Both approaches identified hemoglobin as one of the most significant predictors of CKD risk. Additional top-ranked features included blood urea, sodium levels, red blood cell count, potassium, and ...

New deep-learning framework reconstructs hourly PM2.5 chemical composition using air-quality and meteorological data ...

Objective Cardiovascular diseases (CVD) remain the leading cause of mortality globally, necessitating early risk identification to improve prevention and management strategies. Traditional risk ...

This challenge is examined in Application of AI in Cyberattack Detection: A Review, published in the journal Sensors, where researchers explore how artificial intelligence techniques, from ensemble ...

ACGRIME is an improved metaheuristic algorithm derived from the original RIME framework. ACGRIME integrates three strategic mechanisms: chaotic initialization, adaptive weighting and Gaussian mutation ...

Machine learning models are increasingly applied across scientific disciplines, yet their effectiveness often hinges on heuristic decisions such as data transformations, training strategies, and model ...

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Machine learning, a key enabler of artificial intelligence, is increasingly used for applications like self-driving cars, medical devices, and advanced robots that work near humans — all contexts ...

Machine learning (ML) has rapidly become one of the most influential technologies across industries, from healthcare and finance to e-commerce and entertainment. But if you’re new to ML, the process ...

Abstract: Hyperparameter tuning is a crucial process in the machine learning (ML) pipeline, as the performance of a learning algorithm is highly influenced by its hyperparameter configuration. This ...

ABSTRACT: This study presents a comprehensive and interpretable machine learning pipeline for predicting treatment resistance in psychiatric disorders using synthetically generated, multimodal data.