Unexpected Cost of Korean Wave during Pandemic Covid-19 in Makassar, South Sulawesi

This study discuss phenomenal success Korean Wave during pandemic covid-19 in Makassar, South Sulawesi, Indonesia. In recent years, Korean wave has become an addiction to young people in Makassar. This paper aims to explore the young people’s habit during pandemic covid-19, how they spend their leisure time throughout large scale social restriction (PSBB). Through the process of interviews, questionnaires, media, and article, this study result that the young people are willing to pay more to fulfill their hobby of watching Kdrama

Relational Autoencoder for Feature Extraction

Feature extraction becomes increasingly important as data grows high dimensional. Autoencoder as a neural network based feature extraction method achieves great success in generating abstract features of high dimensional data. However, it fails to consider the relationships of data samples which may affect experimental results of using original and new features. In this paper, we propose a Relation Autoencoder model considering both data features and their relationships. We also extend it to work with other major autoencoder models including Sparse Autoencoder, Denoising Autoencoder and Variational Autoencoder. The proposed relational autoencoder models are evaluated on a set of benchmark datasets and the experimental results show that considering data relationships can generate more robust features which achieve lower construction loss and then lower error rate in further classification compared to the other variants of autoencoders.Comment: IJCNN-201

Artificial immune system for attribute weighted Naive Bayes classification

Naive Bayes (NB) is a popularly used classification method. One potential weakness of NB is the strong conditional independence assumption between attributes, which may deteriorate the classification accuracy. In this paper, we propose a new Artificial Immune System based Weighted Naive Bayes (AISWNB) classifier. AISWNB uses immunity theory in artificial immune systems to find optimal weight values for each attribute. The adjusted weight values will alleviate the conditional independence assumption and help calculate the conditional probability in an accurate way. Because AISWNB uses artificial immune system search mechanism to find optimal weights, it does not need to know the importance of individual attributes nor the relevance among attributes. As a result, it can obtain optimal weight value for each attribute during the learning process. Experiments and comparisons on 36 benchmark data sets demonstrate that AISWNB outperforms other state-of-the-art attribute weighted NB algorithms. © 2013 IEEE