Deep learning technology has driven continuous advancements in the visual tracking field. In order to overcome various challenges, Siamese-based trackers and Attention-based trackers improve tracking performance by ad...
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Aligning aspects and related viewpoints for aspect-specific sentiment polarity categorization is the goal of Aspect-Based Sentiment Analysis (ABSA), a fine-grained sentiment analysis endeavor. Dependency tree-based gr...
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The precise prediction of molecular properties is essential for advancements in drug development,particularly in virtual screening and compound *** recent introduction of numerous deep learningbased methods has shown ...
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The precise prediction of molecular properties is essential for advancements in drug development,particularly in virtual screening and compound *** recent introduction of numerous deep learningbased methods has shown remarkable potential in enhancing Molecular Property Prediction(MPP),especially improving accuracy and insights into molecular ***,two critical questions arise:does the integration of domain knowledge augment the accuracy of molecular property prediction and does employing multi-modal data fusion yield more precise results than unique data source methods?To explore these matters,we comprehensively review and quantitatively analyze recent deep learning methods based on various *** discover that integrating molecular information significantly improves Molecular Property Prediction(MPP)for both regression and classification ***,regression improvements,measured by reductions in Root Mean Square Error(RMSE),are up to 4.0%,while classification enhancements,measured by the area under the receiver operating characteristic curve(ROC-AUC),are up to 1.7%.Additionally,we discover that,as measured by ROC-AUC,augmenting 2D graphs with 3D information improves performance for classification tasks by up to 13.2%and enriching 2D graphs with 1D SMILES boosts multi-modal learning performance for regression tasks by up to 9.1%.The two consolidated insights offer crucial guidance for future advancements in drug discovery.
This paper suggests a suitable feature selection (FS) approach FSFPA using flower pollination algorithm (FPA). It is based on the concept of flower pollination, to choose a set of important features or variables from ...
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The development of extensive clothing databases has significantly advanced the field of clothing recognition and recommendation systems. However, existing datasets often suffer from a limited number of annotations and...
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Neurodegenerative diseases with memory impairment and difficulties in recognizing people pose significant challenges to patients' well-being and independence. This research proposes an approach leveraging extended...
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In this study, the trends of traffic data are analyzed using predictive analysis techniques applied to the NYC taxi dataset. Various models were tested for forecasting travel time, fare, and speed. Notably, the Decisi...
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In a rapidly evolving digital era, the convergence of blockchain technology and healthcare is poised to revolutionize the industry in unprecedented ways. This research paper presents an innovative exploration of the t...
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The growing use of vectors for unstructured data has made efficient hybrid queries-combining boolean filters with vector similarity searches-essential. However, publicly available datasets for evaluating DBMS performa...
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This research introduces a novel machine learning methodology for classifying electrocardiogram (ECG) images, integrating deep learning models like VGG16, Inception V3, and a custom CNN, alongside ensemble methods suc...
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