Design of a logistics warehouse robot positioning and recognition model based on improved EKF and calibration algorithm by Yunbo Wang, Chao Ye

“…It showcased excellent mapping fidelity and integrity (above 0.9) across various datasets, with a high loop detection success rate (0.91) enhancing map precision. …”
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LN-DETR: cross-scale feature fusion and re-weighting for lung nodule detection by Dibin Zhou, Honggang Xu, Wenhao Liu, Fuchang Liu

“…This paper introduces an enhanced lung nodule detection algorithm base on RT-DETR, called LN-DETR. First, we designed a Deep and Shallow Detail Fusion layer that effectively fuses cross-scale features from both shallow and deep layers. …”
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HSF-DETR: A Special Vehicle Detection Algorithm Based on Hypergraph Spatial Features and Bipolar Attention by Kaipeng Wang, Guanglin He, Xinmin Li

“…Cross-domain validation on VisDrone2019 and BDD100K datasets confirms the method’s generalization capability and robustness across diverse scenarios, establishing HSF-DETR as an effective solution for special vehicle detection in complex environments.…”
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Multi robot exploration using an advanced multi-objective salp swarm algorithm for efficient coverage and performance by Ali El Romeh, Seyedali Mirjalili

“…AMET combines the deterministic structure of Coordinated Multi-Robot Exploration (CME) with the adaptive search capabilities of the Multi-Objective Salp Swarm Algorithm (MSSA) to achieve a balanced trade-off between exploration efficiency and mapping accuracy. …”
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A Comprehensive Overview of Single-Source and Dual-Source Energy Balance Algorithms ‎for Estimating Actual Evapotranspiration by Maryam Rezaei

“…No need for a land use map. One disadvantage of this model is that it can only be used in cases where atmospheric conditions across the entire image are constant. …”
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Comparative Analysis of Machine Learning Algorithms and Statistical Techniques for Data Analysis in Crop Growth Monitoring with NDVI by M. Arunachalam, S. Sekar, A. M. Erdmann, V. V. Sajith Variyar, R. Sivanpillai

“…Results generated from ML algorithms were compared to the output generated by the ISODATA algorithm. …”
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Advanced Trajectory Analysis of NASA’s Juno Mission Using Unsupervised Machine Learning: Insights into Jupiter’s Orbital Dynamics by Ashraf ALDabbas, Zaid Mustafa, Zoltan Gal

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FCMI-YOLO: An efficient deep learning-based algorithm for real-time fire detection on edge devices. by Junjie Lu, Yuchen Zheng, Liwei Guan, Bing Lin, Wenzao Shi, Junyan Zhang, Yunping Wu

“…The rapid development of Internet of Things (IoT) technology and deep learning has propelled the deployment of vision-based fire detection algorithms on edge devices, significantly exacerbating the trade-off between accuracy and inference speed under hardware resource constraints. …”
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A small‐target traffic sign detection algorithm based on partial conv and atrous spatial pyramid by Yuqi Li, Zijian Wang, Han Zhang, Xinpeng Yao, Zhou Zhou, Xin Cheng

“…Experiments have demonstrated the superiority of the proposed algorithm, as testing on the TT100K dataset resulted in a mAP@0.5 of 91.2% and a mAP@0.5:0.95 of 71.8% using the proposed algorithm.…”
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Cross-Network User Identity Linkage Method with Deep Learning Based on SDNE Embedding Representation by CHENG Jialin, YUAN Deyu, SUN Zeyu, CHEN Ziyan

“…In order to determine whether users from two different social networks belong to the same natural person, a deep learning algorithm based on SDNE (structural deep network embedding) embedding expression (eSUIL) is proposed to solve the problem of cross-network user identity linkage, and a unified framework is constructed. …”
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Defect Detection in the DR Images of Aluminum and Magnesium Alloy Castings Based on the Improved YOLOv8 Algorithm by Mingfei CHEN, Shuai TIAN

“…Experimental results showed that the precision, recall, and mean average precision (mAP) of the improved YOLOv8 algorithm on the defect dataset reached 92.7%, 98.5%, and 92.4%, respectively, and the detection speed was 138 FPS that met the real-time requirements of intelligent production lines for defect detection in the DR images of aluminum and magnesium alloy castings.…”
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GCS-YOLO: A Lightweight Detection Algorithm for Grape Leaf Diseases Based on Improved YOLOv8 by Qiang Hu, Yunhua Zhang

