AIMC Topic: Neural Networks, Computer

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A Dynamic Prediction Neural Network Model of Cross-Border e-Commerce Sales for Virtual Community Knowledge Sharing.

Computational intelligence and neuroscience
In this paper, a neural network algorithm is used to conduct in-depth research and analysis on the sales dynamics prediction of virtual community knowledge sharing in cross-border e-commerce. Both the expected returns and the social network structure...

A Deep Ranking Weighted Multihashing Recommender System for Item Recommendation.

Computational intelligence and neuroscience
Collaborative filtering (CF) techniques are used in recommender systems to provide users with specialised recommendations on social websites and in e-commerce. But they suffer from sparsity and cold start problems (CSP) and fail to interpret why they...

MRBENet: A Multiresolution Boundary Enhancement Network for Salient Object Detection.

Computational intelligence and neuroscience
Salient Object Detection (SOD) simulates the human visual perception in locating the most attractive objects in the images. Existing methods based on convolutional neural networks have proven to be highly effective for SOD. However, in some cases, th...

Efficient learning representation of noise-reduced foam effects with convolutional denoising networks.

PloS one
This study proposes a neural network framework for modeling the foam effects found in liquid simulation without noise. The position and advection of the foam particles are calculated using the existing screen projection method, and the noise problem ...

A KG-Enhanced Multi-Graph Neural Network for Attentive Herb Recommendation.

IEEE/ACM transactions on computational biology and bioinformatics
Traditional Chinese Medicine (TCM) has the longest clinical history in Asia and contributes a lot to health maintenance worldwide. An essential step during the TCM diagnostic process is syndrome induction, which comprehensively analyzes the symptoms ...

DeepFusionDTA: Drug-Target Binding Affinity Prediction With Information Fusion and Hybrid Deep-Learning Ensemble Model.

IEEE/ACM transactions on computational biology and bioinformatics
Identification of drug-target interaction (DTI) is the most important issue in the broad field of drug discovery. Using purely biological experiments to verify drug-target binding profiles takes lots of time and effort, so computational technologies ...

Minimum Functional Length Analysis of K-Mer Based on BPNN.

IEEE/ACM transactions on computational biology and bioinformatics
BP neural network (BPNN), as a multilayer feed-forward network, can realize the deep cognition to target data and high accuracy to output results. However, there were still no related research of k-mer based on BPNN yet. In present study, BPNN was us...

ASFold-DNN: Protein Fold Recognition Based on Evolutionary Features With Variable Parameters Using Full Connected Neural Network.

IEEE/ACM transactions on computational biology and bioinformatics
Protein fold recognition contribute to comprehend the function of proteins, which is of great help to the gene therapy of diseases and the development of new drugs. Researchers have been working in this direction and have made considerable achievemen...

Using Artificial Neural Networks to Model Errors in Biochemical Manipulation of DNA Molecules.

IEEE/ACM transactions on computational biology and bioinformatics
In recent years, the non-biological applications of DNA molecules have made considerable progress; most of these applications were performed in vitro, involving biochemical operations such as synthesis, amplification and sequencing. Because errors ma...

Document-Level Chemical-Induced Disease Relation Extraction via Hierarchical Representation Learning.

IEEE/ACM transactions on computational biology and bioinformatics
Over the past decades, Chemical-induced Disease (CID) relations have attracted extensive attention in biomedical community, reflecting wide applications in biomedical research and healthcare field. However, prior efforts fail to make full use of the ...