The use of photovoltaic systems has increased in recent years due to their decreasing costs and improved performance. However, these systems can be susceptible to faults that can reduce efficiency and energy yield. To prevent and reduce these problems, preventive or predictive maintenance and effective monitoring are necessary. PV health monitoring systems and automatic fault detection and diagnosis methods are critical for ensuring PV plants’ reliability, high-efficiency operation, and safety. This paper presents a new framework for developing fault detection in photovoltaic (PV) systems. The proposed approach uses machine learning algorithms to predict energy power production and detect anomalies in PV plants by comparing the predicted power from a model and the measured power from sensors. The framework utilizes historical data to train the prediction model, and live data is compared with predicted values to analyze residuals and detect abnormal scenarios. The proposed approach has been shown to accurately distinguish anomalies using constructed thresholding, either static or dynamic thresholds. The paper also reports experimental results using the Matthews correlation coefficient, a more reliable statistical rate for an imbalanced dataset. The proposed approach leads to a reasonable anomaly detection rate, with an MCC of 0.736 and a balanced ACC of 0.863.

Background/Objectives: Osteoporosis is a major contributor to vertebral compression fractures (VCFs) and other skeletal complications, yet quantitative bone mineral density (BMD) assessment using dual-energy X-ray absorptiometry (DEXA) is not routinely available in many spine surgery workflows. This study proposes an anatomically guided and optimization-refined radiomics framework for opportunistic osteoporosis assessment from routine lumbar spine magnetic resonance imaging (MRI). Methods: The proposed pipeline employs a hierarchical template-matching strategy to automatically localize the L1–L4 vertebral region, followed by an optimization-based refinement procedure that adapts vertebral regions of interest (ROIs) using intensity, texture, boundary, and geometric constraints. Anatomically consistent ROIs are subsequently used for extraction of handcrafted radiomic descriptors, including statistical, textural, gradient-based, frequency-domain, and shape-related features. The extracted features were evaluated using conventional support vector classification (SVC) and a NeurodynamicSVMRBFTanh classification framework for osteoporosis-related classification. Results: Experimental results demonstrated robust and anatomically consistent vertebral localization across heterogeneous lumbar MRI acquisitions. The NeurodynamicSVMRBFTanh framework achieved the best screening-oriented performance, yielding 85.2% classification accuracy and 100.0% sensitivity on an independent test set. In addition, exploratory BMD regression analysis demonstrated the feasibility of estimating DEXA-derived BMD directly from MRI-derived radiomic features, achieving mean absolute percentage errors of approximately 15–20% across lumbar vertebral levels. Conclusions: These findings suggest that anatomically guided vertebral radiomics extracted from routine lumbar spine MRI contain clinically meaningful information associated with osteoporosis-related bone quality changes and may provide a practical tool for automated opportunistic osteoporosis assessment in settings where DEXA measurements are unavailable.

Glaucoma affects a significant proportion of people worldwide, and if it progresses to a severe stage, it can lead to blindness. Furthermore, screening and accurately diagnosing glaucoma present a challenge for ophthalmologists. Early detection of glaucoma is crucial because it allows for timely treatment, potentially preventing severe complications that could lead to blindness. Typically, ophthalmologists diagnose glaucoma by analyzing eye fundus photographs to assess the ratio of the optic cup and optic disc (CDR). Machine learning algorithms can assist in glaucoma detection by classifying fundus images. This study introduces image preprocessing techniques for optic disc localization, combined with an integrating multi-view network for accurate glaucoma classification. The dataset used in this research was obtained from Naresuan University Hospital. The study found that EfficientNet underwent training using the Adam optimizer at a fixed learning rate of 0.0001. The multi-view network achieved Accuracy 90.48%, AUC 95.14%, Precision 81.95%, Recall 75.90%, and F1-score 78.72%. This study presents an effective approach to assist ophthalmologists in detecting early-stage glaucoma and glaucoma, thereby improving diagnostic efficiency.