Application of the Bidirectional Encoder Representations from Transformers Model for Predicting the Abbreviated Injury Scale in Patients with Trauma: Algorithm Development and Vali... by Jun Tang, Yang Li, Keyu Luo, Jiangyuan Lai, Xiang Yin, Dongdong Wu

“…We compared the BERT model with previous research results and current mainstream machine learning methods to verify its advantages in prediction tasks. …”
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Utilizing Vision Transformers for Predicting Early Response of Brain Metastasis to Magnetic Resonance Imaging-Guided Stage Gamma Knife Radiosurgery Treatment by Simona Ruxandra Volovăț, Diana-Ioana Boboc, Mădălina-Raluca Ostafe, Călin Gheorghe Buzea, Maricel Agop, Lăcrămioara Ochiuz, Dragoș Ioan Rusu, Decebal Vasincu, Monica Iuliana Ungureanu, Cristian Constantin Volovăț

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A reliable and privacy-preserved federated learning framework for real-time smoking prediction in healthcare by Siddhesh Fuladi, D. Ruby, N. Manikandan, Animesh Verma, M. K. Nallakaruppan, Shitharth Selvarajan, Shitharth Selvarajan, Shitharth Selvarajan, Preeti Meena, V. P. Meena, Ibrahim A. Hameed

“…The ever-evolving domain of machine learning has witnessed significant advancements with the advent of federated learning, a paradigm revered for its capacity to facilitate model training on decentralized data sources while upholding data confidentiality. …”
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Dynamic AI-Enhanced Therapeutic Framework for Precision Medicine Using Multi-Modal Data and Patient-Centric Reinforcement Learning by R. Gayathri, S. K. B. Sangeetha, R. Sangeetha, G. Leena Rosalind Mary, Sandeep Kumar Mathivanan, Usha Moorthy

“…The integration of Artificial Intelligence (AI) and Machine Learning (ML) is revolutionizing precision medicine by enabling the customization of therapeutic strategies to individual patient profiles. …”
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A review of enhancing wind power with AI: applications, economic implications, and green innovations by Yanmiao Sun, Weixue Han

“…The findings indicate that AI, predominantly represented by ML and hybrid AI models, contributes to wind energy systems in three primary domains: first, the forecasting and analysis of variables, second the optimization of wind turbines (WTs) performance through advanced maintenance management and condition monitoring, and finally wind farm layout and optimization. …”
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Medical Device Failure Predictions Through AI-Driven Analysis of Multimodal Maintenance Records by Noorul Husna Abd Rahman, Khairunnisa Hasikin, Nasrul Anuar Abd Razak, Ayman Khallel Al-Ani, D. Jerline Sheebha Anni, Prabu Mohandas

“…This study proposes a data-driven machine-learning model for predicting medical device failure. …”
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Forecasting Eruptions at Steamboat Geyser: Time Scales, Differentiability, and Detectability of Seismic Precursors Through Data‐Driven Methods by Alberto Ardid, Anna Barth, David Dempsey, Michael Manga, Shane J. Cronin

“…A template matching analysis identified an optimal 18‐hr window for detecting precursors. We applied a random forest to classify pre‐eruptive and non‐eruptive data for out‐of‐sample eruptions (eruptions that were not included in the model's training data), showing ability to distinguish between the two. …”
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Data-driven electrochemical behavior prediction for refractory high-entropy alloys by global and focused learning by Xinpeng Zhao, Haiyou Huang, Yanjing Su, Lijie Qiao, Yu Yan

“…Taking refractory high-entropy alloys (RHEAs) as a case study, we establish prediction models for their corrosion behavior based on potentiodynamic polarization curve data and interpretable GFL. …”
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Dynamic Orbital Resource Management (DORM) for 6G Networks: Enhancing Cybersecurity in Non-Terrestrial Systems by Fatemah Alharbi, Abeer Alhuzali, Easa Alalwany, Abdullah Alfahaid

“…DORM leverages predictive modeling and real-time monitoring to detect anomalies such as jamming, spoofing, and DoS attacks while simultaneously optimizing network performance. …”
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Soil Organic Matter (SOM) Mapping in Subtropical Coastal Mountainous Areas Using Multi-Temporal Remote Sensing and the FOI-XGB Model by Hao Zhang, Xiaomei Li, Jinming Sha, Jiangning Ouyang, Zhipeng Fan

