STar-DETR: A Lightweight Real-Time Detection Transformer for Space Targets in Optical Sensor Systems by Yao Xiao, Yang Guo, Qinghao Pang, Xu Yang, Zhengxu Zhao, Xianlong Yin

“…First, the improved MobileNetv4 (IMNv4) backbone network is developed to significantly reduce the model’s parameters and computational complexity. Second, group shuffle convolution (GSConv) is incorporated into the efficient hybrid encoder, which reduces convolution parameters while facilitating information exchange between channels. …”
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An Experimental Study of the Gemination in Arabic Language by Kamel FERRAT, Mhania GUERTI

“…To extract the feature characteristics, we have carried out an acoustic analysis by computing the values of frequency formants, energy and durations of the consonants and subsequent vowels in the various [VCV] and [VCgV] utterances (Cg: geminate consonant). …”
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A Lightweight Greenhouse Tomato Fruit Identification Method Based on Improved YOLOv11n by Xingyu Gao, Fengyu Li, Jun Yan, Qinyou Sun, Xianyong Meng, Pingzeng Liu

“…Meanwhile, the model size is only 3.3 MB, the number of parameters is 1.6 M, and the floating-point computation is 3.9 GFLOPs. …”
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Single-Pixel Imaging Reconstruction Network with Hybrid Attention and Enhanced U-Net by Bingrui Xiao, Huibin Wang, Yang Bu

“…This method takes the Generative Adversarial Network (GAN) as the basic architecture, combines the dense residual structure and the deep separable attention mechanism, and reduces the parameters while ensuring the diversity of feature extraction. …”
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Solution of clusterization problem by graph optimization methods by I.V. Konnov, O.A. Kashina, E.I. Gilmanova

“…For example, we know the market segmentation problem in economics, the feature typologization problem in sociology, faces diagnostics in geology, etc. …”
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PS-SLAM: A Visual SLAM for Semantic Mapping in Dynamic Outdoor Environment Using Panoptic Segmentation by Gang Li, Jinxiang Cai, Chen Huang, Hao Luo, Jian Yu

“…In addition, we use a stereo matching model to generate disparity images and compute depth information based on camera parameters. …”
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An Enhanced VMD with the Guidance of Envelope Negentropy Spectrum for Bearing Fault Diagnosis by Haien Wang, Xingxing Jiang, Wenjun Guo, Juanjuan Shi, Zhongkui Zhu

“…Currently, study on the relevant methods of variational mode decomposition (VMD) is mainly focused on the selection of the number of decomposed modes and the bandwidth parameter using various optimization algorithms. Most of these methods utilize the genetic-like algorithms to quantitatively analyze these parameters, which increase the additional initial parameters and inevitably the computational burden due to ignoring the inherent characteristics of the VMD. …”
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Dynamic Relevance-Weighting-Based Width-Adaptive Auto-Encoder by Malak Almejalli, Ouiem Bchir, Mohamed Maher Ben Ismail

“…Unlike traditional autoencoders with fixed architectures, the proposed method introduces a dynamic relevance weighting mechanism that assigns adaptive importance to each node in the hidden layer. This distinctive feature enables the simultaneous learning of both the model parameters and its structure. …”
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LiteMP-VTON: A Knowledge-Distilled Diffusion Model for Realistic and Efficient Virtual Try-On by Shufang Zhang, Lei Wang, Wenxin Ding

“…Despite their advantages, the substantial number of parameters and intensive computational requirements pose significant barriers to deployment on low-resource platforms. …”
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Balancing complexity and accuracy for defect detection on filters with an improved RT-DETR by Maoyuan Zhang, Xiaojuan Wei, Guojun Liu, Mengxu Chen, Chunxia Zhao, Yingxiao Liu, Zhikang Bao, Yunfeng Guo, Run An, Pengcheng Zhao

“…Furthermore, the model reduces parameter count by 6.9% and computational load by 13.1%, demonstrating its improved efficiency. …”
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Optimization of a multi-environmental detection model for tomato growth point buds based on multi-strategy improved YOLOv8 by Jiang Liu, Jingxin Yu, Changfu Zhang, Huankang Cui, Jinpeng Zhao, Wengang Zheng, Fan Xu, Xiaoming Wei

“…Three key innovations address YOLOv8’s limitations: (1) an SE attention module boosts feature representation in cluttered environments, (2) GhostConv replaces standard convolution to reduce computational load by 19% while preserving feature discrimination, and (3) a scale-adaptive WIoU_v2 loss function optimizes gradient allocation for variable-quality data. …”
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White Blood Cell Detection Based on FBDM-YOLOv8s by Borui Sun, Xiangsuo Fan, Jie Meng, Jinfeng Wang, Huajin Chen, Lei Liu

“…Additionally, to attain diversified feature extraction, this article introduces the DBBNCSPELAN4 module, which can perform diversified feature extraction in a multi-branch manner while lightweighting the model, reducing the number of parameters, and speeding up computation. …”
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An AI-Based Horticultural Plant Fruit Visual Detection Algorithm for Apple Fruits by Bin Yan, Xiameng Li, Rongshan Yan

“…The Depthwise Separable Convolution (DWConv) module has many advantages: (1) It has high computational efficiency, reducing the number of parameters and calculations in the model; (2) It makes the model lightweight and easy to deploy in hardware; (3) DWConv can be combined with other modules to enhance the multi-scale feature extraction capability of the detection network and improve the ability to capture multi-scale information; (4) It balances the detection accuracy and speed of the model; (5) DWConv can flexibly adapt to different network structures. …”
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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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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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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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Direction-Aware Lightweight Framework for Traditional Mongolian Document Layout Analysis by Chenyang Zhou, Monghjaya Ha, Licheng Wu

“…Our framework introduces three key innovations: a modified MobileNetV3 backbone with asymmetric convolutions for efficient vertical feature extraction, a dynamic feature enhancement module with channel attention for adaptive multi-scale information fusion, and a direction-aware detection head with <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mo>(</mo><mo form="prefix">sin</mo><mi>θ</mi><mo>,</mo><mo form="prefix">cos</mo><mi>θ</mi><mo>)</mo></mrow></semantics></math></inline-formula> vector representation for accurate orientation modeling. …”
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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&#x2019;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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Meat analogues: The relationship between mechanical anisotropy, macrostructure, and microstructure by Miek Schlangen, Iris van der Doef, Atze Jan van der Goot, Mathias P. Clausen, Thomas E. Kodger

“…The relationship between microstructural air bubbles and macrostructure and mechanical properties is apparent in all correlation analyses. Last, univariate feature selection provided insight into which parameters are most important for selected target features.…”
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Research on Correction Method of pin-by-pin Homogenization Environmental Effect of PWR Based on Machine Learning by LI Tianya1, 2, , LUO Qi3, , YAO Dong2, HE Caiyun1, CHAI Xiaoming2, CAI Yun2, ZHANG Bin2, ZHANG Hongbo2, LIAO Hongkuan1, DUAN Yongqiang1

“…By analyzing the machine learning model’s feature sensitivity, the combination features of pin material, location, surrounding assembly, energy spectrum, and leakage-related information were determined. …”