An improved lightweight method based on EfficientNet for birdsong recognition by Haolun He, Hui Luo

“…The proposed method introduces the ECA attention mechanism to reduce the parameter complexity while improving feature expression. …”
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Predicting the bounds of large chaotic systems using low-dimensional manifolds. by Asger M Haugaard

“…Here, a method is presented which treats extrema of chaotic systems as belonging to discretised manifolds of low dimension (low-D) embedded in high-dimensional (high-D) phase space. As a central feature, the method exploits that strange attractor dimension is generally much smaller than parent system phase space dimension. …”
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AgriLiteNet: Lightweight Multi-Scale Tomato Pest and Disease Detection for Agricultural Robots by Chenghan Yang, Baidong Zhao, Madina Mansurova, Tianyan Zhou, Qiyuan Liu, Junwei Bao, Dingkun Zheng

“…Real-time detection of tomato pests and diseases is essential for precision agriculture, as it requires high accuracy, speed, and energy efficiency of edge-computing agricultural robots. This study proposes AgriLiteNet (Lightweight Networks for Agriculture), a lightweight neural network integrating MobileNetV3 for local feature extraction and a streamlined Swin Transformer for global modeling. …”
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A multimodal functional structure-based graph neural network for fatigue detection by Dongrui Gao, Zhihong Zhou, Zongyao Peng, Haokai Zhang, Shihong Liu, Manqing Wang, Hongli Chang

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CAPSE-ViT: A Lightweight Framework for Underwater Acoustic Vessel Classification Using Coherent Spectral Estimation and Modified Vision Transformer by Najamuddin NAJAMUDDIN, Usman Ullah SHEIKH, Ahmad Zuri SHA’AMERI

“…The results, evaluated on standard DeepShip and ShipsEar datasets, show that the proposed model achieved a classification accuracy of 97.98 % and 99.19 % while utilizing just 1.90 million parameters, outperforming other models such as ResNet18 and UATR-Transformer in terms of both accuracy and computational efficiency. …”
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Classification of Epileptic Seizures by Simple Machine Learning Techniques: Application to Animals’ Electroencephalography Signals by Illia Pidvalnyi, Anna Kostenko, Oleksandr Sudakov, Dmytro Isaev, Oleksandr Maximyuk, Oleg Krishtal, Olena Iegorova, Ievgen Kabin, Zoya Dyka, Steffen Ortmann, Peter Langendorfer

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A Lightweight Algorithm for Detection and Grading of Olive Ripeness Based on Improved YOLOv11n by Fengwu Zhu, Suyu Wang, Min Liu, Weijie Wang, Weizhi Feng

“…Concurrently, existing deep learning-based detection models face issues such as insufficient feature extraction for small targets and difficulties in deployment due to their need for large numbers of parameters. …”
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A Multi-Encoder BHTP Autoencoder for Robust Lithium Battery SOH Prediction Under Small-Sample Scenarios by Chang Liu, Shunli Wang, Zhiqiang Ma, Siyuan Guo, Yixiong Qin

“…By utilizing a pre-training and fine-tuning strategy, the proposed method effectively reduces computational complexity and the number of model parameters while maintaining high prediction accuracy. …”
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The structure of the local detector of the reprint model of the object in the image by A. A. Kulikov

“…The local detector is able, in addition to determining the modified object, to determine the original shape of the object as well. A special feature of TA is the representation of image sections in a compact form and the evaluation of the parameters of the affine transformation. …”
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Comparative Analysis of Automated Machine Learning for Hyperparameter Optimization and Explainable Artificial Intelligence Models by Muhammad Salman Khan, Tianbo Peng, Hanzlah Akhlaq, Muhammad Adeel Khan

