Medical image segmentation is a new biomedical image processing method that has made a significant contribution to sustainable health care. It has now become a major study area in the realm of computer vision research...
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The severe shortage of organs requires the efficient management and collaboration of practices. Therefore, this paper introduces the Regional Collaboration System (RCS) to deal with this critical issue. The system aim...
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Social media is now indispensable, used by all social, economic, age, and demographic groups. It provides extensive user-generated data, offering insights into users' mental states. This paper introduces a novel T...
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Genetic algorithms are a class of optimization algorithms that simplify genetic processes to solve a variety of problems. Utilizing a genetic algorithm increases the security of the key. The application of the genetic...
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Twitter, as is well known, is one of the most active social media platforms, with millions of tweets posted every day, in which different people express their opinions on topics such as travel, economic concerns, poli...
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Sequence sets with minimum total squared correlation (TSC) have important application in communications. In a multi-cell system, due to the difference in channel strength between the home cell and the neighboring cell...
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The primary method of operation for the vehicle authorization system is the reading and processing of license plates. Automatic Number Plate Recognition (ANPR) is the name given to image processing technology that use...
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The valuation of property rights of cultural products relies on professional knowledge and experience. Existing valuation approaches are vulnerable to third parties, leading to price manipulation. Moreover, neural net...
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All-atom dynamics simulations are an indispensable quantitative tool in physics,chemistry,and materials science,but large systems and long simulation times remain challenging due to the trade-off between computational...
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All-atom dynamics simulations are an indispensable quantitative tool in physics,chemistry,and materials science,but large systems and long simulation times remain challenging due to the trade-off between computational efficiency and predictive *** address this challenge,we combine effective two-and three-body potentials in a cubic B-spline basis with regularized linear regression to obtain machine-learning potentials that are physically interpretable,sufficiently accurate for applications,as fast as the fastest traditional empirical potentials,and two to four orders of magnitude faster than state-of-the-art machine-learning *** data from empirical potentials,we demonstrate the exact retrieval of the *** data from density functional theory,the predicted energies,forces,and derived properties,including phonon spectra,elastic constants,and melting points,closely match those of the reference *** introduced potentials might contribute towards accurate all-atom dynamics simulations of large atomistic systems over long-time scales.
Natural or man-made disasters result in significant loss of life, property damage, disruption of communication networks, and immense suffering for survivors. When a disaster strikes, the communication infrastructure i...
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