CM-YOLO: A Multimodal PCB Defect Detection Method Based on Cross-Modal Feature Fusion by Haowen Lan, Jiaxiang Luo, Hualiang Zhang, Xu Yan

“…By integrating information from RGB images and depth images, the feature perception capability of a defect detection algorithm can be enhanced, making it more robust and reliable in detecting subtle defects on printed circuit boards. …”
Get full text

Sagittal Plane Kinematic Deviations and Spatio-Temporal Gait Characteristics in Children with Idiopathic Toe Walking: A Comparative Analysis Using Statistical Parametric Mapping by Rocio Pozuelo-Calvo, Almudena Serrano-Garcia, Yolanda Archilla-Bonilla, Angel Ruiz-Zafra, Manuel Noguera-Garcia, Kawtar Benghazi-Akhlaki, Miguel Membrilla-Mesa, Carla DiCaudo, Jose Heredia-Jimenez

“…Sagittal plane kinematics of the pelvis, hip, knee, and ankle were compared between groups using SPM to identify significant deviations across the gait cycle. <b>Results</b>: Significant differences were identified in the single support and swing phases, with higher values observed in the ITW group (<i>p</i> < 0.05). …”
Get full text

UDCNet: A U-Net Guided Dual-Branch Cross-Attention Network for SAR Object Detection by Siyang Huang, Liushun Hu, Zhangjunjie Cheng, Shaojing Su, Junyu Wei, Xiaozhong Tong, Zongqing Zhao

“…Synthetic aperture radar (SAR) object detection often suffers from speckle noise and deformation of diverse target shapes, leading to an inability for the algorithm to effectively distinguish between foreground and background. …”
Get full text

Enhancing sentiment analysis in tourism reviews: A comparative study of algorithms in ASPECT-BASED SENTIMENT ANALYSIS and EMOTION DETECTION by Viktor Handrianus Pranatawijaya, Putu Bagus Adidyana Anugrah Putra, Ressa Priskila, Novera Kristianti

“…This research aims to combine Aspect-Based Sentiment Analysis (ABSA) and emotion detection for a more in-depth analysis of tourism reviews in Palangka Raya City and compare the performance of various algorithms. Review data was taken from Google Maps and analyzed using BoW, LDA, NRC Emotion Lexicon, machine learning, and deep learning algorithms such as Logistic Regression (LR), Naïve Bayes (NB), Random Forest (RF), Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Gradient Boosting (GB), Decision Tree (DT), and BERT. …”
Get full text

FECI-RTDETR a Lightweight Unmanned Aerial Vehicle Infrared Small Target Detector Algorithm Based on RT-DETR by Renzheng Xue, Shijie Hua, Haiqiang Xu

“…Experimental results indicate that compared to RT-DETR, the FECI-RTDETR model reduces the number of parameters by 24.56% and floating-point operations by 19.12% on the HIT-UAV dataset. The mAP50 and mAP50:95 metrics improved by 4.2% and 2.9%, respectively, with the mAP50 reaching 84.2%. …”
Get full text

LLD-YOLO: A Low-Light Object Detection Algorithm Based on Dynamic Weighted Fusion of Shallow and Deep Features by Wenhao Cai, Yajun Chen, Xiaoyang Qiu, Meiqi Niu, Jianying Li

“…Object detection in low-light scenarios has a wide range of applications, but existing algorithms often struggle to preserve the scarce low-level features in dark environments and exhibit limitations in localization accuracy for blurred edges and occluded objects, leading to suboptimal performance. …”
Get full text

FP-YOLOv8: Surface Defect Detection Algorithm for Brake Pipe Ends Based on Improved YOLOv8n by Ke Rao, Fengxia Zhao, Tianyu Shi

“…The experimental results show that FP-YOLOv8 achieves a mAP50 of 89.5% and an F1-score of 87% on the ends surface defects dataset, representing improvements of 3.3% and 6.0%, respectively, over the YOLOv8n algorithm, Meanwhile, it reduces model parameters and computational costs by 14.3% and 21.0%. …”
Get full text

Detecting tropical freshly-opened swidden fields using a combined algorithm of continuous change detection and support vector machine by Ningsang Jiang, Peng Li, Zhiming Feng

“…However, the CCD-derived temporal attributes and other multi-dimension features are seldom utilized to monitor swidden agriculture. Here, a combined algorithm integrating CCD and Support Vector Machine (SVM) is firstly developed to comprehensively highlight fundamental characteristics of swidden agriculture for maximumly and effectively mapping freshly opened swiddens. …”
Get full text

A global daily mesoscale front dataset from satellite observations: in situ validation and cross-dataset comparison by Q. Xing, H. Yu, W. Yu, W. Yu, W. Yu, W. Yu, X. Chen, X. Chen, X. Chen, X. Chen, H. Wang, H. Wang, H. Wang

“…However, the lack of comprehensive validation and comparison of cross-satellite products against in situ observations, along with limited accessibility to frontal datasets, must be addressed to enable the broader application of front detection algorithms. …”
Get full text

Mapping recent timber harvest activity in a temperate forest using single date airborne LiDAR surveys and machine learning: lessons for conservation planning by G. Burch Fisher, Andrew J. Elmore, Matthew C. Fitzpatrick, Darin J. McNeil, Jeff W. Atkins, Jeffery L. Larkin

“…In this paper, we develop a timber harvest mapping workflow using machine learning (XGBoost algorithm) and single campaign airborne light detection and ranging (LiDAR) surveys for the state of Pennsylvania, USA. …”
Get full text

