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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This paper presents a comprehensive framework of machine learning and blockchain to bolster women’s safety through the integration of wearable technology. Human wearables in this framework include bracelets and earri...
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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.
Background: Despite its importance to animal production potential, geneticgain for forage nutritive value has been limited in perennial ryegrass (Loliumperenne L.) breeding. The objective of this study was to phenotyp...
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Background: Despite its importance to animal production potential, geneticgain for forage nutritive value has been limited in perennial ryegrass (Loliumperenne L.) breeding. The objective of this study was to phenotype a trainingpopulation and develop prediction models to assess the potential of predictingorganic matter digestibility (OMD) and neutral detergent fiber (NDF) withgenotyping-by-sequencing ***: Near infra-red reflectance spectroscopy calibrations for OMD andNDF were developed and used to phenotype a spaced plant training populationof n = 1606, with matching genotype-by-sequencing data, for developinggenomic selection models. F2 families derived from the training populationwere also evaluated for OMD and NDF in sward plots and used to empiricallyvalidate prediction ***: Sufficient genotypic variation exists in breeding populations toimprove forage nutritive value, and spectral bands contributing to calibrationswere identified. OMD and NDF can be predicted from genomic data withmoderate accuracy (predictive ability in the range of 0.51-0.59 and 0.33-0.57,respectively) and models developed on individual plants outperform thosedeveloped from family means. Encouragingly, genomic prediction modelsdeveloped on parental plants can predict OMD in subsequent generationsgrown as competitive ***: These findings suggest that genetic improvement in foragenutritive value can be accelerated through the application of genomic predictionmodels.
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.
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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Sample recognition is the system of changing complicated, multidimensional data into significant patterns that may be used for numerous analytical or predictive purposes. It's used to discover, classify, and disco...
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Digital tools have greatly improved the detection and diagnosis of oral and dental disorders like cancer and gum disease. Lip or oral cavity cancer is more likely to develop in those with potentially malignant oral di...
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Digital tools have greatly improved the detection and diagnosis of oral and dental disorders like cancer and gum disease. Lip or oral cavity cancer is more likely to develop in those with potentially malignant oral disorders. A potentially malignant disorder (PMD) and debilitating condition of the oral mucosa, oral submucous fibrosis (OSMF), can have devastating effects on one's quality of life. Incorporating deep learning into diagnosing conditions affecting the mouth and oral cavity is challenging. Mouth and Oral Diseases Classification using InceptionResNetV2 Method was established in the current study to identify diseases such as gangivostomatitis (Gum), canker sores (CaS), cold sores (CoS), oral lichen planus (OLP), oral thrush (OT), mouth cancer (MC), and oral cancer (OC). The new collection, termed "Mouth and Oral Diseases" (MOD), comprises seven distinct categories of data. Compared to state-of-the-art approaches, the proposed InceptionResNetV2 model's 99.51% accuracy is significantly higher.
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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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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