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| http://dx.doi.org/10.1016/j.jmccpl.2022.100025 | DOI Listing |
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Regression study on fruit-setting days of purple eggplant fruit based on in situ VIS-NIRS and attention cycle neural network.
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College of Engineering, Jiangxi Agricultural University, Nanchang, China.
In the intelligent harvesting of eggplant, the lack of in situ identification technology makes it challenging to determine the maturity of purple eggplant fruit. The length of the fruit-setting date can determine when the eggplant is ready to be harvested. This study uses deep learning techniques to predict the date of fruit maturity.
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Jiangxi Tellhow Power Technology Co., Ltd, Nanchang, 330031, China.
This paper presents a surrogate-assisted global and distributed local collaborative optimization (SGDLCO) algorithm for expensive constrained optimization problems where two surrogate optimization phases are executed collaboratively at each generation. As the complexity of optimization problems and the cost of solutions increase in practical applications, how to efficiently solve expensive constrained optimization problems with limited computational resources has become an important area of research. Traditional optimization algorithms often struggle to balance the efficiency of global and local searches, especially when dealing with high-dimensional and complex constraint conditions.
View Article and Find Full Text PDFSelf-interactive learning: Fusion and evolution of multi-scale histomorphology features for molecular traits prediction in computational pathology.
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Nuffield Department of Medicine, University of Oxford, Oxford, UK; Department of Engineering Science, University of Oxford, Oxford, UK; Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK; Ludwig Institute for Cancer Research, Nuffield Department of Clinical Medicine, University of Oxford, Oxford, UK; Oxford National Institute for Health Research (NIHR) Biomedical Research Centre, Oxford, UK. Electronic address:
Predicting disease-related molecular traits from histomorphology brings great opportunities for precision medicine. Despite the rich information present in histopathological images, extracting fine-grained molecular features from standard whole slide images (WSI) is non-trivial. The task is further complicated by the lack of annotations for subtyping and contextual histomorphological features that might span multiple scales.
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Departmento de Química, Facultad de Ciencias, Universidad de Tarapacá, Arica, Chile.
Data analysis is a major task for Computational Chemists. The diversity of modeling tools currently available in Computational Chemistry requires the development of flexible analysis tools that can adapt to different systems and output formats. As a contribution to this need, we report the implementation of goChem, a versatile open-source library for multiscale analysis of computational chemistry data.
View Article and Find Full Text PDFAn Adaptive Spatio-Temporal Traffic Flow Prediction Using Self-Attention and Multi-Graph Networks.
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Department of Computer Science, King AbdulAziz University, Jeddah 21589, Saudi Arabia.
Traffic flow prediction is a pivotal element in Intelligent Transportation Systems (ITSs) that provides significant opportunities for real-world applications. Capturing complex and dynamic spatio-temporal patterns within traffic data remains a significant challenge for traffic flow prediction. Different approaches to effectively modeling complex spatio-temporal correlations within traffic data have been proposed.
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