Prediction of Rut Depth in Soil Caused by Wheels Using Artificial Neural Networks by N. Farhadi, A. Mardani, A. Hosainpour, B. Golanbari

“…The complexity of these interactions necessitates using machine learning models, especially artificial neural networks (ANNs), to predict rut depth based on input parameters. …”
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Effectiveness and user experience of a virtual reality intervention in a cohort of patients with chronic musculoskeletal pain syndromes. by Tiffany Prétat, Pedro Ming Azevedo, Chris Lovejoy, Thomas Hügle

“…Follow-up interviews were conducted after one month. An unsupervised machine learning model explored response patterns. …”
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Control of Overfitting with Physics by Sergei V. Kozyrev, Ilya A. Lopatin, Alexander N. Pechen

“…While there are many works on the applications of machine learning, not so many of them are trying to understand the theoretical justifications to explain their efficiency. …”
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Peritumoral features for assessing invasiveness of lung adenocarcinoma manifesting as ground-glass nodules by Xiao Wang, Hui Xue, Wei Ding, Fei Huang, Yu Zhang, Xin Pang

“…Radiomic features from tumor margins of 1, 2, 3, 4, and 5 mm were extracted. Eight machine learning models were constructed following dimensionality reduction. …”
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Efficient and Privacy-Preserving Decision Tree Inference via Homomorphic Matrix Multiplication and Leaf Node Pruning by Satoshi Fukui, Lihua Wang, Seiichi Ozawa

“…With the growing adoption of machine learning as a service (MLaaS), machine learning models are being increasingly deployed on cloud platforms. …”
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Diabetes-focused food recommender system (DFRS) to enabling digital health. by Esmael Ahmed, Mohammed Oumer, Medina Hassan

“…The methodology involves data collection from diverse patient profiles and model development using Graph Neural Networks (GNN) and other machine learning techniques. …”
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Impurity rates detection for pepper harvesting based on YOLOv8n-Seg-ASB and random forest by Lijian Lu, Jin Lei, Chenming Cheng, Shiguo Wang, Chengfu Wang, Xinyan Qin

“…Experimental results show that the YOLOv8n-Seg-ASB model achieves enhanced combined segmentation performance, with a 14.3 % increase in mAP@0.5, a 17.35 % reduction in model parameters, and an inference speed of 82.2 FPS. …”
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Navigating cognitive boundaries: the impact of CognifyNet AI-powered educational analytics on student improvement by Mrim M. Alnfiai, Faiz Abdullah Alotaibi, Mona Mohammed Alnahari, Nouf Abdullah Alsudairy, Asma Ibrahim Alharbi, Saad Alzahrani

“…Evaluated through rigorous 5-fold cross-validation on a comprehensive dataset of 1200 anonymized student records and validated across multiple educational platforms, including UCI Student Performance and Open University Learning Analytics datasets, CognifyNet demonstrates superior performance over conventional approaches, achieving 10.5% reduction in mean squared error and 83% reduction in mean absolute error compared to baseline random forest models, with statistical significance confirmed through paired t-tests (p < 0.01). …”
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Improving prediction of solar radiation using Cheetah Optimizer and Random Forest. by Ibrahim Al-Shourbaji, Pramod H Kachare, Abdoh Jabbari, Raimund Kirner, Digambar Puri, Mostafa Mehanawi, Abdalla Alameen

“…In the contemporary context of a burgeoning energy crisis, the accurate and dependable prediction of Solar Radiation (SR) has emerged as an indispensable component within thermal systems to facilitate renewable energy generation. Machine Learning (ML) models have gained widespread recognition for their precision and computational efficiency in addressing SR prediction challenges. …”
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Prediction for Tunnelling-Induced Ground Settlement in Multilayered Soils: An Improved Gradient Boosting Approach by Hongbin An, Yangyang Chen, Jingjie Lei, Hanbin Luo, Elton J. Chen

“…The research is based on the machine learning algorithm to establish a prediction model of stratum settlement caused by shield tunneling, which provides a new idea for real time prediction of the ground response caused by shield tunneling and risk reduction. …”
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Linear regressive weighted Gaussian kernel liquid neural network for brain tumor disease prediction using time series data by Firoz Khan, Sardar Irfanullah Amanullah, Shitharth Selvarajan

