In a significant breakthrough, researchers have developed an advanced explainable deep learning model to predict and analyze harmful algal blooms (HABs) in freshwater lakes and reservoirs across China ...

This retrospective study included 643 patients who had undergone NSCLC resection. ML models (random forest, gradient boosting, extreme gradient boosting, and AdaBoost) and a random survival forest ...

Yeshwanth Macha developed explainable AI models improving insurance fraud detection accuracy, transparency, compliance and ...

This course explores the field of Explainable AI (XAI), focusing on techniques to make complex machine learning models more transparent and interpretable. Students will learn about the need for XAI, ...

Scientists have developed and tested a deep-learning model that could support clinicians by providing accurate results and clear, explainable insights – including a model-estimated probability score ...

In todays fast evolving financial landscape, artificial intelligence and machine learning are changin how credit decisions get made. But the traditional “black box” models cause worry 'cause nobody ...

A new study published in Genome Research presents an interpretable artificial intelligence framework that improves both the accuracy and transparency of genomic prediction, a key challenge in fields ...

This article examines the work of data scientist Sai Prashanth Pathi in AI for credit risk, focusing on explainable machine learning in regulated finance, governance alignment, fairness, compliance, ...

The financial landscape of 2026 is defined by a paradox: machine learning systems are now more powerful and autonomous than ever, yet they operate under the strictest regulatory scrutiny in history.

In data analysis, time series forecasting relies on various machine learning algorithms, each with its own strengths. However, we will talk about two of the most used ones. Long Short-Term Memory ...