A novel method of BiFormer with temporal-spatial characteristics for ECG-based PVC detection by Siyuan Chen, Zhen Wang, Hao Wang, Shuai Wang, Yang Li, Yang Li, Bing Wang

“…This approach filtered out redundant information, and optimized both computational efficiency and memory usage.ResultsOur algorithm achieved a detection accuracy of 99.45%, outperforming other commonly-used PVC detection algorithms.DiscussionBy integrating MTF and BiFormer, we effectively detected PVCs, facilitating an increased convergence between medicine and deep learning technology. …”
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Audio copy-move forgery detection with decreasing convolutional kernel neural network and spectrogram fusion by Canghong Shi, Xin Qiu, Min Wu, Xianhua Niu, Xiaojie Li, Sani M. Abdullahi

“…In addition, our method shows better performance in the detection of forged audio with multiple attacks. Compared to the state-of-the-art algorithms, the proposed algorithm has advantages in terms of accuracy, precision, and F1 score.…”
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A density-based MS disease diagnosis model using the capuchin search algorithm and an ensemble of deep neural networks by LiJuan Bai, Jiao Wu, Li Chen, Xin Jiang, ZhuYin Song

“…When the target view is detected, the features of each ROI are extracted through three techniques: local binary pattern (LBP), multi-linear principal component analysis (MPCA), and gray level co-occurrence matrix (GLCM). …”
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SD-YOLOv5: a rapid detection method for personal protective equipment on construction sites by ChunYa Li, ChunYa Li, Jianhua Wang, Jianhua Wang, Bingfeng Luo, Tubing Yin, Baohua Liu, Baohua Liu, Jianfei Lu

“…The proposed model incorporates a dedicated feature layer for small target detection and integrates the DilateFormer attention mechanism to balance detection performance and computational efficiency. …”
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Efficient automated detection of power quality disturbances using nonsubsampled contourlet transform & PCA-SVM by Pampa Sinha, Kaushik Paul, Asit Mohanty, IM Elzein, Chandra Sekhar Mishra, Mohamed Metwally Mahmoud, Daniel Eutyche Mbadjoun Wapet, Abdulrahman Al Ayidh, Ahmed Althobaiti, Hany S Hussein, Thamer AH Alghamdi, Ahmed M Ewais

“…High-frequency NSCT subbands are fused to extract oscillatory portions, while low-frequency subbands are averaged to detect transients. Morphological component analysis (MCA) and the split-augmented Lagrangian shrinkage algorithm (SALSA) optimize this process. …”
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Pulse wave time series unsupervised clustering with importance ratios for heart failure subgroups detection by Dandan WU, Ryohei ONO, Sirui WANG, Yoshio KOBAYASHI, Hao LIU

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Enhanced Defect Detection in Additive Manufacturing via Virtual Polarization Filtering and Deep Learning Optimization by Xu Su, Xing Peng, Xingyu Zhou, Hongbing Cao, Chong Shan, Shiqing Li, Shuo Qiao, Feng Shi

“…The main challenge lies in that under extreme lighting conditions, strong reflected light obscures defect feature information, leading to a significant decrease in the defect detection rate. …”
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A comparative analysis of fault detection and process diagnosis methods based on a signal processing paradigm by Dorel Aiordăchioaie

“…Abstract An important paradigm in industrial engineering for fault detection and diagnosis purposes is signal processing. …”
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Object Detection in High-Resolution UAV Aerial Remote Sensing Images of Blueberry Canopy Fruits by Yun Zhao, Yang Li, Xing Xu

“…We also introduced a non-maximal suppression algorithm, Cluster-NMF, which accelerates inference speed through matrix parallel computation and merges multiple high-quality target detection frames to generate an optimal detection frame, enhancing the efficiency of blueberry canopy fruit detection without compromising inference speed.…”
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Exploring Multi-Channel GPS Receivers for Detecting Spoofing Attacks on UAVs Using Machine Learning by Mustapha Mouzai, Mohamed Amine Riahla, Amor Keziou, Hacène Fouchal

“…(b) We have been able to detect most types of attacks and distinguish them.…”
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Classifying metro drivers’ cognitive distractions during manual operations using machine learning and random forest-recursive feature elimination by Haiyue Liu, Yue Zhou, Chaozhe Jiang