“…The CBAM attention mechanism is added to the model to improve the extraction of subtle features of lesions in complex environments. Cross-scale shared convolution parameters and separated batch normalization techniques are used to optimize the detection head, achieving a lightweight design and improving the detection efficiency of the algorithm. …”
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A Novel Deep Learning Approach for Yarn Hairiness Characterization Using an Improved YOLOv5 Algorithm by Filipe Pereira, Helena Lopes, Leandro Pinto, Filomena Soares, Rosa Vasconcelos, José Machado, Vítor Carvalho

“…Key performance indicators include precision, recall, F1-score, mAP0.5:0.95, and mAP0.5. The experimental results show significant improvements, with the proposed algorithm increasing model mAP0.5 by 5% to 6% and mAP0.5:0.95 by 11% to 12% compared to the standard YOLOv5s6 model. …”
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Research on a Target Detection Algorithm for Common Pests Based on an Improved YOLOv7-Tiny Model by He Gong, Xiaodan Ma, Ying Guo

“…Additionally, compared to algorithms like Faster R-CNN, SSD, and RetinaNet, the improved model delivers superior detection performance. …”
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A Comparative Study of K-means Clustering Algorithms Using Euclidean and Manhattan Distance for Climate Data. by Bakhshan Hamad

“…It features a user-friendly interface, a wide range of algorithms, robust pre-processing and visualization tools, and cross-platform compatibility.Focusing on efficiency and reducing variation within clusters, the results revealed that within Euclidean distance, all algorithms formed two clusters. …”
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BCSM-YOLO: An Improved Product Package Recognition Algorithm for Automated Retail Stores Based on YOLOv11 by Pingqing Hou, Shaoze Huang

“…According to the experimental results, BCSM-YOLO improved precision, recall, mAP@0.5, and mAP@0.5-0.95 by 5.5%, 6.2%, 6%, and 5.8%, respectively, under identical hyperparameter and training dataset (Ultralytics SKU-110K). …”
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HPRT-DETR: A High-Precision Real-Time Object Detection Algorithm for Intelligent Driving Vehicles by Xiaona Song, Bin Fan, Haichao Liu, Lijun Wang, Jinxing Niu

“…To address these challenges, we propose a High-Precision Real-Time object detection algorithm (HPRT-DETR). We designed a Basic-iRMB-CGA (BIC) Block for a backbone network that efficiently extracts features and reduces the model’s parameters. …”
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An efficient algorithm for pedestrian fall detection in various image degradation scenarios based on YOLOv8n by Jianhui Xun, Xuefeng Wang, Xiufang Wang, Xiaoliang Fan, Peishuai Yang, Zhifei Zhang

“…To address these issues, this paper proposes an enhanced pedestrian fall detection algorithm called Pedestrian Chain-of-Thought Prompted Adaptive Enhancer YOLO (PCE-YOLO), based on YOLOv8n. …”
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Improved Aerial Surface Floating Object Detection and Classification Recognition Algorithm Based on YOLOv8n by Lili Song, Haixin Deng, Jianfeng Han, Xiongwei Gao

“…To address the aforementioned challenges, we proposed an improved YOLOv8-HSH algorithm based on YOLOv8n. The proposed algorithm introduces several key enhancements: (1) an enhanced HorBlock module to facilitate multi-gradient and multi-scale superposition, thereby intensifying critical floating object characteristics; (2) an optimized CBAM attention mechanism to mitigate background noise interference and substantially elevate detection accuracy; (3) the incorporation of a minor target recognition layer to augment the model’s capacity to discern floating objects of differing dimensions across various environments; and (4) the implementation of the WIoU loss function to enhance the model’s convergence rate and regression accuracy. …”
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Payload- and Energy-Aware Tactical Allocation Loop-Based Path-Planning Algorithm for Urban Fumigation Robots by Prithvi Krishna Chittoor, Bhanu Priya Dandumahanti, Abishegan M., Sriniketh Konduri, S. M. Bhagya P. Samarakoon, Mohan Rajesh Elara

“…This combined strategy minimizes travel distance and reduces energy consumption across extended fumigation missions. To evaluate its effectiveness, a comparative study was performed between PETAL and prominent algorithms such as A*, a hybrid Dijkstra with A*, random search, and a greedy distance-first approach, using both randomly generated environments and a real-time map from an actual deployment site. …”
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