Mapping expanded prostate cancer index composite to EQ5D utilities to inform economic evaluations in prostate cancer: Secondary analysis of NRG/RTOG 0415. by Rahul Khairnar, Stephanie L Pugh, Howard M Sandler, W Robert Lee, Ester Villalonga Olives, C Daniel Mullins, Francis B Palumbo, Deborah W Bruner, Fadia T Shaya, Soren M Bentzen, Amit B Shah, Shawn C Malone, Jeff M Michalski, Ian S Dayes, Samantha A Seaward, Michele Albert, Adam D Currey, Thomas M Pisansky, Yuhchyau Chen, Eric M Horwitz, Albert S DeNittis, Felix Y Feng, Mark V Mishra

“…<h4>Conclusions</h4>This is the first study to identify a comprehensive set of mapping algorithms to generate EQ5D utilities from EPIC domain/ sub-domain scores. …”
Get full text

Evaluating Sugarcane Yield Estimation in Thailand Using Multi-Temporal Sentinel-2 and Landsat Data Together with Machine-Learning Algorithms by Jaturong Som-ard, Savittri Ratanopad Suwanlee, Dusadee Pinasu, Surasak Keawsomsee, Kemin Kasa, Nattawut Seesanhao, Sarawut Ninsawat, Enrico Borgogno-Mondino, Filippo Sarvia

“…Furthermore, the mapped yields across the region closely aligned with the official statistical data from the Office of the Cane and Sugar Board (with a range value of 36,000 ton). …”
Get full text

Multi-stage bidirectional informed-RRT * plant protection UAV path planning method based on A * algorithm domain guidance by Jian Li, Jian Li, Yuan Gao, Yuan Gao, Zheng Li, Zheng Li, Weijian Zhang, Weijian Zhang, Weilin Yu, Weilin Yu, Yating Hu, He Liu, Changtian Li

Get full text

Ecological and Statistical Evaluation of Genetic Algorithm (GARP), Maximum Entropy Method, and Logistic Regression in Predicting Spatial Distribution of Astragalus sp. by Amir Ghahremanian, Abbas Ahmadi, Hamid Toranjzar, Javad Varvani, Nourollah Abdi

“…Soil sampling was conducted at varying depths to capture essential soil properties, including physical (clay, gravel, silt, and sand) and chemical factors (organic matter, electrical conductivity, pH, and lime). Soil maps were generated using interpolation techniques to visualize soil variation across the area. …”
Get full text

Externally validated and clinically useful machine learning algorithms to support patient-related decision-making in oncology: a scoping review by Catarina Sousa Santos, Mário Amorim-Lopes

“…Convolutional neural networks were most prevalent, demonstrating high performance, followed by gradient- and decision tree-based algorithms. Other algorithms, though underrepresented, showed promise. …”
Get full text

Protection Algorithm Based on Two-Dimensional Spatial Current Trajectory Image and Deep Learning for Transmission Lines Connecting Photovoltaic Stations by Panrun Jin, Jianling Liao, Wenqin Song, Xushan Zhao, Yankui Zhang

“…In this algorithm, the PV side current and the system side current are, respectively, mapped to the two-dimensional space plane as X- and Y-axes to form the current trajectory image. …”
Get full text

Bi-directional virtual search algorithm for efficient and collision-free path planning in autonomous robots navigating static and dynamic environments by M.D. Yeshwanth Kumar, K. Rajchandar

“…Extensive experiments were conducted across multiple static and dynamic scenarios. The proposed model achieved a path efficiency improvement of 17.9 % and a computational time reduction of 23.4 % compared to traditional A* and Dijkstra’s algorithms. …”
Get full text

Identification of fresh leaves of Anji White Tea: S-YOLOv10-ASI algorithm fusing asymptotic feature pyra-mid network. by Chunhua Yang, Wenxia Yuan, Qiang Zhao, Zejun Wang, Bowu Song, Xianqiu Dong, Yuandong Xiao, Shihao Zhang, Baijuan Wang

“…This study proposes the S-YOLOv10-ASI algorithm to improve the accuracy of tea identification and harvesting by robots, integrating a slice-assisted super-reasoning technique. …”
Get full text

Computationally Enhanced UAV-Based Real-Time Pothole Detection Using YOLOv7-C3ECA-DSA Algorithm by Siti Fairuz Mat Radzi, Mohd Amiruddin Abd Rahman, Muhammad Khairul Adib Muhammad Yusof, Nurin Syazwina Mohd Haniff, Romi Fadillah Rahmat

“…Although YOLO-based algorithms have been widely adopted for their speed and efficiency in object detection, achieving a balance between high accuracy and low inference time remains a challenge, particularly in scenarios involving small objects and complex features. …”
Get full text

DPD-YOLO: dense pineapple fruit target detection algorithm in complex environments based on YOLOv8 combined with attention mechanism by Cong Lin, Wencheng Jiang, Weiye Zhao, Lilan Zou, Zhong Xue

“…The results indicate that, compared to existing mainstream target detection models, the proposed DPD-YOLO demonstrated superior detection performance for pineapples in situations where the background is complex and the targets are occluded. The mAP@0.5 reached 62.0%, representing an improvement of 6.6% over the original YOLOv8 algorithm, Precision increased by 2.7%, Recall improved by 13%, and F1-score rose by 10.3%.…”
Get full text