Showing 261 - 280 results of 2,064 for search 'network evaluation patterns', query time: 0.18s Refine Results
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    Exploring the predictive value of structural covariance networks for the diagnosis of schizophrenia by Clara S. Vetter, Clara S. Vetter, Clara S. Vetter, Annika Bender, Dominic B. Dwyer, Dominic B. Dwyer, Dominic B. Dwyer, Max Montembeault, Anne Ruef, Katharine Chisholm, Lana Kambeitz-Ilankovic, Linda A. Antonucci, Stephan Ruhrmann, Joseph Kambeitz, Marlene Rosen, Theresa Lichtenstein, Anita Riecher-Rössler, Rachel Upthegrove, Raimo K. R. Salokangas, Jarmo Hietala, Christos Pantelis, Christos Pantelis, Rebekka Lencer, Rebekka Lencer, Eva Meisenzahl, Stephen J. Wood, Stephen J. Wood, Paolo Brambilla, Paolo Brambilla, Stefan Borgwardt, Peter Falkai, Peter Falkai, Alessandro Bertolino, Nikolaos Koutsouleris, Nikolaos Koutsouleris, Nikolaos Koutsouleris, PRONIA Consortium

    Published 2025-06-01
    “…Structural covariance networks (SCN) describe the shared variation in morphological properties emerging from coordinated neurodevelopmental processes, This study evaluates the potential of SCNs as diagnostic biomarker for schizophrenia.MethodsWe compared the diagnostic value of two SCN computation methods derived from regional gray matter volume (GMV) in 154 patients with a diagnosis of first episode psychosis or recurrent schizophrenia (PAT) and 366 healthy control individuals (HC). …”
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  3. 263

    Trait dissociation in borderline personality disorder: influence on immediate therapy outcomes, follow-up assessments, and self-harm patterns by Ana Macchia, David Mikusky, Cedric Sachser, Annabel Sandra Mueller-Stierlin, Sandra Nickel, Niklas Sanhüter, Birgit Abler

    Published 2025-12-01
    “…Prior to therapy, we evaluated trait dissociation (Dissociative Experience Scale), early life trauma (Childhood Trauma Questionnaire), and self-harm patterns (clinical interview). …”
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    Towards a methodology for validation of centrality measures in complex networks. by Komal Batool, Muaz A Niazi

    Published 2014-01-01
    “…Whereas Betweenness Centrality varied according to network topology and did not demonstrate any noticeable pattern. …”
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    Evaluating the effects of volume censoring on fetal functional connectivity by Jung-Hoon Kim, Josepheen De Asis-Cruz, Kevin M. Cook, Catherine Limperopoulos

    Published 2025-04-01
    “…Fetuses’ FC profiles significantly predicted average FD (r = 0.09 ± 0.08; p < 10–3) after regression, suggesting a lingering effect of motion on whole-brain patterns. To dissociate head motion and the FC, we used volume censoring and evaluated its efficacy in correcting motion at different thresholds. …”
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    Neural connectivity biotypes: predictors of clinical outcomes and improvement patterns of iTBS treatment in adolescents and young adults with depression by Xiaoyu Chen, Min Zhang, Fan Zhang, Yanling Zhou, Yuping Ning, Roger S McIntyre, Hanna Lu, Haiyan Liu, Yiying Chen, Xiaofeng Lan, Chengyu Wang, Weicheng Li, Zhibo Hu, Siming Mai, Yanan Yin, Zerui You, Guanxi Liu, Zhanjie Luo, Yexian Zeng, Yifang Chen, Robin Shao

    Published 2025-04-01
    “…This study investigated functional network connectivity and predictors of iTBS treatment outcomes in adolescents and young adults with depression.Aim This study aimed to identify default mode network (DMN)-based connectivity patterns associated with varying iTBS treatment outcomes in depression.Methods Data from a randomised controlled trial of iTBS in depression (n=82) were analysed using a data-driven approach to classify homogeneous subgroups based on the DMN. …”
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    Optimization of Flipped Classroom Teaching Model Based on Social Cognitive Network by Xinyue Wang

    Published 2021-01-01
    “…An evaluation model based on learners’ cognitive network analysis is designed and constructed by integrating cognitive visualization analysis techniques such as network analysis. …”
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  14. 274

    Neural Network VS Genetic and Particle Swarm Optimization Algorithms in Bankruptcy by Alireza Azarberahman

    Published 2025-04-01
    “…Neural networks (NNs) choose the optimal network with the least error in training and evaluating patterns in the second phase. …”
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  15. 275

    A spatiotemporal graph wavelet neural network for traffic flow prediction by Linjie Zhang, Jianfeng Ma

    Published 2025-03-01
    “…Furthermore, we dig the location and time patterns to evaluate the temporal dependence and the spatial proximity correlation. …”
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  16. 276

    Identification of core sub-team on scientific collaboration networks with Shapley method by Lixin Zhou, Chen Liu, Xue Song

    Published 2025-07-01
    “…Identifying the core sub-teams that drive productivity in scientific collaboration networks is essential for research evaluation and team management. …”
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  17. 277

    Analysis of Encrypted Network Traffic for Enhancing Cyber-security in Dynamic Environments by Faeiz Alserhani

    Published 2024-12-01
    “…As a result, there is a fundamental need for methodologies based on intelligent analysis of patterns and attributes of encrypted network traffic. …”
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    Spatial Correlation Network Characteristics of Comprehensive Transportation Green Efficiency in China by Qifei Ma, Sujuan Li, Zhenchao Zhang

    Published 2025-04-01
    “…Furthermore, the standard deviational ellipse (SDE) model and social network analysis (SNA) method are adopted to delineate the spatiotemporal evolution patterns and spatial correlation network characteristics of CTGE, based on input–output data from the transportation industry across 30 provinces (municipalities and autonomous regions) between 2003 and 2020. …”
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  20. 280

    An optimized ensemble model with advanced feature selection for network intrusion detection by Afaq Ahmed, Muhammad Asim, Irshad Ullah, Zainulabidin, Abdelhamied A. Ateya

    Published 2024-11-01
    “…In today’s digital era, advancements in technology have led to unparalleled levels of connectivity, but have also brought forth a new wave of cyber threats. Network Intrusion Detection Systems (NIDS) are crucial for ensuring the security and integrity of networked systems by identifying and mitigating unauthorized access and malicious activities. …”
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