Sparse Decomposition-Based Anti-Spoofing Framework for GNSS Receiver: Spoofing Detection, Classification, and Position Recovery by Yuxin He, Xuebin Zhuang, Bing Xu

“…A sparse decomposition algorithm with non-negative constraints limited by signal power magnitudes is proposed to achieve accurate spoofing detections while extracting key features of the received signals. …”
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Real-Time Fire Detection Method Based on Computer Vision for Electric Vehicle Charging Safety Monitoring by Yuchen Gao, Qing Yang, Shiyu Zhang, Dexin Gao

“…Therefore, a target detection model based on the improved YOLOv5 (You Only Look Once) algorithm is proposed for the features generated by lithium battery combustion, using the K-means algorithm to cluster and analyse the target locations within the dataset, while adjusting the residual structure and the number of convolutional kernels in the network and embedding a convolutional block attention module (CBAM) to improve the detection accuracy without affecting the detection speed. …”
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Detection and classification of hypertensive retinopathy based on retinal image analysis using a deep learning approach by Bambang Krismono Triwijoyo, Ahmat Adil, Muhammad Zulfikri

“…The next step is the image analysis process, which involves extracting retinal blood vessels using the Otsu segmentation algorithm. A Morphological Approach is used to obtain comprehensive features of the blood vessels around the Optic Disc (OD). …”
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Blockchain enabled deep learning model with modified coati optimization for sustainable healthcare disease detection and classification by Heba G. Mohamed, Fadwa Alrowais, Fahd N. Al-Wesabi, Mesfer Al Duhayyim, Anwer Mustafa Hilal, Abdelwahed Motwakel

“…For the optimal subset of features, the spotted hyena optimization algorithm (SHOA) model is used. …”
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Two-Dimensional Geometry Representation Learning-Based Construction Workers Activities Detection with Flexible IMU Solution by Hainan Chen, Guiwen Liu, Jianjun Li

“…The approach employs a 2D geometric representation algorithm that extracts features at the application level, independent of the IMU axes. …”
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An abnormal traffic detection method for chain information management system network based on convolutional neural network by Chao Liu, Chunxiang Liu, Changrong Liu

“…CBAM AE-CRF integrates the convolutional block attention module (CBAM) into convolutional neural network to enhance the model’s ability to learn anomalous features in network traffic. CBAM improves the detection accuracy of abnormal traffic in chain information management system by adaptively adjusting channel attention. …”
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Enhancing pancreatic cancer detection in CT images through secretary wolf bird optimization and deep learning by Sandhya Mekala, Phani Kumar S

“…The SeWBO is designed by incorporating Wolf Bird Optimization (WBO) and the Secretary Bird Optimization Algorithm (SBOA). Then, features like Complete Local Binary Pattern (CLBP) with Discrete Wavelet Transformation (DWT), statistical features, and Shape Local Binary Texture (SLBT) are extracted. …”
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Enhanced detection of accounting fraud using a CNN-LSTM-Attention model optimized by Sparrow search by Peifeng Wu, Yaqiang Chen

“…This paper proposes an enhanced approach to fraud detection by integrating convolutional neural networks (CNN) and long short-term memory (LSTM) networks, complemented by an attention mechanism to prioritize relevant features. …”
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From image to insight deep learning solutions for accurate identification and object detection of Acorus species slices by Yinghui Liu, Haitao Liu, Linlan Li, Ying Ding

“…Meanwhile, the YOLOv8 algorithm achieved rapid object detection in mixed states of the two, with a detection accuracy of 98.6% and a detection frame rate of 22fps. …”
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Obstacle Detection in Hybrid Cross-Country Environment Based on Markov Random Field for Unmanned Ground Vehicle by Feng Ding, Yibing Zhao, Lie Guo, Mingheng Zhang, Linhui Li

“…Then, based on K-means clustering algorithm, the same properties of the line are combined. …”
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A deep learning model for fault detection in distribution networks with high penetration of electric vehicle chargers by Seyed Amir Hosseini, Behrooz Taheri, Seyed Hossein Hesamedin Sadeghi, Adel Nasiri

“…The results show the proposed method's ability to detect all types of faults within 5 ms. Since it employs a machine learning algorithm for fault detection, the method's accuracy is 98.5 %, surpassing the accuracy of k-nearest neighbors (KNN) and conventional LSTM models. …”
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Multispectral Imaging-Based System for Detecting Tissue Oxygen Saturation With Wound Segmentation for Monitoring Wound Healing by Chih-Lung Lin, Meng-Hsuan Wu, Yuan-Hao Ho, Fang-Yi Lin, Yu-Hsien Lu, Yuan-Yu Hsueh, Chia-Chen Chen

