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AI outperforms traditional models in bankruptcy forecasting

Bankruptcy prediction has traditionally relied on statistical approaches such as Altman’s Z-score, which use financial ratios ...

Using Machine Learning to Predict User Needs and Provide Personalized Support

1. Demand Prediction Engine: A Technological Leap from "Passive Response" to "Active Anticipation" ...
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Agentic AI in Contract Analytics Harnessing Machine Learning for Risk Assessment and Compliance in Government Procurement Contracts ()

Government procurement contracts can be complicated, with extensive risk analysis and compliance reviews. The traditional methods of contract analytics are time-consuming and often inexact, thus ...

In Conversation With Ramakrishnan Sathyavageeswaran: Scaling Machine Learning Excellence in Enterprise Systems

The landscape of machine learning engineering has evolved dramatically over the past decade, with organizations increasingly demanding scalable, production-ready solutions that deliver measurable ...

AI and the Humanities: Literature Meets Machine Learning

As AI continues to evolve, its role in the humanities will likely expand. Future projects may integrate multimodal ...

Essential Guide for Institution Selection: Balancing Cost and Value of Smart Learning Room Systems

The cost analysis of the smart learning room system needs to clarify the balance between 'explicit investment' and 'implicit ...
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Machine learning to predict high-risk coronary artery disease on CT in the SCOT-HEART trial

Background Machine learning based on clinical characteristics has the potential to predict coronary CT angiography (CCTA) findings and help guide resource utilisation.Methods From the SCOT-HEART ...

Why do you see what I see? Now machine model can tell the colour the brain hints at

This meant that a model could look at the neural signals of a person and infer what colour they were viewing—even without ...