Modeling of Carbon Dioxide Fixation Rate by Micro Algae Using Hybrid Artificial Intelligence and Fuzzy Logic Methods and Optimization by Genetic Algorithm

Abstract In this study we are reporting a prediction model for the estimation of carbon dioxide (CO2) fixation based on the Adaptive Neuro-Fuzzy Inference System (ANFIS) and Genetic Algorithm (GA) hybrid approach. The experimental parameters such as temperature and pH conditions of the micro-algae-based carbon dioxide uptake process were taken as the input variables and the CO2 fixation rate was taken as the output variable. The optimization of ANFIS parameters and formation of the model structure were performed by genetic algorithm (GA) algorithm in order to achieve optimum prediction capability and industrial applicability. The best-fitting model was figured out using statistical analysis parameters such as RMSE, R2 and AARD. According to the analysis, GA-ANFIS model depicted a superior prediction capability over ANFIS optimized model. The Root Mean Square Error (RMSE), coefficient of determination (R2) and AARD for GA-ANFIS were determined as 0.000431, 0.97865 and 0.044354 in the training phase and 0.00056, 0.98457 and 0.032156 in the testing phase, respectively for the GA-ANFIS Model. As a result, it can be concluded that the proposed GA-ANFIS model is an efficient technique having very high potential to accurately calculate CO2 fixation rate and the exploration of the industrial scale-up process for commercial activities.

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Biomass higher heating value prediction analysis by ANFIS, PSO-ANFIS and GA-ANFIS models

Global NEST Journal ◽

10.30955/gnj.002772 ◽

2018 ◽

Vol 20 (3) ◽

pp. 589-597 ◽

Cited By ~ 6

Keyword(s):

Fuzzy Inference ◽

Coefficient Of Determination ◽

Mean Bias Error ◽

Bias Error ◽

Anfis Model ◽

Inference System ◽

Heating Value ◽

Wide Range ◽

Hhv Prediction ◽

Prediction Capability

In this study, a new model for biomass higher heating value (HHV) prediction based on the Adaptive Neuro-Fuzzy Inference System (ANFIS) approach was proposed. Proximate analysis (volatile matter, fixed carbon and ash content) data for a wide range of various biomass types from the literature were used as input in model studies. Optimization of ANFIS parameters and formation of the model structure were performed by genetic algorithm (GA) and particle swarm optimization (PSO) algorithm in order to achieve optimum prediction capability. The best-fitting model was selected using statistical analysis tools. According to the analysis, PSO-ANFIS model showed a superior prediction capability over ANFIS and GA optimized ANFIS model. The Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Bias Error (MBE) and coefficient of determination (R2) for PSO-ANFIS were determined as 0.3138, 0.2545, -0.00129 and 0.9791 in the training phase and 0.3287, 0.2748, 0.00120 and 0.9759 in the testing phase, respectively. As a result, it can be concluded that the proposed PSO-ANFIS model is an efficient technique and has potential to calculate biomass HHV prediction with high accuracy.

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Predictive Algorithm for Handover Decisions between LTE and LTE-A Networks

Australian Journal of Telecommunications and the Digital Economy ◽

10.18080/jtde.v9n4.370 ◽

2021 ◽

Vol 9 (4) ◽

pp. 110-126

Author(s):

Wafa Benaatou ◽

Adnane Latif ◽

Vicent Pla

Keyword(s):

Power Consumption ◽

Mean Square Error ◽

Fuzzy Inference ◽

Hybrid Approach ◽

Mobile Terminal ◽

Base Station ◽

Coefficient Of Determination ◽

Mean Square ◽

Statistical Parameters ◽

Inference System

A heterogeneous wireless network needs to maintain seamless mobility and service continuity; for this reason, we have proposed an approach based on the combination of particle swarm optimization (PSO) and an adaptive neuro-fuzzy inference system (ANFIS) to forecast a handover during a movement of a mobile terminal from a serving base station to target base station. Additionally, the handover decision is made by considering several parameters, such as peak data rate, latency, packet loss, and power consumption, to select the best network for handover from an LTE to an LTE-A network. The performance efficiency of the new hybrid approach is determined by computing different statistical parameters, such as root mean square error (RMSE), coefficient of determination (R2), mean square error (MSE), and error standard deviation (StD). The execution of the proposed approach has been performed using MATLAB software. The simulation results show that the hybrid PSO-ANFIS model has better performance than other approaches in terms of prediction accuracy and reduction of handover latency and the power consumption in the network.

