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| http://dx.doi.org/10.2147/AMEP.S120438 | DOI Listing |
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Artificial intelligence-based framework for early detection of heart disease using enhanced multilayer perceptron.
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Department of Computer Science and Artificial Intelligence, College of Computing and Information Technology, University of Bisha, Bisha, Saudi Arabia.
Cardiac disease refers to diseases that affect the heart such as coronary artery diseases, arrhythmia and heart defects and is amongst the most difficult health conditions known to humanity. According to the WHO, heart disease is the foremost cause of mortality worldwide, causing an estimated 17.8 million deaths every year it consumes a significant amount of time as well as effort to figure out what is causing this, especially for medical specialists and doctors.
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Chemical Sciences Division, Oak Ridge National Laboratory Oak Ridge TN 37830 USA
The successful design and deployment of next-generation nuclear technologies heavily rely on thermodynamic data for relevant molten salt systems. However, the lack of accurate force fields and efficient methods has limited the quality of thermodynamic predictions from atomistic simulations. Here we propose an efficient free energy framework for computing chemical potentials, which is the central free energy quantity behind many thermodynamic properties.
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Empa Swiss Federal Laboratories for Materials Science and Technology, Überlandstrasse 129, 8600 Dübendorf, Switzerland.
Fast and accurate representation of heat transfer in laser powder-bed fusion of metals (PBF-LB/M) is essential for thermo-mechanical analyses. As an example, it benefits the detection of thermal hotspots at the design stage. While traditional physics-based numerical approaches such as the finite element (FE) method are applicable to a wide variety of problems, they are computationally too expensive for PBF-LB/M due to the space- and time-discretization requirements.
View Article and Find Full Text PDFPartial directed coherence analysis of resting-state EEG signals for alcohol use disorder detection using machine learning.
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Center of Excellence in Intelligent Engineering Systems (CEIES), Department of Electrical and Computer Engineering, Faculty of Engineering, King Abdulaziz University, Jeddah, Saudi Arabia.
Introduction: Excessive alcohol consumption negatively impacts physical and psychiatric health, lifestyle, and societal interactions. Chronic alcohol abuse alters brain structure, leading to alcohol use disorder (AUD), a condition requiring early diagnosis for effective management. Current diagnostic methods, primarily reliant on subjective questionnaires, could benefit from objective measures.
View Article and Find Full Text PDFPrediction-powered inference (PPI) [1] and its subsequent development called PPI++ [2] provide a novel approach to standard statistical estimation leveraging machine learning systems to enhance unlabeled data with predictions. We use this paradigm in clinical trials. The predictions are provided by disease progression models, providing prognostic scores for all the participants as a function of baseline covariates.
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