A technique of data collection

Kumar, Sandeep; Roy, Uponika Barman

doi:10.1016/b978-0-323-91776-6.00011-7

Cited by 7 publications

(3 citation statements)

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“…The XGBoost procedure, including the tuning of model hyperparameters, was performed using the tidymodels version 1.1.0 45 package in R version 4.3.1, 46 with support from other packages listed in the Supplementary material for this article. The Supplementary material also provide more details about the tuned hyperparameters.…”

Section: Methodsmentioning

confidence: 99%

Development of a machine learning algorithm to predict the residual cognitive reserve index

Gavett,

Tomaszewski Farias,

Fletcher

et al. 2024

Brain Communications

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Elucidating the mechanisms by which late-life neurodegeneration causes cognitive decline requires understanding why some individuals are more resilient than others to the effects of brain change on cognition (cognitive reserve). Currently, there is no way of measuring cognitive reserve that is valid (e.g., capable of moderating brain-cognition associations), widely accessible (e.g., does not require neuroimaging and large sample sizes), and able to provide insight into resilience-promoting mechanisms. To address these limitations, this study sought to determine whether a machine learning approach to combining standard clinical variables could (1) predict a residual-based cognitive reserve criterion standard and (2) prospectively moderate brain-cognition associations. In a training sample combining data from the University of California Davis and the Alzheimer’s Disease Neuroimaging Initiative-2 (ADNI-2) cohort (N=1665), we operationalized cognitive reserve using an MRI-based residual approach. An eXtreme Gradient Boosting machine learning algorithm was trained to predict this residual reserve index using three models: Minimal (basic clinical data, such as age, education, anthropometrics, and blood pressure), Extended (Minimal model plus cognitive screening, word reading, and depression measures), and Full (Extended model plus Clinical Dementia Rating and Everyday Cognition scale). External validation was performed in an independent sample of ADNI 1/3/GO participants (N=1640), which examined whether the effects of brain change on cognitive change were moderated by the machine learning models’ cognitive reserve estimates. The three machine learning models differed in their accuracy and validity. The Minimal model did not correlate strongly with the criterion standard (r=.23) and did not moderate the effects of brain change on cognitive change. In contrast, the Extended and Full models were modestly correlated with the criterion standard (r=.49 and .54, respectively) and prospectively moderated longitudinal brain-cognition associations, outperforming other cognitive reserve proxies (education, word reading). The primary difference between the Minimal model – which did not perform well as a measure of cognitive reserve – and the Extended and Full models – which demonstrated good accuracy and validity – is the lack of cognitive performance and informant-report data in the Minimal model. This suggests that basic clinical variables like anthropometrics, vital signs, and demographics are not sufficient for estimating cognitive reserve. Rather, the most accurate and valid estimates of cognitive reserve were obtained when cognitive performance data – ideally augmented by informant-reported functioning – was used. These results indicate that a dynamic and accessible proxy for cognitive reserve can be generated for individuals without neuroimaging data and gives some insight into factors that may promote resilience.

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Section: Methodsmentioning

confidence: 99%

Development of a machine learning algorithm to predict the residual cognitive reserve index

Gavett,

Tomaszewski Farias,

Fletcher

et al. 2024

Brain Communications

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“…The first source was a well-known in Kazakhstan online store "White Wind", specializing in the sale of various electronics, including smartphones. Web page scraping was used to collect feedback from the pages of the online store [6]. In order to increase the diversity of the collected data, it is supplemented with reviews from the THEO VALL dataset from the Kaggle website.…”

Section: Materials and Methods Of Researchmentioning

confidence: 99%

Aspect Oriented Sentiment Analysis of User Text Messages

Zhomartkyzy,

Manapov,

Bazarova

et al. 2023

BEKTU

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Aspect-oriented sentiment analysis plays a crucial role in understanding users' opinions and sentiments towards specific product or service features. This study investigates intelligent algorithms for aspect-oriented tone analysis of user text messages, focusing on smartphone reviews as a case study. The study includes data collection and preprocessing, studying the methods and performance of models for aspect extraction and tone analysis. The performance of these models was compared on test datasets containing customer reviews. For aspect extraction, we combine cross-lingual syntactic analysis with topic models to improve accuracy over the combination of Russian-language syntactic analysis and topic models. In particular, the BERT transformer-based model, BER Topic, shows high performance in aspect detection due to its ability to understand the context in sentences. In the sentiment analysis task, the RuBERT-tiny model based on the BERT transformer outperforms the others, showing higher accuracy in sentiment classification in smartphone reviews. This study provides valuable insights into aspect-oriented tonality analysis, emphasizing the importance of selecting appropriate methods and approaches. The results provide researchers and practitioners with recommendations for effective aspect-oriented tonality analysis.

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“…Training sets are used to train models, which means models are "learned" from patterns and relationships in the data. Instead, a testing set is used to test the performance of models on data that have never been seen before (Kumar, 2020). This study applies the separation of data with an 80/20 ratio.…”

Section: B Performance Of Regression Models On Synthetic Datamentioning

confidence: 99%

Optimizing Sustainability: A Deep Learning Approach on Data Augmentation of Indonesia Palm Oil Products Emission

Tahyudin,

Tikaningsih,

Suryana

et al. 2023

Preprint

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Life Cycle Assessment (LCA) is a widely used methodology for quantifying the environmental impacts of products, including the carbon footprint. However, conducting LCA studies for complex systems, such as the palm oil industry in Indonesia, can be challenging due to limited data availability. This study proposes a novel approach called the Anonymization Through Data Synthesis (ADS-GAN) based on a deep learning approach to augment carbon footprint data for LCA assessments of palm oil products in Indonesia. This approach addresses the data size limitation and enhances the comprehensiveness of carbon footprint assessments. An original dataset comprising information on various palm oil life cycle stages, including plantation operations, milling, refining, transportation, and waste management. The number of original data is 195 obtained from the Sustainable Production Systems and Life Assessment Research Centre of Indonesia's National Innovation Research Agency (BRIN). To measure the performance of prediction accuracy, this study used regression models: Random Forest Regressor (RFR), Gradient Boosting Regressor (GBR), and Adaptive Boosting Regressor (ABR). The best-augmented data size is 1000 data. In addition, the best algorithm is the Random Forest Regressor, resulting in the MAE, MSE, and MSLE values are 0.0031, 6.127072889081567e-05, and 5.838479552074619e-05 respectively. The proposed ADS-GAN offers a valuable tool for LCA practitioners and decision-makers in the palm oil industry to conduct more accurate and comprehensive carbon footprint assessments. By augmenting the dataset, this technique enables a better understanding of the environmental impacts of palm oil products, facilitating informed decision-making and the development of sustainable practices.

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A technique of data collection

Cited by 7 publications

References 0 publications

Development of a machine learning algorithm to predict the residual cognitive reserve index

Development of a machine learning algorithm to predict the residual cognitive reserve index

Aspect Oriented Sentiment Analysis of User Text Messages

Optimizing Sustainability: A Deep Learning Approach on Data Augmentation of Indonesia Palm Oil Products Emission

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