Passive indoor human daily behavior detection method based on channel state information by Xiaochao DANG, Yaning HUANG, Zhanjun HAO, Xiong SI

“…The daily behavior detection of indoor human based on CSI is developing rapidly in the field of WSN.At present,most of the research is still in the environment of 2.4 GHz,so the detection rate,robustness and overall performance still need to be improved.In order to solve this problem,a passive indoor human behavior detection method HDFi (Human Detection with Wi-Fi) based on CSI signal was proposed.The method was used to detect the indoor human daily behavior in a 5 GHz band environment,which was divided into three steps:data acquisition,data processing,feature extraction,online detection.Firstly,the experiment collected typical daily behavioral data in complex laboratory and relatively empty meeting room.Secondly,the amplitude and phase data with more obvious features were extracted and processed by low-pass filtering to obtain a set of stable and noise-free data,and then the fingerprint database was established effectively.Finally,in the real-time detection stage,the collected data features were classified by SVM algorithm to extract more stable eigenvalues,and a classification model of indoor human daily behavior detection was established,and then matched the data in the fingerprint database.The experimental results show that the proposed method has the characteristics of high efficiency,high precision and good robustness,and the method does not need any testing personnel to carry any electronic equipment,so it has high practicability.…”
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A novel feature extractor based on constrained cross network for detecting sleep state by Chenlei Tian, Fei Song

“…Most existing methods that utilize wrist-worn devices data for detection rely on heuristic algorithms or traditional machine learning, which suffer from low classification efficiency and insufficient accuracy. …”
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YOLOv8-LSW: A Lightweight Bitter Melon Leaf Disease Detection Model by Shuang Liu, Haobin Xu, Ying Deng, Yixin Cai, Yongjie Wu, Xiaohao Zhong, Jingyuan Zheng, Zhiqiang Lin, Miaohong Ruan, Jianqing Chen, Fengxiang Zhang, Huiying Li, Fenglin Zhong

“…Bitter melon, an important medicinal and edible economic crop, is often threatened by diseases such as downy mildew, powdery mildew, viral diseases, anthracnose, and blight during its growth. Efficient and accurate disease detection is of significant importance for achieving sustainable disease management in bitter melon cultivation. …”
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Detecting Fraudulent Transactions for Different Patterns in Financial Networks Using Layer Weigthed GCN by Shaziya Islam, Gagan Raj Gupta, Apu Chakraborty, Santosh Singh, Anisha Soni, Chhavi Patle

“…Additionally, the flexible architecture of LayerWeighted-GCN enhances its ability to model complex financial relationships, improving fraud detection accuracy and robustness. …”
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Explainable one-class feature extraction by adaptive resonance for anomaly detection in quality assurance. by Hootan Kamran, Dionne Aleman, Chris McIntosh, Tom Purdie

“…By offering clear insights into decision-making processes, our method allows healthcare professionals to quickly identify and address specific deficiencies in RT plans deemed unacceptable, thereby streamlining the QA process and enhancing patient care efficiency and safety.…”
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DSF-YOLO for weld defect detection in X-ray images with dynamic staged fusion by Meng Zhang, Yanzhu Hu, Binbin Xu, Lisha Luo, Song Wang

“…These results establish DSF-YOLO as an efficient and accurate solution addressing critical challenges in industrial weld defect detection with significant practical value.…”
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Anomaly detection method for cyber physical power system based on bilateral data fusion by Tianlei Zang, Shijun Wang, Chuangzhi Li, Yunfei Liu, Yujian Xiao, Zian Wang, Xueying Yu

“…It is a challenge to fully quantify the complex characteristics of CPPS. A cyber-physical bilateral data-driven composite model is proposed in this paper to achieve efficient and accurate anomaly detection of CPPS. …”
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Research on insider threat detection based on personalized federated learning and behavior log analysis by Xiaoyun Ye, Faqin Luo, Huangrongbin Cui, Jinlong Wang, Xiaoyun Xiong, Wencui Zhang, Jiawei Yu, Wenhao Zhao

“…While machine learning methods offer certain advantages, they still face challenges in complex data scenarios. This study focuses on the application of Federated Learning in insider threat detection. …”
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MMYFnet: Multi-Modality YOLO Fusion Network for Object Detection in Remote Sensing Images by Huinan Guo, Congying Sun, Jing Zhang, Wuxia Zhang, Nengshuang Zhang

