Graph-Based Collaborative Filtering with MLP

Lu, Shengyu; Chen, Hangping; Zhou, Xiuze; Wang, Beizhan; Wang, Hongji; Hong, Qingqi

doi:10.1155/2018/8314105

Cited by 14 publications

(4 citation statements)

References 31 publications

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“…Random forest: Random forest is an ensemble learning algorithm. It consists of multiple decision trees and obtains the final result through a voting mechanism from all decision trees [12].…”

Section: Baseline Methodsmentioning

confidence: 99%

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Deep learning for Protein-Protein Interactions Predication

2020

Proceedings of 2020 the 10th International Workshop on Computer Science and Engineering

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As the main component, proteins play an important role in the cell activities of organisms. Most organisms carry out the cell activities by protein-protein interactions (PPI), so deep researches about PPI is of great significance. Some machine learning methods have been applied to predict PPI by extracting features from massive protein data and then training the models to implement classification. However, these methods can only be used to deal with balanced datasets and their effects can be improved further. We proposed a deep learning method based on Bi-LSTM model to predict PPI. For protein sequences, our method automatically encoded the amino acids and represented the protein sequences, and then extracted the sequence features and implemented classification. Experimental results showed that our method can achieve higher accuracy than advanced methods, and can solve the problem of unbalanced datasets.

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Section: Baseline Methodsmentioning

confidence: 99%

“…in the distance-clustering matrix [11]. Although these algorithms have made some progress in PPI predication, they only solved some certain problems and still had their own defects [12].…”

Section: Proceedings Of 2020 the 10th International Workhop On Computer Science And Engineering (Wcse 2020)mentioning

confidence: 99%

Deep learning for Protein-Protein Interactions Predication

2020

Proceedings of 2020 the 10th International Workshop on Computer Science and Engineering

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“…In this section, it uses several data sets and comparative methods to experiment [18]. It uses different evaluation metrics to describe the experimental results in detail, and then demonstrates the effectiveness and adaptability of our method.…”

Section: Methodsmentioning

confidence: 99%

A Novel Object Detection Algorithm in Video

2019

Proceedings of 2019 the 9th International Workshop on Computer Science and Engineering

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Deep learning technology performs effect in feature extraction of images. Nowadays, with the development of video monitoring, the application of deep learning technology to surveillance video has profound implications. The effects of traditional video recognition are not satisfactory, but deep learning methods perform effect in many scenes of image classification. This paper proposed a novel object detection algorithm in video. It combined the traditional methods of extracting feature and deep learning algorithm to realize vehicle identification based on surveillance video. The method used the frame difference method and background subtraction to preprocess the image, and then trained a network model based on YOLO to perform object detection and obtain the categories and location information of the monitored vehicle. Compared with the existing object detection algorithms faster RNN, our method can achieve higher accuracy and can significantly shorten the time for detection, which can recognize the object of vehicle video quickly and efficiently. The method can meet the requirements of real-time detection.

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“…Although the research on computer-aided pulmonary nodule diagnosis system based on CT detection images has attracted people's attention, there are gaps in clinical application. The main problems are as follows [6]: (1) due to the nature of lung nodules, it is difficult to accurately detect and segment lesions from CT images;…”

Section: Introductionmentioning

confidence: 99%

Lung Nodule Classification Algorithm Based on Fusion Features

2019

Proceedings of 2019 the 9th International Workshop on Computer Science and Engineering

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Lung nodules are the lesion areas of lung, and malignant lung nodules may led to lung cancer. Nowadays, computer-aided diagnosis based on CT images are important for lung cancer. However, the existing methods of feature extraction of CT image did not perform effect. This paper proposes the DSS algorithm for lung nodules classification based on fusion features. The DSS adopts the local-global model to extract the depth features of images, and jointly uses the shape descriptors based on medical knowledge. Besides, it combines the fusion features into the Support Vector Machine (SVM) for lung nodule classification. This paper has evaluated the DSS algorithm on LIDC-IDRI data set and the method performs effect for lung nodules classification.

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Graph-Based Collaborative Filtering with MLP

Cited by 14 publications

References 31 publications

Deep learning for Protein-Protein Interactions Predication

Deep learning for Protein-Protein Interactions Predication

A Novel Object Detection Algorithm in Video

Lung Nodule Classification Algorithm Based on Fusion Features

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