Inteligencia Artificial

Regularized Transfer Learning in Brain Tumor Classification: Leveraging Pretrained Models for Improved Diagnostic Accuracy

2025-11-14T14:47:26+01:00

Classification of medical images for precise detection and treatment of diseases is very vital. This study focuses mainly on classifying MRI images of brain tumors for neurological disorders. This study introduces a Systematic deep learning framework based on an enhanced ResNet50 architecture to improve brain tumor classification. The proposed approach incorporates dropout regularization, Global Average PoolingGAP), additional dense layers, and leaky ReLU activation to boost feature extraction and reduce overfitting. This work uses a varied set of MRI images. The dataset has gone through a different enhancement and preprocessing pipeline that includes cropping, resizing, contrast enhancement, and smoothing. The model evaluation process incorporated stratified 5-fold cross-validation. Different performance Indicators were measured through accuracy, precision, recall, and F1-score. The proposed architecture attained an average testing accuracy of 99.62% (95% Confidence Interval (CI)). These measures surpass benchmark models such as VGG16, MobileNet, InceptionV3, and Xception, in addition to previously documented methodologies in the existing literature. The result shows the stability across the folds and was statistically corroborated by ablation. Its ablation studies revealed that although the architectural alterations yielded only marginal enhancements over the baseline.

A novel approach for Rare Disease symptom extraction from Clinical Texts Using Knowledge Graph Embeddings and Large Language Models

2025-09-02T19:13:55+02:00

Rare diseases are medical conditions affecting a small percentage of the population, often leading to diagnostic challenges due to limited clinical data and symptom overlap with common diseases. Due to the lack of knowledge about these disorders and the fact that many of their symptoms are similar to those of common disorders, doctors find it difficult to diagnose these conditions. This paper leverages DeepSeek Plus Knowledge Graph for high-precision symptom extraction in rare diseases, integrating natural language processing (NLP) and graph-based reasoning to enhance disease recognition. This study presents a novel model called “Rarepheno” that extracts symptoms related to rare diseases from clinical text using DeepSeek Large Language Model (LLM) and knowledge graphs (KG). The proposed model integrates DeepSeek AI, N-GRAM based term extraction, and the Human Phenotype Ontology (HPO) knowledge graph to enhance symptom identification. To improve contextual precision and reduce false positives, the system leverages semantic similarity through BioClinicalBERT embeddings. The proposed model outperforms the state-of-the-art rare disease phenotyping algorithms, with a precision score of 0.83, compared to PhenoGPT (GPT-J) (0.809), PhenoTagger (0.720), and ClinPhen (0.590). Compared to rule-based systems like MetaMap (0.707) and NCBO (0.777), Rarepheno demonstrated superior accuracy in extracting standardized symptom terms. This performance gain reflects its ability to reduce false positives and improve symptom-disease association, particularly in heterogeneous and unstructured clinical texts. This study demonstrates how the new-age technologies, such as GenAI and Natura Language Processing (NLP) can aid in collecting valuable clinical data, improving patient safety and rare disease identification.

Prompt engineering applied to code generation: a preliminary systematic review

2026-01-26T13:56:19+01:00

This Systematic Literature Review examines prompt engineering in automatic code generation using large language models (LLMs). A methodological protocol identified 26 relevant primary studies to characterize the status, trends, challenges, and opportunities of prompt engineering in software development. The results show that prompt engineering has been established as a key discipline for optimizing interaction with LLMs and improve the accuracy, robustness, and applicability of the generated code. The findings were grouped into six recurring thematic categories: structured methodologies, pedagogical strategies, accuracy and robustness, code improvement techniques, security through prompts, and the relevance of prompt engineering in the interaction with LLMs. It also highlights that the accelerated growth of publications between 2021 and 2025, as well as the sustained academic interest, reflect the strategic value of prompt engineering in the software lifecycle. This review provides a critical knowledge base for researchers and developers seeking to integrate prompt engineering into their processes and practices effectively.

Unveiling the Frontiers of Cyber Threat Hunting: A Comprehensive Review of LLM-Based Approaches

2026-01-27T10:28:29+01:00

The dynamic and ever-evolving cyber threat landscape necessitates proactive and sophisticated defensive strategies. Cyber threat hunting, a critical practice, moves beyond reactive security measures. It actively searches for undetected malicious activities. Traditionally reliant on human expertise and structured data analysis, threat hunting is increasingly augmented and transformed by the capabilities of artificial intelligence (AI) and machine learning (ML). Among the latest advancements, large language models (LLMs) have emerged as powerful tools with significant potential to revolutionize how cybersecurity professionals detect, analyze, and respond to threats. This paper presents a comprehensive and systematic review of the state-of-the-art LLM-based approaches for cyber threat hunting, focusing significantly on research published in 2025 and grounding these advances in foundational work published before 2023. We explore the diverse applications of LLMs throughout the threat-hunting lifecycle, including advanced cyber threat intelligence (CTI) analysis, real-time anomaly detection, incident response enhancement, and vulnerability prediction. Furthermore, we examine the foundational AI and LLM concepts driving these innovations. We delve into emergent datasets and evaluation methodologies crucial for progress and critically assess the substantial challenges, limitations, and ethical considerations such as robustness, explainability, data privacy, and adversarial vulnerabilities that are inherent in their deployment. Our review provides a novel synthesis of recent Large Language Model (LLM) applications in threat hunting and identifies critical research gaps. Additionally, it presents a refined perspective on the practical implementation and future trajectory of these technologies.

