Inteligencia Artificial

Analyzing Municipal Patterns of Suicide and Depression in Mexico: A Multilayer Network Approach

Jorge Manuel Pool Cen — 2025-12-08

This study employs a multilayer network approach to analyze the spatial and temporal patterns of suicide and depression across Mexican municipalities from 2015 to 2020. Using a panel dataset of mental health cases, substance use, and healthcare infrastructure, we constructed a multilayer graph based on cosine similarity. The Infomap clustering algorithm was then applied to identify communities of municipalities with similar mental health profiles. Our results reveal five distinct clusters with significant variations in the levels and temporal dynamics of the analyzed indicators. Notably, two clusters consistently exhibited higher rates of substance use and adverse mental health outcomes. These findings demonstrate the efficacy of network-based methods for identifying at-risk
municipal groupings, thereby informing targeted public health interventions.

From the algorithm to the clinical interpretation of childbirth anxiety: analysis and explainability of obstetric predictive models based on psychological indicators

Juan A. Recio-Garcia — 2025-12-08

Anxiety during pregnancy constitutes a relevant factor that can significantly influence labor development. This study presents a novel approach based on explainable artificial intelligence to predict both the type and duration of labor using psychological indicators of anxiety prior to delivery. Employing data from 235 full-term pregnant women from two Spanish hospitals, we developed a multilayer perceptron model to classify eutocic and dystocic deliveries, achieving a capacity to identify 88\% of dystocic deliveries. Additionally, we implemented a regression model that predicts labor time with a mean error of 2 hours, correctly predicting 86% of cases with an error margin of less than 3 hours. The application of explainability techniques to the developed models allows for understanding the specific influence of each anxiety factor on labor development. These results demonstrate the potential of AI models to improve obstetric care and optimize healthcare resource allocation.

Multimodal Emotion Recognition for Empathic Virtual Agents in Mental Health Interventions

Marcelo Alejandro Huerta-Espinoza — 2025-12-08

Depression and anxiety disorders affect millions of individuals globally and are commonly addressed through psychological interventions. A growing technological approach to support such treatments involves the use of embodied conversational agents that employ motivational interviewing, a method that promotes behavioral change through empathic engagement. Despite its critical role in therapeutic efficacy, empathy remains a significant challenge for virtual agents to emulate. Emotion Recognition (ER) technologies offer a potential solution by enabling agents to perceive and respond appropriately to users' emotional states. Given the inherently multimodal nature of human emotion, unimodal ER approaches often fall short in accurately interpreting affective cues. In this work, we propose a multimodal emotion recognition model that integrates verbal and non-verbal signals (text and video) using a Cross-Modal Attention fusion strategy. Trained and evaluated on the IEMOCAP dataset, our approach leverages Ekman's taxonomy of basic emotions and demonstrates superior performance over unimodal baselines across key metrics such as accuracy and F1-score. By prioritizing text as the main modality and dynamically incorporating complementary visual cues, the model proves effective in complex emotion classification tasks. The proposed model is designed for integration into an existing conversational agent aimed at supporting individuals experiencing emotional and psychological distress. Future work will involve embedding the model in the conversational agent platform for emotionally distressed users, aiming to assess its real-world impact on engagement, user experience, and perceived empathy.

Integrated Feature Fusion in Multiclass Maize Leaf Disease Recognition

Prabhnoor Bachhal — 2025-12-08

Plant diseases are the main factor in plant mortality and destruction, especially in trees. Early discovery, however, can assist to manage and treat this issue efficiently. To increase output, crop and plant lesions are detected and stopped as soon as feasible. Because it relies solely on visual observation, manual inspection of plant leaf diseases is time-consuming and expensive. The authors offer methods for identifying and categorizing plant leaf diseases using computer vision. Pre-processing original images to visualize contaminated areas, feature extraction from unprocessed or segmented images, feature fusion, feature selection, and classification are a few examples of computer vision approaches. The fusion technique is used to combine the target's numerical data features, which go beyond the picture, with the extracted image features to increase the target's feature representation. The following are the principal issues that researchers found in the literature: Low-contrast infected regions. Extract redundant and irrelevant information, which degrades classification accuracy; Redundant and irrelevant information may lengthen computation times and the targeted models performance will suffer as a result. This study proposed a framework for classifying plant leaf diseases based on the best feature selection and a deep learning fusion model. In the suggested approach, contrast is first enhanced using a pre-processing model, and then the issue of an unbalanced dataset is resolved via data augmentation. The proposed Deep Fusion Learning Model (DFLM) shows an accuracy of 98.8% in comparison with other models.

HFFN: A Hybrid Feature Fusion Network for Representing Source Code

Shruthi D — 2026-02-01

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 language summaries, benchmarking performance against established baselines. Experiments were conducted on the CanonCode Corpus, a high-quality dataset of 8,542 validated C programs. Individual feature extractors-Convolutional Neural Networks (CNN) for lexical features, Tree-LSTM for syntactic features and Graph Neural Networks (GNN) for semantic features were evaluated and compared with the proposed Hybrid Feature Fusion Network (HFFN). The fused feature vector from HFFN was decoded using a transformer to generate summaries. Performance was measured using ROUGE, BLEU, CodeBLEU, BERTScore, and Exact Match metrics. The HFFN model consistently outperformed all baselines across standard natural language generation metrics, achieving a ROUGE-L score of 0.94 and a BERTScore of 0.93. An ablation study confirmed the complementary contributions of each feature type, with syntactic features providing the greatest individual impact. The improvement over the strongest baseline (CodeBERT) was statistically significant (p < 0.001). The proposed HFFN framework demonstrates the value of combining diverse
code representations for summarization. It offers a robust and interpretable architecture that advances multiview representation learning in software engineering and provides a foundation for future research in automated documentation.

Highly explainable predictive models with DoME for the management of DSP-related harmful algal blooms in the shellfish industry

Andres Molares-Ulloa — 2026-02-01

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 learning model is particularly notable for its high interpretability, as the trained model is expressed as a mathematical equation, allowing for transparent analysis and a better understanding of the factors driving the predictions. This characteristic distinguishes DoME from other black-box models, making it a valuable tool for stakeholders seeking not only accurate predictions but also insights into the dynamics behind HAB events. In this study, we evaluated the novel DoME (Development of Mathematical Expressions) algorithm for the prediction of Harmful Algal Blooms (HAB) associated with Diarrhoeic Shellfish Poisoning (DSP), a significant concern for the shellfish industry. Our testing involved analysing the model’s performance in various environmental conditions, demonstrating its robustness and adaptability. DoME achieved a F1-score of 97.80%, which corresponds to an improvement of around 8% over previous studies. This superior performance, combined with its explainability, underscores the model’s potential as a practical and reliable solution for early warning systems in the shellfish industry, helping to protect both public health and economic stability.