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India’s Digital Public Infrastructure (DPI)—including the Unified Payments Interface (UPI) and Aadhaar—has been globally recognized for advancing financial inclusion and efficient governance. This study analyzes data from 2016–17 to 2023–24 the impact of these services on India's GDP.

The authors looked at how the length of an audio clip used of a song impacted the ability to properly classify it by musical genre.

The authors looked at different factors, such as age, pre-existing conditions, and geographic region, and their ability to predict what an individual's health insurance premium would be.

While cephalopods play significant roles in both ecosystems and medical research, there is currently no assembled genome. In an attempt to sequence the Sepia bandensis genome, it was found that there was inhibition from the organism during DNA extraction, resulting in PCR failure. In this study, researchers tested the hypothesis that S. bandensis ink inhibits PCR. They then assessed the impact of ink on multiple methods of DNA extraction

The authors compare current machine learning algorithms with a new Explainable AI algorithm that produces a human-comprehensible decision tree alongside predictions.

Improving the classifier of the difficulty level of Sudoku.

Skin cancer is a common and potentially deadly form of cancer. This study’s purpose was to develop an automated approach for early detection for skin cancer. We hypothesized that convolutional neural network-based models using transfer learning could accurately differentiate between benign and malignant moles using natural images of human skin.

The authors investigate the ability of machine learning models to developing new drug-like molecules by learning desired chemical properties versus simply generating molecules that similar to those in the training set.

In this study, the authors investigate associations between use of histamine H₂ receptor antagonists and suicidal ideation in different age groups.

Seeking to investigate the effects of ambient pollutants on human respiratory health, here the authors used machine learning to examine asthma in Lost Angeles County, an area with substantial pollution. By using machine learning models and classification techniques, the authors identified that nitrogen dioxide and ozone levels were significantly correlated with asthma hospitalizations. Based on an identified seasonal surge in asthma hospitalizations, the authors suggest future directions to improve machine learning modeling to investigate these relationships.