We live in a world where huge amount of data is collected daily. Analyzing such data is an important need. For example, businesses often struggle to recognize their potential customers; to make it ...

Purpose: Brain tumor segmentation with MRI is a challenging task, traditionally relying on manual delineation of regions-of-interest across multiple imaging sequences. However, this data-intensive ...

SAVANA uses a machine learning algorithm to identify cancer-specific structural variations and copy number aberrations in long-read DNA sequencing data. The complex structure of cancer genomes ...

Developed a Machine Learning code for creating customer segmentation (Cluster Analysis, Elbow Point, Insights) - Pull requests · Gezatan/Customer-Segmentation-Using-Machine-Learning ...

In this work, we demonstrate the machine learning-driven optimization of a photoredox tertiary amine synthesis with six continuous variables (e.g., concentration, temperature, residence time) and ...

AI-Powered Forecasting: Predict expected cash inflows using AI trained on historical collections data, delivering greater confidence in month-end outcomes and forward-looking cash flow Contextual ...

Typical Azure Machine Learning Project Lifecycle (source: Microsoft). At the upcoming Visual Studio Live! @ Microsoft HQ 2025 conference in Redmond, Eric D. Boyd, founder and CEO of responsiveX, will ...

Deep learning–derived chamber volumes and left ventricular mass from CT attenuation correction were predictive of heart failure hospitalization and reduced myocardial flow reserve in patients ...

AI and machine learning are reshaping how brands interact with their audiences and redefining campaign management, customer engagement and data-driven decision making.

Automatic segmentation and quantitative analysis of brain CT volume in 2-year-olds using deep learning model ...