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The Importance of Authentication and Encryption in Cloud Computing Framework Security
Issue:
Volume 4, Issue 1, March 2018
Pages:
1-5
Received:
18 March 2018
Accepted:
29 March 2018
Published:
20 April 2018
Abstract: The issues of cybersecurity these days are extremely relevant. With the massive use of the Cloud Computing system, new concerns about the processes to provide this technology with security appeared. The Cloud Computing infrastructure is based on virtualization and distributed computing, often using the shared resource pooling system. For these scenarios, key issues are considered: authentication, and access control. These issues make relevant the following items: data security, regulatory data, privileged access and data recovery. The issue of security in cloud computing involves encryption, it is important to specify the advantages and disadvantages of symmetric encryption and asymmetric encryption. Parallel to this it is important to develop a set of policies for the creation of passwords and subsequent maintenance and alteration of them, as well as their security. the two mandatory pillars for security in Cloud Computing are encryption and a strong passwords policy.
Abstract: The issues of cybersecurity these days are extremely relevant. With the massive use of the Cloud Computing system, new concerns about the processes to provide this technology with security appeared. The Cloud Computing infrastructure is based on virtualization and distributed computing, often using the shared resource pooling system. For these scen...
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The Outliers and Prediction Analysis of University Talents Introduced Based on Data Mining
Junlong Zhang,
Dan Zhao,
Huijie Wang
Issue:
Volume 4, Issue 1, March 2018
Pages:
6-14
Received:
26 April 2018
Published:
27 April 2018
Abstract: To create profits for colleges and universities, introduction of talents is an important indicator of the value evaluation of talent introduction in colleges and universities. It can meet the needs of the large data system demand for abnormal detection and prediction in the process of talent introduction. In this article, after reducing the dimension of data by principal component analysis, using the method based on distance (markov distance), the method based on density (local outlier factor) and the method based on clustering (two-step, k-means), we establish the outlier detection model. We find 15 significant outliers and find that the publication of SSCI papers and the experience in C9 institutions have a significant effect on obtaining National Foundation of China. Finally, we use support vector machine, decision tree (C4.5, C5.0), bayes, and random forest to establish the talent prediction model after eliminating abnormal values. By comparing four methods, we find that support vector machine method and decision tree method’s prediction accuracies are higher. After optimization, their accuracies can reach 75.00% and 72.09% respectively.
Abstract: To create profits for colleges and universities, introduction of talents is an important indicator of the value evaluation of talent introduction in colleges and universities. It can meet the needs of the large data system demand for abnormal detection and prediction in the process of talent introduction. In this article, after reducing the dimensi...
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Clustering Analysis on the Introduction of Talents in Colleges
Fang Dan,
Chen Xinhui,
Xi Xin
Issue:
Volume 4, Issue 1, March 2018
Pages:
15-23
Received:
26 April 2018
Published:
27 April 2018
Abstract: With the development of economy and technology, introducing and training talents have become the key driving force in the world which can enhance the competitive strength of the whole countries. Therefore, the strategies of strengthening the universities and colleges with more talented people and making efforts to implement the construction of “Double top” are put forward in the same time. Methods of clustering analysis have been widely used in the actual researches. In this study, an effective clustering analysis model by comparing the clustering analysis under different dimensionality reduction methods is established. Firstly, preprocess the data about talent introduction which is collected from Zhejiang University of Finance and Economics, and use Principal Component Analysis (PCA), Weighted Principal Component Analysis (Weighted-PCA) and Random Forest (RF) to reduce the dimensions of the data. Next, use K-means clustering algorithm and K-medoids clustering algorithm to cluster the preprocessed data. The classification results indicate that the K-medoids algorithm with Weighted-PCA is superior to other clustering methods in this illustrative case. In addition, the experiment divides talents into high-end talents and mid-end talents. By looking into the analysis of the characteristics of the clustering results, some targeted advices on the talents introduction in colleges can be provided.
Abstract: With the development of economy and technology, introducing and training talents have become the key driving force in the world which can enhance the competitive strength of the whole countries. Therefore, the strategies of strengthening the universities and colleges with more talented people and making efforts to implement the construction of “Dou...
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Use of Space Time Model for Forecasting Mortality due to Malaria: A Case of Ifakara and Rufiji Health and Demographic Surveillance System Sites
Majige Selemani,
Sigilbert Mrema
Issue:
Volume 4, Issue 1, March 2018
Pages:
24-34
Received:
4 May 2017
Accepted:
6 June 2017
Published:
7 May 2018
Abstract: Malaria is a leading cause of morbidity and mortality in developing countries especially in rural areas where local resources are limited. Accurate disease forecasts can provide information to public and clinical health services to design targeted interventions for malaria control that make effective use of limited resources. Using verbal autopsy data, space-time model was used to forecast mortality due to malaria. The study used longitudinal data which were collected from Rufiji and Ifakara Health Demographic Surveillance System (HDSS) sites for the period of 1999 to 2011 and 2002 to 2012 respectively to assess models. The models included environmental factors and mosquito net ownership as predictor variables for mortality due to malaria. Deviance information criteria (DIC), logarithm score and root mean square error (RMSE) were used to assess the goodness of fit and forecasting accuracy of the models. The results indicate that the model included spatial and temporal random effect terms had small deviance information criteria, logarithm score and root mean square error. This model was the best model for forecasting and prediction of mortality due to malaria in both HDSS sites. In addition, mosquito net ownership and rainfall were significantly associated with mortality due to malaria. The model with spatial and temporal random effect terms is useful tool to provide reasonably reliable forecasts for mortality due to malaria. This might help to design appropriate strategies for targeting malaria control. On the other hand, including spatially and temporal varying random terms in the model is necessary and good strategy for modelling mortality due to malaria.
Abstract: Malaria is a leading cause of morbidity and mortality in developing countries especially in rural areas where local resources are limited. Accurate disease forecasts can provide information to public and clinical health services to design targeted interventions for malaria control that make effective use of limited resources. Using verbal autopsy d...
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Using Logistic Regression Model to Predict the Success of Bank Telemarketing
Issue:
Volume 4, Issue 1, March 2018
Pages:
35-41
Received:
20 June 2018
Published:
21 June 2018
Abstract: Term deposit is always an essential business of a bank and a good market campaign plays an essential role in financial selling. Nowadays, the telephone marketing, which can assist consulting institution to extract potential clients, has been one of the most general marketing campaigns. Previous research shows that data mining has gradually stood out on the era of Big Data and has been incorporated to deal with massive data precisely. The purpose of this study is to predict the success of bank telemarketing to select the best consumer set. A relationship is observed between success and other factors through constructing logistic regression model. To validate the effectiveness of prediction, some basic classification models have been compared in this study, including Bayes, Support Vector Machine, Neural Network and Decision Tree. As a result, the prediction accuracy and the area under ROC curve prove the logistic regression model performs best in classifying than other models. All of the experiments are implemented by R language software. And the experimental results can provide some suggestions and instructions towards the management of the bank.
Abstract: Term deposit is always an essential business of a bank and a good market campaign plays an essential role in financial selling. Nowadays, the telephone marketing, which can assist consulting institution to extract potential clients, has been one of the most general marketing campaigns. Previous research shows that data mining has gradually stood ou...
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