Exploratory Data Analysis Methods for Functional Magnetic Resonance Imaging (fMRI): A Comprehensive Review of Software Programs Used in Research DOI Creative Commons
Hussain A. Jaber,

Basma A. Al-Ghali,

Muna M. Kareem

et al.

Al-Nahrain Journal for Engineering Sciences, Journal Year: 2024, Volume and Issue: 27(4), P. 491 - 500

Published: Dec. 20, 2024

This extensive and thorough review aims to systematically outline, clarify, examine the numerous exploratory data analysis techniques that are employed in intriguing rapidly advancing domain of functional MRI research. We will particularly focus on wide array software applications instrumental facilitating improving these complex often nuanced analyses. Throughout this discourse, we meticulously assess various strengths limitations associated with each analytical tool, offering invaluable insights relevant their application overall efficacy across diverse research contexts environments. Our aim is create a comprehensive understanding how tools can be best utilized enhance outcomes. Through analysis, aspire equip researchers critical knowledge essential information could profoundly influence methodological selections upcoming studies. By carefully considering factors, hope contribute positively ongoing progression important field inquiry, fostering innovation enhancing impact future findings

Language: Английский

From A-to-Z Review of Clustering Validation Indices DOI
Bryar A. Hassan,

Noor Bahjat Tayfor,

Alla Ahmad Hassan

et al.

Neurocomputing, Journal Year: 2024, Volume and Issue: 601, P. 128198 - 128198

Published: July 18, 2024

Language: Английский

Citations

7
Integrated distributed flexible job shop scheduling and vehicle routing problem via Q-learning-based evolutionary algorithms DOI
Yaping Fu,

Zhengpei Zhang,

Kaizhou Gao

et al.

Information Sciences, Journal Year: 2025, Volume and Issue: unknown, P. 122169 - 122169

Published: April 1, 2025

Language: Английский

Citations

0
Patrones de Comportamiento en usuarios de transporte interprovincial en Ecuador mediante Técnicas de Machine Learning DOI Creative Commons
G Aguilar, José Fernando López Aguirre, Juan Carlos Pomaquero Yuquilema

et al.

Revista Venezolana de Gerencia, Journal Year: 2025, Volume and Issue: 30(110), P. 1047 - 1061

Published: April 4, 2025

Este estudio tiene como objetivo analizar y predecir patrones de comportamiento los usuarios transporte interprovincial en Ecuador mediante técnicas aprendizaje automático. Se utilizó un conjunto datos proporcionado por la Unión Cooperativas Transporte Interprovincial que abarca viajes realizados entre 2022 2024. La metodología incluyó implementación K-means para segmentación PCA reducción dimensional. Inicialmente, identificó cuatro clústeres, pero el solapamiento grupos motivó aplicación PCA, mejorando separación. Los resultados revelaron grupos: Ritmo Diario, Exploradores Fin Semana, Nómadas Eventos Viajeros Flexibles. Esta ofrece información clave optimizar servicios mejorar experiencia del usuario al ajustar recursos a las necesidades cada grupo.

Citations

0
Clustering validation by distribution hypothesis learning DOI
Ariel E. Bayá, Mónica G. Larese

Statistics and Computing, Journal Year: 2024, Volume and Issue: 34(6)

Published: Oct. 9, 2024

Language: Английский

Citations

0
Robust Parameter Optimisation of Noise-Tolerant Clustering for DENCLUE Using Differential Evolution DOI Creative Commons
Omer Ajmal, Humaira Arshad, Muhammad Asad Arshed

et al.

Mathematics, Journal Year: 2024, Volume and Issue: 12(21), P. 3367 - 3367

Published: Oct. 27, 2024

Clustering samples based on similarity remains a significant challenge, especially when the goal is to accurately capture underlying data clusters of complex arbitrary shapes. Existing density-based clustering techniques are known be best suited for capturing arbitrarily shaped clusters. However, key limitation these methods difficulty in automatically finding optimal set parameters adapted dataset characteristics, which becomes even more challenging contain inherent noise. In our recent work, we proposed Differential Evolution-based DENsity CLUstEring (DE-DENCLUE) optimise DENCLUE parameters. This study evaluates DE-DENCLUE its robustness accurate presence noise data. performance compared against three other algorithms—DPC weighted local density sequence and nearest neighbour assignment (DPCSA), Density-Based Spatial Applications with Noise (DBSCAN), Variable Kernel Density Estimation–based (VDENCLUE)—across several datasets (i.e., synthetic real). The has consistently shown superior results models most different levels. quality metrics such as Silhouette Index (SI), Davies–Bouldin (DBI), Adjusted Rand (ARI), Mutual Information (AMI) show SI, ARI, AMI values across at some cases regarding DBI, DPCSA performed better. conclusion, method offers reliable noise-resilient solution datasets.

Language: Английский

Citations

0
A comprehensive systematic review of machine learning in the retail industry: classifications, limitations, opportunities, and challenges DOI

D.O. Hassan,

Bryar A. Hassan

Neural Computing and Applications, Journal Year: 2024, Volume and Issue: unknown

Published: Dec. 20, 2024

Language: Английский

Citations

0
Exploratory Data Analysis Methods for Functional Magnetic Resonance Imaging (fMRI): A Comprehensive Review of Software Programs Used in Research DOI Creative Commons
Hussain A. Jaber,

Basma A. Al-Ghali,

Muna M. Kareem

et al.

Al-Nahrain Journal for Engineering Sciences, Journal Year: 2024, Volume and Issue: 27(4), P. 491 - 500

Published: Dec. 20, 2024

This extensive and thorough review aims to systematically outline, clarify, examine the numerous exploratory data analysis techniques that are employed in intriguing rapidly advancing domain of functional MRI research. We will particularly focus on wide array software applications instrumental facilitating improving these complex often nuanced analyses. Throughout this discourse, we meticulously assess various strengths limitations associated with each analytical tool, offering invaluable insights relevant their application overall efficacy across diverse research contexts environments. Our aim is create a comprehensive understanding how tools can be best utilized enhance outcomes. Through analysis, aspire equip researchers critical knowledge essential information could profoundly influence methodological selections upcoming studies. By carefully considering factors, hope contribute positively ongoing progression important field inquiry, fostering innovation enhancing impact future findings

Language: Английский

Citations

0