Computers

Dengue case forecasting is important for the prevention and early control of outbreaks, as well as for the optimization of healthcare resources, among other aspects. This study addresses the need to develop increasingly accurate forecasting models that can support informed decision-making before and during dengue epidemics. Accordingly, two new models based on convolutional and recurrent neural networks, namely ConvLSTM and ConvBiLSTM, combined with data augmentation based on linear interpolation, are proposed. As a case study, weekly dengue cases in Peru from 2000 to 2024 are used. The proposed models are compared with well-known recurrent neural network-based models such as LSTM, BiLSTM, GRU, and BiGRU, both with and without data augmentation. The results show that the proposed models with data augmentation achieve comparable and superior performance to the benchmark models, while also exhibiting a lower average computational cost.

Nowadays, longitudinal data are common—typically high-dimensional, large, complex, and collected using various methods, with repeated outcomes. For example, the growing elderly population experiences health deterioration, including limitations in Instrumental Activities of Daily Living (IADLs), thereby increasing demand for long-term care. Understanding the risk of repeated IADLs and estimating the trajectory risk by identifying significant predictors will support effective care planning. Such data analysis requires a complex modeling framework. We illustrated a regressive modeling framework employing statistical and machine learning (ML) models on the Health and Retirement Study data to predict the trajectory of IADL risk as a function of predictors. Based on the accuracy measure, the regressive logistic regression (RLR) and the Decision Tree (DT) models showed the highest prediction accuracy: 0.90 to 0.93 for follow-ups 1–6; and 0.89 and 0.90 for follow-up 7, respectively. The Area Under the Curve and Receiver Operating Characteristics curve also showed similar findings. Depression scores, mobility score, large muscle score, and Difficulties of Activities of Daily Living (ADLs) score showed a significant positive association with IADLs (p < 0.05). The proposed modeling framework simplifies the analysis and risk prediction of repeated outcomes from complex datasets and could be automated by leveraging Artificial Intelligence (AI).

Student performance is an important factor for any education process to succeed; as a result, early detection of students at risk is critical for enabling timely and effective educational interventions. However, most educational datasets are complex and do not have a stable number of features. As a result, in this paper, we propose a new algorithm called MOHHO-NSGA-III, which is a multi-objective feature-selection framework that jointly optimizes classification performance, feature subset compactness, and prediction stability with cross-validation folds. The algorithm combines Harris Hawks Optimization (HHO) to obtain a good balance between exploration and exploitation, with NSGA-III to preserve solution diversity along the Pareto front. Moreover, we control the diversity management strategy to figure out a new solution to overcome the issue, thereby reducing the premature convergence status. We validated the algorithm on Portuguese and Mathematics datasets obtained from the UCI Student Performance repository. Selected features were evaluated with five classifiers (k-NN, Decision Tree, Naive Bayes, SVM, LDA) through 10-fold cross-validation repeated over 21 independent runs. MOHHO-NSGA-III consistently selected 12 out of 30 features (60% reduction) while achieving 4.5% higher average accuracy than the full feature set (Wilcoxon test; $p<0.01$ across all classifiers). The most frequently selected features were past failures, absences, and family support aligning with educational research on student success factors. This suggests the proposed algorithm produces not just accurate but also interpretable models suitable for deployment in institutional early warning systems.