A Study on the Issues Related to Building a Library Information System Based on Deep Learning

2021 ◽  
Author(s):  
Ju Hyung Kim ◽  
Jung Hee Lee ◽  
Kyoung Jin Lee
Keyword(s):  
Information System ◽  
Deep Learning

scholarly journals A Step-by-Step Guide for Automated Plant Canopy Delineation Using Deep Learning: An Example in Strawberry Using ArcGIS Pro Software

EDIS ◽  
2021 ◽  
Vol 2021 (5) ◽  
Author(s):  
Amr Abd-Elrahman ◽  
Katie Britt ◽  
Vance Whitaker
Keyword(s):  
Neural Networks ◽  
Information System ◽  
Image Analysis ◽  
Deep Learning ◽  
Geographic Information System ◽  
Precision Agriculture ◽  
System Analysis ◽  
Plant Canopy ◽  
Geographic Information System Analysis ◽  
Agriculture Management

This publication presents a guide to image analysis for researchers and farm managers who use ArcGIS software. Anyone with basic geographic information system analysis skills may follow along with the demonstration and learn to implement the Mask Region Convolutional Neural Networks model, a widely used model for object detection, to delineate strawberry canopies using ArcGIS Pro Image Analyst Extension in a simple workflow. This process is useful for precision agriculture management.

On the Use of Deep Learning for Geodata Enrichments

2021 ◽  
pp. 182-192
Author(s):  
Alaeddine Moussa ◽  
Sébastien Fournier ◽  
Bernard Espinasse
Keyword(s):  
Machine Learning ◽  
Information System ◽  
Deep Learning ◽  
Geographic Information System ◽  
Spatial Information ◽  
Central Element ◽  
Geographic Information ◽  
Topic Identification ◽  
Substantial Investment ◽  
Identification Phase

Data is the central element of a geographic information system (GIS) and its cost is often high because of the substantial investment that allows its production. However, these data are often restricted to a service or a category of users. This has highlighted the need to propose and optimize the means of enriching spatial information relevant to a larger number of users. In this chapter, a data enrichment approach that integrates recent advances in machine learning; more precisely, the use of deep learning to optimize the enrichment of GDBs is proposed, specifically, during the topic identification phase. The evaluation of the approach was completed showing its performance.

Aspects of Content Generation Through Narrative Communication and Simulation

2018 ◽  
pp. 1-47 ◽  
Author(s):  
Takashi Ogata ◽  
Shin Asakawa
Keyword(s):  
Neural Network ◽  
Information System ◽  
Deep Learning ◽  
Generation System ◽  
Theoretical Frameworks ◽  
Narrative Generation ◽  
Narrative Structures ◽  
Multiple Structures ◽  
Network Technologies ◽  
Neural Network Technologies

In this chapter, the authors focus on narrative contents by considering and analyzing narrative communication and simulation. In particular, the authors present the multiple narrative structures model and informational narratology as original theoretical frameworks in seeking to undertake narrative hierarchical and multiple structures and micro and macro structures. The authors also introduce, as designing and developing systems, the integrated narrative generation system (INGS) for implementing the narrative micro mechanism, and the geinō information system (GIS) for designing the macro mechanism. Furthermore, neural network technologies including deep learning are also introduced to show the technological possibility of implementing narrative generation systems. These show a synthesized approach or establish a paradigm for narrative generation studies.

scholarly journals SIR-DL: AN ARCHITECTURE OF SEMANTIC-BASED IMAGE RETRIEVAL USING DEEP LEARNING TECHNIQUE AND RDF TRIPLE LANGUAGE

2019 ◽  
Vol 35 (1) ◽  
pp. 39-56
Author(s):  
Van The Thanh ◽  
Do Quang Khoi ◽  
Le Huu Ha ◽  
Le Manh Thanh
Keyword(s):  
Information System ◽  
Deep Learning ◽  
Image Retrieval ◽  
Multimedia Systems ◽  
Visual Feature ◽  
Query Image ◽  
Semantic Classification ◽  
Visual Words ◽  
Learning Technique ◽  
Similar Images

The problem of finding and identifying semantics of images is applied in multimedia applications of many different fields such as Hospital Information System, Geographic Information System, Digital Library System, etc. In this paper, we propose the semantic-based image retrieval (SBIR) system based on the deep learning technique; this system is called as SIR-DL that generates visual semantics based on classifying image contents. At the same time we identify the semantics of similar images on Ontology, which describes semantics of visual features of images. Firstly, the color and spatial features of segmented images are we extracted and these visual feature vectors are trained on the deep neural network to obtain visual words vectors. The process of image retrieval is executed rely on semantic classification of SIR-DL according to the visual feature vector of the query image from which it produces a visual word vector. Then, we retrieve it on Ontology to provide the identities and the semantics of similar images corresponds to a similarity measure. In order to carry out SIR-DL, the algorithms and diagram of this image retrieval system are proposed after that we implement them on ImageCLEF@IAPR, which has 20,000 images. On the base of the experimental results, the effectiveness of our method is evaluated by the accuracy, precision, recall, and F-measure; these results are compared with some of works recently published on the same image dataset. It shows that SIR-DL effectively solves the problem of semantic-based image retrieval and can be used to build multimedia systems in many different fields.

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