A STATE-OF-THE-ART REVIEW OF NEURAL NETWORKS FOR PERMEABILITY PREDICTION

2000 ◽  
Vol 40 (1) ◽  
pp. 341 ◽  
Author(s):  
A.G. Bruce ◽  
P.M. Wong ◽  
Y. Zhang ◽  
H.A. Salisch ◽  
C.C. Fung ◽  
...  
Keyword(s):  
Neural Networks ◽  
Oil And Gas ◽  
State Of The Art ◽  
Future Research ◽  
Good Prediction ◽  
New Era ◽  
Multiple Networks ◽  
Permeability Prediction ◽  
Future Research Directions ◽  
Oil And Gas Companies

This paper reviews the state-of-the-art of neural networks for permeability prediction from well logs. Good prediction of permeability is necessary for reservoir characterisation and is important for improving the reliability of the asset value of oil and gas companies. Two particular models, known as backpropagation and radial basis function networks, have been applied. From previous work, six innovative aspects are identified:choice of inputs;outlier detection and removal;data splitting;scaling;multiple networks; andprediction confidence.We have also provided a list of future research directions in the area, reflecting the current deficiencies of the use of neural networks. The topics are:the quality and quantity of core data;the maximum use of the logs;the compatibility of scales;the use of soft computing; andthe management of prediction confidence.The current applications are certainly the beginning of a new era. It is important for petrophysicists to take advantage of the advanced technologies.

Emotional networks: The heart of brain design

2000 ◽  
Vol 23 (2) ◽  
pp. 198-199
Author(s):  
John C. Fentress
Keyword(s):  
Neural Networks ◽  
Single Unit ◽  
State Of The Art ◽  
Future Research ◽  
Research Directions ◽  
Behavioral Issues ◽  
Current State ◽  
Adaptive Action ◽  
Future Research Directions

The concept of emotion as defined by Rolls is based upon reinforcement mechanisms and their underlying neural networks. He shows how these networks process signals at many levels, through both separate and convergent pathways essential for adaptive action. While many behavioral issues related to emotion are omitted from his review, he succeeds admirably in summarizing both the “current state of the art” in single unit analyses and in pointing out how future research directions may be crafted.

scholarly journals Social commerce—state-of-the-art and future research directions

2016 ◽  
Vol 26 (3) ◽  
pp. 269-290 ◽  
Author(s):  
Catherine Baethge ◽  
Julia Klier ◽  
Mathias Klier
Keyword(s):  
State Of The Art ◽  
Future Research ◽  
Social Commerce ◽  
Research Directions ◽  
Future Research Directions

MetaLearning with Graph Neural Networks

2021 ◽  
Vol 23 (2) ◽  
pp. 13-22
Author(s):  
Debmalya Mandal ◽  
Sourav Medya ◽  
Brian Uzzi ◽  
Charu Aggarwal
Keyword(s):  
Neural Networks ◽  
Deep Neural Networks ◽  
Future Research ◽  
Open Problems ◽  
Research Directions ◽  
Graph Problems ◽  
Meta Learning ◽  
Comprehensive Survey ◽  
Future Research Directions ◽  
Graph Neural Networks

Graph Neural Networks (GNNs), a generalization of deep neural networks on graph data have been widely used in various domains, ranging from drug discovery to recommender systems. However, GNNs on such applications are limited when there are few available samples. Meta-learning has been an important framework to address the lack of samples in machine learning, and in recent years, researchers have started to apply meta-learning to GNNs. In this work, we provide a comprehensive survey of different metalearning approaches involving GNNs on various graph problems showing the power of using these two approaches together. We categorize the literature based on proposed architectures, shared representations, and applications. Finally, we discuss several exciting future research directions and open problems.

scholarly journals Beyond Intra-modality: A Survey of Heterogeneous Person Re-identification

2020 ◽  
Author(s):  
Zheng Wang ◽  
Zhixiang Wang ◽  
Yinqiang Zheng ◽  
Yang Wu ◽  
Wenjun Zeng ◽  
...  
Keyword(s):  
Video Analysis ◽  
State Of The Art ◽  
Future Research ◽  
Low Resolution ◽  
Research Directions ◽  
Comprehensive Review ◽  
Practical Applications ◽  
Future Research Directions

An efficient and effective person re-identification (ReID) system relieves the users from painful and boring video watching and accelerates the process of video analysis. Recently, with the explosive demands of practical applications, a lot of research efforts have been dedicated to heterogeneous person re-identification (Hetero-ReID). In this paper, we provide a comprehensive review of state-of-the-art Hetero-ReID methods that address the challenge of inter-modality discrepancies. According to the application scenario, we classify the methods into four categories --- low-resolution, infrared, sketch, and text. We begin with an introduction of ReID, and make a comparison between Homogeneous ReID (Homo-ReID) and Hetero-ReID tasks. Then, we describe and compare existing datasets for performing evaluations, and survey the models that have been widely employed in Hetero-ReID. We also summarize and compare the representative approaches from two perspectives, i.e., the application scenario and the learning pipeline. We conclude by a discussion of some future research directions. Follow-up updates are available at https://github.com/lightChaserX/Awesome-Hetero-reID

Energy-Storage Modeling: State-of-the-Art and Future Research Directions

2021 ◽  
pp. 1-1
Author(s):  
Ramteen Sioshansi ◽  
Paul Denholm ◽  
Juan Arteaga ◽  
Sarah Awara ◽  
Shubhrajit Bhattacharjee ◽  
...  
Keyword(s):  
Energy Storage ◽  
State Of The Art ◽  
Future Research ◽  
Research Directions ◽  
Future Research Directions
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