MultiRepPI: a cross-modal feature fusion-based multiple characterization framework for plant peptide-protein interaction prediction by Yu Zhiguo, Li Zixuan, Li Peng

“…First, most methods fail to adequately integrate multimodal information such as sequence, structure, and disorder properties, leading to inadequate characterization of complex interaction patterns. …”
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A comprehensive review of machine learning for heart disease prediction: challenges, trends, ethical considerations, and future directions by Raman Kumar, Raman Kumar, Sarvesh Garg, Rupinder Kaur, M. G. M. Johar, Sehijpal Singh, Sehijpal Singh, Soumya V. Menon, Pulkit Kumar, Pulkit Kumar, Ali Mohammed Hadi, Shams Abbass Hasson, Jasmina Lozanović

“…This review showcases the progression of ML approaches from traditional classifiers to hybrid DL structures and federated learning (FL) frameworks. It also discusses ethical issues, dataset limitations, and model transparency. …”
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A web-based artificial intelligence system for label-free virus classification and detection of cytopathic effects by Zeynep Akkutay-Yoldar, Mehmet Türkay Yoldar, Yiğit Burak Akkaş, Sibel Şurak, Furkan Garip, Oğuzcan Turan, Bengisu Ekizoğlu, Osman Can Yüca, Aykut Özkul, Barış Ünver

“…AIRVIC’s hierarchical structure highlights its adaptability to virological diagnostics, providing unbiased infectivity scoring and facilitating viral isolation and antiviral efficacy testing. …”
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An improved U-net and attention mechanism-based model for sugar beet and weed segmentation by Yadong Li, Ruinan Guo, Rujia Li, Rongbiao Ji, Mengyao Wu, Dinghao Chen, Cong Han, Ruilin Han, Yongxiu Liu, Yuwen Ruan, Jianping Yang, Jianping Yang

“…To address this issue, this paper proposes an efficient crop-weed segmentation model based on an improved UNet architecture and attention mechanisms to enhance both recognition accuracy and processing speed.MethodsThe model adopts the encoder-decoder structure of UNet, utilizing MaxViT (Multi-Axis Vision Transformer) as the encoder to capture both global and local features within images. …”
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FinSafeNet: securing digital transactions using optimized deep learning and multi-kernel PCA(MKPCA) with Nyström approximation by Ahmad Raza Khan, Shaik Shakeel Ahamad, Shailendra Mishra, Mohd Abdul Rahim Khan, Sunil Kumar Sharma, Abdullah AlEnizi, Osama Alfarraj, Majed Alowaidi, Manoj Kumar

“…MKPCA seeks to analyze and understand a non-linear structure of data whereas, Nyström Approximation reduces the burden on computational power hence allows the model to work in situations where large sizes of datasets are available but with no loss of efficiency of the model. …”
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Ensemble Streamflow Simulations in a Qinghai–Tibet Plateau Basin Using a Deep Learning Method with Remote Sensing Precipitation Data as Input by Jinqiang Wang, Zhanjie Li, Ling Zhou, Chi Ma, Wenchao Sun

“…Streamflow simulations were carried out using models with diverse structures, including the physically based BTOPMC (Block-wise use of TOPMODEL) and two machine learning models, i.e., Random Forest (RF) and Long Short-Term Memory Neural Networks (LSTM). …”
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Automatic Mushroom Species Classification Model for Foodborne Disease Prevention Based on Vision Transformer by Boyuan Wang

“…Mushrooms are the fleshy, spore-bearing structure of certain fungi, produced by a group of mycelia and buried in a substratum. …”
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Traffic environment perception algorithm based on multi-task feature fusion and orthogonal attention by Zhengfeng LI, Mingen ZHONG, Yihong ZHANG, Kang FAN, Zhiying DENG, Jiawei TAN

“…A notable advancement introduced in MTEPN is the cross-task feature aggregation structure. This module promotes information complementarity between tasks by implicitly modeling the global context relationships among different visual tasks. …”
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A Picking Point Localization Method for Table Grapes Based on PGSS-YOLOv11s and Morphological Strategies by Jin Lu, Zhongji Cao, Jin Wang, Zhao Wang, Jia Zhao, Minjie Zhang

“…To address these issues, this study proposes a novel picking point localization method for table grapes based on an instance segmentation network called Progressive Global-Local Structure-Sensitive Segmentation (PGSS-YOLOv11s) and a simple combination strategy of morphological operators. …”
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Rice Leaf Disease Image Enhancement Based on Improved CycleGAN by YAN Congkuan, ZHU Dequan, MENG Fankai, YANG Yuqing, TANG Qixing, ZHANG Aifang, LIAO Juan

