A Skeleton-based Approach for Campus Violence Detection

In this paper, we propose a skeleton-based method to identify violence and aggressive behavior. The approach does not necessitate high-processing 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.     

An indirect approach for egg weight sorting using image processing

An indirect approach for egg weight sorting, using image processing, is proposed in the paper. The eggs are sorted in four classes by weight. Regression analysis is used for approximation of relationship between egg weight and egg geometric parameters—perimeter, area, major and minor axis, shape index and shape factor. The values of the geometric parameters, collected by image processing and the one, collected by traditional method, are compared for each egg sample, using percent differences between data. The experimental results show that the most significant parameter for egg weight indirect measurement is the egg area, with correlation coefficient 0.989. The mathematical model for the relationship between weight and area of the egg is defined with coefficient of determination 0.978. The classification accuracy is achieved within the eggs test sample sorting. The total classification error is 2.5% for test set and 12.5% for training set.     

Artificial Intelligence-Enabled Chatbots in Mental Health: A Systematic Review

Clinical applications of Artificial Intelligence (AI) for mental health care have experienced a meteoric rise in the past few years. AI-enabled chatbot software and applications have been administering significant medical treatments that were previously only available from experienced and competent healthcare professionals. Such initiatives, which range from “virtual psychiatrists” to “social robots” in mental health, strive to improve nursing performance and cost management, as well as meeting the mental health needs of vulnerable and underserved populations. Nevertheless, there is still a substantial gap between recent progress in AI mental health and the widespread use of these solutions by healthcare practitioners in clinical settings. Furthermore, treatments are frequently developed without clear ethical concerns. While AI-enabled solutions show promise in the realm of mental health, further research is needed to address the ethical and social aspects of these technologies, as well as to establish efficient research and medical practices in this innovative sector. Moreover, the current relevant literature still lacks a formal and objective review that specifically focuses on research questions from both developers and psychiatrists in AI-enabled chatbot-psychologists development. Taking into account all the problems outlined in this study, we conducted a systematic review of AI-enabled chatbots in mental healthcare that could cover some issues concerning psychotherapy and artificial intelligence. In this systematic review, we put five research questions related to technologies in chatbot development, psychological disorders that can be treated by using chatbots, types of therapies that are enabled in chatbots, machine learning models and techniques in chatbot psychologists, as well as ethical challenges.