Integrating Machine Learning and Material Feeding Systems for Competitive Advantage in Manufacturing by Müge Sinem Çağlayan, Aslı Aksoy

“…The research employs six machine learning (ML) algorithms—logistic regression (LR), decision trees (DT), random forest (RF), support vector machines (SVM), K-nearest neighbors (K-NN), and artificial neural networks (ANN)—to develop a multi-class classification model for material feeding system selection. …”
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The Application of the Gestalt Theory in Music Psychotherapy for Piano by Fukun Cai

“…Compared with the classification accuracies of DBN, restricted Boltzmann machine (RBM), and K nearest neighbor (kNN) algorithms in mixed music environments, the classification effects were improved by about 3.49%, 12.89%, and 7.24%. …”
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Optimized Quality of Service for Real-Time Wireless Sensor Networks Using a Partitioning Multipath Routing Approach by Mohammed Zaki Hasan, Tat-Chee Wan

“…The proposed approach significantly improves the network lifetime, while reducing energy consumption and decreasing average end-to-end delays within the sensor network via optimised resource sharing in intermediate nodes compared with existing routing algorithms.…”
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Regional Sea Surface Skin Temperature Retrieval From HY-1C and HY-1D COCTS by Zhuomin Li, Rui Chen, Mingkun Liu, Lei Guan

“…These satellites carry Chinese ocean color and temperature scanners (COCTS), containing two thermal bands with central wavelengths of 10.8 and 12.0 μm and enabling sea surface temperature (SST) observations. Herein, regional algorithms for SST retrieval are developed for the South China Sea (SCS) by applying radiative transfer modeling. …”
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Applicability Evaluation and Correction of Cover and Management Factor Calculation Methods in the Purple Soil Hilly Region by Ruiyin Chen, Yonggang Zhu, Derong Wu, Jia Zhong, Anbang Wen, Wenwu Wang, Biao Bi, Yuetian Li, Jing Feng, Tiancai Jing

“…The cover and management factor (C/B factor) in the Universal Soil Loss Equation (USLE) series models indicates the effects of vegetation cover and management practices on water erosion. …”
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Artificial Intelligence in the Diagnostic Use of Transcranial Doppler and Sonography: A Scoping Review of Current Applications and Future Directions by Giuseppe Miceli, Maria Grazia Basso, Elena Cocciola, Antonino Tuttolomondo

“…Developing robust, generalizable AI models and integrating multimodal imaging data promise to enhance diagnostic and prognostic capabilities in TCD and ultrasound. …”
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A Quantitative Evaluation of UAV Flight Parameters for SfM-Based 3D Reconstruction of Buildings by Inho Jo, Yunku Lee, Namhyuk Ham, Juhyung Kim, Jae-Jun Kim

“…This study aims to address the critical lack of standardized guidelines for unmanned aerial vehicle (UAV) image acquisition strategies utilizing structure-from-motion (SfM) by focusing on 3D building exterior modeling. A comprehensive experimental analysis was conducted to systematically investigate and quantitatively evaluate the effects of various shooting patterns and parameters on SfM reconstruction quality and processing efficiency. …”
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What is the impact of longer patient travel distances and times on perioperative outcomes following revision knee replacement: a retrospective observational study using data for En... by Sarah Lamb, Tim Briggs, Andrew James Price, William Gray, Andrew D Toms, Jonathan Peter Evans, Jonathan Thomas Evans, Alexander Handel Matthews

“…Evolving practice in England aims to manage these patients in specialised centres with the intention of improving outcomes. This practice will result in longer travel distances and times in this frailer group of patients. …”
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SNUH methylation classifier for CNS tumors by Kwanghoon Lee, Jaemin Jeon, Jin Woo Park, Suwan Yu, Jae-Kyung Won, Kwangsoo Kim, Chul-Kee Park, Sung-Hye Park

“…Compared to two published CNS tumor methylation classification models (DKFZ-MC: Deutsches Krebsforschungszentrum Methylation Classifier v11b4: RandomForest, 767-MC: Multi-Layer Perceptron), our SNUH-MC showed improved performance in F1-score. …”
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Human face localization and detection in highly occluded unconstrained environments by Abdulaziz Alashbi, Abdul Hakim H.M. Mohamed, Ayman A. El-Saleh, Ibraheem Shayea, Mohd Shahrizal Sunar, Zieb Rabie Alqahtani, Faisal Saeed, Bilal Saoud

