A Novel Transformer-Based Self-Supervised Learning Method to Enhance Photoplethysmogram Signal Artifact Detection by Thanh-Dung Le, Clara Macabiau, Kevin Albert, Philippe Jouvet, Rita Noumeir

“…Recent research has revealed that traditional machine learning methods, such as semi-supervised label propagation and K-nearest neighbors, outperform Transformer-based models in artifact detection from photoplethysmogram (PPG) signals, mainly when data is limited. …”
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Development of a LAMP method for detection of carbapenem-resistant Acinetobacter baumannii during a hospital outbreak by Carolina Garciglia Mercado, Ramon Gaxiola Robles, Felipe Ascencio, Jesus Silva-Sanchez, Maria Teresa Estrada-Garcia, Gracia Gomez-Anduro

“…Methodology: A set of six primers were designed for recognizing eight distinct sequences on six targets: blaOXA-23-like, blaOXA-24-like, blaOXA-51-like, blaOXA-58-like, blaIMP, and blaVIM. A LAMP method was developed, optimized and evaluated for the identification of CRAB in thirty-three environmental samples from an outbreak in an Intensive Care Unit (ICU) facility. …”
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Research on detection and location method of safflower filament picking points during the blooming period in unstructured environments by Bangbang Chen, Feng Ding, Baojian Ma, Qijun Yao, Shanping Ning

“…Abstract To address the challenges encountered by safflower filament harvesting robots in detecting and localizing harvesting points in unstructured environments, this study proposes a harvesting point detection and localization model based on the DSOE (Detect-Segment-OpenCV Extraction) method, integrated with a localization system using a depth camera. …”
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DAM-Faster RCNN: few-shot defect detection method for wood based on dual attention mechanism by Xingyu Tong, Zhihong Liang, Mingming Qin, Fangrong Liu, Jiayu Yang, Hengjiang Xiao, Wei Dai

“…After synergistic optimization of all modules, the model demonstrates superior detection accuracy and category discrimination capability. …”
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Self-Sensing of Piezoelectric Micropumps: Gas Bubble Detection by Artificial Intelligence Methods on Limited Embedded Systems by Kristjan Axelsson, Mohammadhossien Sheikhsarraf, Christoph Kutter, Martin Richter

“…Different types of sensors are used to detect gas bubbles: inline on the fluidic channels or inside the pump chamber itself. …”
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State-of-the-Art Fault Detection and Diagnosis in Power Transformers: A Review of Machine Learning and Hybrid Methods by Lebo Dina Mashifane, Bongumsa Mendu, Bessie Baakanyang Monchusi

“…Hybrid models combining machine learning with optimization have made detection more accurate. New tools, including optical sensors, now allow for real-time monitoring. …”
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Mobile malware detection method using improved GhostNetV2 with image enhancement technique by Yao Du, CaiXia Gao, Xi Chen, MengTian Cui, LiLi Xu, AoJi Ning

“…Abstract In recent years, image-based feature extraction and deep learning classification methods are widely used in the field of malware detection, which helps improve the efficiency of automatic malicious feature extraction and enhances the overall performance of detection models. …”
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Comparison of Sample Preparation and Detection Methods for the Quantification of Synthetic Musk Compounds (SMCs) in Carp Fish Samples by Jungmin Jo, Eunjin Lee, Na Rae Choi, Ji Yi Lee, Jae Won Yoo, Dong Sik Ahn, Yun Gyong Ahn

“…This study deals with the separation and detection methods for 12 synthetic musk compounds (SMCs), which are some of the emerging contaminants in fish samples, are widely present in environmental media, and can be considered serious risks due to their harmful effects. …”
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YOLO11-ARAF: An Accurate and Lightweight Method for Apple Detection in Real-World Complex Orchard Environments by Yangtian Lin, Yujun Xia, Pengcheng Xia, Zhengyang Liu, Haodi Wang, Chengjin Qin, Liang Gong, Chengliang Liu

“…Third, we applied knowledge distillation to transfer the enhanced model to a compact YOLO11n framework, maintaining high detection efficiency while reducing computational cost, and optimizing it for deployment on devices with limited computational resources. …”
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SIGKD: A Structured Instance Graph Distillation Method for Efficient Object Detection in Remote Sensing Images by Fangzhou Liu, Wenzhe Zhao, Haoxiang Qi, Guangyao Zhou

“…To this end, this paper introduces an innovative deep structured instance graph distillation method that endeavors to delve into the underlying information between instance features, thereby optimizing detection performance. …”
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YOLORM: An Advanced Key Point Detection Method for Accurate and Efficient Rotameter Reading in Low Flow Environments by Huang Yong, Xia Xing, Xiao Shengwang

