SpaMask: Dual masking graph autoencoder with contrastive learning for spatial transcriptomics. by Wenwen Min, Donghai Fang, Jinyu Chen, Shihua Zhang

“…SpaMask combines Masked Graph Autoencoders (MGAE) and Masked Graph Contrastive Learning (MGCL) modules, with MGAE using node masking to leverage spatial neighbors for improved clustering accuracy, while MGCL applies edge masking to create a contrastive loss framework that tightens embeddings of adjacent nodes based on spatial proximity and feature similarity. We conducted a comprehensive evaluation of SpaMask on eight datasets from five different platforms. …”
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High-Resolution Direction of Arrival Estimation of Underwater Multitargets Using Swarming Intelligence of Flower Pollination Heuristics by Nauman Ahmed, Huigang Wang, Shanshan Tu, Norah A.M. Alsaif, Muhammad Asif Zahoor Raja, Muhammad Kashif, Ammar Armghan, Yasser S. Abdalla, Wasiq Ali, Farman Ali

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TARGE: large language model-powered explainable hate speech detection by Muhammad Haseeb Hashir, Memoona, Sung Won Kim

“…The proliferation of user-generated content on social networking sites has intensified the challenge of accurately and efficiently detecting inflammatory and discriminatory speech at scale. …”
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GNNs surpass transformers in tumor medical image segmentation by Huimin Xiao, Guanghua Yang, Zhuocheng Li, Changhua Yi

“…Abstract To assess the suitability of Transformer-based architectures for medical image segmentation and investigate the potential advantages of Graph Neural Networks (GNNs) in this domain. We analyze the limitations of the Transformer, which models medical images as sequences of image patches, limiting its flexibility in capturing complex and irregular tumor structures. …”
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An advanced deep learning method for pepper diseases and pests detection by Xuewei Wang, Jun Liu, Qian Chen

“…Built upon YOLOv10n, YOLO-Pepper incorporates four major innovations: (1) an Adaptive Multi-Scale Feature Extraction (AMSFE) module that improves feature capture through multi-branch convolutions; (2) a Dynamic Feature Pyramid Network (DFPN) enabling context-aware feature fusion; (3) a specialized Small Detection Head (SDH) tailored for minute targets; and (4) an Inner-CIoU loss function that enhances localization accuracy by 18% compared to standard CIoU. …”
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Pose estimation for health data analysis: advancing AI in neuroscience and psychology by Juan Yu, Daoyu Zhu

“…The framework integrates multi-modal data sources and applies temporal graph convolutional networks, ensuring both scalability and adaptability to diverse tasks. …”
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Mitosis detection in histopathological images using customized deep learning and hybrid optimization algorithms. by Afnan M Alhassan, Nouf I Altmami

“…The CDL model comprises a Transfer Learning-based Mitosis Detection module under which extracted features from pre-trained deep networks are used to bolster feature extraction and alleviate class imbalance through skip connections to better localize mitosis. …”
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Auto-embedding transformer under multi-source information fusion for few-shot fault diagnosis by Bo Wang, Shuai Zhao, Qian Zhao, Yang Bai

“…Visualization tools such as t-SNE and ROC curves further confirm its ability to effectively distinguish fault categories and capture critical fault-related features.…”
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Harnessing machine learning for high-entropy alloy catalysis: a focus on adsorption energy prediction by Qi Wang, Yonggang Yao

“…Both strategies can leverage handcrafted features or end-to-end frameworks such as graph neural networks. …”
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Automated Deadlift Techniques Assessment and Classification Using Deep Learning by Wegar Lien Grymyr, Isah A. Lawal

“…These keypoints not only served as visualization aids in the training of Convolutional Neural Networks (CNNs) but also acted as the primary features for Long Short-Term Memory (LSTM) models, both of which we employed to classify the various deadlift techniques. …”
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A k-nearest text similarity-BiGRU approach for duration prediction of traffic accidents on expressways by Jiaona Chen, Jin Zhang, Peng Wang, Yinli Jin

