A new approach to K-nearest neighbors distance metrics on sovereign country credit rating

“…Key findings indicate that FIKNN effectively manages datasets with varying feature relevance and demonstrates a positive correlation between feature diversity and model performance. …”
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Clinical Applicability and Cross-Dataset Validation of Machine Learning Models for Binary Glaucoma Detection by David Remyes, Daniel Nasef, Sarah Remyes, Joseph Tawfellos, Michael Sher, Demarcus Nasef, Milan Toma

“…Data preprocessing included resizing, normalization, and feature extraction to ensure consistency. Among the models, the deep neural network demonstrated the highest generalizability with stable performance across datasets, while the convolutional neural network showed moderate but consistent results. …”
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A YOLO-Based Model for Detecting Stored-Grain Insects on Surface of Grain Bulks by Xueyan Zhu, Dandan Li, Yancheng Zheng, Yiming Ma, Xiaoping Yan, Qing Zhou, Qin Wang, Yili Zheng

“…Compared to other mainstream approaches, the YOLO-SGInsects model achieves better detection and counting performance and is capable of effectively handling tiny stored-grain insect pest detection in grain bulk surfaces. …”
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Machine learning-based prediction of diabetic peripheral neuropathy: model development and clinical validation by Meng Sun, Xingling Sun, Fei Wang, Li Liu

“…Nine machine learning models were developed and compared for DPN risk prediction.ResultsStochastic Gradient Boosting (SGBT) demonstrated the best performance (training AUC: 0.933, 95% CI: 0.921–0.946; testing AUC: 0.811, 95% CI: 0.776–0.843). …”
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YOLOv10-CBRC: A high-precision document image layout analysis model by Zhenjie Wu, Weilan Wang, Hongrui Li

“…Building upon YOLOv10 as the baseline model, the proposed model implements three key improvements: (1) the replacement of the Partial Self-Attention (PSA) module with the Convolutional Block Attention Module (CBAM), which enhances perception of channel-wise information and spatial localization of objects,(2) the adoption of dual-branch Re-upsample and Re-SCDown modules (2Re), which facilitates more effective utilization of multi-scale information,(3) the design of a novel classification feature processor, CIBwithResidualCV3 (CRCV3), which improves performance in classification tasks.Experimental results demonstrate that YOLOv10-CBRC achieves a mAP $$_{50\text {-}95}$$ 50 - 95 of 77.6% on the AbaTND, while YOLOv10-RC reaches mAP $$_{50\text {-}95}$$ 50 - 95 scores of 70.6% and 74.6% on the $$D^4LA$$ D 4 L A and IIIT-AR-13K datasets, respectively, significantly outperforming baseline model. …”
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Connected Vehicles Security: A Lightweight Machine Learning Model to Detect VANET Attacks by Muawia A. Elsadig, Abdelrahman Altigani, Yasir Mohamed, Abdul Hakim Mohamed, Akbar Kannan, Mohamed Bashir, Mousab A. E. Adiel

“…Accordingly, this study introduces a lightweight machine learning model with a gain information feature selection method to detect VANET attacks. …”
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Creep constitutive model of logarithmic fractal structure derivative of deep hard brittle rock by LIU Yipin, LIU Wenbo, ZHANG Shuguang, HUANG Xiang, LI Yingbo, ZHAO Shutian

“…Based on the comprehensive performance, it can be fully determined that the construction process of the model is scientific and reasonable. …”
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Prediction of microbe-drug associations using a CNN-Bernoulli random forest model by Zihao Song, Qingnuo Li, Jincheng Zhao, Qinggang Bu, Zekang Bian, Jia Qu

“…The model constructs feature vectors for all microbe-drug pairs based on known associations, microbe similarity, and drug similarity. …”
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Advancing Spanish Speech Emotion Recognition: A Comprehensive Benchmark of Pre-Trained Models by Alex Mares, Gerardo Diaz-Arango, Jorge Perez-Jacome-Friscione, Hector Vazquez-Leal, Luis Hernandez-Martinez, Jesus Huerta-Chua, Andres Felipe Jaramillo-Alvarado, Alfonso Dominguez-Chavez

“…Feature extraction for speech emotion recognition (SER) has evolved from handcrafted techniques through deep learning methods to embeddings derived from pre-trained models (PTMs). …”
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Data-driven predictive model of coal permeability based on microscopic fracture structure characterization by Tianhao Yan, Xiaomeng Xu, Jiafeng Liu, Yihuai Zhang, Muhammad Arif, Xiaowei Xu, Qiang Wang

