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Modern Network Analytics Architecture Stack to Enterprise Networks

K. Palanivel
2019 International Journal for Research in Applied Science and Engineering Technology  
With this architecture, it strongly believes that it can improve agility, while at the same time having control over data integration and distribution.  ...  There is no specific network analytics architecture stack has been proposed yet. Hence, it is proposed to design a network analytics architecture stack to an enterprise network environment.  ...  The data analytics focus is on scalable machine learning on time series, sequence, and graph data.  ... 
doi:10.22214/ijraset.2019.4480 fatcat:otcfqi7c4beb7mid5chcp2nsyi

Deep neural networks in psychiatry

Daniel Durstewitz, Georgia Koppe, Andreas Meyer-Lindenberg
2019 Molecular Psychiatry  
Here we will first give an overview of machine learning methods, with a focus on deep and recurrent neural networks, their relation to statistics, and the core principles behind them.  ...  They are powerful tools for large scale data analysis, prediction and classification, especially in very data-rich environments ("big data"), and have started to find their way into medical applications  ...  Acknowledgements DD was supported by grants from the German  ... 
doi:10.1038/s41380-019-0365-9 pmid:30770893 fatcat:4azz2evu4jdszhggq64yl7qp6i

Deep Neural Mobile Networking [article]

Chaoyun Zhang
2020 arXiv   pre-print
In particular, deep learning based solutions can automatically extract features from raw data, without human expertise.  ...  This makes monitoring and managing the multitude of network elements intractable with existing tools and impractical for traditional machine learning algorithms that rely on hand-crafted feature engineering  ...  This opens a new research direction toward embedding machine learning towards greening cellular networks.  ... 
arXiv:2011.05267v1 fatcat:yz2zp5hplzfy7h5kptmho7mbhe

Implementation and Design of Wireless IoT Network using Deep Learning

S.V.Manikanthan Et.al
2021 Turkish Journal of Computer and Mathematics Education  
for incorporating wireless communicationenvironment, computers, organizations, and governments.  ...  WC is a budding technology in IoT, and it involves further learning. This dilemma motivates the authors to suggest a standardized IoT WC system.  ...  ., voice or text) or time series problems (data from the sensor) of varying lengths.  ... 
doi:10.17762/turcomat.v12i3.761 fatcat:6zrmbsn4gbettoldvpyjouhp64

Supporting Intelligence in Disaggregated Open Radio Access Networks: Architectural Principles, AI/ML Workflow and Use Cases

Anastasios Giannopoulos, Sotirios Spantideas, Nikolaos Kapsalis, Panagiotis Gkonis, Lambros Sarakis, Christos Capsalis, Massimo Vecchio, Panagiotis Trakadas
2022 IEEE Access  
Additionally, a Network Telemetry (NT) architecture is also proposed to ensure end-to-end data collection and real-time analytics.  ...  the training data of an open dataset and (ii) a Deep Reinforcement Learning (DRL) based algorithm for energy-efficiency maximization using a 5G-compliant simulator to obtain RAN measurements.  ...  Special thanks to Simon Pryor for his contribution to Near-RT RIC architecture. VOLUME 10, 2022  ... 
doi:10.1109/access.2022.3166160 fatcat:vlr7boftxvc7tluax6i7yfrn5i

Regulatory Analytics and Data Architecture (RADAR)

Kingsley Jones
2015 Social Science Research Network  
Data Architecture.  ...  In this paper, we develop a framework to consider how the technology forces of: faster computing; networked data portals; and growth in organizational models for parallelism might facilitate a new Regulatory  ...  This research was supported by the Centre for International Finance and Regulation Project Number T-019: Regulatory Data Architecture and Analytics, which is a Centre of Excellence for research and education  ... 
doi:10.2139/ssrn.2628939 fatcat:picudx6dnnd2niaamjqmftgwaq

Analysis of Deep Neural Networks For Human Activity Recognition in Videos – A Systematic Literature Review

Hadiqa Aman Ullah, Sukumar Letchmunan, M. Sultan Zia, Umair Muneer Butt, Fadratul Hafinaz Hassan
2021 IEEE Access  
BiLSTM Bidirectional long short-term memory can learn long term dependencies in both directions between time steps of time series or sequence data.  ...  They also categorize deep learning architectures for action recognition, such as spatiotemporal, multiple streams, deep generative, and temporal coherency networks.  ...  She has previously served as a lecturer in the field of Computer Science and IT and has one journal publication before. Her research interests are Data Science, Machine learning, and Computer vision.  ... 
doi:10.1109/access.2021.3110610 fatcat:ussooxm7azfljpb5prsm7creaa

Network neuroscience and the connectomics revolution [article]

Richard Betzel
2020 arXiv   pre-print
From the construction of networks using functional and diffusion MRI data, to their subsequent analysis using methods from network neuroscience, this review highlights key findings, commonly-used methodologies  ...  Connectomics and network neuroscience offer quantitative scientific frameworks for modeling and analyzing networks of structurally and functionally interacting neurons, neuronal populations, and macroscopic  ...  While there is an agreed-upon correspondence between communities and the brain's intrinsic functional architecture, there remains many open questions.  ... 
arXiv:2010.01591v1 fatcat:zp3xhlgidzbstl4ni2jqhoz3qu