“…Therefore, the independent variables for predicting accident duration are obtained through a text similarity evaluation, which serves as the feature vector. Finally, BiGRU networks are utilized for the regression prediction of traffic accident duration using the constructed feature vectors. …”
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DSF-YOLO for robust multiscale traffic sign detection under adverse weather conditions by Jun Li, QinWen Deng, WenXin Gao, Bing Yang, Lan Jia, Ju Zhou, HaiBo Pu

“…This model employs an attention-based dynamic sequence fusion feature pyramid, which enhances recognition accuracy for small-target traffic sign instances in adverse weather, as opposed to traditional feature pyramid networks. …”
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YOLO-MARS: An Enhanced YOLOv8n for Small Object Detection in UAV Aerial Imagery by Guofeng Zhang, Yanfei Peng, Jincheng Li

“…Thirdly, a multi-scale SGCS-FPN fusion architecture is proposed, adding a shallow feature guidance branch to establish cross-level semantic associations, thereby effectively addressing the issue of small target loss in deep networks. …”
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Deterministic and Stochastic Machine Learning Classification Models: A Comparative Study Applied to Companies’ Capital Structures by Joseph F. Hair, Luiz Paulo Fávero, Wilson Tarantin Junior, Alexandre Duarte

“…Deterministic models, represented by logistic regression and multilevel logistic regression, and stochastic approaches that incorporate a certain degree of randomness or probability, including decision trees, random forests, Gradient Boosting, Support Vector Machines, and Artificial Neural Networks, were evaluated using usual metrics. The results indicate that decision trees, random forest, and XGBoost excelled in the training phase but showed higher overfitting when evaluated in the test sample. …”
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Resilient Cities: Spatial Planning and Transportation by Arzu Maltaş Erol

“…Within the scope of the study, the concept of resilient, its features, and the impacts of disasters on spatial planning are first addressed. …”
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Deep Transfer Learning for Lip Reading Based on NASNetMobile Pretrained Model in Wild Dataset by Ashwaq Waleed Abdul Ameer, Pedram Salehpour, Mohammad Asadpour

“…The proposed framework involves a process that extracts features from video frames in a time sequence, employing methods such as Convolutional Neural Networks (CNN), CNN-Gated Recurrent Units (CNN-GRU), Temporal CNN, and Temporal PoinWise. …”
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Detection of Mucous Retention Cysts Using Deep Learning Methods on Panoramic Radiographs by Seda Arslan Tuncer, Çağla Danacı, Sümeyye Coşgun Baybars

“…The efficient features obtained from these networks were given as input to the support vector machine classifier, and healthy individuals and patients with mucous retention cysts were classified.Results: As a result of the model training, it was determined that the EfficientNetB6 model performed the best. …”
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Beef Carcass Grading with EfficientViT: A Lightweight Vision Transformer Approach by Hyunwoo Lim, Eungyeol Song

“…Unlike conventional two-stage methods that require prior segmentation of the loin region, our model directly predicts beef quality grades from raw RGB images, significantly simplifying the pipeline and reducing computational overhead. We evaluate the proposed model against representative convolutional neural networks (VGG-16, ResNeXt-50, DenseNet-121) as well as two-stage combinations of segmentation and classification models. …”
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A strategy for out-of-roundness damage wheels identification in railway vehicles based on sparse autoencoders by Jorge Magalhães, Tomás Jorge, Rúben Silva, António Guedes, Diogo Ribeiro, Andreia Meixedo, Araliya Mosleh, Cecília Vale, Pedro Montenegro, Alexandre Cury

“…The proposed procedure is based on a machine learning methodology and includes the following stages: (1) data collection, (2) damage-sensitive feature extraction from the acquired responses using a neural network model, i.e., the sparse autoencoder (SAE), (3) data fusion based on the Mahalanobis distance, and (4) unsupervised feature classification by implementing outlier and cluster analysis. …”
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A Cluster‐Based Deep Learning Model Perceiving Series Correlation for Accurate Prediction of Phonon Spectrum by Chao Liang, Yilimiranmu Rouzhahong, Shunwei Yao, Junhao Liang, Chunlin Yu, Biao Wang, Huashan Li

“…The high performance of CSGN model can be attributed to the pertinent feature extraction and the appropriate similarity evaluation, which enable the natural perception of structure‐property relation and intrinsic series correlations as confirmed in the predictive results. …”
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