Discriminating global ore deposit genetic types using chalcopyrite trace elements: Insights from interpretable machine learning

Towards Early and Accurate Disease Detection Through Multimodal Predictive Modeling: Fusion of Electronic Health Records, Medical Imaging, And Omics Data Using Interpretable Machine Learning.

Muhammad Ahsan Hayat, Jahangir Baig, Shayan Ahmed, Ahmed Faraz Ayubi

Early detection of disease is a cornerstone for improving patient outcomes, reducing costs, and enabling preventative interventions. Traditional predictive models often rely on a single type of data (e.g., imaging, clinical labs, or genomics). However, human health is inherently…Read more

Also available via: European Organization for Nuclear Research

Predictive Maintenance of Power Transformers using Machine Learning- A Case Study

AMANING RICHMOND OFORI, Electronic Engineering, DR JOSEPH C. ATTACHIE

The importance of power transformers in electrical power systems cannot be overstated, as their failures can lead to considerable economic losses and disruptions. The typical malfunctions encountered by a power transformer comprise dielectric issues, thermal losses due to copper…Read more

Event-Based Prediction of Liquidity Sweep Dynamics in XAUUSD Using Machine Learning

Vanshvardhan Sharma

This paper develops a machine learning framework for detecting and predicting liquidity sweep events in XAUUSD using event-based market microstructure analysis. Using 15-minute data from 2014–2024, the study formalizes liquidity sweeps as a binary classification problem evaluated…Read more

Interpretable machine-learning risk stratification at diagnosis for 3-year mortality in de novo metastatic prostate cancer (SEER): reproducibility code

Xin Wang, Guanglei Yao, Wei Ding

This archive contains the analysis code, the predictor dictionary, and the retrained primary model objects underlying the manuscript "Interpretable machine-learning risk stratification at the time of diagnosis for 3-year mortality in de novo metastatic prostate cancer: developmen…Read more

Data and code for: Frequent Mental Distress Across Texas Census Tracts: Social-Environmental Co-Exposure, Spatial Dependence, and Interpretable Machine Learning

kwadwo Frimpong

This repository contains the processed analytic dataset and analysis code supporting the study "Environmental Co-Exposure, Green Space, and Frequent Mental Distress in Texas Census Tracts: An Interpretable Machine Learning and Spatial Analysis." The dataset includes tract-level f…Read more

Resume Ranking Engine Using Machine Learning and Natural Language Processing for Automated Candidate Screening

V. Satish Mantripragada Akshaya Pranathi

Organizations often receive hundreds of resumes for a single job opening, making manual candidate screening slow and difficult to manage. Conventional recruitment methods generally depend on keyword matching or manual evaluation, both of which may overlook qualified applicants be…Read more