Sensors and Actuators in Smart Cities

Mohammad Hammoudeh, Mounir Arioua
2018 Journal of Sensor and Actuator Networks  
Author Contributions: Alex Adim Obinikpo and Burak Kantarci conceived and pursued the literature survey on deep learning techniques on big sensed data for smart health applications, reviewed the state  ...  Ateya and Ammar Muthanna built the network model and perform the simulation process.  ...  As those challenges arise from the nature of deep learning, sensor deployment and sensory data acquisition, addressing those challenges paves the 158 Books MDPI way towards robust smart health applications  ... 
doi:10.3390/jsan7010008 fatcat:pt7nkf4oaraijkmsndohahqtnq

Trustworthy Graph Neural Networks: Aspects, Methods and Trends [article]

He Zhang, Bang Wu, Xingliang Yuan, Shirui Pan, Hanghang Tong, Jian Pei
2022 arXiv   pre-print
Graph neural networks (GNNs) have emerged as a series of competent graph learning methods for diverse real-world scenarios, ranging from daily applications like recommendation systems and question answering  ...  In this survey, we introduce basic concepts and comprehensively summarise existing efforts for trustworthy GNNs from six aspects, including robustness, explainability, privacy, fairness, accountability  ...  As the generalisation of deep neural networks on the graph domain, current GNNs are data-driven and designed to learn desired mappings from graph data.  ... 
arXiv:2205.07424v1 fatcat:f3iul7soqvgzbgaeqw7nhypbju

Learning Deep and Wide: A Spectral Method for Learning Deep Networks

Ling Shao, Di Wu, Xuelong Li
2014 IEEE Transactions on Neural Networks and Learning Systems  
With the recent resurgence of neural networks invoked by Hinton and others [11] , deep neural architectures have been proposed as an effective solution for extracting high level features from data.  ...  Videos and other high-dimensional time series data are challenging areas where learning based techniques are gaining more and more momentums. Still there are many more broad and open questions.  ...  Deep Learning Toolbox The Matlab Deep Learning Toolbox with pedagogic purposes including Gaussian Bernoulli Deep Belief Network, Maxpooling Convolutional Neural Networks and Multimodal Deep Belief Networks  ... 
doi:10.1109/tnnls.2014.2308519 pmid:25420251 fatcat:4mnl6tv2xnf3jpzwhp76cvl4ti

Circles: Networks of Reading

Massimo Lollini
2015 Humanist Studies & The Digital Age  
Then the brain learned to make connections, circuits, and pathways between the visual areas and those responsible for linguistic processes, essential for the development of written language (14).  ...  To do so we need to adjourn, and for certain aspects suspend, the modern notions of author and book which have been considered crucial for both the writer and the reader since the age of printing.  ...  Starting from twelfth century, the city became the privileged site for the book and this change, along with the transformation of the book from a symbolic to an instrumental object, provoked intense reactions  ... 
doi:10.5399/uo/hsda.4.1.3685 fatcat:udvat43qujalbflp4ipg6iuadi

Research Exhibition "Design Probes" - Deliverable 3.1 (The Innochain Network Journal #2)

Zeynep Aksöz, Efilena Baseta, Klaus Bollinger, Anja Jonkhans, Clemens Preisinger
2017 Zenodo  
The Innochain Network Journal #3 is structured around three activities: the workshop seminar 2 series, the Midterm Review with a focus on a training environment for interdisciplinary design collaboration  ...  1 Introduction This deliverable reports on the Workshop-Seminars series 2, which took place in the second year of the Innochain network.  ...  , etc. over time and to give measurable data.  ... 
doi:10.5281/zenodo.3948522 fatcat:2u5vy4brubcntapku7dkd5kfsu

A Generative Neural Network Framework for Automated Software Testing [article]

Leonid Joffe, David J. Clark
2020 arXiv   pre-print
We believe this proof of concept opens new directions for future work at the intersection of SBST and neural networks.  ...  We demonstrate through a series of experiments that this architecture is possible and practical. It generates diverse, sensible program inputs, while exploring the space of program behaviours.  ...  Citations to standard deep learning concepts are omitted for clarity, and the reader is referred to the Deep Learning Book [22] for reference.  ... 
arXiv:2006.16335v1 fatcat:n6as2mm77zfb5o7zn4whou5gy4

Testing and verification of neural-network-based safety-critical control software: A systematic literature review

Jin Zhang, Jingyue Li
2020 Information and Software Technology  
Results: To reach our result, we selected 83 primary papers published between 2011 and 2018, applied the thematic analysis approach for analyzing the data extracted from the selected papers, presented  ...  tools that are invented, and to identify challenges and gaps for future studies.  ...  Acknowledgments The authors would like to thank Weifeng Liu for commenting on and improving this paper.  ... 
doi:10.1016/j.infsof.2020.106296 fatcat:3bamhnoqsjcmfkwg33jizjxype
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