Research on multi-target recognition method based on WSN and blind source separation by Pengju HE, Gangyi LIU, Siyi LIU

“…Aiming at the problem of signal aliasing in multi-target detection and recognition using wireless sensor network (WSN),a blind source separation algorithm was proposed,which can determine the number of targets and obtain the accurate source signals.In this algorithm,the multichannel mixed signal was used as the analysis object,the number of source signals was determined based on the eigenvalue method and then the blind source separation algorithm based on the non-negative matrix factorization was used to obtain the separation signals.The experimental results indicate that the number of targets can be determined and the accurate separation signals can be obtained by the proposed scheme.It can be applied to solve the problem of signal aliasing in multi-target detection and recognition.…”
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Stacking ensemble learning models diagnose pulmonary infections using host transcriptome data from metatranscriptomics by Tian Zhang, Ying Deng, Wentao Wang, Zhe Zhao, Yiling Wu, Haoqian Wang, Shutao Xia, Weifang Liao, Weijie Liao

“…In this study, we conducted metatranscriptomic sequencing on bronchoalveolar lavage fluid (BALF) collected from critically ill, severely ill, and ICU patients. Based on microbial detection results, patients were classified into four types: negative, bacterial infection, viral infection, and fungal infection. …”
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Adult Hope Scale: validation in older adults by Kate Labrosciano, Jillian Dorrian, Braam Lowies, Roslyn Russell, Kurt Lushington

“…Dimensionality was tested by (i) bifactor modelling (one-factor, two-factor and a bifactor model with a general factor, Hope, and two specific factors, Agency and Pathways) and (ii) exploratory graph analysis (which uses community detection algorithms to cluster variables into factors). …”
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Oxygen isotope values of charred tree bark as an indicator of forest fire severity by Elizabeth McDonald, Elizabeth A. Webb, Jeffery P. Dech

“…We also analyzed pre- and post-fire Sentinel-2 imagery of the burn area to compute various Normalized Burn Ratio (NBR)-based change detection algorithms, which are known to produce reliable predictions of CBI. …”
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Identification of atrial fibrillation using heart rate variability: a meta-analysis by Ziwei Yin, Changxin Liu, Chenggong Xie, Zixing Nie, Jiaming Wei, Wen Zhang, Hao Liang

“…Subgroup analyses revealed that both deep learning algorithms (sensitivity = 0.95, specificity = 0.98, AUC = 0.99) and multi-database studies (sensitivity = 0.96, specificity = 0.97, AUC = 0.99) demonstrated enhanced accuracy in AF identification compared to other approaches.ConclusionMachine learning can effectively identify AF with HRV in ECG, especially in diagnosis and detection, with deep learning algorithms and multiple-databases outperforming other diagnostic methods.Systematic Review Registrationhttps://www.crd.york.ac.uk/PROSPERO/, PROSPERO (CRD42025634406).…”
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Early Attrition Prediction for Web-Based Interpretation Bias Modification to Reduce Anxious Thinking: A Machine Learning Study by Sonia Baee, Jeremy W Eberle, Anna N Baglione, Tyler Spears, Elijah Lewis, Hongning Wang, Daniel H Funk, Bethany Teachman, Laura E Barnes

“…Features involving passive detection of user behavior contributed the most to the prediction relative to other features. …”
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Diagnostic accuracy of NS1 ELISA and lateral flow rapid tests for dengue sensitivity, specificity and relationship to viraemia and antibody responses. by Vu Ty Hang, Nguyen Minh Nguyet, Dinh The Trung, Vianney Tricou, Sutee Yoksan, Nguyen Minh Dung, Tran Van Ngoc, Tran Tinh Hien, Jeremy Farrar, Bridget Wills, Cameron P Simmons

“…The presence of measurable DENV-reactive IgG and to a lesser extent IgM in the test sample was associated with a significantly lower rate of NS1 detection in both assays. NS1 positivity was associated with the underlying viraemia, as NS1-positive samples had a significantly higher viraemia than NS1-negative samples matched for duration of illness. …”
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Statistical Characteristics of Nighttime Medium‐Scale Traveling Ionospheric Disturbances From 10‐Years of Airglow Observation by the Machine Learning Method by Chang Lai, Jiyao Xu, Zhishuang Lin, Kun Wu, Donghe Zhang, Qinzeng Li, Longchang Sun, Wei Yuan, Yajun Zhu

“…The classification model and detection model have accuracies of 96.9% and 70%–85%, respectively. …”
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Cyberattack Monitoring Architectures for Resilient Operation of Connected and Automated Vehicles by Zulqarnain H. Khattak, Brian L. Smith, Michael D. Fontaine

“…The proposed algorithm was also compared to convolutional neural network (CNN) and other classical algorithms. …”
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Current Tasks in Identifying Invalid Events in Critical Information Infrastructure by D. A. Evdokimova, A. A. Mikryukov

