Development of Dual ‘RT‐LAMP‐LFA’ Rapid Detection Technology With Gold Magnetic Nanoparticles for Influenza Virus by Haiyang Fan, Yonglong Gong, Mengying Chang, Juan Gao, Mengjia Li, Siyu Chen, Ruoyi Yang, Muxue Zhao, Yali Cui, Wenli Hui

“…However, current diagnostic tools often face limitations in speed, accuracy or complexity of result interpretation; there is a great need for more efficient detection technology for influenza virus, especially for use in resource‐limited settings or during large‐scale outbreaks. …”
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MCFNet: Multi-Scale Contextual Fusion Network for Salient Object Detection in Optical Remote Sensing Images by Jinting Ding, Yueqian Quan, Honghui Xu

“…Despite increasing attention, salient object detection for optical remote sensing images (ORSI-SOD) remains highly challenging due to the intrinsic complexities of remote sensing scenes. …”
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Ship crack detection method based on lightweight fast convolution and bidirectional weighted feature fusion network by Chong WANG, Yuhui ZHU

“…ObjectiveAs traditional ship crack detection methods based on artificial visual inspection and ultrasonic methods in ship repair and inspection processes have the characteristics of low efficiency, high cost and high danger, a ship crack detection method based on deep learning is proposed. …”
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MSCD-YOLO: A Lightweight Dense Pedestrian Detection Model with Finer-Grained Feature Information Interaction by Qiang Liu, Zhongmin Li, Lei Zhang, Jin Deng

“…The experimental results show that under the same experimental conditions, MSCD-YOLO significantly outperforms the original model in terms of detection accuracy, efficiency, and model complexity.…”
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Detection of Mycotoxins in Cereal Grains and Nuts Using Machine Learning Integrated Hyperspectral Imaging: A Review by Md. Ahasan Kabir, Ivan Lee, Chandra B. Singh, Gayatri Mishra, Brajesh Kumar Panda, Sang-Heon Lee

“…Mycotoxins exhibit heightened sensitivity to specific spectral bands within HSI, facilitating accurate detection. Additionally, selecting only relevant spectral features reduces ML model complexity and enhances reliability in the detection process. …”
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BurgsVO: Burgs-Associated Vertex Offset Encoding Scheme for Detecting Rotated Ships in SAR Images by Mingjin Zhang, Yaofei Li, Jie Guo, Yunsong Li, Xinbo Gao

“…Moreover, oriented bounding box-based detection methods often prioritize accuracy excessively, leading to increased parameters and computational costs, which in turn elevate computational load and model complexity. …”
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Towards automatic farrowing monitoring-A Noisy Student approach for improving detection performance of newborn piglets. by Martin Wutke, Clara Lensches, Ulrich Hartmann, Imke Traulsen

“…Challenges such as frequent animal occlusions, high overlapping rates or strong heterogeneous animal postures increase the complexity and place new demands on the detection model. …”
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Analyzing the Efficacy of Computer-Aided Detection in Cerebral Aneurysm Diagnosis Using MRI Modality: A Review by Keerthi A. S. Pillai, Preena K. P., Madhu S. Nair

“…CNNs have proven to be a crucial component in improving the accuracy and efficiency of aneurysm detection by automatically learning features from raw imaging data, bypassing the need for manual feature extraction. …”
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ContourTL-Net: Contour-Based Transfer Learning Algorithm for Early-Stage Brain Tumor Detection by N. I. Md. Ashafuddula, Rafiqul Islam

“…An increasing patient longevity rate requires prompt detection; however, the complexities of brain tissue make early diagnosis challenging. …”
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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

“…Secondly, a novel lightweight segmentation detection head (LSDH) was introduced, based on the YOLO-SaFi model, to efficiently segment safflower filaments and fruit balls. …”
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Data leakage detection in machine learning code: transfer learning, active learning, or low-shot prompting? by Nouf Alturayeif, Jameleddine Hassine

