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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An Efficient Method for Offset Mitigation in Free-Space Optical Systems by Omar A. Saraereh, Imran Khan, Jeong Woo Lee

“…The system performance parameters such as the bit error rate (BER), mean square error (MSE), and computational complexity are evaluated. …”
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Soft-sensor modeling of silicon content in hot metal based on sparse robust LS-SVR and multi-objective optimization by GUO Dong-wei, ZHOU Ping

“…First, owing to the issue that the Lagrange multiplier of the standard least squares support vector machine (LS-SVR) is directly proportional to the error term and solves the lack of sparsity, the maximal independent set of sample data in the feature space mapping set was extracted to realize the sparse of the training sample set and reduce the computational complexity of modeling. …”
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ADSTrack: adaptive dynamic sampling for visual tracking by Zhenhai Wang, Lutao Yuan, Ying Ren, Sen Zhang, Hongyu Tian

“…Moreover, the adaptive dynamic sampling strategy is a parameterless token sampling strategy that does not use additional parameters. We add several extra tokens as auxiliary tokens to the backbone to further optimize the feature map. …”
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The application of deep learning technology in smart agriculture: Lightweight apple leaf disease detection model by Luo Man

“…With just 29.3 million parameters and 57.6 GFLOPs, AppleLite-YoloV8 is computationally lightweight and suitable for resource-constrained devices. …”
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Lightweight convolutional neural networks using nonlinear Lévy chaotic moth flame optimisation for brain tumour classification via efficient hyperparameter tuning by Amin Abdollahi Dehkordi, Mehdi Neshat, Alireza Khosravian, Menasha Thilakaratne, Ali Safaa Sadiq, Seyedali Mirjalili

“…Abstract Deep convolutional neural networks (CNNs) have seen significant growth in medical image classification applications due to their ability to automate feature extraction, leverage hierarchical learning, and deliver high classification accuracy. …”
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Question-matching approach based on gradual machine learning by Xuejian HE, Anqi CHEN, Zhiqiang GUO, Zhiru WANG, Qun CHEN

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Remote Sensing Image Dehazing via Dual-View Knowledge Transfer by Lei Yang, Jianzhong Cao, He Bian, Rui Qu, Huinan Guo, Hailong Ning

“…In particular, the distilled model achieves a significant speedup with less than 6% of the parameters and computational cost of the original model, while maintaining a state-of-the-art dehazing performance.…”
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Disease Detection Algorithm for Tea Health Protection Based on Improved Real-Time Detection Transformer by Zhijie Lin, Zilong Zhu, Lingling Guo, Jingjing Chen, Jiyi Wu

“…The proposed method integrates three novel components: Faster-LTNet, CG Attention Module, and RMT Spatial Prior Block, to significantly improve computational efficiency, feature representation, and detection capabilities. …”
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State-of-Health Estimation for Lithium-Ion Batteries Based on Lightweight DimConv-GFNet by Kehao Huang, Jianqiang Kang, Jing V. Wang, Qian Wang, Oukai Wu

“…Remarkably, the DimConv-GFNet substantially reduces computational demands, requiring fewer than one-third of the Floating Point Operations (FLOPs) and parameters of DimConv-Transformer. …”
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Artificial intelligence for prediction of atrial fibrillation in the stroke unit: a retrospective derivation validation cohort studyResearch in context by Maximilian Schoels, Laura Krumm, Alexander Nelde, Manuel C. Olma, Christian H. Nolte, Jan F. Scheitz, Markus G. Klammer, Christoph Leithner, Andreas Meisel, Franziska Scheibe, Michael Krämer, Karl Georg Haeusler, Matthias Endres, Christian Meisel

“…The most valuable input features and most effective model design for AF prediction are also unclear. …”
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Temporal and Modality Awareness-Based Lightweight Residual Network With Attention Mechanism for Human Activity Recognition Using a Lower-Limb Exoskeleton Robot by Chang-Sik Son, Won-Seok Kang

“…The model adopts an asymmetric convolutional architecture composed of depthwise and pointwise layers to efficiently capture temporal and modality-specific features while significantly reducing the number of trainable parameters. …”
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CAREC: Continual Wireless Action Recognition with Expansion–Compression Coordination by Tingting Zhang, Qunhang Fu, Han Ding, Ge Wang, Fei Wang

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Kans-Unet Model and Its Application in Image Patch-Shaped Detection by Xingsu Li, Zhong Li, Jianping Huang, Ying Han, Kexin Zhu, Bo Hao, Junjie Song, Yumeng Huo

“…It solves the problem of long model training time caused by insufficient computing power and provides a new method for the detection and analysis of abnormal features in power spectrum images.…”
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Research on the Classification of Sun-Dried Wild Ginseng Based on an Improved ResNeXt50 Model by Dongming Li, Zhenkun Zhao, Yingying Yin, Chunxi Zhao

“…First, each convolutional layer in the Bottleneck structure is replaced with the corresponding Ghost module, reducing the model’s computational complexity and parameter count without compromising performance. …”
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A Frequency Domain-Enhanced Transformer for Nighttime Object Detection by Yaru Li, Li Shen

“…Our approach integrates physics-prior enhancement to improve the visibility of objects in low-light conditions, frequency domain feature extraction to capture structural information potentially lost in the spatial domain, and window cross-attention fusion that efficiently combines complementary features while reducing computational complexity, significantly improving detection performance without increasing the parameter count. …”
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A Hybrid Strategy for Forward Kinematics of the Stewart Platform Based on Dual Quaternion Neural Network and ARMA Time Series Prediction by Jie Tao, Huicheng Zhou, Wei Fan

“…The DQ-BPNN is partitioned into real and dual parts, composed of parameters such as driving-rod lengths, maximum and minimum lengths, to extract more features. …”
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Exploring Trial-and-Error in Deep Learning: Initial Application to Isotope Detection in Mass Spectrometry by Qihong Jiao, Yuxiao Wang, Yongshuai Wang, Shiwei Sun, Xuefeng Cui

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An improved ShuffleNetV2 method based on ensemble self-distillation for tomato leaf diseases recognition by Shuiping Ni, Yue Jia, Mingfu Zhu, Mingfu Zhu, Yizhe Zhang, Wendi Wang, Shangxin Liu, Yawei Chen

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Voice as a sensitive biomarker for predicting exercise intensity: a modelling study by Shuyi Zhou, Ruisi Ma, Wangjing Hu, Dandan Zhang, Rui Hu, Shengwei Zou, Dingyi Cai, Zikang Jiang, Hexiao Ding, Ting Liu

“…Speech data were collected before, during, and after these activities using professional recording equipment. Acoustic features were extracted using the openSMILE toolkit, focusing on the Geneva Minimalistic Acoustic Parameter Set (GeMAPS) and the Computational Paralinguistics Challenge (ComParE) feature sets. …”
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