Wear fault diagnosis in journal bearings using vibration analysis and AI by Jebur Nazik A., Soud Wafa A.

“…In this study, machine learning models for identifying wear defects in journal bearings under various operating circumstances are compared. …”
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A fuzzy-optimized multi-level random forest (FOMRF) model for the classification of the impact of technostress by Gabriel James, Ifeoma, David, John, Samuel, Enefiok, Imeh Umoren, Ubong Etuk, Aloysius, Anietie, Saviour, Chikodili

“…It increasingly affects corporate productivity, well-being, and effectiveness in digital settings. Traditional machine learning models often struggle with the complexity and non-linearity of technostress classification. …”
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ELD-YOLO: A Lightweight Framework for Detecting Occluded Mandarin Fruits in Plant Research by Xianyao Wang, Yutong Huang, Siyu Wei, Weize Xu, Xiangsen Zhu, Jiong Mu, Xiaoyan Chen

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Tackling multimodal device distributions in inverse photonic design using invertible neural networks by Michel Frising, Jorge Bravo-Abad, Ferry Prins

“…We show how conditional generative neural networks can be used to efficiently find nanophotonic devices with desired properties, also known as inverse photonic design. Machine learning has emerged as a promising approach to overcome limitations imposed by the dimensionality and topology of the parameter space. …”
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Providing contexts for classification of transients in a wide-area sky survey: An application of noise-induced cluster ensemble by Tossapon Boongoen, Natthakan Iam-On, James Mullaney

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Predictive for patients with pneumonia in pediatric intensive care unit by Mingxuan Jia, Xiyan Hu, Lin Ji, Jiawen Lin, Jialin Liu, Yong Wang

“…For prognostic model construction, we used stepwise regression to filter 28 variables, then Spearman and Pearson correlation analyses to identify an intersection of 14 key indicators from the top 20 features. Twelve machine learning algorithms underwent parameter tuning and combination, forming 113 model combinations for survival outcome prediction.ResultsThe “Stepglm [both] + GBM” combination achieved the highest average accuracy (79.4%) in both training and testing sets. …”
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A kinetic-based regularization method for data science applications by Abhisek Ganguly, Alessandro Gabbana, Vybhav Rao, Sauro Succi, Santosh Ansumali

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The analytical basis for evaluating the innovative reflection engineering enterprises by O.Koleshchuk

“…The author substantiates that the parameter that evaluates the potential capabilities for a machine-building enterprise, as well as determines the direction of innovation management on a strategic scale, is to assess the degree of innovation reflection, that is, to determine the level of readiness of the enterprise's line activity in the innovation process and on what scale. …”
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Channel Estimation for Wideband Multi-RIS-Assisted mmWave Massive-MIMO OFDM System With Beam Squint Effect by Thabang C. Rapudu, Olutayo O. Oyerinde

“…Therefore, in this paper, a beam squint aware machine learning (ML)-based uplink CE scheme for wideband multi-RIS-assisted mmWave massive-MIMO orthogonal frequency division multiplexing (OFDM) system is proposed. …”
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Artificial intelligence and numerical weather prediction models: A technical survey by Muhammad Waqas, Usa Wannasingha Humphries, Bunthid Chueasa, Angkool Wangwongchai

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Long-term forecasting of shield tunnel position and attitude deviation using the 1DCNN-informer method by Jiajie Zhen, Ming Huang, Shuang Li, Kai Xu, Qianghu Zhao

“…Accurate prediction of shield machine position and attitude is crucial for ensuring the quality of tunnel construction. …”
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Real-Time Detection of Varieties and Defects in Moving Corn Seeds Based on YOLO-SBWL by Yuhang Che, Hongyi Bai, Laijun Sun, Yanru Fang, Xinbo Guo, Shanbing Yin

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Micro hole drilling and multi criteria optimization of soda lime glass via ultrasonic assisted rotary electrochemical discharge drilling by Sahil Grover, Viveksheel Rajput, Sanjay Kumar Mangal, Sarbjit Singh, Sandeep Singh, Shubham Sharma, Ehab El Sayed Massoud, Dražan Kozak, Jasmina Lozanovic

“…MRR, HOC and CE serve as a response parameter while tool vibration, tool feed rate, working material rotation, applied voltage and electrolyte concentration are control variables. …”
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Forecasting yield and market classes of Vidalia sweet onions: A UAV-based multispectral and texture data-driven approach by Marcelo Rodrigues Barbosa Júnior, Lucas de Azevedo Sales, Regimar Garcia dos Santos, Rônega Boa Sorte Vargas, Chris Tyson, Luan Pereira de Oliveira

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A Distributional Regression Network With Data Transformation for Calibrating Rainfall Forecasts by Zeqing Huang, Andrew Schepen, James C. Bennett, David E. Robertson, Tongtiegang Zhao, Eun‐Soon Im, Quan J. Wang

“…Abstract Machine learning methods provide a promising approach for exploiting relationships between raw forecasts and observations for forecast calibration. …”
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Determinação da eficiência de campo de conjuntos de máquinas convencionais de preparo do solo, semeadura e cultivo Field efficiency determination of set of conventional machines of... by Gastão M. da Silveira, Kyoshi Yanai, Sergio A. H. Kurachi

“…For that, in mechanized farms, the careful controlling of agricultural machine and implements, always, deserved a great attention. …”
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A data-driven approach utilizing machine learning (ML) and geographical information system (GIS)-based time series analysis with data augmentation for water quality assessment in M... by Abhijeet Das

“…These approaches were additionally applied to the water quality datasets, generated during two-year period namely, 2022–2024, at nineteen different sites for 21 parameters. From the results, it produces two metrics, TKN and coliform, that are higher than WHO guidelines while maintaining the optimal level of DO throughout the duration of the study. …”
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Newly synthesized 1-butyl-3-methylimidazolium p-toluenesulfonate ionic liquid for acid corrosion of API 5L X70 steel: Experimental, DFT/MD-simulation, statistical and machine learn... by Daniel Iheanacho Udunwa, Okechukwu Dominic Onukwuli, Simeon Chukwudozie Nwanonenyi, Callistus Nonso Ude, Innocent Okechi Arukalam, Remy Uche

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Wind Power Prediction Method and Outlook in Microtopographic Microclimate by Jia He, Fangchun Tang, Junxin Feng, Chaoyang Liu, Mengyan Ni, Youguang Chen, Hongdeng Mei, Qin Hu, Xingliang Jiang

“…Then, the accurate prediction of wind power under icing weather is considered, and two possible research directions for wind power prediction under icy weather are proposed: a statistical prediction method for classifying and clustering wind turbines according to microtopography, combining large-scale meteorological parameters with small-scale meteorological parameter correlation models and using machine learning for cluster power prediction, and a power prediction model converted from the power prediction model during normal operation of the wind turbine to the power prediction model during icing. …”
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An Efficient Framework for Personalizing EMG-Driven Musculoskeletal Models Based on Reinforcement Learning by Joseph Berman, I-Chieh Lee, Jie Yin, He Huang

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