the rapid expansion of solar energy implementation has highlighted critical challenges related to temperature variations and environmental factors impacting photovoltaic (PV) systems. this paper addresses the need for...
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Bridge Health Monitoring (BHM) is a crucial research domain in engineering, involving the analysis of infrastructure conditions like bridges using sensor data. Structural Health Monitoring (SHM) systems comprise disti...
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In a world with an overgrowing elderly population, there exists a critical need for a greater number of skilled individuals in the nursing industry. AI-based systems can be useful, compared to traditional ones with re...
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ISBN:
(纸本)9798350375510;9798350375503
In a world with an overgrowing elderly population, there exists a critical need for a greater number of skilled individuals in the nursing industry. AI-based systems can be useful, compared to traditional ones with require in-person assistance, to accurately identify nursing activities and assess the nursing trainees to help them become proficient. this paper addresses classifying activities in one such procedure, endotracheal suctioning, using skeletal keypoint data of the subject performing the procedure. A multi-step structured prompt engineering method was established and utilized on several LLMs to select or calculate key features from the data. then the features are passed onto several tuned machinelearning models to obtain results. A tuned XGBoost prevailed across all models, achieving 90% accuracy on the validation set.
Basic military training (BMT) is important and initial period for soldiers, especially the military basic throwing performance is one of the key military tactical motor skill. In recent years, machinelearning (ML) te...
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ISBN:
(纸本)9783031863226;9783031863233
Basic military training (BMT) is important and initial period for soldiers, especially the military basic throwing performance is one of the key military tactical motor skill. In recent years, machinelearning (ML) techniques have been increasingly adopted to analyze extensive datasets and derive meaningful insights. To evaluate the effectiveness of fitness training strategies for military throwing, this study analyzed seven common input features: pull-ups, push-ups, squat concentric/eccentric peak power, squat maximum strength, leg tuck, and a 3 km run. Four datamining models-Random Forest, Multilayer Perceptron, AdaboostM1, and Bagging classifier-were tested to identify the most effective method for predicting performance. the Random Forest model excelled, achieving the highest accuracy, precision, recall, and F1 score, indicating its superiority in this context. these seven attributes were thus identified as key predictors of military throwing performance. However, further research is necessary to establish a definitive ranking and to fully understand the importance of each feature in refining training strategies.
machinelearning and Deep learning models had enabled several smart applications such as classification and recognition of objects. Smart machine and deep learning models can provide effective solutions to real life p...
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In today's world of globalized commerce, cross-market recommendation systems (CMRs) are crucial for providing personalized user experiences across diverse market segments. However, traditional recommendation algor...
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ISBN:
(纸本)9798400716379
In today's world of globalized commerce, cross-market recommendation systems (CMRs) are crucial for providing personalized user experiences across diverse market segments. However, traditional recommendation algorithms have difficulties dealing with market specificity and data sparsity, especially in new or emerging markets. In this paper, we propose the CrossGR model, which utilizes Graph Isomorphism Networks (GINs) to improve CMR systems. It outperforms existing benchmarks in NDCG@10 and HR@10 metrics, demonstrating its adaptability and accuracy in handling diverse market segments. the CrossGR model is adaptable and accurate, making it well-suited for handling the complexities of cross-market recommendation tasks. Its robustness is demonstrated by consistent performance across different evaluation timeframes, indicating its potential to cater to evolving market trends and user preferences. Our findings suggest that GINs represent a promising direction for CMRs, paving the way for more sophisticated, personalized, and context-aware recommendation systems in the dynamic landscape of global e-commerce.
In emotion recognition, the requirement for authenticity and real-time of the medium under study has always been a primary consideration. thus electroencephalogram (EEG) is most suitable as the primary medium for emot...
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Low-rank-based methods are frequently employed for dimensionality reduction and feature extraction in machinelearning. To capture local structures, these methods often incorporate graph embedding, which requires cons...
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ISBN:
(纸本)9789819985395;9789819985401
Low-rank-based methods are frequently employed for dimensionality reduction and feature extraction in machinelearning. To capture local structures, these methods often incorporate graph embedding, which requires constructing a zero-one weighted neighborhood graph to extract local information from the original data. However, these methods are incapable of learning an adaptive graph that reveals intricate relationships among distinct samples within noisy data. To address this issue, we propose a novel unsupervised feature extraction method called Robust Subspace learning with Double Graph Embedding (RSL_DGE). RSL_DGE incorporates a low-rank graph into the graph embedding process to preserve more discriminative information and remove noise simultaneously. Additionally, the l(2,1)-norm constraint is also imposed on the projection matrix, making RSL_DGE more flexible in selecting feature dimensions. Several experiments demonstrate that RSL_DGE achieves competitive performance compared to other state-of-the-art methods.
the epiDAMIK workshop serves as a platform for advancing the utilization of data-driven methods in the fields of epidemiology and public health research. these fields have seen relatively limited exploration of data-d...
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ISBN:
(纸本)9798400701030
the epiDAMIK workshop serves as a platform for advancing the utilization of data-driven methods in the fields of epidemiology and public health research. these fields have seen relatively limited exploration of data-driven approaches compared to other disciplines. therefore, our primary objective is to foster the growth and recognition of the emerging discipline of data-driven and computational epidemiology, providing a valuable avenue for sharing state-of-the-art research and ongoing projects. the workshop also seeks to showcase results that are not typically presented at major computing conferences, including valuable insights gained from practical experiences. Our target audience encompasses researchers in AI, machinelearning, and data science from both academia and industry, who have a keen interest in applying their work to epidemiological and public health contexts. Additionally, we welcome practitioners from mathematical epidemiology and public health, as their expertise and contributions greatly enrich the discussions. Homepage: https://***/.
In the real world, naturally collected data often exhibits a long-tailed distribution, where the head classes have a larger number of samples compared to the tail classes. this long-tailed data distribution often intr...
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ISBN:
(纸本)9789819985425;9789819985432
In the real world, naturally collected data often exhibits a long-tailed distribution, where the head classes have a larger number of samples compared to the tail classes. this long-tailed data distribution often introduces a bias in classification results, leading to incorrect classifications that harm the tail classes. Mixup is a simple but effective data augmentation method that transforms data into a new shrinking space, resulting in a regularization effect that is beneficial for classification. therefore, many researchers consider using Mixup to promote the performance of long-tailed learning. However, these methods do not consider the special space transformation of data caused by Mixup in long-tail learning. In this paper, we present the Space-Transform Margin (STM) loss function, which offers a novel approach to dynamically adjusting the margin between classes by leveraging the shrinking strength introduced by Mixup. In this way, the margin of data can adapt to the special space transformation of Mixup. In the experiments, our solution achieves state-of-the-art performance on benchmark datasets, including CIFAR10-LT, CIFAR100-LT, ImageNet-LT, and iNaturalist 2018.
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