VECTORIZATION OF REGULATORY - REFERENCE INFORMATION USING THE BERT NEURAL NETWORK

2020 ◽

Vol XLII-3/W10 ◽

pp. 975-979

Author(s):

H. Huang ◽

L. L. Liu

Keyword(s):

Neural Network ◽

Bp Neural Network ◽

Site Selection ◽

Large Scale ◽

Machine Learning Algorithms ◽

Training Data ◽

Location Analysis ◽

Analysis Model ◽

Reference Information ◽

Neural Network Algorithm

Abstract. Site selection is a key first step in the operation of large-scale shopping malls, and most of the existing site selection methods lack practicality and efficiency. Therefore, it is necessary to carry out a scientific modeling of the site selection problem and provide effective reference information for site selection. With the development of machine learning algorithms, the modeling of such problems becomes more and more simple. In this paper, using matlab software as a tool, based on BP neural network algorithm, Nanning urban area is selected as the research object. After analyzing the influencing factors of location problem, the large-scale mall location analysis modeling is carried out. After repeated training and testing of the training data and the test data, the data for testing the usability is input into the model and applied for analysis. It turns out that the large-scale mall location analysis model is usable and can meet the site selection needs of the mall.

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A genotype imputation method for de-identified haplotype reference information by using recurrent neural network

PLoS Computational Biology ◽

10.1371/journal.pcbi.1008207 ◽

2020 ◽

Vol 16 (10) ◽

pp. e1008207 ◽

Cited By ~ 1

Author(s):

Kaname Kojima ◽

Shu Tadaka ◽

Fumiki Katsuoka ◽

Gen Tamiya ◽

Masayuki Yamamoto ◽

...

Keyword(s):

Neural Network ◽

Recurrent Neural Network ◽

Imputation Method ◽

Genotype Imputation ◽

Reference Information

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Microscopic Image Retrieval Scheme Using Neural Network For Multi Image Queries

Turkish Journal of Computer and Mathematics Education (TURCOMAT) ◽

10.17762/turcomat.v12i2.1346 ◽

2021 ◽

Vol 12 (2) ◽

pp. 1357-1363

Author(s):

Jaipal Reddy Yeruva , Et. al.

Keyword(s):

Neural Network ◽

Image Retrieval ◽

Normal Order ◽

Microscopic Images ◽

Low Level ◽

Multiple Image ◽

Reference Information ◽

Retrieval Scheme ◽

Highlight Extraction ◽

Weighting Technique

In this paper, we describe the plan and advancement of a neural network based image retrieval framework for microscopic images using a reference information base that contains images of more than one information. Such an extraction requires a point by point assessment of retrieval execution of image highlights. This paper presents a survey of crucial parts of content based image retrieval including highlight extraction of color and surface highlights. The proposed neural network based image retrieval framework utilizes a multitier way to deal with arrange and recover microscopic images including their particular subtypes, which are generally hard to separate and characterize. Broad examinations on neural network based image retrieval frameworks show that low-level image highlights can't generally depict elevated level semantic ideas in the clients mind. This framework empowers multi-image inquiry to ensure the semantic consistency among the recovered images. New weighting terms, roused from information retrieval hypothesis, are characterized for multiple-image inquiry and retrieval. The multi-image inquiry calculation with the proposed weighting technique accomplishes about normal order exactness at the main position retrieval, beating the image-level retrieval precision by about ideal rate focuses for different infections separately. Utilizing low level highlights just does exclude human insight. In the event that human mediation is permitted in the image retrieval framework the proficiency supports up.

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