Academic literature on the topic 'Network meta-analyze'
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Journal articles on the topic "Network meta-analyze"
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Gupta, A. K., Bianca Maria Piraccini, Vincent Piguet, and Antonella Tosti. "15952 Network meta-analysis to analyze alopecia areata treatment." Journal of the American Academy of Dermatology 83, no. 6 (December 2020): AB164. http://dx.doi.org/10.1016/j.jaad.2020.06.746.
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Albareda, Laura, and Sandra Waddock. "Networked CSR Governance: A Whole Network Approach to Meta-Governance." Business & Society 57, no. 4 (January 13, 2016): 636–75. http://dx.doi.org/10.1177/0007650315624205.
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Meta-governance is Earth system governance for dealing with the global commons. This article develops a whole network approach to meta-governance to explore the potential for collective action for sustainable development by a loosely coupled network of networks. Networked corporate social responsibility (CSR) governance has emerged around corporate sustainability and responsibility in the first years of the 21st century. Growing agreements and interactions among CSR initiatives suggest the development, structure, and governance of networked CSR governance as a network that can analytically be viewed as a whole and as a platform for learning about systemic change. Using the evolution of CSR initiatives from about 1990 to 2014, the authors differentiate four developmental stages: independent and fragmented multistakeholder networks as CSR governance, collaborative CSR governance, networked CSR governance, and integrated networked CSR governance. The authors then present a framework to analyze networked CSR governance as a whole network experimenting with meta-governance.
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Sun, Yuhong, Qinghai Liu, and Hua Wang. "SIR Meta Distribution in the Heterogeneous and Hybrid Networks." Wireless Communications and Mobile Computing 2020 (December 1, 2020): 1–13. http://dx.doi.org/10.1155/2020/8856459.
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With the development of the technology, the wireless systems are becoming more heterogeneous with the introduction of various power nodes including femtocells, relays, or distributed antennas. Among the research of wireless network performance, the meta distribution of the signal-to-interference ratio (SIR) has attracted significant attention. Compared to the standard success (coverage) probability, the meta distribution provides much more fine-grained information about the network performance. In this paper, we analyze the meta distribution of the SIR in the multi-tier heterogeneous and hybrid networks, where each tier is based on a homogeneous independent Poisson point process model. For the open tiers (the users can associate with any tier) and the closed tiers (the users can only associate with a certain tier), we study the b th moment of the conditional success probability for the typical user and give the beta approximation of the meta distribution from analysis and simulations. Furthermore, we analyze the per-link rate control for open tiers and closed tiers, which answers the question: “how to set the SIR threshold to meet a target reliability?”. We give the approximate value of the SIR threshold to meet a target reliability and show how the value is related to the path loss exponent and densities.
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Ardila, Alfredo, Byron Bernal, and Monica Rosselli. "Language and Visual Perception Associations: Meta-Analytic Connectivity Modeling of Brodmann Area 37." Behavioural Neurology 2015 (2015): 1–14. http://dx.doi.org/10.1155/2015/565871.
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Background.Understanding the functions of different brain areas has represented a major endeavor of neurosciences. Historically, brain functions have been associated with specific cortical brain areas; however, modern neuroimaging developments suggest cognitive functions are associated to networks rather than to areas.Objectives.The purpose of this paper was to analyze the connectivity of Brodmann area (BA) 37 (posterior, inferior, and temporal/fusiform gyrus) in relation to (1) language and (2) visual processing.Methods.Two meta-analyses were initially conducted (first level analysis). The first one was intended to assess the language network in which BA37 is involved. The second one was intended to assess the visual perception network. A third meta-analysis (second level analysis) was then performed to assess contrasts and convergence between the two cognitive domains (language and visual perception). The DataBase of Brainmap was used.Results.Our results support the role of BA37 in language but by means of a distinct network from the network that supports its second most important function: visual perception.Conclusion.It was concluded that left BA37 is a common node of two distinct networks—visual recognition (perception) and semantic language functions.
