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This Reprint presents a carefully curated selection of peer-reviewed articles from the Special Issue Applied Machine Learning III, published in Applied Sciences. The featured contributions reflect the current state of machine learning research, with a strong emphasis on methodological innovation and practical deployment across diverse applied domains. Bringing together studies that address key challenges in modern machine learning, including data quality and preprocessing, model robustness, hybrid modeling strategies, and interpretability, the Reprint features articles that illustrate how classical machine learning methods and modern deep learning architectures are increasingly combined with domain knowledge, optimization techniques, and explainable frameworks to achieve reliable and actionable results. A distinctive feature of this Reprint is its cross-domain perspective. The included works span environmental and climate modeling, wearable sensing and human movement analysis, clinical risk prediction, financial forecasting, evolutionary optimization, and intelligent manufacturing systems. Despite varied applications, the contributions focus on handling complex, noisy, or incomplete data and translating computational methods into real-world decision support. By combining theoretical advances with applied case studies, this Reprint provides an overview of how machine learning evolves as a key enabling technology in applied sciences. It serves as a reference for researchers, practitioners, and decision makers seeking insight into contemporary approaches that balance performance, transparency, and practical relevance.