“…To overcome the limitations of existing fusion methods in terms of complexity and insufficient information utilization, we innovatively propose a Cosine Similarity-based Image Feature Fusion (CSIFF) module and integrate it into a dual-branch YOLOv8 network, constructing a lightweight and efficient target detection network called Multi-Modality YOLO Fusion Network (MMYFNet). …”
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Intelligent back-to-back testing with denoising autoencoder-based fault detection and DBSCAN clustering by Mohammad Abboush, Christoph Knieke, Andreas Rausch

“…The approach is capable of automatically detecting and clustering the faults in an efficient manner considering the noisy conditions. …”
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A hybrid parallel convolutional spiking neural network for enhanced skin cancer detection by K. Anup Kumar, C. Vanmathi

“…PCSN-Net outperforms various complex algorithms, including EfficientNet, DenseNet, and Inception-ResNet-V2, despite preserving effective training and inference times. …”
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Deep Learning–Based Enhanced Optimization for Automated Rice Plant Disease Detection and Classification by P. Preethi, R. Swathika, S. Kaliraj, R. Premkumar, J. Yogapriya

“…ABSTRACT Ensuring global food security necessitates innovative solutions for early detection and precise classification of diseases in staple crops like rice. …”
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Sentiment analysis for depression detection: A stacking ensemble-based deep learning approach by Kinza Noor, Mariam Rehman, Maria Anjum, Afzaal Hussain, Rabia Saleem

“…Due to the widespread prevalence of this disorder in society, novel and efficient methods must be developed for effective detection and treatment. …”
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Aero-Engine Blade Defect Detection: A Systematic Review of Deep Learning Models by Yusra Abdulrahman, M. A. Mohammed Eltoum, Abdulla Ayyad, Brain Moyo, Yahya Zweiri

“…This is the first systematic review of deep learning models for aero-engine blade defect detection. The findings of this review demonstrate the potential of deep learning in detecting blade defects and improving the accuracy and efficiency of visual inspection. …”
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Adaptive Fusion of LiDAR Features for 3D Object Detection in Autonomous Driving by Mingrui Wang, Dongjie Li, Josep R. Casas, Javier Ruiz-Hidalgo

“…However, traditional early or late fusion methods face challenges such as high bandwidth and computational resources, which make it difficult to balance data transmission efficiency with the accuracy of perception of the surrounding environment, especially for the detection of smaller objects such as pedestrians. …”
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Mitosis detection in histopathological images using customized deep learning and hybrid optimization algorithms. by Afnan M Alhassan, Nouf I Altmami

“…Identifying mitosis is crucial for cancer diagnosis, but accurate detection remains difficult because of class imbalance and complex morphological variations in histopathological images. …”
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HOIL-1L interacting protein (HOIP) as an NF-kappaB regulating component of the CD40 signaling complex. by Bruce S Hostager, Daniel K Fox, Douglas Whitten, Curtis G Wilkerson, Betty A Eipper, Victor P Francone, Paul B Rothman, John D Colgan

“…One family member, CD40, is important for the efficient activation of antibody-producing B cells and other antigen-presenting cells. …”
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An explainable unsupervised learning approach for anomaly detection on corneal in vivo confocal microscopy images by Ningning Tang, Qi Chen, Yunyu Meng, Daizai Lei, Li Jiang, Yikun Qin, Xiaojia Huang, Fen Tang, Shanshan Huang, Qianqian Lan, Qi Chen, Lijie Huang, Rushi Lan, Xipeng Pan, Huadeng Wang, Fan Xu, Wenjing He

“…BackgroundIn vivo confocal microscopy (IVCM) is a crucial imaging modality for assessing corneal diseases, yet distinguishing pathological features from normal variations remains challenging due to the complex multi-layered corneal structure. Existing anomaly detection methods often struggle to generalize across diverse disease manifestations. …”
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Automated Antithrombin Activity Detection with Whole Capillary Blood Based on Digital Microfluidic Platform by Dongshuo Li, Hanqi Hu, Hanzhi Zhang, Lei Shang, Tao Zhao, Qingchen Zhao, Shuhao Zhang, Fucun Ma, Guowei Liang, Rongxin Fu, Xuekai Liu

“…However, traditional detection methods often require large sample volumes, complex procedures, and lengthy processing times. …”
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Federated Learning for Fall Detection With Multimodal Residual Fusion and Pareto-Optimized Client Selection by Bao-Quan Wang, Fan Yang, Yi Wang, Fan Zhao, Yun-Fei Han, Yu-Peng Ma

“…However, challenges such as multimodal data integration and joint analysis in Internet of Medical Things (IoMT) environments and data heterogeneity across sources hinder efficient and accurate fall detection. This paper proposes a Federated Learning-based framework with Multimodal Residual Fusion and Pareto-optimized Client Selection (FLPCS-MRF). …”
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