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Sembroiz, David, Behnam Ojaghi, Davide Careglio, and Sergio Ricciardi. "A GRASP Meta-Heuristic for Evaluating the Latency and Lifetime Impact of Critical Nodes in Large Wireless Sensor Networks." Applied Sciences 9, no. 21 (October 27, 2019): 4564. http://dx.doi.org/10.3390/app9214564.
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Wireless Sensor Networks (WSN) have lately been gaining momentum thanks to the hardware improvements and standardization software efforts. Moreover, the appearance of Internet of Things (IoT) and its reliance on sensors are helping to widely extend the usage of WSNs. However, such networks present drawbacks, mainly because of limited sensor batteries and their vulnerability against physical attacks due to the lack of protection and security. Additionally, not all the sensors inside the network have the same responsibility in terms of traffic handling. In this paper, we firstly analyze the fact that some nodes are more critical than others, considering the most critical node the one that, once incapacitated, causes the most deterioration on the network performance. Such performance is analyzed using two metrics, namely network latency and lifetime. We present a result comparison between a Mixed Integer Programming (MIP) model and a Greedy Randomized Adaptive Search Procedure (GRASP) meta-heuristic for small networks. For bigger networks, GRASP meta-heuristic results are presented to understand how the network degrades as the number of both critical and network nodes increase, by distributing them into two different areas: fixed and incremental to maintain node density.
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Yang, Zheming, and Wen Ji. "Meta measurement of intelligence with crowd network." International Journal of Crowd Science 4, no. 3 (June 13, 2020): 295–307. http://dx.doi.org/10.1108/ijcs-03-2020-0008.
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Purpose The multiple factors of intelligence measurement are critical in intelligent science. The intelligence measurement is typically built as a model based on multiple factors. The different agent is generally difficult to measure because of the uncertainty between multiple factors. The purpose of this paper is to solve the problem of uncertainty between multiple factors and propose an effective method for universal intelligence measurement for the different agents. Design/methodology/approach In this paper, the authors propose a universal intelligence measurement method based on meta-analysis for crowd network. First, the authors get study data through keywords in the database and delete the low-quality data. Second, they compute the effect value by odds ratio, relative risk and risk difference. Then, they test the homogeneity by Q-test and analyze the bias by funnel plots. Third, they select the fixed effect and random effect as a statistical model. Finally, through the meta-analysis of time, complexity and reward, the weight of each factor in the intelligence measurement is obtained and then the meta measurement model is constructed. Findings This paper studies the relationship among time, complexity and reward through meta-analysis and effectively combines the measurement of heterogeneous agents such as human, machine, enterprise, government and institution. Originality/value This paper provides a universal intelligence measurement model for crowd network. And it can provide a theoretical basis for the research of crowd science.
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Fazlollahtabar, Hamed, Iraj Mahdavi, and Nezam Mahdavi-Amiri. "Meta modelling of job satisfaction effective factors for improvement policy making in organizations." Benchmarking: An International Journal 23, no. 2 (March 7, 2016): 388–405. http://dx.doi.org/10.1108/bij-11-2013-0107.
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Purpose – The purpose of this paper is to propose a Meta modeling based on regression, neural network, and clustering to analyze the job satisfaction factors and improvement policy making. Design/methodology/approach – Since any job satisfaction evaluation supposes to improve the status by prescribing specific strategies to be performed in the organization, proposing applicable strategies is decisively important. Task demand, social structure and leader-member exchange (LMX) are general applications easily conceptualized while proposing job satisfaction improvement strategies. Findings – On the basis of these empirical findings, the authors first aim to identify relationships between LMX, task demand, social structure and individual factors, organizational factors, job properties, which are easier to be employed in strategy formulation for job satisfaction, and then determine the sub-factors and subsequently cluster them. The effectiveness of the proposed model is verified by a case study. Originality/value – Here, a Meta modeling based on regression, neural network, and clustering is proposed to analyze the job satisfaction factors and improvement policy making.
