Cancer is a pestilent disease. One of the most important cancer kinds, cervical cancer is a malignant tumor which threats women's life. In this study, the importance of test variables for cervical cancer disease is investigated by utilizing Stability Selection method. Also, Random Under-Sampling and Random Over-Sampling methods are implemented on the dataset. In this context, the learning model is designed by using Random Forest algorithm. The experimental results show that Stability Selection, Random Over-Sampling and Random Forest based model are more successful, approximately 98% accuracy.

Researchers around the world are publishing their scientific research results in different forms such as books, journal articles, reference works and project reports. Publishers of these scientific documents usually describe them by using metadata for organizational purposes. This metadata provides a rich information about scientific documents that can be used for analysis purposes such as measuring the impact of researchers and research centers. It can also be used to find scientific documents published in domain of some ones interest, which ultimately can be used to raise the state of the art to the next level. Scientific publications metadata can also be used to analyze the quality and directions of common and highly cited individuals and organizations, and based on this analysis other individuals and organizations can define directions for their future work and research. However, the main limitation of this metadata is that it is available in different formats that might not facilitate the analysis of scientific documents. Therefore, in this paper we clarify that how our SPedia knowledge base (a semantic based knowledge base of scientific publications metadata which we extracted by using SpringerLink as information source) facilitates the analysis of scientific data for policy making. We discuss different kind of questions that can be answered through SPedia knowledge base and we show that how results of these questions can be used to analyze the performance of individuals as well as organizations. We also show that how results of such analysis can help in making organizational policies regarding future research directions.

Taxation has an important role as state revenue in the State Budget. In 2017, the target of tax revenues in the State Budget is about Rp. 1,498.9 trillion so that the Ministry of Finance of the Republic of Indonesia contrives as well as possible through the fiscal policy reform to fulfill the targets. One of the efforts which undertaken by the Directorate General of Taxes is applied tax payment system using e-billing with the electronic deposit slip. E-billing (electronic billing) is an online service which uses the system through the issued code on a kind of tax payment. The tax payment system is based on self-assessment to make deposits or tax payments independently by the taxpayer. Developments in technology influenced the emergence of innovations particularly financial technology in the finance industry. An industry which worked in the finance sector utilizing financial technology in the payment system. This kind of condition can provide opportunities for financial transaction service electronically. In this research proposed a tax payment system design which can be integrated with financial technology industry through Service Oriented Architecture (SOA) approach. The design which included in this paper is technology architecture, data modeling, use case diagram of service and system integration design. A conceptual design is realized as an effort to utilize financial technology to provide alternative tax payment transactions in achieving the target of state revenue.

Because logistic is a process-oriented business, we propose in this paper a measurement system of decision support for assessing the costs associated with each logistics process. This system allows calculating economic, environmental and social costs of logistics process to ensure a sustainable logistics. We have formulated the problem and we present some simulation for testing our system. This proposition allows the decision-maker to have knowledge of economic, ecological and social cost before making a decision.

The present study bridges the gap between the two intersecting domains, data science and supply chain management. The data can be analyzed for inventory management, forecasting and prediction, which is in the form of reports, queries and forecasts. Because of the price, weather patterns, economic volatility and complex nature of business, the forecasts may not be accurate. This has resulted in the growth of Supply chain analytics. It is the application of qualitative and quantitative methods to solve relevant problems and to predict the outcomes by considering quality of data. The issues like increased collaboration between companies, customers, retailers and governmental organizations, companies are adopting Big Data solutions. Big Data applications can be linked for Supply Chain Management across the fields like procurement, transportation, warehouse operations, marketing and also for smart logistics. As supply chain networks becoming vast, more complex and driven by demands for more exacting service levels, the type of data that is managed and analyzed also becomes more complex. The present work aims at providing an overview of adoption of capabilities of Data Analytics as part of a “next generation” architecture by developing a linear regression model on a sales-data. The paper also covers the survey of how big data techniques can be used for storage, processing, managing, interpretation and visualization of data in the field of Supply chain.

Evolutionary Algorithms (EAs) are found to be effective for solving a large variety of optimization problems. In this Paper Dual Population Genetic Algorithm (DPGA) is used for solving the test functions of Congress on Evolutionary Computation 2013 (CEC’2013), by using two different crossovers. Dual Population Genetic Algorithm is found to be better in performance than traditional Genetic Algorithm. It is also able to solve the problem of premature convergence and diversity of the population in genetic algorithm. This paper proposes Dual Population Genetic Algorithm for solving the problem regarding unconstrained optimization. Dual Population Genetic Algorithm is used as meta-heuristic which is verified against 28 functions from Problem Definitions and Evaluation Criteria for the Congress on Evolutionary Computation 2013 on unconstrained set of benchmark functions using two different crossovers. The results of both the crossovers are compared with each other. The results of both the crossovers are also compared with the existing results of Standard Particle Swarm Optimization algorithm. The Experimental results showed that the algorithm found to be better for finding the solution of multimodal functions of the problem set.