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Stroke ranks as one of the deadliest diseases globally, emphasizing the crucial need for early diagnosis. This study aims to create a two-stage classification system for stroke and non-stroke images to support early clinical detection. Deep learning, a cornerstone of diagnosis, detection, and prompt treatment, is the primary methodology. Transfer learning adapts successful deep learning architectures for diverse problems, and ensemble learning combines multiple classifiers for enhanced results. These two techniques are applied to classify stroke using a dataset of stroke and normal images. In the initial stage, six pre-trained models are fine-tuned, with DenseNet, Xception, and EfficientNetB2 emerging as the top performers, achieving validation accuracies of 98.4%, 98.4%, and 98%, respectively. These models serve as base learners within an ensemble framework. A weighted average ensemble method combines them, resulting in a remarkable 99.84% accuracy on a reserved test dataset. This approach exhibits promise for stroke detection, a life-threatening condition, while also demonstrating the effectiveness of ensemble techniques in enhancing model performance. Received: 27 October 2023 | Revised: 24 November 2023 | Accepted: 19 December 2023 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement Since the data used in the study were removed from the Kaggle platform after the study was conducted, no data link is provided. However, the study data can be sent to researchers who request it, with privacy and ethical restrictions.

Agriculture has notably become one of the fields experiencing intensive digital transformation. Leveraging state-of-the-art techniques in this domain has provided numerous advantages for agricultural activities. Deep learning (DL) algorithms have proven beneficial in addressing various agricultural challenges. This study presents a comprehensive investigation into applying DL models for palm disease detection and classification in the context of smart agriculture. The research aims to address the limitations observed in previous studies and improve the robustness and generalizability of the results. To achieve this, a two-stage optimization methodology is employed. First, transfer learning and fine-tuning techniques are applied using various pre-trained deep neural network models. The experiments show promising results, with all models achieving high accuracy rates during training and validation. Furthermore, their performance on unseen test data is also assessed to ensure practical applicability. The top-performing models are MobileNetV2 (92.48 %), ResNet (92.42 %), ResNetRS50 (92.30 %), and DenseNet121 (92.01 %). Second, a deep ensemble learning approach is applied to enhance the models' generalization capability further. The best-performing models with different criteria are combined using the ensemble technique, resulting in remarkable improvements in disease detection tasks. DELM1 emerges as the most successful ensemble model, achieving an ROC AUC Score of 99 %. This study demonstrates the effectiveness of deep ensemble learning models in palm disease detection and classification for smart agriculture applications. The findings contribute to advancing disease detection systems and emphasize the potential of ensemble learning. The study provides valuable insights for future research, guiding the application of DL techniques to address critical agricultural challenges and improve crop health monitoring systems. Another contribution is combining various plant diseases and insect pest classes using diverse datasets. A comprehensive classification system is achieved by considering different disease classes and stages within the white scale category, improving the model's robustness.