Unique data protection is must in the modern world. Security for information technology and access to restricted areas, such as airports, governments, healthcare facilities, and military bases, are two areas where bio...
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Advanced machinelearning techniques have shown significant promise in predicting student performance in E-Iearning systems, but challenges such as handling imbalanced datasets, integrating multimodal behavioral data,...
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ISBN:
(数字)9798331523923
ISBN:
(纸本)9798331523930
Advanced machinelearning techniques have shown significant promise in predicting student performance in E-Iearning systems, but challenges such as handling imbalanced datasets, integrating multimodal behavioral data, and managing the complexities of feature selection persist. Recent techniques, such as SMOTE for class balancing and BorutaNetCV for feature selection, have made strides in improving prediction accuracy; however, they often fail to address real-time cognitive state integration and the influence of diverse engagement patterns. To overcome these limitations, this study employs a comprehensive methodology that combines robust preprocessing methods, advanced feature selection, and ensemble machinelearning models such as Random Forest, AdaBoost, and XGBoost. the findings demonstrate that predictive accuracy can be significantly enhanced through these techniques, and the proposed framework enables the development of personalized interventions that can foster improved student engagement and success in dynamic E-learning environments.
Plant diseases and abiotic disorders are crucial elements in affecting the yield and quality of plants. thus, an early detection is obligatory to increase the productiveness. Diagnosing a plant disease is very pivotal...
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the use of integrated data management information systems not only ensures the smooth operation of each system throughout the enterprise, but also encourages the maximum removal of manual production management by inte...
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the proceedings contain 16 papers. the topics discussed include: towards text simplification in Spanish: a brief overview of deep learning approaches for text simplification;improving semi-supervised deep learning by ...
ISBN:
(纸本)9781665470483
the proceedings contain 16 papers. the topics discussed include: towards text simplification in Spanish: a brief overview of deep learning approaches for text simplification;improving semi-supervised deep learning by using automatic thresholding to deal with out of distribution data for Covid-19 detection using chest X-ray images;analysis software for the principal physical properties in food matrices;recognition of grammatical classes of overt speech using electrophysiological signals and machinelearning;comparison of Nextera XT and Collibri Es library preparation kits: from wet lab to bioinformatics analysis;data quality metrics for unlabeled datasets;towards an understanding of the lipophilicity of non-coded amino acids: computational simulations of proline analogs;gene expression analyses of gingival tissue of patients with periodontitis using public transcriptomic data;and miRNA networks regulating gene expression in response to tension or compression forces in the cells of the periodontal ligament.
Shuttlecock tracking is required for examining the trajectory of the shuttle-cock in badminton matches. Player Service Fault Detection identifies service faults during badminton matches. the match point scored by play...
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ISBN:
(数字)9783031390593
ISBN:
(纸本)9783031390586;9783031390593
Shuttlecock tracking is required for examining the trajectory of the shuttle-cock in badminton matches. Player Service Fault Detection identifies service faults during badminton matches. the match point scored by players is analyzed by the first referee based on the shuttlecock landing point and player service faults. If the first referee cannot decide, they use technology such as a third umpire system to assist. the current challenge withthe third umpire system is based on the high number of marginal errors in predicting the match score. this research proposes a machinelearning Framework to improve the accuracy of Shuttlecock Tracking and player service fault detection. the proposed framework combines a shuttlecock trajectory model and a player service fault model. the shuttlecock trajectory model is implemented using a pre-trained Convolutional Neural Network (CNN), namely Track-Net. the player service fault detection model uses Google MediaPipe Pose. A Random Forest classifier is used to classify the player's service faults. the framework is trained using the badminton world federation channel dataset. the dataset consists of 100000 images of badminton players and shuttlecock positions. the models are evaluated using a confusion matrix based on loss, accuracy, precision, recall, and F1 scores. Results demonstrate that the optimized TrackNet model has an accuracy of 90%, which is 5% more with 2.84% less positioning error compared to the current state of the art. the player service fault detection model can classify player faults with 90% accuracy using Google MediaPipe Pose, 10% more compared to the Open-pose model. the machinelearning framework for shuttlecock tracking and player service fault detection is of use to referees and the Badminton World Federation (BWF) for improving referee decision-making.
In general terms, the selection of EOR methods has been carried out through the use of comparative tables, where the application of each of these methods is determined by specific ranges of reservoir parameters. Howev...
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Named entity recognition (NER) is an essential Natural Language Processing (NLP) task to find key entities from sentences. In this paper, NewsCorpus Marathi monolingual data set is created, which contains 900 sentence...
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the accurate prediction of wind speed is an important part of the weather forecasting service for the Winter Olympic Games. the Zhangjiakou zone of the Beijing 2022 Winter Olympic Games is located in the complex terra...
the accurate prediction of wind speed is an important part of the weather forecasting service for the Winter Olympic Games. the Zhangjiakou zone of the Beijing 2022 Winter Olympic Games is located in the complex terrain of the Dama Mountains, and the current numerical forecasts have large deviations in forecasting wind speed in small-scale complex terrain. Based on the unsupervised machinelearning method, this paper performs historical modeling on the basis of five element forecasts of two numerical forecasts for nine stations in the Zhangjiakou Zone of the Beijing 2022 Winter Olympic Games, thus achieving objective correction to the numerical forecasts of average winds and extreme winds. the results show that the unsupervised machinelearning-based winds forecasts during the Beijing 2022 Winter Olympics Test Games are better, with significant improvements over the original numerical forecasts in terms of mean absolute error and mean squared error.
Deep neural networks are a powerful tool for a wide range of applications, including natural language processing (NLP) and computer vision (CV). However, training these networks can be a challenging task, as it requir...
Deep neural networks are a powerful tool for a wide range of applications, including natural language processing (NLP) and computer vision (CV). However, training these networks can be a challenging task, as it requires careful selection of hyperparameters such as learning rates and scheduling strategies. Despite significant advances in designing dynamic (and adaptive) learning rate schedulers, choosing the right learning rate / schedule for a machinelearning task is still more art than science. In this paper, we introduce Zeroth order GreedyLR, a novel scheduler that adaptively adjusts the learning rate during training based on the current loss and gradient information. To validate the effectiveness of our proposed method, we conduct experiments on several NLP and CV tasks. the results show that our approach outperforms several state-of-the-art schedulers in terms of accuracy, speed, and convergence. Furthermore, our method is easy to implement, computationally efficient, and requires minimal hyperparameter tuning. Overall, our study provides a useful tool for researchers and practitioners in the field of deep learning.
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