Federated learning based reference evapotranspiration estimation for distributed crop fields. by Muhammad Tausif, Muhammad Waseem Iqbal, Rab Nawaz Bashir, Bayan AlGhofaily, Alex Elyassih, Amjad Rehman Khan

“…The Random Forest Regressor (RFR) performance yields the local machine learning models against each selected site. …”
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Simulated Annealing-Based Hyperparameter Optimization of a Convolutional Neural Network for MRI Brain Tumor Classification by Sofia El Amoury, Youssef Smili, Youssef Fakhri

“…Recent advances in deep learning, particularly through the application of Convolutional Neural Networks (CNNs), have transformed medical image analysis by enabling automated, high-accuracy feature extraction. Despite their promise, the performance of CNNs is highly contingent upon optimal hyperparameter tuning, a process that can be both computationally demanding and pivotal for model efficacy. …”
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PlantNet: Scalable Convolutional Neural Network for Image-Based Plant Disease Detection by Sinha Anupa, Kumaraswamy Balasubramaniam

“…By leveraging deep learning techniques, PlantNet processes large-scale image datasets to detect disease symptoms with high precision. The model employs transfer learning, utilizing pre-trained networks on vast image repositories before fine-tuning on a specialized plant disease dataset, thereby enhancing feature extraction while minimizing computational complexity. …”
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Predicting mortgage credit defaults in morocco using machine learning approaches by Amine Hade, Mohamed Elhia

“…A bootstrap test, using the F1-score as the primary criterion, was conducted to determine whether the performance differences among the three top-performing models were statistically significant. …”
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Optimizing the core loading pattern and fuel composition in a hexagonal small modular nuclear reactor via ANN-PSO approach by G.R. Ansarifar, M.R. Karimi, H. Zayermohammadi Rishehri

“…The study investigates the impact of altering the arrangement pattern of FAs and the quantity of gadolinia-containing rods within these assemblies. Various models featuring hexagonal FAs, containing different mixed fuels, were tested through a series of simulations. …”
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Embedded Rough-Neck Helmholtz Resonator Low-Frequency Acoustic Attenuator by Xianming Sun, Tao Yu, Lipeng Wang, Yunshu Lu, Changzheng Chen

“…To achieve low-frequency absorption within a limited spatial volume, this study proposes an embedded Helmholtz resonator featuring a roughened neck and establishes a numerical computational model that incorporates thermos viscous effects. …”
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VAE-Based Real-Time Anomaly Detection Approach for Enhanced V2X Communication Security by Yonas Teweldemedhin Gebrezgiher, Sekione Reward Jeremiah, Stefanos Gritzalis, Jong Hyuk Park

“…Our model processes streaming data using a sliding window mechanism, ensuring prompt detection of anomalies in the dynamic V2X environment. …”
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Fusion of Personalized Federated Learning (PFL) with Differential Privacy (DP) Learning for Diagnosis of Arrhythmia Disease. by Syed Mohsin Bokhari, Sarmad Sohaib, Muhammad Shafi

“…By employing gated recurrent units (GRUs) for temporal sequence modeling along with feature fusion techniques and local differential privacy enforcement, FLPMDP ensures robust classification performance with data confidentiality. …”
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Research on a UAV-View Object-Detection Method Based on YOLOv7-Tiny by Yuyang Miao, Xihan Wang, Ning Zhang, Kai Wang, Lianhe Shao, Quanli Gao

“…To address the issues of missed and false detections caused by small object sizes, dense object distribution, and complex scenes in drone aerial images, this study proposes a drone-view object-detection algorithm based on YOLOv7-tiny with a Partial_C_Detect detection head. The algorithm’s performance in handling object occlusion and multi-scale detection is enhanced by introducing the VarifocalLoss loss function and improving the feature fusion network to BiFPN. …”
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Stroke Risk Classification Using the Ensemble Learning Method of XGBoost and Random Forest by Gullam Almuzadid, Egia Rosi Subhiyakto

