Johns Hopkins University Applied Physics Laboratory

Johns Hopkins APL Pioneering Digital Calibration to Create ‘Born-Qualified’ Parts

APL is developing a comprehensive suite of capabilities to ensure that additively manufactured parts can perform predictably in mission-critical applications — no matter where, when, or on what ...
Scientific American

AI techniques speed up forensic analysis of crucial crime scene larvae

To use this evidence, investigators typically must grow the larvae until adulthood in a laboratory setting and then identify ...
Rheumatology Advisor

Machine Learning Helps Predict MPA Exposure in Childhood-Onset Lupus Nephritis

A machine learning-driven framework accurately predicts MPA exposure and supports individualized dosing in childhood-onset LN.
IEEE

Cluster Head Selection Algorithm using Machine Learning

Abstract: Wireless Sensor Networks (WSNs) find extensive applications in environmental monitoring, healthcare, and smart cities. Energy efficiency, however, continues to be a significant challenge ...

Beyond the Black Box A Framework for Explainable AI (XAI) in Credit Scoring

The promise of artificial intelligence in credit scoring is undeniable. By analyzing vast, non-traditional datasets from ...
Unite.AI

Book Review: The Worlds I See by Dr. Fei-Fei Li

Artificial intelligence is often explained through algorithms, hardware breakthroughs, and the rapid rise of powerful models. What is frequently missing from that narrative is the human story behind ...
IEEE

Comparative Analysis of Deep Learning Algorithms for Image Classification

Abstract: Image classification is one of the highly explored fields in artificial intelligence and computer vision, due to its widespread applications such as medical image analysis, autonomous cars, ...