An outlier removal method based on PCA-DBSCAN for blood-SERS data analysis

Liu, Miaomiao; Wang, Tingyin; Zhang, Qiyi; Pan, Changbin; Liu, Shuhang; Chen, Yuanmei; Lin, Duo; Feng, Shangyuan

doi:10.1039/d3ay02037a

Cited by 6 publications

(1 citation statement)

References 45 publications

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“…In particular, the Local Outlier Factor (LOF) technique makes use of this system [ 117 , 126 ]. On the other hand, following the same principle, the Density-based spatial clustering of applications with noise (DBSCAN) algorithm classifies the data into clusters based on the proper density of the data [ 127 , 128 ]. However, this method is dependent on the specified density parameter, and therefore does not perform well on databases with different densities.…”

Section: Sitting Posture Anomaly Detection Techniquesmentioning

confidence: 99%

Intelligent systems for sitting posture monitoring and anomaly detection: an overview

Vermander,

Mancisidor,

Cabanes

et al. 2024

J NeuroEngineering Rehabil

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The number of people who need to use wheelchair for proper mobility is increasing. The integration of technology into these devices enables the simultaneous and objective assessment of posture, while also facilitating the concurrent monitoring of the functional status of wheelchair users. In this way, both the health personnel and the user can be provided with relevant information for the recovery process. This information can be used to carry out an early adaptation of the rehabilitation of patients, thus allowing to prevent further musculoskeletal problems, as well as risk situations such as ulcers or falls. Thus, a higher quality of life is promoted in affected individuals. As a result, this paper presents an orderly and organized analysis of the existing postural diagnosis systems for detecting sitting anomalies in the literature. This analysis can be divided into two parts that compose such postural diagnosis: on the one hand, the monitoring devices necessary for the collection of postural data and, on the other hand, the techniques used for anomaly detection. These anomaly detection techniques will be explained under two different approaches: the traditional generalized approach followed to date by most works, where anomalies are treated as incorrect postures, and a new individualized approach treating anomalies as changes with respect to the normal sitting pattern. In this way, the advantages, limitations and opportunities of the different techniques are analyzed. The main contribution of this overview paper is to synthesize and organize information, identify trends, and provide a comprehensive understanding of sitting posture diagnosis systems, offering researchers an accessible resource for navigating the current state of knowledge of this particular field.

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Section: Sitting Posture Anomaly Detection Techniquesmentioning

confidence: 99%

Intelligent systems for sitting posture monitoring and anomaly detection: an overview

Vermander,

Mancisidor,

Cabanes

et al. 2024

J NeuroEngineering Rehabil

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Machine Learning in Solid‐State Hydrogen Storage Materials: Challenges and Perspectives

Zhou,

Xiao

et al. 2024

Advanced Materials

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Machine learning (ML) has emerged as a pioneering tool in advancing the research application of high‐performance solid‐state hydrogen storage materials (HSMs). This review summarizes the state‐of‐the‐art research of ML in resolving crucial issues such as low hydrogen storage capacity and unfavorable de‐/hydrogenation cycling conditions. First, the datasets, feature descriptors, and prevalent ML models tailored for HSMs are described. Specific examples include the successful application of ML in titanium‐based, rare‐earth‐based, solid solution, magnesium‐based, and complex HSMs, showcasing its role in exploiting composition–structure–property relationships and designing novel HSMs for specific applications. One of the representative ML works is the single‐phase Ti‐based HSM with superior cost‐effective and comprehensive properties, tailored to fuel cell hydrogen feeding system at ambient temperature and pressure through high‐throughput composition‐performance scanning. More importantly, this review also identifies and critically analyzes the key challenges faced by ML in this domain, including poor data quality and availability, and the balance between model interpretability and accuracy, together with feasible countermeasures suggested to ameliorate these problems. In summary, this work outlines a roadmap for enhancing ML's utilization in solid‐state hydrogen storage research, promoting more efficient and sustainable energy storage solutions.

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Non-invasive screening and subtyping for breast cancer by serum SERS combined with LGB-DNN algorithms

Zhang,

Lin,

Lin

et al. 2024

Talanta

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An outlier removal method based on PCA-DBSCAN for blood-SERS data analysis

Abstract: Surface-enhanced Raman spectroscopy (SERS) has shown promising potential in cancer screening. In practical applications, Raman spectra are often affected by deviations from the spectrometer, changes in measurement environments, and anomalies...

Cited by 6 publications

References 45 publications

Intelligent systems for sitting posture monitoring and anomaly detection: an overview

Intelligent systems for sitting posture monitoring and anomaly detection: an overview

Machine Learning in Solid‐State Hydrogen Storage Materials: Challenges and Perspectives

Non-invasive screening and subtyping for breast cancer by serum SERS combined with LGB-DNN algorithms

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