Image Recommendation System Based on Environmental and Human Face Information.

With the advancement of computer hardware and communication technologies, deep learning technology has made significant progress, enabling the development of systems that can accurately estimate human emotions. Factors such as facial expressions, gender, age, and the environment influence human emotions, making it crucial to understand and capture these intricate factors. Our system aims to recommend personalized images by accurately estimating human emotions, age, and gender in real time. The primary objective of our system is to enhance user experiences by recommending images that align with their current emotional state and characteristics. To achieve this, our system collects environmental information, including weather conditions and user-specific environment data through APIs and smartphone sensors. Additionally, we employ deep learning algorithms for real-time classification of eight types of facial expressions, age, and gender. By combining this facial information with the environmental data, we categorize the user's current situation into positive, neutral, and negative stages. Based on this categorization, our system recommends natural landscape images that are colorized using Generative Adversarial Networks (GANs). These recommendations are personalized to match the user's current emotional state and preferences, providing a more engaging and tailored experience. Through rigorous testing and user evaluations, we assessed the effectiveness and user-friendliness of our system. Users expressed satisfaction with the system's ability to generate appropriate images based on the surrounding environment, emotional state, and demographic factors such as age and gender. The visual output of our system significantly impacted users' emotional responses, resulting in a positive mood change for most users. Moreover, the system's scalability was positively received, with users acknowledging its potential benefits when installed outdoors and expressing a willingness to continue using it. Compared to other recommender systems, our integration of age, gender, and weather information provides personalized recommendations, contextual relevance, increased engagement, and a deeper understanding of user preferences, thereby enhancing the overall user experience. The system's ability to comprehend and capture intricate factors that influence human emotions holds promise in various domains, including human-computer interaction, psychology, and social sciences.

Reliable Data Collection Methodology for Face Recognition in Preschool Children.

Most face datasets target adults who can make their own decisions. In the case of children, consent from parents or guardians is necessary to collect biometric information, thus making it very difficult. As a result, the amount of data on children is quite small and inevitably private. In this work, we built a database by collecting face data of 74 children aged 2-7 years in daycare facilities. In addition, we conducted an experiment to determine the best location to perform face recognition on children by installing cameras in various locations. This study presents the points and methods to be considered to build a children's face dataset and also studies the optimal camera installation setups for the face recognition of children.

Juglone Suppresses LPS-induced Inflammatory Responses and NLRP3 Activation in Macrophages.

The NLRP3 (NACHT, LRR and PYD domains-containing protein 3) inflammasome has been implicated in a variety of diseases, including atherosclerosis, neurodegenerative diseases, and infectious diseases. Thus, inhibitors of NLRP3 inflammasome have emerged as promising approaches to treat inflammation-related diseases. The aim of this study was to explore the effects of juglone (5-hydroxyl-1,4-naphthoquinone) on NLRP3 inflammasome activation. The inhibitory effects of juglone on nitric oxide (NO) production were assessed in lipopolysaccharide (LPS)-stimulated J774.1 cells by Griess assay, while its effects on reactive oxygen species (ROS) and NLRP3 ATPase activity were assessed. The expression levels of NLRP3, caspase-1, and pro-inflammatory cytokines (IL-1ß, IL-18) and cytotoxicity of juglone in J774.1 cells were also determined. Juglone was non-toxic in J774.1 cells when used at 10 µM (p < 0.01). Juglone treatment inhibited the production of ROS and NO. The levels of NLRP3 and cleaved caspase-1, as well as the secretion of IL-1ß and IL-18, were decreased by treatment with juglone in a concentration-dependent manner. Juglone also inhibited the ATPase activities of NLRP3 in LPS/ATP-stimulated J774.1 macrophages. Our results suggested that juglone could inhibit inflammatory cytokine production and NLRP3 inflammasome activation in macrophages, and should be considered as a therapeutic strategy for inflammation-related diseases.