Advanced retinal disease detection from OCT images using a hybrid squeeze and excitation enhanced model. by Gülcan Gencer, Kerem Gencer

“…EfficientNetB0 achieves high accuracy with fewer parameters through model scaling strategies, while Xception offers powerful feature extraction using deep separable convolutions. …”
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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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YOLO-Pika: a lightweight improved model of YOLOv8n incorporating Fusion_Block and multi-scale fusion FPN and its application in the precise detection of plateau pikas by Yihao Liu, Jianyun Zhao, Changjun Xu, Yuedi Hou, Yuxiang Jiang

“…We propose YOLO-Pika, a lightweight detector built on YOLOv8n that integrates (1) a Fusion_Block into the backbone, leveraging high-dimensional mapping and fine-grained gating to enhance feature representation with negligible computational overhead, and (2) an MS_Fusion_FPN composed of multiple MSEI modules for multi-scale frequency-domain fusion and edge enhancement. …”
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A study on the prediction of mountain slope displacement using a hybrid deep learning model by Yuyang Ma, Xiangxiang Hu, Yuhang Liu, Yaya Shi, Zhiyuan Yu, Xinmin Wang, Liangbai Hu, Shuailing Liu, Dongdong Pang

“…The method employs an Improved Whale Optimization Algorithm (IWOA) to fine-tune parameters for GNSS data fitting, ensuring accurate signal feature extraction. …”
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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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Contrast Limited Adaptive Local Histogram Equalization Method for Poor Contrast Image Enhancement by Ibrahim Majid Mohammed, Nor Ashidi Mat Isa

“…However, each approach has its gaps, such as complexity, parameter tuning sensitivity, dependence on initial image quality and long computational time. …”
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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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Research on Laser Radar Inspection Station Planning of Vehicle Body-In-White (BIW) with Complex Constraints by Lijuan Li, Siyi Wang, Jichao Ma, Xiaobing Gao

“…Firstly, a parametric geometric modeling approach is developed to define measurement spaces for individual features, accompanied by an innovative maximal complete subgraph mining algorithm to intelligently identify shared feasible measurement regions among multiple features. …”
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An overview of the activation functions used in deep learning algorithms by Mete Çelik, Kemal Adem, Serhat Kılıçarslan

“…Also, in deep learning algorithms, activation functions have been developed by taking into account features such as performing the learning process in a healthy way, preventing excessive learning, increasing the accuracy performance, and reducing the computational cost. …”
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RETRACTED: Intelligent power grid energy supply forecasting and economic operation management using the snake optimizer algorithm with Bigur-attention model by Lingling Cui, Jiacheng Liao

“…The model evaluation phase calculates metrics such as prediction error, accuracy, and stability, and also examines the model’s training time, inference time, number of parameters, and computational complexity to assess its efficiency and scalability. …”
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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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Machine learning of 27Al NMR electric field gradient tensors for crystalline structures from DFT by He Sun, Shyam Dwaraknath, Handong Ling, Kristin A. Persson, Sophia E. Hayes

“…We developed a fast, low-cost machine learning model to predict EFG parameters based on local structural motifs and elemental parameters. …”
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Multi-kernel inception-enhanced vision transformer for plant leaf disease recognition by Sk Mahmudul Hassan, Kumar Sekhar Roy, Ruhul Amin Hazarika, Mehbub Alam, Mithun Mukherjee

“…The proposed IEViT architecture extracts local as well as global features, which improves feature learning. The use of multiple filters with different kernel sizes efficiently uses computing resources to extract relevant features without the need for deeper networks. …”
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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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Identification of glass eel capture equipment in the Yangtze River estuary based on high-spatial -resolution imagery and an improved YOLOv8 model by Pengfei Zhu, Weifeng Zhou

“…To avoid the false detection of small targets, we introduce the asymptotic feature pyramid network to replace the original detection head, and add a detection layer for small targets, which improves the accuracy but increases the parameters and computation volume. …”
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A Real-Time Green and Lightweight Model for Detection of Liquefied Petroleum Gas Cylinder Surface Defects Based on YOLOv5 by Burhan Duman

“…The architecture integrates ghost convolution and ECA blocks to improve feature extraction with less computational overhead in the network’s backbone. …”
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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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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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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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Recognition of anxiety and depression using gait data recorded by the kinect sensor: a machine learning approach with data augmentation by Milad Shoryabi, Ahmad Hajipour, Afshin Shoeibi, Ali Foroutannia

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