ISEP - DM – Engenharia de Inteligência Artificial
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Browsing ISEP - DM – Engenharia de Inteligência Artificial by advisor "Conceição, Luís Manuel Silva"
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- Ai-driven emotion recognition for mental health diagnoses: Assessing mental health through emotional state evaluationPublication . PRETO, PEDRO MIGUEL PERES; Conceição, Luís Manuel Silva; Figueiredo, Ana Maria Neves Almeida BaptistaMental health conditions remain a concerning challenge across the globe, requiring timely and reliable approaches to correctly make accurate diagnoses and effective interventions. Traditional assessment methods often rely on subjective self-reports and clinical interviews, which may not always capture the full spectrum of an individual’s emotional state. In this context, computational techniques for emotion analysis provide a complementary perspective by identifying patterns in facial expressions, speech, and language. This dissertation evaluates the potential of multimodal emotional state analysis and its contribution to mental health assessment, through the development of a computational application. A systematic review was conducted to evaluate existing methodologies and highlight their strengths, limitations, and applicability in clinical contexts. Building on this review, the present work explores an integration of visual, vocal, textual patterns, assessing the contribution of their combined capacity to improve the consistency and depth of emotional interpretation. An analysis centered on methodological design was conducted by applying techniques such as preprocessing, fine-tuning, and data augmentation on the datasets to enhance the model’s capacity. Ethical and security considerations were also incorporated to strengthen system robustness and ensure responsible deployment in the market. The proposed solution consists of an artificial intelligence based multimodal system that integrates the analysis of emotions present in facial expressions, voice, and text patterns to provide a comprehensive assessment of the user’s emotional state. The application’s modular architecture enables real-time processing and the generation of clinical reports. The experimental validation of the system revealed promising results across several DSM-5 domains, the clinical reference manual that defines diagnostic criteria for mental disorders cases. High F1-scores were recorded in domains such as Anger (0.84) and Personality Functioning (0.87), while more subtle domains, such as Dissociation (0.43) and Repetitive Behaviors (0.52), revealed more modest performance. The overall analysis resulted in an observed agreement level of 71.9% and a Cohen’s Kappa of 0.42, indicating moderate agreement with the DSM-5. The findings underline the promise of computational emotion analysis as a supplementary tool for mental health professionals, while also emphasizing the importance of critical evaluation of its limitations and careful integration into clinical practice.
- Assistente virtual inteligente para acesso a dados de negócioPublication . FRANCO, GUILHERME LIMA; Conceição, Luís Manuel SilvaModern organisations increasingly struggle to access and interpret enterprise data that is dispersed across isolated Business Information Systems (BIS). These silos hinder the ability to obtain a unified view of information, which is essential for timely and informed decisionmaking. Advances in Large Language Models (LLMs) offer the possibility of querying such data in natural language, thereby lowering the technical barrier for business users. However, the adoption of these models in corporate environments is constrained by concerns over data privacy, regulatory compliance, and the high operational costs of cloud-based solutions. These challenges underline the need for on-premises, resource-efficient approaches that preserve control over sensitive information. This dissertation presents an intelligent virtual assistant that answers business questions by orchestrating Model Context Protocol (MCP) tools to inspect schemas, draft explicitprojection SQL, validate read-only execution, and ground responses in results from a local Microsoft SQL Server instance of AdventureWorksDW2022. No model fine-tuning is performed; instead, the approach combines runtime schema filtering, deny-list validation, and prompt scaffolding to minimise hallucinations and enforce governance. A controlled evaluation over 52 representative prompts compares three configurations: a prompt-only baseline (B0), MCP with unfiltered schemas (B1), and a curated setup with filtering and explicit projections (S). The curated configuration yields substantially higher execution accuracy and fewer schema-error incidents than both baselines, demonstrating that governed tool use materially increases correctness without relaxing the privacy posture on a single on-premises workstation. Latency observations are reported descriptively and are attributable primarily to model generation rather than orchestration. These findings support the feasibility of privacy-preserving, on-premises conversational analytics under the EU General Data Protection Regulation (GDPR) and the EU Artificial Intelligence Act (Regulation (EU) 2024/1689), and suggest practical next steps: broadening schema coverage, refining curation policies, and exploring lighter local models and decoding strategies to improve interactivity.