“…These included user perception evaluation (UPE), structural similarity index (SSIM), peak signal-to-noise ratio (PSNR), and the performance of disease recognition within object detection frameworks. …”
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MDFT-GAN: A Multi-Domain Feature Transformer GAN for Bearing Fault Diagnosis Under Limited and Imbalanced Data Conditions by Chenxi Guo, Vyacheslav V. Potekhin, Peng Li, Elena A. Kovalchuk, Jing Lian

“…While generative adversarial networks (GANs) have shown promise in data augmentation, their efficacy deteriorates in the presence of multi-category and structurally complex fault distributions. To address these challenges, this paper proposes a novel fault diagnosis framework based on a Multi-Domain Feature Transformer GAN (MDFT-GAN). …”
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A lightweight intelligent compression method for fast Sea Level Anomaly data transmission. by Xiaodong Ma, Xiang Wan, Lei Zhang, Dong Wang, Zeyuan Dai

“…., peak signal-to-noise ratio, PSNR; structural similarity index, SSIM). The architecture integrates global-local dual discriminators to enforce spatiotemporal coherence of mesoscale vortices, employs dilated convolutions to enhance feature receptive fields without computational overhead, and incorporates vortex recognition rate as a physics-aware evaluation metric. …”
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Predicting peak ground acceleration using the ConvMixer networkKey points by Mona Mohammed, Omar M. Saad, Arabi Keshk, Hatem M. Ahmed

“…The proposed ConvMixer is a patch-based model that extracts global features from input seismic data and predicts the PGA of an earthquake by combining depth and pointwise convolutions. …”
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XTNSR: Xception-based transformer network for single image super resolution by Jagrati Talreja, Supavadee Aramvith, Takao Onoye

“…A multi-layer feature fusion block with skip connections, part of this hybrid architecture, guarantees efficient local and global feature fusion. The experimental results show better performance in Peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM), and visual quality than the state-of-the-art techniques. …”
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MAMNet: Lightweight Multi-Attention Collaborative Network for Fine-Grained Cropland Extraction from Gaofen-2 Remote Sensing Imagery by Jiayong Wu, Xue Ding, Jinliang Wang, Jiya Pan

“…Second, the global–local Transformer block (GLTB) decoder uses multi-head self-attention mechanisms to dynamically fuse multi-scale features across layers, effectively restoring the topological structure of fragmented farmland boundaries. …”
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One Health interventions and challenges under rural African smallholder farmer settings: A scoping review by Evanson R. Omuse, Honest Machekano, Bonoukpoè M. Sokame, Daniel M. Mutyambai, Thomas Dubois, Sevgan Subramanian, Frank Chidawanyika

“…The global human population is rapidly increasing, escalating interactions of people, animals and the environment. …”
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Automated recognition of deep-sea benthic megafauna in polymetallic nodule mining areas based on deep learning by Guofan Long, Wei Song, Xiangchun Liu, Ziyao Fang, Jinqi An, Kun Liu, Yaqin Huang, Xuebao He

“…Its backbone integrates deformable convolutions, attention mechanisms, and ResNet structures to improve feature extraction and reduce background interference. …”
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LWSARDet: A Lightweight SAR Small Ship Target Detection Network Based on a Position–Morphology Matching Mechanism by Yuliang Zhao, Yang Du, Qiutong Wang, Changhe Li, Yan Miao, Tengfei Wang, Xiangyu Song

“…Furthermore, we propose a Position–Morphology Matching IoU loss function, P-MIoU, which integrates center distance constraints and morphological penalty mechanisms to more precisely capture the spatial and structural differences between predicted and ground truth bounding boxes. …”
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PC3D-YOLO: An Enhanced Multi-Scale Network for Crack Detection in Precast Concrete Components by Zichun Kang, Kedi Gu, Andrew Yin Hu, Haonan Du, Qingyang Gu, Yang Jiang, Wenxia Gan

“…To address these limitations, we propose PC3D-YOLO, an enhanced framework derived from YOLOv11, which strengthens long-range dependency modeling through multi-scale feature integration, offering a novel approach for crack detection in precast concrete structures. Our methodology involves three key innovations: (1) the Multi-Dilation Spatial-Channel Fusion with Shuffling (MSFS) module, employing dilated convolutions and channel shuffling to enable global feature fusion, replaces the C3K2 bottleneck module to enhance long-distance dependency capture; (2) the AIFI_M2SA module substitutes the conventional SPPF to mitigate its restricted receptive field and information loss, incorporating multi-scale attention for improved near-far contextual integration; (3) a redesigned neck network (MSCD-Net) preserves rich contextual information across all feature scales. …”
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A Low Complexity Algorithm for 3D-HEVC Depth Map Intra Coding Based on MAD and ResNet by Erlin Tian, Jiabao Zhang, Qiuwen Zhang

“…As an extension of HEVC, 3D-HEVC retains the quadtree structure inherent to HEVC and is currently recognized as the most widely adopted international standard for stereoscopic video coding. …”
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