Detection of random vectors using an unsupervised neural network

1996 ◽  
Author(s):  
Chuan Wang ◽  
LiKang Yen ◽  
Jose C. Principe
Keyword(s):  
Neural Network ◽  
Random Vectors ◽  
Unsupervised Neural Network

scholarly journals Emotion Detection of Human Face

2019 ◽  
Vol 9 (1) ◽  
pp. 5521-5524 ◽  
Keyword(s):  
Neural Network ◽  
Facial Expressions ◽  
Language Translation ◽  
Online Marketing ◽  
Facial Emotion ◽  
Good Resolution ◽  
Human Beings ◽  
Emotion Detection ◽  
Unsupervised Neural Network ◽  
User Device

Facial emotion analysis is the basic idea to train the system to understand the different facial expressions of human beings. The Facial expressions are recorded by the use of camera which is attached to user device. Additionally this project will be helpful for the online marketing of the products as it can detect the facial expressions and sentiment of the person. It is the study of people sentiment, opinions and emotions. Sentiment analysis is the method by which information is taken from the facial expressions of people in regard to different situations. The main aim is to read the facial expressions of the human beings using a good resolution camera so that the machine can identify the human sentiments. Convolutional neural network is used as an existing system which is unsupervised neural network to replace that with a supervised mechanism which is called supervised neural network. It can be used in gaming sector, unlock smart phones, automated facial language translation etc.

Unravel Carbonate Reservoir Heterogeneities Through 3D Seismic Neural Network Application: A Machine Learning Based Approach and Workflow

2021 ◽  
Author(s):  
Dimmas Ramadhan ◽  
Krishna Pratama Laya ◽  
Ricko Rizkiaputra ◽  
Esterlinda Sinlae ◽  
Ari Subekti ◽  
...  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Seismic Data ◽  
Carbonate Reservoir ◽  
3D Seismic ◽  
Carbonate Facies ◽  
Full Field ◽  
Facies Classification ◽  
Unsupervised Neural Network ◽  
Tight Gas Reservoir

Abstract The availability of 3D seismic data undoubtedly plays an important role in reservoir characterization. Currently seismic technology continues to advance at a rapid pace not only in the acquisition but also in processing and interpretation domain. The advance on this is well supported by the digitalization era which urges everything to run reliably fast, effective and efficient. Thanks to continuous development of IT peripherals we now have luxury to process and handle big data through the application of machine learning. Some debates on the effectiveness and threats that this process may automating certain task and later will decrease human workforce are still going on in many forums but still like it or not this machine learning is already embraced in almost every aspect of our life including in oil & gas industry. Carbonate reservoir on the other hand has been long known for its uniqueness compared to siliciclastic reservoir. The term heterogeneous properties are quite common for carbonate due to its complex multi-story depositional and diagenetic facies. In this paper, we bring up our case where we try to unravel carbonate heterogeneity from a massive tight gas reservoir through our machine learning application using the workflow of supervised and unsupervised neural network. In this study, we incorporate 3D PSTM seismic data and its stratigraphic interpretation coupled with the core study result, BHI (borehole image) log interpretation, and our regional understanding of the area to develop a meaningful carbonate facies model through seismic neural network exercises. As the result, we successfully derive geological consistent carbonate facies classification and distribution honoring all the supporting data above though the limitation of well penetration in the area. This result then proved to be beneficial to build integrated 3D geomodel which later can explain the issue on different gas compositions happens in the area. The result on unsupervised neural network also able to serves as a quick look for further sweetspot analysis to support full-field development.

KLN, a new biological koniocortex based unsupervised neural network: competitive results on credit scoring

2018 ◽  
Vol 18 (2) ◽  
pp. 265-273 ◽  
Author(s):  
J. Fombellida ◽  
I. Martín-Rubio ◽  
A. Romera-Zarza ◽  
D. Andina
Keyword(s):  
Neural Network ◽  
Credit Scoring ◽  
Unsupervised Neural Network
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