Distributed Processing Network Survivability

This article has been retracted by IOP Publishing following an allegation that this article contains text overlap from multiple unreferenced sources [1, 2]. IOP Publishing has investigated and agree the article constitutes plagiarism. IOP Publishing also expresses concern regarding a number of nonsensical phrases used in the article, which suggests the article may have been created at least partly by artificial intelligence or translation software. IOP Publishing also notes sections of this article were published in multiple other journals at a similar time [3, 4, 5, 6], by different author groups. These issues all bring the legitimacy of this article into serious doubt. The authors have not responded to confirm whether they agree or disagree to this retraction. IOP Publishing wishes to credit Problematic Paper Screener [7] for bringing some of these issues to our attention. 1. "Machine learning" Wikipedia, Wikimedia Foundation,https://en.wikipedia.org/wiki/Machine_learning 2. "Cardiovascular disease" Wikipedia, Wikimedia Foundation, https://en.wikipedia.org/wiki/Cardiovascular_disease 3. Sukanth, N. et al., 2021. Heart Disease Classification using Machine Learning Algorithm. International Journal of Innovative Research in Computer and Communication Engineering, 9(3), pp.1108-1114. 4. Karthikeyan, N. et al., 2021. Machine learning based classification models for heart disease prediction. Journal of Physics: Conference Series, 1916. 5. Priyadharshini, K. et al., 2021. Coronary Infarction Prediction Using Correlation Analysis aspects based on Parallel Distributed Processing Network. Annals of the Romanian Society for Cell Biology, 25(4), pp.2864-2869. 6. Vennila, V. et al., 2021. Enhanced Deep Learning Assisted Convolutional Neural Network for Heart Disease Prediction. Annals of the Romanian Society for Cell Biology, 25(3), pp.8467-8474. 7. Cabanac G, Labbe C, Magazinov A, 2021, arXiv:2107.06751v1 Retraction published: 17 December 2021

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Reconfigurable real-time distributed processing network

10.1117/12.885696 ◽

2011 ◽

Author(s):

S. F. Page ◽

R. D. Seely ◽

D. Hickman

Keyword(s):

Real Time ◽

Distributed Processing ◽

Processing Network

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Automated instrument design and optimisation using a genetic algorithm running on a distributed processing network

Nuclear Instruments and Methods in Physics Research Section B Beam Interactions with Materials and Atoms ◽

10.1016/j.nimb.2007.04.076 ◽

2007 ◽

Vol 263 (1) ◽

pp. 156-162 ◽

Cited By ~ 3

Author(s):

Greg Roach ◽

James Tickner

Keyword(s):

Genetic Algorithm ◽

Distributed Processing ◽

Instrument Design ◽

Automated Instrument ◽

Processing Network

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Receptive Field Characteristics That Allow Parietal Lobe Neurons to Encode Spatial Properties of Visual Input: A Computational Analysis

Journal of Cognitive Neuroscience ◽

10.1162/jocn.1990.2.2.141 ◽

1990 ◽

Vol 2 (2) ◽

pp. 141-155 ◽

Cited By ~ 8

Author(s):

Randall C. O'Reilly ◽

Stephen M. Kosslyn ◽

Chad J. Marsolek ◽

Christopher F. Chabris

Keyword(s):

Parietal Lobe ◽

Distributed Processing ◽

Receptive Fields ◽

Spatial Properties ◽

Crucial Difference ◽

Cortex Area ◽

Area 7A ◽

Response Profiles ◽

Processing Network ◽

Visually Sensitive Neurons

A subset of visually sensitive neurons in the parietal lobe apparently can encode the locations of stimuli, whereas visually sensitive neurons in the inferotemporal cortex (area IT) cannot. This finding is puzzling because both sorts of neurons have large receptive fields, and yet location can be encoded in one case, but not in the other. The experiments reported here investigated the hypothesis that a crucial difference between the IT and parietal neurons is the spatial distribution of their response profiles. In particular, IT neurons typically respond maximally when stimuli are presented at the fovea, whereas parietal neurons do not. We found that a parallel-distributed-processing network could map a point in an array to a coordinate representation more easily when a greater proportion of its input units had response peaks off the center of the input array. Furthermore, this result did not depend on potentially implausible assumptions about the regularity of the overlap in receptive fields or the homogeneity of the response profiles of different units. Finally, the internal representations formed within the network had receptive fields resembling those found in area 7a of the parietal lobe.

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