“…The findings demonstrate that Optuna consistently outperforms its counterparts, achieving the highest predictive accuracy and the lowest computational training time. The findings also highlight SHAP’s superiority in offering detailed, consistent, and actionable insights, making it the preferred method for both global feature importance and individual feature analysis in high-stakes engineering applications.…”
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Transferable deep learning with coati optimization algorithm based mitotic nuclei segmentation and classification model by Amal Alshardan, Nazir Ahmad, Achraf Ben Miled, Asma Alshuhail, Yazeed Alzahrani, Ahmed Mahmud

“…Numerous pattern recognition algorithms for cell-sized objects in HIs depend upon segmentation to assess features. The correct description of the segmentation has been difficult, and feature outcomes can be highly complex to the segmentation. …”
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RSDCNet: An efficient and lightweight deep learning model for benign and malignant pathology detection in breast cancer by Yuan Liu, Haipeng Li, Zhu Zhu, Chen Chen, Xiaojing Zhang, Gongsheng Jin, Hongtao Li

“…This integration aims to reduce model parameters while enhancing key feature extraction capabilities, thereby achieving both lightweight design and high efficiency. …”
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Automatic Identification of the Main Ionospheric Trough in Total Electron Content Images by Gregory Starr, Sebastijan Mrak, Yukitoshi Nishimura, Michael Hirsch, Prakash Ishwar, Joshua Semeter

“…We show that statistics computed on the resulting labels are robust to our choice of algorithm parameters and that we are able to match the results of Aa, Zou, et al. (2020, doi:https://doi.org/10.1029/2019JA027583) with a particular selection of the parameters. …”
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Enhancing lung disease diagnosis with deep-learning-based CT scan image segmentation by Rima Tri Wahyuningrum, Achmad Bauravindah, Indah Agustien Siradjuddin, Budi Dwi Satoto, Amillia Kartika Sari, Anggraini Dwi Sensusiati

“…Whereas on the Kaggle dataset it achieved a Dice coefficient of 0.961, IoU of 0.930, computational time of 1.189 s, and 9.16 million trainable parameters. …”
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YOLOv8-GABNet: An Enhanced Lightweight Network for the High-Precision Recognition of Citrus Diseases and Nutrient Deficiencies by Qiufang Dai, Yungao Xiao, Shilei Lv, Shuran Song, Xiuyun Xue, Shiyao Liang, Ying Huang, Zhen Li

“…This model incorporates several key enhancements: A lightweight ADown subsampled convolutional block is utilized to reduce both the model’s parameter count and its computational demands, replacing the traditional convolutional module. …”
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Research on Thickness Error Optimization Method of Rolling System Based on Improved Sparrow Search Algorithm–Bidirectional Long Short-Term Memory Network–Attention by Qingyun Wu, Xinchen Li, Jiafei Ji, Bowen Xing

“…Firstly, a mechanical model is established, and the parameters involved are analyzed to extract suitable parameters as inputs to the network to reduce the feature loss of the network inputs. …”
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A software pipeline for systematizing machine learning of speech data by Jimuel Celeste, Mashrura Tasnim, Amable J. Valdés Cuervo, Enrique A. de la Cal, Eleni Stroulia

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Ghost Module-Enhanced MTCNN: A Lightweight Cascade Framework for High-Accuracy Face Detection in Edge-Deployable Scenarios by Chen Wang, Fen Liu

“…This modification reconstructs the network’s feature extraction capabilities, resulting in a new model. …”
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Novel channel attention-based filter pruning methods for low-complexity semantic segmentation models by Md. Bipul Hossain, Na Gong, Mohamed Shaban

“…This is realized by recognizing the contextual importance of the feature maps in each layer of the models and the significance of each filter to the final model performance. …”
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A Lightweight Neural Network for Cell Segmentation Based on Attention Enhancement by Shuang Xia, Qian Sun, Yiheng Zhou, Zhaoyuxuan Wang, Chaoxing You, Kainan Ma, Ming Liu

“…Deep neural networks have made significant strides in medical image segmentation tasks, but their large-scale parameters and high computational complexity limit their applicability on resource-constrained edge devices. …”
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