Inteligencia Artificial

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

Sun, 26 Jul 2026 00:00:00 +0200

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

Anuradha Dhull, Kartik Pal, Manoj Kumar — Wed, 22 Jul 2026 00:00:00 +0200

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

Erika Camacho, Yazmin Gutierrez, Cesar Pardo — Wed, 22 Jul 2026 00:00:00 +0200

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

Dilkhaz Mohammed, Shahram Jamali — Wed, 22 Jul 2026 00:00:00 +0200

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

David Davó, Javier Arroyo — Tue, 28 Jul 2026 00:00:00 +0200

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.