The data mining tools market is set to expand to USD 3.89 billion by 2034, registering a 12.30% CAGR as data-driven ...

Based Detection, Linguistic Biomarkers, Machine Learning, Explainable AI, Cognitive Decline Monitoring Share and Cite: de Filippis, R. and Al Foysal, A. (2025) Early Alzheimer’s Disease Detection from ...

Introduction Armed conflict severely impacts health, with indirect deaths often exceeding direct casualties two to four times ...

Worse, the most recent CERN implementation of the FPGA-Based Level-1 Trigger planned for the 2026-2036 decade is a 650 kW system containing an incredibly high number of transistor, 20 trillion in all, ...

Discover how the Luhn Algorithm verifies credit card accuracy, supports secure transactions, and helps prevent errors in inputting Social Security numbers.

Abstract: The study of protein-protein interactions (PPIs) and predicting the protein structure plays a critical role in understanding cellular processes and designing therapeutic interventions. In ...

The global computer vision in healthcare market is projected to expand at a compound annual growth rate (CAGR) of approximately 25% over the forecast period. This robust growth is driven by the ...

This project provides a powerful and flexible PDF analysis microservice built with Clean Architecture principles. The service enables OCR, segmentation, and classification of different parts of PDF ...

TikTok’s algorithm favors mental health content over many other topics, including politics, cats and Taylor Swift, according to a Washington Post analysis. At first, the mental health-related videos ...

Meta is giving Instagram users a rare glimpse into why certain posts are showing up on their Reels, the platform’s feed of algorithmically curated videos. Starting today, users will now see a list of ...

You chose selected. Each dot here represents a single video about selected. While you’re on the app, TikTok tracks how you interact with videos. It monitors your watch time, the videos you like, the ...

Abstract: Conventional soft clustering algorithms perform well on linearly distributed features, but their performance degrades on nonlinearly distributed features in high-dimensional space. In this ...