Relevant bibliographies by topics / Neural networks (Computer science) Building

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers

Academic literature on the topic 'Neural networks (Computer science) Building'

Author: Grafiati
Published: 4 June 2021
Last updated: 12 February 2022

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Journal articles on the topic "Neural networks (Computer science) Building"

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Seungyup Paek. "Building Neural Networks [Book Reviews]." IEEE Communications Magazine 36, no. 6 (1998): 20. http://dx.doi.org/10.1109/mcom.1998.685339.

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Maher, Gabriel, Nathan Wilson, and Alison Marsden. "Accelerating cardiovascular model building with convolutional neural networks." Medical & Biological Engineering & Computing 57, no. 10 (2019): 2319–35. http://dx.doi.org/10.1007/s11517-019-02029-3.

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Xu, Xiaolong, Guohui Feng, Dandan Chi, Ming Liu, and Baoyue Dou. "Optimization of Performance Parameter Design and Energy Use Prediction for Nearly Zero Energy Buildings." Energies 11, no. 12 (2018): 3252. http://dx.doi.org/10.3390/en11123252.

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Optimizing key parameters with energy consumption as the control target can minimize the heating and cooling needs of buildings. In this paper we focus on the optimization of performance parameters design and the prediction of energy consumption for nearly Zero Energy Buildings (nZEB). The optimal combination of various performance parameters and the Energy Saving Ratio (ESR)are studied by using a large volume of simulation data. Artificial neural networks (ANNs) are applied for the prediction of annual electrical energy consumption in a nearly Zero Energy Building designs located in Shenyang
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Dai, Dawei, Chengfu Tang, Guoyin Wang, and Shuyin Xia. "Building partially understandable convolutional neural networks by differentiating class-related neural nodes." Neurocomputing 452 (September 2021): 169–81. http://dx.doi.org/10.1016/j.neucom.2021.04.003.

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Wang, Hanli, Peiqiu Chen, and Sam Kwong. "Building Correlations Between Filters in Convolutional Neural Networks." IEEE Transactions on Cybernetics 47, no. 10 (2017): 3218–29. http://dx.doi.org/10.1109/tcyb.2016.2633552.

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NAZARI, ALI, and SHADI RIAHI. "COMPUTER-AIDED PREDICTION OF PHYSICAL AND MECHANICAL PROPERTIES OF HIGH STRENGTH CEMENTITIOUS COMPOSITE CONTAINING Cr2O3 NANOPARTICLES." Nano 05, no. 05 (2010): 301–18. http://dx.doi.org/10.1142/s1793292010002219.

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In the present paper, two models based on artificial neural networks (ANN) and genetic programming (GEP) for predicting flexural strength and percentage of water absorption of concretes containing Cr2O3 nanoparticles have been developed at different ages of curing. For purpose of building these models, training and testing using experimental results for 144 specimens produced with 16 different mixture proportions were conducted. The data used in the multilayer feed forward neural networks models and input variables of genetic programming models are arranged in a format of eight input parameter
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Runge, Jason, and Radu Zmeureanu. "Forecasting Energy Use in Buildings Using Artificial Neural Networks: A Review." Energies 12, no. 17 (2019): 3254. http://dx.doi.org/10.3390/en12173254.

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During the past century, energy consumption and associated greenhouse gas emissions have increased drastically due to a wide variety of factors including both technological and population-based. Therefore, increasing our energy efficiency is of great importance in order to achieve overall sustainability. Forecasting the building energy consumption is important for a wide variety of applications including planning, management, optimization, and conservation. Data-driven models for energy forecasting have grown significantly within the past few decades due to their increased performance, robustn
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Tijskens, Astrid, Hans Janssen, and Staf Roels. "Optimising Convolutional Neural Networks to Predict the Hygrothermal Performance of Building Components." Energies 12, no. 20 (2019): 3966. http://dx.doi.org/10.3390/en12203966.

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Performing numerous simulations of a building component, for example to assess its hygrothermal performance with consideration of multiple uncertain input parameters, can easily become computationally inhibitive. To solve this issue, the hygrothermal model can be replaced by a metamodel, a much simpler mathematical model which mimics the original model with a strongly reduced calculation time. In this paper, convolutional neural networks predicting the hygrothermal time series (e.g., temperature, relative humidity, moisture content) are used to that aim. A strategy is presented to optimise the
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Wei, Minghui, Jingjing Tang, Haotian Tang, Rui Zhao, Xiaohui Gai, and Renying Lin. "Adoption of Convolutional Neural Network Algorithm Combined with Augmented Reality in Building Data Visualization and Intelligent Detection." Complexity 2021 (June 29, 2021): 1–13. http://dx.doi.org/10.1155/2021/5161111.

