The rapid adoption of cloud computing in healthcare has improved data accessibility but introduced significant challenges in privacy preservation. This study presents a secure cloud-based framework fo...
Background: The prevalence of suicidal behavior in children and adolescents is a critical public health issue, with rates in the coffee region exceeding the national average. This study analyzed respo...
Federated learning (FL) has emerged as a paradigm-shifting approach to distributed machine learning, enabling multiple participants to collaboratively train models without exposing raw data. However, ...
Background Given that machine learning is one of the most effective approaches for identifying disease risk factors, this study aimed to explore the predictors of preeclampsia through machine learning...
Background Our study aimed to explore the correlations between Auditory Brainstem Response outcomes and Autism phenotypes.Methods A total of 1883 children with or suspected of being with ASD were enro...
Objective To analyze the current status and influencing factors of social isolation in patients with colorectal cancer after stoma surgery, and to construct a risk prediction model for social isolatio...
Sarcasm detection is a challenging task in Natural Language Processing (NLP) due to its reliance on subtle and implicit linguistic cues that are often not explicitly expressed in text. Designing model...
This study proposes a Machine Learning-Optimized 3D Hybrid Chaotic Map (Logistic–Tent–Hénon–Chebyshev) for secure and energy-efficient encryption of images and multimedia data in resource-constrained ...
Context: Modern society increasingly relies on complex and interconnected software systems. Systems-of-Systems (SoS) then enable interoperability among heterogeneous and independent systems, deliverin...
The stimulator of interferon genes (STING) is a key signaling adaptor in the cGAS-STING pathway of the innate immune system, playing a significant role in autoimmune diseases, viral infections, and ca...
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