Enhancing Voter Engagement in Decentralized Autonomous Organizations Through Recommender Systems

2026-05-05T10:38:30+02:00

Decentralized Autonomous Organizations (DAOs) offer a novel approach to collectively governing projects through a democratic mechanism facilitated by blockchain. DAOs allow members to put forward and vote on proposals, thereby shaping the organization’s future. However, low voter turnout is common in DAO decision-making, particularly in large and active DAOs, where the high volume of proposals makes it unrealistic to expect members to track all proposals. Abstentionism threatens the voting system's effectiveness and the legitimacy of the results. We consider that recommender systems can help boost voter engagement. This article details the design of a recommendation approach tailored to aid DAO members in identifying proposals of interest, alongside its implementation and evaluation. The design accommodates the domain constraints that render off-the-shelf recommendation approaches inadequate, namely that proposals are short-lived and can only be recommended while available for voting. To the best of our knowledge, this is the first study to examine recommendation in DAO governance. To carry out our research, we have compiled a dataset, made publicly available, covering 12 of the most active DAOs. We compare a baseline, specifically designed to accommodate DAO-specific constraints, against a range of recommendation techniques. The findings confirm that personalized recommenders can often anticipate voting preferences, significantly outperforming the baseline. In turn, given the limitations of offline evaluation, an online evaluation using A/B testing would also be needed to fully assess their impact on participation. We also discuss how their implementation must carefully incorporate fairness and transparency to ensure community trust and adoption. We believe that proposal recommender systems in DAOs not only could drive engagement improvements similar to those observed in other online collaborative projects, but can also provide insights for collective governance settings beyond blockchain, such as cooperative organizations or participatory budgeting platforms.

A MUST-Former: A Family of Transformer-Based Models for Robust Multi-Modal Flood Mapping from Satellite Data.

2026-01-23T19:47:07+01:00

Flood disasters pose severe threats to human lives and infrastructure worldwide. Accurate and timely flood detection using satellite imagery is essential for emergency response and disaster management. This paper introduces MUST-Former, a family of five deep learning models designed for robust global flood mapping using multi-modal satellite data. The proposed architectures leverage Sentinel-1 radar and Sentinel-2 optical imagery from the Sen1Floods11 dataset, which covers 11 flood-affected regions across 6 continents. Three distinct fusion strategies are presented: Projection-based fusion, Cross-Attention fusion, and the flagship Principal Component Analysis-inspired fusion. The models address critical challenges in flood detection, including severe class imbalance, geographic bias, and sensor-specific limitations. The MUST-Former-PCA variant achieves a mean Intersection over Union of 90.35% on the test set and 89.32% on the unseen Bolivia region, demonstrating superior generalization. Compared to existing baselines, MUST-Former-SAR shows a 160% improvement in mean IoU over the original Sen1Floods11 baseline, while MUST-Former-Optical achieves competitive performance against foundation models using 3.6 times fewer parameters and superior geographic generalization on the unseen Bolivia split. The fusion models successfully mitigate single-modality failures across diverse geographic regions. Statistical reproducibility is confirmed across three independent random seeds. These results prove the effectiveness of multi-modal approaches for operational flood mapping systems.

A Misaligned Compass: Evaluating the Conventional Bias and Moral Divergence of Large Language Models

2025-11-03T22:44:01+01:00

Recent advances in large language models (LLMs) have enabled impressive performance across reasoning tasks, yet their capacity for autonomous moral judgment remains underexplored. Prior research has not systematically benchmarked LLMs against expert standards of postconventional moral reasoning. This study aims to evaluate the moral reasoning capabilities of twelve state-of-the-art LLMs by comparing their responses to complex socio-moral dilemmas with those of a moral psychology expert, using Kohlberg’s framework as a referential standard. Models were prompted with four dilemmas (passive euthanasia, incurable diseases decisions, population control, and capital punishment) across three zero-shot tasks: moral recognition, option selection, and justification. Responses were coded using Kohlberg’s levels of moral development and statistically compared to expert judgments. Quantitative metrics included weighted kappa for concordance and logistic regression for explanatory depth. Qualitative analysis examined the structure and coherence of LLM justifications. LLMs predominantly exhibited conventional reasoning (47.7%), diverging sharply from the expert’s consistently postconventional judgment. Concordance was weak to negative, with kappa coefficients ranging from –0.333 to 0.286, indicating systemic misalignment. Binary logistic regression analysis showed that model classification significantly predicted the capacity for moral articulation; the odds ratio was 7.00 (p = 0.005), accounting for 23.4% of the variance according to Nagelkerke’s R² = 0.234. The Kruskal–Wallis test yielded H = 20.34 with p < .05, confirming significant differences in moral reasoning levels between the models and the expert. Finally, a qualitative analysis revealed that although 84.3% of justifications aligned with the selected options, they often lacked principled depth, replicating normative patterns and rejecting postconventional stances through lower-order reasoning. In conclusion, contemporary LLMs reproduce social conventions rather than engaging in autonomous ethical deliberation. The observed limitations have substantive implications for the ethical design and deployment of AI systems in morally sensitive domains. To address this, the study proposes six complementary strategies for ethical alignment, aiming to foster principled moral reasoning in AI systems: staged fine-tuning, symbolic ontology integration, expert supervision, reinforcement learning with philosophical rewards, adversarial training, and continual calibration.