The different discrete transform techniques such as discrete cosine transform (DCT), discrete sine transform (DST), discrete wavelet transform (DWT), and mel-scale frequency cepstral coefficients (MFCCs) are powerful feature extraction techniques. This article presents a proposed computer-aided diagnosis (CAD) system for extracting the most effective and significant features of Alzheimer's disease (AD) using these different discrete transform techniques and MFCC techniques. Linear support vector machine has been used as a classifier in this article. Experimental results conclude that the proposed CAD system using MFCC technique for AD recognition has a great improvement for the system performance with small number of significant extracted features, as compared with the CAD system based on DCT, DST, DWT, and the hybrid combination methods of the different transform techniques.
Download full-text PDF |
Source |
|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10852668 | PMC |
| http://dx.doi.org/10.1177/1533317515603957 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Automated mold defects classification in paintings: A comparison of machine learning and rule-based techniques.
PLoS One
January 2025
Faculty of Science and Engineering, School of Computer Science, University of Hull, Hull, United Kingdom.
Mold defects pose a significant risk to the preservation of valuable fine art paintings, typically arising from fungal growth in humid environments. This paper presents a novel approach for detecting and categorizing mold defects in fine art paintings. The technique leverages a feature extraction method called Derivative Level Thresholding to pinpoint suspicious regions within an image.
View Article and Find Full Text PDFComparative analysis of data-driven models for spatially resolved thermometry using emission spectroscopy.
PLoS One
January 2025
Department of Computer Science, Khalifa University, Abu Dhabi, UAE.
A methodology is proposed, which addresses the caveat that line-of-sight emission spectroscopy presents in that it cannot provide spatially resolved temperature measurements in non-homogeneous temperature fields. The aim of this research is to explore the use of data-driven models in measuring temperature distributions in a spatially resolved manner using emission spectroscopy data. Two categories of data-driven methods are analyzed: (i) Feature engineering and classical machine learning algorithms, and (ii) end-to-end convolutional neural networks (CNN).
View Article and Find Full Text PDFImpacts of continuing education on Primary Health Care professionals-A scoping review protocol.
PLoS One
January 2025
Postgraduate Program in Family Health (RENASF), Federal University of Rio Grande do Norte, Natal, Rio Grande do Norte, Brazil.
Introduction: Continuing Health Education is a strategy that integrates learning into the work process to transform health practices. Primary health care has proved to be a powerful space for consolidating continuing education, as it promotes reflection and learning based on the local singularities of the territory. Continuing health education is an important strategy for transforming the reality of Primary health care, reinventing work, and consequently changing practices.
View Article and Find Full Text PDFFunctional profiling of the sequence stockpile: a protein pair-based assessment of in silico prediction tools.
Bioinformatics
January 2025
Department of Biology, Emory University, Atlanta, GA 30322, United States.
Motivation: In silico functional annotation of proteins is crucial to narrowing the sequencing-accelerated gap in our understanding of protein activities. Numerous function annotation methods exist, and their ranks have been growing, particularly so with the recent deep learning-based developments. However, it is unclear if these tools are truly predictive.
View Article and Find Full Text PDFSelf-Adaptive Quantum Kernel Principal Component Analysis for Compact Readout of Chemiresistive Sensor Arrays.
Adv Sci (Weinh)
January 2025
Data61, CSIRO, Clayton, VIC, 3168, Australia.
The rapid growth of Internet of Things (IoT) devices necessitates efficient data compression techniques to manage the vast amounts of data they generate. Chemiresistive sensor arrays (CSAs), a simple yet essential component in IoT systems, produce large datasets due to their simultaneous multi-sensor operations. Classical principal component analysis (cPCA), a widely used solution for dimensionality reduction, often struggles to preserve critical information in complex datasets.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!