A Lightweight Network for Water Body Segmentation in Agricultural Remote Sensing Using Learnable Kalman Filters and Attention Mechanisms by Dingyi Liao, Jun Sun, Zhiyong Deng, Yudong Zhao, Jiani Zhang, Dinghua Ou

“…However, the spectral noise caused by complex light and shadow interference and water quality differences, combined with the diverse shapes of water bodies and the high computational cost of image processing, severely limits the accuracy of water body recognition in agricultural watersheds. …”
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Adaptive Global Dense Nested Reasoning Network into Small Target Detection in Large-Scale Hyperspectral Remote Sensing Image by Siyu Zhan, Yuxuan Yang, Muge Zhong, Guoming Lu, Xinyu Zhou

“…Secondly, we present a novel approach for computing physicochemical parameters, which enhances the spectral characteristics of targets while minimizing environmental interference. …”
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MRFP-Mamba: Multi-Receptive Field Parallel Mamba for Hyperspectral Image Classification by Xiaofei Yang, Lin Li, Suihua Xue, Sihuan Li, Wanjun Yang, Haojin Tang, Xiaohui Huang

“…Deep learning has achieved remarkable success in hyperspectral image (HSI) classification, attributed to its powerful feature extraction capabilities. However, existing methods face several challenges: Convolutional Neural Networks (CNNs) are limited in modeling long-range spectral dependencies because of their limited receptive fields; Transformers are constrained by their quadratic computational complexity; and Mamba-based methods fail to fully exploit spatial–spectral interactions when handling high-dimensional HSI data. …”
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Brosimine B and the biphasic dose-response: insights into hormesis and retinal neuroprotection by Susanne Suely Santos Fonseca, Susanne Suely Santos Fonseca, Natacha M. S. Port’s, Gisele Priscila Soares Aguiar, Eliã P. Botelho, Nádia M. G. Couto, Wandson Braamcamp Souza Pinheiro, André Salim Khayat, Elizabeth S. Yamada, Edmar T. Costa, Chubert Bernardo C. Sena, Mara Silvia P. Arruda, Carlomagno P. Bahia, Antonio Pereira

“…However, higher concentrations (>10 µM) induced cytotoxic effects. A computational modeling approach using a hormetic (inverted U-shaped) model revealed biologically interpretable parameters, including a peak response at 10.2 µM and a hormetic zone width (σ = 6.5 µM) (R2 = 0.984).DiscussionThese results confirm that Brosimine B exhibits hormetic neuroprotective effects within a well-defined concentration window, supporting its potential as a therapeutic agent for oxidative stress–related retinal damage. …”
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Real-Time Lightweight Morphological Detection for Chinese Mitten Crab Origin Tracing by Xiaofei Ma, Nannan Shen, Yanhui He, Zhuo Fang, Hongyan Zhang, Yun Wang, Jinrong Duan

“…In the first stage, an improved YOLOv10n-based model is designed by incorporating omni-dimensional dynamic convolution, a SlimNeck structure, and a Lightweight Shared Convolutional Detection head, which effectively enhances the detection accuracy of crab targets under complex multi-scale environments while reducing computational cost. In the second stage, an Improved GoogleNet’s Inception Net for Crab is developed based on the Inception module, with further integration of Asymmetric Convolution Blocks and Squeeze and Excitation modules to improve the feature extraction and classification ability for regional origin. …”
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Advanced holographic convolutional dense networks and Tangent runner optimization for enhanced polycystic ovarian disease classification by Prathibanandhi Jeyashanker, Annie Grace Vimala Georgewilliam Sundaram, Padmakala Sadagopan, Abid Yahya, Ravi Samikannu, Irfan Anjum Badruddin, Sarfaraz Kamangar, Mohamed Ghouse Shukur

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Precise Spatial Prediction of Rice Seedlings From Large-Scale Airborne Remote Sensing Data Using Optimized Li-YOLOv9 by Jayakrishnan Anandakrishnan, Arun Kumar Sangaiah, Hendri Darmawan, Nguyen Khanh Son, Yi-Bing Lin, Mohammed J. F. Alenazi

“…However, the limited computational capabilities of UAVs make it challenging to deploy complex object detection models onboard. …”
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Adapting SAM2 Model from Natural Images for Tooth Segmentation in Dental Panoramic X-Ray Images by Zifeng Li, Wenzhong Tang, Shijun Gao, Yanyang Wang, Shuai Wang

“…In addition, high-precision deep models typically require significant computational resources for inference, making deployment in real-world applications challenging. …”
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P-GELU: A Novel Activation Function to Optimize Whisper for Darija Speech Translation by Maria Labied, Abdessamad Belangour, Mouad Banane

