Haengkon Kim - Software Engineering in IoT, Big Data, Cloud and Mobile Computing

The series "Studies in Computational Intelligence" (SCI) publishes new developments and advances in the various areas of computational intelligencequickly and with a high quality. The intent is to cover the theory, applications, and design methods of computational intelligence, as embedded in the fields of engineering, computer science, physics and life sciences, as well as the methodologies behind them. The series contains monographs, lecture notes and edited volumes in computational intelligence spanning the areas of neural networks, connectionist systems, genetic algorithms, evolutionary computation, artificial intelligence, cellular automata, self-organizing systems, soft computing, fuzzy systems, and hybrid intelligent systems. Of particular value to both the contributors and the readership are the short publication timeframe and the world-wide distribution, which enable both wide and rapid dissemination of research output.

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The purpose of International Semi-Virtual Workshop on Software Engineering in IoT, Big Data, Cloud and Mobile Computing (SE-ICBM 2020) held on October 17, 2020, Soongsil University, Seoul, Korea, is aimed at bringing together researchers and scientists, businessmen and entrepreneurs, teachers and students to discuss the numerous fields of computer science and to share ideas and information in a meaningful way. This special session Software Engineering with ICBM will discuss the analysis, evaluation and implementation of IoT, big data, cloud and mobile computing as well as the appreciation of mobile platform development. Through the special session, advanced hands-on theory and practical projects approach.

This publication captures 17 of the conferences most promising papers. The selected papers have not been published in the workshop proceedings or elsewhere, but only in this book. We impatiently await the important contributions that we know these authors will bring to the field.

In Chapter , Aman Jaffari, Jihyun Lee and Eunmi Kim introduce the majority of the studies proposed techniques for modeling variability in a separate model rather than modeling variability as an integral part of the development artifact.

In Chapter , Lai Yin Xiang, Ha Jin Hwang, Haeng Kon Kim, Monowar Mahmood and Norazryana Mat Dawi discuss a bigger picture of how the use of big data analytics can improve the supply chain performance in logistics industry. Logistics industry could benefit from the results of this research by understanding the key success factors of big data analytics to improve supply chain performance in logistics industry.

In Chapter , Symphorien Karl Yoki Donzia and Haeng Kon Kim propose a personalized service to each user to provide differentiated medical care and change their perception of how to provide services smart U-healthcare. The achievement of service-oriented smart healthcare information systems focused on the healthcare cloud environment.

In Chapter , Sun-Myung Hwang and Hee-Gyun Yeom develop an image translator that combines existing OCR technology and translation technology and verify its effectiveness. Before developing, we presented what functions are needed to implement this system and how to implement them, and then tested their performance.

In Chapter , Jun-hee Choi and Hyun-Sug Cho present that an artificial neural network algorithm was applied to infer the ignition factors using about 10 data per fire accident, and the prediction accuracy was about 80%, rather than determining the ignition factors.

In Chapter , Symphorien Karl Yoki Donzia, Yong Pil Geum and Haeng Kon Kim propose an algorithm to detect driver drowsiness through analysis of heart rate variability and compare it with EEG-based sleep scores to verify the proposed method in bigdata. The ECG sensor provides various detection methods to detect RR interval big data from ECG data and only transmit abnormal data.

In Chapter , Jeong-Hoon Shin presented a technique for applying the stimuli allowing all subjects to maintain their psychologically steady state by using the customized stimuli optimized for individual users and has attempted to promote the psychologically steady state of the subjects by using stimuli tailored to each user.

In Chapter , Jeong Ah Kim present that unplugged activities are designed to evaluate how much the students studied in pre-class and identify the part which students cannot understands. For post-class, just homework for identifying the application of algorithm in real life is issued.

In Chapter , Symphorien Karl Yoki Donzia and Haeng-Kon Kim proposed the general process of a situational contexed awareness system and the design considerations for the situational awareness system. In addition, it summarizes the characteristics of the existing situational awareness system and presents a trend analysis of the system. In conclusion, we propose future work for a better situational awareness system.