RFAG-YOLO: A Receptive Field Attention-Guided YOLO Network for Small-Object Detection in UAV Images by Chengmeng Wei, Wenhong Wang

“…Moreover, compared to the larger YOLOv8s model, RFAG-YOLO achieved 97.98% of its mAP50 performance while utilizing only 53.51% of the parameters, highlighting its exceptional efficiency in terms of the performance-to-parameter ratio and making it highly suitable for resource-constrained UAV applications. …”
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The Imbalanced Target Classification Method Based on Mixed Learning of Virtual and Real Data by Fengyu Yang, Peng Wang, Wutao Qin, Zhangze Liao

“…We proposes a category imbalance classification model based on mixed feature enhancement between virtual and real domains to address the class imbalance problem in maritime target classification applications. …”
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AI-Based Ransomware Detection: A Comprehensive Review by Jannatul Ferdous, Rafiqul Islam, Arash Mahboubi, Md Zahidul Islam

“…We then review the existing literature on the core components of AI-based ransomware detection models, including the datasets and challenges arising during data collection, data pre-processing, feature engineering techniques, model training, and performance evaluation for effective model training. …”
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Machine learning-based analysis on pharmaceutical compounds interaction with polymer to estimate drug solubility in formulations by Ahmad J. Obaidullah, Wael A. Mahdi

“…To further optimize performance, Recursive Feature Elimination (RFE) is employed for feature selection with the number of features treated as a hyperparameter. …”
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A Cross-Stage Focused Small Object Detection Network for Unmanned Aerial Vehicle Assisted Maritime Applications by Gege Ding, Jiayue Liu, Dongsheng Li, Xiaming Fu, Yucheng Zhou, Mingrui Zhang, Wantong Li, Yanjuan Wang, Chunxu Li, Xiongfei Geng

“…This study introduces an innovative detection framework, CFSD-UAVNet, designed to boost the accuracy of detecting minor objects within imagery captured from elevated altitudes. To improve the performance of the feature pyramid network (FPN) and path aggregation network (PAN), a newly designed PHead structure was proposed, focusing on better leveraging shallow features. …”
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Device Modeling Based on Cost-Sensitive Densely Connected Deep Neural Networks by Xiaoying Tang, Zhiqiang Li, Lang Zeng, Hongwei Zhou, Xiaoxu Cheng, Zhenjie Yao

“…Experimental results on a test dataset of 116 NWFETs demonstrate the effectiveness of this method. The DenseDNN model with cost-sensitive learning exhibits better performance than traditional deep neural networks (DNN) with various widths and depths, with a prediction error below 1.62%. …”
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SUNet: Coffee Leaf Disease Detection Using Hybrid Deep Learning Model by Deepak Thakur, Tanya Gera, Ambika Aggarwal, Madhushi Verma, Manjit Kaur, Dilbag Singh, Mohammed Amoon

“…SUNet achieved significant performance improvement over other models in terms of accuracy, Intersection over Union (IoU), F1-score, precision, and recall by 1.22%, 1.21%, 1.17%, 1.19%, and 1.24%, respectively. …”
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An Optimized Hybrid PSO-ELM for Parkinson’s Disease Diagnosis by Gunakala Archana, Afzal Hussain Shahid

“…Various top-ranked feature subsets are created to identify the most discriminative features and enhance the performance of the proposed hybrid PSO-ELM model for all datasets. …”
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An exploratory analysis of longitudinal artificial intelligence for cognitive fatigue detection using neurophysiological based biosignal data by Sameer Nooh, Mahmoud Ragab, Rania Aboalela, Abdullah AL-Malaise AL-Ghamdi, Omar A. Abdulkader, Ghadah Alghamdi

“…Primarily, the EALAI-CFDNBD model utilized the linear scaling normalization (LSN) model to ensure that the input features were appropriately scaled for subsequent analysis. …”
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Smart grid stability prediction using Adaptive Aquila Optimizer and ensemble stacked BiLSTM by Safwan Mahmood Al-Selwi, Mohd Fadzil Hassan, Said Jadid Abdulkadir, Mohammed Gamal Ragab, Alawi Alqushaibi, Ebrahim Hamid Sumiea