“…The proposed P-GELU offers a promising balance between computational efficiency and performance gains, presenting a viable solution for enhancing Transformer-based speech models in low-resource language scenarios.…”
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Rep-MobileViT: Texture and Color Classification of Solid Wood Floors Based on a Re-Parameterized CNN-Transformer Hybrid Model by Anning Duanmu, Sheng Xue, Zhenye Li, Yajun Zhang, Chao Ni

“…Specifically, the RepAIRB module is introduced, incorporating an asymmetric convolutional block (ACB) and a re-parameterized structure within the inverted residual block (IRB) module to enhance the network’s receptive field without increasing computational costs. Additionally, the RepMFF-MobileViT modu le is designed, utilizing a multi-feature fusion strategy and the RepTransformer structure to reduce the number of parameters while improving the network’s expressiveness and stability. …”
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Metasurface-Based Solar Absorption Prediction System Using Artificial Intelligence by Md. Mottahir Alam, Ahteshamul Haque, Asif Irshad Khan, Samir Kasim, Amjad Ali Pasha, Aasim Zafar, Kashif Irshad, Anis Ahmad Chaudhary, Md. Samsuzzaman, Rezaul Azim

“…The optimization technique is used to increase the effectiveness of the solar absorber by optimizing the design parameters. The features from the WTa-SiO2 design are extracted by the proposed Principal Component-Autoencoder (PC-AE) method. …”
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Benchmarking Hook and Bait Urdu news dataset for domain-agnostic and multilingual fake news detection using large language models by Sheetal Harris, Jinshuo Liu, Hassan Jalil Hadi, Naveed Ahmad, Mohammed Ali Alshara

“…The lightweight LoRA fine-tuning method, with  0.032% trainable parameters, ensured robust data handling, computational efficiency while leveraging early stopping and optimized hyperparameters for reliable and high-performing monolingual and multilingual FND. …”
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Deep learning-based action recognition for joining and welding processes of dissimilar materials by Tao He, Xinyuan Jin, Yiming Zou

“…The framework integrates adaptive frame sampling and lightweight temporal modeling to ensure computational efficiency, making it practical for real-time applications without sacrificing accuracy. …”
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Ultra-Wide-Band and Polarization-Insensitive Metamaterial Absorber With Resistive Ink Sheet for RCS Reduction Applications by Ramesh Amugothu, Vakula Damera

“…The structure of the unit cell is designed using a dielectric FR4 substrate with resistive ink patterns printed on it. With the help of computational optimization using the Ansys HFSS tool, the proposed unit cell's various geometric parameters are fine-tuned to achieve outstanding results. …”
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Wavelet-Guided Multi-Scale ConvNeXt for Unsupervised Medical Image Registration by Xuejun Zhang, Aobo Xu, Ganxin Ouyang, Zhengrong Xu, Shaofei Shen, Wenkang Chen, Mingxian Liang, Guiqi Zhang, Jiashun Wei, Xiangrong Zhou, Dongbo Wu

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Lightweight deep neural network for contour detection and extraction of wheat spikes in complex field environments by Xin Xu, Haiyang Zhang, Jiangchuan Lu, Ziyi Guo, Juanjuan Zhang, Jibo Yue, Hongbo Qiao, Xinming Ma

“…The algorithm innovatively constructs a lightweight depthwise separable network (LDSNet) as backbone, balancing computational efficiency and accuracy through channel re-parameterization strategy; incorporates an Efficient Local Attention (ELA) module to build feature enhancement networks, and employs dual-path feature fusion mechanisms to strengthen edge texture responses, significantly improving discrimination of overlapping spikes and complex backgrounds. …”
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NID-DETR: A novel model for accurate target detection in dark environments by Qingyuan Pan, Qiang Liu, Wei Huang

“…Current mainstream algorithms face challenges in extracting meaningful features under low-light conditions, which significantly limits their effectiveness. …”
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scAMZI: attention-based deep autoencoder with zero-inflated layer for clustering scRNA-seq data by Lin Yuan, Zhijie Xu, Boyuan Meng, Lan Ye

“…Abstract Background Clustering scRNA-seq data plays a vital role in scRNA-seq data analysis and downstream analyses. Many computational methods have been proposed and achieved remarkable results. …”
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Prediction of heart failure risk factors from retinal optical imaging via explainable machine learning by Sona M. Al Younis, Samit Kumar Ghosh, Hina Raja, Feryal A. Alskafi, Siamak Yousefi, Siamak Yousefi, Ahsan H. Khandoker

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VHR Multispectral Satellite Image Classification with Kolmogorov-Arnold Networks for Urban Applications by M. Fawzy, M. Fawzy, Á. Barsi

“…Obtained KAN classification accuracy and computational efficiency are evaluated compared to SNN, highlighting its strengths in modelling complexity with optimized key model parameters. …”
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