LAVID: A Lightweight and Autonomous Smart Camera System for Urban Violence Detection and Geolocation by Mohammed Azzakhnini, Houda Saidi, Ahmed Azough, Hamid Tairi, Hassan Qjidaa

“…Due to the advancement of artificial intelligence methods, many automatic video analysis tasks like violence detection have been studied from a research perspective, and are even beginning to be commercialized in industrial solutions. …”
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SCCA-YOLO: Spatial Channel Fusion and Context-Aware YOLO for Lunar Crater Detection by Jiahao Tang, Boyuan Gu, Tianyou Li, Ying-Bo Lu

“…Lunar crater detection plays a crucial role in geological analysis and the advancement of lunar exploration. …”
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Detection Algorithm for Air Duct Clamp on Trains Based on RSA-YOLOv10n by WANG Dairong, ZHAO Yuhong, QU Xiaozhang, LIU Yi

“…Through ablation experiments and comparative experiments with other models, including YOLOv3-tiny, YOLOv5n, and YOLOv8n, the analysis and verification showcased the superiority of the proposed detection algorithm in terms of overall accuracy, recall rate and detection time, suggesting its potential for widespread application in the field of detecting air duct clamps on trains. …”
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Oriented ice eddy detection network based on the Sentinel-1 dual-polarization data by Jinqun Wu, Jinqun Wu, Yiqin Zheng, Tingting Wang, Chunyong Ma, Chunyong Ma, Ge Chen, Ge Chen

“…In this paper, we employed high-resolution synthetic aperture radar (SAR) satellite imagery and proposed an oriented ice eddy detection network (OIEDNet) framework to conduct automated detection and spatiotemporal analysis of ice eddies in the Nordic Seas. …”
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Detection of Immunochemical Reactions Using Piezoquartz Immunosensor. Regeneration of the Electrode Bio-layer (Review) by S. S. Gogina, E. A. Smirnova, Ya. M. Stanishevskii, A. M. Stoinova

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Deep learning-based time series prediction in multispectral and hyperspectral imaging for cancer detection by Lijun Hao, Changmin Wang, Jinshan Che, Mingming Sun, Yuhong Wang

“…Traditional approaches for cancer detection rely on handcrafted features and conventional machine learning algorithms, which struggle with high-dimensional spectral data, noise interference, and domain adaptation challenges. …”
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Automated Detection of Aberrant Episodes in Epileptic Conditions: Leveraging EEG and Machine Learning Algorithms by Uddipan Hazarika, Bidyut Bikash Borah, Soumik Roy, Manob Jyoti Saikia

“…To achieve this, we analyzed the long-term memory characteristics of EEG by employing time-dependent Hurst analysis. Together, the Hurst exponent and the Daubechies 4 discrete wavelet transformation constitute the basis of this unique feature extraction. …”
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User Comment-Guided Cross-Modal Attention for Interpretable Multimodal Fake News Detection by Zepu Yi, Chenxu Tang, Songfeng Lu

“…In order to address the pressing challenge posed by the proliferation of fake news in the digital age, we emphasize its profound and harmful impact on societal structures, including the misguidance of public opinion, the erosion of social trust, and the exacerbation of social polarization. Current fake news detection methods are largely limited to superficial text analysis or basic text–image integration, which face significant limitations in accurately identifying deceptive information. …”
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Multi-Class Urinary Sediment Particles Detection Based on YOLOv7 With Attention Modules by Tatsuki Komori, Hiroki Nishikawa, Keita Sasaki, Ittetsu Taniguchi, Takao Onoye

“…Traditional machine learning techniques approach the task of urine sediment particle detection as an image classification problem, wherein the particles are segmented based on features like edges or thresholds. …”
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Optimized Demand Forecasting for Bike-Sharing Stations Through Multi-Method Fusion and Gated Graph Convolutional Neural Networks by Hebin Guo, Kexin Li, Yutong Rou

“…Additionally, user characteristics are included as node features, enabling a more comprehensive analysis. The study utilizes the 2020 dataset from Jersey City’s bike-sharing system, starting with the application of the Isolation Forest algorithm to detect and filter anomalous data points. …”
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Cell‐free epigenomes enhanced fragmentomics‐based model for early detection of lung cancer by Yadong Wang, Qiang Guo, Zhicheng Huang, Liyang Song, Fei Zhao, Tiantian Gu, Zhe Feng, Haibo Wang, Bowen Li, Daoyun Wang, Bin Zhou, Chao Guo, Yuan Xu, Yang Song, Zhibo Zheng, Zhongxing Bing, Haochen Li, Xiaoqing Yu, Ka Luk Fung, Heqing Xu, Jianhong Shi, Meng Chen, Shuai Hong, Haoxuan Jin, Shiyuan Tong, Sibo Zhu, Chen Zhu, Jinlei Song, Jing Liu, Shanqing Li, Hefei Li, Xueguang Sun, Naixin Liang

