Machine Learning For Data Processing Research Articles

Dynamic Light Scattering in Biomedical Applications: feature issue introduction

The feature Issue on “Dynamic Light Scattering in Biomedical Applications” presents a compilation of research breakthroughs and technological advancements that have shaped the field of biophotonics, particularly in the non-invasive exploration of biological tissues. Highlighting the significance of dynamic light scattering (DLS) alongside techniques like laser Doppler flowmetry (LDF), diffusing wave spectroscopy (DWS), and laser speckle contrast imaging (LSCI), this issue underscores the versatile applications of these methods in capturing the intricate dynamics of microcirculatory blood flow across various tissues. Contributions explore developments in fluorescence tomography, the integration of machine learning for data processing, enhancements in microscopy for cancer detection, and novel approaches in optical biophysics, among others. Innovations featured include a high-resolution speckle contrast tomography system for deep blood flow imaging, a rapid estimation technique for real-time tissue perfusion imaging, and the use of convolutional neural networks for efficient blood flow mapping. Additionally, studies delve into the impact of skin strain on spectral reflectance, the sensitivity of cerebral blood flow measurement techniques, and the potential of photobiomodulation for enhancing brain function. This issue not only showcases the latest theoretical and experimental strides in DLS-based imaging but also anticipates the continued evolution of these modalities for groundbreaking applications in disease detection, diagnosis, and monitoring, marking a pivotal contribution to the field of biomedical optics.

Targeted MRM Quantification of Urinary Proteins in Chronic Kidney Disease Caused by Glomerulopathies.

Glomerulopathies with nephrotic syndrome that are resistant to therapy often progress to end-stage chronic kidney disease (CKD) and require timely and accurate diagnosis. Targeted quantitative urine proteome analysis by mass spectrometry (MS) with multiple-reaction monitoring (MRM) is a promising tool for early CKD diagnostics that could replace the invasive biopsy procedure. However, there are few studies regarding the development of highly multiplexed MRM assays for urine proteome analysis, and the two MRM assays for urine proteomics described so far demonstrate very low consistency. Thus, the further development of targeted urine proteome assays for CKD is actual task. Herein, a BAK270 MRM assay previously validated for blood plasma protein analysis was adapted for urine-targeted proteomics. Because proteinuria associated with renal impairment is usually associated with an increased diversity of plasma proteins being present in urine, the use of this panel was appropriate. Another advantage of the BAK270 MRM assay is that it includes 35 potential CKD markers described previously. Targeted LC-MRM MS analysis was performed for 69 urine samples from 46 CKD patients and 23 healthy controls, revealing 138 proteins that were found in ≥2/3 of the samples from at least one of the groups. The results obtained confirm 31 previously proposed CKD markers. Combination of MRM analysis with machine learning for data processing was performed. As a result, a highly accurate classifier was developed (AUC = 0.99) that enables distinguishing between mild and severe glomerulopathies based on the assessment of only three urine proteins (GPX3, PLMN, and A1AT or SHBG).

Towards Smart Irrigation: A Literature Review on the Use of Geospatial Technologies and Machine Learning in the Management of Water Resources in Arboriculture

Agriculture consumes an important ratio of the water reserve in irrigated areas. The improvement of irrigation is becoming essential to reduce this high water consumption by adapting supplies to the crop needs and avoiding losses. This global issue has prompted many scientists to reflect on sustainable solutions using innovative technologies, namely Unmanned Aerial Vehicles (UAV), Machine Learning (ML), and the Internet of Things (IoT). This article aims to present an overview of the use of these new technologies in the analysis of the water status of crops for better irrigation management, with an emphasis on arboriculture. The review demonstrated the importance of UAV-ML-IoT technologies. This contribution is due to the relevant information that can be collected from IoT sensors and extracted from UAV images through various sensors (RGB, multispectral, hyperspectral, thermal), and the ability of ML models to monitor and predict water status. The review in this paper is organized into four main sections: the use of UAV in arboriculture, UAV for irrigation management in arboriculture, IoT systems and irrigation management, and ML for data processing and decision-making. A discussion is presented regarding the prospects for smart irrigation using geospatial technologies and machine learning.

