Advanced Classifiers and Feature Reduction for Accurate Insomnia Detection Using Multimodal Dataset by Ameya Chatur, Mostafa Haghi, Nagarajan Ganapathy, Nima TaheriNejad, Ralf Seepold, Natividad Martinez Madrid

“…Our results demonstrate that the ensemble classifiers generalize well on the dataset regardless of the feature count, while other algorithms are hindered by the curse of dimensionality.…”
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A Piezoelectric Sensor Based on MWCNT-Enhanced Polyvinyl Chloride Gel for Contact Perception of Grippers by Qiyun Zhong, Qingsong He, Diyi Liu, Xinyu Lu, Siyuan Liu, Yuze Ye, Yefu Wang

“…The optimal PMPG (PVC:DBA = 1:5, 1 wt% MWCNTs) exhibited outstanding performance. …”
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A Systematic Integration of Artificial Intelligence Models in Appendicitis Management: A Comprehensive Review by Ivan Maleš, Marko Kumrić, Andrea Huić Maleš, Ivan Cvitković, Roko Šantić, Zenon Pogorelić, Joško Božić

“…Artificial intelligence (AI) and machine learning (ML) are transforming the management of acute appendicitis by enhancing diagnostic accuracy, optimizing treatment strategies, and improving patient outcomes. …”
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Literature Review of Prognostic Factors in Secondary Generalized Peritonitis by Valerii Luțenco, Adrian Beznea, Raul Mihailov, George Țocu, Verginia Luțenco, Oana Mariana Mihailov, Mihaela Patriciu, Grigore Pascaru, Liliana Baroiu

“…The Mannheim Peritonitis Index (MPI) remains a widely validated prognostic tool, while APACHE II and SOFA scores also provide valuable risk estimates. …”
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Enhanced Feature Selection via Hierarchical Concept Modeling by Jarunee Saelee, Patsita Wetchapram, Apirat Wanichsombat, Arthit Intarasit, Jirapond Muangprathub, Laor Boongasame, Boonyarit Choopradit

“…Moreover, this study is considered to provide a mathematical feature selection approach with optimization based on a paired-samples <i>t</i>-test. …”
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Blockchain Approach for Healthcare Using Fog Topology and Lightweight Consensus by Aya Laouamri, Sarra Cherbal, Yacine Mosbah, Chahrazed Benrebbouh, Kamir Kharoubi

“…The proposed architecture addresses critical challenges and offers practical benefits such as resource efficiency and system stability. While promising, the framework requires real-world testing and further optimization to overcome potential scalability bottlenecks in large-scale healthcare deployments.…”
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Beyond Linearity: Uncovering the Complex Spatiotemporal Drivers of New-Type Urbanization and Eco-Environmental Resilience Coupling in China’s Chengdu–Chongqing Economic Circle with... by Caoxin Chen, Shiyi Wang, Meixi Liu, Ke Huang, Qiuyi Guo, Wei Xie, Jiangjun Wan

“…A random forest (RF) model, interpreted with Shapley Additive exPlanations (SHAP) and Partial Dependence Plot (PDP) algorithms, explores nonlinear driving mechanisms, while Geographically and Temporally Weighted Regression (GTWR) assesses drivers’ spatiotemporal heterogeneity. …”
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Advanced Interpretation of Bullet-Affected Chest X-Rays Using Deep Transfer Learning by Shaheer Khan, Nirban Bhowmick, Azib Farooq, Muhammad Zahid, Sultan Shoaib, Saqlain Razzaq, Abdul Razzaq, Yasar Amin

“…Special deep learning algorithms went through a process of optimization before researchers improved their ability to detect and place objects. …”
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Comparative study of machine learning methods for mapping forest fire areas using Sentinel-1B and 2A imagery by Xinbao Chen, Xinbao Chen, Yaohui Zhang, Shan Wang, Zecheng Zhao, Chang Liu, Junjun Wen

“…The combination of spectral and Normalized Difference Vegetation Index (NDVI) features achieved an optimal accuracy for identifying forest areas with an overall accuracy (OA) of 93.52%. …”
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Diagnostic Models for Differentiating COVID-19-Related Acute Ischemic Stroke Using Machine Learning Methods by Eylem Gul Ates, Gokcen Coban, Jale Karakaya

“…These findings demonstrate the potential of AI-driven diagnostic tools to identify high-risk patients, support optimized treatment strategies, and ultimately improve clinical implications.…”
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Development and validation of a machine learning model for online predicting the risk of in heart failure: based on the routine blood test and their derived parameters by Jianchen Pu, Yimin Yao, Xiaochun Wang

