arXiv AI
Jul 20
AquaAugmentor: A Novel Feature Augmentation Algorithm for Water Potability Prediction
arXiv:2607. 15775v1 Announce Type: cross Abstract: Access to potable water is crucial for health, economic development, and sustainability.
A Comparative Analysis of Machine Learning Algorithms for Multi-Task Prediction of the Parameters of the Pectin Hydrolysis--Extraction Process
arXiv:2606. 00821v1 Announce Type: new Abstract: This study addresses the challenge of controlling a complex, multi-parameter technological process -- pectin hydrolysis--extraction -- using machine learning methods.
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arXiv Machine Learning
Sep 10
A Machine Learning Framework for Predicting Restaurant Food Waste to Support Sustainable Food Management
arXiv:2609.08078v1 Announce Type: new Abstract: Food waste in the restaurant sector poses a substantial challenge to environmental sustainability and economic efficiency. This paper presents an explo...
arXiv Machine Learning
Jul 22
Predicting Activities in Aqueous Electrolyte Solutions with Hybrid Machine Learning
arXiv:2607. 19114v1 Announce Type: new Abstract: Activities in aqueous electrolyte solutions, usually described by ionic activity and osmotic coefficients, are important properties for modeling many processes in industry and nature.
arXiv Machine Learning
4d ago
An Efficient Machine Learning Approach for Degradation Forecasting in AEM Water Electrolysis
arXiv:2609.37941v1 Announce Type: new Abstract: This study provides a data-driven analysis of a novel dataset of single-cell Anion Exchange Membrane water electrolyzers (AEMWE), operated under consta...
arXiv AI
Aug 24
Decision Tree and K-Means Analysis of Raman Spectra for Edible Oils: A Physics-Informed AI Approach
The paper presents a Raman spectroscopy and machine‑learning framework that uses t‑SNE, K‑means, Decision Trees, and NNLS‑based spectral decomposition to authenticate edible oils. In pure oils, Decision Trees achieved 100% accuracy using only four Raman variables out of 1866 features, while in matrix‑containing samples, NNLS‑based PI‑AI improved classification to about 85–86% with only four to five key variables. The approach yields highly compact, interpretable spectral representations that enable accurate oil identification with minimal data footprint.
arXiv AI
Jul 14
Task-Conditioned Synthetic Data Generation for Improving Machine Learning Performance in Agricultural Prediction Tasks
arXiv:2607. 09751v1 Announce Type: new Abstract: Machine Learning (ML) algorithms have been widely used to estimate agricultural variables across diverse contexts.
arXiv Machine Learning
Jul 2
Spectroscopy Analysis with Machine Learning Regression for the Quantification of Carbon and Nitrogen Contents in Inceptisol and Oxisol Soil Types: Comparing Different Preprocessing and Validation methods as well as Feature Importance
arXiv:2607. 00834v1 Announce Type: new Abstract: Near-Infrared (NIR) spectroscopy has emerged as a promising alternative to traditional soil analysis methods, offering advantages such as speed, low cost, and non-destructive testing.
arXiv Machine Learning
2d ago
ElectrolyteFM: Unifying Electrolyte Property Prediction through Cross-Property Knowledge Learning
arXiv:2609.39340v1 Announce Type: new Abstract: Electrolyte formulation design requires balancing multiple physicochemical properties, yet existing models often focus on a limited subset. Learning ea...
arXiv Machine Learning
Aug 14
A Multispectral Framework for the Detection of Calcium Carbide-Induced Ripening and Shelf-Life Estimation in Climacteric Fruits
arXiv:2608. 13073v1 Announce Type: new Abstract: Significant health risks are associated with the illegal, yet commonly practiced use of industrial-grade Calcium Carbide (CaC2) for ripening climacteric fruits like mango and banana, which leaves behind trace residues of arsenic and phosphorus.
arXiv AI
2d ago
Atoms to Processes: The Role of Artificial Intelligence and Machine Learning in Chemical Engineering
The article discusses how the rapid development of artificial intelligence (AI) and machine learning (ML) is transforming chemical engineering by influencing problem formulation, analysis, and solution across a wide range of applications, from atomic-scale simulations to industrial operations. It highlights recent methodological advances and representative uses, noting a shift from purely black-box models to hybrid and physics-informed frameworks that incorporate conservation laws, thermodynamic consistency, and structural constraints. These integrated approaches enhance robustness, reliability, and human-AI collaboration, ultimately amplifying rather than replacing core chemical engineering principles.
arXiv Machine Learning
Jul 10
Physics-Informed Machine Learning Under Small-Data Constraints: Lessons from Abrasive Waterjet Milling
arXiv:2607. 07863v1 Announce Type: new Abstract: In physically dominated machining processes, experimental datasets are small, expensive, and material-specific; in this regime, data curation, evaluation design, and the form of physics integration can matter as much as the learning algorithm.
arXiv AI
Jun 29
Calibrating Biophysical Models for Grape Phenology Prediction via Multi-Task Learning
arXiv:2508. 03898v2 Announce Type: replace-cross Abstract: Accurate prediction of grape phenology is essential for timely vineyard management decisions, such as scheduling irrigation and fertilization, to maximize crop yield and quality.