Artificial Intelligence Meets Bioequivalence: Using Generative Adversarial Networks for Smarter, Smaller Trials by Anastasios Nikolopoulos, Vangelis D. Karalis

“…This study highlights the potential of WGANs to improve data augmentation and optimize subject recruitment in BE studies.…”
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Numerical modeling of electromagnetic wave propagation in spatially-varying evaporation duct conditions via 3D parabolic equation method by Hanjie Ji, Hanjie Ji, Lixin Guo, Yan Zhang, Tianhang Nie, Yiwen Wei, Jinpeng Zhang, Qingliang Li, Xiangming Guo, Yusheng Zhang

“…Quantitative analysis reveals that the 3DPE framework delivers over 40% performance improvement compared to the 2D model. This approach significantly enhances predictive accuracy for over-the-horizon radar assessments in maritime environments, providing crucial support for optimizing next-generation communication systems.…”
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Time-Dependent Vehicle Routing Problem with Drones Under Vehicle Restricted Zones and No-Fly Zones by Shuo Wei, Houming Fan, Xiaoxue Ren, Xiaolong Diao

“…Compared to the genetic neighborhood search algorithm and the hybrid genetic algorithm, the improvement rates are 5.1% and 13.0%, respectively. …”
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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. …”
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Sentinel-2 image based smallholder crops classification and accuracy assessment by UAV data by Kadierye Maolan, Yusufujiang Rusuli, Zhang XuHui, Yimuran Kuluwan

“…The Sentinel-2 image based smallholder crops classification results indicate that: The effective combination of optimal input feature variables selection and classification models significantly improves crop classification accuracy. …”
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Optimisation of the adaptive neuro-fuzzy inference system for adjusting low-cost sensors PM concentrations by Martina Casari, Piotr A. Kowalski, Laura Po

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Development and validation of novel machine learning-based prognostic models and propensity score matching for comparison of surgical approaches in mucinous breast cancer by Chunmei Chen, Jundong Wu, Yutong Fang, Yong Li, Qunchen Zhang

“…We have successfully developed 6 optimal prognostic models utilizing the XGBoost algorithm to accurately predict the survival of MBC patients. …”
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TAE Predict: An Ensemble Methodology for Multivariate Time Series Forecasting of Climate Variables in the Context of Climate Change by Juan Frausto Solís, Erick Estrada-Patiño, Mirna Ponce Flores, Juan Paulo Sánchez-Hernández, Guadalupe Castilla-Valdez, Javier González-Barbosa

“…Additionally, data remediation techniques improve data set quality. The ensemble combines Long Short-Term Memory neural networks, Random Forest regression, and Support Vector Machines, optimizing their contributions using heuristic algorithms such as Particle Swarm Optimization. …”
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Applying a Four-Way Factorial Experimental Model to Diagnose Optimum kNN Parameters for Precise Aboveground Biomass Mapping by Chinsu Lin, Nova D. Doyog

“…This protocol effectively identifies suitable kNN-AGB models, enhancing the ability to delineate areas with low biomass productivity for precision management, while also supporting forest improvement initiatives and biomass-related studies.…”
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GAB-YOLO: a lightweight deep learning model for real-time detection of abnormal behaviors in juvenile greater amberjack fish by Mingxin Liu, Mingxin Liu, Chun Zhang, Cong Lin, Cong Lin

“…Meanwhile, existing automated detection algorithms often struggle with a trade-off between detection accuracy and model size. …”
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A machine learning model for predicting acute respiratory distress syndrome risk in patients with sepsis using circulating immune cell parameters: a retrospective study by Kaihuan Zhou, Lian Qin, Yin Chen, Hanming Gao, Yicong Ling, Qianqian Qin, Chenglin Mou, Tao Qin, Junyu Lu

“…Early identification of patients with sepsis at high risk of developing ARDS is crucial for timely intervention, optimization of treatment strategies, and improvement of clinical outcomes. …”
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Advanced intrusion detection technique (AIDT) for secure communication among devices in internet of medical things (IoMT) by M. Ramya, Pradeep Sudhakaran, Yuvaraj Sivagnanam, C. Santhana Krishnan

