A Machine Learning-Based Method for Lithology Identification of Outcrops Using TLS-Derived Spectral and Geometric Features by Yanlin Shao, Peijin Li, Ran Jing, Yaxiong Shao, Lang Liu, Kunpeng Zhao, Binqing Gan, Xiaolei Duan, Longfan Li

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Quantitative assessment of factors that influence heat vulnerability in residential areas using machine learning and unmanned aerial vehicle by Jawoon Gu, Dongwoo Kim, Chulmin Jun, Seungwoo Son

“…High-resolution thermal imagery captured by unmanned aerial vehicles (UAVs) and interpretable machine learning (ML) techniques were used to model and analyze thermal patterns at the microscale. …”
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Machine learning analysis of thermo-bioconvection in a micropolar hybrid nanofluid-filled square cavity with oxytactic microorganisms by Ahlawat Anil, Chaudhary Shilpa, Karuppusamy Loganathan, Mohammed Salman Arafath, Kandasamy Thirumalaisamy, Thameem Basha Hayath

“…Numerical simulations utilize finite-difference approach to discretize the ensuing equations and boundary conditions, solving the resulting algebraic system iteratively via successive over-relaxation, under-relaxation, and Gauss–Seidel techniques utilizing in-house MATLAB codes. In addition, a machine learning approach was employed to accurately predict fluid transport properties using a multilayer artificial neural network structured with a feed-forward backpropagation model and optimized using the Levenberg–Marquardt algorithm. …”
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Comparative performance evaluation of quartz and snail shell powders modified concrete: Mechanical, machine learning, and microstructural assessments by Md. Habibur Rahman Sobuz, Md. Kanan Chowdhury Tilak, SM Arifur Rahman, Fahim Shahriyar Aditto, Faiz Uddin Ahmed Shaikh, Md. Kawsarul Islam Kabbo, M Jameel, Md. Munir Hayet Khan

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Information models of agricultural objects by V. V. Alt, S. N. Olshevsky

“…The hypothesis of possibility of their uniform information description on the basis of the law on substance circulation in the nature and dialectic interrelation of objects of agricultural production is formulated: earth, plants, animals, machines, environment and society. The interrelation of these resources and its optimality (rationality) is possible on the basis of use of information technologies that in many respects defines efficiency of agrarian production. …”
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Enhancing soil organic carbon prediction by unraveling the role of crop residue coverage using interpretable machine learning by Yi Dong, Xinting Wang, Sheng Wang, Baoguo Li, Junming Liu, Jianxi Huang, Xuecao Li, Yelu Zeng, Wei Su

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Data Preprocessing Techniques for AI and Machine Learning Readiness: Scoping Review of Wearable Sensor Data in Cancer Care by Bengie L Ortiz, Vibhuti Gupta, Rajnish Kumar, Aditya Jalin, Xiao Cao, Charles Ziegenbein, Ashutosh Singhal, Muneesh Tewari, Sung Won Choi

“…Moreover, preprocessing pipelines to clean, transform, normalize, and standardize raw data have not yet been fully optimized. ObjectiveThis study aims to conduct a scoping review of preprocessing techniques used on raw wearable sensor data in cancer care, specifically focusing on methods implemented to ensure their readiness for artificial intelligence and machine learning (AI/ML) applications. …”
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An explainable federated blockchain framework with privacy-preserving AI optimization for securing healthcare data by Tanisha Bhardwaj, K. Sumangali

“…PPFBXAIO employs Secure Hash Algorithm 256 (SHA-256) for blockchain-backed secure model updates, Min-Max normalization for feature scaling, and the Levy Grasshopper Optimization Algorithm (LGOA) for optimal feature selection and federated model tuning. …”
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Electrical Discharge Machining of Al (6351)-5% SiC-10% B4C Hybrid Composite: A Grey Relational Approach by S. Suresh Kumar, M. Uthayakumar, S. Thirumalai Kumaran, P. Parameswaran, E. Mohandas

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Forecasting Short- and Long-Term Wind Speed in Limpopo Province Using Machine Learning and Extreme Value Theory by Kgothatso Makubyane, Daniel Maposa

“…This study adds value to the literature and knowledge of modelling wind speed using both EVT and machine learning. …”
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Midspan Deflection Prediction of Long-Span Cable-Stayed Bridge Based on DIWPSO-SVM Algorithm by Lilin Li, Qing He, Hua Wang, Wensheng Wang

