An efficient enhanced stacked auto encoder assisted optimized deep neural network for forecasting Dry Eye Disease.

Meibomian Gland Dysfunction (MGD) and Dry Eye Disease (DED) comprise two of the most significant eye diseases, impacting millions of sufferers worldwide. Several etiological factors influence the early symptoms of DED. Early diagnosis and treatment of erectile dysfunction may significantly improve the Quality of Life (QoL) for people. The current study introduces the ESAE-ODNN, an improved stacked autoencoder-aided optimised deep neural network, as a new way to predict DED using feature selection (FS), feature extraction (FE), and classification. The approach described here is novel because it merges chaotic maps into FS, employs SLSTM-STSA for improved classification accuracy (CA), and optimizes with the adaptive quantum rotation of the Enhanced Quantum Bacterial Foraging Optimisation Algorithm (EQBFOA). The present study enhances prediction functions by extracting MGD-related features and complicated relationships from the DED dataset. To ensure essential feature identification, the ESAE minimizes irrelevant and redundant features. To predict the DED, the ESAE first applies FE and then implements an ODNN classifier. This method fine-tunes the ODNN framework to enhance the effectiveness of the classification. The proposed ESAE-ODNN classification system efficiently assists in the early diagnosis of DED. Combining advanced Deep Learning (DL) methods with optimization can help us understand MGD features better and sort the data with the best accuracy (96.34%). The experimental evaluation with relevant performance metrics indicates that the proposed method is efficient in diverse aspects: accurate identification, reduced complexity, and fine-tuned performance. The ESAE-ODNN's robustness in handling intricate feature indications and high-dimensional data outperforms the existing state-of-the-art techniques.

Performance of Nano-Silica Modified Self-Compacting Glass Mortar at Normal and Elevated Temperatures.

This research aims to combine the effects of nanosilica and glass powder on the properties of self-compacting mortar at normal and at higher temperatures. The fine aggregate was replaced by waste glass powder at various percentage levels of 10%, 20%, 30%, 40% and 50%. The mechanical properties of self-compacting glass mortar (SGCM) were studied at elevated temperatures of 200, 400, 600 and 800 °C. Furthermore the effect of sudden and gradual cooling technique on the residual strength of glass mortar was also investigated In order to enhance the behavior of SCGM the nanosilica of 3% by weight of cement was added. From the results it was obtained that the glass powder replacement effectively contributed towards the thermal performance while the addition of nanosilica enhanced the mechanical performance. The enhanced physical properties were obtained mainly at the glass transition temperature thus showing the active participation of glass powders during high temperatures. Moreover the gradually cooled specimens exhibited improved strength characteristics than the suddenly cooled specimens.