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Machine learning approaches are increasingly being employed to forecast the key characteristics of strong ground motions, including the challenging classification of Pulse-Like (PL) ground motions. The PL ground motions are characterized by their impulsive nature and have the potential to cause significant damage to structures. The classification of PL ground motions continues to be a significant challenge due to the absence of consensus on their definition and categorization. This paper investigates the potential benefits of several Machine Learning Classifiers (MLCs) algorithms such as decision tree, random forest, logistic regression, naive Bayes, support vector machine, K-nearest neighbor, ensemble model, and artificial neural network models for predicting PL and Non-Pulse-Like (NPL) ground motions. In this regard, a dataset comprising 200 near-fault ground motions records compiled from active tectonic regions like Taiwan, Turkey, Iran, and Japan, was divided into 2 portions, with 75% used to train the model and the remaining 25% used for testing. Plots of performance metrics, confusion matrix, and receiver operating curve indicate that the ensemble classifier outperforms the other classifier with 86.2% accuracy and the lowest misclassification of PL and NPL ground motions. Additionally, the trained MLC has been compared with the existing ground motion classification models to further assess the accuracy of the different classifiers in the present study. [ABSTRACT FROM AUTHOR]

This research concentrates on segmenting credit card clients of Taiwan into optimal groups. Unsupervised Learning plays a significant role in dividing customers into similar groups based on several parameters. If customers are clustered in groups optimally, it leads towards the retrieval of better precision from machine learning models applied to customers associated with the clusters. Different machine learning algorithms (Linear discriminant analysis, logistic regression and random forest) were applied on the obtained clusters through K-means, hierarchical and HK Means clustering algorithm, and predictive accuracy is compared with the accuracy obtained via applying mentioned machine learning models on the whole dataset. In this paper, a novel approach of combining K Means and hierarchical clustering (HK Means) is used. In this approach, HK means clustering algorithms are applied on the factorial coordinates, obtained from multiple correspondence analyses for segmenting customers into optimal groups has been proposed. The accuracy of the clustering techniques is evaluated from the decomposition of inertia. The results demonstrate that the combination of K Means and hierarchical clustering proved to be optimal clustering techniques for customer segmentation which can be used further for applying Machine Learning techniques for credit risk analysis. [ABSTRACT FROM AUTHOR]

The wrapping layer's foaming process of the car steering wheel industry usually relies on the manual parameter setting by experienced engineers, but the reliability and validity are usually hard to control due to many foaming factors that affect the hardness of steering wheel wrapping layer (e.g., vulcanization time, mold temperature, material liquid temperature, material discharge pressure, humidity). This paper first proposes an intelligent process parameter recommendation system architecture, then develops a neural network-based hardness prediction (NNHP) model to predict the car steering wheel's wrapping hardness. Consequently, we combine NNHP and genetic algorithm (GA) to develop a foaming process parameter recommendation model (NNHP-GA) to effectively recommend the most suitable combinations of process parameters for achieving the desired wrapping hardness in an IoT-based intelligent manufacturing environment. An empirical study is applied in Taiwan's world-leading car steering wheel company. By analyzing the 16 important process sensor data installed in each casting machine, the five experimental results show that the NNHP-GA recommendation system can successfully predict the hardness of the steering wheel's wrapping layer and recommend appropriate process parameters (e.g., sensors S1-vulcanization time, S2-mold temperature, S5-A.P. pressure, S6-B.I. pressure, and "pressurization time") under a specific hardness target. The manufacturing company may employ the NNHP-GA model to build a process parameter recommendation system and increase the quality of process parameter setting. [ABSTRACT FROM AUTHOR]

Pairs trading is an effective statistical arbitrage strategy considering the spread of paired stocks in a stable cointegration relationship. Nevertheless, rapid market changes may break the relationship (namely structural break), which further leads to tremendous loss in intraday trading. In this paper, we design a two-phase pairs trading strategy optimization framework, namely structural break-aware pairs trading strategy (SAPT), by leveraging machine learning techniques. Phase one is a hybrid model extracting frequency- and time-domain features to detect structural breaks. Phase two optimizes pairs trading strategy by sensing important risks, including structural breaks and market-closing risks, with a novel reinforcement learning model. In addition, the transaction cost is factored in a cost-aware objective to avoid significant reduction of profitability. Through large-scale experiments in real Taiwan stock market datasets, SAPT outperforms the state-of-the-art strategies by at least 456% and 934% in terms of profit and Sortino ratio, respectively. [ABSTRACT FROM AUTHOR]