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3 articles for “Physics-Informed AI”
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Quasar: Quantum-Accelerated Sustainable Anomaly Recognition in Climate Systems
Abstract: Accurate detection of climate anomalies is vital for disaster alleviation and policy making in a sustainable manner, but customary detection methods face the challenges of computational inefficiency and physical inconsistency. In this study, we propose a novel approach called Quantum-Optimized Fuzzy Physics-Informed Neural Networks (QFuzzy-PINNs), which integrates quantum computing, fuzzy logic, and physics-informed deep learning. As a first step, we employ quantum annealing for conventional optimization to adjust multiple Gaussian …
Published in International Journal of Climate Conditions · Vol. 2, Issue 2, 2025 · pp. 18–27 Read article
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Diet(Nutrition) Recommender using Machine Learning
Abstract: In an era where maintaining a healthy lifestyle has become a priority, personalized dietary recommendations play a crucial role in guiding individuals towards achieving their nutritional goals. The Diet Recommendation System is an inno- vative application that combines user-specific data with machine learning algorithms to provide tailored dietary suggestions. This system is designed to simplify the process of meal planning and promote healthier eating habits. The Diet Recommendation System is …
Published in International Journal of Nutritions · Vol. 2, Issue 2, 2025 Read article
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FMR1 Key Biomarker in Fragile X Syndrome- A Comprehensive Review
Abstract: Fragile X Syndrome (FXS) is a complicated neurodevelopmental condition that causes intellectual disabilities, behavioural issues, and a variety of physical symptoms. Central to understanding FXS is the Fragile X Mental Retardation 1 (FMR1) gene, pivotal in the disorder's pathogenesis. This review examines FMR1 as a key biomarker in FXS, drawing on recent research insights. The FMR1 gene, situated on the X chromosome, encodes the fragile X mental retardation protein (FMRP), …
Published in International Journal of Brain Sciences · Vol. 1, Issue 2, 2024 · pp. 8–18 Read article