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Special issue on the engineering applications of neural networks
2016
Neural computing & applications (Print)
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The paper authored by Furquim et al. is entitled "Improving the accuracy of a flood forecasting model by means of machine learning and chaos theory". ...
An intelligent model based on recurrent neural networks with nonlinear autoregressive architecture is applied for the enhancement of hybrid renewable energy systems control in the paper of Chatziagorakis ...
The paper authored by Furquim et al. is entitled ''Improving the accuracy of a flood forecasting model by means of machine learning and chaos theory''. ...
doi:10.1007/s00521-016-2318-4
fatcat:z4lu3i6d5nb3xbzuqkmhr7z2oq
Chaos theory-based time series analysis of in-cylinder pressure and its application in combustion control of SI engines
2020
Journal of Thermal Science and Technology
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Second, with the determined system state variable, the in-cylinder pressure series during combustion and the combustion phase are learned and estimated by a machine learning method, namely, extreme learning ...
Chaos theory is also applied to traffic flow information analysis, and short-term traffic speed forecasting is completed by the support vector machine model (Wang et al., 2013) . ...
., Visual analysis of nonlinear dynamical systems: chaos, fractals, self-similarity and the limits of prediction, Systems, Vol.4, No.4 (2016), 37. ...
doi:10.1299/jtst.2020jtst0001
fatcat:6qosbxvgavcebda6lksd4eezsq
Fault Diagnosis of Rotating Machine
2020
Applied Sciences
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Rotating machines have been used in a wide variety of industries, such as manufacturing tools [...] ...
A new nonlinear blind source separation method with chaos indicators for decoupling diagnosis of hybrid failures: A marine propulsion gearbox case with a large speed variation. Chaos Soliton. ...
Glowacz [48] applied the acoustics analysis and Lee et al. [49] adopted the deep learning for motor failure detection. ...
doi:10.3390/app10061961
fatcat:tiefdmpyqzgjvc7hebb4z5n47i
Particle swarm optimization for feature selection with application in obstructive sleep apnea diagnosis
2011
Neural computing & applications (Print)
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To evaluate the effectiveness of PSO + C4.5 algorithm, it is implemented on 5 different data sets of life sciences obtained from UCI machine learning databases. ...
Moreover, the results of PSO + C4.5 implementation are compared to logistic regression (LR), back propagation neural network (BPNN), support vector machine (SVM), and decision tree (C4.5). ...
This proposed method is applied on 5 data sets obtained from UCI machine learning databases. ...
doi:10.1007/s00521-011-0632-4
fatcat:x3cmqcbujjdihmdbyhubxmpiyy
Improved Approach for Identification of Real and Fake Smile using Chaos Theory and Principal Component Analysis
2019
Journal of Southwest Jiaotong University
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The projected work is analyzed using assorted parameters and it has been found that the deep learning integration approach for chaos and PCA is quite important and performance aware in the multiple parameters ...
with the different datasets in evaluations. ...
RESEARCH METHODOLOGY USING DEEP LEARNING WITH CHAOS AND PCA Chaos Theory with the Deep Learning is more accurate and performance aware flavor of machine learning in which the error rate is much less as ...
doi:10.35741/issn.0258-2724.54.5.20
fatcat:ynbe4pzzonhkthk4qagfn6bmii
The Influence of Sentiments in Digital Currency Prediction Using Hybrid Sentiment-based Support Vector Machine with Whale Optimization Algorithm (SVMWOA)
2021
2021 International Congress of Advanced Technology and Engineering (ICOTEN)
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This study proposes a machine learning model that applies a combination of sentimentbased support vector machine that is optimized by the whale optimization algorithm for predicting the daily price of ...
The proposed method is compared with Support Vector Machine Optimized by Genetic Algorithm (SVMGA) and the Support Vector Machine Optimized by Harmony Search (SVMHS). ...
An efficient hybrid machine learning method for time series stock The Whale Optimization Algorithm market forecasting Cited 3431 times (2011) PEAM 2011 -Proceedings: 2011 IEEE Power Engineering and Automation ...
doi:10.1109/icoten52080.2021.9493454
fatcat:muon2ww6znh45pm5nko7q7olx4
Supervised machine learning based signal demodulation in chaotic communications
2022
Zenodo
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This paper presents the machine learning based demodulation approach for the bifurcation parameter keying. ...
It presents the structure of a convolutional neural network as well as performance metrics values for signals generated with the chaotic logistic map. ...
It is based on supervised machine learning and considers communication in the presence of additive white Gaussian noise.
II. RELATED WORK Chaos has been applied in communication since 90s [11] . ...
doi:10.5281/zenodo.7512426
fatcat:swv2krodjralhgir5gbqqamwx4
Review on Machine Learning Techniques for Diagnosis of Heart Disease
2021
International Journal for Research in Applied Science and Engineering Technology
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In this paper, different machine learning approach for heart disease diagnosis are reviewed. Keywords: Heart disease, CVD, Machine Learning ...
