scholarly journals Research on Big Data Digging of Hot Topics about Recycled Water Use on Micro-Blog Based on Particle Swarm Optimization

2018 ◽  
Vol 10 (7) ◽  
pp. 2488 ◽  
Author(s):  
Hanliang Fu ◽  
Zhaoxing Li ◽  
Zhijian Liu ◽  
Zelin Wang
Keyword(s):  
Big Data ◽  
Particle Swarm Optimization ◽  
Key Words ◽  
Water Use ◽  
Particle Swarm ◽  
Recycled Water ◽  
Swarm Optimization ◽  
The Public ◽  
Key Factor ◽  
Important Significance

The public’s acceptance level of recycled water use is a key factor that affects the popularization of this technology; therefore, it is critical to know the public’s attitude in order to make guiding policies effectively and scientifically. To examine the major focuses and hot topics among the public about recycled water use, one of the major platforms for social opinion in China, the micro blog, is used as a source to obtain data related to the topic. Through the “follow-be followed” and “forward-dialogue” behaviors, a network of discussion of recycled water use among micro-blog users has been constructed. Improved particle swarm optimization has been used to allow deep digging for key words. Ultimately, key words about the topic of have been clustered into three categories, namely, the popularization status of recycled water use, the main application, and the public’s attitude. The conclusion accurately describes the concerns of Chinese citizens regarding recycled water use, and has important significance for the popularization of this technology.

Particle Swarm Optimization based dictionary learning for remote sensing big data

2015 ◽  
Vol 79 ◽  
pp. 43-50 ◽  
Author(s):  
Lizhe Wang ◽  
Hao Geng ◽  
Peng Liu ◽  
Ke Lu ◽  
Joanna Kolodziej ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Big Data ◽  
Particle Swarm Optimization ◽  
Dictionary Learning ◽  
Particle Swarm ◽  
Swarm Optimization

scholarly journals An Enhanced Framework To Secure Big Data Based on Hybrid Machine Learning Technique:ANN-PSO

2021 ◽  
Vol 9 (6) ◽  
pp. 76-84
Author(s):  
Salim Raza Qureshi
Keyword(s):  
Big Data ◽  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Smart Devices ◽  
Data Sets ◽  
Academic Impact ◽  
Swarm Optimization ◽  
Data Source

With the advancement of smart devices and cloud computing, more and more public health data can be collected from various sources and analyzed in unprecedented ways. The enormous social and academic impact of this development has led to a global buzz for bigdata. Moreover, due to the massive data source, the security of big data in the cloud is becoming an important issue. In these days, various issues have arisen in the field of big data security, such as Infrastructure security, data confidentiality, data management and data integrity. In this paper, we propose a novel technique based on Artificial Neural Network-and Particle Swarm Optimization Algorithm (ANNPSO) for enabling a highly secured framework. The ANN-PSO method was created to predict health status from a database and its functions were selected from these data sets. The particle swarm optimization algorithm matches the ANN for better results by reducing errors. The results show the potential of the ANNPSO-based methodology for satisfactory health prediction results. This proposed approach will be tested using large medical data in a Hadoop environment. The proposed work will be carried out in the JAVA work phase.

scholarly journals Determination of Customer Satisfaction of Tax Service Office Services Using C4.5 and PSO

2020 ◽  
Vol 4 (2) ◽  
pp. 296-302
Author(s):  
Ikhsan Romli ◽  
Fairuz Kharida ◽  
Chandra Naya
Keyword(s):  
Feature Selection ◽  
Particle Swarm Optimization ◽  
Decision Tree ◽  
Particle Swarm ◽  
Service Satisfaction ◽  
Swarm Optimization ◽  
The Public ◽  
C4.5 Algorithm ◽  
Directorate General ◽  
Using Data

Tax Service Office is a work unit of the Directorate General of Taxation that carries out services in the field of taxation to the public, both registered and unregistered taxpayers, within the working area of the Directorate General of Taxes. The number of Primary Tax Service Offices in Indonesia, one of which is the Primary Tax Service Office in Bekasi, has various ways to increase the satisfaction of taxpayers for the services provided. This study aims to determine the accuracy of taxpayers' satisfaction using data mining techniques using the Decision Tree C4.5 Algorithm with Particle Swarm Optimization (PSO) feature selection, validation uses cross validation techniques while accuracy is measured by the confussion matrix, which is to determine the level of service satisfaction conducted by distributing questionnaires to taxpayers in the Primary Tax Service Office in Bekasi as many as 500 questionnaires. The results show the accuracy value of Taxpayers' service satisfaction at the Pratama Tax Service Office using the Decision Tree C4.5 Algorithm with a feature selection of Particle Swarm Optimization (PSO) of 98,85%, Precission of 98,85% and Recall of 100%.

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