“…However, automated tools with advanced ML techniques are increasingly recognized as essential for efficiently identifying quality issues in large and complex codebases, enhancing the overall effectiveness of code review processes. …”
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LS-MambaNet: Integrating Large Strip Convolution and Mamba Network for Remote Sensing Object Detection by Lingyu Yan, Zijian He, Zhiqi Zhang, Guangqi Xie

“…To address the above challenges, we propose a new target detection framework for complex remote sensing images, LS-MambaNet. …”
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A Dual-Task Approach for Onset Time Picking and the Detection of Microseismic Waveforms Based on Deep Learning by Hang Zhang, Ruoyu Li, Chunchi Ma, Xiaobing Cheng, Simeng Meng, Zhenxing Huang, Di Li

“…Before the analysis of source information and further prediction of possible disasters, it is generally necessary to perform onset time picking and detection of microseismic signals. To improve the accuracy and efficiency of these tasks, this paper proposes an advanced deep dual-task neural network, which sequentially integrates the two processes. …”
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SOFTWARE COMPONENT DEVELOPMENT FOR PARALLEL GATEWAYS DETECTION AND QUALITY ASSESSMENT IN BPMN MODELS USING FUZZY LOGIC by Andrii Kopp, Ľuboš Cibák, Dmytro Orlovskyi, Dmytro Kudii

“…Errors in their implementation, such as incorrect synchronization or termination of parallel branches, are common and difficult to detect by traditional metrics such as the Number of Activities (NOA) or Control-Flow Complexity (CFC). …”
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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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Fast Anomaly Detection for Vision-Based Industrial Inspection Using Cascades of Null Subspace PCA Detectors by Muhammad Bilal, Muhammad Shehzad Hanif

“…In this study, we introduce a novel anomaly detection framework that leverages feature maps from a lightweight convolutional neural network (CNN) backbone, MobileNetV2, and cascaded detection to achieve notable accuracy as well as computational efficiency. …”
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LCCDMamba: Visual State Space Model for Land Cover Change Detection of VHR Remote Sensing Images by Junqing Huang, Xiaochen Yuan, Chan-Tong Lam, Yapeng Wang, Min Xia

“…Recently, with the advent of Mamba, which maintains linear time complexity and high efficiency in processing long-range data, it offers a new solution to address feature-fusion challenges in LCCD. …”
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Enhanced diabetic retinopathy detection using U-shaped network and capsule network-driven deep learning by Govindharaj I, Poongodai A, Gnanajeyaraman Rajaram, Santhakumar D, Ravichandran S, Vijaya Prabhu R, Udayakumar K, Yazhinian S

“…Glaucoma, a severe eye disease leading to irreversible vision loss if untreated, remains a significant challenge in healthcare due to the complexity of its detection. Traditional methods rely on clinical examinations of fundus images, assessing features like optic cup and disc sizes, rim thickness, and other ocular deformities. …”
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Optimization and Validation of Universal Real-Time RT-PCR Assay to Detect Virulent Newcastle Disease Viruses by Ellen Ruth Alexander Morris, Megan E. Schroeder, Phelue N. Anderson, Lisa J. Schroeder, Nicholas Monday, Gabriel Senties-Cue, Martin Ficken, Pamela J. Ferro, David L. Suarez, Kiril M. Dimitrov

“…The considerable genetic diversity of the virus adds complexity to maintaining the high sensitivity and specificity of molecular detection assays. …”
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Design of an Improved Model for Anomaly Detection in CCTV Systems Using Multimodal Fusion and Attention-Based Networks by V. Srilakshmi, Sai Babu Veesam, Mallu Shiva Rama Krishna, Ravi Kumar Munaganuri, Dulam Devee Sivaprasad

“…Long Short-Term Memory (LSTM) can capture long-range dependencies in sequential data, while Temporal Convolutional Network (TCN) efficiently models temporal patterns using convolutional layers and Transformer Networks fathom the relative importance of temporal features against one another through self-attention, thus improving their detection accuracy for anomalies that happen over a long duration. …”
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