Improving the accuracy and interpretability of neural networks for wind power forecasting by Wenlong Liao, Birgitte Bak-Jensen, Zhe Yang, Gonghao Zhang, Jiannong Fang, Fernando Porté-Agel

“…To address these two challenges, this paper firstly proposes simple but effective triple optimization strategies (TriOpts) to accelerate the training process and improve the model performance of DNNs in wind power forecasting. …”
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Severe-hail detection with C-band dual-polarisation radars using convolutional neural networks by V. Forcadell, V. Forcadell, C. Augros, O. Caumont, O. Caumont, K. Dedieu, M. Ouradou, C. David, J. Figueras i Ventura, O. Laurantin, H. Al-Sakka

“…The study demonstrates that many of the existing hail proxies can be adjusted using a threshold value and a threshold area to achieve better performance. Finally, the output of 10 fitted CNN models in inference mode on a hail event is shown.…”
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Lipidomic analysis reveals metabolism alteration associated with subclinical carotid atherosclerosis in type 2 diabetes by Maria Barranco-Altirriba, Joana Rossell, Núria Alonso, Ralf J. M. Weber, Emilio Ortega, Gavin R. Lloyd, Marta Hernandez, Oscar Yanes, Jordi Capellades, Catherine Winder, Alexandra Junza, Mireia Falguera, Josep Franch-Nadal, Warwick B. Dunn, Alexandre Perera-Lluna, Esmeralda Castelblanco, Didac Mauricio

“…Multiple linear regression models were used to assess the association between features and the presence and burden of SCA in subjects with T1D, T2D, and controls separately. …”
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Differentiation of Early Sacroiliitis Using Machine-Learning- Supported Texture Analysis by Qingqing Zhu, Qi Wang, Xi Hu, Xin Dang, Xiaojing Yu, Liye Chen, Hongjie Hu

“…The diagnostic efficacies of the two readers were judged and compared using an assigned Likert score, conducting a Kappa consistency test of the diagnostic results between two readers. Texture analysis models (the T1WI-TA model and the FsT2WI-TA model) were constructed through feature extraction and feature screening. …”
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Preoperative CT-based radiomics nomogram for progression-free survival prediction in pediatric posterior mediastinal malignancies by Shucheng Bi, Chenghao Chen, Jie Yu, Ting Yang, Jihang Sun, Zunying Hu, Qi Zeng, Yun Peng

“…Selection of the radiomic features was performed using maximum relevance minimum redundancy and the least absolute shrinkage and selection operator. …”
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Enhancing Breast Cancer Diagnosis With Multi-Resolution Vision Transformers and Robust Decision-Making by Margo Sabry, Hossam Magdy Balaha, Khadiga M. Ali, Tayseer Hassan A. Soliman, Dibson Gondim, Mohammed Ghazal, Norah Saleh Alghamdi, Ayman El-Baz

“…This study aims to improve breast cancer (BC) diagnosis through a novel multi-resolution Vision Transformer (ViT)-based framework with ensemble decision-making, addressing limitations in traditional single-magnification models. The proposed framework uses multiscale feature extraction at three magnification levels (L0, L1, L2 or 16x, 4x, 2x) to capture both fine-grained and high-level tumor features. …”
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Optimizing Chlorophyll-a Concentration Inversion in Coastal Waters Using SVD and Deep Learning Approach by Lili Zhan, Yongxin Xu, Jinshan Zhu, Zhangshuo Liu

“…Other machine learning methods, such as random forest (RF) and the support vector machine (SVM) are also used to establish the inversion models for the comparison. At the same time, a comparative analysis was performed with Chl-a retrieval models created using a feature selection method based on the correlation of band combinations. …”
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Prediction and evaluation of environmental quality for nursing sow buildings via multisource sensor information fusion by Chong Chen, Xingqiao Liu, Chaoji Liu, Chengyang Yu

“…The Random Forest (RF) model was selected for the feature selection. There were six feature factors that were closely related to environmental quality, including temperature, relative humidity, concentrations of NH3, CO2,H2S and air speed. …”
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Integrating machine learning and symbolic regression for predicting damage initiation in hybrid FRP bolted connections by Sherif Samy Sorour, Chahinaz Abdelrahman Saleh, Mostafa Shazly

