Titlebook: Applied Computing and Information Technology; Roger Lee Book 2020 Springer Nature Switzerland AG 2020 Computational Intelligence.Applied C

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1860-949X erence’s most promising papers were selected for this Springer (SCI) book and not the conference proceedings. We eagerly await the important contributions that we know these authors will make to the field of co978-3-030-25219-9978-3-030-25217-5Series ISSN 1860-949X Series E-ISSN 1860-9503

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1860-949X Applied Computing and Information Technology ACIT 2019, heldThis book gathers the outcomes of the 7th International Conference on Applied Computing and Information Technology (ACIT 2019), which was held on May 29–31, 2019 in Honolulu, Hawaii. The aim of the conference was to bring together researche

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A Comparative Study of Using Bag-of-Words and Word-Embedding Attributes in the Spoiler Classificatiag of words could be due to its scalability. Between Word2Vec and FastText embeddings, the former was favorable when few out-of-vocabulary (OOV) words were present. Finally, although FastText was expected to be helpful with a large number of OOV words, its benefit was hardly seen for Thai language.

Microgram

Fall Detection of Elderly Persons by Action Recognition Using Data Augmentation and State Transitioy using the result of action recognition. Since it is difficult to capture dangerous scenes such as fall of elderly people and to capture a lot of data, this paper proposes a new method that can create a large amount of data necessary for learning from a small amount of data.

ALOFT

Transforming YAWL Workflows into Petri Nets,still be time-consuming. In this paper, we propose an alternative to transform a YAWL workflow into a corresponding Petri nets model. A set of mapping rules would be proposed to cope with the non well formed model of YAWL. The resulting Petri nets model of the YAWL workflows is correct and ready for model checking.

Cerebrovascular

Frictional Behaviours and Mechanismsr Indonesian civilian number plate. Testing shows that the ANPR system has been implemented successfully. Evaluation of the system gives a precision value of 1 and a recall value of 0.78. These values are obtained with hidden layer nodes of 75, 85, and 95. These number of hidden nodes delivers an F-score of 0.88 with the accuracy of 88%.

Salivary-Gland

Labeling Algorithm and Fully Connected Neural Network for Automated Number Plate Recognition Systemr Indonesian civilian number plate. Testing shows that the ANPR system has been implemented successfully. Evaluation of the system gives a precision value of 1 and a recall value of 0.78. These values are obtained with hidden layer nodes of 75, 85, and 95. These number of hidden nodes delivers an F-score of 0.88 with the accuracy of 88%.

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