Titlebook: Soft Computing in Data Science; 7th International Co Marina Yusoff,Tao Hai,Eisuke Kita Conference proceedings 2023 The Editor(s) (if applic

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Cox Point Process with Ridge Regularization: A Better Approach for Statistical Modeling of Earthquakss is one of the popular models for the analysis of earthquake occurrences involving geological variables. The standard two-step procedure does not however perform well when such variables exhibit high correlation. Since ridge regularization has a reputation in handling multicollinearity problems, i

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Discovering Popular Topics of Sarawak Gazette (SaGa) from Twitter Using Deep Learningfeasible to analyze historical document from social media. This study aims to understand more about how people use their social media to share the content of the Sarawak Gazette (SaGa), one of the valuable historical documents of Sarawak. In the study, a short text of Tweet corpus relating to SaGa w

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Comparison Analysis of LSTM and CNN Variants with Embedding Word Methods for Sentiment Analysis on FCOVID-19 pandemic. During the implementation of the Movement Control Order, Malaysians’ food preferences are already shifting away, influencing new consumption behavior. Since it has played a significant role in many areas of natural language, mainly using social media data from Twitter, there has b

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Short-Time Fourier Transform with Optimum Window Type and Length: An Application for Sag, Swell and d by the Hanning window. The proposed technique can accurately characterize the power quality signals averagely by 95%, as well as the complexity and memory usage is low. Finally, the paper is concluded by the recommendation of pre-setting for optimum window type and length for real-time power quality measurement.

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Cox Point Process with Ridge Regularization: A Better Approach for Statistical Modeling of Earthquakased regression. We apply our proposed method to model the earthquake distribution in Sumatra. The results show that considering ridge regularization in the model is advantageous to obtain a smaller value of the Akaike Information Criterion (AIC). Especially, Cox point process model with a logistic-based regression has the smallest AIC.

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Explainability for Clustering Modelsty of the proposed solution on most practiced unsupervised problems. We also have thoroughly investigated the methods to validate the results of both supervised and unsupervised explainability modules.

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Multi-source Heterogeneous Data Fusion Algorithm Based on Federated Learning to capture the high-dimensional features of heterogeneous data and solve the fusion problem of heterogeneous data assuming no interaction. Combining and remembering. This method, unlike its predecessors, can efficiently integrate multi-source heterogeneous data without data transfer, therefore over