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The role of optimality in characterizing CO2 seepage from geologic carbon sequestration sites
2008
International Journal of Greenhouse Gas Control
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Storage of large amounts of carbon dioxide (CO 2 ) in deep geological formations for greenhousegas mitigation is gaining momentum and moving from its conceptual and testing stages towards widespread application ...
Artificial neural networks are considered as regression models useful for distinguishing natural system behavior from anomalous behavior suggestive of CO 2 seepage without need for detailed understanding ...
Riley (LBNL) for assistance with the ISOLSM model computations and ...
doi:10.1016/j.ijggc.2008.04.008
fatcat:6q5px3l5jbe4loanyuyuof6iki
A Decade of Kasabov's Evolving Connectionist Systems: A Review
2009
IEEE Transactions on Systems Man and Cybernetics Part C (Applications and Reviews)
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A decade on, the number of ECoS algorithms, and the problems to which they have been applied, have multiplied. ...
It reviews (1) the motivations for ECoS, (2) the major ECoS algorithms in use, (3) previously existing constructive algorithms that are similar to ECoS, (4) empirical evaluations of ECoS networks over ...
Evolving Fuzzy Neural Networks EFuNN was the first ECoS network described [65] , [66] and is an application of the ECoS principles to the Fuzzy Neural Network (FuNN) [85] . ...
doi:10.1109/tsmcc.2008.2012254
fatcat:zqozz7v3wza7voylgofytyrgpa
Table of contents
2018
2018 International Conference on Signal Processing and Information Security (ICSPIS)
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A new framework that can categorize the devices and technologies of the IoT according to their security threats and security requirements is highlighted. Several case studies are discussed. ...
It has been projected that billions of devices will surround us and fundamentally alter the way we interact with our physical environment in countless applications such as healthcare, home automation, ...
The only downfall of VGI approach is its inability to update the new road developments. In this paper, we introduce deep learning approach to update the road network. ...
doi:10.1109/cspis.2018.8642789
fatcat:koekisxlhfbklg6atcf4kkwq6m
Image Processing and Machine Learning for Automated Fruit Grading System: A Technical Review
2013
International Journal of Computer Applications
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In India, demand for various fruits and vegetables are increasing as population grows.Automation in agriculture plays a vital role in increasing the productivity and economical growth of the Country, therefore ...
The objective of the paper is to provide introduction to machine learning and color based grading algorithms, its components and current work reported on an automatic fruit grading system. ...
All parameters and weight of the fruit were given as a input to neural networks. Output of neural networks was evaluated based on reference value of sugar content or pH of the fruit. ...
doi:10.5120/14209-2455
fatcat:5heza3tkkrhnbeotnhxdhs6vvm
Application of Computational Intelligence Algorithms in Radio Propagation: A Systematic Review and Metadata Analysis
2021
Mobile Information Systems
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This paper presents a comprehensive and systematic literature review on the application of nature-inspired computational approaches in radio propagation analysis. ...
In particular, we cover artificial neural networks (ANNs), fuzzy inference systems (FISs), swarm intelligence (SI), and other computational techniques. ...
[47] To propose a new artificial neural network prediction model for railway environments ANN and Ericsson models. ...
doi:10.1155/2021/6619364
fatcat:mx6o6rfsqbhpfnuqcedtpunrwm
An Introduction to Biologically Inspired Engineering: Genetic Algorithms, Evolutionary Computing and Artificial Immune Systems –Theory & Applications in Classifying Remote Sensing Satellite Image Data
2005
Zenodo
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A comparison would also be drawn between evolutionary computing (GAs + AIS) and other biologically inspired methods including neural networks and neural fuzzy systems and traditional engineering methods ...
Some of the common methods used in this regard are neural networks, artificial intelligence and neural fuzzy models against methods like maximum likelihood, machine learning and data mining from a statistical ...
I am also appreciative of my peers for their reviews on the study and their constructive suggestions for the betterment of the research and the presentation of the final paper. ...
doi:10.5281/zenodo.10258070
fatcat:ic4u4tybbvdk3f6bx36onfkbei
Determination of the Stability of a High and Steep Highway Slope in a Basalt Area Based on Iron Staining Anomalies
2023
Remote Sensing
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The research findings reveal that it is feasible to judge the stability of a high and steep slope in a basalt area via the use of iron staining anomalies as an indicator. ...
The artificial neural network–cellular automata (ANN-CA) model is established, and the rock fragmentation classification data obtained based on iron staining anomalies are used to simulate the area. ...
The ANN is a nonlinear mathematical model that imitates the structure and function of biological neural networks. It is composed of a large number of nodes and connections between nodes. ...
doi:10.3390/rs15123021
fatcat:l5wyrdl7uffujplpch2quttlju
Hyperspectral Band Selection Using Attention-based Convolutional Neural Networks
2020
IEEE Access
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In this paper, we introduce a novel algorithm for hyperspectral band selection that couples new attention-based convolutional neural networks used to weight the bands according to their importance with ...
