A Novel Quadrilateral Contour Disentangled Algorithm for Industrial Instrument Reading Detection by Xiang Li, Changchang Zeng, Yong Yao, Jide Qian, Haiding Zhang, Sen Zhang, Suixian Yang

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Optimizing deep learning models to combat amyotrophic lateral sclerosis (ALS) disease progression by Haoshen Qin, Lal Hussain, Ziang Liu, Xu Yan, Fuad A. Awwad, Faisal Mehmood Butt, Umair Ahmad Salaria, Emad A.A. Ismail

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A lightweight model for echo trace detection in echograms based on improved YOLOv8 by Jungang Ma, Jianfeng Tong, Minghua Xue, Junfan Yao

“…It reduces computational complexity by 18.5%, decreases model parameters by 40%, and improves mAP0.5 to 79.5% and mAP0.5:0.95 to 58.2%, making it suitable for echosounders with limited resources.…”
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RE-YOLO: An apple picking detection algorithm fusing receptive-field attention convolution and efficient multi-scale attention. by Jinxue Sui, Li Liu, Zuoxun Wang, Li Yang

“…This module makes the spatial semantic features uniformly distributed to each feature group through partial channel reconstruction and feature grouping, which emphasizes the interaction of spatial channels, improves the ability to detect subtle differences, can effectively discriminate the apple occlusion, and reduces the computational cost. …”
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LI-YOLOv8: Lightweight small target detection algorithm for remote sensing images that combines GSConv and PConv. by Pingping Yan, Xiangming Qi, Liang Jiang

“…In the domain of remote sensing image small target detection, challenges such as difficulties in extracting features of small targets, complex backgrounds that easily lead to confusion with targets, and high computational complexity with significant resource consumption are prevalent. …”
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Lightweight Infrared Small Target Detection Method Based on Linear Transformer by Bingshu Wang, Yifan Wang, Qianchen Mao, Jingzhuo Cao, Han Zhang, Laixian Zhang

“…The model consists of two parts: a multi-scale linear transformer and a lightweight dual feature pyramid network. It combines the strengths of a lightweight feature extraction module and the multi-head attention mechanism, effectively representing the small targets in the complex background at an economical computational cost. …”
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Fault Diagnosis of Electric Impact Drills Based on Time-Varying Loudness and Logistic Regression by Yapeng Jing, Haitao Su, Shao Wang, Wenhua Gui, Qing Guo

“…A feature extraction peak-to-average ratio (PAR) method based on the time-varying loudness spectrum was described and implemented to compute the feature vectors. …”
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The Study of Roadside Visual Perception in Internet of Vehicles Based on Improved YOLOv5 and CombineSORT by LI Xiaohui, YANG Jie, XIA Qin

“…The algorithm is then compared with other classical algorithms using video streams from intersections with varying traffic volumes, all executed on a mobile edge computer (MEC) with limited computing power.Results and DiscussionsThrough ablation test, original YOLOv5 achieved mAP@90 at 0.894 and parameter quantity at 21.2M. …”
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Deep Separable Hypercomplex Networks by Nazmul Shahadat, Anthony S. Maida

“…Deep hypercomplex-inspired convolutional neural networks (CNNs) have recently enhanced feature extraction for image classification by allowing weight sharing across input channels. …”
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Tomato Ripening Detection in Complex Environments Based on Improved BiAttFPN Fusion and YOLOv11-SLBA Modeling by Yan Hao, Lei Rao, Xueliang Fu, Hao Zhou, Honghui Li

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A lightweight algorithm for steel surface defect detection using improved YOLOv8 by Shuangbao Ma, Xin Zhao, Li Wan, Yapeng Zhang, Hongliang Gao

“…Firstly, GhostNet is utilized as the backbone network in order to reduce the number of model parameters and computational complexity. Secondly, the MPCA (MultiPath Coordinate Attention) attention mechanism is integrated to enhance feature extraction capabilities. …”
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The Development of a Lightweight DE-YOLO Model for Detecting Impurities and Broken Rice Grains by Zhenwei Liang, Xingyue Xu, Deyong Yang, Yanbin Liu

“…Firstly, changing the CBS module to the DBS module in the entire network model and replacing the standard convolution with Depthwise Separable Convolution (DSConv) can effectively reduce the number of parameters and the computational complexity, making the model lightweight. …”
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Multidimensional State Data Reduction and Evaluation of College Students’ Mental Health Based on SVM by Han Peiqing

“…A model experiment containing internal and external personality tendency classification, anxiety, and depression dichotomy was designed using logistic regression analysis, information entropy, and SVM algorithm to construct the feature dimensions of the network behavior data, combined with the labeled data of mental state to derive the sample data set for model experiments. …”
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Sea Surface Temperature Prediction Based on Spatio-Temporal Graph Contrastive Learning Network by Weihan Dai, Xiaoyu He, Xiulin Geng, Shuai Zhang, Ziheng Gao

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Investigating lightweight and interpretable machine learning models for efficient and explainable stress detection by Debasish Ghose, Ayan Chatterjee, Indika A. M. Balapuwaduge, Yuan Lin, Soumya P. Dash

“…We have developed ML models incorporating efficient feature selection techniques and hyper-parameter tuning. …”
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Lightweight Apple Leaf Disease Detection Algorithm Based on Improved YOLOv8 by LUO Youlu, PAN Yonghao, XIA Shunxing, TAO Youzhi

“…SPD-Conv was introduced to replace the original convolutional layers to retain fine-grained information and reduce model parameters and computational costs, thereby improving the accuracy of disease detection. …”
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Improved YOLO for long range detection of small drones by Sicheng Zhou, Lei Yang, Huiting Liu, Chongqin Zhou, Jiacheng Liu, Yang Wang, Shuai Zhao, Keyi Wang

“…Inspired by ARM CPU efficiency optimizations, the model uses depthwise separable convolutions and efficient activation functions to reduce parameter size. The neck structure is enhanced with a collaborative attention mechanism and multi-scale fusion, improving feature representation. …”
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A Detection Method of Lentinus Edodes Based on Improved YOLOv4 Algorithm by HUANG Ying-lai, LI Da-ming, LU Xin, YANG Liu-song

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An improved DeepLabv3 + railway track extraction algorithm based on densely connected and attention mechanisms by Yanbin Weng, Jie Yang, Changfan Zhang, Jing He, Cheng Peng, Lin Jia, Hui Xiang

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SHIFA: SBERT-Based Healthcare Information Focused Arabic Question Answering by Rahaf Alruwaithi, Sarah AlHumoud

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