Feature Selection Using Pearson Correlation for Ultra-Wideband Ranging Classification by Gita Indah Hapsari, Rendy Munadi, Bayu Erfianto, Indrarini Dyah Irawati

“…The Random Forest and Gradient Boosting models exhibit superior performance, maintaining classification accuracy above 90%. …”
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Enhanced Prediction of Bond Strength in Corroded RC Structures Using Advanced Feature Selection and Ensemble Learning Framework by Jin-Yang Gui, Zhao-Hui Lu, Chun-Qing Li

“…Compared to traditional machine learning and analytical models, it demonstrates enhanced accuracy, generalization, and interpretability. …”
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Combination of group method of data handling neural network with multi-objective gray wolf optimizer to predict the viscosity of MWCNT-TiO2 -oil SAE50 nanofluid by Hongfei Zhou, Ali B.M. Ali, Hussein Zekri, Hanaa Kadhim Abdulaali, Pardeep Singh Bains, Rohit Sharma, Dilsora Abduvalieva, Mohammadreza Baghoolizadeh, Soheil Salahshour, Mohammad Hashemian

“…Finally, by using the extracted equations by the GMDH neural network, the best models or the Pareto front can be obtained using the MOGWO meta-heuristic algorithm. …”
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Neural network-based method for measuring the impacts of epileptic brain activities on cardiac cycles by Cândida H. L. Alves, Gilberto S. Alves, Gilberto S. Alves, Rômulo Kunrath, Mariélia Barbosa L. de Freitas, João Pedro G. Castor, Allan Kardec Barros, Diego Dutra Sampaio, Jonathan Araújo Queiroz

“…Conversely, wearable methods, easily accessible, such as electrocardiogram (ECG), represent an alternative for indirectly monitoring brain activity through cardiac cycles. A computational model was developed based on statistical cycles and neural networks to measure changes in the morphology of ECG waves. …”
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Prediction of Survival in Patients With Esophageal Cancer After Immunotherapy Based on Small-Size Follow-Up Data by Yuhan Su, Chaofeng Huang, Chen Yang, Qin Lin, Zhong Chen

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Influence of next-generation artificial intelligence on headache research, diagnosis and treatment: the junior editorial board members’ vision – part 2 by Igor Petrušić, Chia-Chun Chiang, David Garcia-Azorin, Woo-Seok Ha, Raffaele Ornello, Lanfranco Pellesi, Eloisa Rubio-Beltrán, Ruth Ruscheweyh, Marta Waliszewska-Prosół, William Wells-Gatnik

“…Furthermore, AI-driven advances in drug discovery leverage machine learning and generative AI to accelerate the identification of novel therapeutic targets and optimize treatment strategies for migraine and other headache disorders. …”
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Zero-Shot Learning for Accurate Project Duration Prediction in Crowdsourcing Software Development by Tahir Rashid, Inam Illahi, Qasim Umer, Muhammad Arfan Jaffar, Waheed Yousuf Ramay, Hanadi Hakami

“…The implementation of the proposed automated duration prediction model is crucial for enhancing the success rate of crowdsourcing projects, optimizing resource allocation, managing budgets effectively, and improving stakeholder satisfaction.…”
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Exploring Task-Related EEG for Cross-Subject Early Alzheimer’s Disease Susceptibility Prediction in Middle-Aged Adults Using Multitaper Spectral Analysis by Ziyang Li, Hong Wang, Jianing Song, Jiale Gong

“…The experimental results reveal that the MSIT task achieves optimal cross-subject classification performance using the Support Vector Machine (SVM) approach with the TFAAT feature set, yielding a Receiver Operating Characteristic Area Under the Curve (ROC AUC) of 58%. …”
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A Reconfigurable 1x2 Photonic Digital Switch Controlled by an Externally Induced Metasurface by Alessandro Fantoni, Paolo Di Giamberardino

“…MMI couplers, based on self-imaging multimode waveguides, are very compact integrated optical components that can perform many different splitting and recombining functions. The input–output model has been defined using a machine learning approach; a high number of images have been generated through simulations, based on the beam propagation algorithm, obtaining a large dataset for an MMI structure under different activation maps of the MOS pixels. …”
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A Detailed Thermal Analysis for Performance Improvement of Axial Transverse-flux-switching PM Wind Turbine Generator by A. Ghaheri, A. Zarghani, E. Afjei, H. Torkaman