“…By employing the integrated patient sensing and network traffic datasets, our research achieves a superior accuracy rate of 96.4% in network intrusion detection compared to the competing algorithms. Furthermore, we incorporated a comprehensive analysis of the implementation of different classification algorithms in IoMT network intrusion detection, further supporting the claim that the proposed framework performs marginally better than alternative models.…”
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Estimation of Leaf Chlorophyll Content of Maize from Hyperspectral Data Using E2D-COS Feature Selection, Deep Neural Network, and Transfer Learning by Riqiang Chen, Lipeng Ren, Guijun Yang, Zhida Cheng, Dan Zhao, Chengjian Zhang, Haikuan Feng, Haitang Hu, Hao Yang

“…Combining the E2D-COS feature selection with TL and DNN significantly improves the estimation accuracy: the R² of the proposed Maize-LCNet model is improved by 0.06–0.11 and the RMSE is reduced by 0.57–1.06 g/cm compared with LCNet-field. …”
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Development and validation of a small-sample machine learning model to predict 5–year overall survival in patients with hepatocellular carcinoma by Tingting Jiang, Xingyu Liu, Wencan He, Hepei Li, Xiang Yan, Qian Yu, Shanjun Mao

“…Additionally, the optimal model was established after rigorous validation. …”
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Advanced Hydroponic Nutrient Management Systems for Vertical Farming Efficiency with IoT and Model Predictive Control to Enhance Sustainable Crop Growth by Almusawi Muntather, Hussein Abbas Hameed Abdul, Raju V. Siva Prasada

“…These technologies are integrated, whereby the aim is to achieve multiple key objectives, such as optimizing nutrient delivery for improved yield, enhancing environmental control for optimal growing conditions, and encouraging sustainable growing practices. …”
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RETRACTED ARTICLE: Screening and identification of susceptibility genes for cervical cancer via bioinformatics analysis and the construction of an mitophagy-related genes diagnosti... by Zhang Zhang, Fangfang Chen, Xiaoxiao Deng

“…Furthermore, using machine learning algorithms, we constructed a clinical prognostic model and validated and optimized it via extensive clinical data. …”
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Analyzing the impact of area of interest (AOI) size and endmember selection on evapotranspiration (ET) estimation through a contextual model (SEBAL) by Hamza Barguache, Jamal Ezzahar, Jamal Elfarkh, Said Khabba, Salah Er-Raki, Valerie Le Dantec, Mohamed Hakim Kharrou, Ghizlane Aouade, Abdelghani Chehbouni

“…Contextual models such as the Surface Energy Balance Algorithm for Land (SEBAL) are particularly valuable for ET estimation. …”
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Computed tomography-based radiomics model for predicting station 4 lymph node metastasis in non-small cell lung cancer by Yanru Kang, Mei Li, Xizi Xing, Kaixuan Qian, Hongxia Liu, Yafei Qi, Yanguo Liu, Yi Cui, Hua Zhang

“…This model serves as an effective auxiliary tool for clinical decision-making and has the potential to optimize treatment strategies and improve prognostic assessment for pN0-pN2 patients. …”
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Development and validation of a machine learning-based risk prediction model for stroke-associated pneumonia in older adult hemorrhagic stroke by Yi Cao, Yi Cao, Haipeng Deng, Shaoyun Liu, Xi Zeng, Yangyang Gou, Weiting Zhang, Yixinyuan Li, Hua Yang, Min Peng

“…The results indicated that among the four machine learning algorithms (XGBoost, LR, SVM, and Naive Bayes), the LR model demonstrated the best and most stable predictive performance. …”
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Minimizing Delay in UAV-Aided Federated Learning for IoT Applications With Straggling Devices by Mudassar Liaq, Waleed Ejaz

“…We then use the concurrent deterministic simplex with root relaxation algorithm. We also propose a deep reinforcement learning (DRL)-based solution to improve runtime complexity. …”
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