“…These features, along with topographic covariates, were processed using an improved Feature-Optimized and Interpretable XGBoost (FOI-XGB) model for key variable selection and spatial mapping. …”
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Short-Term Forecasting of Non-Stationary Time Series by Amir Aieb, Antonio Liotta, Alexander Jacob, Muhammad Azfar Yaqub

“…In this work, we use a daily standardized precipitation index dataset as an example of NTS, whereby the heterogeneous variability of daily precipitation poses complexities for traditional machine-learning models in predicting future events. …”
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Development and Validation of the Relational Tissue Altered (RTA) Index: Applied Artificial Intelligence for the Assessment of Structural Impact from Laser Vision Correction by Aydano P. Machado, Louise Pellegrino G. Esporcatte, Marcella Q. Salomão, Bernardo T. Lopes, João Marcelo A. G. Lyra, Renato Ambrósio

“…Methods An artificial intelligence-based machine learning approach was used to create a predictive model for the impact of corneal refractive surgery. …”
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Linear B-cell epitope prediction for SARS and COVID-19 vaccine design: Integrating balanced ensemble learning models and resampling strategies by Fatih Gurcan

“…The implemented resampling methods were designed to improve class balance and enhance model training. The rebalanced datasets were then used in model building with ensemble classifiers employing Boosting, Bagging, and Balancing strategies. …”
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Identifying Disinformation on the Extended Impacts of COVID-19: Methodological Investigation Using a Fuzzy Ranking Ensemble of Natural Language Processing Models by Jian-An Chen, Wu-Chun Chung, Che-Lun Hung, Chun-Ying Wu

“…ResultsAfter training on the dataset, various classification methods were evaluated on the test set, including the fuzzy rank-based method and state-of-the-art large language models. Experimental results reveal that language models, particularly XLNet, outperform traditional approaches that combine term frequency–inverse document frequency features with support vector machine or utilize deep models like HAN. …”
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Deep learning based stacking ensembles for tropical sorghum classification by Muhammad Aqil, Muhammad Azrai, Roy Efendi, Nining Nurini Andayani, Suwardi, Bunyamin Zainuddin, Suarni, Herawati, Andi Irma Damayanti, Muhammad Jihad, Syafruddin, Ramlah Arief, Paesal, Yustisia, Rahman

“…Grid and Bayesian search optimizations were applied for parameter tuning, and model performance was assessed using an 80/20 train-validation split and tenfold cross-validation. …”
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Considering the effect of non-landslide sample selection on landslide susceptibility assessment by Youchen Zhu, Deliang Sun, Haijia Wen, Qiang Zhang, Qin Ji, Changming Li, Pinggen Zhou, Jianjun Zhao

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Effective Breast Cancer Classification Using Deep MLP, Feature-Fused Autoencoder and Weight-Tuned Decision Tree by Nagham Rasheed Hameed Alsaedi, Mehmet Fatih Akay

“…This paper presents an advanced machine learning algorithm designed to improve classification accuracy in breast cancer diagnosis. …”
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Precise prediction of choke oil rate in critical flow condition via surface data by Qing Wang, Muntadher Abed Hussein, Bhavesh Kanabar, Anupam Yadav, Asha Rajiv, Aman Shankhyan, Sachin Jaidka, Mehul Manu, Issa Mohammed Kadhim, Zainab Jamal Hamoodah, Fadhil Faez, Mohammad Mahtab Alam, Hojjat Abbasi

“…Accurate estimation of choke oil flow rate from surface-related parameters are critical to production optimization of oil fields. This is the first study that addresses the challenge of accurately predicting oil production rates by utilizing various advanced machine learning methods including Random Forest, convolutional neural network, support vector machine, multilayer perceptron artificial neural network and ridge regression methods. …”
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Use of Artificial Intelligence in the Digital Economy by N. A. Olimov

“…Particular attention is paid to machine learning models that allow analyzing large volumes of data for strategic decision-making. …”
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Enhanced Hybrid Artificial Fish Swarm Algorithm for Three-Dimensional Path Planning Applied to Robotic Systems by Ilias Chouridis, Gabriel Mansour, Vasileios Papageorgiou, Michel Theodor Mansour, Apostolos Tsagaris

“…Finally, a method called “multiple laser activation” is introduced to overcome both the main disadvantage of the application of AFSAs in path planning and the limitation of the finite number of possible movement points that appear when bio-inspired algorithms are applied to generate the optimal path in a grid environment. The resulting path is applied to real-world challenges with drones, coordinate measuring machines, and industrial robotic arms.…”
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