Prediction of High-ozone Events Using GAM, SMOTE, and Tail Dependence Approaches in Texas (2005–2019) by Benjamin Brown-Steiner, Xiong Zhou, Matthew J. Alvarado, Brook T. Russell

“…We also find that the tail dependence approach is capable of predicting extreme ozone events, but algorithmic stability and configuration complexity can make this approach difficult to operationalize on a broad scale and that the selection of the threshold needs to be carefully considered. Finally, the feature selection via the tail dependence method performs comparably to other forms of machine learning-based feature selection and we find that there are multiple parameter sets that can predict MDA8 O3 with equal success.…”
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Grape cluster detection based on spatial-to-depth convolution and attention mechanism by Shuai Rong, Xinghai Kong, Ruibo Gao, Zhiwei Hu, Hua Yang

“…These findings demonstrate that the enhanced YOLOX model performs well for grape cluster detection. …”
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A Synergistic Approach to Colon Cancer Detection: Leveraging EfficientNet and NSGA-II for Enhanced Diagnostic Performance by Noushin Saba, Afia Zafar, Mohsin Suleman, Kainat Zafar, Shahneer Zafar, Adil Ali Saleem, Hafeez Ur Rehman Siddiqui, Muhammad Iqbal, Syed Sajid Ullah

“…We employed EfficientNet, a state-of-the-art convolutional neural network, to extract intricate features from histopathological images, alongside the Non-dominated Sorting Genetic Algorithm II for optimal feature selection. …”
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Textures Recognition using Elman Neural Network by Fawziya Ramo, Alaa Mohamed

“…The system consists of two phases: phase extraction important feature of each texture by using an algorithm Principal Components Analysis (PCA)   and recognition phase which recognize these feature by using  Elman network were trained network on a number of various texture  models down to the steady-state network  and then test the network by input  samples  of textures.  …”
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Comparative Evaluation of Multimodal Large Language Models for No-Reference Image Quality Assessment with Authentic Distortions: A Study of OpenAI and Claude.AI Models by Domonkos Varga

“…Our results demonstrate that these LLMs outperform traditional methods based on hand-crafted features. However, more advanced deep learning models, especially those based on deep convolutional networks, surpass LLMs in performance. …”
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Active sonar target recognition method based on multi‐domain transformations and attention‐based fusion network by Qingcui Wang, Shuanping Du, Wei Zhang, Fangyong Wang

“…Traditional methods have limited classification performance in time and spatially varying ocean channels. …”
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Enhanced pulmonary nodule detection using a transformer framework with dual fusion and gated mechanism by Kelei Sun, Yihang Wang, Huaping Zhou

“…Furthermore, a Gated Sampling and Dual Fusion Enhanced Path Aggregation Network (GDPAN) is proposed, which dynamically extracts informative features by embedding weight information into the feature maps and performs efficient multiscale fusion, thereby improving the detection of small-sized nodules. …”
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The Complex Action Recognition via the Correlated Topic Model by Hong-bin Tu, Li-min Xia, Zheng-wu Wang

“…Finally, we use the topic model of correlated topic model (CTM) to classify action. …”
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Mathematical Model of Energy Processes in an Industrial Electric Screwdriver by A. Nieoczym, K. Drozd, p Veselik

“…An electric screwdriver was designed to work in an automatic cycle on assembly lines. A characteristic feature of the screwdriver is the use of a low power motor in comparison with commonly used devices. …”
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Integrated analysis of single-cell RNA-seq and bulk RNA-seq unravels the molecular feature of tumor-associated neutrophils of head and neck squamous cell carcinoma by Haiyang Cui, Zhikai Li, Yukun Liu, Yuchuan Yang, Linkuan Huangfu, Jinglin Kong, Xiaocong Sun, Shibo Gao, Daoke Yang, Yingjuan Zheng