But, now-a-days, medical fields are using machine learning to predict or diagnose different diseases. Implementation of machine learning techniques provides faster and mostly accurate results. ...
[5] developed a machine learning model consisting of factor analysis of mixed data (FAMD) and Random Forest-based machine learning algorithm. ...
doi:10.22214/ijraset.2021.37202
fatcat:qhiuymvyhraldjp3xxzcrh4krm
Methods for optimizing the assignment of cloud computing resources and the scheduling of related tasks
2024
Indonesian Journal of Electrical Engineering and Computer Science
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This study presents a chaos bird swarm algorithm (Chaos BSA) approach that use machine learning to consider task priority while allocating tasks to the cloud platform. ...
The scheduler will select tasks that align with the specified priorities and are compatible with the virtual machines. ...
Mahout machine learning techniques [34] are employed to analyze extensive datasets. ...
doi:10.11591/ijeecs.v33.i2.pp1092-1099
fatcat:6nnzw7szjzfnvpuugklkwbcrxu
Influence of database noises to machine learning for spatiotemporal chaos
[article]
2021
arXiv
pre-print
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Thus, we must use a "clean" database for machine learning of some spatiotemporal chaos. This surprising result might open a new door and possibility to study machine learning. ...
Here we illustrate that machine learning (ML) can always give good enough fitting predictions of a spatiotemporal chaos by using, separately, two quite different training sets: one is the "clean database ...
Thus, we must use a "clean" database for machine learning of some spatiotemporal chaos. This surprising result might open a new door and possibility to study machine learning. ...
arXiv:2109.03823v2
fatcat:ep3jfcdtvjb77i7azlm7dxxiyu
Recent Advances in Uncertainty Quantification Methods for Engineering Problems
[article]
2022
arXiv
pre-print
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This chapter describes the two most popular meta-modeling methods for uncertainty quantification suitable for engineering applications (Polynomial Chaos Method and Gaussian Process). ...
Further, the UQ methods are applied to an engineering test problem under multiple uncertainties. The test problem considered here is a supersonic nozzle under operational uncertainties. ...
For large-scale problems and real-world engineering applications, more recent methods based on machine learning approaches such as Polynomial Chaos Method (PCM) Xiu and Karniadakis [2002b] , Najm [2009 ...
arXiv:2211.03012v1
fatcat:2ldvmebynjesnm45okaswut6ba
PerfCE: Performance Debugging on Databases with Chaos Engineering-Enhanced Causality Analysis
[article]
2023
arXiv
pre-print
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Recently, chaos engineering has been applied to test complex real-world software systems. ...
This paper identifies novel usage of chaos engineering on helping developers diagnose performance anomalies in databases. ...
learning DML + CE-enabled IV : Comparing existing works and PERFCE.CE stands for chaos engineering." ...
arXiv:2207.08369v2
fatcat:qdfipcsn6bbgpbhf7hb6ankxde
Chaotic Time Series Forecasting Approaches Using Machine Learning Techniques: A Review
2022
Symmetry
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Table 1 . 1 Summary of works focused on chaos forecasting using machine-learning-based approaches. ...
In [21] , the MLP model for enhancing the accuracy of a flood prediction through machine learning and chaos theory was presented. ...
doi:10.3390/sym14050955
dblp:journals/symmetry/RamadeviB22
fatcat:3oa3go7rdzdurjl4yxcivjsbf4
Artificial Intelligence Profits from Biological Lessons
2004
IEEE Distributed Systems Online
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If you're interested in new sociology, psychology, engineering, or computer science, you'll learn a great deal and find a wealth of additional sources of information. ...
VALUABLE REFERENCES
CONCLUSION Swarm Intelligence is aimed at computer scientists; applied mathematicians; computer engineers; cognitive scientists who want to move into evolutionary computing, social ...
doi:10.1109/mdso.2004.2
fatcat:blcxfwexsnfqhpuwjvqodfknum
A Heuristic Feature Selection Approach for Text Categorization by Using Chaos Optimization and Genetic Algorithm
2013
Mathematical Problems in Engineering
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The proposed algorithm selects the optimal subsets in both empirical and theoretical work in machine learning and presents a general framework for text categorization. ...
Experimental results show that the proposed algorithm simplifies the feature selection process effectively and can obtain higher classification accuracy with a smaller feature set. ...
Feature selection has been widely applied to various fields including text categorization [1] , signal processing [2] , data mining [3] , machine learning [4] , neural networks, and pattern recognition ...
doi:10.1155/2013/524017
fatcat:btfzr56blvcurezhrsxnabx2im
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