Human vision depends heavily on retinal tissue. The loss of eyesight may result from infections of the retinal tissues that are treated slowly or do not work at all. Additionally, the diagnosis is susceptible to inaccuracies when a large dataset is involved. Therefore, a fully automated transfer learning approach for diagnosing diabetic retinopathy (DR) is suggested to minimize human intervention while maintaining high classification accuracy. To address this issue, we proposed a transfer learning-based trilateral attention network (TaNet) for the classification. To boost the visual quality of the DR pictures, a contrast constrained adaptive histogram equalization approach is applied. The pre-processed pictures are then segmented using a bilateral segmentation network (BiSeNet). The BiSeNet segmented the optic disc and blood vessels individually. After the completion of segmentation, the features are extracted. Feature extraction is based on the wavelet scattering transformation approach. The results of many trials were evaluated against the Messidor-2, EYEPACS, and APTOS 2019 datasets. The proposed model was created using a refined pre-trained technique and transfer learning methodology. Finally, the suggested framework was tested using efficiency assessment methods, and the classification rate was recorded as having above 98% sensitivity, specificity, precision, and accuracy. The proposed approach yields greater performance and shows enhancement towards the existing approach.

The life expectancy of a population is a vital measure of its overall health and healthcare quality. This study use machine learning methods, notably XGBoost, to predict life expectancy in industrialized and emerging countries. XGBoost, or Extreme Gradient Boosting, is a robust machine-learning technique renowned for its precision and efficiency in handling structured data. The objective is to develop a precise prediction model and identify the primary factors influencing life expectancy. The experimental findings indicate that our XGBoost-based model effectively forecasts life expectancy in both industrialized and developing nations. Mean Absolute Error, Mean Squared Error, and R² score are used for model selection. XGBoost is selected because to its mean squared error of 2.45, mean absolute error of 1.03, and R² scores of 0.99 for the training set and 0.97 for the testing set. We conduct feature significance analysis to identify the most relevant factors affecting life expectancy. Our results underscore the significance of variables such as HIV/AIDS, income composition, and adult mortality in influencing life expectancy. The model's findings may assist lawmakers and healthcare professionals in making educated decisions to enhance public health outcomes, allocate resources efficiently, and formulate focused solutions.

Protein structure comparison is one of the most challenging problem in bioinformatics. This problem is modeled as a contact map overlap problem in which the similarity of the two proteins being compared is measured by the amount of overlap between their corresponding protein contact maps. To find a maximum overlap is proved to be an NP-hard problem in this area. Protein contact map is a two dimensional representation of protein tertiary structure. The goal of this work is to find frequent sub-graphs from contact maps. We propose a simple and computationally inexpensive algorithm is depth first search approach to contact map overlap problem for finding sub graphs from whole contact map of each protein structure. The sub-graph approach is evaluating on two bench mark data sets are SOKOL and SKOLNICK. It is interesting to note that along with achieving better sub-graphs is also obtained for certain protein folds. Further these sub graphs are used successfully in addressing the problem of protein fold classification.