Appraisal of Industrial Pollutants in Sewage and Biogas Production Using Multivariate Analysis and Unsupervised Machine Learning Clustering by Wiktor Halecki, Anna Młyńska, Krzysztof Chmielowski

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Producing Landslide Susceptibility Maps Using Statistics and Machine Learning Techniques: The Rize-Taşlıdere Basin Example by Arif Çağdaş Aydınoğlu, Gehver Altürk

“…Using the landslide inventory and input parameters, a parameter analysis was performed for the landslide susceptibility map in five classes by employing the frequency ratio (FR), logistic regression (LR), and artificial neural network (ANN) methods. …”
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Use of Machine Learning Algorithms to Predict Almen (Shot Peening) Intensity Values of Various Steel Materials by Murat İnce, Hatice Varol Özkavak

“…One of these processes is shot peening (SP). Process parameters are crucial for SP. This necessitates the optimization of SP process parameters. …”
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Data-Driven Optimization of Aspect Ratio in Permanent Magnet Machines Using Deep Learning and SHAP Analysis by Kyeong Jin Kim, Ji Hoon Park, Dong Hoo Min, Seun Guy Min

“…The aspect ratio, defined as the ratio of the outer diameter to the stack length, is a critical parameter in permanent magnet (PM) machine design, with a profound impact on motor performance. …”
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Dementia Classification Based on Magnetic Resonance Scans Comparing Traditional and Modern Machine Learning Models’ Quintessence by Andreea POPOVICIU, Diogen BABUC, Todor IVAŞCU

“…This paper aimed to analyze and compare several machine learning models used for the classification of Magnetic Resonance Imaging (MRI) scans of patients with or without dementia. …”
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Calculation Model for Multi-Roller Load Distribution of Planetary Threaded Roller Bearings Considering Machining Errors by Zhijie Xie, Mingyang Li, Yicheng Hou, Zhiwei Wang, Kailiang Zhang

“…The limited literature explores the influence of machining errors on PTRB’s load-bearing performance. …”
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A Comparative Study between Different Machine Learning Algorithms for Estimating the Vehicular Delay at Signalized Intersections by Yazan Alatoom, Abdallah Al-Hamdan

“…The delay at signalized intersections is a crucial parameter that determines the performance and level of service (LOS). …”
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Hybrid machine learning-enabled multivariate bridge-specific seismic vulnerability and resilience assessment of UHPC bridges by Tadesse G. Wakjira, M. Shahria Alam

“…However, existing single-parameter-based probabilistic seismic demand (PSD) models overlook critical bridge‐specific characteristics and uncertainties. …”
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Modeling Equatorial to Mid‐Latitudinal Global Night Time Ionospheric Plasma Irregularities Using Machine Learning by Ephrem Beshir Seba, Giovanni Lapenta

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Unveiling hydrogen chemical states in supersaturated amorphous alumina via machine learning-driven atomistic modeling by Simon Gramatte, Olivier Politano, Noel Jakse, Claudia Cancellieri, Ivo Utke, Lars P. H. Jeurgens, Vladyslav Turlo

“…Guided by experiments on atomic layer deposited alumina, a fast atomistic simulation technique is introduced using an ab initio-based machine learning interatomic potential to generate amorphous structures with realistic hydrogen contents. …”
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Optimization and performance study of large aspect ratio SiC microgrooves by waterjet assisted laser machining by Qijian Zhang, Wenzhao Yang, Xinwei Zhang, Jinjin Han, Yunxia Guo, Weining Lei

“…Furthermore, this research systematically optimized the machining parameters for LAR microgrooves through orthogonal experiments and grey-relational analysis (GRA). …”
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Optimizing casting process using a combination of small data machine learning and phase-field simulations by Xiaolong Pei, Jiaqi Pei, Hua Hou, Yuhong Zhao

“…Abstract It has been a challenge to employ machine learning (ML) to optimize casting processes due to the scarcity of data and difficulty in feature expansion. …”
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Forest Aboveground Biomass Estimation Based on Unmanned Aerial Vehicle–Light Detection and Ranging and Machine Learning by Yan Yan, Jingjing Lei, Yuqing Huang

“…In this study, the performance of predictive biomass regression equations and machine learning algorithms, including multivariate linear stepwise regression (MLSR), support vector machine regression (SVR), and k-nearest neighbor (KNN) for constructing a predictive forest AGB model was analyzed and compared at individual tree and stand scales based on forest parameters extracted by Unmanned Aerial Vehicle–Light Detection and Ranging (UAV LiDAR) and variables screened by variable projection importance analysis to select the best prediction method. …”
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Methodology for Estimating the Cost of Construction Equipment Based on the Analysis of Important Characteristics Using Machine Learning Methods by Nataliya Boyko, Oleksii Lukash

“…In this study, the assessment of abnormal data was applied separately to each set of grouped data with the same parameters. The study built and analyzed models using machine learning methods (linear and polynomial regression, decision trees, random forest, support vector machine, and neural network). …”
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Leveraging the Power of Hybrid and Standalone Machine Learning for Enhanced FRP-Confined Concrete Columns Strength Prediction by Mehdi Ghasri, Mohammad Ghasemi, Abdolhamid Salarnia

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Balancing Predictive Performance and Interpretability in Machine Learning: A Scoring System and an Empirical Study in Traffic Prediction by Fabian Obster, Monica I. Ciolacu, Andreas Humpe

“…To address this gap, we introduce a novel interpretability scoring system - a Machine Learning Interpretability Rank-based scale - that combines objective measures such as the number of model parameters with subjective interpretability rankings across different model types. …”
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A machine learning approach for significant utilization of high-ash Indian coals by metal chloride modification by Aparna Singh, Deepak Singh Panwar, Satish Kumar Maurya, Ajay Oraon, Sanjeev Srivastava, Md Irfanul Haque Siddiqui, Saurav Dixit, Choon Kit Chan, Chandrakant Sonawane

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A combined improved dung beetle optimization and extreme learning machine framework for precise SOC estimation by Kaihua Yao, Xinyu Yan, Xiling Mao, Mengwei Li, Xiao Li, Ziyu Lian, Yuxiang Han

“…In this work, we propose a combined Improved Dung Beetle Optimization (IDBO) and Extreme Learning Machine (ELM) framework for SOC estimation and evaluate the efficiency of the BMS. …”
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Multi-Domain Controversial Text Detection Based on a Machine Learning and Deep Learning Stacked Ensemble by Jiadi Liu, Zhuodong Liu, Qiaoqi Li, Weihao Kong, Xiangyu Li

“…Secondly, we design a two-tier stacked ensemble architecture, which not only combines the strengths of multiple machine learning algorithms, e.g., gradient-boosted decision tree (GBDT), random forest (RF), and extreme gradient boosting (XGBoost), with deep learning models, e.g., gated recurrent unit (GRU) and long short-term memory (LSTM), but also implements the support vector machine (SVM) for efficient meta-learning. …”
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Oil well productivity capacity prediction based on support vector machine optimized by improved whale algorithm by Kuiqian Ma, Chunxin Wu, Yige Huang, Pengfei Mu, Peng Shi

“…Abstract Oil well productivity capacity is an important parameter in oilfield development, which is of great significance for efficient development. …”
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