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SE-ResUNet Using Feature Combinations: A Deep Learning Framework for Accurate Mountainous Cropland Extraction Using Multi-Source Remote Sensing Data by Ling Xiao, Jiasheng Wang, Kun Yang, Hui Zhou, Qianwen Meng, Yue He, Siyi Shen

“…The results showed the following: (1) feature fusion (NDVI + TerrainIndex + SAR) had the best performance (OA: 97.11%; F1-score: 96.41%; IoU: 93.06%), significantly reducing shadow/cloud interference. (2) SE-ResUNet outperformed ResUNet by 3.53% for OA and 8.09% for IoU, emphasizing its ability to recalibrate channel-wise features and refine edge details. (3) The model exhibited robustness across diverse slopes/aspects (OA > 93.5%), mitigating terrain-induced misclassifications. …”
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The structure of the local detector of the reprint model of the object in the image by A. A. Kulikov

“…A transforming autocoder (TA), a model of a neural network, was developed for the local detector. …”
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A review of deep learning in blink detection by Jianbin Xiong, Weikun Dai, Qi Wang, Xiangjun Dong, Baoyu Ye, Jianxiang Yang

“…Compared with traditional methods, the blink detection method based on deep learning offers superior feature learning ability and higher detection accuracy. …”
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CLIP-Llama: A New Approach for Scene Text Recognition with a Pre-Trained Vision-Language Model and a Pre-Trained Language Model by Xiaoqing Zhao, Miaomiao Xu, Wushour Silamu, Yanbing Li

“…Currently, pre-trained vision-language models have become the foundation for various downstream tasks. …”
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A lightweight multi-path convolutional neural network architecture using optimal features selection for multiclass classification of brain tumor using magnetic resonance images by Amreen Batool, Yung-Cheol Byun

“…Furthermore, an optimal features module implemented to select the most promising features to enhance our proposed multi-path architecture's performance and computational efficiency. …”
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Software Defect Prediction For Quality Evaluation Using Learning Techniques Ensemble Stacking by Muhammad Romadhona Kusuma, Windu Gata, Sigit Kurniawan, Dedi Dwi Saputra, Supriadi Panggabean

“…We performed hyperparameter tuning with grid search CV on Machine Learning algorithms such as Ada Boost and Gradient Boosting. …”
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Multi-Condition Magnetic Core Loss Prediction and Magnetic Component Performance Optimization Based on Improved Deep Forest by Haotian Shi, Zipeng Jin

“…Next, the deep forest algorithm is employed to perform multi-granularity scanning for feature extraction from the samples, which are then fed into an enhanced cascade forest (with two random forests replaced by XGBoost and LightGBM), key parameters (maximum tree depth and number of trees) are optimized via grid search, constructing a highly accurate and robust core loss prediction model. …”
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Performance Analysis of MobileNetV3-based Convolutional Neural Network for Facial Skin Disorder Classification by Herimanto, Arie Satia Dharma, Junita Amalia, David Largo, Christin Adelia Pratiwi Sihite

“…This superior performance is attributed to MobileNetV3's advanced architecture, which is optimized for efficient feature extraction, and particularly relevant for capturing the subtle variations in facial skin types. …”
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Generalized Extraction of Bolts, Mesh, and Rock in Tunnel Point Clouds: A Critical Comparison of Geometric Feature-Based Methods Using Random Forest and Neural Networks by Luke Weidner, Gabriel Walton

“…In general, we found that the NN and RF models had similar performance to each other, and that same-site classification was generally successful, but cross-site performance was much lower and judged as not practically useful. …”
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Hybrid feature fusion in cervical cancer cytology: a novel dual-module approach framework for lesion detection and classification using radiomics, deep learning, and reproducibilit... by Shurong Niu, Lili Zhang, Lina Wang, Xue Zhang, Erniao Liu

“…Four deep learning models, Swin Transformer, YOLOv11, Faster R-CNN, and DETR (DEtection TRansformer), were employed for lesion detection, and their performance was compared using mAP, IoU, precision, recall, and F1-score. …”
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