arXiv Machine Learning By Jianglan Wei, Zhenyu Zhang, Pengcheng Wang, Mingjie Zeng, Zhigang Zeng

HD3C: Efficient Medical Data Classification for Edge Devices

Read the original on arXiv Machine Learning →

arXiv:2509. 14617v4 Announce Type: replace Abstract: Efficient medical data classification is essential for modern disease screening, particularly in resource-constrained environments where power budgets and computing capabilities are limited.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

arXiv Machine Learning
Jun 9

AMS-HD: Hyperdimensional Computing for Real-Time and Energy-Efficient Acute Mountain Sickness Detection

arXiv:2602. 08916v3 Announce Type: replace-cross Abstract: Objective: Acute mountain sickness (AMS) is the most prevalent altitude illness, affecting unacclimatized individuals ascending above 2,500 m and potentially escalating to life threatening cerebral or pulmonary edema.

By Abu Masum, Mehran Moghadam, M. Hassan Najafi, Bige Unluturk, Ulkuhan Guler, Beth A. Beidleman, Sercan Aygun
arXiv AI
Sep 7

A Hybrid Predictive Ensemble of Machine Learning and Deep Neural Networks for Early Cardiovascular Disease Risk Assessment

The paper presents a hybrid predictive ensemble that merges machine learning and deep neural network techniques to detect and prognosticate cardiovascular disease early. It processes real‑time physiological data from IoMT devices, applying preprocessing, feature selection, and optimized classifiers (SVM, Random Forest, XGBoost) within an ensemble architecture. The cloud‑based system achieves higher accuracy, fewer false positives, and consistent performance on real‑world datasets, supporting continuous patient monitoring and clinical decision support.

By Balaji Venkateswaran