Traditional machine learning algorithms for classification tasks operate under the assumption of balanced class distributions. However, this assumption only holds in some practical scenarios. In most ...

ABSTRACT: The growing demand for energy-efficient Wireless Sensor Networks (WSNs) in applications such as IoT, environmental monitoring, and smart cities has sparked exhaustive research into practical ...

Even by Silicon Valley’s hyper-competitive standards, the past few weeks have provided a generational example of one-upmanship in the AI space. First, in June, the data-labeling startup Scale AI ...

A machine learning random forest regression system predicts a single numeric value. A random forest is an ensemble (collection) of simple decision tree regressors that have been trained on different ...

In today’s world, you’ve probably heard the term “Machine Learning” more than once. It’s a big topic, and if you’re new to it, all the technical words might feel confusing. Let’s start with the basics ...

Innovation: Identified a subset of the model (a circuit) that explains most of the model’s behavior for basic arithmetic logic and examine its functionality. Tasks: Analyzed attention patterns using ...

In earthquake-prone areas like Japan, there is a need for better prediction of soil stability to mitigate liquefaction risks. Towards this end, researchers have used machine learning models, including ...