Machine Learning Genetic Algorithms Research Articles

Assessment and prediction of water quality indices by machine learning-genetic algorithm and response surface methodology

Assessment and prediction of water quality indices by machine learning-genetic algorithm and response surface methodology

Precision agriculture based on convolutional neural network in rice production nutrient management using machine learning genetic algorithm

Precision Agriculture technology can use high-tech sensors and analytical tools to help farmers better manage their crop yields, allowing them to demonstrate their techniques on the field. The greater use of remote sensing technologies in farm productivity and nitrogen management tools to increase yields and reduce operational costs contributes to reduced energy consumption. This concept describes the need to fulfill nutrient and crop production objectives for the near and long term. Based on our proposed approach, we concluded that the predictors of individual flow occurrences are variables linked to surface temperatures, moisture, and nitrogen availability. We have developed an automated Convolutional Neural Network creation approach genetic algorithms approach to handling image classification problems more efficiently. The best feature of the method is that it is “automated” and doesn't need domain knowledge about CNNs. When compared to conventional encoding, we compare the performance and adaptability of our processing. Moreover, the comparison of individual Genetic algorithms with CNN schemes employed for training the systems was compared to that of other GENCNN schemes ResNet-34, ResNet-50, AlexNet, DenseNet, VGGNet in terms of convergence speed and overall accuracy, among other things. Our studies data indicate that our Soil Nitrogen Management significantly reduced commercial system costs while providing notable outcomes. The experimental results of soil nutrient deficiency 99%and 97% show that the suggested method may be used to automatically identify the most efficient GENCNN model compared to existing models in a wide range of domains.

An Algorithm Design for a Secure Full Duplex Non-Orthogonal Multiple Access in Wireless Communication System

In this article, a new Component-Forward (CF) cooperative communication algorithm is proposed for Full Duplex Modulation-based Non-Orthogonal Multiple Access and is referred as FD-MNOMA. The FD-MNOMA prevents security issues in the FD mode of a cooperative communication system, where a near user assists a far user to receive a better quality of signal. For further improving the security issues, Artificial Noise (AN) is also added to the signal and called it FA-MNOMA. In this article, the secrecy capacity equation is derived for the FA-MNOMA algorithm. Performance evaluation shows that FA-MNOMA and FD-MNOMA outperforms the traditional NOMA. The secrecy capacity of FA-MNOMA is further optimized using Machine Learning Genetic Algorithm (MLGA). This article also shows the implementation of FA-MNOMA in the Intelligent Transportation System (ITS) as one of its applications.

Portfolio optimization through hybrid deep learning and genetic algorithms vine Copula-GARCH-EVT-CVaR model

This study investigates the potential benefits of using the Conditional Value at Risk (CVaR) portfolio optimization approach with a GARCH model, Extreme Value Theory (EVT), and Vine Copula to obtain the optimal allocation decision for a portfolio consisting of Bitcoin, gold, oil, and stock indices. First, we fit a suitable GARCH model to the return series for each asset, followed by employing the Generalized Pareto Distribution (GPD) to model the innovation tails. Next, we construct a Vine Copula-GARCH-EVT model to capture the interdependence structure between the assets. To refine risk assessment, we combine our model with a Monte Carlo simulation and Mean-CVaR model to optimize the portfolio. In addition, we utilize a novel version of deep machine learning's genetic algorithm to address the optimization decision. This research contributes new evidence to the CVaR portfolio optimization approach and provides insights for portfolio managers seeking to optimize multi-asset portfolios.

A hybrid machine learning-genetic algorithm (ML-GA) model to predict optimal process parameters of nanoporous Cu for CO2 reduction

The electrocatalytic performance of nanoporous copper (NPC) in carbon dioxide reduction reaction (CO2RR) varies based on given features during the sample preparation phases and reaction conditions of each sample. This work aims to improve the CO2RR capabilities of NPC regarding C2 products in a flow cell setup by altering process parameters during electrode preparation. A dataset consisting of five electrode process parameters and five Faradaic efficiencies (FE) of major products was employed as the respective inputs and outputs. A machine learning-genetic algorithm (ML-GA) structure was subsequently used to discover NPC's maximum sum of C2-specific faradaic efficiencies. Our results show that the extreme gradient boosting regression (XGBR) algorithm would be the favorable option as the base model and is later combined with the evolutionary GA to predict the best process parameters for optimal C2 FE. A validation test using the best results shows that the sum of C2-specific FE (ethylene, ethanol and acetic acid) was 66.71%. The space-time yield of C2 products in this work, 4693 μmol/(h∗g), was higher than that of other Cu catalysts. The results of this work demonstrate the potential of machine learning methods in effectively finding the optimal NPC electrode process parameters for enhanced CO2RR results.

Interference Management with Reflective In-Band Full-Duplex NOMA for Secure 6G Wireless Communication Systems.