“…Cognitive distractions in parking phase are difficult to be detected using HR-HRV features.…”
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Feasibility of snapshot testing using wearable sensors to detect cardiorespiratory illness (COVID infection in India) by Olivia K. Botonis, Jonathan Mendley, Shreya Aalla, Nicole C. Veit, Michael Fanton, JongYoon Lee, Vikrant Tripathi, Venkatesh Pandi, Akash Khobragade, Sunil Chaudhary, Amitav Chaudhuri, Vaidyanathan Narayanan, Shuai Xu, Hyoyoung Jeong, John A. Rogers, Arun Jayaraman

“…SHAP values were mapped to visualize feature importance and directionality, yielding engineered features from core temperature, cough, and lung sounds as highly important. …”
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An AutoEncoder enhanced light gradient boosting machine method for credit card fraud detection by Lianhong Ding, Luqi Liu, Yangchuan Wang, Peng Shi, Jianye Yu

“…This paper proposes an AutoEncoder enhanced LightGBM method for credit card detection. The method inherits the advantages of each component, using an AutoEncoder for feature reconstruction on the dataset, and integrating the LightGBM algorithm for improving the GBDT (Gradient Boosting Decison Tree) to detect abnormal data more accurately and efficiently. …”
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A Shadow Detection Method Combining Topography and Spectra for Remote Sensing Images in Mountainous Environments by Huagui Xu, Jingxing Zhu, Feng Wang, Hongjian You, Wenzhi Wang

“…Shadow in remote sensing images can obscure important details of land features, making shadow detection crucial for enhancing the accuracy of subsequent analyses and applications. …”
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Integrating the landscape scale supports SAR-based detection and assessment of the phenological development at the field level by Johannes Löw, Steven Hill, Insa Otte, Christoph Friedrich, Michael Thiel, Tobias Ullmann, Christopher Conrad

“…Unlike previous approaches that focus on local algorithm optimisation or SAR feature selection, this work integrates two scales: (1) landscape patterns derived from annual distributions of time series metrics (TSMs) and (2) field-level phenology, both linked to growing degree days (GDD). …”
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STIED: a deep learning model for the spatiotemporal detection of focal interictal epileptiform discharges with MEG by Raquel Fernández-Martín, Alfonso Gijón, Odile Feys, Elodie Juvené, Alec Aeby, Charline Urbain, Xavier De Tiège, Vincent Wens

“…Here, we developed and validated STIED, a simple yet powerful supervised DL algorithm combining two convolutional neural networks with temporal (1D time-course) and spatial (2D topography) features of MEG signals inspired from current clinical guidelines. …”
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A Multi-Strategy Active Learning Framework for Enhanced Peripheral Blood Cell Image Detection by Yuheng Feng, Jiangtao He, Linjin Wang, Wuchen Yang, Sihan Deng, Lanlin Li, Xinwei Li

“…The process begins with entropy-based uncertainty selection to identify the most uncertain samples, followed by clustering analysis to capture diverse samples from the feature space, and concludes with density-based selection using the k-nearest neighbors algorithm to prioritize samples from high-density regions. …”
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Accurate detection of low concentrations of microplastics in soils via short-wave infrared hyperspectral imaging by Huan Chen, Taesung Shin, Bosoon Park, Kyoung Ro, Changyoon Jeong, Hwang-Ju Jeon, Pei-Lin Tan

“…This study evaluated the effectiveness of coupling machine learning algorithms with short-wave infrared hyperspectral imaging in detecting two types of microplastics - polyamide and polyethylene - with the maximum particle sizes of 50 and 300 ​μm, respectively, across three concentration ranges (0.01–0.10, 0.10–1.0, and 1.0–12 ​%) in soils. …”
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Integrated pixel-level crack detection and quantification using an ensemble of advanced U-Net architectures by Rakshitha R, Srinath S, N Vinay Kumar, Rashmi S, Poornima B V

“…Automated pavement crack detection faces significant challenges due to the complex shapes of crack patterns, their similarity to non-crack textures, and varying environmental conditions such as lighting and noise. …”
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Severe-hail detection with C-band dual-polarisation radars using convolutional neural networks by V. Forcadell, V. Forcadell, C. Augros, O. Caumont, O. Caumont, K. Dedieu, M. Ouradou, C. David, J. Figueras i Ventura, O. Laurantin, H. Al-Sakka

“…Two datasets of images of 60 km <span class="inline-formula">×</span> 60 km containing 19 different radar-derived features are built. The first is created from severe-hail cases (<span class="inline-formula">≥2</span> cm), and the second is obtained from rain or small-hail cases (rain or hail <span class="inline-formula">&lt;2</span> cm) selected with the help of a cell identification algorithm above densely populated areas with no hail reports. …”
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