In this paper, we design and develop a brand new application for Persian stock-market chatbot using the retrieval approach namely ChatParse. The proposed architecture for this system consists of the Persian version of the BERT called ParsBERT in which we also add fully-connected and softmax layers to consider the number of classes according to our designed dataset. We manually design an appropriate Persian dataset for bourse application including 17 classes because we have found no Persian corpus for this application. ChatParse is able to have multi-turn conversations with users on the stock-market topic. The performance of the proposed system is evaluated in terms of accuracy, recall, precision, and F1-score on validation set. We also examine our application with test data acquired from users in real time. The average accuracy of the validation set over 17 classes is 68.29% showing the effectiveness of ChatParse as a new Persian Chatbot.

This article presents a thorough examination of eight state-of-the-art AI chatbots that are designed for general use. Through detailed descriptions and empirical research, which include the use of confusion matrices and a pairwise comparison methodology, the study systematically assesses these chatbots based on eight specific criteria. The main objective of this research is to pinpoint the most efficient AI chatbot for general purposes, thereby providing invaluable insights for both users and developers within the AI industry. By utilizing the results of this study, developers can gain access to crucial information and actionable recommendations that help in enhancing the performance of their chatbots. Furthermore, these findings act as a vital resource for budding developers who are venturing into the creation of their own chatbot projects. In a rapidly evolving technological landscape, where AI is increasingly infiltrating various aspects of daily life, such detailed evaluations play a crucial role. They guide both users and developers towards selecting and developing AI chatbots that are not just effective, but also user-friendly and adaptable to the shifting needs and preferences of their user base.

Background: Optimal maternal care requires timely access to accurate information. Recent technological advancements have led to the development of chatbots that provide essential information to women, empowering informed decision-making and enhancing healthcare experiences. These tools can significantly improve maternal health outcomes and support child well-being. However, research in this area is limited, highlighting the need for further exploration. This systematic review analyzed the existing literature on using chatbots in maternal care.  Methods: This systematic review followed the PRISMA-ScR guidelines to identify studies focused on using chatbots in maternal care. We searched PubMed, Web of Science, Scopus, and Google Scholar without limitations on time or geography to locate relevant studies. Data were collected using a standardized extraction form, and the results were presented in tables. Results: This study reviewed 10 articles out of 245 retrieved. Most of the research was conducted in Brazil and Singapore in 2024, utilizing a mixed-methods approach. Most of them designed the chatbot as a mobile application. The average age of the participants was 32.56 years, and the total follow-up period across the studies was 33 months. The primary objective was to enhance the mental health of pregnant women both during pregnancy and after childbirth. The chatbots provided timely health information on prenatal and postnatal care, dietary guidelines, and mental health support. Further research is needed to validate these findings and to advance the development of chatbot technology within healthcare. Conclusion: This systematic review suggests that chatbots can positively impact maternal health by providing essential support to pregnant women and new mothers. However, more studies are needed to confirm these benefits and to find the best ways to use this technology in healthcare.

Timely access to healthcare is crucial in order to maintain a high standard of living. However, obtaining medical consultations can be difficult, especially for those living in remote areas or during a pandemic when face-to-face consultations are not always possible. The ability to accurately diagnose diseases is essential for effective treatment, and recent technological advancements offer a potential solution. Machine learning (ML) and Natural language processing (NLP) enables computer programs to understand human language and extract desired features from responses, allowing for human-like interaction with users. By leveraging these technologies, healthcare professionals can potentially provide more accessible and efficient medical consultations to individuals, regardless of their location. The concept is to establish an online platform where users can ask medical-related queries and receive responses from both medical professionals and fellow users. The platform would feature a Medical Chatbot, which employs advanced ML techniques to analyze user-provided symptoms and provide initial disease diagnosis and related information prior to consulting with a doctor. This disease prediction chatbot interacts dynamically with the users to enter the symptoms of the diseases and based on syntactic and semantic similarity response is given. In this work the threshold of similarity score is kept of 0.7. K-Nearest neighbors, Random forest, Support vector machine, Naive bayes and Logistic regression algorithms are used for prediction of disease based on symptoms which are faced by users. The syntactic similarity, fuzzy string matching and semantic similarity using all-MiniLM-L6-v2 model is used to improve the efficiency of the result.