Reinforcement learning is an important technique in various fields, particularly in automated machine learning for reinforcement learning (AutoRL). The integration of transfer learning (TL) with AutoRL in combinatorial optimization is an area that requires further research. This paper employs both AutoRL and TL to effectively tackle combinatorial optimization challenges, specifically the asymmetric traveling salesman problem (ATSP) and the sequential ordering problem (SOP). A statistical analysis was conducted to assess the impact of TL on the aforementioned problems. Furthermore, the Auto_TL_RL algorithm was introduced as a novel contribution, combining the AutoRL and TL methodologies. Empirical findings strongly support the effectiveness of this integration, resulting in solutions that were significantly more efficient than conventional techniques, with an 85.7% improvement in the preliminary analysis results. Additionally, the computational time was reduced in 13 instances (i.e., in 92.8% of the simulated problems). The TL-integrated model outperformed the optimal benchmarks, demonstrating its superior convergence. The Auto_TL_RL algorithm design allows for smooth transitions between the ATSP and SOP domains. In a comprehensive evaluation, Auto_TL_RL significantly outperformed traditional methodologies in 78% of the instances analyzed.

The growing energy demands and environmental impact of Machine Learning (ML) models have established Green Machine Learning (GML) as an important field for sustainable AI progress. This systematic review incorporates recent advances in energy-aware ML, contributing an overall assessment of algorithmic strategies, energy-aware design, and sustainability practices that reduce carbon footprints while preserving performance. The review identifies five basic dimensions of GML: algorithmic efficiency, energy optimization, hardware efficiency, carbon footprint tracking, and green benchmarks. Key findings indicate that pruning, quantization, and low-precision training can reduce energy consumption without significant loss of accuracy. Energy-aware federated learning and lightweight architectures have demonstrated potential savings in distributed environments. In contrast to prior work, this review integrates a full ML life-cycle perspective and contextualizes technical methods within broader sustainability frameworks, including the UN Sustainable Development Goals. It also surveys emerging trends, including energy-aware Federated Learning, Automated ML under $$\text{CO}_2$$ constraints, and low-power inference on edge devices. Critical challenges remain, including the lack of standardized reporting metrics, barriers to reproducibility, and the trade-off between energy savings and predictive accuracy. This review offers structured research opportunities to support the development of GML across various domains. It serves as a reference for researchers and practitioners committed to integrating environmental responsibility into ML innovation.

The Covid-19 pandemic has been declared in 2020 by the World Health Organization. One of the most relevant aspects of this respiratory disease is the fact that the infection caused by the new coronavirus has a high rate of spread. Thus, rapid and accurate diagnosis can contribute to reducing the transmission rate. In this aspect, in the literature, Deep Learning techniques are studied for application in the detection of this disease through X-ray images of the patient’s lung. However, one of the challenges in this area is the training of Convolutional Neural Network models with a database with few samples. One possibility is the generation of artificial images through Data Augmentation techniques. Thus, the objective of this work is to propose a careful methodology for the tuning of Data Augmentation hyperparameters for the classification of lung X-ray images in Covid-19 detection with Deep Learning. The proposed method consists of analyzing the accuracy of 36 Data Augmentation transformations applied to generate new images for training with balanced and unbalanced database. After the selection of hyperparameters, the classifier system achieved accuracies up to 100% on the testing stage, both for combinations and individual transformations with balanced database. Therefore, it is recommended to use a balanced database with the use of zoom, rotation, brightness in combination or individually, for Covid-19 versus Normal and Covid-19 versus Pneumonia classification.