After the Covid 19 pandemic, the property management industry is slowly starting to revive. Even though the occupancy rate has not returned to normal like the Covid19 pandemic, its growth is starting to move in a posi...
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Indonesia's tourism sector, a cornerstone of its economy, has seen significant growth with both domestic and international tourists, drawn by its diverse landscapes and cultural sites. In 2022, the country welcome...
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Underground mining is a hazardous environment, with frequent accidents leading to significant loss of life each year. To enhance safety, sensor nodes monitor key environmental factors such as temperature, toxic gases,...
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Spatial, temporal, and weather elements like ballast, loose nuts, misalignment, and cracks due to rain, snow, and earthquakes may lead to railway accidents and cause human and financial loss. Manual inspection is erro...
Spatial, temporal, and weather elements like ballast, loose nuts, misalignment, and cracks due to rain, snow, and earthquakes may lead to railway accidents and cause human and financial loss. Manual inspection is erroneous, labor-intensive, and *** automatic inspection provides a fast, reliable, and unbiased solution in this regard, however, ensuring high accuracy for fault detection is challenging due to the lack of public datasets, noisy data, high computer processing requirements, and inefficient models. This study presents an approach that uses Mel frequency cepstral coefficient features from the acoustic data. The dataset gathered using a customized railway cart from our previous research is used for experiments. The focus of the study is to increase the fault detection performance using selective features from the acoustic data. This study employs Chisquare(Chi2) for the selection of important features and involves performance analysis of machine learning and deep learning models using selected features. Experimental results suggest that using 60 features, 40 original features, and 20 Chi2 features, produces optimal results both regarding accuracy and computational complexity. A 100%accuracy can be obtained using the proposed approach with machine learning models. Moreover, this performance is significantly better than existing approaches.
With the growing demand for global trade transportation, the shipping container market has gained an increasingly important position. As a key issue of the market, container pricing is regarded as an important indicat...
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In the production process of automobiles, parts procurement is invariably a crucial step. In order to find an optimal decision, it is a challenge to match parts to suppliers for the limited financial and material capa...
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Facial age estimation is a complex and essential task with applications in biometrics, healthcare, and personalized services. This study explores the use of pre-trained deep convolutional neural network (CNN) architec...
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ISBN:
(数字)9798331513320
ISBN:
(纸本)9798331513337
Facial age estimation is a complex and essential task with applications in biometrics, healthcare, and personalized services. This study explores the use of pre-trained deep convolutional neural network (CNN) architectures, including Xception, Inception, MobileNet, ResNet, and Inception ResNet, to predict age from facial images. These models leverage their hierarchical feature extraction capabilities to capture age-relevant patterns accurately. The evaluation was conducted using key regression metrics, such as Mean Absolute Error (MAE), Mean Squared Error (MSE), and Root Mean Squared Error (RMSE). Among the tested models, Xception demonstrated the best performance, achieving a MAE of 2.95, MSE of 48.59, and RMSE of 6.97, making it the most accurate and reliable architecture in this study. The findings underscore the effectiveness of pre-trained CNNs in handling the complexities of facial age estimation and emphasize the importance of selecting an appropriate model architecture and optimization techniques. While Xception showed strong accuracy, challenges such as overfitting and dataset bias were partially mitigated through fine-tuning. Future research could focus on incorporating multi-modal data and optimizing these models for deployment on resource-constrained devices. This study provides a robust foundation for advancing facial age estimation systems in both research and practical applications.
We introduce a novel approach by fabricating van der Waals (vdW) asymmetric metal contacts on wide bandgap semiconductor gallium nitride (GaN). This pioneering method effectively mitigates the Fermi level pinning effe...
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The integration of the Internet of Things (IoT) into the healthcare industry has led to the development of the Internet of Medical Things (IoMT). In IoMT, healthcare professionals diagnose and treat patients by analyz...
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The improvement of some aspects in tourism industry needs further study through aspect-based sentiment analysis based on tourist experience. The aim of this study is presenting the empiric results of aspect-based sent...
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