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Wang, Shudong, Jixiao Wang, Xinzeng Wang, Yuanyuan Zhang, and Tao Yi. "Identification of Critical Core Genes of Sarcoma Based on Centrality Analysis of Networks Nodes." Journal of Medical Imaging and Health Informatics 10, no. 7 (July 1, 2020): 1776–84. http://dx.doi.org/10.1166/jmihi.2020.3080.
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Genome-wide association studies (GWAS) are powerful tools for identifying pathogenic genes of complex diseases and revealing genetic structure of diseases. However, due to gene-to-gene interactions, only a part of the hereditary factors can be revealed. The meta-analysis based on GWAS can integrate gene expression data at multiple levels and reveal the complex relationship between genes. Therefore, we used meta-analysis to integrate GWAS data of sarcoma to establish complex networks and discuss their significant genes. Firstly, we established gene interaction networks based on the data of different subtypes of sarcoma to analyze the node centralities of genes. Secondly, we calculated the significant score of each gene according to the Staged Significant Gene Network Algorithm (SSGNA). Then, we obtained the critical gene set HYC of sarcoma by ranking the scores, and then combined Gene Ontology enrichment analysis and protein network analysis to further screen it. Finally, the critical core gene set Hcore containing 47 genes was obtained and validated by GEPIA analysis. Our method has certain generalization performance to the study of complex diseases with prior knowledge and it is a useful supplement to genome-wide association studies.
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Takahashi, Marissa, Marta Indulska, and John Steen. "Collaborative Research Project Networks." Project Management Journal 49, no. 4 (July 17, 2018): 36–52. http://dx.doi.org/10.1177/8756972818781630.
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Many businesses seeking enhanced innovation have corporate research teams that engage in collaborative research projects (CRPs), with external entities such as universities, public organizations, or customers. Anecdotal evidence, however, suggests mixed outcomes of CRPs in terms of corporate research impact, which implies successful transfer of novel knowledge generated within CRPs to company-internal business networks to develop radically innovative products. We use the multiple regression quadratic assignment procedure (MRQAP) and meta-analysis to analyze six CRP networks. Our findings indicate that the network’s relational characteristics (tie strength) and structural characteristics (network range) are important determinants of knowledge transfer at the fuzzy front end of innovation.
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Markowski, Marcin. "Heuristic algorithms for joint optimization of unicast and anycast traffic in elastic optical network–based large–scale computing systems." International Journal of Applied Mathematics and Computer Science 27, no. 3 (September 1, 2017): 605–22. http://dx.doi.org/10.1515/amcs-2017-0043.
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AbstractIn recent years elastic optical networks have been perceived as a prospective choice for future optical networks due to better adjustment and utilization of optical resources than is the case with traditional wavelength division multiplexing networks. In the paper we investigate the elastic architecture as the communication network for distributed data centers. We address the problems of optimization of routing and spectrum assignment for large-scale computing systems based on an elastic optical architecture; particularly, we concentrate on anycast user to data center traffic optimization. We assume that computational resources of data centers are limited. For this offline problems we formulate the integer linear programming model and propose a few heuristics, including a meta-heuristic algorithm based on a tabu search method. We report computational results, presenting the quality of approximate solutions and efficiency of the proposed heuristics, and we also analyze and compare some data center allocation scenarios.
Dissertations / Theses on the topic "Network meta-analyze"
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Caruba, Thibaut. "Analyses médico-économiques de la prise en charge de la maladie coronarienne stable : méta-analyse en réseau et modélisation." Phd thesis, Université René Descartes - Paris V, 2013. http://tel.archives-ouvertes.fr/tel-00921072.