The electromagnetic spectrum is used as a medium for modern wireless communication. Most of the spectrum is being utilized by the existing communication system. For technological breakthroughs and fulfilling the demands of better utilization of such natural resources, a novel Reflective In-Band Full-Duplex (R-IBFD) cooperative communication scheme is proposed in this article that involves Full-Duplex (FD) and Non-Orthogonal Multiple Access (NOMA) technologies. The proposed R-IBFD provides efficient use of spectrum with better system parameters including Secrecy Outage Probability (SOP), throughput, data rate and secrecy capacity to fulfil the requirements of a smart city for 6th Generation (6thG or 6G). The proposed system targets the requirement of new algorithms that contribute towards better change and bring the technological revolution in the requirements of 6G. In this article, the proposed R-IBFD mainly contributes towards co-channel interference and security problem. The In-Band Full-Duplex mode devices face higher co-channel interference in between their own transmission and receiving antenna. R-IBFD minimizes the effect of such interference and assists in the security of a required wireless communication system. For a better understanding of the system contribution, the improvement of secrecy capacity and interference with R-IBFD is discussed with the help of SOP derivation, equations and simulation results. A machine learning genetic algorithm is one of the optimization tools which is being used to maximize the secrecy capacity.

A machine learning-based framework to design capillary-driven networks.

We present a novel approach for the design of capillary-driven microfluidic networks using a machine learning genetic algorithm (ML-GA). This strategy relies on a user-friendly 1D numerical tool specifically developed to generate the necessary data to train the ML-GA. This 1D model was validated using analytical results issued from a Y-shaped capillary network and experimental data. For a given microfluidic network, we defined the objective of the ML-GA to obtain the set of geometric parameters that produces the closest matching results against two prescribed curves of delivered volume against time. We performed more than 20 generations of 10 000 simulations to train the ML-GA and achieved the optimal solution of the inverse design problem. The optimisation took less than 6 hours, and the results were successfully validated using experimental data. This work establishes the utility of the presented method for the fast and reliable design of complex capillary-driven devices, enabling users to optimise their designs via an easy-to-use 1D numerical tool and machine learning technique.

Wastewater Quality Estimation Through Spectrophotometry-Based Statistical Models.

Local administrations are increasingly demanding real-time continuous monitoring of pollution in the sanitation system to improve and optimize its operation, to comply with EU environmental policies and to reach European Green Deal targets. The present work shows a full-scale Wastewater Treatment Plant field-sampling campaign to estimate COD, BOD5, TSS, P, TN and NO3−N in both influent and effluent, in the absence of pre-treatment or chemicals addition to the samples, resulting in a reduction of the duration and cost of analysis. Different regression models were developed to estimate the pollution load of sewage systems from the spectral response of wastewater samples measured at 380–700 nm through multivariate linear regressions and machine learning genetic algorithms. The tests carried out concluded that the models calculated by means of genetic algorithms can estimate the levels of five of the pollutants under study (COD, BOD5, TSS, TN and NO3−N), including both raw and treated wastewater, with an error rate below 4%. In the case of the multilinear regression models, these are limited to raw water and the estimate is limited to COD and TSS, with less than a 0.5% error rate.

Engine Combustion System Optimization Using Computational Fluid Dynamics and Machine Learning: A Methodological Approach

Abstract Gasoline compression ignition (GCI) engines are considered an attractive alternative to traditional spark-ignition and diesel engines. In this work, a Machine Learning-Grid Gradient Ascent (ML-GGA) approach was developed to optimize the performance of internal combustion engines. ML offers a pathway to transform complex physical processes that occur in a combustion engine into compact informational processes. The developed ML-GGA model was compared with a recently developed Machine Learning-Genetic Algorithm (ML-GA). Detailed investigations of optimization solver parameters and variable limit extension were performed in the present ML-GGA model to improve the accuracy and robustness of the optimization process. Detailed descriptions of the different procedures, optimization tools, and criteria that must be followed for a successful output are provided here. The developed ML-GGA approach was used to optimize the operating conditions (case 1) and the piston bowl design (case 2) of a heavy-duty diesel engine running on a gasoline fuel with a research octane number (RON) of 80. The ML-GGA approach yielded >2% improvements in the merit function, compared with the optimum obtained from a thorough computational fluid dynamics (CFD) guided system optimization. The predictions from the ML-GGA approach were validated with engine CFD simulations. This study demonstrates the potential of ML-GGA to significantly reduce the time needed for optimization problems, without loss in accuracy compared with traditional approaches.

A Machine Learning-Genetic Algorithm (ML-GA) Approach for Rapid Optimization Using High-Performance Computing

A Machine Learning-Genetic Algorithm (ML-GA) Approach for Rapid Optimization Using High-Performance Computing