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Inteligencia Artificial
Scopus
ISSN 1137-3601 · Asociación Española para la Inteligencia Artificial · 276 artikel di indeks
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Artikel Jurnal
Empirical Evaluation of Audio Encoders and Multimodal Fusion for Active Speaker Detection
Active Speaker Detection (ASD) aims to identify the speaking individual within a visual scene across consecutive frames. This task has gained significant prominence due to its role in applications like Human-Robot Interaction (HRI). While existing multimodal approaches primarily focus on innovating…
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LT-SignDiff: Long-Tail Video Sign Language Recognition via Diffusion-Synthesized Rare-Class Prototypes
Video-based Sign Language Recognition (SLR) is a fundamental accessibility technology, yet real-world sign vocabularies follow an extreme long-tailed distribution, a problem sharpened for low-resource languages such as Vietnamese Sign Language (VSL), where classical imbalance tools (re-weighting…
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A MUST-Former: A Family of Transformer-Based Models for Robust Multi-Modal Flood Mapping from Satellite Data.
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…
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A Misaligned Compass: Evaluating the Conventional Bias and Moral Divergence of Large Language Models
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…
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Regularized Transfer Learning in Brain Tumor Classification: Leveraging Pretrained Models for Improved Diagnostic Accuracy
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…
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A novel approach for Rare Disease symptom extraction from Clinical Texts Using Knowledge Graph Embeddings and Large Language Models
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…
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Prompt engineering applied to code generation: a preliminary systematic review
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…
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Unveiling the Frontiers of Cyber Threat Hunting: A Comprehensive Review of LLM-Based Approaches
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…
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Enhancing Voter Engagement in Decentralized Autonomous Organizations Through Recommender Systems
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…
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Text Separation from Digital Images: a Pair-Copula Based Approach and Performance Analysis
Automatic separation of text from digital images holds significant importance in various domains, including document processing and content-based image retrieval. This paper presents a statistical model-based approach for automatic text component extraction from digital images. The methodology…
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HFFN: A Hybrid Feature Fusion Network for Representing Source Code
This study aims to automate source code summarization by introducing a novel machine learning architecture that integrates multiple feature perspectives. Specifically, it combines lexical, syntactic, and semantic representations of code and employs a transformer-based decoder to generate natural…
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Highly explainable predictive models with DoME for the management of DSP-related harmful algal blooms in the shellfish industry
The occurrence of HAB has a direct impact on shellfish farming, leading to economic losses due to the contamination of shellfish with toxins harmful to human health. Predicting these blooms accurately is therefore crucial for minimizing their negative effects on the industry. The DoME machine…
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A Machine Learning and Explainable Artificial Intelligence approach for Insurance Fraud Classification
This study addresses the critical issue of fraud in the vehicle insurance market by introducing a comprehensive framework that employs advanced detection models, including explainable artificial intelligence (XAI) and heterogeneous classifiers to effectively identify fraudulent activities. The…
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Combinatorial Optimization for All: Using LLMs to Aid Non-Experts in Improving Optimization Algorithms
We investigate whether Large Language Models (LLMs) can refine the given codebase of an optimization algorithm without requiring specialized user expertise. This is in contrast to works that study optimization algorithm code generation from scratch. To this end, 10 baseline algorithms covering…
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UttaraRisk-Next: A Multi-Task Ensemble Learning Framework for Maternal Health Risk Prediction
In India's mountainous areas, maternal mortality is still a serious public health concern, especially in Uttarakhand, where access to healthcare is hampered by geographical obstacles. UttaraRisk-Next, a multi-task ensemble learning framework for thorough maternal health risk assessment, is…
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To What Extent Is LLM Performance on Multiple-Choice Questions Driven by Data Leakage? A Case Study with Contamination-Controlled Spanish Undergraduate Exams
The performance of Large Language Models (LLMs) on multiple-choice university-level exam benchmarks such as MMLU is often reported as highly competitive; however, such results raise persistent concerns regarding contamination of public datasets, English-centric bias, and over-reliance on aggregate…
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Artificial Intelligence (AI) in Dental Age Estimation Studies: A Scientometric Analysis
Dental age estimation (DAE) is important in age-related studies ranging from forensics, clinical dentistry and bioanthropology. DAE heavily relies on image analysis and morphometrics and has underwent academic scrutiny to improve its level of reliability and accuracy. The recent rise of artificial…
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A Review of Cyber Security Challenges and Solutions in Unmanned Aerial Vehicles (UAVs)
The widespread use of unmanned aerial vehicles in a variety of sectors poses major challenges to these issues of cyber security and data protection. In this paper, a detailed study of the security vulnerabilities in UAV-based systems was conducted and analytically categorized into three types…
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R-UNET: Improving performance of rumex detection using unet based selective regions
In particular, Rumex weed detection is regarded as a crucial step in real-world data under many circumstances. The detection task suffers from several issues, such as overlapping weeds, occlusion, varying leaf colour distributions, leaf size and shape, and growth stage. Many machine learning…
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Two competitive hybridization approaches based on combining of Giza Pyramids Construction with Particle Swarm Optimization for solving global optimization problems
Optimization problems are complex problems that are very difficult to solve. Although these types of problems are solved in the real world using exact methods, these methods are very time-consuming and costly. By using soft computing methods, the time and cost of problem-solving can be reduced to…