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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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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Kernel-based response extraction approach for efficient configurable ring oscillator PUF by Enas Abulibdeh, Hani Saleh, Baker Mohammad, Mahmoud Al-Qutayri, Asif Hussain

“…The NIST tests are successfully passed, and the average prediction accuracy of machine learning models is found to be 65.3%.…”
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Reducing bias in coronary heart disease prediction using Smote-ENN and PCA. by Xinyi Wei, Boyu Shi

“…This indicates that combining data balancing and feature dimensionality reduction techniques significantly improves model accuracy and makes the random forest model the best model. …”
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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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Enhancing Unmanned Aerial Vehicle Object Detection via Tensor Decompositions and Positive–Negative Momentum Optimizers by Ruslan Abdulkadirov, Pavel Lyakhov, Denis Butusov, Nikolay Nagornov, Dmitry Reznikov, Anatoly Bobrov, Diana Kalita

“…However, the growing number and difficulty of such problems cause the developers to construct machine learning models with higher computational complexities, such as an increased number of hidden layers, epochs, learning rate, and rate decay. …”
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History Matching of a Channelized Reservoir Using a Serial Denoising Autoencoder Integrated with ES-MDA by Sungil Kim, Baehyun Min, Seoyoon Kwon, Min-gon Chu

“…The SDAE learns how to eliminate the noise and restore the clean reservoir models. …”
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Indoor Multidimensional Reconstruction Based on Maximal Cliques by Yongtong Zhu, Lei Li, Na Liu, Qingdu Li, Ye Yuan

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Leveraging Digital Twins for Stratification of Patients with Breast Cancer and Treatment Optimization in Geriatric Oncology: Multivariate Clustering Analysis by Pierre Heudel, Mashal Ahmed, Felix Renard, Arnaud Attye

“…MethodsA retrospective analysis was conducted on data from women aged 70+ years with HER2-negative early-stage breast cancer treated at the French Léon Bérard Cancer Center between 1997 and 2016. Manifold learning and machine learning algorithms were applied to uncover complex data relationships and develop predictive models. …”
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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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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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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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Enhanced mastitis severity classification in dairy cows using DNN and RF: A study on PCA and correlation-based feature selection by Manar Lashin, Ayman Samir Farid, Abdullah T. Elgammal

“…These findings highlight the broader applicability of machine learning advancements beyond veterinary sciences, demonstrating the potential for interdisciplinary research across veterinary medicine, computer science, and engineering to drive innovation in automated diagnostics and resource management in dairy farming. …”
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Evaluating cognitive decline detection in aging populations with single-channel EEG features based on two studies and meta-analysis by Lior Molcho, Neta B. Maimon, Talya Zeimer, Ofir Chibotero, Sarit Rabinowicz, Vered Armoni, Noa Bar On, Nathan Intrator, Ady Sasson

“…The study highlights the feasibility of using a single-channel EEG device combined with advanced machine learning models, offering a practical and accessible method for evaluating cognitive function and identifying individuals at risk in various settings.…”
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Artificial intelligence for severity triage based on conversations in an emergency department in Korea by Jae Won Seo, Sung-Joon Park, Young Jae Kim, Jung-Youn Kim, Kwang Gi Kim, Young-Hoon Yoon

“…To verify the robustness of the models, we performed tenfold cross-validation. Based on the area under the receiver operating characteristic curve (AUROC) values, the support vector machine achieved the best performance among the term frequency-inverse document frequency-based conventional machine learning models with an AUROC of 0.764 (95% CI 0.019). …”
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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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Seismic Attribute Extraction and Application Based on the Gabor Wavelet Transform by Ran Xiong, Xuri Huang, Liang Guo, Xuan Zou, Haonan Tian

“…This can be captured by machine learning and deep learning methods to stratigraphic segmentation as many people investigated in industry. …”
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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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Rewiring Sustainability: How Digital Transformation and Fintech Innovation Reshape Environmental Trajectories in the Industry 4.0 Era by Zhuoqi Teng, Han Xia, Yugang He

“…Robustness checks via the system-generalized method of moments confirm the stability of these relationships, while machine learning models—Random Forest and XGBoost—highlight digital variables as top predictors of emissions reduction. …”
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Big Data Analytics for Uncovering Voxel Connectivity Patterns in Attention Deficit Hyperactivity Disorder by Caraka RE, Supardi K, Gio PU, Isnaniawardhani V, Chen RC, Djatmiko B, Pardamean B

“…The performance of different activation functions—ReLU, Sigmoid, and Tanh—was evaluated within deep neural networks.Results: Several key brain regions, including the Fusiform Gyrus, Thalamus, and Superior Temporal Gyrus, were identified as significant predictors for ADHD. The integration of machine learning models demonstrated improved classification accuracy, with ReLU-based neural networks outperforming others in most evaluation metrics.Discussion: The study demonstrates the potential of a robust, integrated machine learning framework to analyze high-dimensional neuroimaging data and identify biologically relevant markers of ADHD. …”
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