Titlebook: Computational Science and Technology; 7th ICCST 2020, Patt Rayner Alfred,Hiroyuki Iida,Patricia Anthony Conference proceedings 2021 The Edi

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Denis Bourguet,Thomas Guillemaudll help researchers to customise the incorporation of blended learning tools in students learning as well help educators in planning and adopting blended learning, and benefit students in their learning. The system developers also should consider designing and develop more suitable systems/apps for students in learning Language subjects.

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Attention Models for Sentiment Analysis Using Objectivity and Subjectivity Word Vectors,d subjectivity, models trained with the objectivity word embeddings performed worse than their counterpart. However, when compared to the BERT model, a model also with Attention Mechanism but has its own word embedding technique, the BERT model achieved higher accuracy even though model training was performed with only transfer learning.

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,Information Technology Students’ Preferences on Blended Learning,ll help researchers to customise the incorporation of blended learning tools in students learning as well help educators in planning and adopting blended learning, and benefit students in their learning. The system developers also should consider designing and develop more suitable systems/apps for students in learning Language subjects.

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Nasheed Song Classification by Fuzzy Soft-Set Approach,ection technique and the classifier were built based on the theory of fuzzy-soft set that have enough parameters to handle uncertainties in data. The result show that the built classifier using the selected features accurately can identify the nasheed genre up to 90%.

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Nitrogen-Fixing Plant-Microbe Symbioses,. Various techniques were employed to obtain data from the images provided for the data mining process. The features used are the colour of the palm fruit bunches and the amount of edges representing visible leaves in the palm fruit bunches, indicating empty sockets. The project is able to achieve an accuracy of up to 79.11%.

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Towards Palm Bunch Ripeness Classification Using Colour and Canny Edge Detection,. Various techniques were employed to obtain data from the images provided for the data mining process. The features used are the colour of the palm fruit bunches and the amount of edges representing visible leaves in the palm fruit bunches, indicating empty sockets. The project is able to achieve an accuracy of up to 79.11%.

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