Stellar Classification by Machine Learning

As an emerging subject with strong comprehensiveness, machine learning has made varying degrees of progress in various fields. In the field of astronomy, it has also been generally used, and there have been quantities of research using machine learning for data processing and model prediction. The paper has used three algorithms (Decision Tree, Random Forest and Support Vector Machine) to build prediction models to classify stars, galaxies, and quasars in the universe and make a comparison among three models. The results of the test have shown that the prediction accuracy of the Random Forest model reaches roughly 98 percent with a great computing efficiency, which performs the best.

Intelligent English resource recommendation and teaching effect based on symmetric SDAE collaborative filtering algorithm

In the process of acquiring English resources, information retrieval based only on keywords has great limitations in many cases. Moreover, the process of browsing irrelevant information will undoubtedly lead to the loss of customers. In addition, search engines are even more powerless for users who have no clear needs. Based on this, this paper combines the symmetric SDAE collaborative filtering algorithm to construct an intelligent English resource recommendation model. The symmetric SDAE model is composed of two stacked denoising automatic codes, one is used to train item information to obtain the item feature matrix, and the other is used to train user information to obtain the user feature matrix. At the same time, in order to study the performance of the model, this paper takes expert ratings and student user ratings as examples for analysis, uses mathematical statistics and machine learning for data processing, and displays the results in graphs. The research results show that the model constructed in this paper has good results, and the next research direction is proposed.

Non-invasive identification of commercial green tea blends using NIR spectroscopy and support vector machine

In this study, we aimed to discriminate four commercial blends of green tea in bagged (inside its sachet) and non-bagged conditions using near-infrared (NIR) spectroscopy and support vector machines (SVM) for data modelling. To choose optimal parameters for the models, we applied Bayesian optimization, which provided accurate models. Two spectrometers were evaluated: a benchtop and a handheld, both presenting reliable results for non-bagged tea (accuracies of 90% and 93%, respectively). However, for bagged tea models, the classification performance of benchtop was superior to handheld equipment, yielding accuracies of 93% and 82%, respectively. Classification accuracies using SVM outperformed partial least squares discriminant analysis (PLS–DA) for handheld and tea inside teabag models. The results indicated that the proposed methodology has the potential to be applied in automatic quality control coupling NIR sensors and machine learning for data processing.

Updating the DCC Curation Lifecycle Model

The DCC Curation Lifecycle Model has played a vital role in the field of data curation for over a decade. During that time, the scale and complexity of data have changed dramatically, along with the contexts of data production and use. This paper reports on a study examining factors impacting data curation practices and presents recommendations for updating the DCC Curation Lifecycle Model. The study was grounded in a review of other lifecycle models and informed by a site visit to the Digital Curation Centre and consultation with expert practitioners and researchers. Framed by contemporary conditions impacting the conduct of research and provision of data services, the analysis and proposed recommendations account for the prominence of machine-actionable data, the importance of machine learning for data processing and analytics, growth of integrated research workflows, and escalating concerns with fairness, accountability, and transparency of data and algorithms.

Laser Welding Quality Monitoring via Graph Support Vector Machine With Data Adaptive Kernel

Laser welding is a rapidly developing technology that is of utmost importance in a number of industrial processes. The physics of the process has been investigated over the past 50 years and is mostly well understood. Nevertheless, online laser-quality monitoring remains an open issue until today due to its dynamic complexity. This paper is a supplement to existing approaches in the field of in situ and real-time laser-quality monitoring that presents a novel combination of state-of-the-art sensors and machine learning for data processing. The investigations were carried out using laser welding of titanium workpieces. The quality was estimated a posteriori by the visual inspection of cross-sections of the welded joints. Four quality categories were defined to cover the two main laser welding regimes: conduction and keyhole. The signals from the laser back reflection and optical and acoustic emissions were recorded during the laser welding process and were decomposed with the $M$ -band wavelets. The relative energies of narrow frequency bands were taken as descriptive features. The correlation of the extracted features with the laser welding quality was carried out using the Laplacian graph support vector machine classifier. Also, an adaptive kernel for the classifier was developed to improve the analysis of the distributions of the complex features and was constructed from Gaussian mixtures. The presented laser welding setup and the developed adaptive kernel algorithm were able to classify the quality for every $2~\mu \text{m}$ of the welded joint with an accuracy ranged between 85.9% and 99.9%. Finally, the results of the developed adaptive kernel were compared with state-of-the-art machine learning methods.