3/11/2023 0 Comments R studio basics![]() ![]() This paper aims to make a comparison between three recently developed models to estimate the ADT at any location of the class-A road network in Sri Lanka. ADT estimation models have been developed using different methods such as regression analysis, and neural networks. In conceptual planning stage, it is sufficient to use estimated ADTs obtained from a model, which saves time and cost. Eventually, the most common techniques for predicting telecommunication churning such as classification, regression analysis, and clustering are included, thus presenting a roadmap for new researchers to build new churn management models.Īverage Daily Traffic (ADT) data are mostly used in transportation engineering for the purpose of planning and designing roads, pavement capacity designing, prioritizing road maintenance investments, accident studies, etc. It epitomizes the present literature in the field of communications by highlighting the impact of service quality on customer satisfaction, detecting churners in the telecoms industry, in addition to the sample size used, the churn variables used and the results of various DM technologies. ![]() This paper supplies a review of nearly 73 recent journalistic articles starting in 2003 to introduce the different DM techniques used in many customerbased churning models. Researchers around the world have conducted important research to understand the uses of Data mining (DM) that can be used to predict customers' churn. These data can be helpfully extracted for analysis and used for predicting churners. This satisfaction raises rivalry between firms to maintain the quality of their services and upgrade them. As well as better awareness of customer requirements and excellent quality that meets their satisfaction. The telecommunication sector has been developed rapidly and with large amounts of data obtained as a result of increasing in the number of subscribers, modern techniques, data-based applications, and services. These results support the novel idea of concurrently cultivating environmental consciousness and CT and build a robust base for future studies that will focus on providing an ecological reflection on CT activities. The exploitation of ordinal logistic regression analysis and machine learning method validated the correlation of the two fields and pointed out that AT levels constitute a predictive factor for performance in the Environmental Study course and vice versa. The adoption of cluster sampling eventuated in a sample of 435 students. Towards this end, we implemented a quantitative research study, employing an innovative assessment framework we propose. Thus, our research aim is to explore the correlation between algorithmic thinking (AT), as a fundamental CT competency, and educational achievements in the Environmental Study course during the early primary school years. ![]() Having in mind that CT does not concern only technocrats but also applies in solving everyday problems, we introduce the novel idea of the synergistic learning of CT and Environmental Study. Concurrently, the COVID-19 pandemic has accentuated the demand for strengthening Environmental Education as a means to improve sustainability and stimulate environmental protection and public health. Presently, computational thinking (CT) is considered necessary for adapting to the future. ![]()
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