Diabetes’ serious complication, diabetic retinopathy (DR), which can potentially be life-threatening, might result in vision loss in certain situations. Although it has no symptoms in the early stages, this illness is regarded as one of the “silent diseases” that go unnoticed. The fact that various datasets have varied retinal features is one of the significant difficulties in this field of study. This information impacts the models created for this purpose. This study’s method can efficiently learn and classify DR from three diverse datasets. Four models based on transfer learning Convolution Neural Network (CNN)—Visual Geometry Group (VGG) 16, Inception version 3 (InceptionV3), Dense Network (DenseNet) 121, and Mobile Network version 2 (MobileNetV2)—are employed in this work, with evaluation parameters, including loss, accuracy, recall, precision, and specificity. The models are also tested by combining the images from the three datasets. The DenseNet121 model performs better with 98.97% accuracy on the combined image set. The study concludes that combining multiple datasets improves performance compared to individual datasets. The obtained model can be utilized globally to accommodate more tests that clinics perform for diabetic patients to prevent DR. It helps health workers refer patients to ophthalmologists before DR becomes serious.

Diabetic retinopathy (DR) is the primary factor leading to vision impairment and blindness in diabetics. Uncontrolled diabetes can damage the retinal blood vessels. Initial detection and prompt medical intervention are vital in preventing progressive vision impairment. Today’s growing medical field presents a more significant workload and diagnostic demands on medical professionals. In the proposed study, a convolutional neural network (CNN) is employed to detect the stages of DR. This research is crucial for studying DR because of its innovative methodology incorporating two different public datasets. This strategy enhances the model’s capacity to generalize unseen DR images, as each dataset encompasses unique demographics and clinical circumstances. The network can learn and capture complicated hierarchical image features with asymmetric weights. Each image is preprocessed using contrast-limited adaptive histogram equalization and the discrete wavelet transform. The model is trained and validated using the combined datasets of Dataset for Diabetic Retinopathy and the Asia-Pacific Tele-Ophthalmology Society. The CNN model is tuned in with different learning rates and optimizers. An accuracy of 72% and an area under curve score of 0.90 was achieved by the CNN model with the Adam optimizer. The recommended study results may reduce diabetes-related vision impairment by early identification of DR severity.

As the COVID-19 pandemic caused many schools to go online, asynchronous distant learning has become popular. One of the main challenges of asynchronous distance learning is keeping students engaged and motivated, as they do not have the same engagement with their peers and teachers as in traditional face-to-face learning environments. HTML 5 package (H5P) is an interactive learning tool that has the potential to fill this need due to its numerous immediate interactive features, such as interactive videos, pop quizzes, and games during media playback. This study investigates the effectiveness of using H5P and Moodle in asynchronous distance learning environments for undergraduate students. The data collection methods included pre-and post-surveys for Moodle and H5P and the questions related to the student perspectives towards H5P features. The technology acceptance model (TAM) is employed to find student satisfaction. The results of this study suggest that both the H5P and Moodle could be valuable tools for making E-learning more effective. The interactive and engaging nature of H5P can provide students with a more enjoyable and effective learning experience, helping to keep them motivated and engaged throughout their studies.

Arabic poetry follows intricate rhythmic patterns known as ‘arūḍ’ (prosody), which makes its automated categorization particularly challenging. While earlier studies primarily relied on conventional machine learning and recurrent neural networks, this work evaluates the effectiveness of transformer-based models—an area not extensively explored for this task. We investigate several pretrained transformer models, including Arabic Bidirectional Encoder Representations from Transformers (Arabic-BERT), BERT base Arabic (AraBERT), Arabic Efficiently Learning an Encoder that Classifies Token Replacements Accurately (AraELECTRA), Computational Approaches to Modeling Arabic BERT (CAMeLBERT), Multi-dialect Arabic BERT (MARBERT), and Modern Arabic BERT (ARBERT), alongside deep learning models such as Bidirectional Long Short-Term Memory (BiLSTM) and Bidirectional Gated Recurrent Units (BiGRU). This study uses half-verse data across 14 m. The CAMeLBERT model achieved the highest performance, with an accuracy of 90.62% and an F1-score of 0.91, outperforming other models. We further analyze feature significance and model behavior using the Local Interpretable Model-Agnostic Explanations (LIME) interpretability technique. The LIME-based analysis highlights key linguistic features that most influence model predictions. These findings demonstrate the strengths and limitations of each method and pave the way for further advancements in Arabic poetry analysis using deep learning.

Background: Diagnostic errors can be substantially diminished, and clinical decision-making can be significantly enhanced through automated image classification. Methods: We implemented a YOLO (You Only Look Once)-based system to classify diabetic retinopathy (DR) utilizing a unique retinal dataset. Although YOLO provides exceptional accuracy and rapidity in object recognition and categorization, its interpretability is constrained. Both binary and multi-class classification methods (graded severity levels) were employed. The Contrast-Limited Adaptive Histogram Equalization (CLAHE) model was utilized to improve image brightness and detailed readability. To improve interpretability, we utilized Eigen Class Activation Mapping (Eigen-CAM) to display areas affecting classification predictions. Results: Our model exhibited robust and consistent performance on the datasets for binary and 5-class tasks. The YOLO 11l model obtained a binary classification accuracy of 97.02% and an Area Under Curve (AUC) score of 0.98. The YOLO 8x model showed superior performance in 5-class classification, with an accuracy of 80.12% and an AUC score of 0.88. A simple interface was created using Gradio to enable real-time interaction. Conclusions: The suggested technique integrates robust prediction accuracy with visual interpretability, rendering it a potential instrument for DR screening in clinical environments.

Diabetic retinopathy (DR) is the primary factor leading to vision impairment and blindness in diabetic people. Uncontrolled diabetes can damage the retinal blood vessels. Initial detection and prompt medical intervention are vital in preventing progressive vision impairment. Today's growing medical field poses more significant workload and diagnostic demands on medical professionals. In the proposed study, a convolutional neural network (CNN) is employed to detect the stages of DR. This research is crucial for studying DR because of its innovative methodology incorporating two different public datasets. This strategy enhances the model's capacity to generalize unseen DR images, as each dataset encompasses unique demographics and clinical circumstances. The network can learn and capture complicated hierarchical image features with asymmetric weights. Each image undergoes preprocessing using contrast-limited adaptive histogram equalization (CLAHE) and the discrete wavelet transform (DWT). The model is trained and validated using the combined datasets of Dataset for Diabetic Retinopathy (DDR) and the Asia Pacific Tele-Ophthalmology Society (APTOS). The CNN model is tuned with different learning rates and optimizers. The CNN model with Adam optimizer achieved 70% accuracy and an area under curve (AUC) score of 0.82. The recommended study results may reduce diabetes-related vision impairment by early DR severity identification.