“…Changes in StO2 levels were detected before laser speckle contrast imaging (LSCI) detected changes in blood flux. …”
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Enhancing Detection of Control State for High-Speed Asynchronous SSVEP-BCIs Using Frequency-Specific Framework by Yufeng Ke, Jiale Du, Shuang Liu, Dong Ming

“…This study proposed a novel frequency-specific (FS) algorithm framework for enhancing control state detection using short data length toward high-performance asynchronous steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCI). …”
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Detection of Rice Leaf Folder in Paddy Fields Based on Unmanned Aerial Vehicle-Based Hyperspectral Images by Shanshan Feng, Shun Jiang, Xuying Huang, Lei Zhang, Yangying Gan, Laigang Wang, Canfang Zhou

“…Then, hyperspectral data and field investigation data from the jointing stage were used to construct a machine learning (extreme gradient boosting, XGBoost) algorithm for detecting rice pests. The results showed that the XGBoost model exhibited the best performance when eight vegetation indices were considered as the selected input features for model construction: the Red-edge Normalized Difference Vegetation Index (red-edge NDVI), Structure Insensitive Pigment Index (SIPI), Enhanced Vegetation Index (EVI), Atmospherically Resistant Vegetation Index (ARVI), Soil-Adjusted Vegetation Index (SAVI), Red-edge Chlorophyll Index (CIred-edge), Pigment-Specific Simple Ratio₆₈₀ (PSSR₆₈₀), and Carotenoid Reflectance Index₇₀₀ (CPI₇₀₀). …”
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Fractional Artificial Protozoa Optimization Enabled Deep Learning for Intrusion Detection and Mitigation in Cyber-Physical Systems by Shaik Abdul Rahim, Arun Manoharan

“…The input log file from the database is normalized using Quantile Normalization (QN). Afterwards, features are selected employing the Skill Optimization Algorithm (SOA). …”
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Detection-Driven Gaussian Mixture Probability Hypothesis Density Multi-Target Tracker for Airborne Infrared Platforms by Mingyu Hong, Jiarong Wang, Ming Zhu, Shenyi Cao, Haitao Nie, Xiangdong Xu

“…However, the limitations inherent in airborne infrared platforms can lead to irregular imaging and inadequate textural features. This study presents a multi-object tracking system specifically designed for weak-textured infrared targets, aimed at enhancing detection accuracy and tracking stability. …”
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Wavelet-Based ensembled intelligent technique for a better quality of fault detection and classification in AC microgrids by Nityananda Giri, Pravati Nayak, Ranjan Kumar Mallick, Sairam Mishra, Aymen Flah, Habib Kraiem, Lukas Prokop, Mohammad Kanan

“…Non-stationary voltage and current signals are analysed using DWT to extract wavelet detailed coefficients, which are then used to compute the energy of these coefficients as input features for the EBDT. The hyperparameters of the EBDT are optimized using a random search algorithm to enhance robustness in fault classification. …”
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CGD-CD: A Contrastive Learning-Guided Graph Diffusion Model for Change Detection in Remote Sensing Images by Yang Shang, Zicheng Lei, Keming Chen, Qianqian Li, Xinyu Zhao

“…However, most SSL algorithms for CD in remote sensing image rely on convolutional neural networks with fixed receptive fields as their feature extraction backbones, which limits their ability to capture objects of varying scales and model global contextual information in complex scenes. …”
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I-YOLOv11n: A Lightweight and Efficient Small Target Detection Framework for UAV Aerial Images by Yukai Ma, Caiping Xi, Ting Ma, Han Sun, Huiyang Lu, Xiang Xu, Chen Xu

“…However, existing detection algorithms still have weak small target representation ability, extensive computational resource overhead, and poor deployment adaptability. …”
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Comparing 2D and 3D Feature Extraction Methods for Lung Adenocarcinoma Prediction Using CT Scans: A Cross-Cohort Study by Margarida Gouveia, Tânia Mendes, Eduardo M. Rodrigues, Hélder P. Oliveira, Tania Pereira

“…Machine learning algorithms have already shown the potential to recognize patterns in CT scans to classify the cancer subtype. …”
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