“…The identification of these events is associated with the complexity of detecting such events, the need to process large volumes of data, insufficient speed in detecting IS events, as well as technological limitations.The relevance of identifying and classifying invalid events in information security, especially for CII, is driven by the need for timely detection and response to incidents that could lead to negative consequences. …”
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Integration of graph neural networks and transcriptomics analysis identify key pathways and gene signature for immunotherapy response and prognosis of skin melanoma by Maodong Ye, Shuai Ren, Huanjuan Luo, Xiumin Wu, Hongwei Lian, Xiangna Cai, Yingchang Ji

“…Graph neural networks (GNNs), alongside other deep learning algorithms and bioinformatics approaches, have demonstrated substantial promise in advancing cancer diagnosis and treatment strategies. …”
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A machine learning model revealed that exosome small RNAs may participate in the development of breast cancer through the chemokine signaling pathway by Jun-luan Mo, Xi Li, Lin Lei, Ji Peng, Xiong-shun Liang, Hong-hao Zhou, Zhao-qian Liu, Wen-xu Hong, Ji-ye Yin

“…Method Peripheral blood samples from breast cancer screening positive and negative people were used for small RNA sequencing of plasma exosomes. …”
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CELIAC DISEASE SCREENING IN A LARGE DOWN SYNDROME COHORT: COMPARISON OF DIAGNOSTIC YIELD OF DIFFERENT SEROLOGICAL SCREENING TESTS by Dilek Uludağ Alkaya, Seçil Sözen, Birol Öztürk, Nuray Kepil, Tülay Erkan, Hüseyin Tufan Kutlu, Beyhan Tüysüz

“…Conclusion: Celiac disease was detected in 2.3% of DS patients. The CD detection rate was 1.3% at initial screening but increased to 4.9% at rescreening. …”
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The Clinical Significance of Abnormal Electroencephalography (EEG) Patterns in Patients with Neuropsychiatric Disorders Due to Anti-NMDA Receptor Encephalitis: A Comparative Study by Alvaro Moreno-Avellán, Arely Juarez-Jaramillo, Maria del Carmen Fernandez Gonzalez-Aragon, Gerardo Quiñones-Pesqueira, Luz Maria Pineda-Centeno, Mariana Espinola-Nadurille, Victoria Martinez-Angeles, Francisco Martinez-Carrillo, Veronica Rivas-Alonso, Daniel San-Juan, Jose Flores-Rivera, Jesus Ramirez-Bermudez

“…These findings support the integration of EEG into diagnostic algorithms to guide early detection and management of autoimmune encephalitis.…”
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An innovative approach to urban parks and perception: a cross-cultural analysis using big and small data by Siqi Lai, Brian Deal

“…This study integrates big data (online reviews, images) and small data (survey results) with large language models (LLMs) and object detection algorithms to analyze public perceptions of urban parks in Stockholm, New York, and Shanghai. …”
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The Impact of a Deep Learning Self-Adaptive Colour Restoration Pipeline for Deep Underwater Images in 3D Reconstruction by M. Vlachos, D. Skarlatos, S. Demesticha

“…Underwater photogrammetry is challenged by image degradation caused by water absorption and scattering, which negatively impacts feature detection and 3D reconstruction quality. …”
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DIO-SLAM: A Dynamic RGB-D SLAM Method Combining Instance Segmentation and Optical Flow by Lang He, Shiyun Li, Junting Qiu, Chenhaomin Zhang

“…Feature points from moving objects can negatively impact the accuracy of Visual Simultaneous Localization and Mapping (VSLAM) algorithms, while detection or semantic segmentation-based VSLAM approaches often fail to accurately determine the true motion state of objects. …”
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A Comparative Study of YOLO, SSD, Faster R-CNN, and More for Optimized Eye-Gaze Writing by Walid Abdallah Shobaki, Mariofanna Milanova

“…Using this dataset, we evaluated the performance of several computer vision algorithms across three key areas. For object detection, we implemented YOLOv8, SSD, and Faster R-CNN. …”
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Capturing drug use patterns at a glance: An n-ary word sufficient statistic for repeated univariate categorical values. by Gabriel J Odom, Laura Brandt, Clinton Castro, Sean X Luo, Daniel J Feaster, Raymond R Balise, CTN-0094 Team

“…As examples, we provide three algorithms to define primary endpoints from seminal SUD clinical trials.…”
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Effective deep learning aided vehicle classification approach using Seismic Data by Sherief Hashima, Mohamed H. Saad, Ahmad B. Ahmad, Takeshi Tsuji, Hamada Rizk

“…Traditional classification algorithms rely heavily on visual or sensor-based data (e.g., radar or image signals), often compromised by adverse weather, poor lighting, or occlusion. …”
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