AutoML based workflow for design of experiments (DOE) selection and benchmarking data acquisition strategies with simulation models by Xukuan Xu, Donghui Li, Jinghou Bi, Michael Moeckel

“…This paper introduces a workflow for conducting DOE comparative studies using automated machine learning. Based on a practical definition of model complexity in the context of machine learning, the interplay of systematic data generation and model performance is examined considering various sources of uncertainty: this includes uncertainties caused by stochastic sampling strategies, imprecise data, suboptimal modeling, and model evaluation. …”
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Hybrid VLC-RF Channel Estimation for GFDM Wireless Sensor Networks Using Tree-Based Regressor by Azam Isam Aladwani, Tarik Adnan Almohamad, Abdullah Talha Sözer, İsmail Rakıp Karaş

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Intelligent agriculture: deep learning in UAV-based remote sensing imagery for crop diseases and pests detection by Hongyan Zhu, Hongyan Zhu, Chengzhi Lin, Chengzhi Lin, Gengqi Liu, Gengqi Liu, Dani Wang, Dani Wang, Shuai Qin, Shuai Qin, Anjie Li, Anjie Li, Jun-Li Xu, Yong He

“…Additionally, some widely used traditional machine learning (ML) algorithms were presented and the performance results were tabulated to form a comparison. …”
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Accelerating Disease Model Parameter Extraction: An LLM-Based Ranking Approach to Select Initial Studies for Literature Review Automation by Masood Sujau, Masako Wada, Emilie Vallée, Natalie Hillis, Teo Sušnjak

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Exploring the thermal attributes of nano-composition (GQDs+Bi2Se3+Ag) suspended in therminol VP-1: An artificial intelligence based approach by Sohail Ahmad, Hessa A. Alsalmah

“…The analysis incorporates a machine learning technique based on recurrent neural network (RNN) to evaluate the nonlinear impacts of the physical parameters. …”
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Enhanced glaucoma detection using U-Net and U-Net+ architectures using deep learning techniques by B.P. Pradeep kumar, Pramod K.B. Rangaiah, Robin Augustine

“…Capsule Networks were utilized for feature extraction and Extreme Learning Machines (ELM) for diagnostic classification. …”
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Development of an autoregressive neural network model for predicting accidents in the Khabarovsk Territory by E. V. Shokhirev, P. P. Volodkin, V. A. Lazarev, A. V. Konstantinov

“…The purpose of the study is to compare the actual accident rates in Khabarovsk Krai for the period from 01.01.2015 to 30.11.2023 with the results of the accident prediction model by training and validation based on recurrent neural network using machine learning methods. …”
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Ultrafine‐Resolution Urban Climate Modeling: Resolving Processes Across Scales by Chenghao Wang, Yongling Zhao, Qi Li, Zhi‐Hua Wang, Jiwen Fan

“…Addressing these limitations requires advances in computational techniques, numerical schemes, and the integration of diverse observational data. Machine learning presents new opportunities by emulating certain computationally expensive processes, enhancing data assimilation, and improving model accessibility for decision‐making. …”
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Deep learning on routine full-breast mammograms enhances lymph node metastasis prediction in early breast cancer by Daqu Zhang, Looket Dihge, Pär-Ola Bendahl, Ida Arvidsson, Magnus Dustler, Julia Ellbrant, Kim Gulis, Malin Hjärtström, Mattias Ohlsson, Cornelia Rejmer, David Schmidt, Sophia Zackrisson, Patrik Edén, Lisa Rydén

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Categorizing E-cigarette-related tweets using BERT topic modeling by D. Murthy, S. Keshari, S. Arora, Q. Yang, A. Loukas, S.J. Schwartz, M.B. Harrell, E.T. Hébert, A.V. Wilkinson

“…In contrast, unsupervised machine learning approaches, such as topic modeling, allow for efficient analysis of large datasets, uncovering patterns and trends that manual methods cannot achieve at scale. …”
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AstroM3: A Self-supervised Multimodal Model for Astronomy by M. Rizhko, J. S. Bloom