“…This study aimed to integrate insights from epigenetic modifications and fragmentomic features of cfDNA using machine learning to develop a more accurate lung cancer detection model. …”
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Stacked random forest model for colorectal cancer detection using complete blood counts by Junfeng Luo, Weiwei Tan, Shaobo Chen, Yijing Chen, Ya Fu, Xiaojuan Jing, Lingling Kang, Qingyun Li, Zhenjian Ma, Tingji Sun, Peng Xiao, Shigui Xue, Xiaozhi Wang, Houde Zhang

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Paraphrase detection for Urdu language text using fine-tune BiLSTM framework by Muhammad Ali Aslam, Khairullah Khan, Wahab Khan, Sajid Ullah Khan, Abdullah Albanyan, Shabbab Ali Algamdi

“…Abstract Automated paraphrase detection is crucial for natural language processing (NL) applications like text summarization, plagiarism detection, and question-answering systems. …”
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Bifunctional nanoprobe for simultaneous detection of intracellular reactive oxygen species and temperature in single cells by Yanmei Ma, Weikang Hu, Jian Hu, Muyang Ruan, Jie Hu, Ming Yang, Yi Zhang, Hanhan Xie, Chengzhi Hu

“…By providing detailed analysis of submicrometer-scale temperature and ROS variations within living cells, this nanoprobe offers valuable insights into cellular processes and holds promise for early disease detection and drug development.…”
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3D Reconstruction and Deformation Detection of Rescue Shaft Based on RGB-D Camera by Hairong Gu, Bokai Liu, Lishun Sun, Mostak Ahamed, Jia Luo

“…This paper proposes a novel method for real-time 3D model reconstruction, deformation detection, and accessibility analysis of rescue shafts using an RGB-D camera. …”
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Explainable AI for lung cancer detection via a custom CNN on CT images by Mohamed Hammad, Mohammed ElAffendi, Ahmed A. Abd El-Latif, Abdelhamied A. Ateya, Gauhar Ali, Pawel Plawiak

“…Early detection is essential for better outcomes, but traditional CT image analysis is time-consuming, prone to error, and relies on subjective judgments. …”
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Detection of Masses in Mammogram Images Based on the Enhanced RetinaNet Network With INbreast Dataset by Wang M, Liu R, Luttrell IV J, Zhang C, Xie J

“…Therefore, we adopted and enhanced RetinaNet to detect masses in mammogram images. Specifically, we introduced a novel modification to the network structure, where the feature map M5 is processed by the ReLU function prior to the original convolution kernel. …”
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Diagnostic accuracy of artificial intelligence for obstructive sleep apnea detection: a systematic review by Sara Haghighat, Muhammed Joghatayi, Julien Issa, Sarina Azimian, Janet Brinz, Ali Ashkan, Akhilanand Chaurasia, Zahra Rahimian, Linda Sangalli

“…Artificial intelligence (AI) algorithms can facilitate diagnosis by detecting patients’ signs and symptoms. In this systematic review, we evaluated the diagnostic accuracy of AI models in detecting sleep apnea. …”
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Online Measurement Method for Circuit Breaker Mechanical–Time Characteristics Based on Transient Voltage and Current Signal Feature Extraction by Liting Weng, Jianhua Xia, Zhaochuang Zhang, Jingang Wang, Lin Chen, Yingbo Zi, Lingyi Ma, Xingyu Zhang, Fan Zhang, Pengcheng Zhao

“…The analysis shows that the calculation error of the opening and closing times is within 0.2 ms. …”
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Transcriptomics‐Based Liquid Biopsy for Early Detection of Recurrence in Locally Advanced Gastric Cancer by Ping'an Ding, Jiaxiang Wu, Haotian Wu, Tongkun Li, Xiaoman Niu, Peigang Yang, Honghai Guo, Yuan Tian, Jinchen He, Jiaxuan Yang, Renjun Gu, Lilong Zhang, Ning Meng, Xiaolong Li, Zhenjiang Guo, Lingjiao Meng, Qun Zhao

“…Four mRNA biomarkers (AGTR1, DNER, EPHA7, and SUSD5) linked to recurrence are identified through transcriptomic data analysis. A Risk Stratification Assessment (RSA) model combining these biomarkers with clinical features showed superior predictive accuracy for postoperative recurrence, with AUCs of 0.919 and 0.935 in surgical and liquid biopsy validation cohorts, respectively. …”
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