“…Key data-driven strategies, including feature selection and hyperparameter tuning, are employed to improve model performance. …”
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Method to Use Transport Microsimulation Models to Create Synthetic Distributed Acoustic Sensing Datasets by Ignacio Robles-Urquijo, Juan Benavente, Javier Blanco García, Pelayo Diego Gonzalez, Alayn Loayssa, Mikel Sagues, Luis Rodriguez-Cobo, Adolfo Cobo

“…The U-Net models, trained solely on synthetic data, showed promising performance (e.g., validation MSE down to 0.0015 for directional filtering) and improved the detectability of faint signals, like bicycles among heavy vehicles, when applied to real DAS measurements from the test site. …”
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A Normative Model Representing Autistic Individuals Amidst Autism Spectrum Phenotypic Heterogeneity by Joana Portolese, Catarina Santos Gomes, Vinicius Daguano Gastaldi, Cristiane Silvestre Paula, Sheila C. Caetano, Daniela Bordini, Décio Brunoni, Jair de Jesus Mari, Ricardo Z. N. Vêncio, Helena Brentani

“…PCA-projected dimensions supported normative modeling where a multivariate normal distribution was used to calculate percentiles. …”
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Detection Model for Cotton Picker Fire Recognition Based on Lightweight Improved YOLOv11 by Zhai Shi, Fangwei Wu, Changjie Han, Dongdong Song, Yi Wu

“…This mechanism aims to enhance the model’s feature extraction capability under challenging environmental conditions, thereby improving overall detection accuracy. …”
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YOLO-SUMAS: Improved Printed Circuit Board Defect Detection and Identification Research Based on YOLOv8 by Ying Tang, Runhao Liu, Sheng Wang

“…Aiming at the demand for defect detection accuracy and efficiency under the trend of high-density and integration in printed circuit board (PCB) manufacturing, this paper proposes an improved YOLOv8n model (YOLO-SUMAS), which enhances detection performance through multi-module collaborative optimization. …”
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A Novel Improved Binary Optimization Algorithm and Its Application in FS Problems by Boyuan Wu, Jia Luo

“…To enhance model prediction accuracy, feature selection (FS) methods have arisen with the goal of eliminating redundant features within datasets. …”
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Effective Identification of Variety and Origin of Chenpi Using Hyperspectral Imaging Assisted with Chemometric Models by Hangxiu Liu, Youyou Wang, Yiheng Wang, Jingyi Wang, Hanqing Hu, Xinyi Zhong, Qingjun Yuan, Jian Yang

“…Feature wavelength selection strategies, such as CARS, simplified the model complexity while maintaining a classification performance comparable to that of the full-spectrum models. …”
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LS-YOLO: A Novel Model for Detecting Multiscale Landslides With Remote Sensing Images by Wenjie Zhang, Zhiheng Liu, Suiping Zhou, Wenjuan Qi, Xinjun Wu, Tianyu Zhang, Ling Han

“…Extensive experiments show that our proposed model has high performance for multiscale landslide detection and outperforms other object detection models (faster RCNN, SSD, EfficientDet-D0, YOLOv5s, YOLOv7, and YOLOX). …”
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Identify suitable artificial groundwater recharge zones using hybrid deep learning models by Navaz Khalillollahi, Mohsen Isari, Hamed Faroqi, Kaywan Othman Ahmed, Kamran Nobakht Vakili, Miklas Scholz, Saad Sh. Sammeng

“…In the end, model performance was validated using Accuracy, Kappa score, Root Mean Square Error (RMSE), F1-score, Confusion Matrix, and Receiver Operating Characteristic curve (ROC). …”
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Flood Index-Enhanced deep learning model for coastal inundation mapping in SAR imagery by Wantai Chen, Yinfei Zhou, Xiaofeng Li

“…This combination enables the model to handle complex flood scenarios more effectively, thereby boosting overall performance. …”
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Evaluating machine learning models comprehensively for predicting maximum power from photovoltaic systems by Samir A. Hamad, Mohamed A. Ghalib, Amr Munshi, Majid Alotaibi, Mostafa A. Ebied

“…Additionally, the study assessed the correlation and feature importance to evaluate model compatibility and the factors impacting the predictive accuracy of the ML models. …”
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