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Titlebook: Applied Intelligence; First International De-Shuang Huang,Prashan Premaratne,Changan Yuan Conference proceedings 2024 The Editor(s) (if ap

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Imputation of Compound Property Assay Data Using a Gene Expression Programming-Based Methodete data and improve the success rate of drug development, researchers often need to effectively impute the missing data. Therefore, this paper proposes a gene expression programming-based method, called GEP-CPI, for imputing missing compound property assay data. In GEP-CPI, the missing data imputat
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Identification of Parkinson’s Disease Associated Genes Through Explicable Deep Learning and Bioinforpression data were collected from the GEO dataset, subjected to rigorous differential expression analysis to curate genes for subsequent scrutiny. Based on the P-Net and PASNet models, we have developed a pathway-related deep learning model that integrates PD-associated gene expression data with est
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Challenges in Realizing Artificial Intelligence Assisted Sign Language Recognitions would appear to solve a certain aspect of a problem, but not the real problem at hand. In the context of a challenging real problem, such approach would simply deceive the researcher into extrapolating the existing capabilities but would not offer any practical realization of the real problem. Com
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Efficient and Accurate Document Parsing and Verification Based on OCR Engineification certificates from electronic documents like images, scans, and PDFs within bid documents. Leveraging an OCR engine and well-trained models with strong error correction capabilities, the accuracy of OCR in bid document processing for Guizhou Power Grid Company is significantly improved. Thi
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Romano Audhoe,Neil Thompson,Karen Verduijnf 42 young athletes (19 males and 23 females, aged 9–17 years) from the diving team of Shenzhen Sports School, and then three morphological data of corneal curvature, astigmatism, and thickness were measured based on the topographic maps. A study was conducted to analyze the influence of diving on c
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https://doi.org/10.1007/978-3-319-71014-3ever, the traditional emotion recognition approach utilizes all EEG channels which may lead to increased computational degree as well as un-wanted interfering information affecting the accuracy. And it is not suitable for all emotion recognition work. In this paper, we propose an EEG selection frame
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