Also, it is modular, easy to implement, seamlessly applicable to any convolutional network, and can be trained end-to-end using gradient descent. ...
ACKNOWLEDGMENT The authors are grateful to the anonymous reviewers for their constructive and valuable comments that helped improve the article. ...
doi:10.1109/access.2020.2977454
fatcat:r5wchsbi6rad5p6ppjn767ckd4
Geospatial Artificial Intelligence (GeoAI) in the Integrated Hydrological and Fluvial Systems Modeling: Review of Current Applications and Trends
2022
Water
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GeoAI effectively harnesses the vast amount of spatial and non-spatial data collected with the new automatic technologies. ...
The fast development of GeoAI provides multiple methods and techniques, although it also makes comparisons between different methods challenging. ...
Conflicts of Interest: The authors declare no conflict of interest. ...
doi:10.3390/w14142211
fatcat:tjiod5qz45f67kbmkm3f6kuv7i
2020 Index IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Vol. 13
2020
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
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., +, JSTARS 2020 3503-3520
The Optimized Small Incidence Angle Setting of a Composite Bragg Scat-
tering Model and its Application to Sea Surface Wind Speed Retrieval. ...
., +, JSTARS 2020 4095-4110 A Variational Approach for Fusion of Panchromatic and Multispectral Images Using a New Spatial-Spectral Consistency Term.An Optimized Deep Neural Network Detecting Small and ...
A New Deep-Learning-Based Approach for Earthquake-Triggered Landslide Detection From Single-Temporal RapidEye Satellite Imagery. Yi, Y., +, JSTARS 2020 ...
doi:10.1109/jstars.2021.3050695
fatcat:ycd5qt66xrgqfewcr6ygsqcl2y
Prediction of seasonal temperature using soft computing techniques: application in Benevento (Southern Italy) area
2016
Journal of Ambient Intelligence and Humanized Computing
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Note that this fulltext copy is not of the "primary" version of this work. The version it corresponds to is:
2016 pre-print arXiv:1611.04767v1 fatcat:wlw4lmfkpfdk7gc6x44pbxnzwm
In this work two soft computing methods, Artificial Neural Networks and Genetic Programming, are proposed in order to forecast the mean temperature that will occur in future seasons. ...
Keywords Seasonal Temperature Forecasting; Soft Computing; Artificial Neural Networks; Genetic Programming; Southern Italy. ...
Acknowledgements The authors are grateful to L. Rampone for the careful reading of the paper.
References Anderson J, van ...
doi:10.1007/s12652-016-0403-2
fatcat:n637zzbinrhwnl7r7ivcpxyqrq
Summer Precipitation Forecast Using an Optimized Artificial Neural Network with a Genetic Algorithm for Yangtze-Huaihe River Basin, China
2022
Atmosphere
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The major ANN employed here is the standard backpropagation neural network (BPNN), which was modified for application to the YHRB. ...
For example, the basin-averaged MAPE and anomaly rate reach 4.7% and 88.3%, respectively, for the YHRB, which can be a good recommendation for future operational services. ...
Institutional Review Board Statement: Not applicable. Informed Consent Statement: Not applicable. ...
doi:10.3390/atmos13060929
fatcat:g5de6s7z5rdnhb5ixkw7jr6azm
The Use of Artificial Intelligence in Tribology—A Perspective
2020
Lubricants
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as, for instance, the coefficient of friction or the oil film thickness. ...
The presentation and discussion of successful case studies using these approaches in a tribological context clearly demonstrates their ability to accurately and efficiently predict these tribological characteristics ...
Acknowledgments: A. Rosenkranz greatly acknowledges the continuous support of the Department of Chemical Engineering, Biotechnology and Materials of the University of Chile including VID. M. ...
doi:10.3390/lubricants9010002
fatcat:cqu2irtku5fhpj3si2cnvgmen4
Machine Learning for Microcontroller-Class Hardware – A Review
[article]
2022
arXiv
pre-print
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We characterize a closed-loop widely applicable workflow of machine learning model development for microcontroller class devices and show that several classes of applications adopt a specific instance ...
of it. ...
NEURAL ARCHITECTURE SEARCH NAS is the automated process of finding the most optimal neural network within a neural network search space given target architecture and network architecture constraints, achieving ...
arXiv:2205.14550v3
fatcat:y272riitirhwfgfiotlwv5i7nu
A/B Testing
[chapter]
2017
Encyclopedia of Machine Learning and Data Mining
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They argued that the case for such a framework as the existence of similar frameworks in other biologically inspired approaches, such as artificial neural networks (ANNs) and evolutionary algorithms (EAs ...
Effective detection of anomalies allows extracting critical information from data which can then be used for a variety of applications, such as to stop malicious intruders, detect and repair faults in ...
In the presence of linear function approximation, the average-reward version of temporal difference learning, which learns a state-based value function for a fixed policy, is shown to converge in the limit ...
doi:10.1007/978-1-4899-7687-1_100507
fatcat:bg6sszljsrax5heho4glbcbicu
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