“…Accurate thermal modeling and temperature measurement of advanced electric machines with complex structures are mandatory to confirm their reliability and safe operation. …”
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Explaining basketball game performance with SHAP: insights from Chinese Basketball Association by Yan Ou-Yang, Wei Hong, Liming Peng, Cheng-Xi Mao, Wen-Jia Zhou, Wei-Tao Zheng, Quan Wang, Feng Qi, Xue-Wei Li, Shi-Huan Chen, Ce Xu, Yu-Fan Wang

“…Utilizing data from 4100 games across 10 CBA seasons (2013–2023), this study constructs CBA game outcome prediction models using seven machine learning algorithms, including XGBoost, LightGBM, Decision Tree, Random Forest, Support Vector Machines, Logistic Regression, and K-Nearest Neighbors. …”
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A Lightweight YOLO-Based Architecture for Apple Detection on Embedded Systems by Juan Carlos Olguín-Rojas, Juan Irving Vasquez, Gilberto de Jesús López-Canteñs, Juan Carlos Herrera-Lozada, Canek Mota-Delfin

“…Our optimized model achieved a high accuracy and sensitivity (94–99%) and was successfully implemented on a Jetson Xavier NX board, where it reached a processing speed of 37 FPS.…”
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MATHEMATICAL MODEL OF THE BIOTECHNIC SYSTEM CONTROLLED BY THE SYSTEM OF ANIMAL SYSTEM ON THE EXAMPLE OF INFRARED IRRADIATION OF PIG-REMOVAL by L. S. Gerasimovich, A. N. Kosko, A. V. Sinenkii

“…This article proposes a model biotechnical system and exposure control algorithm on the behavior of piglets using machine vision technology. …”
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Improving the efficiency and security of passport control processes at airports by using the R-CNN object detection model by Elhoucine Ouassam, Yassine Dabachine, Nabil Hmina, Belaid Bouikhalene

“… The use of real-time machine learning to optimize passport control procedures at airports can greatly improve both the efficiency and security of the processes. …”
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The Role of Artificial Intelligence in Sports Analytics: A Systematic Review and Meta-Analysis of Performance Trends by Przemysław Pietraszewski, Artur Terbalyan, Robert Roczniok, Adam Maszczyk, Kajetan Ornowski, Daria Manilewska, Szymon Kuliś, Adam Zając, Artur Gołaś

“…Sixteen peer-reviewed studies spanning 13 distinct sports disciplines were included, employing a variety of AI techniques—from classical machine learning algorithms to advanced deep learning and computer vision models. …”
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Automatic Feature Selection for Imbalanced Echocardiogram Data Using Event-Based Self-Similarity by Huang-Nan Huang, Hong-Min Chen, Wei-Wen Lin, Rita Wiryasaputra, Yung-Cheng Chen, Yu-Huei Wang, Chao-Tung Yang

“…<b>Background and Objective:</b> Using echocardiogram data for cardiovascular disease (CVD) can lead to difficulties due to imbalanced datasets, leading to biased predictions. Machine learning models can enhance prognosis accuracy, but their effectiveness is influenced by optimal feature selection and robust classification techniques. …”
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Enhancing Predictive Models in E-Commerce: A Comparative Study Using XGBoost Across Diverse Scenarios by Zhang Jianhao

“…After comparing the performance of these models, the results indicate that XGBoost is the optimal choice. …”
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Artificial Intelligence in Wind Turbine Fault Detection and Diagnosis: Advances and Perspectives by Nejad Alagha, Anis Salwa Mohd Khairuddin, Zineddine N. Haitaamar, Obada Al-Khatib, Jeevan Kanesan

“…Drawing on a curated selection of 55 studies (identified via structured searches across IEEE Xplore, ScienceDirect, and Web of Science), the paper prioritizes research employing data-driven or model-based methodologies with explicit experimental validation and clearly documented data sources. …”
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Hybrid Intelligence approach to study post-processing impact on the mechanical performance of notched additively manufactured AlSi10Mg by Erfan Maleki, Sara Bagherifard, Okan Unal, Mario Guagliano

“…AI identified patterns and developed predictive models that provided deeper insights into how process parameters affect material properties and performance. …”
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Exploring the role of repetitive negative thinking in the transdiagnostic context of depression and anxiety in children by Kuiliang Li, Lei Ren, Xiao Li, Chang Liu, Xuejiao Tan, Ming Ji, Xi Luo

“…Network analysis revealed that RNT’s core features exhibited the highest bridge betweenness and bridge expected influence, indicating a critical mediating role in the co-occurrence of symptoms. The random forest model showed optimal predictive performance (AUC = 0.90, recall = 0.95), supporting its applicability for early screening. …”
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