“…The AAO uses a Sigmoid Factor to balance exploration and exploitation, adapting the transition from broad searches to focused ones based on iteration progress. It is utilized for feature selection by identifying and excluding irrelevant and redundant features and methodically evaluates seven key hyperparameters to fine-tune the model's performance. …”
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Enhancing Object Detection in Underground Mines: UCM-Net and Self-Supervised Pre-Training by Faguo Zhou, Junchao Zou, Rong Xue, Miao Yu, Xin Wang, Wenhui Xue, Shuyu Yao

“…We propose utilizing the combined backbone and neck portions of the detection model as the encoder of an image-masking pre-training structure to strengthen feature acquisition and improve the performance of small models in self-supervised learning. …”
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Quantifying Suicide Risk in Prostate Cancer: A SEER-Based Predictive Model by Jiaxing Du, Fen Zhang, Weinan Zheng, Xue Lu, Huiyi Yu, Jian Zeng, Sujun Chen

“…The results were visualized using nomogram. Model performance was evaluated with time-dependent receiver operating characteristic (ROC) curves, concordance index (C-index), and internal validation. …”
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Predictive modeling of air quality in the Tehran megacity via deep learning techniques by Abdullah Kaviani Rad, Mohammad Javad Nematollahi, Abbas Pak, Mohammadreza Mahmoudi

“…R-squared (R2), root-mean-square error (RMSE), mean absolute error (MAE), and mean-square error (MSE) were used to assess and compare the models. This research demonstrated that DL models typically outperform ML models in forecasting air pollution. …”
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A Novel Foundation Model-Based Framework for Multimodal Retinal Age Prediction by Christopher Nielsen, Matthias Wilms, Nils D. Forkert

“…Technology or Method: Feature representations from CFP and OCT images were extracted using RETFound, a powerful foundation model for retinal image analysis. …”
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An enhanced lightweight model for apple leaf disease detection in complex orchard environments by Ge Wang, Wenjie Sang, Fangqian Xu, Yuteng Gao, Yue Han, Qiang Liu

“…In order to enhance the network’s anti-interference ability in complex backgrounds and its capacity to differentiate between similar diseases, we incorporate an Efficient Multi-Scale Attention (EMA) within the deep structure of the network for in-depth feature extraction. Additionally, we design a detail-enhanced shared convolutional scaling detection head (DESCS-DH) to enable the model to effectively capture edge information of diseases and address issues such as poor performance in object detection across different scales. …”
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A Systematic Survey of AI Models in Financial Market Forecasting for Profitability Analysis by Bilal Hassan Ahmed Khattak, Imran Shafi, Abdul Saboor Khan, Emmanuel Soriano Flores, Roberto Garcia Lara, Md. Abdus Samad, Imran Ashraf

“…Moreover, hybrid models employing AI algorithms for feature engineering have great potential at par with ensemble techniques. …”
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Efficient approximate Bayesian inference for quantifying uncertainty in multiscale animal movement models by Majaliwa M. Masolele, J. Grant C. Hopcraft, Colin J. Torney

“…In our simulations, individuals respond to two environmental covariates and we employ all combinations of positive and negative selection coefficients corresponding to attraction to an environmental feature and avoidance of an environmental feature, respectively. …”
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Predicting suicidal behavior outcomes: an analysis of key factors and machine learning models by Mohammad Bazrafshan, Kourosh Sayehmiri

“…A combination of statistical models for feature selection and machine learning algorithms for prediction was used, with Random Forest showing the best performance. …”
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An explainable GeoAI approach for the multimodal analysis of urban human dynamics: a case study for the COVID-19 pandemic in Rio de Janeiro by David Hanny, Dorian Arifi, Steffen Knoblauch, Bernd Resch, Sven Lautenbach, Alexander Zipf, Antonio Augusto de Aragão Rocha

“…At the health-area level, local feature selection generally yielded better model performance compared to city-wide feature selection. …”
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An Improved Segformer for Semantic Segmentation of UAV-Based Mine Restoration Scenes by Feng Wang, Lizhuo Zhang, Tao Jiang, Zhuqi Li, Wangyu Wu, Yingchun Kuang

“…To address challenges such as small-object recognition, insufficient multi-scale feature fusion, and blurred boundaries in UAV-based remote sensing imagery, this paper proposes an enhanced semantic segmentation model based on Segformer. …”
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