Geometric Deep Lean Learning: Evaluation Using a Twitter Social Network

The goal of this work is to evaluate a deep learning algorithm that has been designed to predict the topological evolution of dynamic complex non-Euclidean graphs in discrete–time in which links are labeled with communicative messages. This type of graph can represent, for example, social networks or complex organisations such as the networks associated with Industry 4.0. In this paper, we first introduce the formal geometric deep lean learning algorithm in its essential form. We then propose a methodology to systematically mine the data generated in social media Twitter, which resembles these complex topologies. Finally, we present the evaluation of a geometric deep lean learning algorithm that allows for link prediction within such databases. The evaluation results show that this algorithm can provide high accuracy in the link prediction of a retweet social network.

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Faultless Decision Making for False Information in Online: A Systematic Approach

Journal of Soft Computing Paradigm - September 2019 ◽

10.36548/jscp.2020.4.004 ◽

2021 ◽

Vol 2 (4) ◽

pp. 226-235

Author(s):

Yasir Babiker Hamdan ◽

Sathish

Keyword(s):

Decision Making ◽

Deep Learning ◽

Systematic Approach ◽

Learning Algorithm ◽

Research Work ◽

High Accuracy ◽

Fake News ◽

False Information ◽

Q Learning ◽

Deep Learning Algorithm

An identifying the news are real or fake instantly with high accuracy is a challenging work. The deep learning algorithm is implementing here to acquire very accurate separation of real and fake news rather than other methods. This research work constructs naïve bayes and CNN classifiers with Q-learning decision making. The two different approaches detect fake news in online and it gives to decision making section which is designed at tail in our research. The deep decision making section compares the input and make the decision wisely and it provides the more accurate output rather than single classifiers in deep learning. This research work comprises compare between our proposed works with single classifiers.

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297 – Deep Learning Algorithm for Characterization of Barrett's Neoplasia Demonstrates High Accuracy on Nbi-Zoom Images

Gastroenterology ◽

10.1016/s0016-5085(19)36929-x ◽

2019 ◽

Vol 156 (6) ◽

pp. S-58 ◽

Cited By ~ 3

Author(s):

Maarten R. Struyvenberg ◽

Jeroen de Groof ◽

Joost van der Putten ◽

Fons van der Sommen ◽

Francisco Baldaque-Silva ◽

...

Keyword(s):

Deep Learning ◽

Learning Algorithm ◽

High Accuracy ◽

Deep Learning Algorithm

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Can Deep Learning Extract Useful Information about Energy Dissipation and Effective Hydraulic Conductivity from Gridded Conductivity Fields?

Water ◽

10.3390/w13121668 ◽

2021 ◽

Vol 13 (12) ◽

pp. 1668

Author(s):

Mohammad Moghaddam ◽

Paul A Ferre ◽

Mohammad Reza Ehsani ◽

Jeffrey Klakovich ◽

Hoshin Vijay Gupta

Keyword(s):

Deep Learning ◽

Energy Dissipation ◽

Hydraulic Conductivity ◽

Learning Algorithm ◽

High Accuracy ◽

Effective Hydraulic Conductivity ◽

Deep Learning Algorithm ◽

Localized Structures ◽

Hydrologic Systems ◽

Controlled Flow

We confirm that energy dissipation weighting provides the most accurate approach to determining the effective hydraulic conductivity (Keff) of a binary K grid. A deep learning algorithm (UNET) can infer Keff with extremely high accuracy (R2 > 0.99). The UNET architecture could be trained to infer the energy dissipation weighting pattern from an image of the K distribution, although it was less accurate for cases with highly localized structures that controlled flow. Furthermore, the UNET architecture learned to infer the energy dissipation weighting even if it was not trained directly on this information. However, the weights were represented within the UNET in a way that was not immediately interpretable by a human user. This reiterates the idea that even if ML/DL algorithms are trained to make some hydrologic predictions accurately, they must be designed and trained to provide each user-required output if their results are to be used to improve our understanding of hydrologic systems.

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Deep learning algorithm detection of Barrett’s neoplasia with high accuracy during live endoscopic procedures: a pilot study (with video)

Gastrointestinal Endoscopy ◽

10.1016/j.gie.2019.12.048 ◽

2020 ◽

Vol 91 (6) ◽

pp. 1242-1250 ◽

Cited By ~ 15

Author(s):

Albert J. de Groof ◽

Maarten R. Struyvenberg ◽

Kiki N. Fockens ◽

Joost van der Putten ◽

Fons van der Sommen ◽

...

Keyword(s):

Deep Learning ◽

Pilot Study ◽

Learning Algorithm ◽

High Accuracy ◽

Endoscopic Procedures ◽

Deep Learning Algorithm

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Design of deep learning on intelligent levelling system for industry 4.0 technology

MATEC Web of Conferences ◽

10.1051/matecconf/201818500026 ◽

2018 ◽

Vol 185 ◽

pp. 00026

Author(s):

Sung-Yu Tsai ◽

Jen-Yuan (James) Chang

Keyword(s):

Deep Learning ◽

Metal Forming ◽

Laser Cutting ◽

Industry 4.0 ◽

Information Transfer ◽

Learning Algorithm ◽

Information Process ◽

Deep Learning Algorithm ◽

Metal Stamping ◽

Real Objects

Sheet metal is widely used in the industry for metal forming purposes, such as metal stamping and laser cutting as shown. It is often winded and stored in a coil form in order for better transportation. In the recent years, industry 4.0 has been a widely discussed topic in terms of industry manufacturing solutions, the manufacturing is required to be more flexible, efficient and also require more customization. In conventional coil levelling system, the machine settings are often tuned by the experienced technicians with many years of experiences. However, as industry 4.0 focused on information process through real objects, it is required to digitize the experience through deep learning method. Therefore, it is required to be adapted through data information transfer between real world and machines, or even machines to machines. In addition, the data information is often processed and analysed through computers which are often desired to mimic the operations of the experienced machine technicians through machine learning or deep learning methods. This paper is aimed to describe and develop the deep learning algorithm with application based on coil levelling system. Finally, through this paper, design of the deep learning algorithm with application based on coil levelling system is verified.

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