High dimensional data are commonly encountered in various scientific fields and pose great challenges to modern statistical analysis. To address this issue different penalized regression procedures have been introduced in the litrature, but these methods cannot cope with the problem of outliers and leverage points in the heavy tailed high dimensional data. For this purppose, a new Robust Adaptive Lasso (RAL) method is proposed which is based on pearson residuals weighting scheme. The weight function determines the compatibility of each observations and downweight it if they are inconsistent with the assumed model. It is observed that RAL estimator can correctly select the covariates with non-zero coefficients and can estimate parameters, simultaneously, not only in the presence of influential observations, but also in the presence of high multicolliearity. We also discuss the model selection oracle property and the asymptotic normality of the RAL. Simulations findings and real data examples also demonstrate the better performance of the proposed penalized regression approach.
Abstract-At present the most challenging issue that the software development industry encounters is less efficient management of software development budget projections. This problem has put the modern day software development companies in a situation wherein they are dealing with improper requirement engineering, ambiguous resource elicitation, uncertain cost and effort estimation. The most indispensable and inevitable aspect of any software development company is to form a counter mechanism to deal with the problems which leads to chaos. An emphatic combative domain to deal with this problem is to schedule the whole development process to undergo proper and efficient estimation process, wherein the estimation of all the resources can be made well in advance in order to check whether the conceived project is feasible and within the resources available. The basic building block in any object oriented design is Use Case diagrams which are prepared in early stages of design after clearly understanding the requirements. Use Case Diagrams are considered to be useful for approximating estimates for software development project. This research work gives detailed overview of Re-UCP (revised use case point) method of effort estimation for software projects. The Re-UCP method is a modified approach which is based on UCP method of effort estimation. In this research study 14 projects were subjected to estimate efforts using Re-UCP method and the results were compared with UCP and e-UCP models. The comparison of 14 projects shows that Re-UCP has significantly outperformed the existing UCP and e-UCP effort estimation techniques.
The pore system in carbonates is complicated because of the associated biological and chemical activity. Secondary porosity, on the other hand, is the result of chemical reactions that occur during diagenetic processes. A thorough understanding of the carbonate pore system is essential to hydrocarbon prospecting. Porosity classification schemes are currently limited to accurately forecast the petrophysical parameters of different reservoirs with various origins and depositional environments. Although rock classification offers a way to describe lithofacies, it has no impact on the application of the poro-perm correlation. An outstanding example of pore complexity (both in terms of type and origin) may be found in the Central Luconia carbonate system (Malaysia), which has been altered by diagenetic processes. Using transmitted light microscopy, 32 high-resolution pictures were collected of each thin segment for quantitative examination. An FESEM picture and a petrographic study of thin sections were used to quantify the grains, matrix, cement, and macroporosity (pore types). Microporosity was determined by subtracting macroporosity from total porosity using a point-counting technique. Moldic porosity (macroporosity) was shown to be the predominant type of porosity in thin sections, whereas microporosity seems to account for 40 to 50% of the overall porosity. Carbonates from the Miocene have been shown to possess a substantial quantity of microporosity, making hydrocarbon estimate and production much more difficult. It might lead to a higher level of uncertainty in the estimation of hydrocarbon reserves if ignored. Existing porosity classifications cannot be used to better understand the poro-perm correlation because of the wide range of geological characteristics. However, by considering pore types and pore structures, which may be separated into macro- and microporosity, the classification can be enhanced. Microporosity identification and classification investigations have become a key problem in limestone reservoirs across the globe.
In today's world, social media provides a valuable platform for conveying expressions, thoughts, point-of-views, and communication between people, from diverse walks of life. There are currently approximately 2.62 billion active users' social networks, and this is expected to exceed 3 billion users by 2021. Social networks used to share ideas and information, allowing interaction across communities, organizations, and so forth. Recent studies have found that the typical individual uses these platforms between 2 and 3 h a day. This creates a vast and rich source of data that can play a critical role in decisionmaking for companies, political campaigns, and administrative management and welfare. Twitter is one of the important players in the social network arena. Every scale of companies, celebrities, different types of organizations, and leaders use Twitter as an instrument for communicating and engaging with their followers. In this paper, we build upon the idea that Twitter data can be analyzed for crowd source sensing and decision-making. In this paper, a new framework is presented that uses Twitter data and performs crowd source sensing. For the proposed framework, real-time data are obtained and then analyzed for emotion classification using a lexicon-based approach. Previous work has found that weather, understandably, has an impact on mood, and we consider these effects on crowd mood. For the experiments, weather data are collected through an application-programming-interface in R and the impact of weather on human sentiments is analyzed. Visualizations of the data are presented and their usefulness for policy/ decision makers in different applications is discussed.INDEX TERMS Big data, crowd-sourced sensing, lexicon-based approach, Twitter, social networks.
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