AIMC Topic: Principal Component Analysis

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Exploring the impact of lenticels on the detection of soluble solids content in apples and pears using hyperspectral imaging and one-dimensional convolutional neural networks.

Food research international (Ottawa, Ont.) Feb 7, 2025
In this work, the effect of lenticels on the predictive performance of apple and pear soluble solids content (SSC) models developed based on hyperspectral imaging (HSI) at 380-1010 nm was investigated for the first time. Variations in the spectral pr...
Malus Pyrus Fruit Hyperspectral Imaging Calibration Principal Component Analysis Least-Squares Analysis Discriminant Analysis Neural Networks, Computer Algorithms Convolutional Neural Networks
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Improving stroke risk prediction by integrating XGBoost, optimized principal component analysis, and explainable artificial intelligence.

BMC medical informatics and decision making Feb 7, 2025
The relevance of the study is due to the growing number of diseases of the cerebrovascular system, in particular stroke, which is one of the leading causes of disability and mortality in the world. To improve stroke risk prediction models in terms of...
Principal Component Analysis Stroke Boosting Machine Learning Algorithms Humans Artificial Intelligence Machine Learning Risk Assessment
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Using near-infrared hyperspectral imaging combined with machine learning to predict the components and the origin of Radix Paeoniae Rubra.

Analytical methods : advancing methods and applications Feb 6, 2025
The efficacy and safety of drugs are closely related to the geographical origin and quality of the raw materials. This study focuses on using near-infrared hyperspectral imaging (NIR-HSI) combined with machine learning algorithms to construct content...
Spectroscopy, Near-Infrared Support Vector Machine Principal Component Analysis Machine Learning Algorithms Hyperspectral Imaging Monoterpenes Paeonia Drugs, Chinese Herbal Least-Squares Analysis Glucosides
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A hybrid approach for intrusion detection in vehicular networks using feature selection and dimensionality reduction with optimized deep learning.

PloS one Feb 6, 2025
Autonomous transportation systems have the potential to greatly impact the way we travel. A vital aspect of these systems is their connectivity, facilitated by intelligent transport applications. However, the safety ensured by the vehicular network c...
Dimensionality Reduction Humans Algorithms Deep Learning Principal Component Analysis
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Development of a method for detecting and classifying hydrocarbon-contaminated soils via laser-induced breakdown spectroscopy and machine learning algorithms.

Environmental science and pollution research international Feb 5, 2025
In recent years, there has been a significant increase in oil exploration and exploitation activities, resulting in spills that pose a severe threat to the environment and public health. The present work aims to develop a method to detect and classif...
Machine Learning Algorithms Soil Soil Pollutants Spectrum Analysis Lasers Environmental Monitoring Principal Component Analysis Discriminant Analysis Hydrocarbons
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Machine learning integrated with in vitro experiments for study of drug release from PLGA nanoparticles.

Scientific reports Feb 4, 2025
This paper investigates delivery of encapsulated drug from poly lactic-co-glycolic micro-/nano-particles. Experimental data collected from about 50 papers are analyzed by machine learning algorithms including linear regression, principal component an...
Polylactic Acid-Polyglycolic Acid Copolymer Principal Component Analysis Hydrogen-Ion Concentration Solubility Drug Liberation Lactic Acid Nanoparticles Polyglycolic Acid Particle Size Drug Carriers Neural Networks, Computer Machine Learning
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Determination of quality differences and origin tracing of green tea from different latitudes based on TG-FTIR and machine learning.

Food research international (Ottawa, Ont.) Jan 27, 2025
Latitude differences can significantly affect the quality of tea, while in-depth research in this field is lacking. This study investigates green teas from different latitudes in China using thermogravimetric analysis coupled with infrared spectrosco...
Tea Principal Component Analysis Kinetics Spectroscopy, Fourier Transform Infrared Pyrolysis Machine Learning Cluster Analysis Thermogravimetry China Camellia sinensis
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Integrating hyperspectrograms with class modeling techniques for the construction of an effective expert system: Quality control of pharmaceutical tablets based on near-infrared hyperspectral imaging.

Journal of pharmaceutical and biomedical analysis Jan 26, 2025
Near-infrared hyperspectral imaging (NIR-HSI) integrated with expert systems can support the monitoring of active pharmaceutical ingredients (APIs) and provide effective quality control of tablet formulations. However, existing quality control method...
Hyperspectral Imaging Spectroscopy, Near-Infrared Chemistry, Pharmaceutical Excipients Particle Size Powders Expert Systems Principal Component Analysis Drug Compounding Tablets Quality Control
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Machine learning classification and biochemical characteristics in the real-time diagnosis of gastric adenocarcinoma using Raman spectroscopy.

Scientific reports Jan 20, 2025
This study aimed to identify biomolecular differences between benign gastric tissues (gastritis/intestinal metaplasia) and gastric adenocarcinoma and to evaluate the diagnostic power of Raman spectroscopy-based machine learning in gastric adenocarcin...
Aged Aged, 80 and over Stomach Neoplasms Male Middle Aged Adenocarcinoma Humans Spectrum Analysis, Raman Principal Component Analysis Machine Learning Discriminant Analysis Female
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Design and realization of compressor data abnormality safety monitoring and inducement traceability expert system.

PloS one Jan 16, 2025
Centrifugal compressors are widely used in the oil and natural gas industry for gas compression, reinjection, and transportation. Fault diagnosis and identification of centrifugal compressors are crucial. To promptly monitor abnormal changes in compr...
Principal Component Analysis Humans Expert Systems Neural Networks, Computer Oil and Gas Industry Algorithms
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