“…This online forecasting tool not only processes a large amount of data but also continuously optimizes and adjusts the accuracy of the model according to the latest medical research and clinical data. …”
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Strategies to Reduce Left Anterior Descending Artery and Left Ventricle Organ Doses in Radiotherapy Planning for Left-Sided Breast Cancer by Umut Diremsizoglu, Nezihan Topal, Aykut Oguz Konuk, Ibrahim Halil Suyusal, Dogacan Genc, Onur Ari, Hasan Furkan Cevik, Aysegul Ucuncu Kefeli, Maksut Gorkem Aksu, Emine Binnaz Sarper

“…The doses to the LAD and LV were added to the optimization algorithms. Two volumetric modulated arc therapy (VMAT) plans were created for each patient. …”
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Long-term planning optimisation of sustainable energy systems: A systematic review and meta-analysis of trends, drivers, barriers, and prospects by Soheil Mohseni, Alan C. Brent

“…Nevertheless, despite the well-documented drivers that promote the adoption of renewable energy, an array of social, technical, economic, and regulatory barriers poses significant impediments to the realisation of optimally sized and operated renewable and sustainable energy systems. …”
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Assessing the temporal transferability of machine learning models for predicting processing pea yield and quality using Sentinel-2 and ERA5-land data by Michele Croci, Manuele Ragazzi, Alessandro Grassi, Giorgio Impollonia, Stefano Amaducci

“…Accurate pre-harvest prediction of yield and quality (tenderometric reading, TR) is crucial for the processing pea industry due to a narrow optimal harvest window. Machine learning (ML) models offer potential, but their real-world utility depends on their performance stability across different years. …”
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Enhanced Position-Aided Beam Prediction Using Real-World Data and Enhanced-Convolutional Neural Networks by Ahmed Abd El Moaty Mohamed Gouda, Ehab K. I. Hamad, Aziza I. Hussein, M. Mourad Mabrook, A. A. Donkol

“…In this work, an Enhanced Convolutional Neural Network model (E-CNN) is proposed for optimal prediction of beam indices with the aid of real-world GPS position data. …”
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A Comparative Performance Evaluation of OFDM, GFDM, and OTFS in Impulsive Noise Channels by Mohsen Sheikh-Hosseini, Farhad Rahdari, Hazhir Ghasemnezhad, Somayeh Ahmadi, Murat Uysal

“…This method examines the impact of variations in the precoder order and explores the application of iterative algorithms for more optimal designing of the precoder. …”
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ST-YOLOv8: Small-Target Ship Detection in SAR Images Targeting Specific Marine Environments by Fei Gao, Yang Tian, Yongliang Wu, Yunxia Zhang

“…Furthermore, the ST-YOLOv8 model outperforms several state-of-the-art multi-scale ship detection algorithms on both datasets. In summary, the ST-YOLOv8 model, by integrating advanced neural network architectures and optimization techniques, significantly improves detection accuracy and reduces false detection rates. …”
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Regularized Kaczmarz Solvers for Robust Inverse Laplace Transforms by Marta González-Lázaro, Eduardo Viciana, Víctor Valdivieso, Ignacio Fernández, Francisco Manuel Arrabal-Campos

“…Quantitative evaluation via mean squared error (MSE), Wasserstein distance, total variation, peak signal-to-noise ratio (PSNR), and runtime demonstrates that Wasserstein–Kaczmarz attains an optimal balance of speed (0.53 s per inversion) and accuracy (MSE = <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>4.7</mn><mo>×</mo><msup><mn>10</mn><mrow><mo>−</mo><mn>8</mn></mrow></msup></mrow></semantics></math></inline-formula>), while TRAIn achieves the highest fidelity (MSE = <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>1.5</mn><mo>×</mo><msup><mn>10</mn><mrow><mo>−</mo><mn>8</mn></mrow></msup></mrow></semantics></math></inline-formula>) at a modest computational cost. …”
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A novel lightweight YOLOv8-PSS model for obstacle detection on the path of unmanned agricultural vehicles by Zhijian Chen, Yijun Fang, Jianjun Yin, Shiyu Lv, Farhan Sheikh Muhammad, Lu Liu

“…When compared with other algorithms, such as Faster RCNN, SSD, YOLOv3-tiny, and YOLOv5, the improved model strikes an optimal balance between parameter count, computational efficiency, detection speed, and accuracy, yielding superior results. …”
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Construction and SHAP interpretability analysis of a risk prediction model for feeding intolerance in preterm newborns based on machine learning by Hui Xu, Xingwang Peng, Ziyu Peng, Rui Wang, Rui Zhou, Lianguo Fu

“…Second, ML models were constructed based on the logistic regression (LR), decision tree (DT), support vector machine (SVM) and eXtreme Gradient Boosting (XGBoost) algorithms, after which random sampling and tenfold cross-validation were separately used to evaluate and compare these models and identify the optimal model. …”
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