Retiming DAGs [direct acyclic graph]

1998 ◽  
Vol 17 (12) ◽  
pp. 1319-1325 ◽  
Author(s):  
P.Y. Calland ◽  
A. Mignotte ◽  
O. Peyran ◽  
Y. Robert ◽  
F. Vivien
Keyword(s):  
Acyclic Graph ◽  
Direct Acyclic Graph

scholarly journals Direct Acyclic Graph-Based Ledger for Internet of Things: Performance and Security Analysis

2020 ◽  
Vol 28 (4) ◽  
pp. 1643-1656 ◽  
Author(s):  
Yixin Li ◽  
Bin Cao ◽  
Mugen Peng ◽  
Long Zhang ◽  
Lei Zhang ◽  
...  
Keyword(s):  
Internet Of Things ◽  
Security Analysis ◽  
Acyclic Graph ◽  
Direct Acyclic Graph

Leaf Recognition for Plant Classification Using Direct Acyclic Graph Based Multi-Class Least Squares Twin Support Vector Machine

2016 ◽  
Vol 16 (03) ◽  
pp. 1650012 ◽  
Author(s):  
Divya Tomar ◽  
Sonali Agarwal
Keyword(s):  
Support Vector Machine ◽  
Least Squares ◽  
Plant Species ◽  
Recognition System ◽  
Twin Support Vector Machine ◽  
Support Vector ◽  
Individual Plant ◽  
Species Classification ◽  
Acyclic Graph ◽  
Direct Acyclic Graph

As most of the plant species are at the risk of extinction, the task of plant identification has become a challenging process and an active area of research. In this paper, we propose a leaf recognition system for plant species classification using leaf image data through a novel direct acyclic graph based multi-class least squares twin support vector machine (DAG-MLSTSVM) classifier. Hybrid feature selection (HFS) approach is used to obtain the best discriminant features for the recognition of individual plant species. Leaves are recognized on the basis of shape and texture features. The experimental results indicate that the proposed DAG-MLSTSVM based plant leaf recognition system is highly accurate and having faster processing speed as compared to artificial neural network and direct acyclic graph based support vector machine.

Toward a Mixed Tangle-Blockchain Architecture

2020 ◽  
Author(s):  
Motaz Ben Hassine ◽  
Mourad Kmimech ◽  
Hussein Hellani ◽  
Layth Sliman
Keyword(s):  
Poisson Distribution ◽  
Scale Up ◽  
Mathematical Foundation ◽  
Validation Process ◽  
Acyclic Graph ◽  
Direct Acyclic Graph ◽  
Industrial Enterprises ◽  
Design And Implementation ◽  
Transaction Fees ◽  
Industrial Context

This paper presents the design and implementation of a new platform that takes into consideration the requirements and constraints resulting from the industrial context based on IoT. This platform combines the “Tangle” and “Blockchain” techniques. Tangle is primarily designed to address scale-up issues and the relatively high cost (time and resource) of transactions in a traditional blockchain-based platform. Unlike the “blockchain” structure, it consists of a solid mathematical foundation called DAG (Direct Acyclic Graph). It uses a validation process in which transactions are entered into the distributed registry after authenticating two other randomly selected transactions according to a Poisson distribution (thus, the locations of the new transactions are chosen using random runs in the graph). Therefore, it is an easily scalable system that does not require mining or transaction fees. We aim to study the integration of Tangle and Blockchain techniques to improve the performance and scalability of distributed registry-based platforms to be adapted in industrial enterprises whose processes incorporate or are based on IoT.

scholarly journals Evaluating the performance of convolutional neural networks with direct acyclic graph architectures in automatic segmentation of breast lesion in US images

2019 ◽  
Vol 19 (1) ◽  
Author(s):  
Marly Guimarães Fernandes Costa ◽  
João Paulo Mendes Campos ◽  
Gustavo de Aquino e Aquino ◽  
Wagner Coelho de Albuquerque Pereira ◽  
Cícero Ferreira Fernandes Costa Filho
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Gold Standard ◽  
Breast Imaging ◽  
Breast Lesion ◽  
Surrounding Tissue ◽  
Ultra Sound ◽  
Mean Values ◽  
Acyclic Graph ◽  
Direct Acyclic Graph

Abstract Background Outlining lesion contours in Ultra Sound (US) breast images is an important step in breast cancer diagnosis. Malignant lesions infiltrate the surrounding tissue, generating irregular contours, with spiculation and angulated margins, whereas benign lesions produce contours with a smooth outline and elliptical shape. In breast imaging, the majority of the existing publications in the literature focus on using Convolutional Neural Networks (CNNs) for segmentation and classification of lesions in mammographic images. In this study our main objective is to assess the ability of CNNs in detecting contour irregularities in breast lesions in US images. Methods In this study we compare the performance of two CNNs with Direct Acyclic Graph (DAG) architecture and one CNN with a series architecture for breast lesion segmentation in US images. DAG and series architectures are both feedforward networks. The difference is that a DAG architecture could have more than one path between the first layer and end layer, whereas a series architecture has only one path from the beginning layer to the end layer. The CNN architectures were evaluated with two datasets. Results With the more complex DAG architecture, the following mean values were obtained for the metrics used to evaluate the segmented contours: global accuracy: 0.956; IOU: 0.876; F measure: 68.77%; Dice coefficient: 0.892. Conclusion The CNN DAG architecture shows the best metric values used for quantitatively evaluating the segmented contours compared with the gold-standard contours. The segmented contours obtained with this architecture also have more details and irregularities, like the gold-standard contours.

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