Worldwide, nearly 2.2 billion individuals face vision impairment or blindness, mainly caused by age-associated eye disorders like cataracts, diabetic retinopathy, and glaucoma. If these ailments are not recognized and treated timely, they may lead to irreversible blindness. The research aims to develop a highly accurate and efficient model for the early prediction of these eye conditions by using fundus pictures. Existing models for detecting eye diseases have made significant progress, but many still fail to provide the necessary accuracy for early detection, which is crucial for efficient treatment. EDCNet seeks to address this challenge by integrating CNNs and GRUs in a hybrid model. CNNs are employed for their strength in feature extraction, capturing intricate details from fundus images. At the same time, GRUs are used for their ability to handle sequential data, enabling the model to analyze temporal patterns in the retinal images. The model’s preprocessing phase is enhanced to increase the quality and clarity of the dataset, which is vital for accurate analysis. EDCNet utilizes the Adam optimizer, known for its efficiency in training deep learning models, to optimize the model’s performance further. This combination of advanced techniques aims to create a reliable system for detecting early signs of age-related eye diseases, facilitating timely intervention and potentially reducing the global burden of blindness. The proposed work aims to contribute to more accurate and accessible eye disease screening, especially in regions where early detection can significantly impact public health outcomes. The EDCnet model has been evaluated and exhibits a high level of accuracy, achieving a precise performance rate of 91.75%.

Accurate prediction of foreign exchange (forex) rates is a critical task for financial markets, influencing decision-making for traders, investors, and policymakers. Traditional time series forecasting models often have difficulty obtaining the complex trends and non-linear dependencies inherent in forex data. To tackle these challenges this study comes up with a combined model that incorporates the powerful qualities of Prophet and LSTM networks. Prophet is leveraged for its ability to model trend and seasonality in time series data while handling irregularities and missing values effectively. In order to capture the residual non-linear dependencies left by Prophet, we integrate an LSTM network, recognized for its capability for modeling long-term dependencies in data that are sequential. The hybrid model obtained an MAE of 0.00051, an RMSE of 0.00064, and a MSE of 0.00375, outperforming traditional models and highlighting the effectiveness of this approach in improving forex rate predictions. This study not only advances time series forecasting techniques but also offers valuable insights for stakeholders in the forex market, enhancing their ability to make informed decisions.