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It aims to improve the degree of visualization of building data, ensure the ability of intelligent detection, and effectively solve the problems encountered in building data processing. Convolutional neural network and augmented reality technology are adopted, and a building visualization model based on convolutional neural network and augmented reality is proposed. The performance of the proposed algorithm is further confirmed by performance verification on public datasets. It is found that the building target detection model based on convolutional neural network and augmented reality has obv
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Wiestler, Benedikt, and Bjoern Menze. "Deep learning for medical image analysis: a brief introduction." Neuro-Oncology Advances 2, Supplement_4 (2020): iv35—iv41. http://dx.doi.org/10.1093/noajnl/vdaa092.

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Abstract Advances in deep learning have led to the development of neural network algorithms which today rival human performance in vision tasks, such as image classification or segmentation. Translation of these techniques into clinical science has also significantly advanced image analysis in neuro-oncology. This has created a need in the neuro-oncology community for understanding the mechanisms behind neural networks and deep learning, as close interaction of computer scientists and neuro-oncology researchers as well as realistic expectations about the possibilities (and limitations) of the
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Dissertations / Theses on the topic "Neural networks (Computer science) Building"

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Künzle, Philippe. "Building topological maps for robot navigation using neural networks." Thesis, McGill University, 2005. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=82266.

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Robots carrying tasks in an unknown environment often need to build a map in order to be able to navigate. One approach is to create a detailed map of the environment containing the position of obstacles. But this option can use a large amount of memory, especially if the environment is large. Another approach, closer to how people build a mental map, is the topological map. A topological map contains only places that are easy to recognize (landmarks) and links them together.<br>In this thesis, we explore the issue of creating a topological map from range data. A robot in a simulated en
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Rouhana, Khalil G. "Neural networks applications in estimating construction costs." Thesis, This resource online, 1994. http://scholar.lib.vt.edu/theses/available/etd-12302008-063358/.

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Sacks, Maxwell. "Neural Networks: Building a Better Index Fund." Scholarship @ Claremont, 2017. http://scholarship.claremont.edu/cmc_theses/1666.

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Big data has become a rapidly growing field amongst firms in the financial sector and thus many companies and researchers have begun implementing machine learning methods to sift through large portions of data. From this data, investment management firms have attempted to automate investment strategies, some successful and some unsuccessful. This paper will investigate an investment strategy by using a deep neural network to see whether the stocks picked from the network will out or underperform the Russell 2000.
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Boetticher, Gary. "A neural network-based bottom-up approach for building a software reuse economic model." Morgantown, W. Va. : [West Virginia University Libraries], 1999. http://etd.wvu.edu/templates/showETD.cfm?recnum=994.

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Thesis (Ph. D.)--West Virginia University, 1999.<br>Title from document title page. Document formatted into pages; contains viii, 226 p. : ill. (some col.) Includes abstract. Includes bibliographical references (p. 152-159).
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Ruedlinger, Benjamin Franklin 1976. "Fundamental building blocks for a compact optoelectronic neural network processor." Thesis, Massachusetts Institute of Technology, 2003. http://hdl.handle.net/1721.1/29621.

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Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2003.<br>Includes bibliographical references (leaves 153-156).<br>The focus of this thesis is interconnects within the Compact Optoelectronic Neural Network Processor. The goal of the Compact Optoelectronic Neural Network Processor Project (CONNPP) is to build a small, rugged neural network co-processing unit. This processor will be optimized for solving various signal processing problems such as image segmentation or facial recognition. These represent a class of problems for which th
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Heathcote, Jonathan David. "Building and operating large-scale SpiNNaker machines." Thesis, University of Manchester, 2016. https://www.research.manchester.ac.uk/portal/en/theses/building-and-operating-largescale-spinnaker-machines(6151916a-ed71-42e4-97d2-2993a4caf5f6).html.

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SpiNNaker is an unconventional supercomputer architecture designed to simulate up to one billion biologically realistic neurons in real-time. To achieve this goal, SpiNNaker employs a novel network architecture which poses a number of practical problems in scaling up from desktop prototypes to machine room filling installations. SpiNNaker's hexagonal torus network topology has received mostly theoretical treatment in the literature. This thesis tackles some of the challenges encountered when building `real-world' systems. Firstly, a scheme is devised for physically laying out hexagonal torus t
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Jiang, Xiaomo. "Dynamic fuzzy wavelet neural network for system identification, damage detection and active control of highrise buildings." Connect to this title online, 2005. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=osu1110266591.

