Johns Hopkins Medicine

Machine Learning Model Improves Accuracy of Liquid Biopsy Results

A machine learning model developed by researchers at the Johns Hopkins Kimmel Cancer Center filters out the biological noise ...

Researchers trained an open source AI search agent, Harness-1, that outperforms GPT-5.4 on recalling relevant information

Harness-1 suggests that the future of agentic AI lies in building better environments for models to work within, rather than ...

Hidden geometry explains why kernel methods separate complex data so well

Are two sets of data genuinely different, or is it because of randomness? This question, known as the two-sample testing problem, becomes notoriously difficult in modern datasets, because they are ...
The Manila Times

WiMi Proposes a New High-Performance Fault-Tolerant Quantum Computing Technology Based on Multi-Hypercube Codes

WiMi Hologram Cloud Inc. (NASDAQ: WiMi) ('WiMi' or the 'Company'), a leading global Hologram Augmented Reality ('AR') Technology provider, proposes a new high-performance fault-tolerant quantum ...
Devdiscourse

Banks could strengthen credit card fraud screening with ensemble machine learning model

Read more about Banks could strengthen credit card fraud screening with ensemble machine learning model on Devdiscourse ...
TMCnet

WiMi Achieves Breakthrough in Deep Convolutional Neural Network Technology Based on Quantum Parameterized Circuits

WiMi Hologram Cloud Inc. (NASDAQ: WiMi) ("WiMi" or the "Company") is a leading global Hologram Augmented Reality ("AR") Technology provider. A quantum deep convolutional neural network technology ...
Scientific Research Publishing

A Multimodal Deep Learning Framework for Early Detection, Mood State Classification, and Episode Prediction in Bipolar Disorder ()

Bipolar disorder (BD) affects approximately 45 million individuals worldwide and is characterized by recurrent episodes of mania, hypomania, and depression, with an average diagnostic delay exceeding ...
IEEE

Comprehensive Analysis of Categorical Encoding Methods Balancing Performance, Efficiency, and Interpretability in Machine Learning

Abstract: Categorical variables are a common feature in real-world datasets; however, most machine learning algorithms require numerical inputs. The method used to convert these categorical features ...
Scientific Research Publishing

Dietterich, T.G. (2000) Ensemble Methods in Machine Learning. In: Kittler, J. and Roli, F., Eds., International Workshop on Multiple Classifier Systems, Springer, 1-15.

ABSTRACT: This paper proposes a hybrid machine learning framework for early diabetes prediction tailored to Sierra Leone, where locally representative datasets are scarce. The framework integrates ...
EurekAlert!

An effective encoding of human medical conditions in disease space provides a versatile framework for deciphering disease associations

Uncovering complex disease patterns from large-scale, heterogeneous health data remains a significant challenge. Traditional statistical methods and conventional machine learning algorithms often ...