Exploring statistical and machine learning methods for modeling probability distribution parameters in downtime length analysis: a paper manufacturing machine case study by Vladimir Koković, Kosta Pavlović, Andjela Mijanović, Slavko Kovačević, Ivan Mačužić, Vladimir Božović

“…This paper explores statistical and machine learning techniques for modeling downtime length probability distributions and correlation with machine vibration measurements. …”
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Multi-criteria decision-making approach for evaluating the critical determinants of machine tool integrity by Olugbenga Adegbemisola Aderoba, Khumbulani Mpofu, Buliaminu Kareem, Olurotimi Akintunde Dahunsi, Olayinka Oladele Awopetu, Ilesanmi Afolabi Daniyan

Subjects: “…Death knell parameters…”
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Recent advances in explainable Machine Learning models for wildfire prediction by Abira Sengupta, Brendon J. Woodford

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Machine learning-based mathematical equations for dengue positivity detection using elementary laboratory parameters by Shirin Dasgupta, Shuvankar Das, Debarghya Chakraborty

“…A comprehensive review of all the input parameters is conducted, and the positivity prediction of dengue infection is correlated with past investigations. …”
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Explainable artificial intelligence driven insights into smoking prediction using machine learning and clinical parameters by S. Aishwarya, P. C. Siddalingaswamy, Krishnaraj Chadaga

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Machine Learning Prediction of Foaming in Anaerobic Co-Digestion from Six Key Process Parameters by Sarah E. Daly, Ji-Qin Ni

“…Cu) associated with digester foaming. Among the tested machine learning models, the support vector machine (SVM) algorithm achieved the highest recognition accuracy of 87%. …”
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Prediction of Soil Field Capacity and Permanent Wilting Point Using Accessible Parameters by Machine Learning by Liwei Liu, Xingmao Ma

“…Overall, our results demonstrated that machine learning is effective in predicting the FC and PWP from easily accessible data from WoSIS, and the GEP model is more preferable for FC and PWP modeling.…”
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Optimization of drilling parameters to minimize delamination in CNT-filled GFRP composites using machine learning by Aveen K P, Ullal Vignesh Nayak, K M Pranesh Rao, Shivaramu H T, V Londhe Neelakantha, Shashikumar C M

“…Torque (T) and thrust force (F) were measured using a digital drilling machine with a dynamometer. A machine learning based multi-output random forest regression model with hyper parameter tuning was used to predict the T, F, and delamination factor (Fd). …”
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Goal Programming Optimisation of Machining Parameters during the Production of Ti-Alloy Components on the CNC Lathe by Nicholas Tayisepi, Daniel M Madyira, Trust Tawanda, Lameck Mugwagwa

“…In this research, outside cutting tests were carry out, on the lathe machine, with coated carbide tipped tools at various parameter setting levels. …”
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Machine learning-based model for acute asthma exacerbation detection using routine blood parameters by Youpeng Chen, Junquan Sun, Yabang Chen, Enzhong Li, Jiancai Lu, Huanhua Tang, Yifei Xie, Jiana Zhang, Lesi Peng, Haojie Wu, Zhangkai J. Cheng, Baoqing Sun

“…This study aimed to develop and validate a diagnostic model for AAE using routine blood parameters through machine learning techniques. Methods: We developed a machine learning-based diagnostic model using routine blood test parameters. …”
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Inferring Parameters in a Complex Land Surface Model by Combining Data Assimilation and Machine Learning by L. T. Keetz, K. Aalstad, R. A. Fisher, C. Poppe Terán, B. Naz, N. Pirk, Y. A. Yilmaz, O. Skarpaas

Subjects: “…machine learning…”
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COMPARISON OF PARAMETERS OF SURFACE INTEGRITY OF MACHINED DUPLEX AND AUSTENITE STAINLESS STEELS IN RELATION TO TOOL GEOMETRY by Jiří Čapek, Zdeněk Pitrmuc, Kamil Kolařík, Libor Beránek, Nikolaj Ganev

“…The goal of this contribution was to describe parameters of surface integrity of two machined materials; austenite and duplex stainless steel. …”
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Identifying Infant Body Position from Inertial Sensors with Machine Learning: Which Parameters Matter? by Joanna Duda-Goławska, Aleksander Rogowski, Zuzanna Laudańska, Jarosław Żygierewicz, Przemysław Tomalski

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Hyperspectral technology and machine learning models to estimate the fruit quality parameters of mango and strawberry crops. by Salah Elsayed, Hoda Gala, Mohamed S Abd El-Baki, Mohamed Maher, Ahmed Elbeltagi, Ali Salem, Abdallah Elshawadfy Elwakeel, Osama Elsherbiny, Nadia G Abd El-Fattah

“…Using chemical laboratory procedures to estimate the fruit quality parameters (biochemical parameters) of mango "Succarri" and strawberry "Florida" as indicators of ripening degrees in a large area presents challenges such as low throughput, labor intensity, time consumption, and the need for multiple samples. …”
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Real-time lithology identification while drilling based on drilling parameters analysis with machine learning by Kun Li, Ting Ren, Ningping Yao, Jon Roberts, Haitao Song, Zhongbei Li, Chunmiao Liang

“…Artificial intelligence is increasingly vital for lithology identification but faces challenges in underground coal mines, especially in accurately interpreting lithology from drilling parameters. These challenges include: the influence of drill string friction, difficulties in extracting valuable data from large datasets, insufficient real-time performance to guide drilling operations, and the limited adaptability of individual machine learning algorithm. …”
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Analytical estimation of influence of dynamic parameters of testing machine fluid drive on accuracy of test operations by D. N. Andrianov, N. V. Andrianov

“…There is shown analytic dependence of absolute and ratio dynamic error of test operations on the parameters of machine, substance and size of the tested sample, testing mode. …”
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Investigating the impact of meteorological parameters on daily soil temperature changes using machine learning models by Farrokh Asadzadeh, Somayeh Emami, Ahmed Elbeltagi, Muhammed Ernur Akiner, Vahid Rezaverdinejad, Farshid Taran, Ali Salem

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RESEARCH ON CHARACTERISTICS OF DAMPING PARAMETERS OF SANDWICH STRUCTURE OF MACHINE TOOL COMPONENTS BASED ON MODAL ANALYSIS by HU Jun, ZHU WenFeng, XU KaiLe, MA ZhuangZhuang

“…The dynamic characteristics are important factors that affect the accuracy of machine tools. Currently,the material combinations are an effective method that is often used. …”
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Robust integer optimization of turning parameters for cutting tool sustainability and machining economics in discrete production by Chunhui Chung, Agus Andrianto, Po-Chieh Wang

“…Machining optimization is crucial for determining cutting parameters that enhance machining economics. …”
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Back analysis of geomechanical parameters based on a data augmentation algorithm and machine learning technique by Hui Li, Weizhong Chen, Xianjun Tan

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