Aims and Scope
Research on Intelligent Manufacturing and Assembly (RIMA) (eISSN: 2972-3329) is an international, peer-reviewed, open access journal dedicated to the latest advancements in intelligent manufacturing and assembly. RIMA serves as a critical bridge between cutting-edge research and practical applications, fostering collaboration between the academic community and industry practitioners. The journal aims to publish high-impact research that pushes the boundaries of knowledge in the design, analysis, manufacturing, and operation of intelligent systems and equipment. RIMA focuses on innovative technologies and methodologies that are transforming the manufacturing landscape, driving efficiency, precision, and sustainability in industrial processes. By publishing rigorous research and fostering a vibrant community of scholars and practitioners, RIMA aims to be the go-to resource for advancing the state-of-the-art in intelligent manufacturing and assembly.
Topics of interest include, but are not limited to the following:
• Digital design and manufacturing
• Theories, methods, and systems for intelligent design
• Advanced processing techniques
• Modelling, control, optimization, and scheduling of systems
• Manufacturing system simulation and digital twin technology
• Industrial control systems and the industrial Internet of Things (IIoT)
• Safety and reliability assessment
• Robotics and automation
• Artificial intelligence and machine learning in manufacturing
• Supply chain optimization and management
• Additive manufacturing and materials science
• Cybersecurity and data privacy in manufacturing
• Sustainability and circular economy in manufacturing
• Bio-fabrication and other advanced manufacturing methods
• Digital Workforce and Automation
• etc.
Current Issue
Vol 4 No 2 (2025)
Published: 2025-12-25
Research Article
Page 219-238
Machine Learning Approaches to Predicting Pacemaker Battery Life
Samikshya Neupane, Tarun Goswami
Accurate prediction of pacemaker battery life is critical to timely generator replacement and patient safety. We evaluated three regression approaches: multilayer perceptron Neural Networks (NN), Random Forests (RF), and Linear Regression (LR), using 42 real‑world interrogation reports spanning single, dual, and triple‑chamber Medtronic devices. Key electrical parameters (battery voltage/current, lead impedance, capture thresholds, pacing percentages, etc.) were modelled. Performance was quantified with mean absolute error (MAE), mean squared error (MSE), and coefficient of determination (R²). NNs achieved the highest accuracy (R² ≈ 1.0; MAE < 0.1 months), RF provided robust results (R² ≈ 0.85), whereas LR exhibited limited predictive fidelity (R² ≤ 0.41). “Monte‑Carlo simulations (n = 1000)” and 95 % prediction intervals characterized predictive uncertainty; residual and Q‑Q analyses verified statistical assumptions. Our findings indicate that a data‑driven NN framework can reliably forecast remaining battery longevity, enabling proactive replacement scheduling and reducing unexpected generator depletion. The methodology is compatible with different manufacturers and suitable to integration within remote device follow‑up systems to enhance longitudinal cardiac care.
| eISSN: 2972-3329 Abbreviation: Res Intell Manuf Assem Editor-in-Chief: Prof. Matthew Chin Heng Chua (Singapore) Publishing Frequency: Continuous publication Article Processing Charges (APC): 0 Publishing Model: Open Access |
