AIMC Topic: Principal Component Analysis

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An extensive experimental analysis for heart disease prediction using artificial intelligence techniques.

Scientific reports Feb 20, 2025
The heart is an important organ that plays a crucial role in maintaining life. Unfortunately, heart disease is one of the major causes of mortality globally. Early and accurate detection can significantly improve the situation by enabling preventive ...
Artificial Intelligence Algorithms Principal Component Analysis Heart Diseases Support Vector Machine Humans
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Compression-enabled interpretability of voxelwise encoding models.

PLoS computational biology Feb 19, 2025
Voxelwise encoding models based on convolutional neural networks (CNNs) are widely used as predictive models of brain activity evoked by natural movies. Despite their superior predictive performance, the huge number of parameters in CNN-based models ...
Principal Component Analysis Data Compression Models, Neurological Algorithms Brain Computational Biology Humans Neural Networks, Computer
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A Novel Improvement of Feature Selection for Dynamic Hand Gesture Identification Based on Double Machine Learning.

Sensors (Basel, Switzerland) Feb 13, 2025
Causal machine learning is an approach that combines causal inference and machine learning to understand and utilize causal relationships in data. In current research and applications, traditional machine learning and deep learning models always focu...
Neural Networks, Computer Hand Machine Learning Pattern Recognition, Automated Algorithms Humans Gestures Principal Component Analysis
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An efficient approach on risk factor prediction related to cardiovascular disease around Kumbakonam, Tamil Nadu, India, using unsupervised machine learning techniques.

Scientific reports Feb 13, 2025
Nowadays, human beings suffer from varieties of diseases due to the environmental circumstances and their residing habits. Cardiovascular diseases (CVD) are the leading cause of mortality among all diseases. CVDs are heart-related diseases. In early ...
Principal Component Analysis India Cluster Analysis Unsupervised Machine Learning Male Cardiovascular Diseases Female Risk Factors Humans
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Contaminant detection in flexible polypropylene packaging waste using hyperspectral imaging and machine learning.

Waste management (New York, N.Y.) Feb 10, 2025
Flexible plastic packaging (FPP) constitutes one of the largest post-consumer plastic streams processed in recycling facilities. To address the key challenges of its sorting and quality control, this study developed and tested a classification proced...
Product Packaging Polypropylenes Recycling Machine Learning Principal Component Analysis Hyperspectral Imaging Spectroscopy, Fourier Transform Infrared
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Incremental Classification for High-Dimensional EEG Manifold Representation Using Bidirectional Dimensionality Reduction and Prototype Learning.

IEEE journal of biomedical and health informatics Feb 10, 2025
In brain-computer interface (BCI) systems, symmetric positive definite (SPD) manifold within Riemannian space has been frequently utilized to extract spatial features from electroencephalogram (EEG) signals. However, the intrinsic high dimensionality...
Machine Learning Algorithms Brain Humans Neural Networks, Computer Signal Processing, Computer-Assisted Brain-Computer Interfaces Dimensionality Reduction Principal Component Analysis Electroencephalography Adult
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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...
Hyperspectral Imaging Discriminant Analysis Neural Networks, Computer Principal Component Analysis Pyrus Calibration Fruit Algorithms Least-Squares Analysis Malus 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...
Humans Artificial Intelligence Stroke Boosting Machine Learning Algorithms Risk Assessment Principal Component Analysis Machine Learning
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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...
Machine Learning Least-Squares Analysis Glucosides Monoterpenes Paeonia Hyperspectral Imaging Support Vector Machine Principal Component Analysis Drugs, Chinese Herbal Algorithms Spectroscopy, Near-Infrared
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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...
Deep Learning Humans Principal Component Analysis Dimensionality Reduction Algorithms
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