scholarly journals Big Data Digging of the Public’s Cognition about Recycled Water Reuse Based on the BP Neural Network

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
Author(s):  
Hanliang Fu ◽  
Zhijian Liu ◽  
Mengmeng Wang ◽  
Zelin Wang
Keyword(s):  
Neural Network ◽  
Big Data ◽  
Bp Neural Network ◽  
Water Reuse ◽  
Clustering Algorithm ◽  
Behavioral Control ◽  
Evaluation Model ◽  
Perceived Behavioral Control ◽  
Recycled Water ◽  
The Public

Reuse of recycled water is very important to both the environment and economy, while the public cognition degree towards recycled water reuse also plays a key role in this process, and it determines the acceptance degree of the public towards recycled water reuse. Under the background of the big data, the Hadoop platform was used to collect and save data about the public’s cognition towards recycled water in one city and the BP neural network algorithm was used to construct an evaluation model that could affect the public’s cognition level. The public’s risk perception, subjective norm, and perceived behavioral control regarding recycled water reuse were selected as key factors. Based on a multivariate clustering algorithm, MATLAB software was used to make real testing on massive effective data and assumption models, so as to analyze the proportion of three evaluation factors and understand the simulation parameter scope of the cognition degree of different groups of citizens. Lastly, several suggestions were proposed to improve the public’s cognition on recycled water reuse based on the big data in terms of policy mechanism.

A Psychological Process of Bureaucratic Whistleblowing: Applying the Theory of Planned Behavior

2021 ◽  
pp. 027507402110033
Author(s):  
Hongseok Lee ◽  
Minsung Michael Kang ◽  
Sun Young Kim
Keyword(s):  
Theory Of Planned Behavior ◽  
Planned Behavior ◽  
Structural Equation ◽  
Structural Equation Models ◽  
Behavioral Control ◽  
Perceived Behavioral Control ◽  
Psychological Process ◽  
Public Employees ◽  
The Public ◽  
Using Data

Whistleblowing is a psychological process that involves the calculation of risks and benefits. While there exists a broad range of research on whistleblowing in the public sector, previous studies have not examined its entire process due to the limited focus on either whistleblowing intention or whistleblowing behavior. This study aims to fill this gap by applying the theory of planned behavior (TPB) to the whistleblowing context. Specifically, we examine how individual beliefs about the likely consequences of whistleblowing (attitude toward whistleblowing), others’ expectations about whistleblowing (subjective norm), and the capability of blowing the whistle (perceived behavioral control) influence public employees’ actual whistleblowing by way of their intention to report wrongdoings. A series of structural equation models are tested using data from the 2010 Merit Principles Survey. The findings show that the more the employees perceive that the consequences of whistleblowing are important, the more the key referents support whistleblowing, and the more the protections for whistleblowers are available, the more likely are their intentions to disclose wrongdoings and then actually engage in whistleblowing behavior. We conduct additional analyses for internal and external whistleblowers separately and find that there are both meaningful similarities and differences between the two groups. This study provides support for the validity of TPB as a theoretical framework for better understanding and explicating the psychological process of bureaucratic whistleblowing.

scholarly journals Differential Diagnosis Model of Hypocellular Myelodysplastic Syndrome and Aplastic Anemia Based on the Medical Big Data Platform

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-12 ◽  
Author(s):  
Jianhui Wu ◽  
Lu Zhang ◽  
Sufeng Yin ◽  
Haidong Wang ◽  
Guoli Wang ◽  
...  
Keyword(s):  
Neural Network ◽  
Logistic Regression ◽  
Big Data ◽  
Differential Diagnosis ◽  
Decision Tree ◽  
Cell Morphology ◽  
Bp Neural Network ◽  
Data Platform ◽  
New Ideas ◽  
Medical Big Data

The arrival of the era of big data has brought new ideas to solve problems for all walks of life. Medical clinical data is collected and stored in the medical field by utilizing the medical big data platform. Based on medical information big data, new ideas and methods for the differential diagnosis of hypo-MDS and AA are studied. The basic information, peripheral blood classification counts, peripheral blood cell morphology, bone marrow cell morphology, and other information were collected from patients diagnosed with hypo-MDS and AA diagnosed in the first diagnosis. First, statistical analysis was performed. Then, the logistic regression model, decision tree model, BP neural network model, and support vector machine (SVM) model of hypo-MDS and AA were established. The sensitivity, specificity, Youden index, positive likelihood ratio (+LR), negative likelihood ratio (−LR), area under curve (AUC), accuracy, Kappa value, positive predictive value (+PV), negative predictive value (−PV) of the four model training set and test set were compared, respectively. Finally, with the support of medical big data, using logistic regression, decision tree, BP neural network, and SVM four classification algorithms, the decision tree algorithm is optimal for the classification of hypo-MDS and AA and analyzes the characteristics of the optimal model misjudgment data.

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.

scholarly journals Efficient and Privacy-Preserving Multi-User Outsourced K-Means Clustering

2021 ◽  
Vol 14 (2) ◽  
pp. 26
Author(s):  
Na Li ◽  
Lianguan Huang ◽  
Yanling Li ◽  
Meng Sun
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Clustering Algorithm ◽  
Privacy Preserving ◽  
Locality Sensitive Hashing ◽  
Sensitive Information ◽  
The Public ◽  
Big Data Mining ◽  
Euclidean Distances ◽  
Computational Resources

In recent years, with the development of the Internet, the data on the network presents an outbreak trend. Big data mining aims at obtaining useful information through data processing, such as clustering, clarifying and so on. Clustering is an important branch of big data mining and it is popular because of its simplicity. A new trend for clients who lack of storage and computational resources is to outsource the data and clustering task to the public cloud platforms. However, as datasets used for clustering may contain some sensitive information (e.g., identity information, health information), simply outsourcing them to the cloud platforms can't protect the privacy. So clients tend to encrypt their databases before uploading to the cloud for clustering. In this paper, we focus on privacy protection and efficiency promotion with respect to k-means clustering, and we propose a new privacy-preserving multi-user outsourced k-means clustering algorithm which is based on locality sensitive hashing (LSH). In this algorithm, we use a Paillier cryptosystem encrypting databases, and combine LSH to prune off some unnecessary computations during the clustering. That is, we don't need to compute the Euclidean distances between each data record and each clustering center. Finally, the theoretical and experimental results show that our algorithm is more efficient than most existing privacy-preserving k-means clustering.

scholarly journals Security and Privacy Risk Assessment of Energy Big Data in Cloud Environment

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Zhiru Li ◽  
Wei Xu ◽  
Huibin Shi ◽  
Yuanyuan Zhang ◽  
Yan Yan
Keyword(s):  
Neural Network ◽  
Risk Factors ◽  
Risk Assessment ◽  
Big Data ◽  
Data Storage ◽  
Bp Neural Network ◽  
Security And Privacy ◽  
Cloud Environment ◽  
Privacy Risk ◽  
Whole Life Cycle

Considering the importance of energy in our lives and its impact on other critical infrastructures, this paper starts from the whole life cycle of big data and divides the security and privacy risk factors of energy big data into five stages: data collection, data transmission, data storage, data use, and data destruction. Integrating into the consideration of cloud environment, this paper fully analyzes the risk factors of each stage and establishes a risk assessment index system for the security and privacy of energy big data. According to the different degrees of risk impact, AHP method is used to give indexes weights, genetic algorithm is used to optimize the initial weights and thresholds of BP neural network, and then the optimized weights and thresholds are given to BP neural network, and the evaluation samples in the database are used to train it. Then, the trained model is used to evaluate a case to verify the applicability of the model.

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