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1. A Hybrid Machine Learning Approach for High-Accuracy Energy Consumption Prediction Using Indoor Environmental Quality Sensors
2. A Pilot Study on Thermal Comfort in Young Adults: Context-Aware Classification Using Machine Learning and Multimodal Sensors
3. A Review of Artificial Intelligence and Deep Learning Approaches for Resource Management in Smart Buildings
4. AI-Powered Building Ecosystems: A Narrative Mapping Review on the Integration of Digital Twins and LLMs for Proactive Comfort, IEQ, and Energy Management
5. Benchmarking Tabular Foundation Models for Total Volatile Fatty Acid Prediction in Anaerobic Digestion
6. Development and evaluation of an intelligent control system for sustainable and efficient energy management
7. Integrating Machine Learning with Intelligent Control Systems for Flow Rate Forecasting in Oil Well Operations
8. IoT-Based Unsupervised Learning for Characterizing Laboratory Operational States to Improve Safety and Sustainability
9. Interpretable Machine Learning-Based Differential Diagnosis of Hip and Knee Osteoarthritis Using Routine Preoperative Clinical and Laboratory Data
10. Non-Imaging Differential Diagnosis of Lower Limb Osteoarthritis: An Interpretable Machine Learning Framework
11. Analyzing the Application of Digital Twin Technology in Manufacturing Processes
12. Architecting the Orthopedical Clinical AI Pipeline: A Review of Integrating Foundation Models and FHIR for Agentic Clinical Assistants and Digital Twins
13. Predicting Industrial Copper Hydrometallurgy Output with Deep Learning Approach Using Data Augmentation
14. A Low-Cost IoT Sensor and Preliminary Machine-Learning Feasibility Study for Monitoring In-Cabin Air Quality: A Pilot Case from Almaty
15. A Wearable IoT-Based Measurement System for Real-Time Cardiovascular Risk Prediction Using Heart Rate Variability
16. Development of an Intelligent Oil Field Management System based on Digital Twin and Machine Learning
17. Digital Cardiovascular Twins, AI Agents, and Sensor Data: A Narrative Review from System Architecture to Proactive Heart Health
18. Enhancing Cardiovascular Disease Classification with Routine Blood Tests Using an Explainable AI Approach
19. Interpretable Machine Learning for Coronary Artery Disease Risk Stratification: A SHAP-Based Analysis
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