Emojis have become increasingly prevalent in today’s digital world, allowing individuals to convey a wide range of emotions, from uncomplicated to intricate, to a greater extent than previously. Consequently, emojis are being utilized in sentiment analysis and tailored marketing strategies. The ongoing research on conducting emotion detection on both tweets and a symbolic expression dataset sourced from Kaggle. Given that tweets are largely commentaries, we utilized two end‐to‐end sentence embedding models, the DistilBERT, USE‐Large3, and RoBERTa, which generate embeddings. These embeddings are further utilized for training with dense neural networks (NNs) and LSTM techniques. Remarkably, it was perceived that the text classification accuracy for both models was consistently high, hovering around 98%. However, when the validation set is constructed with that of symbolic expression or emojis not included in the training dataset, a significant drop in accuracy for both models, plummeting to 75%, had been observed. Additionally, a distributed training methodology is utilized as a substitute for the conventional single‐threaded model to enhance scalability. This approach resulted in a roughly 17% reduction in the runtime while maintaining accuracy. Lastly, in pursuit of explainable AI, the SHAP and LIME algorithms are employed to elucidate the model’s behavior and assess any potential biases in the dataset. The creative use of advanced deep NN techniques customized for the delicate complexities of hybrid‐data sentiment analysis indicates a significant leap forward. Our proposed work provides the critical gap in existing sentiment analysis methods, which primarily aimed at either text or emojis in isolation, thereby exploring more holistic understanding of sentiment in digital communications. Moreover, the application of explainable AI techniques, SHAP and LIME, to demystify model decisions emphasizes commitment to advancing transparent and trustworthy deep learning technologies in sentiment analysis.

Glaucoma is a major cause of blindness, often undetected in early stages due to lack of symptoms. Addressing this, research study developed a deep learning framework integrating Generative Adversarial Networks (GANs) with Residual Neural Networks (ResNet) to enhance glaucoma detection from fundus images. Utilizing GANs for data augmentation, we enriched the training set with synthetic images that improve feature recognition, while ResNet, fine-tuned on this data, performed high-precision classification. The GAN's discriminator, trained using binary cross-entropy loss, concentrating to extract key indicators of glaucoma from these fundus images, with its performance assessed by its accuracy in distinguishing real from synthetic images. The GAN-ResNet channel exploited the discriminator's feature extraction coupled with ResNet's deep learning capabilities to classify the fundus images with refined accuracy. The proposed model final layer is fine-tuned for binary classification between glaucomatous and healthy images, with the loss function modified for medical dataset imbalances. Through wide testing, the GAN-ResNet model proven a remarkable 98% accuracy in analysing glaucoma, showing high predictive results. This validates that the proposed model is helpful in detecting glaucoma early. It highlights how well-advanced neural networks work for analysing medical images.