“…To address these issues, this study introduces YOLORM, an advanced key point detection method for rotameters, built upon the YOLOv8n model. …”
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Research on Concrete Crack and Depression Detection Method Based on Multi-Level Defect Fusion Segmentation Network by Zhaochen Yao, Yanjuan Li, Hao Fu, Jun Tian, Yang Zhou, Chee-Loong Chin, Chau-Khun Ma

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A Ratiometric Fluorescence Method Based on PCN-224-DABA for the Detection of Se(IV) and Fe(III) by Mao-Ling Luo, Guo-Ying Chen, Wen-Jia Li, Jia-Xin Li, Tong-Qing Chai, Zheng-Ming Qian, Feng-Qing Yang

“…After the experimental parameters were systematically optimized, the developed method shows good selectivity and interference resistance for Fe(III) and Se(IV) detection, and has good linearity in the ranges of 0.01–4 μM and 0.01–15 μM for Fe(III) and Se(IV) with a limit of detection of 0.045 μM and 0.804 μM, respectively. …”
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A low illumination target detection method based on a dynamic gradient gain allocation strategy by Zhiqiang Li, Jian Xiang, Jiawen Duan

“…This method optimizes the model through enhancements in multi-scale feature fusion, feature extraction, detection head, and loss function. …”
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LDDFSF-YOLO11: A Lightweight Insulator Defect Detection Method Focusing on Small-Sized Features by Peng Shen, Keyu Mei, Huiqiong Cao, Yongxiang Zhao, Guoqing Zhang

“…This study proposes a lightweight insulator defect detection method (LDDFSF-YOLO11) that focuses on small-sized features to efficiently detect and identify insulator defects. …”
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Comparison of molecular with conventional methods for detection of genital tuberculosis in infertile women: A comparative prospective study by Anupama Hari, Anusha Reddy Thota, Sravanthi Katamreddy

“…Employing the best clinical practices for diagnosing and treating genital TB may help optimize fertility rate. The current study aimed to evaluate the molecular and conventional methods to diagnose genital TB in women with infertility. …”
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Development and evaluation of fluorescent recombinase polymerase amplification (RPA)-based method for rapid detection of Necator americanus. by Jia-Rui Liang, Shu-Ning Yan, Han-Yin Yang, Shuo Yang, Yu-Juan Shen, Le-Le Huo, Yu-Chun Cai, Zi-Ran Mo, Bin Zheng, Bin Xu, Wei Hu

“…The method's detection limit was assessed via serial dilution of genomic DNA. …”
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ANN-SVM-IP: An Innovative Method for Rapidly and Efficiently Detecting and Classifying of External Defects of Apple Fruits by Nashaat M. Hussain Hassan, Mohamed M. Hassan Mahmoud, Mohamed A. Ismeil, M. Mourad Mabrook, A. A. Donkol, A. M. Mabrouk

“…This paper attempts to address these issues by offering a novel method (ANN-SVM-IP) that integrates image processing (IP)-based segmentation and ML algorithms (ANN-SVM) for extracting and classifying exterior defects in apple fruits. …”
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Enhancing deep learning methods for brain metastasis detection through cross-technique annotations on SPACE MRI by Tassilo Wald, Benjamin Hamm, Julius C. Holzschuh, Rami El Shafie, Andreas Kudak, Balint Kovacs, Irada Pflüger, Bastian von Nettelbladt, Constantin Ulrich, Michael Anton Baumgartner, Philipp Vollmuth, Jürgen Debus, Klaus H. Maier-Hein, Thomas Welzel

“…HAQ alone achieves about 40% of the performance improvements seen with SPACE images as input, allowing for fast and accurate, fully automated detection of small (< 1 cm) BMs. Relevance statement Training with higher-quality annotations, created using the SPACE sequence, improves the detection and delineation sensitivity of DL methods for the detection of brain metastases (BMs)on MPRAGE images. …”
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Analysis and Selection Method for Radar Echo Features in Challenging Scenarios by Yunlong Dong, Xiao Luo, Hao Ding, Ningbo Liu, Zheng Cao

“…Feature selection was then performed from the clusters based on mean feature value, coefficient of variation, and Bhattacharyya distance, yielding an optimal feature set for the current scenario. Validation on the SDRDSP dataset under sea states 4–5 showed that the proposed method achieved an average detection probability 10.64% higher than existing methods. …”
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