With the development of cities and the prevalence of networks, interpersonal relationships have become increasingly distant. When people crave communication, they hope to find someone to confide in. With the rapid advancement of deep learning and big data technologies, an enabling environment has been established for the development of intelligent chatbot systems. By effectively combining cutting-edge technologies with human-centered design principles, chatbots hold the potential to revolutionize our lives and alleviate feelings of loneliness. A multi-topic chat companion robot based on a state machine has been proposed, which can engage in fluent dialogue with humans and meet different functional requirements. It can chat with users about movies, music, and other related topics, and recommend movies and music that may interest them to alleviate their loneliness and provide companionship. The interaction platform of the companion robot is realized through the QQ communication platform, with two chat modes: Conversation mode and recommendation mode. First, the KdConv open-source corpus was selected, and Python was used to crawl information on movies and music from Douban and QQ Music to establish and pre-process the dataset. Then, the dialogue function was implemented using generative language models and retrieval systems, while the recommendation function was achieved using user profiling and collaborative filtering. Finally, a state machine algorithm was used to achieve real-time switching between the two chat modes of the companion robot. In conclusion, test participants gave high ratings for the accuracy of the companion robot's responses and the satisfaction with its content recommendations. Compared to traditional large-scale integrated models, this robot employs a state-machine framework to achieve diverse functions through seamless state transitions, thereby enhancing computational speed and precision. Additionally, the robot can recommend movies and music, providing companionship and alleviating loneliness for users, which is of great significance in modern society where interpersonal relationships are increasingly alienated.
As a result of the introduction of new infectious illnesses, key infection prevention measures were implemented. Now, a new coronavirus (SARS-CoV-2) epidemic has expanded swiftly, causing the coronavirus illness 2019 (COVID-19). Many microorganisms spread illness via hospital surfaces due to environmental pollution. This virus has been associated to close contact between persons in tight situations such as houses, hospitals, assisted living, and residential institutions. Aside from health care settings, public buildings, faith-based community centers, marketplaces, transportation, and corporate environments are prone to COVID-19 transmission. Physical contact with the sanitizer device may cause for spread Covid virus. That’s why we have proposed an automatic fogger mechanism-based hand sanitizer that may be able to reduce covid risk. Disinfectant fog will flow when an object will pass through the machine. This project will save cost, time, and wastage along with Covid spreading risk. This project is about designing a good healthcare system. In recent years, sophisticated automation has influenced the health industry. Health care in poor nations is costly. So, the project is an attempt to tackle this issue.
YouTube videos on sustainable fashion enable the public to gain basic knowledge about this concept. In this paper, we analyse user comments on YouTube videos that contain sustainable fashion content. The paper’s main objective is to help content creators and business managers effectively understand the perspectives of viewers, thus improving video quality and developing business. We analysed a dataset of 17,357 comments collected from 15 sustainable fashion YouTube videos. First, we use Latent Dirichlet Allocation (LDA), a topic modelling technique, to discover the abstract topics. In addition, we use two approaches to rank these topics: ranking based on proportion and Rank-1 method. Second, we apply sentiment analysis to identify the user’s emotional tone in the comments. As a result, 14 topics were identified. The most common positive and negative scores are 1 and −1, respectively. In total, there are 28.42% positive comments, 22.35% negative comments and 49.23% neutral comments.
