be applied more profoundly in this field and further provides recommendations for future research [14]. [PERSON] et al. developed a system for the detection and prevention of domestic violence incidents by incorporating NLP and ML techniques in their 2023 study. The main objective of the study was to alleviate the difficulties faced by victims of domestic violence and allow for emergency interventions through the early detection of such incidents. In this respect, the researchers proposed a mobile application that contains a voice recognition module, an image processing module, and an alert system. The app identifies the violent pattern of speech using NLP techniques, while image processing algorithms identify violent physical gestures. The proposed system analyzes both audio and image data for the detection of violent incidents with a high accuracy. The Convolutional Neural Networks, Random Forest, and K-Nearest Neighbor algorithms were used to analyze the audio data within the scope of the study. You Only Look Once was preferred for processing the image data. In the tests, the accuracy rates obtained with the CNN model were 93%, while the accuracy rate reached 91% in the KNN model. In the voice recognition module, the Random Forest algorithm showed 96% accuracy, while for the CNN algorithm, the accuracy was 94%. These results depict that the developed system performs highly in detecting and intervening in domestic violence. The system sends warnings of the detected incidents of violence to the emergency contacts and law enforcement agencies, and hence victims can be assisted as soon as possible. This work demonstrates that technology can be employed effectively in combating domestic violence and thus provides important grounds for new works in this area [15]. [PERSON] et al., in 2023, came up with a novel approach using deep learning techniques and natural language processing to detect violence through image and audio channels captured by surveillance cameras. The paper primarily worked on designing a system that could automatically detect incidents of violence, especially in crowded areas or dangerous areas, and help security forces to reach those incidents as quickly as possible. By applying TensorFlow, researchers created an object detection model that is able to recognize incidents of violence that could include weapons, fire, fisticuffs, purse snatching, and sexual harassment. They also designed an NLP-based model for detecting hate speech, sexual harassment, and offensive language using audio channels from surveillance cameras. The research used specially created image and audio datasets to improve the model's success. Violence detection has been performed based on a model that showed 84% accuracy in its work and gave effectual results under variant conditions, especially night vision ones.