Ciências e Tecnologia | Comunicações em congressos, conferências, seminários/Communications in congresses, conferences, seminars
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Percorrer Ciências e Tecnologia | Comunicações em congressos, conferências, seminários/Communications in congresses, conferences, seminars por Domínios Científicos e Tecnológicos (FOS) "Engenharia e Tecnologia::Engenharia Eletrotécnica, Eletrónica e Informática"
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- Enhancing competency development and organizational effectiveness through advanced technologies: a position paperPublication . Dias, J. T.; Santos, Arnaldo; Martins, P.; São Mamede, HenriqueIn recent years, companies have faced increasing pressure from glob-alization, requiring them to adapt not only to survive but also to thrive in a highly competitive environment. This adaptation has been facilitated by the ef-ficient integration of technology, achieved through digital processes and collab-oration tools. Digital transformation has emerged as a critical element for main-taining competitiveness as economies become increasingly digital. To succeed in this ever-evolving environment, companies must balance leveraging existing strengths with seeking new organizational agility. Integrating advanced tech-nologies like Artificial Intelligence (AI) and Web Technologies, into education and professional training is a strategic response to the challenges posed by the current digital landscape. AI, with its adaptability and automation capabilities, offers benefits such as increased efficiency, personalized learning, and stream-lined administrative processes. Continuous evaluation of teaching and learning, along with data extraction and predictive analysis, enhances e-learning quality and informs organizational decisions. This research aims to investigate how ad-vanced technologies can predict and adapt organizational training needs to im-prove competency development and overall effectiveness. The research adopts a Design Science Research (DSR) methodology, focusing on the development and implementation of an AI-based framework for personalized training rec-ommendations. Expected outcomes include integrating AI-driven predictive models with existing Human Resources Management Systems to identify and address training needs, fostering employee skill development, organizational agility, and competitiveness in a rapidly changing market. Additionally, ad-dressing this issue promotes a more inclusive and empowering work environ-ment, enabling employees to thrive in an increasingly digital world.
- Enhancing digital libraries through NLP and recommender systems: current trends and future prospects with large language modelsPublication . Cardoso, Heike da Silva; Rocio, VitorIn an era characterized by rapid proliferation of scientific publications and overwhelming volumes of digital content, researchers, students, and faculty members face significant challenges in identifying literature relevant to their academic pursuits. This saturation of information has heightened the need for advanced Recommender Systems within university libraries, tailored specifically for navigating and discovering scientific literature. This paper proposes leveraging insights from librarians’ direct interactions with users to adapt existing Recommender Systems, augmented with NLP and LLMs, to better serve the specific needs of academic researchers. It should streamline the research process by delivering precise, relevant, and personalized literature recommendations, centered on a curated database of bibliographic information.
- Enhancing recruitment with LLMs and chatbotsPublication . Novais, Liliana; Rocio, Vitor; Morais, A. JorgeTraditional approaches in the competitive recruitment landscape frequently encounter difficulties in effectively identifying exceptional applicants, resulting in delays, increased expenses, and biases. This study proposes the utilisation of contemporary technologies such as Large Language Models (LLMs) and chatbots to automate the process of resume screening, thereby diminishing prejudices and enhancing communication between recruiters and candidates. Algorithms based on LLM can greatly transform the process of screening by improving both its speed and accuracy. By integrating chatbots, it becomes possible to have personalised interactions with candidates and streamline the process of scheduling interviews. This strategy accelerates the hiring process while maintaining principles of justice and ethics. Its objective is to improve algorithms and procedures to meet changing requirements and enhance the competitive advantage of talent acquisition within organisations.
- Formação Flash sobre IA Generativa: algumas notas para o desenvolvimento profissional docentePublication . Rocio, Vitor; Cruz, Elisabete; Freitas, João Correia de; Pereira, Carolina
- Professional training for effective adoption of Generative AI in the corporate world: bridging the gapPublication . Guedes, Fernando; Rocio, Vitor; Martins, PauloThis position paper emphasizes the critical role of professional training in facilitating the effective adoption of Generative AI (GenAI) in the corporate world. GenAI, with its ability to create new content from existing data, holds immense potential for transforming business processes, enhancing decision-making, and driving innovation. However, the adoption of GenAI faces significant challenges, including a shortage of skilled professionals, high implementation costs, data privacy concerns, and the complexity of integrating these technologies into existing systems. To address these challenges, this paper highlights the importance of comprehensive education and training programs tailored to equip employees with the necessary skills and knowledge. Such programs should focus on developing technical competencies and understanding the operational implications of GenAI. By analyzing current literature and case studies, this paper identifies key strategies for effective training and outlines best practices for integrating GenAI into corporate environments. The findings underscore the need for a strategic approach to training that aligns with the evolving demands of AI-driven innovation. This includes continuous learning and development initiatives, the promotion of a culture of innovation, and the implementation of responsible AI practices. By investing in professional training, organizations can bridge the skills gap, mitigate risks, and fully leverage the transformative potential of GenAI technologies, ultimately gaining a competitive edge in the market. Through this comprehensive exploration, the paper advocates for the integration of robust training frameworks that support the sustainable adoption of GenAI, ensuring that businesses are well-prepared to navigate the complexities and opportunities of the digital age.
- Pylung: a supporting tool for comparative study of ViT and CNN-based models used for lung nodules classificationPublication . Marques, Felipe; Pestana, Pedro Duarte; Filipe, VitorLung cancer is a significant global health concern, and accu- rate classification of lung nodules plays a crucial role in its early detec- tion and treatment. This paper evaluates and compares the performance of Vision Transformer (ViT) and Convolutional Neural Network (CNN) models for lung nodule classification using the Pylung tool proposed in this work. The study aims to address the lack of research on ViT in lung nodule classification and proposes ViT as an alternative to CNN. The Lung Image Database Consortium and Image Database Resource Ini- tiative (LIDC-IDRI) dataset is utilized for training and evaluation. The Pylung tool is employed for dataset preprocessing and comparison of models. Three models, ViT, VGG16, and ResNet50, are analyzed, and their hyperparameters are optimized using Optuna. The results show that ViT achieves the highest accuracy (99.06%) in nodule classifica- tion compared to VGG16 (98.71%) and ResNet50 (98.46%). The study contributes by introducing ViT as a model for lung nodule classification, presenting the Pylung tool for model comparison, and suggesting further investigations to improve the accuracy.
- Sistema de Classificação de Sinalética Gestual em competições de karatéPublication . Violante, Sónia Correia; Filipe, Vítor; Morais, A. Jorge; Rocha, Álvaro; Peñalvo, Francisco; Gonçalves, Ramiro; Garcia Holgado, Alicia; Moreira, FernandoEm contexto de Kumite (combate de Karate) propõe-se investigar um modelo de classificação da sinalética gestual do árbitro para atribuição de pontos, com recurso a Visão Computacional e técnicas de Aprendizagem Profunda. Foram realizadas três abordagens, todas tendo como base o recurso a modelos de Redes Neuronais Convolucionais (Convolutional Neural Network – CNN): Classificação de imagens com recurso a uma CNN; Deteção da pose humana com o modelo MoveNet; e a deteção e classificação de gestos com o modelo YOLOv5, via RoboFlow. A última abordagem obteve melhores resultados, com 100% de precisão para todas as classes, pelo que se testou a sua aplicação para a deteção e classificação dos gestos em vídeo.