“…While machine-learned models are now routinely employed to facilitate astronomical inquiry, model inputs tend to be limited to a primary data source (namely images or time series) and, in the more advanced approaches, some metadata. …”
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Data-driven intelligent productivity prediction model for horizontal fracture stimulation by Qian Li, Yiyong Sui, Mengying Luo, Bin Guan, Lu Liu, Yuan Zhao

“…Under the assumption of similar characteristics and mechanisms, correlation analysis was conducted for each fracturing interval category to identify the dominant controlling factors affecting post-fracturing productivity in each reservoir type. Machine learning algorithms were used to establish intelligent models describing the relationships between post-fracturing production enhancement effects, dominant factors, and production time for each reservoir category. …”
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A Novel Approach for Automatic Detection of Concrete Surface Voids Using Image Texture Analysis and History-Based Adaptive Differential Evolution Optimized Support Vector Machine by Nhat-Duc Hoang, Quoc-Lam Nguyen

“…To improve the productivity of the inspection work, this study develops a hybrid intelligence approach that combines image texture analysis, machine learning, and metaheuristic optimization. …”
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Modeling, optimization, and thermal management strategies of hydrogen fuel cell systems by Abubakar Unguwanrimi Yakubu, Liu Qingsheng, Meng Kai, Chen Jinwei, Omer Abbaker Ahmed Mohammed, Jiahao Zhao, Qi Jiang, Xuanhong Ye, Junyi Liu, Qinglong Yu, Muhammad Aurangzeb, Shusheng Xiong

“…The review also explores hybrid physical-AI models, CFD-based surrogate models, and predictive machine-learning methods like LSTM and CNN. …”
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Advanced graph embedding for intelligent heating, ventilation, and air conditioning optimization: An ensemble learning-based recommender system by Shouliang Lai, Xiyu Yi, Peiling Zhou, Lu Peng, Wentao Liu, Shi Sun, Binrong Huang

“…Utilizing advanced graph embedding techniques combined with ensemble learning models, we developed a recommender system tailored for Heating, Ventilation, and Air Conditioning (HVAC) optimization in Shenzhen Qianhai Smart Community. …”
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Using AI for Optimizing Packing Design and Reducing Cost in E-Commerce by Hayder Zghair, Rushi Ganesh Konathala

“…In the second phase, a random forest (RF) machine learning model was developed to predict optimal packaging configurations using key product features: weight, volume, and fragility. …”
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In vivo electrophysiology recordings and computational modeling can predict octopus arm movement by Nitish Satya Sai Gedela, Ryan D. Radawiec, Sachin Salim, Julianna Richie, Cynthia Chestek, Anne Draelos, Galit Pelled

“…For kinematic analysis, deep learning models and unsupervised dimensionality reduction identified a consistent set of features that could be used to distinguish different types of arm movements. …”
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Internet of things driven hybrid neuro-fuzzy deep learning building energy management system for cost and schedule optimization by Deepshikha Shrivastava, Deepshikha Shrivastava, Prerna Goswami

“…The data collected was preprocessed, cleaned, transformed and used for training a machine learning model. Based on the previous literature, a hybrid DL model was developed using artificial neural networks and fuzzy logic by integrating fuzzy layers in the deep neural architecture. …”
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Intelligent deep learning for human activity recognition in individuals with disabilities using sensor based IoT and edge cloud continuum by Mohammed Maray

“…It presents valuable insights into the health, fitness, and overall wellness of individuals outside of hospital settings. Therefore, the machine learning (ML) model is mostly used for the growth of the HAR system to discover the models of human activity from the sensor data. …”
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Privacy-Preserving Glycemic Management in Type 1 Diabetes: Development and Validation of a Multiobjective Federated Reinforcement Learning Framework by Fatemeh Sarani Rad, Juan Li

“… Abstract BackgroundEffective diabetes management requires precise glycemic control to prevent both hypoglycemia and hyperglycemia, yet existing machine learning (ML) and reinforcement learning (RL) approaches often fail to balance competing objectives. …”
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