AIMC Topic: Principal Component Analysis

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Differentially Private Singular Value Decomposition for Training Support Vector Machines.

Computational intelligence and neuroscience
Support vector machine (SVM) is an efficient classification method in machine learning. The traditional classification model of SVMs may pose a great threat to personal privacy, when sensitive information is included in the training datasets. Princip...

Fruit classification using attention-based MobileNetV2 for industrial applications.

PloS one
Recent deep learning methods for fruits classification resulted in promising performance. However, these methods are with heavy-weight architectures in nature, and hence require a higher storage and expensive training operations due to feeding a larg...

Mood State Detection in Handwritten Tasks Using PCA-mFCBF and Automated Machine Learning.

Sensors (Basel, Switzerland)
In this research, we analyse data obtained from sensors when a user handwrites or draws on a tablet to detect whether the user is in a specific mood state. First, we calculated the features based on the temporal, kinematic, statistical, spectral and ...

Near-Infrared Spectral Characteristic Extraction and Qualitative Analysis Method for Complex Multi-Component Mixtures Based on TRPCA-SVM.

Sensors (Basel, Switzerland)
Quality identification of multi-component mixtures is essential for production process control. Artificial sensory evaluation is a conventional quality evaluation method of multi-component mixture, which is easily affected by human subjective factors...

Machine Learning-Based Cry Diagnostic System for Identifying Septic Newborns.

Journal of voice : official journal of the Voice Foundation
BACKGROUND AND OBJECTIVE: Processing the newborns' cry audio signal (CAS) provides valuable information about the newborns' condition. This information can be used to diagnose the disease. This article analyzes the CASs of newborns under two months o...

Identification of coumarin-based food additives using terahertz spectroscopy combined with manifold learning and improved support vector machine.

Journal of food science
The purpose of this paper is to use terahertz (THz) spectroscopy combined with manifold learning and improved support vector machine (SVM) model to identify the coumarin-based food additives. The 216 THz absorbance spectra (144 for calibration set an...

Universal prediction of cell-cycle position using transfer learning.

Genome biology
BACKGROUND: The cell cycle is a highly conserved, continuous process which controls faithful replication and division of cells. Single-cell technologies have enabled increasingly precise measurements of the cell cycle both as a biological process of ...

PCA driven mixed filter pruning for efficient convNets.

PloS one
Deployment of the deep neural networks (DNNs) on resource-constrained devices is a challenging task due to their limited memory and computational power. In most cases, the pruning techniques do not prune the DNNs to full extent and redundancy still e...

Adaptively Optimized Gas Analysis Model with Deep Learning for Near-Infrared Methane Sensors.

Analytical chemistry
Noise significantly limits the accuracy and stability of retrieving gas concentration with the traditional direct absorption spectroscopy (DAS). Here, we developed an adaptively optimized gas analysis model (AOGAM) composed of a neural sequence filte...

Automated selection of mid-height intervertebral disc slice in traverse lumbar spine MRI using a combination of deep learning feature and machine learning classifier.

PloS one
Abnormalities and defects that can cause lumbar spinal stenosis often occur in the Intervertebral Disc (IVD) of the patient's lumbar spine. Their automatic detection and classification require an application of an image analysis algorithm on suitable...