TRIZ has been well-established and is widely applied in multi-national enterprises to enhance design innovation and problem solving for design and manufacturing problems. Most applications of TRIZ utilise the engineering contradiction tools to solve design and manufacturing problems. This paper initially applied the engineering contradiction matrix to solve a semi-processed material transfer misalignment problem while passing through a furnace via a long mesh metal conveyor belt in the productions of automotive parts. Then, using another TRIZ tool, substance-field analysis to verify the solutions obtained earlier. This verification is to reduce the possible of errors in solving problems using the engineering contradiction matrix.
It is vital to remain vigilant during pandemic COVID-19. Wearing a face mask is one of the crucial steps that people must take to ensure that they are a step away from spreading and infecting the virus. However, controlling and monitoring people in a densely crowded place is tough. Hence, a face mask detection system in public area is needed to remotely monitor if one is wearing a face mask or vice versa. In this study, two face masks datasets are downloaded from GitHub with 3834 images and 11800 colour images. Data pre-processing steps are carried out before the classification, which includes image resizing, converting images into array and label encoding. Two deep learning models, MobileNetV2 and VGG19, are developed for detection and evaluation. The experimental results performed by MobileNetV2 outperformed the VGG19 with achieving accuracy of 98.96% and 99.55% on Dataset 1 and Dataset 2 respectively.
This research present the notion of subjectivity and objectivity in Bahasa Melayu language. Word2Vec and BERT word embedding models are created for the purpose of subjectivity classification and sentiment classification. Two types of embeddings are developed (Word2Vec and BERT) with Wikipedia data as objectivity dataset, Twitter data as subjectivity dataset and combination of both datasets. A pre-trained BERT embedding model called Bert-Base-Bahasa-Cased is used as a reference. First, the datasets are fed into every embedding model to be embedded as vectors. The subjectivity classification and sentiment classification are carried out via 70:30 train-test splits. Both classification tasks are carried out using Logistic Regression, Random Forest, and Double Layer Neural Network classifiers. Logistic Regression on Bert-Base-Bahasa-Cased model achieved the highest result of 99.95% in subjectivity classification and 74.30% in sentiment classification.
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