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La maladie coronaire stable est une maladie chronique pour laquelle de nombreuses stratégies thérapeutiques sont disponibles, dont le traitement par médicaments seuls et les traitements invasifs par angioplastie avec stent ou par pontage aortocoronaire. Face aux résultats de plusieurs méta-analyses mettant en évidence un taux de mortalité comparable entre ces traitements, nous avons décidé d'effectuer un travail de recherche comparant leurs coûts. Dans la première partie de mon travail, nous avons comparé, après une période de un an et une autre de 3 ans de suivi des patients, les données cliniques et économiques publiées pour 5 traitements de l'angor stable : les médicaments seuls, le pontage aortocoronaire, l'angioplastie sans stent, l'angioplastie avec stent nu et l'angioplastie avec stent actif. La mortalité et le taux d'IDM étaient nos critères de jugement clinique. Les coûts directs, liés au traitement effectué et liés à la prise en charge des éventuelles complications, ont été uniformisés via la parité de pouvoir d'achat et exprimés en US $ 2008. Il s'agissait de notre critère de jugement économique. Un total de 19 études cliniques a été retenu dans notre méta-analyse en réseau. Nos résultats mettent en évidence une absence de différence significative sur le critère clinique. En revanche, nous avons observé une différence concernant le coût moyen de chaque traitement après un an et 3 ans de suivi. Le traitement le moins onéreux était le traitement par médicaments seuls, après un an et 3 ans de suivi, avec respectivement un coût moyen par patient de 3 069 US $ et 13 854 US $. Le coût moyen le plus élevé a toujours été obtenu avec le traitement par pontage aortocoronaire : 27 003 US $ après un an et 28 670 US $ après 3 ans de suivi. Cependant, nos conclusions sont limitées d'une part, par la variabilité des méthodes économiques utilisées dans les études sélectionnées dans notre méta-analyse et, d'autre part, par l'évolution des traitements dans le temps. Dans la seconde partie de mon travail de recherche, nous avons calculé le coût de prise en charge d'un patient angoreux stable traité par l'une des 4 stratégies thérapeutiques suivantes : médicaments seuls, pontage aortocoronaire, angioplastie avec stent nu et angioplastie avec stent actif. Pour se faire, nous avons défini d'une part 6 situations cliniques correspondant aux possibles états cliniques du patient un an après l'instauration du traitement étudié et, d'autre part, déterminé les quantités de soins consommés pour chacune de ces situations cliniques. La perspective retenue était celle de l'Assurance Maladie. Les coûts calculés étaient liés aux hospitalisations, aux soins ambulatoires et aux moyens de transport utilisés pour accéder à l'hôpital. La stratégie médicamenteuse était la moins onéreuse avec un coût moyen annuel de 1 518 € ; ce coût prenant en compte les probabilités de survenue des 6 états cliniques. Le traitement par pontage aortocoronaire était le plus onéreux des 4 traitements étudiés, avec un coût moyen annuel de 15 237 €. La perspective de mes travaux est de modéliser la prise en charge d'un patient angoreux stable en envisageant un second traitement si le premier traitement effectué conduit à une situation d'échec thérapeutique. Les arbres que nous avons construits nous permettront ensuite d'effectuer une analyse coût-efficacité de deux stratégies thérapeutiques avec une durée totale de suivi des patients de 2 ans. Enfin, si nos travaux mettent en avant l'intérêt économique du traitement par médicaments, nous soulignons que ces résultats sont obtenus après avoir suivi les patients sur une courte durée (études à un an et à 3 ans), alors que l'angor stable est une maladie chronique où les stratégies thérapeutiques peuvent se succéder en cas d'échec à l'un des traitements...
Book chapters on the topic "Network meta-analyze"
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Wahyono, Teguh, and Yaya Heryadi. "Machine Learning Applications for Anomaly Detection." In Computational Intelligence in the Internet of Things, 49–83. IGI Global, 2019. http://dx.doi.org/10.4018/978-1-5225-7955-7.ch003.