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Thesis (Ph. D.)--Ohio State University, 2005.<br>Title from first page of PDF file. Document formatted into pages; contains xvii, 221 p.; also includes graphics (some col.). Includes bibliographical references (p. 210-221). Available online via OhioLINK's ETD Center
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Sloan, Cooper Stokes. "Neural bus networks." Thesis, Massachusetts Institute of Technology, 2018. http://hdl.handle.net/1721.1/119711.

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Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2018.<br>This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.<br>Cataloged from student-submitted PDF version of thesis.<br>Includes bibliographical references (pages 65-68).<br>Bus schedules are unreliable, leaving passengers waiting and increasing commute times. This problem can be solved by modeling the traffic network, and delivering predicted arrival times to passengers. Research att
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Lindblom, Ellen, and Isabelle Almquist. "Data-Driven Predictions of Heating Energy Savings in Residential Buildings." Thesis, Uppsala universitet, Byggteknik, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-387395.

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Along with the increasing use of intermittent electricity sources, such as wind and sun, comes a growing demand for user flexibility. This has paved the way for a new market of services that provide electricity customers with energy saving solutions. These include a variety of techniques ranging from sophisticated control of the customers’ home equipment to information on how to adjust their consumption behavior in order to save energy. This master thesis work contributes further to this field by investigating an additional incentive; predictions of future energy savings related to indoor temp
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Zaghloul, Waleed A. Lee Sang M. "Text mining using neural networks." Lincoln, Neb. : University of Nebraska-Lincoln, 2005. http://0-www.unl.edu.library.unl.edu/libr/Dissertations/2005/Zaghloul.pdf.

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Thesis (Ph.D.)--University of Nebraska-Lincoln, 2005.<br>Title from title screen (sites viewed on Oct. 18, 2005). PDF text: 100 p. : col. ill. Includes bibliographical references (p. 95-100 of dissertation).
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Books on the topic "Neural networks (Computer science) Building"

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Building neural networks. ACM Press, 1996.

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Molina, Alfonso Hernán. Building up a neural network sociotechnical constituency: A contribution to the formulation of the UK strategy. Research Centre for Social Sciences, University of Edinburgh, 1990.

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Blum, Adam. Neural networks in C++: An object-oriented framework for building connectionist systems. Wiley, 1992.

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Blum, Adam. Neural networks in C [plus plus]: An object-oriented framework for building connectionist systems. Wiley, 1992.

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Dominique, Valentin, and Edelman Betty, eds. Neural networks. Sage Publications, 1999.

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Neural networks. Palgrave, 2000.

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Bischof, Horst. Pyramidal neural networks. Lawrence Erlbaum Associates, 1995.

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Kwon, Seoyun J. Artificial neural networks. Nova Science Publishers, 2010.

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Hoffmann, Norbert. Simulating neural networks. Vieweg, 1994.

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Baram, Yoram. Nested neural networks. National Aeronautics and Space Administration, Ames Research Center, 1988.

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Book chapters on the topic "Neural networks (Computer science) Building"

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Kaiser, Florian, and Fridtjof Feldbusch. "Building a Bridge Between Spiking and Artificial Neural Networks." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-74690-4_39.

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Salama, Khalid, and Ashraf M. Abdelbar. "A Novel Ant Colony Algorithm for Building Neural Network Topologies." In Lecture Notes in Computer Science. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-09952-1_1.

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Khan, Imran, Alfonso Capozzoli, Fiorella Lauro, Stefano Paolo Corgnati, and Stefano Pizzuti. "Building Energy Management Through Fault Detection Analysis Using Pattern Recognition Techniques Applied on Residual Neural Networks." In Communications in Computer and Information Science. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-12745-3_1.

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Parygin, Danila, Nikolay Matyushin, Anton Finogeev, Natalia Sadovnikova, Tatyana Petrova, and Ekaterina Fadeeva. "Neural Network Processing of Natural Russian Language for Building Intelligent Dialogue Systems." In Communications in Computer and Information Science. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-67238-6_17.

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Nguyen, Thieu, Binh Minh Nguyen, and Giang Nguyen. "Building Resource Auto-scaler with Functional-Link Neural Network and Adaptive Bacterial Foraging Optimization." In Lecture Notes in Computer Science. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-14812-6_31.

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Jiang, Kaiyu, and Qingpeng Li. "TQR-Net: Tighter Quadrangle-Based Convolutional Neural Network for Dense Building Instance Localization in Remote Sensing Imagery." In Lecture Notes in Computer Science. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-34113-8_24.

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ElAarag, Hala. "Neural Networks." In SpringerBriefs in Computer Science. Springer London, 2012. http://dx.doi.org/10.1007/978-1-4471-4893-7_3.

