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.

Emotion plays a significant role in interpersonal communication and also improving social life. In recent years, facial emotion recognition is highly adopted in developing human-computer interfaces (HCI) and humanoid robots. In this work, a triangulation method for extracting a novel set of geometric features is proposed to classify six emotional expressions (sadness, anger, fear, surprise, disgust, and happiness) using computer-generated markers. The subject’s face is recognized by using Haar-like features. A mathematical model has been applied to positions of eight virtual markers in a defined location on the subject’s face in an automated way. Five triangles are formed by manipulating eight markers’ positions as an edge of each triangle. Later, these eight markers are uninterruptedly tracked by Lucas- Kanade optical flow algorithm while subjects’ articulating facial expressions. The movement of the markers during facial expression directly changes the property of each triangle. The area of the triangle (AoT), Inscribed circle circumference (ICC), and the Inscribed circle area of a triangle (ICAT) are extracted as features to classify the facial emotions. These features are used to distinguish six different facial emotions using various types of machine learning algorithms. The inscribed circle area of the triangle (ICAT) feature gives a maximum mean classification rate of 98.17% using a Random Forest (RF) classifier compared to other features and classifiers in distinguishing emotional expressions.

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.