“…However, conventional machine learning and deep learning detection models face challenges in achieving high accuracy in brain tumor disease prediction while minimizing time complexity. …”
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A neural network-based synthetic diagnostic of laser-accelerated proton energy spectra by Christopher J. G. McQueen, Robbie Wilson, Timothy P. Frazer, Martin King, Matthew Alderton, Ewan F. J. Bacon, Ewan J. Dolier, Thomas Dzelzainis, Jesel K. Patel, Maia P. Peat, Ben C. Torrance, Ross J. Gray, Paul McKenna

“…Abstract Machine learning can revolutionize the development of laser-plasma accelerators by enabling real-time optimization, predictive modeling and experimental automation. …”
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A Correlation-Based Feature Selection Algorithm for Operating Data of Nuclear Power Plants by Yuxuan He, Hongxing Yu, Ren Yu, Jian Song, Haibo Lian, Jiangyang He, Jiangtao Yuan

“…Nuclear power plant operating data are characterized by a large variety, strong coupling, and low data value density. When using machine learning techniques for fault diagnosis and other related research, feature selection enables dimensionality reduction while maintaining the physical meaning of the original features, thus improving the computational efficiency and generalization ability of the learning model. …”
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An Intelligent Diagnostic System to Analyze Early-Stage Chronic Kidney Disease for Clinical Application by N. I. Md. Ashafuddula, Bayezid Islam, Rafiqul Islam

“…Thus, this study presents a novel, fully automated machine learning approach to tackle these complexities by incorporating feature selection (FS) and feature space reduction (FSR) techniques, leading to a substantial enhancement of the model’s performance. …”
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Baseline [18F]FDG PET/CT radiomics for predicting interim efficacy in follicular lymphoma treated with first-line R-CHOP by Zeying Wen, Xiaohe Gao, Qingxia Wu, Jianwei Yang, Jian Sun, Keliu Wu, Hongfei Zhao, Ruihua Wang, Yanmei Li

“…Univariate analysis was employed to identify clinical risk factors, and correlation coefficients, MRMR, and LASSO algorithms were used for dimensionality reduction and selection of radiomics features. Finally, a logistic regression machine learning model was developed to predict the interim efficacy of FL using a five-fold cross-validation strategy. …”
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An evaluation of multi-fidelity methods for quantifying uncertainty in projections of ice-sheet mass change by J. D. Jakeman, M. Perego, D. T. Seidl, T. A. Hartland, T. R. Hillebrand, M. J. Hoffman, S. F. Price

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AdaGram in Python: An AI Framework for Multi-Sense Embedding in Text and Scientific Formulas by Arun Josephraj Arokiaraj, Samah Ibrahim, André Then, Bashar Ibrahim, Stephan Peter

“…In this work, we present a Python-based reimplementation of AdaGram that facilitates broader integration with modern machine learning tools. Our implementation expands the model’s applicability beyond natural language, enabling the analysis of scientific notation—particularly chemical and physical formulas encoded in LaTeX. …”
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Improved Intelligent Condition Monitoring with Diagnostic Indicator Selection by Urszula Jachymczyk, Paweł Knap, Krzysztof Lalik

“…By applying the proposed method, it was possible to successfully filter out redundant features, enabling simpler machine learning (ML) models to match or even surpass the performance of more complex deep learning (DL) architectures. …”
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Civil Aircraft Landing Attitude Ultra-Limit Warning System Based on mRMR-LSTM by Fei Lu, Tong Jing, Chunsheng Xie, Haonan Chen

“…The early warning system includes four modules: data pretreatment, feature dimensionality reduction, prediction, and judgment. Subsequently, through data pretreatment methods such as data cleaning, frequency normalization, data standardization, and feature classification, the experimental dataset is transformed into a form recognizable by machine learning algorithms and neural network models. …”
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Positron emission tomography imaging biomarker and artificial intelligence for the characterization of solitary pulmonary nodule by Ashish Kumar Jha, Ashish Kumar Jha, Umeshkumar Baburao Sherkhane, Umeshkumar Baburao Sherkhane, Nilendu C. Purandare, Nilendu C. Purandare, Leonard Wee, Andre Dekker, Venkatesh Rangarajan, Venkatesh Rangarajan

“…A total of 1,098 features were extracted from PET images using PyRadiomics. To optimize model performance two strategies i.e., (a) feature selection and (b) feature reduction techniques were employed, including hierarchical clustering, RFE in feature selection, and PCA in feature reduction. …”
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