We will create a Deep Neural Network python from scratch. We are not going to use Tensorflow or any built-in model to write ...

NITK develops SVALSA, a machine learning-based landslide warning system for the Western Ghats, enhancing disaster ...

The native just-in-time compiler in Python 3.15 can speed up code by as much as 20% or more, although it’s still experimental ...

From disaster zones to underground tunnels, robots are increasingly being sent where humans cannot safely go. But many of ...

Discover the power of predictive modeling to forecast future outcomes using regression, neural networks, and more for improved business strategies and risk management.

Background: Maternal and child health remains a global public health issue, particularly in low- and middle-income countries where maternal and child mortality are extremely high. The World Health ...

Abstract: The K-nearest neighbors (kNNs) algorithm, a cornerstone of supervised learning, relies on similarity measures constrained by real-number-based distance metrics. A critical limitation of ...

Objectives This study aimed to employ machine learning algorithms to predict the factors contributing to zero-dose children in Tanzania, using the most recent nationally representative data. Design ...

The video game industry has evolved leaps and bounds over the last half century, from simple arcade-like gameplay to highly immersive, intelligent, and interactive gaming. With advancements in ...

The emergence of using Machine Learning Techniques in software testing started in the 2000s with the rise of Model-Based Testing and early bug prediction models trained on historical defect data. It ...