“…This study proposes a novel hybrid model, DIWPSO-SVM, which integrates dynamic inertia weight particle swarm optimization (DIWPSO) with support vector machines (SVMs) to enhance the prediction accuracy of midspan deflection. …”
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Extended Maximum Actor–Critic Framework Based on Policy Gradient Reinforcement for System Optimization by Jung-Hyun Kim, Yong-Hoon Choi, You-Rak Choi, Jae-Hyeok Jeong, Min-Suk Kim

“…Specifically, Reinforcement Learning, a branch of Machine Learning, has been used in several research fields to solve optimal control problems. …”
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Optimizing colorectal polyp detection and localization: Impact of RGB color adjustment on CNN performance by Jirakorn Jamrasnarodom, Pharuj Rajborirug, Pises Pisespongsa, Kitsuchart Pasupa

“…Using datasets from Harvard Dataverse for training and internal validation, and LDPolypVideo-Benchmark for external validation, RGB color adjustments were applied, and YOLOv8s was used to develop models. Bayesian optimization identified the best RGB adjustments, with performance assessed using mean average precision (mAP) and F1-scores. …”
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Machine Learning Accurately Predicts Need for Critical Care Support in Patients Admitted to Hospital for Community-Acquired Pneumonia by George S. Chen, BSc, Terry Lee, PhD, Jennifer L.Y. Tsang, MD, Alexandra Binnie, MD, Anne McCarthy, MD, Juthaporn Cowan, MD, Patrick Archambault, MD, Francois Lellouche, MD, Alexis F. Turgeon, MD, MSc, Jennifer Yoon, MD, Francois Lamontagne, MD, Allison McGeer, MD, Josh Douglas, MD, Peter Daley, MD, Robert Fowler, MD, David M. Maslove, MD, Brent W. Winston, MD, Todd C. Lee, MD, Karen C. Tran, MD, Matthew P. Cheng, MD, Donald C. Vinh, MD, John H. Boyd, MD, Keith R. Walley, MD, Joel Singer, PhD, John C. Marshall, MD, James A. Russell, MD, for the Community-Acquired Pneumonia: Toward InnoVAtive Treatment (CAPTIVATE) Investigators

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The Study on the Co-Optimization of Guidance Decision-Making and Chassis Control for Self-Driving Vehicles by Jia Zili

“…It ends by summarizing possible solutions and discussing future developments, such as integrating AI and machine learning in optimization, and expanding it to networked and intelligent traffic environments, among others. …”
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Multi-objective optimization of dual-stator permanent magnet motor based on composite algorithm by Xiaoguang Kong, Zhuo Yang

“…Then, the Taguchi method optimizes the significant variables, the genetic algorithm based on the Kriging response surface model optimizes the non-significant variables, and finally, the optimal solution is selected on the Pareto front. …”
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Optimizing Genomic Selection Methods to Improve Prediction Accuracy of Sugarcane Single-Stalk Weight by Zihao Wang, Chengcai Xia, Yanjie Lu, Qi Liu, Meiling Zou, Fenggang Zan, Zhiqiang Xia

“…Additionally, we incorporated inbred and outbred populations as fixed effects into the model. The optimized SSBLUP model achieved a prediction accuracy of 0.44, which was a 17% improvement over the original SSBLUP model and a 9% increase compared to the originally optimal GBLUP model. …”
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Individualizing glioma radiotherapy planning by optimization of a data and physics-informed discrete loss by Michal Balcerak, Jonas Weidner, Petr Karnakov, Ivan Ezhov, Sergey Litvinov, Petros Koumoutsakos, Tamaz Amiranashvili, Ray Zirui Zhang, John S. Lowengrub, Igor Yakushev, Benedikt Wiestler, Bjoern Menze

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Multi-Objective Optimization of Injection Parameters and Energy Consumption Based on ANN-Differential Evolution by Devic Oktora, Yu-Hung Ting, Sukoyo

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Composite Bayesian Optimization in function spaces using NEON—Neural Epistemic Operator Networks by Leonardo Ferreira Guilhoto, Paris Perdikaris

“…Abstract Operator learning is a rising field of scientific computing where inputs or outputs of a machine learning model are functions defined in infinite-dimensional spaces. …”
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