Vitor Manuel Filipe

Abstract
Deep learning and large language models (LLMs) have recently enabled studies in state-of-the-art technologies that enhance recommender systems. This research focuses on solving the next-item recommendation problem using these challenging technologies in Web applications, specifically focusing on a case study in the wine domain. This paper presents the characterization of the framework developed for the object of study: adaptive recommendation based on new modeling of the initial data to explore the user’s dynamic taste profile. Following the design science research methodology, the following contributions are presented: (i) a novel dataset of wines called X-Wines; (ii) an updated recommender model called X-Model4Rec—eXtensible Model for Recommendation supported in attention and transformer mechanisms which constitute the core of the LLMs; and (iii) a collaborative Web platform to support adaptive wine recommendation to users in an online environment. The results indicate that the solutions proposed in this research can improve recommendations in online environments and promote further scientific work on specific topics.

Abstract
This study examines the effectiveness of employing Advanced Rider Assistance System (ARAS) for enhancing motorcycle safety by reducing crashes and improving rider safety. The system includes both single solution approaches, like braking systems, and multi-sensor solutions that integrate data from LiDARs, radars, and cameras through sensor fusion. A systematic literature review was conducted to collect data from 2008 to 2024 across various sources related to ARAS. The review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines to ensure a comprehensive and transparent process. Data were extracted from the included studies, focusing on study design, sample size, intervention details, and outcomes. The risk of bias was assessed using a customized checklist. The review included 31 studies that met the inclusion criteria. Findings were summarized for single sensor solutions and sensor fusion approaches.The review indicates that single-solution systems are effective ARAS technologies. In contrast, the application of sensor fusion in motorcycles has been only minimally explored, making it difficult to draw definitive conclusions about its impact in this context. Evidence from four-wheeled vehicles, however, shows that sensor fusion can enhance perception robustness, improve performance under adverse conditions, and contribute to measurable safety gains. These results suggest that similar advantages could be realized for motorcycles as fusion-based ARAS technologies become more widely implemented. Moreover, sensor fusion holds the potential to provide riders with broader situational awareness and more comprehensive safety assistance than single-system solutions. Future research should focus on addressing the identified challenges and optimizing these systems for broader implementation. This review underscores the critical role of ARAS in reducing motorcycle-related incidents and improving rider safety, highlighting the need for ongoing research to refine sensor fusion algorithms and address technical challenges for real-world applications.

Abstract

Abstract

Abstract
The textile industry faces economic and environmental challenges due to low recycling rates and contamination from fasteners like buttons, rivets, and zippers. This paper proposes an Red, Green, Blue (RGB) vision system using You Only Look Once version 11 (YOLOv11) with a sliding window technique for automated fastener detection. The system addresses small object detection, occlusion, and fabric variability, incorporating Grounding DINO for garment localization and U2-Net for segmentation. Experiments show the sliding window method outperforms full-image detection for buttons and rivets (precision 0.874, recall 0.923), while zipper detection is less effective due to dataset limitations. This work advances scalable AI-driven solutions for textile recycling, supporting circular economy goals. Future work will target hidden fasteners, dataset expansion and fastener removal. © 2025 IEEE.