Managing high disease burden: a study of medication rationality in diabetes and hypertension in Andhra Pradesh.

Objectives: To assess the burden of chronic Non-communicable diseases (NCDs) such as diabetes and hypertension, and evaluate the utilization of drugs for their treatment among the population of Andhra Pradesh, India.

Methods: A cross-sectional study was conducted to analyse the prevalence of diabetes and hypertension, as well as the pattern of drug usage for their management. Data were collected through surveys and medical records review.

Link Prediction in Complex Networks Using Average Centrality-Based Similarity Score.

Link prediction plays a crucial role in identifying future connections within complex networks, facilitating the analysis of network evolution across various domains such as biological networks, social networks, recommender systems, and more. Researchers have proposed various centrality measures, such as degree, clustering coefficient, betweenness, and closeness centralities, to compute similarity scores for predicting links in these networks. These centrality measures leverage both the local and global information of nodes within the network.

Optimized attention-induced multihead convolutional neural network with efficientnetv2-fostered melanoma classification using dermoscopic images.

Melanoma is an uncommon and dangerous type of skin cancer. Dermoscopic imaging aids skilled dermatologists in detection, yet the nuances between melanoma and non-melanoma conditions complicate diagnosis. Early identification of melanoma is vital for successful treatment, but manual diagnosis is time-consuming and requires a dermatologist with training.

Community-Based Matrix Factorization (CBMF) Approach for Enhancing Quality of Recommendations.

Matrix factorization is a long-established method employed for analyzing and extracting valuable insight recommendations from complex networks containing user ratings. The execution time and computational resources demanded by these algorithms pose limitations when confronted with large datasets. Community detection algorithms play a crucial role in identifying groups and communities within intricate networks.