Search results for "NEURAL NETWORK"
showing 10 items of 1385 documents
Neural Networks to Determine the Relationships Between Business Innovation and Gender Aspects
2021
Gender aspects of management, innovation and entrepreneurship are gaining more and more importance as cross-cutting issues for researchers, practitioners and decision makers. Extant literature pays a growing attention to the hypothesis that there exists a correlation between the gender diversity of corporate boards of directors and the business attitude to innovation. In this paper we introduce a working framework to test the aforementioned hypothesis and to examine the correlation between board diversity and innovation perception of a business. This framework is based on correlation computation and feed-forward neural networks, and it is used to evaluate whether the gender component may be…
Slow and fast methyl group rotations in fragile glass-formers studied by NMR
2000
Abstract The spin-lattice relaxation times of the selectively ring deuterated, fragile glass-formers propylene carbonate and toluene were compared with those measured for species which were specifically labeled at the methyl groups. It was found that the dynamics of the CD 3 group is strongly decoupled from that associated with the primary response of toluene, while for propylene carbonate the degree of decoupling is relatively weak. The experimental results could be described successfully using a model which takes into account the ring dynamics as well as those of the methyl group.
Neural networks as effective techniques in clinical management of patients: some case studies
2004
In this paper, we present four examples of effective implementation of neural systems in the daily clinical practice. There are two main goals in this work; the first one is to show that neural networks are especially well-suited tools for solving different kind of medical/pharmaceutical problems, given the complex input output relationships and the few a priori knowledge about data distribution and variable relations. The second goal is to develop specific software applications, which enclose complex mathematical models, to clinicians; thus, the use of such models as decision support systems is facilitated. Four important pharmaceutical problems are considered in this study: identificatio…
Architectural improvements and FPGA implementation of a multimodel neuroprocessor
2003
Since neural networks (NNs) require an enormous amount of learning time, various kinds of dedicated parallel computers have been developed. In the paper a 2-D systolic array (SA) of dedicated processing elements (PEs) also called systolic cells (SCs) is presented as the heart of a multimodel neural-network accelerator. The instruction set of the SA allows the implementation of several neural algorithms, including error back propagation and a self organizing feature map algorithm. Several special architectural facilities are presented in the paper in order to improve the 2-D SA performance. A swapping mechanism of the weight matrix allows the implementation of NNs larger than 2-D SA. A systo…
Topology management in unstructured P2P networks using neural networks
2007
Resource discovery is an essential problem in peer-to-peer networks since there is no centralized index in which to look for information about resources. In a pure P2P network peers act as servers and clients at the same time and in the Gnutella network for example, peers know only their neighbors. In addition to developing different kinds of resource discovery algorithms, one approach is to study the different topologies or structures of the P2P network. In many cases topology management is based on either technical characteristics of the peers or their interests based on the previous resource queries. In this paper, we propose a topology management algorithm which does not predetermine fa…
Machine learning techniques to estimate the degree of binder activity of reclaimed asphalt pavement
2022
Part of this research was funded by the project RTI2018-096224-J-I00 that has been cofounded by the Spanish Ministry of Science and Innovation, inside the National Program for Fostering Excellence in Scientific and Technical Research, National Subprogram of Knowledge Generation, 2018 call, in the framework of the Spanish National Plan for Scientific and Technical Research and Innovation 2017-2020, and by the European Union, through the European Regional Development Fund, with the main objective of Promoting technological development, innovation and quality research. Part of this work was financially supported by the Italian Ministry of University and Research with the research Grant PRIN 20…
Concurrent TMS-fMRI for causal network perturbation and proof of target engagement
2021
The experimental manipulation of neural activity by neurostimulation techniques overcomes the inherent limitations of correlative recordings, enabling the researcher to investigate causal brain-behavior relationships. But only when stimulation and recordings are combined, the direct impact of the stimulation on neural activity can be evaluated. In humans, this can be achieved non-invasively through the concurrent combination of transcranial magnetic stimulation (TMS) with functional magnetic resonance imaging (fMRI). Concurrent TMS-fMRI allows the assessment of the neurovascular responses evoked by TMS with excellent spatial resolution and full-brain coverage. This enables the functional ma…
PGAC: A Parallel Genetic Algorithm for Data Clustering
2005
Cluster analysis is a valuable tool for exploratory pattern analysis, especially when very little a priori knowledge about the data is available. Distributed systems, based on high speed intranet connections, provide new tools in order to design new and faster clustering algorithms. Here, a parallel genetic algorithm for clustering called PGAC is described. The used strategy of parallelization is the island model paradigm where different populations of chromosomes (called demes) evolve locally to each processor and from time to time some individuals are moved from one deme to another. Experiments have been performed for testing the benefits of the parallelisation paradigm in terms of comput…
Predicting lorawan behavior. How machine learning can help
2020
Large scale deployments of Internet of Things (IoT) networks are becoming reality. From a technology perspective, a lot of information related to device parameters, channel states, network and application data are stored in databases and can be used for an extensive analysis to improve the functionality of IoT systems in terms of network performance and user services. LoRaWAN (Long Range Wide Area Network) is one of the emerging IoT technologies, with a simple protocol based on LoRa modulation. In this work, we discuss how machine learning approaches can be used to improve network performance (and if and how they can help). To this aim, we describe a methodology to process LoRaWAN packets a…
A 3D deep learning approach based on Shape Prior for automatic segmentation of myocardial diseases
2020
Accurate three-dimensional (3D) cardiac segmentation from late gadolinium enhancement (LGE)-MRI plays a critical role in designing a structure of reference for diagnosing many cardiac pathologies such as ischemia, myocarditis and myocardial infarction. This segmentation is however still a non-trivial task, due to the motion artifacts during acquisition, and heterogeneous intensity distributions. In this study, we develop a fully 3D automated model based on deep neural networks (DNN) for LGE-MRI myocardial pathologies (scar and No-reflow tissues) segmentation in a new expert annotated dataset. Considering that damaged tissue constitutes a small area of the whole LGE-MRI, we concentrated on m…