Controlling Data Dissemination

2012 ◽  
pp. 303-309 ◽  
Author(s):  
Helge Janicke ◽  
Mohamed Sarrab ◽  
Hamza Aldabbas
Keyword(s):  
Data Dissemination

Performance analysis of Broadcast Data Dissemination over VANETs

2014 ◽  
Vol 2 (4) ◽  
pp. 1-5
Author(s):  
G. Mary Valantina ◽  
◽  
S. Jayashri ◽  
Keyword(s):  
Performance Analysis ◽  
Data Dissemination

On Connectivity-Aware and Reliable Data Dissemination in VANETs

2014 ◽  
Vol 36 (4) ◽  
pp. 701-715 ◽  
Author(s):  
Li-Feng ZHANG ◽  
Bei-Hong JIN ◽  
Wei ZHUO
Keyword(s):  
Data Dissemination ◽  
Reliable Data

scholarly journals Human-centric Data Dissemination in the IoP

2020 ◽  
Vol 14 (3) ◽  
pp. 1-25 ◽  
Author(s):  
Matteo Mordacchini ◽  
Marco Conti ◽  
Andrea Passarella ◽  
Raffaele Bruno
Keyword(s):  
Data Dissemination

scholarly journals Identifying protein subcellular localisation in scientific literature using bidirectional deep recurrent neural network

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Rakesh David ◽  
Rhys-Joshua D. Menezes ◽  
Jan De Klerk ◽  
Ian R. Castleden ◽  
Cornelia M. Hooper ◽  
...  
Keyword(s):  
Neural Network ◽  
Language Processing ◽  
Data Dissemination ◽  
Short Term Memory ◽  
Biological Data ◽  
Experimental Methodology ◽  
Subcellular Localisation ◽  
Crop Species ◽  
Deep Recurrent Neural Network ◽  
Functional Features

AbstractThe increased diversity and scale of published biological data has to led to a growing appreciation for the applications of machine learning and statistical methodologies to gain new insights. Key to achieving this aim is solving the Relationship Extraction problem which specifies the semantic interaction between two or more biological entities in a published study. Here, we employed two deep neural network natural language processing (NLP) methods, namely: the continuous bag of words (CBOW), and the bi-directional long short-term memory (bi-LSTM). These methods were employed to predict relations between entities that describe protein subcellular localisation in plants. We applied our system to 1700 published Arabidopsis protein subcellular studies from the SUBA manually curated dataset. The system combines pre-processing of full-text articles in a machine-readable format with relevant sentence extraction for downstream NLP analysis. Using the SUBA corpus, the neural network classifier predicted interactions between protein name, subcellular localisation and experimental methodology with an average precision, recall rate, accuracy and F1 scores of 95.1%, 82.8%, 89.3% and 88.4% respectively (n = 30). Comparable scoring metrics were obtained using the CropPAL database as an independent testing dataset that stores protein subcellular localisation in crop species, demonstrating wide applicability of prediction model. We provide a framework for extracting protein functional features from unstructured text in the literature with high accuracy, improving data dissemination and unlocking the potential of big data text analytics for generating new hypotheses.

A UAV-Enabled Data Dissemination Protocol with Proactive Caching and File Sharing in V2X Networks

2021 ◽  
pp. 1-1
Author(s):  
Rongqing Zhang ◽  
Rui Lu ◽  
Xiang Cheng ◽  
Ning Wang ◽  
Liuqing Yang
Keyword(s):  
Data Dissemination ◽  
File Sharing
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