![]() ![]() The best performance is chosen as an optimality criterion. In the established Azure ML, the regression algorithms such as, boost decision tree regression, Bayesian linear regression, neural network, and decision forest regression are selected. Azure ML enables algorithms that can learn from data and experiences and accomplish tasks without having to be coded. For prediction, four parameters are used as inputs: the receiver radius, transmitter radius, distance between receiver and transmitter, and diffusion coefficient, while the output is mAP (mean average precision) of the received signal. This paper applies Azure Machine Learning (Azure ML) for flexible pavement maintenance regressions problems and solutions. Machine learning (ML) is one of the intelligent methodologies that has shown promising results in the domain. In this paper, we concentrate on one critical aspect of the MC system, modelling MC received signal until time t, and demonstrate that using tools from ML makes it promising to train detectors that can be executed well without any information about the channel model. In these cases, a new method to analyze and design is needed. However, the underlying channel models are unknown in some systems, such as MC systems, where chemical signals are used to transfer information. Analysis and designs of communication systems usually rely on developing mathematical models that describe the communication channel. Even though, the impact of incredibly slow molecule diffusion and high variability environments remains unknown. ![]() Molecular communication (MC) implemented on Nano networks has extremely attractive characteristics in terms of energy efficiency, dependability, and robustness. The development of complex programming concepts can also be demonstrated. Our results indicate that SRA-programming with visual, on-screen output yields a significant increase in the development of CT, as opposed to SRA-programming with a tangible output. It was expected that the observed effect of pupils' programming actions through the application of SRA would show that the type of output influences the understanding of complex programming concepts at a higher level. In this research, we therefore explore whether characteristic differences in the development of CT can be measured when SRA-programming is applied in a visual programming environment with an on-screen output or a tangible output. It is important to investigate whether the type of output has a characteristic influence on the level of development of CT in visual programming environments. Visual programming environments are diverse in appearance and prove to be an excellent way to teach pupils the basic ideas of programming. SRA-programming has been identified as an instrumental way of thinking for learning to program robots and encourages the development of the more complex concepts of programming. The application of sense-reason-act (SRA) programming in contemporary education can ensure the development of computational thinking (CT) at a more advanced level. ![]()
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