Consideration of the geomechanical state of a fractured porous reservoir in reservoir simulation modelling by Yurii A. Kashnikov, Denis V. Shustov, Sergei Yu. Yakimov

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Development and validation of a risk prediction model for acute kidney injury in coronary artery disease by Ming Ye, Chang Liu, Duo Yang, Hai Gao

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An Optimized Multi-Stage Framework for Soil Organic Carbon Estimation in Citrus Orchards Based on FTIR Spectroscopy and Hybrid Machine Learning Integration by Yingying Wei, Xiaoxiang Mo, Shengxin Yu, Saisai Wu, He Chen, Yuanyuan Qin, Zhikang Zeng

“…The proposed framework includes (1) FTIR spectral acquisition; (2) a comparative evaluation of nine spectral preprocessing techniques; (3) dimensionality reduction via three representative feature selection algorithms, namely the Successive Projections Algorithm (SPA), Competitive Adaptive Reweighted Sampling (CARS), and Principal Component Analysis (PCA); (4) regression modeling using six machine learning algorithms, namely the Random Forest (RF), Support Vector Regression (SVR), Gray Wolf Optimized SVR (SVR-GWO), Partial Least Squares Regression (PLSR), Principal Component Regression (PCR), and the Back-propagation Neural Network (BPNN); and (5) comprehensive performance assessments and the identification of the optimal modeling pathway. …”
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Achieving cloud resource optimization with trust-based access control: A novel ML strategy for enhanced performance by Bala Subramanian C, Bharathi ST, Shanmugapriya S

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Implementation of a hierarchical cluster model to analyze wind and solar availability in the department of Antioquia, Colombia by Alejandro Restrepo Román, Didier J. Villegas, Carolina Rodriguez, Adrián Cogollo, Iván D. Bedoya, Andres A. Amell Arrieta

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A hybrid approach for intrusion detection in vehicular networks using feature selection and dimensionality reduction with optimized deep learning. by Fayaz Hassan, Zafi Sherhan Syed, Aftab Ahmed Memon, Saad Said Alqahtany, Nadeem Ahmed, Mana Saleh Al Reshan, Yousef Asiri, Asadullah Shaikh

“…The intended use of CFS and PCA in the machine learning pipeline serves two folds benefit, first is that the resultant feature matrix contains attributes that are most useful for recognizing malicious traffic, and second that after CFS and PCA, the feature matrix has a smaller dimensionality which in turn means that smaller number of weights need to be trained for the dense layers (connections are required for the dense layers) which resulting in smaller model size. …”
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Prediction of KRAS gene mutations in colorectal cancer using a CT-based radiomic model by Wenjing Wang, Qingbiao Zhang, Shimei Fan, Yuyin Wang, Xingyan Le, Min Ai, Chunqi Du, Junbang Feng, Chuanming Li

“…After dimensionality reduction, machine learning methods such as extremely randomized trees (ERT), random forest (RF), XGBoost, Bagging, and CatBoost were used for model construction. …”
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One-Class Anomaly Detection for Industrial Applications: A Comparative Survey and Experimental Study by Davide Paolini, Pierpaolo Dini, Ettore Soldaini, Sergio Saponara

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Optimizing unsupervised feature engineering and classification pipelines for differentiated thyroid cancer recurrence prediction by Emmanuel Onah, Uche Jude Eze, Abdullahi Salahudeen Abdulraheem, Ugochukwu Gabriel Ezigbo, Kosisochi Chinwendu Amorha, Fidele Ntie-Kang

“…This study aimed to enhance predictive performance by refining feature engineering and evaluating a diverse ensemble of machine learning models using the UCI DTC dataset. …”
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Comparative performance analysis of ensemble learning methods for fetal health classification by Tasnim Bill Zannah, Sadia Islam Tonni, Md. Alif Sheakh, Mst. Sazia Tahosin, Afjal Hossan Sarower, Mahbuba Begum

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A study on the application of the latent dirichlet allocation model in production optimization by Youbin Chen, Da Huang, Lifei Wang

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Advanced Zero-Shot Learning (AZSL) Framework for Secure Model Generalization in Federated Learning by Muhammad Asif, Surayya Naz, Faheem Ali, Amerah Alabrah, Abdu Salam, Farhan Amin, Faizan Ullah

“…Federated learning (FL) introduces new perspectives in machine learning (ML) by enabling model training across decentralized devices. …”
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Cyber epidemic spread forecasting based on the entropy-extremal dynamic interpretation of the SIR model by Viacheslav Kovtun, Krzysztof Grochla, Mohammed Al-Maitah, Saad Aldosary, Tetiana Gryshchuk

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The development of a multimodal prediction model based on CT and MRI for the prognosis of pancreatic cancer by Zheng Dou, Jiaxi Lin, Chenghao Lu, Xiaoting Ma, Ruoxu Zhang, Jinzhou Zhu, Songbing Qin, Chao Xu, Jinli Li

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Revealing the effects of environmental and spatio-temporal variables on changes in Japanese sardine (Sardinops melanostictus) high abundance fishing grounds based on interpretable... by Yongchuang Shi, Lei Yan, Shengmao Zhang, Fenghua Tang, Shenglong Yang, Wei Fan, Haibin Han, Yang Dai

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What are the future directions for microplastics characterization? A regex-llama data mining approach for identifying emerging trends by FERNANDO GOMES, SHEKHAR BHANSALI, FABÍOLA MARANHÃO, VIVIANE VALLADÃO, KARINE VELASCO

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Applications of Artificial Intelligence in Drug Repurposing by Zhaoman Wan, Xinran Sun, Yi Li, Tianyao Chu, Xueyu Hao, Yang Cao, Peng Zhang

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Towards generalizable machine learning prediction of downskin surface roughness in laser powder bed fusion by Jigar Patel, Mihaela Vlasea, Sagar Patel

“…While numerical or experimental approaches alone can be significantly resource intensive, data-driven approaches such as machine learning (ML) have the potential to be more practical. …”
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Dynamic demand response strategies for load management using machine learning across consumer segments by Ravi Kumar Goli, Nazeer Shaik, Manju Sree Yalamanchili

“…These systems efficiently support load adjustment tactics, such as load shifting and curtailment, to achieve notable peak load reductions by utilizing sophisticated prediction approaches, such as machine learning, statistical methods, and reinforcement learning. …”
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A step forward in the diagnosis of urinary tract infections: from machine learning to clinical practice by Emilio Flores, Laura Martínez-Racaj, Álvaro Blasco, Elena Diaz, Patricia Esteban, Maite López-Garrigós, María Salinas

“…The aim of this study was to improve UTI diagnostics in clinical practice by application of machine learning (ML) models for real-time UTI prediction. …”
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