“…Unconstrained face identification has been significantly improved by the advancements in Deep Learning algorithms (DL). …”
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Labelling Training Samples Using Crowdsourcing Annotation for Recommendation by Qingren Wang, Min Zhang, Tao Tao, Victor S. Sheng

“…The experimental results demonstrate that crowdsourcing annotation significantly improves the performance of machine annotation. Among the ground truth inference algorithms, both HILED and HILI improve the performance of baselines; meanwhile, HILED performs better than HILI.…”
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Leveraging Advanced Technologies for Real-Time Plant Disease Monitoring by Mishra Archana, Afzal Md

“…The ensemble methods improve the accuracy and robustness of the diagnostic through using multiple ML models. …”
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DNA familial binding profiles made easy: comparison of various motif alignment and clustering strategies. by Shaun Mahony, Philip E Auron, Panayiotis V Benos

“…In addition, multiple-alignment strategies for binding profiles and tree-building methods are tested for their efficiency in constructing generalized binding models. A new method for automatic determination of the optimal number of clusters is developed and applied in the construction of a new set of familial binding profiles which improves upon TF classification accuracy. …”
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Spatiotemporal Dynamics of Urban Green Spaces and Climatic Vulnerability of Togolese Cities in the Context of Rapid Urbanisation: The Case of Lome and Kara by Yetondé Deton, Hodabalo Kamou, Abalo Atato, Bimare Kombate, Abalo Mabafei, Josef Yoka, Marra Dourma, Atsu Kudzo Guelly, Komlan Batawila, Koffi Akpagana

“…Additionally, vulnerability models for 2022, 2060, and 2100 were developed using Shared Socioeconomic Pathways (SSP 2.45 and SSP 5.85) and a multifactorial modelling approach. …”
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An Ensemble Learning-Based Predictive Parameterization Approach for Permanent Magnet Synchronous Machines by Sema Nur Ipek, Nur Bekiroglu, Murat Taskiran

“…An averaging voting ensemble model is developed by integrating the two highest-performing algorithms, LRNN and TRF, leveraging the strengths of both algorithms. …”
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A Comprehensive Review of Smartphone and Other Device-Based Techniques for Road Surface Monitoring by Saif Alqaydi, Waleed Zeiada, Ahmed El Wakil, Ali Juma Alnaqbi, Abdelhalim Azam

“…Future work could focus on improving sensor calibration, data synchronization, and machine learning models to handle diverse real-world conditions. …”
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Measuring the impact of technological innovation, green energy, and sustainable development on the health system performance and human well-being: Evidence from a machine learning-... by Alaeddine Mihoub, Montassar Kahia, Mohannad Alswailim

“…Within this context, this study intended to examine the impact of recent innovations, logistical measures, Information and Communication Technology (ICT) diffusion, environmental quality improvements, economic growth, and green (renewable) energy exploitation on health performance and well-being, in Saudi Arabia from 1990 to 2022, by implementing machine learning models (random forest and gradient boosting) and regression algorithms (ridge and lasso). …”
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Artificial Intelligence and MCDA in Circular Economy: Governance Strategies and Optimization for Reverse Supply Chains of Solid Waste by Joel Joaquim de Santana Filho, Arminda do Paço, Pedro Dinis Gaspar

“…The results indicate that hybrid MCDA-AI models, employing techniques, such as TOPSIS, AHP, neural networks, and genetic algorithms, enhance decision-making automation, reduce costs, and improve waste traceability. …”
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Machine learning and transfer learning techniques for accurate brain tumor classification by Seyed Matin Malakouti, Mohammad Bagher Menhaj, Amir Abolfazl Suratgar

“…These results demonstrate the effectiveness of combining ML and transfer learning techniques for accurate brain tumor classification, addressing limitations of prior approaches and offering improved diagnostic reliability. All coding and model implementations were conducted on the Python platform.…”
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Development and validation of a nomogram for predicting in-hospital mortality in older adult hip fracture patients with atrial fibrillation: a retrospective study by Zhenli Li, Jing He, Tiezhu Yao, Guang Liu, Jing Liu, Ling Guo, Mengjia Li, Mengjia Li, Zhengkun Guan, Zhengkun Guan, Ruolian Gao, Jingtao Ma

“…Logistic regression (LR) and Least Absolute Shrinkage and Selection Operator (LASSO) algorithms were employed to screen features. We further used Extreme Gradient Boosting (XGBoost) based on features selected by LR and LASSO algorithms to assist in identifying the final model-established features. …”
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