Machine Learning and Digital-Twins-Based Internet of Robotic Things for Remote Patient Monitoring by Sehat Ullah, Sangeen Khan, David Vanecek, Inam Ur Rehman

“…It uses the virtual twin (VT) to navigate the physical twin (PT) for collecting data from the patient-mounted sensors and applies ML techniques to predict health anomalies. It evaluated six ML algorithms to determine the most accurate model. …”
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Estimation of Ground-Level NO₂ Concentrations Over Megacities Using Sentinel-5P and Machine Learning Models: A Case Study of Istanbul by N. Yagmur Aydin, I. Aydin

“…The performance of three ML algorithms, namely multi-layer perceptron (MLP), support vector regression (SVR), and XGBoost regression (XGB), in estimating the ground level-NO₂ parameter was evaluated both quantitatively using RMSE and MAE accuracy metrics and qualitatively by visual analysis. …”
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Optimization of clustering parameters for single-cell RNA analysis using intrinsic goodness metrics by Nicolina Sciaraffa, Antonino Gagliano, Luigi Augugliaro, Claudia Coronnello, Claudia Coronnello

“…Consequently, fifteen intrinsic measures have been calculated and used to train an ElasticNet regression model in both intra- and cross-dataset approaches to evaluate the possibility of predicting the clustering accuracy.Results and discussionThe first-order interactions demonstrated that the use of the UMAP method for the generation of the neighborhood graph and an increase in resolution has a beneficial impact on accuracy. …”
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Predictions of Multilevel Linguistic Features to Readability of Hong Kong Primary School Textbooks: A Machine Learning Based Exploration by Zhengye Xu, Yixun Li, Duo Liu

“…Fifteen combinations of linguistic features were trained using Support Vector Machine (SVM) and Random Forest (RF) algorithms. …”
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Complex Large-Deformation Multimodality Image Registration Network for Image-Guided Radiotherapy of Cervical Cancer by Ping Jiang, Sijia Wu, Wenjian Qin, Yaoqin Xie

“…On the basis of deep learning, bistructural morphology is added to the model to train the pelvic area registration evaluator, and the model can obtain parameters covering large deformation for loss function. …”
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Predictive modelling employing machine learning, convolutional neural networks (CNNs), and smartphone RGB images for non-destructive biomass estimation of pearl millet (Pennisetum... by Faten Dhawi, Abdul Ghafoor, Norah Almousa, Sakinah Ali, Sara Alqanbar

“…This study employed a transfer learning approach using pre-trained convolutional neural networks (CNNs) alongside shallow machine learning algorithms (Support Vector Regression, XGBoost, Random Forest Regression) to estimate AGB. …”
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Image preference estimation with a data-driven approach: A comparative study between gaze and image features by Yusuke Sugano, Yasunori Ozaki, Hiroshi Kasai, Keisuke Ogaki, Yoichi Sato

“…A dataset of eye movements is collected while the participants are viewing pairs of natural images, and it is used to train image preference label classifiers. The input feature is defined as a combination of various fixation and saccade event statistics, and the use of the random forest algorithm allows us to quantitatively assess how each of the statistics contributes to the classification task. …”
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Disturbance Rejection and Uncertainty Analysis in Wind Turbines Using Model Predictive Control by Alok Kumar, Atul Kelkar

“…For effective wind turbine operations, it is essential to maintain the power limit and reduce the stress on the drive train in the presence of disturbance and uncertain conditions. …”
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Generalizable Solar Irradiance Prediction for Battery Operation Optimization in IoT-Based Microgrid Environments by Ray Colucci, Imad Mahgoub

“…We evaluated five popular machine learning algorithms and applied ensemble methods, achieving a substantial improvement in predictive accuracy. …”
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Speaker Recognition Using Wavelet Cepstral Coefficient, I-Vector, and Cosine Distance Scoring and Its Application for Forensics by Lei Lei, She Kun

“…Moreover, linear discriminant analysis (LDA) and the within-class covariance normalization (WCNN) are added to the CDS algorithm to deal with the channel variability problem. …”
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Machine learning approaches for predicting the structural number of flexible pavements based on subgrade soil properties by Asadullah Ziar

