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Performance Engineering for Scientific Computing with R
Issue:
Volume 4, Issue 2, June 2018
Pages:
42-48
Received:
25 June 2018
Published:
26 June 2018
Abstract: R has been adopted as a popular data analysis and mining tool in many domain fields over the past decade. As Big Data overwhelms those fields, the computational needs and workload of existing R solutions increases significantly. With recent hardware and software developments, it is possible to enable massive parallelism with existing R solutions with little to no modification. In this paper, three different approaches are evaluated to speed up R computations with the utilization of the multiple cores, the Intel Xeon Phi SE10P Co-processor, and the general purpose graphic processing unit (GPGPU). Performance engineering and evaluation efforts in this study are based on a popular R benchmark script. The paper presents preliminary results on running R-benchmark with the above packages and hardware technology combinations.
Abstract: R has been adopted as a popular data analysis and mining tool in many domain fields over the past decade. As Big Data overwhelms those fields, the computational needs and workload of existing R solutions increases significantly. With recent hardware and software developments, it is possible to enable massive parallelism with existing R solutions wi...
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Sentiment Analysis Using Text Mining: A Review
Swati Redhu,
Sangeet Srivastava,
Barkha Bansal,
Gaurav Gupta
Issue:
Volume 4, Issue 2, June 2018
Pages:
49-53
Received:
25 June 2018
Published:
26 June 2018
Abstract: Text mining and sentiment analysis have received huge attention recently, specially because of the availability of vast data in form of text available on social media, e-commerce websites, blogs and other similar sources. This data is usually unstructured and contains noise, therefore the task of gaining information is complex and expensive. There is a growing need for developing different methodologies and models for efficiently processing the texts and extracting apt information. One way to extract information is text mining and sentiment analysis, that include: data acquisition, data pre-processing and normalization, feature extraction and representation, labelling, and finally the application of various Natural Language Processing (NLP) and machine learning algorithms. This paper provides an overview of different methods used in text mining and sentiment analysis elaborating on all subtasks.
Abstract: Text mining and sentiment analysis have received huge attention recently, specially because of the availability of vast data in form of text available on social media, e-commerce websites, blogs and other similar sources. This data is usually unstructured and contains noise, therefore the task of gaining information is complex and expensive. There ...
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The L(2, 1)-labeling on β-product of Graphs
Issue:
Volume 4, Issue 2, June 2018
Pages:
54-59
Received:
11 May 2018
Accepted:
1 June 2018
Published:
3 July 2018
Abstract: The L(2, 1)-labeling (or distance two labeling) of a graph G is an integer labeling of G in which two vertices at distance one from each other must have labels differing by at least 2 and those vertices at distance two must differ by at least 1. The L(2, 1)-labeling number of G is the smallest number k such that G has an L(2, 1)-labeling with maximum of f(v) is equal to k, where v belongs to vertex set of G. In this paper, upper bound for the L(2, 1)-labeling number for the β-product of two graphs has been obtained in terms of the maximum degrees of the graphs involved.
Abstract: The L(2, 1)-labeling (or distance two labeling) of a graph G is an integer labeling of G in which two vertices at distance one from each other must have labels differing by at least 2 and those vertices at distance two must differ by at least 1. The L(2, 1)-labeling number of G is the smallest number k such that G has an L(2, 1)-labeling with maxim...
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Research on Vehicle Lane-Change Driving Behavior Based on Optimal Velocity Model
Ma Yu-yue,
Wang Ji-zhong,
Gao Li,
Zhang Hui
Issue:
Volume 4, Issue 2, June 2018
Pages:
60-66
Received:
17 May 2018
Accepted:
1 June 2018
Published:
4 July 2018
Abstract: Vehicle lane-change driving behavior affects the safety of vehicle driving and the stability of traffic flow, and it has great significance to establish a reasonable lane-change driving behavior model for studying lane-change driving characteristics and developing driver assistance system. The influence of the associated vehicle driving state on the lane-change behavior during the changing process is analyzed, and the driving behavior model based on optimal velocity model is established by using the vehicle following theory. The Theil`s U objective function is used to calibrate the model parameters, the prediction results of the model are compared with the actual measured results. The study shows that the lane-change behavior can be approximately described as the two kinds of car following behavior in the original lane and the target lane to the front car. The lane-change model established can truly describe the lane-change driving characteristics.
Abstract: Vehicle lane-change driving behavior affects the safety of vehicle driving and the stability of traffic flow, and it has great significance to establish a reasonable lane-change driving behavior model for studying lane-change driving characteristics and developing driver assistance system. The influence of the associated vehicle driving state on th...
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Multilevel Modelling of Determinants of Youth Unemployment in Urban Ethiopia: Bayesian Estimation Approach
Issue:
Volume 4, Issue 2, June 2018
Pages:
67-78
Received:
20 May 2018
Accepted:
5 June 2018
Published:
4 July 2018
Abstract: The main objective of this study was to identify and explain the effects of the Demographic and Socio-economic determinant factors of Youth unemployment in urban of Ethiopia. The data used for this study is the 2016 Ethiopian Urban Employment Unemployment Survey (UEUS) which was conducted by Central Statistical Agency (CSA) of Ethiopia. The statistical methods of data analysis are multilevel logistic regression models and Bayesian multilevel models and the parameters are estimated by using maximum likelihood estimation method and Bayesian estimation method by Stata and WinBUGS software. The analysis result revealed that Out of the 3870 youth considered in the analysis, 1,757 (45.4%) youth were unemployed, while 2113 (54.6%) youth were employed at the time of data collection. Region, Sex of youth, Age of youth, Literacy status, marital status, Type of Training, Steps taken to search work, Household size and Educational level are found to be the significant determinants of youth unemployment in urban Ethiopia. The multilevel logistic model revealed that the random intercept is better fit than null and random coefficient multilevel models. The intra correlation coefficient suggests that there is clear variation of youth unemployment status across the region of urban Ethiopia. The result of classical and Bayesian multilevel shows high prevalence of unemployment among youth and the probability of being unemployed for youth was found to decline with increasing age, literacy level, training, educational level and household size.
Abstract: The main objective of this study was to identify and explain the effects of the Demographic and Socio-economic determinant factors of Youth unemployment in urban of Ethiopia. The data used for this study is the 2016 Ethiopian Urban Employment Unemployment Survey (UEUS) which was conducted by Central Statistical Agency (CSA) of Ethiopia. The statist...
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