ISEP - DM – Engenharia de Inteligência Artificial
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Percorrer ISEP - DM – Engenharia de Inteligência Artificial por orientador "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.
- An explainable and privacy-preserving machine learning pipeline for early detection of endometriosis leveraging liquid biopsy and minimally-invasive CPublication . MANESSE, CIRO MIGUEL POÇAS FERREIRA; Martinho, Diogo Emanuel Pereira; Conceição, Luís Manuel SilvaEndometriosis a!ects approximately one in ten women of reproductive age, yet it is diagnosed, on average, seven to eight years after the onset of symptoms, and in some studies up to twelve. This diagnostic delay is associated with prolonged symptoms, uncertainty, repeated healthcare contacts and delayed therapeutic intervention, largely because a definitive diagnosis still depends on an invasive surgical procedure, namely laparoscopy. Recent progress in Machine Learning, together with the growing availability of clinical and molecular data, o!ers a realistic opportunity to shorten this interval. This dissertation investigates whether a non-invasive, explainable and privacy-preserving Machine Learning pipeline can support an earlier clinical suspicion of Endometriosis from micro-RNA (miRNA) measured in a blood sample, that is, a liquid biopsy. Three requirements are addressed: the test must be non-invasive; its decisions must be explainable, so that a clinician can examine and verify them; and the training procedure must protect sensitive patient data. To the best of the author’s knowledge, no previous work brings these three properties together for Endometriosis detection. Working exclusively with real, public data, a leakage-safe pipeline was built for the serummiRNA cohort GSE279435 (127 samples; 67 Endometriosis, 60 benign controls). A central observation in understanding the data is that the measurements are left-censored at the assay’s limit of detection, so that a missing value is itself informative. Additionally, every preprocessing step was fitted strictly inside each cross-validation fold to avoid optimistic bias. Five classifiers were compared under nested, repeated, stratified cross-validation. The two strongest (Random Forest and LightGBM) reached an area under the ROC curve of 0.77– 0.78 with balanced sensitivity and specificity, a performance comparable to that of the previously published eleven-miRNA model evaluated on the same cohort. Explainability was provided with SHAP, which makes each prediction inspectable both globally and at the level of an individual patient, allowing the model to be examined rather than trusted blindly. Five of the ten most influential miRNAs coincide with the published diagnostic panel, which grounds the model’s reasoning in established biology; the remaining five are plausible additional candidates, consistent with the di!erential-expression analysis, that would warrant follow-up in larger studies. On privacy, this is, to the best of the author’s knowledge, the first work to apply both Di!erential Privacy and Federated Learning to non-invasive miRNA-based Endometriosis detection. In a simulated federated setting, created by partitioning the public cohort into four virtual sites, a Federated Learning implementation built with NVIDIA FLARE trained a shared model without moving any raw data and recovered approximately 95% of the accuracy of a model trained on the pooled data, well above what any single site achieved in isolation. Di!erential Privacy, in contrast, reduced accuracy to little above chance at the privacy levels that would meaningfully protect patients, illustrating how costly strong, sample-level privacy guarantees become when the cohort is this small (n = 127). A cross-platform exploratory test on an independent plasma cohort indicated that the general signal — that circulating miRNA carries an Endometriosis-related signal — holds across biofluids, even though the specific serum signature does not transfer directly to plasma. The contribution of this work is therefore not a higher headline accuracy but a pipeline that, using real public data, reaches the performance of the previously published model while adding two properties that model did not have: explanations a clinician can recognise, and training that never centralises patient data. The proposed pipeline is presented not as a medical device, but as a reproducible and ethically framed foundation for the larger, multiinstitutional validation studies needed to support earlier clinical suspicion of Endometriosis.