Classifying apartment defect repair tasks in South Korea: a machine learning approach

Managing building defects in the residential environment is an important social issue in South Korea. Therefore, most South Korean construction companies devote a large amount of human resources and economic costs in managing such defects. This paper proposes a machine learning approach for investigating whether a specific defect can be autonomously categorized into one of the categories of repair tasks. To this end, we employed a dataset of 310,044 defect cases (from 656,266 validated cases of 717,550 total collected cases). Three machine learning classifiers (support vector machine, random forest, and logistic regression) with three word embedding methods (bag-of-words, term frequency-inverse document frequency, and Word2Vec) were employed for the classification tasks. The highest yielded results showed more than 99% accuracy, precision, recall, and F1-scores for the random forest classifier with the Word2Vec embedding. Finally, based on these findings, the implications and limitations of this study are discussed. Representatively, the findings of this research can improve the defect management effectiveness of the apartment construction industry in South Korea. Moreover, to contribute to future research, we have made the dataset publicly available

Fused deep neural networks for sustainable and computational management of heat-transfer pipeline diagnosis

We propose deep learning-based models for the risk detection of underground pipelines. To build effective diagnosis models, we construct two types of deep neural network frameworks. First, we propose an image segmentation model with two parallel encoder structures for analyzing RGB and thermal images to detect risk-suspected regions due to pipeline rupture. In addition, for pipeline risk-stage recognition, we compare the performance of various image classification models and select the optimal model. Then, by integrating the image segmentation and classification models, we propose a fused model that performs risk-suspected region detection and risk-level detection using a single inference. The proposed image segmentation model achieves IoU and Dice coefficient values of 0.8373 and 0.9024, respectively, indicating higher performance in suspected-region detection compared with other competing models and demonstrating robust performance against false recognition. In risk-level detection, we confirm that the DenseNet-based model has the highest performance among the classification models, scoring 94.10% and 95.65% in terms of heat and leak accuracy, respectively. Finally, we validate that the fusion model, which recorded 0.8160, 0.8711, 93.90%, and 95.05% in terms of IoU, Dice coefficient, heat accuracy, and leak accuracy, respectively, can process 30 frames of video per second in real-time at the cost of a slight drop in performance

Deep learning model based on expectation-confirmation theory to predict customer satisfaction in hospitality service

Customer satisfaction is one of the most important measures in the hospitality industry. Therefore, several psychological and cognitive theories have been utilized to provide appropriate explanations of customer perception. Owing to recent rapid developments in artifcial intelligence and big data, novel methodologies have presented to examine several psychological theories applied in the hospitality industry. Within this framework, this study combines deep learning techniques with the expectation-confrmation theory to elucidate customer satisfaction in hospitality services. Customer hotel review comments, hotel information, and images were employed to predict customer satisfaction with hotel service. The results show that the proposed fused model achieved an accuracy of 83.54%. In addition, the recall value that predicts dissatisfaction improved from 16.46–33.41%. Based on the fndings of this study, both academic and managerial implications for the hospitality industry are presented

A deep hybrid learning model for customer repurchase behavior

Smartphones have become an integral part of our daily lives, which has led to the rapid growth of the smartphone market. As the global smartphone market tends to remain stable, retaining existing customers has become a challenge for smartphone manufacturers. This study investigates whether a deep hybrid learning approach with various customer-oriented types of data can be useful in exploring customer repurchase behavior of same-brand smartphones. Considering data from more than 74,000 customers, the proposed deep learning approach showed a prediction accuracy higher than 90%. Based on the results of deep hybrid learning models, we aim to provide better understanding on customer behavior, such that it could be used as valuable assets for innovating future marketing strategies