“…Abstract This study presents a machine learning approach to predict the structural number of flexible pavements using subgrade soil properties and environmental conditions. Four algorithms were evaluated, including random forest, extreme gradient boosting, gradient boosting, and K nearest neighbors. …”
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Non-variational quantum random access optimization with alternating operator ansatz by Zichang He, Rudy Raymond, Ruslan Shaydulin, Marco Pistoia

“…However, to date QRAO has only been implemented using variational algorithms, which suffer from the need to train instance-specific variational parameters, making them difficult to scale. …”
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Development of Recurrent Neural Networks for Thermal/Electrical Analysis of Non-Residential Buildings Based on Energy Consumptions Data by Elisa Belloni, Flavia Forconi, Gabriele Maria Lozito, Martina Palermo, Michele Quercio, Francesco Riganti Fulginei

“…Consumptions trends for a building are generated using the EnergyPlus™ dynamic simulation software over a timespan of a year in different locations, and the data are then used to train neural network models. Uncertainty analyses are carried out to evaluate the behavior effectiveness of the artificial neural networks (ANNs) in different weather conditions, and the root mean square error (RMSE) is calculated in terms of mean air temperatures. …”
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Federated Learning Framework Based on Distributed Storage and Diffusion Model for Intrusion Detection on IoT Networks by Ricardo Manzano, Marzia Zaman, Darshana Upadhyay, Nishith Goel, Srinivas Sampalli

“…The proposed framework is trained and evaluated in two datasets. The MNIST (Modified National Institute of Standards and Technology) dataset and BoT-IoT dataset. …”
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The Role of Education in Building National Soft Power: An Empirical Analysis From a Global Perspective Using Deep Neural Networks by Yun Bai

“…Thirdly, we evaluate the model’s performance using several performance metrics, such as accuracy, RMSE, MAE, and precision. …”
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Improving ACS prediction in T2DM patients by addressing false records in electronic medical records using propensity score by David Seung U Lee, Jung-Hyun Won, Howard Lee

“…Notably, 6.9% experienced ACS during their insulin treatment. The ML models trained on PS datasets generally outperformed the models trained on raw datasets. …”
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Synthetic Data-Enhanced Classification of Prevalent Osteoporotic Fractures Using Dual-Energy X-Ray Absorptiometry-Based Geometric and Material Parameters by Luca Quagliato, Jiin Seo, Jiheun Hong, Taeyong Lee, Yoon-Sok Chung

“…To model the association of the bone’s current health status with prevalent FXs, three prediction algorithms—extreme gradient boosting (XGB), support vector machine, and multilayer perceptron—were trained using two-dimensional dual-energy X-ray absorptiometry (2D-DXA) analysis results and subsequently benchmarked. …”
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Utilizing artificial intelligence for the detection of hemarthrosis in hemophilia using point-of-care ultrasonography by Pascal N. Tyrrell, María Teresa Alvarez-Román, Nihal Bakeer, Brigitte Brand-Staufer, Victor Jiménez-Yuste, Susan Kras, Carlo Martinoli, Mauro Mendez, Azusa Nagao, Margareth Ozelo, Janaina B.S. Ricciardi, Marek Zak, Johannes Roth

“…Results: The algorithms exhibited high performance across all joints and all cohorts. …”
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Opposition-Based White Shark Optimizer for Optimizing Modified EfficientNetV2 in Road Crack Classification by Mohammed Al-Shalabi, Mohammed A. Mahdi, Malik Braik, Mohammed Azmi Al-Betar, Shahanawaj Ahamad, Sawsan A. Saad

“…Although Convolutional Neural Networks (CNNs) and meta-heuristic algorithms have proven effective in solving real-world problems, their use in low-contrast pavement crack images is worth investigating. …”
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Efficient Argan Tree Deforestation Detection Using Sentinel-2 Time Series and Machine Learning by Younes Karmoude, Soufiane Idbraim, Souad Saidi, Antoine Masse, Manuel Arbelo

“…This study monitors changes in an argan forest near Agadir, Morocco, from 2017 to 2023 using Sentinel-2 satellite imagery and advanced image processing algorithms. Various machine learning models were evaluated for argan tree detection, with LightGBM achieving the highest accuracy when trained on a dataset integrating spectral bands, temporal features, and vegetation indices information. …”
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