Deep learning-based automated classification of dermatoscopic skin lesions has exhibited promising potential in diagnostics. However, two prominent issues need to be addressed before achieving high-quality diagnostic tools: inconsistent performance in the case of imbalanced classes and poor clinical interpretability of models. Even though some studies have attempted to leverage both deep and shallow learning by combining pretrained convolutional neural networks (CNNs)-based feature extraction with classical machine learning (ML) models, very few of them systematically explore several model combinations based on various clinically important metrics, such as F1-score, precision, recall, accuracy, etc., and utilize decision threshold calibration techniques. In this research, we present an evaluation of a systematic framework with threshold calibration for the comparison of several hybrid models on seven-class skin lesion classification (multi-class) on the HAM10000 dataset. In particular, we used deep features extracted from three pretrained CNN architectures, i.e., DenseNet201, InceptionV3 and EfficientNet-B4. These deep features were used as inputs for six different classical classifiers. As a result, we obtained 18 comparable hybrid models that were then systematically compared by multiple clinically relevant metrics: accuracy, macro-precision, macro-recall, macro-F1, ROC-AUC, Precision-Recall-AUC, and log loss. Also, fold-wise optimization of decision thresholds was performed, which was based on the maximization of the macro-F1 score. Finally, we found out that DenseNet201 with an SVM-RBF classifier yielded the highest performance among all 18 tested models, showing 90.88% accuracy, 90.7% macro-precision, and 0.921 ROC-AUC. To analyze the clinical plausibility, top-performing models were further explained with explainable artificial intelligence (XAI) techniques: Grad-CAM, LIME and Occlusion Sensitivity. Results show that the most successful models concentrated mostly on lesion-specific areas. Overall, this study contributes a reproducible hybrid-XAI model-selection framework rather than a single black-box classifier, supporting more transparent and clinically meaningful skin lesion diagnosis.

The power of digital twins, virtual replicas of physical assets and systems that are revolutionizing the efficiency and performance is significant for optimization for renewable energy. Agriculture sector uses digital twins in the context of precision agriculture, fishing and reforestation, manufacturing can simulate machinery and robots predict maintenance breakdowns before they happen or calculate needs for supplies. Bringing new perspectives on cutting-edge technologies in the area of renewables it simulates wind turbine. With a real-time digital twin in place, energy operators can observe how the system behaves across diverse operating conditions, detect inefficiencies and work on data to make judicious decisions toward enhancing performance. In case of solar power systems, they monitor the performance efficiency of solar panels and predict probable failures which may result into loss from energy production.

The need for strong watermarking methods to safeguard IP and guarantee authenticity has increased due to the widespread use of digital images across many platforms. At the same time, there has been a meteoric rise in the need for visual quality improvement techniques for images. This work introduces a new method that combines watermarking, picture augmentation, and machine learning classification to provide security, visual integrity, and durability. Common assaults on traditional watermarking systems include cropping, filtering, and compression. On the other hand, our suggested method uses ML classifiers to adaptively embed watermarks in the frequency or spatial domains, making it more resistant to typical assaults. The embedding approach ensures resilience while minimising perceptual distortion by dynamically adapting to picture content features using a classification model trained on a broad dataset. Images that have been watermarked have their visual quality improved since image enhancement is a part of the watermarking framework. The suggested method finds target areas and selectively conducts improvement operations using feature extraction and analysis. This customised method reduces the effect of watermark insertion while preserving crucial picture characteristics.

The need for strong watermarking methods to safeguard IP and guarantee authenticity has increased due to the widespread use of digital images across many platforms. At the same time, there has been a meteoric rise in the need for visual quality improvement techniques for images. This work introduces a new method that combines watermarking, picture augmentation, and machine learning classification to provide security, visual integrity, and durability. Common assaults on traditional watermarking systems include cropping, filtering, and compression. On the other hand, our suggested method uses ML classifiers to adaptively embed watermarks in the frequency or spatial domains, making it more resistant to typical assaults. The embedding approach ensures resilience while minimising perceptual distortion by dynamically adapting to picture content features using a classification model trained on a broad dataset. Images that have been watermarked have their visual quality improved since image enhancement is a part of the watermarking framework. The suggested method finds target areas and selectively conducts improvement operations using feature extraction and analysis. This customised method reduces the effect of watermark insertion while preserving crucial picture characteristics.