Physics-Based AI-Driven Surrogate Modeling for Structural Displacement Prediction in Mechanical Systems With Limited Sensor Data by Ali Hashemi, Javad Beheshti, Mahdieh Mohammadi

“…This study introduces a machine learning (ML)-based surrogate model for finite element analysis, designed to predict structural strain distributions using a minimal number of strategically placed virtual sensors. …”
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Microstructural characterization and hardness prediction of AlCuCrFeNi high entropy alloys using Transformer-based Physics-Informed Neural Networks (T-PINN) by Mohamed Yasin Abdul Salam, Enoch Nifise Ogunmuyiwa, Victor Kitso Manisa, Abid Yahya, Irfan Anjum Badruddin

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SHERA: SHAP-Enhanced Resource Allocation for VM Scheduling and Efficient Cloud Computing by Ashwin Singh Slathia, Abhiram Sharma, P. B. Krishna, Saksham Anand, Ayush Rathi, Linda Joseph, Xiao-Zhi Gao

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Método para definição de layout em sistemas job-shop baseado em dados históricos Historical data-based job-shop layout systems definition method by Maurício Tomelin, João Carlos Colmenero

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Buried No longer: recent computational advances in explicit interfacial modeling of lithium-based all-solid-state battery materials by Julia H. Yang, Xinqiang Rao, Amanda Whai Shin Ooi

“…Lastly, we highlight universal machine learning potentials, challenging datasets, and opportunities for tighter integration with experiments, all of which broaden the scope of modeling. …”
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Identification method of canned food for production line sorting robot based on improved PSO-SVM by GAO Haiyan, GAO Jinyang, WANG Weicheng

“…By improving the particle swarm optimization algorithm to optimize support vector machine parameters, an optimized support vector machine classification model was obtained. …”
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Full-chain comprehensive assessment and multi-scenario simulation of geological disaster vulnerability based on the VSD framework: a case study of Yunnan province in China by Li Xu, Shucheng Tan, Runyang Li

“…Furthermore, the Ordered Weighted Averaging (OWA) algorithm and the Partical Swarm Optimization-Support Vector Machine (PSO-SVM) model were combined to simulate future GDV scenarios for 2030–2050 under three development preferences: environment oriented, status quo, and economically oriented. …”
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Online Learning of Entrainment Closures in a Hybrid Machine Learning Parameterization by Costa Christopoulos, Ignacio Lopez‐Gomez, Tom Beucler, Yair Cohen, Charles Kawczynski, Oliver R. A. Dunbar, Tapio Schneider

“…To avoid drift and instability that plague offline‐trained machine learning parameterizations that are subsequently coupled with climate models, we frame learning as an inverse problem: Data‐driven models are embedded within the EDMF parameterization and trained online in a one‐dimensional vertical global climate model (GCM) column. …”
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Multi-task deep learning for simultaneous prediction of steel purity and carbon capture rate using membrane separation technology in integrated steelmaking processes by Somboon Sukpancharoen, Pakon Sakdee, Natacha Phetyim, Rinlada Sirisangsawang, Chayut Sungsook

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Link Prediction in Social Networks Using the HTOA by Foad Asef, Vahid Majidnezhad, Mohammad-Reza Feizi-Derakhshi

“…HTOA, inspired by the physical phenomenon of heat transfer, identifies an optimal subset of topological features to feed XGBoost machine learning model. …”
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Outage Performance and Novel Loss Function for an ML-Assisted Resource Allocation: An Exact Analytical Framework by Nidhi Simmons, David E. Simmons, Michel Daoud Yacoub

“…In this paper, we present Machine Learning (ML) solutions to address the reliability challenges likely to be encountered in advanced wireless systems (5G, 6G, and indeed beyond). …”
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Advancing Geotechnical Evaluation of Wellbores: A Robust and Precise Model for Predicting Uniaxial Compressive Strength (UCS) of Rocks in Oil and Gas Wells by Mohammadali Ahmadi

“…This study examines the efficacy of various machine learning models for predicting the uniaxial compressive strength (UCS) of rocks in oil and gas wells, which are essential for ensuring wellbore stability and optimizing drilling operations. …”
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Development and validation of an interpretable multi-task model to predict outcomes in patients with rhabdomyolysis: a multicenter retrospective cohort studyResearch in context by Chunli Liu, Jie Shi, Fengjuan Wang, Duo Li, Yu Luo, Bofan Yang, Yunlong Zhao, Li Zhang, Dingwei Yang, Heng Jin, Jie Song, Xiaoqin Guo, Haojun Fan, Qi Lv

“…Early and accurate prediction of acute kidney injury (AKI), disease severity, renal replacement therapy (RRT) requirements, and mortality risk is essential for timely identification of high-risk individuals, personalized treatment planning, and optimal allocation of healthcare resources. We aimed to develop and externally validate an interpretable multi-task machine learning (ML) model to predict four clinical outcomes in patients with rhabdomyolysis: AKI, disease severity, the need for RRT, and in-hospital mortality. …”
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Advanced Planning Systems in Production Planning Control: An Ethical and Sustainable Perspective in Fashion Sector by Martina De Giovanni, Mariangela Lazoi, Romeo Bandinelli, Virginia Fani

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Speech emotion recognition based on a stacked autoencoders optimized by PSO based grass fibrous root optimization by Chi Zeng, Jialing Li, Abbas Habibi

“…Specifically, a stacked autoencoder (SAE) serves as the primary model, and its performance is fine-tuned using a nature-inspired hybrid algorithm that combines particle swarm optimization (PSO) with Grass Fibrous Root Optimization (GFRO). …”
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Inspiring from Galaxies to Green AI in Earth: Benchmarking Energy-Efficient Models for Galaxy Morphology Classification by Vasileios Alevizos, Emmanouil V. Gkouvrikos, Ilias Georgousis, Sotiria Karipidou, George A. Papakostas

“…Concurrently, the considerable energy consumption of machine learning (ML) has fostered the emergence of Green AI, emphasizing sustainable, energy-efficient computational practices. …”
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MaxEnt-Based Distribution Modeling of the Invasive Species <i>Phragmites australis</i> Under Climate Change Conditions in Iraq by Nabaz R. Khwarahm

“…Consequently, this study seeks to model the current and future potential distribution of this invasive species in Iraq using machine learning techniques (i.e., MaxEnt) alongside geospatial tools integrated within a GIS framework. …”
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Optimizing Power Consumption in Aquaculture Cooling Systems: A Bayesian Optimization and XGBoost Approach Under Limited Data by Sina Ghaemi, Hessam Gholmohamadi, Amjad Anvari-Moghaddam, Birgitte Bak-Jensen

“…To address this challenge, we presents a black-box optimization model for optimizing the energy consumption of cooling systems in the aquaculture industry using Extreme Gradient Boosting (XGBoost) and Bayesian Optimization (BO). …”
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Application of artificial intelligence in intelligent healthcare by WANG Zuheng, WEI Chunmeng, LU Wenhao, LI Xiao, WANG Fubo

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Methods and Algorithms for Predictive Analytics of Time Series in Energy Consumption by Aleksandr Karmanov

“…States must minimize carbon dioxide emissions and businesses must optimize their energy costs. In this regard, improving energy efficiency plays a key role in solving the climate crisis and reducing costs for businesses. …”
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