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Siegelmann, Hava T. "Recurrent neural networks." In Computer Science Today. Springer Berlin Heidelberg, 1995. http://dx.doi.org/10.1007/bfb0015235.

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Ertel, Wolfgang. "Neural Networks." In Undergraduate Topics in Computer Science. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-58487-4_9.

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Ertel, Wolfgang. "Neural Networks." In Undergraduate Topics in Computer Science. Springer London, 2011. http://dx.doi.org/10.1007/978-0-85729-299-5_9.

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Conference papers on the topic "Neural networks (Computer science) Building"

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Dunaeva, Alexandra. "Building Footprint Extraction from Stereo Satellite Imagery Using Convolutional Neural Networks." In 2019 International Multi-Conference on Engineering, Computer and Information Sciences (SIBIRCON). IEEE, 2019. http://dx.doi.org/10.1109/sibircon48586.2019.8958117.

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Mughaz, Dror, Michael Cohen, Sagit Mejahez, Tal Ades, and Dan Bouhnik. "From an Artificial Neural Network to Teaching [Abstract]." In InSITE 2020: Informing Science + IT Education Conferences: Online. Informing Science Institute, 2020. http://dx.doi.org/10.28945/4557.

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[This Proceedings paper was revised and published in the "Interdisciplinary Journal of e-Skills and Lifelong Learning," 16, 1-17.] Aim/Purpose: Using Artificial Intelligence with Deep Learning (DL) techniques, which mimic the action of the brain, to improve a student’s grammar learning process. Finding the subject of a sentence using DL, and learning, by way of this computer field, to analyze human learning processes and mistakes. In addition, showing Artificial Intelligence learning processes, with and without a general overview of the problem that it is under examination. Applying the idea o
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Cappart, Quentin, Didier Chételat, Elias B. Khalil, Andrea Lodi, Christopher Morris, and Petar Veličković. "Combinatorial Optimization and Reasoning with Graph Neural Networks." In Thirtieth International Joint Conference on Artificial Intelligence {IJCAI-21}. International Joint Conferences on Artificial Intelligence Organization, 2021. http://dx.doi.org/10.24963/ijcai.2021/595.

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Combinatorial optimization is a well-established area in operations research and computer science. Until recently, its methods have mostly focused on solving problem instances in isolation, ignoring the fact that they often stem from related data distributions in practice. However, recent years have seen a surge of interest in using machine learning, especially graph neural networks, as a key building block for combinatorial tasks, either directly as solvers or by enhancing the former. This paper presents a conceptual review of recent key advancements in this emerging field, aiming at research
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"Forecast of Building Energy Consumption Using RBF Neural Network based on L-GEM." In 2018 the 8th International Workshop on Computer Science and Engineering. WCSE, 2018. http://dx.doi.org/10.18178/wcse.2018.06.062.

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Rababaah, Aaron R., and Eniye Tebekaemi. "Electric load monitoring of residential buildings using goodness of fit and multi-layer perceptron neural networks." In 2012 IEEE International Conference on Computer Science and Automation Engineering (CSAE). IEEE, 2012. http://dx.doi.org/10.1109/csae.2012.6272871.

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Zhang Zhen-Ya, Cheng Hong-Mei, and Zhang Shu-Guang. "An approach to the identification of temperature in intelligent building based on feed forward neural network and genetic algorithm." In 2010 3rd IEEE International Conference on Computer Science and Information Technology (ICCSIT 2010). IEEE, 2010. http://dx.doi.org/10.1109/iccsit.2010.5564154.

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Hang, Ju, and Li-jun Wan. "Application of Artificial Neural Network Approach for Intelligent Building in China." In 2009 Fourth International Conference on Computer Sciences and Convergence Information Technology. IEEE, 2009. http://dx.doi.org/10.1109/iccit.2009.220.

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Pandurangan, G., P. Raghavan, and E. Upfal. "Building low-diameter P2P networks." In Proceedings 42nd IEEE Symposium on Foundations of Computer Science. IEEE, 2001. http://dx.doi.org/10.1109/sfcs.2001.959925.

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Zhao, Ritchie, Yuwei Hu, Jordan Dotzel, Christopher De Sa, and Zhiru Zhang. "Building Efficient Deep Neural Networks With Unitary Group Convolutions." In 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2019. http://dx.doi.org/10.1109/cvpr.2019.01156.

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Doncow, Sergey, Leonid Orbachevskyi, Valentin Birukow, and Nina V. Stepanova. "Artificial Kohonen's neural networks for computer capillarometry." In Optical Information Science and Technology, edited by Andrei L. Mikaelian. SPIE, 1998. http://dx.doi.org/10.1117/12.304962.

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