The paper discusses the concept of mineral resources associated with coal measures. A rational and scientific classification of such mineral resources becomes more necessary with the development of science and technol...The paper discusses the concept of mineral resources associated with coal measures. A rational and scientific classification of such mineral resources becomes more necessary with the development of science and technology. A classification scheme is proposed based on compositions and physical properties and the utilization of these associated minerals.展开更多
The market trends rapidly changed over the last two decades.The primary reason is the newly created opportunities and the increased number of competitors competing to grasp market share using business analysis techniq...The market trends rapidly changed over the last two decades.The primary reason is the newly created opportunities and the increased number of competitors competing to grasp market share using business analysis techniques.Market Basket Analysis has a tangible effect in facilitating current change in the market.Market Basket Analysis is one of the famous fields that deal with Big Data and Data Mining applications.MBA initially uses Association Rule Learning(ARL)as a mean for realization.ARL has a beneficial effect in providing a plenty benefit in analyzing the market data and understanding customers’behavior.An important motive of using such techniques is maximizing the business profit as well as matching the exact customer needs as closely as possible.In this survey paper,we discussed several applications and methods of MBA based on ARL.Also,we reviewed some association rule learning measurements including trust,lift,leverage,and others.Furthermore,we discuss some open issues and future topics in the area of market basket analysis and association rule learning.展开更多
This paper presents a new approach to identify and estimate the dispersion parameters for bivariate, trivariate and multivariate correlated binary data, not only with scalar value but also with matrix values. For this...This paper presents a new approach to identify and estimate the dispersion parameters for bivariate, trivariate and multivariate correlated binary data, not only with scalar value but also with matrix values. For this direction, we present some recent studies indicating the impact of over-dispersion on the univariate data analysis and comparing a new approach with these studies. Following the property of McCullagh and Nelder [1] for identifying dispersion parameter in univariate case, we extended this property to analyze the correlated binary data in higher cases. Finally, we used these estimates to modify the correlated binary data, to decrease its over-dispersion, using the Hunua Ranges data as an ecology problem.展开更多
Objective To investigate the relationship between olanzapine induced metabolic disturbance related measures and TCF7L2 gene expression.Methods Thirty adult C57BL/61 mice,in accordance with the random number table,were...Objective To investigate the relationship between olanzapine induced metabolic disturbance related measures and TCF7L2 gene expression.Methods Thirty adult C57BL/61 mice,in accordance with the random number table,were divided into 3 groups that were展开更多
Accurate nuclear classification(e.g., grading of renal cell carcinoma(RCC) biopsy images) is important to better understand fundamental phenomena such as tumor growth. In this paper, an automated pipeline is proposed ...Accurate nuclear classification(e.g., grading of renal cell carcinoma(RCC) biopsy images) is important to better understand fundamental phenomena such as tumor growth. In this paper, an automated pipeline is proposed to quantitatively analyze RCC data. A novel segmentation methodology is firstly used to delineate cell nuclei based on minimum description length(MDL) constrained B-spline curve fitting. From the obtained segmentations, thirteen features are then extracted based on five types of characteristics. These features are used to classify cell nuclei in biopsy images. Associations among nuclei are computed and represented by graphical networks to enable further analysis. Finally, a support vector machine(SVM) based decision-graph classifier is introduced to classify the biopsy images with the purpose of grading. Experimental results on real RCC data show that our SVM-based decision-graph classifier achieves 95.20% of classification accuracy while the SVM classifiers achieve 93.33% of classification accuracy.展开更多
文摘The paper discusses the concept of mineral resources associated with coal measures. A rational and scientific classification of such mineral resources becomes more necessary with the development of science and technology. A classification scheme is proposed based on compositions and physical properties and the utilization of these associated minerals.
文摘The market trends rapidly changed over the last two decades.The primary reason is the newly created opportunities and the increased number of competitors competing to grasp market share using business analysis techniques.Market Basket Analysis has a tangible effect in facilitating current change in the market.Market Basket Analysis is one of the famous fields that deal with Big Data and Data Mining applications.MBA initially uses Association Rule Learning(ARL)as a mean for realization.ARL has a beneficial effect in providing a plenty benefit in analyzing the market data and understanding customers’behavior.An important motive of using such techniques is maximizing the business profit as well as matching the exact customer needs as closely as possible.In this survey paper,we discussed several applications and methods of MBA based on ARL.Also,we reviewed some association rule learning measurements including trust,lift,leverage,and others.Furthermore,we discuss some open issues and future topics in the area of market basket analysis and association rule learning.
文摘This paper presents a new approach to identify and estimate the dispersion parameters for bivariate, trivariate and multivariate correlated binary data, not only with scalar value but also with matrix values. For this direction, we present some recent studies indicating the impact of over-dispersion on the univariate data analysis and comparing a new approach with these studies. Following the property of McCullagh and Nelder [1] for identifying dispersion parameter in univariate case, we extended this property to analyze the correlated binary data in higher cases. Finally, we used these estimates to modify the correlated binary data, to decrease its over-dispersion, using the Hunua Ranges data as an ecology problem.
文摘Objective To investigate the relationship between olanzapine induced metabolic disturbance related measures and TCF7L2 gene expression.Methods Thirty adult C57BL/61 mice,in accordance with the random number table,were divided into 3 groups that were
基金the National Natural Science Foundation of China(Nos.61171165,11431015 and 61571230)the Natural Science Foundation of Jiangsu Province(Nos.BK20161500 and BK20150784)+2 种基金the National Scientific Equipment Developing Project of China(No.2016YFF0103604)the China Postdoctoral Science Foundation(No.2015M581800)the Fundamental Research Funds for the Central Universities of China(No.30915012204)
文摘Accurate nuclear classification(e.g., grading of renal cell carcinoma(RCC) biopsy images) is important to better understand fundamental phenomena such as tumor growth. In this paper, an automated pipeline is proposed to quantitatively analyze RCC data. A novel segmentation methodology is firstly used to delineate cell nuclei based on minimum description length(MDL) constrained B-spline curve fitting. From the obtained segmentations, thirteen features are then extracted based on five types of characteristics. These features are used to classify cell nuclei in biopsy images. Associations among nuclei are computed and represented by graphical networks to enable further analysis. Finally, a support vector machine(SVM) based decision-graph classifier is introduced to classify the biopsy images with the purpose of grading. Experimental results on real RCC data show that our SVM-based decision-graph classifier achieves 95.20% of classification accuracy while the SVM classifiers achieve 93.33% of classification accuracy.