Bridging the Gap: A Review of Machine Learning in Water Quality Control by Herlina Abdul Rahim, Nur Athirah Syafiqah Noramli, Indrabayu

“…Emerging solutions like federated learning (35% accuracy improvement in pan-African E. coli prediction) and explainable AI (SHAP-guided nitrate models) address these barriers. …”
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Enhancing Early Breast Cancer Detection with Infrared Thermography: A Comparative Evaluation of Deep Learning and Machine Learning Models by Reem Jalloul, Chethan Hasigala Krishnappa, Victor Ikechukwu Agughasi, Ramez Alkhatib

“…This study investigates and compares the performance of various deep learning and machine learning models in analyzing thermographic data to classify breast tissue as healthy, benign, or malignant. …”
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Accurate estimation of permeability reduction resulted from low salinity water flooding in clay-rich sandstones by Xiaojuan Zhang, Muntadher Abed Hussein, Tarak Vora, Anupam Yadav, Asha Rajiv, Aman Shankhyan, Sachin Jaidka, Mehul Manu, Farzona Alimova, Issa Mohammed Kadhim, Zainab Jamal Hamoodah, Fadhil Faez, Ahmad Khalid

Subjects: “…Machine learning…”
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Feature extraction and classification of digital rock images via pre-trained convolutional neural network and unsupervised machine learning by Masashige Shiga, Masao Sorai, Tetsuya Morishita, Masaatsu Aichi, Naoki Nishiyama, Takashi Fujii

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Comparative evaluation of feature reduction methods for drug response prediction by Farzaneh Firoozbakht, Behnam Yousefi, Olga Tsoy, Jan Baumbach, Benno Schwikowski

“…Our analysis employs six distinct machine learning models, with a total of more than 6,000 runs to ensure a robust evaluation. …”
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Machine Learning-Based Lithium Battery State of Health Prediction Research by Kun Li, Xinling Chen

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Investigating the Effects of Labeled Data on Parameterized Physics-Informed Neural Networks for Surrogate Modeling: Design Optimization for Drag Reduction over a Forward-Facing Ste... by Erik Gustafsson, Magnus Andersson

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Improving Voice Spoofing Detection Through Extensive Analysis of Multicepstral Feature Reduction by Leonardo Mendes de Souza, Rodrigo Capobianco Guido, Rodrigo Colnago Contreras, Monique Simplicio Viana, Marcelo Adriano dos Santos Bongarti

“…This study proposes a novel experimental analysis that extensively explores various dimensionality reduction strategies in conjunction with supervised machine learning models to effectively identify spoofed voice signals. …”
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Accelerating Energy Forecasting with Data Dimensionality Reduction in a Residential Environment by Rafael Gonçalves, Diogo Magalhães, Rafael Teixeira, Mário Antunes, Diogo Gomes, Rui L. Aguiar

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Using Permutation-Based Feature Importance for Improved Machine Learning Model Performance at Reduced Costs by Adam Khan, Asad Ali, Jahangir Khan, Fasee Ullah, Muhammad Faheem

“…This task is commonly achieved through Machine Learning (ML) techniques, but improving model performance typically incurs significant computational costs. …”
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A novel machine learning model for perimeter intrusion detection using intrusion image dataset. by Shahneela Pitafi, Toni Anwar, I Dewa Made Widia, Zubair Sharif, Boonsit Yimwadsana

“…To address these challenges, a new machine learning model is developed. This model utilizes the pre-trained InceptionV3 for feature extraction on PID intrusion image dataset, followed by t-SNE for dimensionality reduction and subsequent clustering. …”
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Analysis of Data and Feature Processing on Stroke Prediction using Wide Range Machine Learning Model by Untari Novia Wisesty, Tjokorda Agung Budi Wirayuda, Febryanti Sthevanie, Rita Rismala

“…In this research, stroke prediction was carried out on the Stroke dataset acquired from the Kaggle dataset using various machine learning models. Then, data sampling techniques are used to handle data imbalance problems in the stroke dataset, which include Random Undersampling, Random Oversampling, and SMOTE techniques. …”
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Using Machine Learning to Develop a Surrogate Model for Simulating Multispecies Contaminant Transport in Groundwater by Thu-Uyen Nguyen, Heejun Suk, Ching-Ping Liang, Yu-Chieh Ho, Jui-Sheng Chen

“…Recent advances in artificial intelligence (AI) offer promising alternatives, particularly data-driven machine learning techniques, for accelerating such simulations. …”
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A Machine Learning-Based Risk Prediction Model During Pregnancy in Low-Resource Settings by Kapil Tomar, Chandra Mani Sharma, Tanisha Prasad, Vijayaraghavan M. Chariar

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Analysis of different IDS-based machine learning models for secure data transmission in IoT networks by Gladić Dejana, Petrovački Jelena, Sladojević Srdan, Arsenović Marko, Ristić Sonja

“…The aim of this study is to develop a machine learning (ML) model for network traffic classification and attack detection in IoT environments. …”
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Urban Flood Susceptibility Mapping for Toronto, Canada, Using Supervised Regression and Machine Learning Models by Baljeet Kaur, Andrew Binns, Edward McBean, Dan Sandink, Karen Castro, Bahram Gharabaghi

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Optimizing Superior Seed Bunches: A New Approach to Artificial Pollination with Machine Learning Models by Yabani Yabani, Retna Astuti Kuswardani

“…Keywords: Seed bunch optimization, pollination strategy, pollen viability, optimization model, machine learning, k-means clustering, multi-objective optimization.…”
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Photovoltaic Farm Power Generation Forecast Using Photovoltaic Battery Model with Machine Learning Capabilities by Agboola Benjamin Alao, Olatunji Matthew Adeyanju, Manohar Chamana, Stephen Bayne, Argenis Bilbao

“…This study presents a machine learning-based photovoltaic (PV) model for energy management and planning in a microgrid with a battery system. …”
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Correcting errors in seasonal Arctic sea ice prediction of Earth system models with machine learning by Z. He, Z. He, Z. He, Y. Wang, J. Brajard, X. Wang, X. Wang, Z. Shen, Z. Shen

“…In this study, we integrate a multilayer perceptron (MLP) machine learning (ML) model into the Norwegian Climate Prediction Model (NorCPM) to improve seasonal sea ice predictions. …”
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Integrating transcriptomics and hybrid machine learning enables high-accuracy diagnostic modeling for nasopharyngeal carcinoma by Hehe Wang, Junge Zhang, Peng Cheng, Lujie Yu, Chunlin Li, Yaowen Wang

“…This study aimed to develop robust machine learning (ML)-driven diagnostic models and identify key biomarkers through integrated analysis of multi-cohort transcriptomic data. …”
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