Neural Networks and Rules-based Systems used to Find Rational and Scientific Correlations between being Here and Now with Afterlife Conditions
Neural Networks and Rules-based Systems used to Find Rational and
This article proposes a research program that integrates recent advances in artificial intelligence with established theory and practice in drowning prevention, lifeguard operations, and aquatic risk management. Building on prior contributions that characterized the Instinctive Drowning Response and clarified the cognitive and perceptual demands placed on rescuers, I outline how intelligent systems can support earlier recognition, better prioritization, and more reliable intervention in aquatic environments. The paper introduces a modular architecture for AI-enhanced water safety that spans perception, causal risk modeling, decision support, education and training, and system-level prevention. It presents practical evaluation criteria, interdisciplinary collaboration paths, and governance principles tailored to the realities of beaches, pools, waterparks, and open water. The agenda emphasizes human-centered design, ethical deployment, and translational research that connects laboratory methods with lifeguard stands, facility control rooms, and public health practice
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Dr. Francesco Pia. 2026. \u201cFrom the Instinctive Drowning Response to Intelligent Water Safety: A Research Agenda for AI-Augmented Aquatic Risk Science\u201d. Global Journal of Medical Research - K: Interdisciplinary GJMR-K Volume 25 (GJMR Volume 25 Issue K4): .
Crossref Journal DOI 10.17406/gjmra
Print ISSN 0975-5888
e-ISSN 2249-4618
The methods for personal identification and authentication are no exception.
Total Score: 101
Country: Italy
Subject: Global Journal of Medical Research - K: Interdisciplinary
Authors: Francesco Pia (PhD/Dr. count: 0)
View Count (all-time): 67
Total Views (Real + Logic): 105
Total Downloads (simulated): 32
Publish Date: 2026 01, Fri
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This article proposes a research program that integrates recent advances in artificial intelligence with established theory and practice in drowning prevention, lifeguard operations, and aquatic risk management. Building on prior contributions that characterized the Instinctive Drowning Response and clarified the cognitive and perceptual demands placed on rescuers, I outline how intelligent systems can support earlier recognition, better prioritization, and more reliable intervention in aquatic environments. The paper introduces a modular architecture for AI-enhanced water safety that spans perception, causal risk modeling, decision support, education and training, and system-level prevention. It presents practical evaluation criteria, interdisciplinary collaboration paths, and governance principles tailored to the realities of beaches, pools, waterparks, and open water. The agenda emphasizes human-centered design, ethical deployment, and translational research that connects laboratory methods with lifeguard stands, facility control rooms, and public health practice
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