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The aim of this chapter is to describe and analyze the application of machine learning for anomaly detection. The study regarding the anomaly detection is a very important thing. The various phenomena often occur related to the anomaly study, such as the occurrence of an extreme climate change, the intrusion detection for the network security, the fraud detection for e-banking, the diagnosis for engines fault, the spacecraft anomaly detection, the vessel track, and the airline safety. This chapter is an attempt to provide a structured and a broad overview of extensive research on anomaly detection techniques spanning multiple research areas and application domains. Quantitative analysis meta-approach is used to see the development of the research concerned with those matters. The learning is done on the method side, the techniques utilized, the application development, the technology utilized, and the research trend, which is developed.
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Vivitsou, Marianna. "Values and Purposes in Digital Pedagogies." In Innovative Applications of Online Pedagogy and Course Design, 25–41. IGI Global, 2018. http://dx.doi.org/10.4018/978-1-5225-5466-0.ch002.
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This chapter aims to use metaphor analysis in order to discuss and analyze the patterns resulting from Finnish and Greek language and science teachers' (N= 8) interviews on how they integrate digital technologies into classroom practices. To this end, the author performs a meta-analysis of findings from two previous studies on teacher's metaphorical thinking. This is done through the theoretical lens of two cognitive science related theories (conceptual metaphor and conceptual integration or blending theory) and a theory originating from philosophy of language (semantic theory of metaphor). To meet the ends of the study, the author examined the input sources that inform the teachers' thinking in order to extract single-scope and double-scope networks of metaphors. In this way, we can see what values and purposes emerge out of the teachers' choices when digital pedagogies are introduced into the classroom practice.
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Galés, Neus Lorenzo, and Ray Gallon. "A Social Constructionist Model for Human-Machine Ecosystems." In Cognitive Analytics, 1938–61. IGI Global, 2020. http://dx.doi.org/10.4018/978-1-7998-2460-2.ch099.
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Future learning experiences will be shared in hybrid communities that include humans and non-human agents. Students will need to be protagonists of their social learning situations, together with interactive “smart” tools, and teachers will share planning responsibilities with high-tech assessment tools. Personal engagement will be a major factor for educational success, and collective constructionism will represent the unified model for understanding human-machine interaction. The authors analyze the factors that influence how this will happen through three specific domains of socio-cognitive development: explicit information acquisition, implicit knowledge development, abstract meta-reflection. Humans will experience unpredictable cognitive changes just by merging their goals and actions with artificial intelligence agents. A social constructionist educational system needs to take this into account, plan for the unknown, and work with these evolutions with the goal of developing a new ethos based on a society that is global, networked, collective, ethical, and inclusive.
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Venkatesan, Anusuya S. "Optimized Clustering Techniques with Special Focus to Biomedical Datasets." In Biomedical Engineering, 1149–79. IGI Global, 2018. http://dx.doi.org/10.4018/978-1-5225-3158-6.ch049.
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The clinical data including clinical test results, MRI images and drug responses of patients are documented and analyzed with machine learning and data mining tools. The scale and complexity of these datasets is a big challenge to machine learning and data mining community as the data is of mixed type. The extraction of meaningful or desired information from these datasets provides knowledge in decision making process which in turn helps for the diagnosis and treatment of the diseases. Biomedical datasets are a collection of data with diverse types as it involves images, clinical studies, statistical reports etc. The recent researches have focused on different clustering and classification methods to manage and analyze the biomedical datasets. The objective of this chapter is to cluster or classify the patterns of interest from Brain MRI images, Liver disorder and Breast cancer datasets using efficient clustering methodologies. Among the different algorithms in data mining for clustering, classification, visualization and interpretation, K Means, Fuzzy C Means and Neural Networks(NN) are frequently used for clustering and classification of biomedical datasets. The performance of these methods are greatly influenced by the initialization of K value and its convergence speed. This chapter discusses about FCM and K Means clustering methods and its optimization with meta heuristics such as Particle Swarm Optimization (PSO) and Quantum Particle Swarm Optimization (QPSO). The experimental section of this paper exhibits analysis in terms of Intra cluster distances, elapsed time and Davis Bouldin Index (DBI).
