Paper 01 · Renewable Energy & Machine Learning
Published
Modeling the Impact of Partial Shading on Photovoltaic Panels
Abstract
Partial shading is a critical challenge for photovoltaic (PV) systems, causing severe power loss and damaging hotspots, yet existing detection approaches either rely on expensive hardware sensors or oversimplified analytical models that fail under dynamic conditions. This work presents an end-to-end machine learning approach for predicting shading percentage in PV systems using regression.
A custom MATLAB Simulink model implementing a physics-based single-diode equivalent circuit was used to generate a large-scale dataset of more than 24 million data points, systematically varying irradiance (200–1000 W/m²), temperature (10–50 °C), twelve series-parallel array configurations, and shading levels (0–100%). A context-aware feature-engineering step normalizes voltage, current, and power into scale-invariant ratios, enabling a single model to generalize across diverse panel configurations. The resulting XGBoost regression model achieves an R² of 0.937 with a mean absolute error of 5.19% and an RMSE of 7.76%, demonstrating that properly processed I–V curve data alone can accurately quantify shading without costly specialized hardware — a practical tool for optimizing residential PV systems.
Photovoltaics
Partial shading
XGBoost
Regression
Simulation dataset
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Paper 02 · AI & Additive Manufacturing
Published
Innovative Applications of AI and 3D Printing in Digital Dentistry: Enhancing Accuracy and Efficiency in Dental Care
Abstract
Digital dentistry has significantly transformed the way dental professionals deliver patient care by integrating digital technologies into various aspects of the field, such as diagnosis, treatment planning, and restoration. This includes a broad array of technologies like computer-aided design and manufacturing (CAD/CAM), 3D printing, and artificial intelligence (AI), all of which are rapidly advancing and reshaping dental practice. While 3D printing has transformed industries, including dentistry, it still has several drawbacks and limitations — namely a complex design process, reliance on manual design adjustments, and limited resolution with occasional inaccuracies in 3D prints.
To address these issues, we propose an AI-enabled framework that provides a simplified process to ensure success and minimize human error. Users can quickly import an STL file into the software, select the desired application, and begin printing within seconds; the system automatically analyzes the 3D object, adjusting its orientation and generating supports for splints, crowns, bridges, and surgical guides. We focus on how AI can enhance the precision, efficiency, and reliability of dental prosthetics and restorations by automating critical steps that were traditionally manual and prone to errors — automatically detecting complex geometries, optimizing support structures, and adjusting printing parameters in real time. This AI-enabled approach reduces the number of design iterations while ensuring higher resolution and accuracy, overcoming the limitations of conventional 3D printing methods.
3D printing
Digital dentistry
Autoencoder
Support optimization
CAD/CAM
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Paper 03 · Machine Learning & Education
Published
Study on the Use of Random Forest Classifier and Multi-Output Classifier Models for Predicting Student Academic Performance and Identifying Areas of Concern
Abstract
This paper explores the use of machine learning to identify key factors that may connect to a student's academic performance, and how it may be used to predict student learning outcomes at an early stage — specifically, by utilizing two machine learning models: the Random Forest classifier and the Multi-Output classifier. The Random Forest Classifier operates by constructing multiple decision trees during training and selecting the mode of their predictions for a given input, identifying the most significant factors affecting outcomes. A Multi-Output Classifier is designed for multi-label tasks where each instance can be linked to multiple output variables, predicting several targets simultaneously — for example, assessing a student's grade and engagement level at once — and our implementation uses a neural network backend.
Several datasets sourced from Kaggle containing student background information and academic engagement and performance data were processed using the two classifier models, with the steps for cleaning, preparing, and analyzing the data discussed in the paper. The results show the Random Forest classifier is very effective at identifying key factors connected to academic performance — such as units completed in the previous semester, prior-semester grades, and tuition fee payment status — with an accuracy of 85.9% on the test data (94.5% correct on non-dropouts and 67.9% correct on dropouts). The same data processed by the Multi-Output Classifier neural network yielded accuracy scores ranging from 83.5% to 94.2% across the five target variables, providing valuable insights to educators for advocating tailored support for at-risk students.
Random forest
Neural networks
Multi-output classifier
Early-warning systems
Education
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