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We investigate and analyze methods to violence detection in this study to completely disassemble the present condition and anticipate the emerging trends of violence discovery research. In this systematic review, we provide a comprehensive assessment of the video violence detection problems that have been described in state-of-the-art researches. This work aims to address the problems as state-of-the-art methods in video violence detection, datasets to develop and train real-time video violence detection frameworks, discuss and identify open issues in the given problem. In this study, we analyzed 80 research papers that have been selected from 154 research papers after identification, screening, and eligibility phases. As the research sources, we used five digital libraries and three high ranked computer vision conferences that were published between 2015 and 2021. We begin by briefly introducing core idea and problems of video-based violence detection; after that, we divided current techniques into three categories based on their methodologies: conventional methods, end-to-end deep learning-based methods, and machine learning-based methods. Finally, we present public datasets for testing video based violence detectionmethods’ performance and compare their results. In addition, we summarize the open issues in violence detection in videoand evaluate its future tendencies.

In this paper, we propose a skeleton-based method to identify violence and aggressive behavior. The approach does not necessitate highprocessing equipment and it can be quickly implemented. Our approach consists of two phases: feature extraction from image sequences to assess a human posture, followed by activity classification applying a neural network to identify whether the frames include aggressive situations and violence. A video violence dataset of 400 min comprising a single person's activities and 20 h of video data including physical violence and aggressive acts, and 13 classifications for distinguishing aggressor and victim behavior were generated. Finally, the proposed method was trained and tested using the collected dataset. The results indicate the accuracy of 97% was achieved in identifying aggressive conduct in video sequences. Furthermore, the obtained results show that the proposed method can detect aggressive behavior and violence in a short period of time and is accessible for real-world applications.

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In recent years, the demand for mental health services has increased exponentially, prompting the need for accessible, cost-effective, and efficient solutions. This paper introduces an Artificial Intelligence (AI) enabled mobile chatbot psychologist that leverages AIML (Artificial Intelligence Markup Language) and Cognitive Behavioral Therapy (CBT) to provide psychological support. The chatbot is designed to facilitate mental health care by offering personalized CBT interventions to individuals experiencing psychological distress. The proposed mobile chatbot psychologist employs AIML, a language created to facilitate human-computer interactions, to understand user inputs and generate contextually appropriate responses. To ensure the efficacy of the chatbot, it is equipped with a knowledge base comprising CBT principles and techniques, enabling it to provide targeted psychological interventions. The integration of CBT allows the chatbot to address a wide range of mental health issues, including anxiety, depression, stress, and phobias, by helping users identify and challenge cognitive distortions. The paper discusses the development and implementation of the mobile chatbot psychologist, detailing the AIML-based conversational engine and the incorporation of CBT techniques. The chatbot's effectiveness is evaluated through a series of user studies involving participants with varying levels of psychological distress. Results demonstrate the chatbot's ability to deliver personalized interventions, with users reporting significant improvements in their mental well-being. The AIenabled mobile chatbot psychologist offers a promising solution to bridge the gap in mental health care, providing an easily accessible, cost-effective, and scalable platform for psychological support. This innovative approach can serve as a valuable adjunct to traditional therapy and help reduce the burden on mental health professionals, while empowering individuals to take charge of their mental well-being.