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Venkatesan, Anusuya S. "Optimized Clustering Techniques with Special Focus to Biomedical Datasets." In Computational Tools and Techniques for Biomedical Signal Processing, 333–60. IGI Global, 2017. http://dx.doi.org/10.4018/978-1-5225-0660-7.ch015.
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The clinical data including clinical test results, MRI images and drug responses of patients are documented and analyzed with machine learning and data mining tools. The scale and complexity of these datasets is a big challenge to machine learning and data mining community as the data is of mixed type. The extraction of meaningful or desired information from these datasets provides knowledge in decision making process which in turn helps for the diagnosis and treatment of the diseases. Biomedical datasets are a collection of data with diverse types as it involves images, clinical studies, statistical reports etc. The recent researches have focused on different clustering and classification methods to manage and analyze the biomedical datasets. The objective of this chapter is to cluster or classify the patterns of interest from Brain MRI images, Liver disorder and Breast cancer datasets using efficient clustering methodologies. Among the different algorithms in data mining for clustering, classification, visualization and interpretation, K Means, Fuzzy C Means and Neural Networks(NN) are frequently used for clustering and classification of biomedical datasets. The performance of these methods are greatly influenced by the initialization of K value and its convergence speed. This chapter discusses about FCM and K Means clustering methods and its optimization with meta heuristics such as Particle Swarm Optimization (PSO) and Quantum Particle Swarm Optimization (QPSO). The experimental section of this paper exhibits analysis in terms of Intra cluster distances, elapsed time and Davis Bouldin Index (DBI).
Conference papers on the topic "Network meta-analyze"
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Hou, Shifu, Yanfang Ye, Yangqiu Song, and Melih Abdulhayoglu. "Make Evasion Harder: An Intelligent Android Malware Detection System." In Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. California: International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/737.
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To combat the evolving Android malware attacks, in this paper, instead of only using Application Programming Interface (API) calls, we further analyze the different relationships between them and create higher-level semantics which require more efforts for attackers to evade the detection. We represent the Android applications (apps), related APIs, and their rich relationships as a structured heterogeneous information network (HIN). Then we use a meta-path based approach to characterize the semantic relatedness of apps and APIs. We use each meta-path to formulate a similarity measure over Android apps, and aggregate different similarities using multi-kernel learning to make predictions. Promising experimental results based on real sample collections from Comodo Cloud Security Center demonstrate that our developed system HinDroid outperforms other alternative Android malware detection techniques.
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Foster, Dylan J., and Vasilis Syrgkanis. "Statistical Learning with a Nuisance Component (Extended Abstract)." In Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}. California: International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/ijcai.2020/654.
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We provide excess risk guarantees for statistical learning in a setting where the population risk with respect to which we evaluate a target parameter depends on an unknown parameter that must be estimated from data (a "nuisance parameter"). We analyze a two-stage sample splitting meta-algorithm that takes as input two arbitrary estimation algorithms: one for the target parameter and one for the nuisance parameter. We show that if the population risk satisfies a condition called Neyman orthogonality, the impact of the nuisance estimation error on the excess risk bound achieved by the meta-algorithm is of second order. Our theorem is agnostic to the particular algorithms used for the target and nuisance and only makes an assumption on their individual performance. This enables the use of a plethora of existing results from statistical learning and machine learning literature to give new guarantees for learning with a nuisance component. Moreover, by focusing on excess risk rather than parameter estimation, we can give guarantees under weaker assumptions than in previous works and accommodate the case where the target parameter belongs to a complex nonparametric class. We characterize conditions on the metric entropy such that oracle rates---rates of the same order as if we knew the nuisance parameter---are achieved. We also analyze the rates achieved by specific estimation algorithms such as variance-penalized empirical risk minimization, neural network estimation and sparse high-dimensional linear model estimation. We highlight the applicability of our results in four settings of central importance in the literature: 1) heterogeneous treatment effect estimation, 2) offline policy optimization, 3) domain adaptation, and 4) learning with missing data.