Dimension reduction techniques: Current status and perspectives

Vachharajani, Bhasha; Pandya, D. M.

doi:10.1016/j.matpr.2021.12.549

Cited by 8 publications

(7 citation statements)

References 7 publications

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“…This step in data-driven modeling helps reduce redundant input variables by identifying the important drivers of the process . This step not only improves the accuracy of predictive modeling by reducing colinearity among the input variables but also improves computational efficiency. , Dimensionality reduction is a common practice followed by data scientists during model development with high-dimensional data sets. , Among the data-driven modeling studies that were reviewed, a considerable number of studies (86% experimental data sets, 67% time-series data sets, and 49% secondary data sets) did not include this step (Figure e; red circles). This discrepancy in the reviewed literature could be attributed to the current limited training and knowledge of environmental engineers in applying data science tools utilizing standard practices followed by data scientists. , The limited studies that followed this step for developing data-driven models using primary data sets applied Pearson’s Correlation (3% experimental and 8% time-series) or Principal Component Analysis (4% experimental and 17% time-series) or Stepwise Regression (7% experimental and 8% time-series) (Figure e; red circles).…”

Section: Assessment Of the Different Data Science Methods Applied For...mentioning

confidence: 99%

“… 730 , 731 Dimensionality reduction is a common practice followed by data scientists during model development with high-dimensional data sets. 732 , 733 Among the data-driven modeling studies that were reviewed, a considerable number of studies (86% experimental data sets, 67% time-series data sets, and 49% secondary data sets) did not include this step ( Figure 4 e; red circles). This discrepancy in the reviewed literature could be attributed to the current limited training and knowledge of environmental engineers in applying data science tools utilizing standard practices followed by data scientists.…”

Section: Assessment Of the Different Data Science Methods Applied For...mentioning

confidence: 99%

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A Critical Review of Data Science Applications in Resource Recovery and Carbon Capture from Organic Waste

Zaki,

Rowles,

Adjeroh

et al. 2023

ACS EST Eng.

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Municipal and agricultural organic waste can be treated to recover energy, nutrients, and carbon through resource recovery and carbon capture (RRCC) technologies such as anaerobic digestion, struvite precipitation, and pyrolysis. Data science could benefit such technologies by improving their efficiency through data-driven process modeling along with reducing environmental and economic burdens via life cycle assessment (LCA) and techno-economic analysis (TEA), respectively. We critically reviewed 616 peer-reviewed articles on the use of data science in RRCC published during 2002−2022. Although applications of machine learning (ML) methods have drastically increased over time for modeling RRCC technologies, the reviewed studies exhibited significant knowledge gaps at various model development stages. In terms of sustainability, an increasing number of studies included LCA with TEA to quantify both environmental and economic impacts of RRCC. Integration of ML methods with LCA and TEA has the potential to costeffectively investigate the trade-off between efficiency and sustainability of RRCC, although the literature lacked such integration of techniques. Therefore, we propose an integrated data science framework to inform efficient and sustainable RRCC from organic waste based on the review. Overall, the findings from this review can inform practitioners about the effective utilization of various data science methods for real-world implementation of RRCC technologies.

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Section: Assessment Of the Different Data Science Methods Applied For...mentioning

confidence: 99%

Section: Assessment Of the Different Data Science Methods Applied For...mentioning

confidence: 99%

A Critical Review of Data Science Applications in Resource Recovery and Carbon Capture from Organic Waste

Zaki,

Rowles,

Adjeroh

et al. 2023

ACS EST Eng.

View full text Add to dashboard Cite

show abstract

“…Common dimensionality reduction approaches are based on the principal component analysis (PCA), linear discriminant analysis (LDA), non‐negative matrix factorization (NMF), and artificial neural network (ANN)‐based approaches. [ 246 ] In addition, current state of art technologies such as GraphSAGE, [ 247 ] Network embeddings, [ 248 ] etc., have also made great progress in dimensionality reduction. Then, the resulting data can be used in subsequent data analysis.…”

Section: Prospects Of Future Directionmentioning

confidence: 99%

Artificial Intelligence‐Enabled Sensing Technologies in the 5G/Internet of Things Era: From Virtual Reality/Augmented Reality to the Digital Twin

Zhang

Wen

Sun

et al. 2022

Advanced Intelligent Systems

230

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With the development of 5G and Internet of Things (IoT), the era of big data‐driven product design is booming. In addition, artificial intelligence (AI) is also emerging and evolving by recent breakthroughs in computing power and software architectures. In this regard, the digital twin, analyzing various sensor data with the help of AI algorithms, has become a cutting‐edge technology that connects the physical and virtual worlds, in which the various sensors are highly desirable to collect environmental information. However, although existing sensor technologies, including cameras, microphones, inertial measurement units, etc., are widely used as sensing elements for various applications, high‐power consumption and battery replacement of them is still a problem. Triboelectric nanogenerators (TENGs) as self‐powered sensors supply a feasible platform for realizing self‐sustainable and low‐power systems. Herein, the recent progress on TENG‐based intelligent systems, that is, wearable electronics, robot‐related systems, and smart homes, followed by prospective future development enabled by sensor fusion technology, is focused on. Finally, how to apply artificial intelligence to the design of intelligent sensor systems for the 5G and IoT era is discussed.

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“…Dimensionality reduction (DR) is one of the main techniques for reducing redundant features, and analysing high-dimensional data (Vachharajani & Pandya, 2022) to improve the model's feature learning accuracy. This is achieved by adjusting an objective function by exploiting redundancy between variables and producing a reduced new set of variables.…”

Section: Dimensionality Reductionmentioning

confidence: 99%

A bottom-up framework for analysing city-scale energy data using high dimension reduction techniques

Khan

Walker

Zeiler

2023

Sustainable Cities and Society

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Dimension reduction techniques: Current status and perspectives

Cited by 8 publications

References 7 publications

A Critical Review of Data Science Applications in Resource Recovery and Carbon Capture from Organic Waste

A Critical Review of Data Science Applications in Resource Recovery and Carbon Capture from Organic Waste

Artificial Intelligence‐Enabled Sensing Technologies in the 5G/Internet of Things Era: From Virtual Reality/Augmented Reality to the Digital Twin

A bottom-up framework for analysing city-scale energy data using high dimension reduction techniques

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