Limited ability for artists and designers to create outline images on vertical surfaces such as walls. Methods, such as hand painting, are time-consuming, labor-intensive, and limit the creativity of professionals and art enthusiasts. Picasso Robotics System aims to revolutionize this process by combining advanced printing technology with artistic creativity, reducing the time required to create each painting while maintaining precision. The main motivation of the project is to bridge the gap in the efficiency and accuracy of traditional methods by integrating design selection and remote control through a dedicated mobile app with artificial intelligence capabilities expanding creative control and simplifying workflow. The mobile application is based on an innovative image processing system based on contour vector elements. This technology provides high accuracy and efficiency in creating designs and also allows you to easily scale and adapt images to different formats.

This article describes the application of a unique artificial intelligence model, trained on the basis of a special dataset, for drawing images. Artificial intelligence is very important in the modern world because it is a key driver of progress, offering innovative solutions to improve processes in various fields. A feature of this work is the use of the Stable Diffusion model and the creation of the LoRa model to create a specific object. The use of a special dataset, which is a classified set of images, to train models based on a stable propagation mechanism. The application of the image-to-image method, which consists in completely generating an image by having the model reproduce all the objects in the image, trying to preserve the maximum amount of detail in the image. As a result, a tool has been developed that can generate images of characters of a certain style.

Design plays a critical role and creates a positive user experience, establishing a solid brand identity and staying competitive in the digital world. This research investigates the impact of user data collecting and analysis in improving user experience. The Data-driven User interface design can create an optimized and relevant UI with individual preferences, which leads to increasing user engagement and usability. The proposal explores methodologies to implement data collection and user research for tailoring digital products.

The classification of banana ripeness remains an important task in the food industry, as it directly affects the quality of the product and its shelf life. This paper presents an automated ripeness assessment system implemented using a comparative analysis of machine learning and deep learning algorithms. We tested the effectiveness of Random Forest, a custom CNN model, as well as the pre-trained ResNet50, EfficientNetB0, and VGG16 models, based on a dataset of 9960 images categorized into 3 ripeness stages (overripe, ripe, unripe). The results show the superiority of deep neural networks over classical methods: the ResNet50 architecture demonstrated 98% accuracy with a macro-averaged F1-score of 96%. The implementation of the proposed solution in the retail sector can automate ripeness monitoring and significantly reduce food waste.

The growing demand for creative and recreational activities has led to the development of an AI-powered platform that automates paint-by-numbers canvas generation. By integrating image segmentation, color extraction (using K-means clustering), and vectorization, the system transforms user-uploaded images into customizable paint-by-number templates with precise color palettes. Leveraging OpenAI’s DALL-E for image generation, the platform simplifies a traditionally labor-intensive process while maintaining high-quality outputs. Usability tests confirm high user satisfaction due to its intuitive interface and high-resolution results. Beyond artistic applications, the tool also offers therapeutic benefits by promoting mindfulness and stress reduction, bridging the gap between technology and creative expression for both hobbyists and businesses.

E-commerce has become a significant sales medium for the HVAC industry, allowing businesses to capture a broader market and enhance customer service. Not all HVAC company e-commerce sites, however, are easy and efficient to shop through. This study identifies the most essential factors that constitute an efficient e-commerce site for HVAC equipment, with focus on usability, accessibility of information, and the reliability of the purchasing process. Based on secondary research, qualitative research, comparative analysis, and expert opinion, the study evaluates leading HVAC online portals such as LG B2B, Daikin, and Midea. The study infers that a simple-to-use interface, precise product description, robust search and filtering, and secure ordering and support system significantly enhance business performance and customer experience. The study concludes that companies that emphasize these factors on their e-commerce sites are likely to improve conversion rates, customer satisfaction, and business efficiency. The future study must explore the impact of newer technologies such as AI-based personalization and augmented reality (AR) in further optimizing HVAC e-commerce experiences.