Predicting urban landslides in the hilly regions of Bangladesh leveraging a hybrid machine learning model and CMIP6 climate projections by Md․ Ashraful Islam, Musabbir Ahmed Arrafi, Mehedi Hasan Peas, Tanvir Hossain, Md Mehedi Hasan, Sanzida Murshed, Monira Jahan Tania

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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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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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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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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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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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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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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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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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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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Data-Driven Pavement Performance: Machine Learning-Based Predictive Models by Mohammad Fahad, Nurullah Bektas

“…However, machine learning models offer a time-efficient solution for predicting pavement performance. …”
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Statistical and machine learning models for predicting university dropout and scholarship impact. by Stephan Romero, Xiyue Liao

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Efficient Encoding and Decoding of Voxelized Models for Machine Learning-Based Applications by Damjan Strnad, Stefan Kohek, Borut Zalik, Libor Vasa, Andrej Nerat

“…Point clouds have become a popular training data for many practical applications of machine learning in the fields of environmental modeling and precision agriculture. …”
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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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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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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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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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Hyperparameter Optimization of ANN, SVM, and KNN Models for Classification of Hazelnuts Images Based on Shell Cracks and Feature Selection Method by H. Bagherpour, F. Fatehi, A. Shojaeian, R. Bagherpour

“…In this study, three famous machine learning models, including Support Vector Machine (SVM), K-nearest neighbors (KNN), and Multi-Layer Perceptron (MLP) were employed. …”
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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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