Research progress on modeling of heavy metal adsorption by biochar based on machine learning by FENG Ding, LIU Jingjing, MA Wendan, LIU Yuxue, YANG Chen, ZHANG Mengmeng

“…Machine learning has demonstrated significant potential in handling high-dimensional data and analyzing complex problems. …”
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Machine learning approaches for improving atomic force microscopy instrumentation and data analytics by Nabila Masud, Jaydeep Rade, Md. Hasibul Hasan Hasib, Adarsh Krishnamurthy, Adarsh Krishnamurthy, Anwesha Sarkar

“…AFM is also crucial for measuring single-molecule interaction forces and important parameters of binding dynamics for receptor-ligand interactions or protein-protein interactions on live cells. …”
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Formulation and evaluation of ocean dynamics problems as optimization problems for quantum annealing machines. by Takuro Matsuta, Ryo Furue

“…In either case, SA successfully reproduces the expected solution when appropriate parameters are chosen. In contrast, QA using the D-Wave quantum annealing machine fails to obtain good solutions for some cases owing to hardware limitations; in particular, the highly limited connectivity graph of the machine limits the size of the solvable problems, at least under currently available algorithms. …”
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Supervised Machine Learning Insights into Social and Linguistic Influences on Cesarean Rates in Luxembourg by Prasad Adhav, María Bélen Farias

“…Subsequently, we employed four machine learning models to predict CS based on the survey data. …”
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Evaluation and Application of Machine Learning Techniques for Quality Improvement in Metal Product Manufacturing by Katarzyna Antosz, Lucia Knapčíková, Jozef Husár

“…This article proposes that optimising key process parameters, such as temperature, machining speed, and the type of coolant used, can markedly reduce the prevalence of production defects. …”
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Automatic titration detection method of organic matter content based on machine vision by Bingjie Zhang, Meng Li, Qing Song, Lujian Xu

“…First, by analysing the colour change characteristics during the titration process, machine learning techniques are used to classify the titration speed, and a titration experiment state recognition model is constructed to divide the titration speed into four categories and improve titration efficiency; Second, through a large number of titration experiments to collect relevant data and extract key feature parameters, an efficient titration algorithm based on histogram similarity was designed to accurately identify titration endpoints and improve detection accuracy. …”
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Gaussian Process Regression and Machine Learning Methods for Carbon-Based Material Adsorption by Manar Ahmed Hamza, Maha M. Althobaiti, Fahd N. Al-Wesabi, Rana Alabdan, Hany Mahgoub, Anwer Mustafa Hilal, Abdelwahed Motwakel, Mesfer Al Duhayyim

“…Below a variety of environmental parameters (e.g., warmth, solution pH) and adsorbent varieties, the created Ml algorithms outperformed classic isotherm models in conditions of generalisation. …”
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Classification of shale gas “sweet spot” based on Random Forest machine learning by NIE Yunli, GAO Guozhong

“…As a result, the proposed Random Forest machine learning method with multi-source information is an effective shale gas “sweet spot” classification and identification method.…”
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A Spatially Informed Machine Learning Method for Predicting Sound Field Uncertainty by Xiangmei Chen, Chao Li, Haibin Wang, Yupeng Tai, Jun Wang, Cyrille Migniot

“…Predicting the uncertain distribution of underwater acoustic fields, influenced by dynamic oceanic parameters, is critical for acoustic applications that rely on sound field characteristics to generate predictions. …”
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Optimized Support Vector Machine Assisted BOTDA for Temperature Extraction With Accuracy Enhancement by Hongna Zhu, Lei Yu, Yufeng Zhang, Le Cheng, Zhenyu Zhu, Jiayin Song, Jinli Zhang, Bin Luo, Kai Yang

