Predictive Modeling for Fetal Health: A Comparative Study of PCA, LDA, and KPCA for Dimensionality Reduction by Ariana Deyaneira Jimenez-Narvaez, Victor David Casa Vaca, Jonathan Javier Loor-Duque, Isidro Rafael Amaro Martin, Ivan Galo Reyes-Chacon, Paulina Vizcaino, Manuel Eugenio Morocho-Cayamcela

“…This study addresses this issue by applying dimensionality reduction techniques—Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), and Kernel Principal Component Analysis (KPCA)—to improve the performance of machine learning (ML) models in predicting fetal health using CTG data. …”
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Machine learning approach for revealing the nickel grade and recovery optimization in reduction process of laterite ores by Vuri Ayu Setyowati, Fakhreza Abdul

“…This research attempts to take a machine learning approach to find the right model for process optimization. …”
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Machine learning-based approach for reduction of energy consumption in hybrid energy storage electric vehicle by T. Paulraj, Yeddula Pedda Obulesu

“…Abstract This research introduces a novel machine learning-based strategy for generating supercapacitor (SC) reference current to optimize energy distribution in Battery Electric Vehicles (BEV) and Hybrid Battery Electric Vehicles (HBEV). …”
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A proposed deep learning model for multichannel ECG noise reduction by Jay Prakash Maurya, Manish Manoria, Sunil Joshi

“…This article proposed a novel deep learning-based solution for multichannel ECG noise reduction, through utilizing the capabilities of fully convolutional neural network along with the Jacobin regularization to ensure confining and preserving local information. …”
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Interpretable Machine Learning Models for PISA Results in Mathematics by Ismael Gomez-Talal, Luis Bote-Curiel, Jose Luis Rojo-Alvarez

“…To address this question, we proposed using advanced Machine Learning techniques through possibly non-linear predictive models that identify key drivers of Mathematics performance to inform data-driven educational policies and interventions that improve learning outcomes. …”
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A stacking ensemble model for food demand forecasting: A preventative approach to food waste reduction by Asmaa Seyam, Sujith Samuel Mathew, Bo Du, May El Barachi, Jun Shen

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A Hybrid Model of Feature Extraction and Dimensionality Reduction Using ViT, PCA, and Random Forest for Multi-Classification of Brain Cancer by Hisham Allahem, Sameh Abd El-Ghany, A. A. Abd El-Aziz, Bader Aldughayfiq, Menwa Alshammeri, Malak Alamri

“…<b>Methods:</b> In recent years, computer-aided diagnosis (CAD) systems incorporating deep learning (DL) and machine learning (ML) technologies have gained popularity as they offer precise predictive outcomes based on MRI images using advanced computer vision techniques. …”
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The data dimensionality reduction and bad data detection in the process of smart grid reconstruction through machine learning. by Bo Yu, Zheng Wang, Shangke Liu, Xiaomin Liu, Ruixin Gou

“…In this study, the bad data detection model based on deep learning has an active role in the realization of the safe and stable operation of the smart grid.…”
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Study on the electrocatalytic CO2 reduction performance of covalent organic framework materials based on machine learning by Zhang Xiangying, Wang Sitan

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Generalized MPC-DSVPWM Methods: Reduction Techniques and Explainable Machine Learning With Conformal Prediction for PMSM Drives by Hasan Ali Gamal Al-Kaf, Sadeq Ali Qasem Mohammed, Kyo-Beum Lee

“…Secondly, we propose robust machine learning algorithm by employing explainable machine learning techniques to select important features. …”
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Task relevant autoencoding enhances machine learning for human neuroscience by Seyedmehdi Orouji, Vincent Taschereau-Dumouchel, Aurelio Cortese, Brian Odegaard, Cody Cushing, Mouslim Cherkaoui, Mitsuo Kawato, Hakwan Lau, Megan A. K. Peters

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Text Classification: How Machine Learning Is Revolutionizing Text Categorization by Hesham Allam, Lisa Makubvure, Benjamin Gyamfi, Kwadwo Nyarko Graham, Kehinde Akinwolere

“…Key applications of TC are explored, alongside an analysis of critical machine learning methods, including document representation techniques and dimensionality reduction strategies. …”
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Leveraging Feature Extraction to Perform Time-Efficient Selection for Machine Learning Applications by Duarte Coelho, Ana Madureira, Ivo Pereira, Ramiro Gonçalves, Susana Nicola, Inês César, Daniel Alves de Oliveira

“…This work presents a cost-effective proposal for feature selection, which is a crucial part of machine learning processes, and intends to partly solve this problem through computational time reduction. …”
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Machine-learning certification of multipartite entanglement for noisy quantum hardware by Andreas J C Fuchs, Eric Brunner, Jiheon Seong, Hyeokjea Kwon, Seungchan Seo, Joonwoo Bae, Andreas Buchleitner, Edoardo G Carnio

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Comparative Study of Machine Learning and Deep Learning Models for Early Prediction of Ovarian Cancer by Hardik Dhingra, Roopashri Shetty

“…This study presents a comparative analysis of machine learning (ML) and deep learning (DL) models for the early prediction of ovarian cancer using clinical and biomarker data. …”
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Benchmarking with a Language Model Initial Selection for Text Classification Tasks by Agus Riyadi, Mate Kovacs, Uwe Serdült, Victor Kryssanov

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Efficient Fault Diagnosis of Elevator Cabin Door Drives Using Machine Learning with Data Reduction for Reliable Transmission by Jakub Gęca, Dariusz Czerwiński, Bartosz Drzymała, Krzysztof Kolano

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Hybridization of stochastic hydrological models and machine learning methods for improving rainfall-runoff modeling by Sianou Ezéckiel Houénafa, Olatunji Johnson, Erick K. Ronoh, Stephen E. Moore

“…It achieves an overall Nash-Sutcliffe Efficiency (NSE) of 0.896, which is 7.30% higher than the NSE of HyMoLAP, and 29.67% and 259.71% higher than those of the standalone machine learning models. The Combined Accuracy (CA) is 38.11, reflecting reductions of 19.81%, 42.30%, and 62.41% compared to the standalone models. …”
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Intelligent Feature Selection Ensemble Model for Price Prediction in Real Estate Markets by Daniel Cristóbal Andrade-Girón, William Joel Marin-Rodriguez, Marcelo Gumercindo Zuñiga-Rojas

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Retraining and evaluation of machine learning and deep learning models for seizure classification from EEG data by Juan Pablo Carvajal-Dossman¹, Laura Guio, Danilo García-Orjuela, Jennifer J. Guzmán-Porras, Kelly Garces, Andres Naranjo, Silvia Juliana Maradei-Anaya, Jorge Duitama

“…However, manual annotation of seizures in EEG data is a major time-consuming step in the analysis process of EEGs. Different machine learning models have been developed to perform automated detection of seizures from EEGs. …”
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