“…Hybrid L-joint specimens were fabricated and tested to determine damage initiation loads, with results validating FEM models in ABAQUS. A design of experiments approach structured the dataset, and feature selection identified key factors influencing joint performance. …”
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Integrating CNN and transformer architectures for superior Arabic printed and handwriting characters classification by Mohammed R. Al-Maamari, Rakesh Ramteke, Aymen M. Al-Hejri, Sultan S. Alshamrani

“…The methodology integrates transfer learning techniques utilizing pre-trained VGG16 and ResNet50 models for feature extraction, followed by a feature ensemble process. …”
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DKA-YOLO: Enhanced Small Object Detection via Dilation Kernel Aggregation Convolution Modules by Yicheng Qiu, Feng Sha, Li Niu

“…Finally, the model’s head layer employs multi-scale convolution kernels detect to enhance feature expression diversity, generalization ability, and computational efficiency of detection. …”
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A Relative Attitude Detection Method for Unmanned Aerial Vehicles Based on You Only Look Once Framework by Jingting Qiu, Feifan Yu, Fengrui Xu, Xinmin Chen, Jiqiang Wang

“…The experimental results demonstrate that our model outperforms the baseline by 10.8% in terms of accuracy while occupying a mere 5.8 M in size, representing a reduction of 6.5%, achieving a satisfactory balance between performance and resource consumption.…”
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Research on Point Cloud Registration and Stitching Fusion Algorithm Based on GCN-PRFNet by Wenhao Zeng, Gongbing Su, Zixuan Su, Rui Li, Jun Chen

“…Many researchers have proposed various algorithms for deep learning-based point cloud registration and stitching methods with good performance, and although there are end-to-end methods that have made progress, they still have limitations in local feature fusion efficiency and geometric detail retention. …”
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Meta-Learning Approach for Adaptive Anomaly Detection from Multi-Scenario Video Surveillance by Deepak Kumar Singh, Dibakar Raj Pant, Ganesh Gautam, Bhanu Shrestha

“…Leveraging the swin transformer as a spatial feature extractor, the model captures rich hierarchical representations from surveillance videos. …”
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Prediction of outpatient rehabilitation patient preferences and optimization of graded diagnosis and treatment based on XGBoost machine learning algorithm by Xuehui Fan, Ruixue Ye, Yan Gao, Kaiwen Xue, Zeyu Zhang, Jing Xu, Jingpu Zhao, Jun Feng, Yulong Wang

“…Addressing class imbalance and enhancing feature engineering can further improve model performance. …”
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Enhancing LoRa-Based Outdoor Localization Accuracy Using Machine Learning by Nur Kelesoglu, Marzena Halama, Anna Strzoda

“…We further propose a Hybrid Model that combines convolutional feature extraction with gradient-boosted regression. …”
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Unfolding the collective functional potential of a synergistic multispecies community through genotypic and phenotypic analyses by Dana Ronin, Mads Frederik Hansen, Maximilian Lukas Flaig, Morten Kam Dahl Dueholm, Anders Ogechi Hostrup Daugberg, Joseph Nesme, Witold Kot, Mette Burmølle

“…In this study, we conducted the first complete genome assemblies for these four strains and performed a manually curated annotation of the genomes to identify genomic features that could guide the selection of relevant phenotypic assays. …”
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Regression machine learning methods for isolation prediction and massive gain broadband MIMO antenna design for 28 GHz applications by Md.Ashraful Haque, Redwan A. Ananta, Jun-Jiat Tiang, Mouaaz Nahas, Md Afzalur Rahman, Narinderjit Singh Sawaran Singh

“…Various metrics are available to evaluate the performance of Machine Learning (ML) models, including variance score, R-squared, Mean Square Error (MSE), Mean Absolute Error (MAE), and Root Mean Square Error (RMSE). …”
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Low-Cost Air Quality Sensors: Biases, Corrections and Challenges in Their Comparability by Idris Hayward, Nicholas A. Martin, Valerio Ferracci, Mohsen Kazemimanesh, Prashant Kumar

“…Some go further and include features with no direct connection to the measurement such as the level of traffic nearby, converting the initial measurement into a modelled value. …”
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A Double Inertial Mann-Type Method for Two Nonexpansive Mappings with Application to Urinary Tract Infection Diagnosis by Krittin Naravejsakul, Pasa Sukson, Waragunt Waratamrongpatai, Phatcharapon Udomluck, Mallika Khwanmuang, Watcharaporn Cholamjiak, Watcharapon Yajai

“…The Extreme Learning Machine (ELM) model is utilized to categorize urinary tract infection cases based on both clinical and demographic features. …”
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