“…Validation on the hold-out test set demonstrated exceptional ensemble model performance with an AUC of 0.99, 98% accuracy, 98% precision, and 98% recall. …”
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Prediction of Monthly Temperature Over China Based on a Machine Learning Method by Ping Mei, Zixin Yin, Haoyu Wang, Changzheng Liu, Yaoming Liao, Qiang Zhang, Liping Yin

“…We identified three statistical challenges in climate prediction: instability of statistical models, complexity of feature factors, and the nonlinearity of the relationship between predictors and predictands. …”
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Substation Equipment Defect Detection Based on Improved YOLOv8 by Yiwei Sun, Xiangran Sun, Ying Lin, Yi Yang, Zhuangzhuang Li, Lun Du, Chaojun Shi

“…This paper proposes an object detection algorithm for substation equipment defect detection based on improvements to the YOLOv8 model. First, the backbone of YOLOv8 is replaced with EfficientViT, which not only reduces computational redundancy but also enhances the model’s feature extraction capabilities, thereby improving overall performance. …”
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Innovative cone resistance and sleeve friction prediction from geophysics based on a coupled geo-statistical and machine learning process by A. Bolève, R. Eddies, M. Staring, Y. Benboudiaf, H. Pournaki, M. Nepveaux

“…A sensitivity study is also performed on input features used to train the ML algorithm to better define the optimal combination of input features for the prediction. …”
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Advances in Infant Cry Paralinguistic Classification—Methods, Implementation, and Applications: Systematic Review by Geofrey Owino, Bernard Shibwabo

“…The overall quality assessment was performed using TRIPOD (Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis) guidelines. …”
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Hybrid boosted attention-based LightGBM framework for enhanced credit risk assessment in digital finance by Chengwei Ying, Anlu Shi, Xiongyi Li

“…The results demonstrate that HBA-LGBM achieves the lowest RMSE (11.53) and MAPE (4.44%), with an R2 score of 0.998, outperforming deep learning and ensemble-based approaches. The model’s superior performance is attributed to its ability to adaptively refine borrower risk assessment, effectively balance computational efficiency with model interpretability, and provide a robust, scalable solution for digital finance applications. …”
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Improvement of a Subpixel Convolutional Neural Network for a Super-Resolution Image by Muhammed Fatih Ağalday, Ahmet Çinar

“…In this model, a subpixel convolution layer is presented that learns a series of filters to enhance low-resolution feature maps to high-resolution images. …”
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Machine learning based screening of biomarkers associated with cell death and immunosuppression of multiple life stages sepsis populations by Jie Yang, Fanyan Ou, Binbin Li, Lixiong Zeng, Qiuli Chen, Houyu Gan, Jianing Yu, Qian Guo, Jihua Feng, Jianfeng Zhang

“…It was significantly positively correlated with M0-type macrophages and significantly negatively correlated with activated CD4 + memory T cells. (3) In the adult sepsis population, models constructed using GBM, SVM, and LASSO algorithms showed the best performance. …”
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Improved RT-DETR Framework for Railway Obstacle Detection by Peng Li, Yanhui Peng, Su-Mei Wang, Cheng Zhong

“…However, existing algorithms face challenges related to insufficient multiscale object detection, high model redundancy, and poor real-time performance. …”
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APGCN-CF: A Spatio-Temporal-Aware Graph Convolutional Network Framework for Minor Agricultural Product Recommendation in Rural E-Commerce by Wenjin Hou, Ke Zhu, Pingzeng Liu, Honghua Jiang, Yan Zhang, Xinran Yu

“…Notably, the framework exhibits superior performance in challenging scenarios characterized by data sparsity and cold-start conditions through its effective feature extraction and relationship modeling capabilities. …”
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An attention mechanism and fully connected layer-based dual-branch network for bioluminescent tomographic reconstruction by Lin Wang, Chenrui Pan, Minghua Zhao, Xin Cao, Xueli Chen

“…The outputs of the AMDB are combined through feature fusion to produce the final result. Numerical simulations and experimental results demonstrate that the FC-AM network significantly improves BLT reconstruction performance compared to existing methods (KNN_LC and AMLC networks), offering enhanced stability and accuracy.…”
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