“…Brillouin optical time domain analyzer (BOTDA) assisted by optimized support vector machine (SVM) algorithm for accurate temperature extraction is presented and experimentally demonstrated. …”
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An Automatic Machine Vision-Guided System for the Propagation of Potato Test-Tube Plantlets by Xu Shengyong, Peng Biye, Wu Haiyang, Li Fushuai, Cai Xingkui, Duan Hongbing

“…Using an agricultural intelligent robot to replace manual operation will greatly improve the efficiency and quality of the propagation of PTTPs. An automatic machine vision-guided system for the propagation of PTTPs was developed and tested. …”
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Harnessing Machine Learning for Predictive Analysis of Crop Resistance to Extreme Weather Conditions by Pan Susovan Kumar, Shivaj Ghorpade Bipin

“…Given the increasing pressure of extreme weather conditions threatening agricultural productivity, it is becoming more and more important to rely on advanced machine learning techniques to predict this type of unpredictable events. …”
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Predictive Technology Assessment by Means of a Structure-Based Method of Machine Learning by Manja Mai-Ly PFAFF, Uwe FRIEß, Andreas OTTO, Matthias PUTZ

“…Classification axioms can be formed by 1-class learning procedures for the predictive state evaluation of subsequent production start-ups based on collected machine and process data from past production start-ups. …”
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Evaluating the Performance of Python-Based Machine Learning in Earthquake-Resistant Building Design by Sameh Fuqaha, Guntur Nugroho

“…This study investigates the feasibility of applying artificial intelligence (AI)-based machine learning techniques, specifically a Multiple Linear Regression (MLR) model implemented in Python, for earthquake-resistant building design. …”
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Solving the Control Synthesis Problem Through Supervised Machine Learning of Symbolic Regression by Askhat Diveev, Elena Sofronova, Nurbek Konyrbaev

“…Symbolic regression methods, which were previously called genetic programming methods, allow one to use a computer to find not only the parameters of a given regression function but also its structure. …”
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Integration of RSM and Machine Learning for Accurate Prediction of Surface Roughness in Laser Processing by Dragan Rodić, Milenko Sekulić, Borislav Savković, Miloš Madić, Milan Trifunović

“…This study investigates the modeling of surface roughness (Ra) in the laser cutting of EN 10130 steel process by integrating classical statistical and machine learning methods. First, a quadratic model was developed using response surface methodology (RSM) based on a Box–Behnken experimental design with 17 runs, using cutting speed, laser power, and auxiliary gas pressure as input parameters. …”
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MLA-Machine Learning Approach for Dependable Battery Condition Monitoring in Electric Vehicles by Tirgar Pravin, Priya R Karpaga, Sampath Kumar Vankadara, Lakhanpal Sorabh, Raj R Gowtham, N K Rayaguru

“…This leads to more accurate predictions of critical parameters like State of Charge (SOC), State of Health (SOH), and Remaining Useful Life (RUL). …”
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AISI 1045 Steel Flat Surfaces Machining Using the Magneto-Abrasive Method by Maiboroda V. S., Belajev O. O., Dzhulii D. Yu., Slobodianiuk I. V.

“…The influence of technological process parameters: the rotation speed of the working heads, the sizes of the working gap, the technological feed on the character of the change in the microgeometry of the machined surface were investigated. …”
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Investigation of different electrolyte effects on micro-hole machining of scrapped wheel alloy by D. Sriram, B.D.Y. Sunil, S. Jeyakrishnan, C. Rakesh, S. Vijayakumar, Ashish Kumar, N. Rao Cheepurupalli, Lakshita Sehgal

“…The optimal process parameters were recognized as VO of 14 V, DU of 85%, and EL of 50 g/L, attaining an MRR of 2.752 g/min and an OC of 0.068 mm. …”
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Exploring the Potential Imaging Biomarkers for Parkinson’s Disease Using Machine Learning Approach by Illia Mushta, Sulev Koks, Anton Popov, Oleksandr Lysenko

“…This study aims to identify a biomarker from DATSCAN images and develop a machine learning (ML) algorithm for PD diagnosis. …”
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