Background/Objectives: This work brings to light the importance of forming large training datasets with diverse images generated and proposes an image dataset merging application, namely, the Data Merger App, to streamline the management and synthesis of large-scale datasets. The Data Merger can recognize common classes across various datasets and provides tools to combine and organize them in a well-structured and easily accessible way. Methods: A case study is then presented, leveraging four different Convolutional Neural Network (CNN) models, VGG16, ResNet50, MobileNetV3-small, and DenseNet-161, and a Visual Transformer (ViT), to benchmark their performance to classify skin cancer images, when trained on single datasets and on enhanced hyperdatasets generated by the Data Merger App. Results: Extended experimental results indicated that enhanced hyperdatasets are efficient and able to improve the accuracies of classification models, whether the models are trained from scratch or by using Transfer Learning. Moreover, the ViT model was reported for higher classification accuracies compared to CNNs on datasets with a limited number of classes, reporting 91.87% accuracy for 9 classes, as well as in the case of enhanced hyperdatasets with multiple numbers of classes, reporting accuracy of 58% for 32 classes. Conclusions: In essence, this work demonstrates the great significance of data combination, as well as the utility value of the developed prototype web application as a critical tool for researchers and data scientists, enabling them to easily handle complex datasets, combine datasets into larger diverse versions, to further enhance the generalization ability of models and improve the quality and impact of their work.

Recent advancements in space exploration have significantly increased the volume of astronomical data, heightening the demand for efficient analytical methods. Concurrently, the considerable energy consumption of machine learning (ML) has fostered the emergence of Green AI, emphasizing sustainable, energy-efficient computational practices. We introduce the first large-scale Green AI benchmark for galaxy morphology classification, evaluating over 30 machine learning architectures (classical, ensemble, deep, and hybrid) on CPU and GPU platforms using a balanced subset of the Galaxy Zoo dataset. Beyond traditional metrics (precision, recall, and F1-score), we quantify inference latency, energy consumption, and carbon-equivalent emissions to derive an integrated EcoScore that captures the trade-off between predictive performance and environmental impact. Our results reveal that a GPU-optimized multilayer perceptron achieves state-of-the-art accuracy of 98% while emitting 20× less CO₂ than ensemble forests, which—despite comparable accuracy—incur substantially higher energy costs. We demonstrate that hardware–algorithm co-design, model sparsification, and careful hyperparameter tuning can reduce carbon footprints by over 90% with negligible loss in classification quality. These findings provide actionable guidelines for deploying energy-efficient, high-fidelity models in both ground-based data centers and onboard space observatories, paving the way for truly sustainable, large-scale astronomical data analysis.