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Optimization of EPB Shield Performance with Adaptive Neuro-Fuzzy Inference System and Genetic Algorithm

Applied Sciences ◽

10.3390/app9040780 ◽

2019 ◽

Vol 9 (4) ◽

pp. 780 ◽

Cited By ~ 27

Author(s):

Khalid Elbaz ◽

Shui-Long Shen ◽

Annan Zhou ◽

Da-Jun Yuan ◽

Ye-Shuang Xu

Keyword(s):

Genetic Algorithm ◽

Fuzzy Inference System ◽

Fuzzy Inference ◽

Pressure Balance ◽

Anfis Model ◽

Inference System ◽

Advance Rate ◽

Multi Objective ◽

Neuro Fuzzy ◽

Epb Shield

The prediction of earth pressure balance (EPB) shield performance is an essential part of project scheduling and cost estimation of tunneling projects. This paper establishes an efficient multi-objective optimization model to predict the shield performance during the tunneling process. This model integrates the adaptive neuro-fuzzy inference system (ANFIS) with the genetic algorithm (GA). The hybrid model uses shield operational parameters as inputs and computes the advance rate as output. GA enhances the accuracy of ANFIS for runtime parameters tuning by multi-objective fitness function. Prior to modeling, datasets were established, and critical operating parameters were identified through principal component analysis. Then, the tunneling case for Guangzhou metro line number 9 was adopted to verify the applicability of the proposed model. Results were then compared with those of the ANFIS model. The comparison showed that the multi-objective ANFIS-GA model is more successful than the ANFIS model in predicting the advance rate with a high accuracy, which can be used to guide the tunnel performance in the field.

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Investigating Entry Capacity Models of Roundabouts under Heterogeneous Traffic Conditions

Transportation Research Record Journal of the Transportation Research Board ◽

10.1177/0361198118777603 ◽

2018 ◽

Vol 2672 (15) ◽

pp. 35-43 ◽

Cited By ~ 5

Author(s):

Ashish Kumar Patnaik ◽

Ankit Raj Ranjan ◽

Prasanta Kumar Bhuyan

Keyword(s):

Fuzzy Inference ◽

Point Of View ◽

Heterogeneous Traffic ◽

Coefficient Of Determination ◽

Efficiency Coefficient ◽

Anfis Model ◽

Inference System ◽

Critical Gap ◽

Traffic Conditions ◽

Traffic Volumes

The primary objectives of this study are to develop two roundabout entry capacity models using a regression-based multiple non-linear regression model (MNLR) and artificial intelligence (AI)-based ANFIS (adaptive neuro-fuzzy inference system) model under heterogeneous traffic conditions. ANFIS is the latest technique in the field of AI that integrates both neural networks and fuzzy logic principles in a single framework. Required data have been collected from 27 roundabouts in eight states of India. To assess the significance of these models and select the best model among them, modified rank index is applied in this study. The coefficient of determination ( R2) and Nash–Sutcliffe model efficiency coefficient ‘ E’ values are found to be 0.92, 0.91 and 0.98, 0.98 for the MNLR and ANFIS model, respectively. The ANFIS model is found to be the best model in this study. However, from a practical point of view, the MNLR model is recommended for determining roundabout entry capacity under heterogeneous traffic conditions. Sensitivity analysis reports that critical gap is the prime variable and shares 18.43% for the development of roundabout entry capacity. As compared with the Girabase formula (France), Brilon wu formula (Germany), and HCM 2010 models, the proposed MNLR model is quite reliable under low to medium ranges of traffic volumes.

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Performance Evaluation of ANFIS and GA-ANFIS for Predicting Stock Market Indices

International Journal of Economics and Finance ◽

10.5539/ijef.v13n7p1 ◽

2021 ◽

Vol 13 (7) ◽

pp. 1

Author(s):

Farnaz Ghashami ◽

Kamyar Kamyar

Keyword(s):

Genetic Algorithm ◽

Stock Market ◽

Mean Squared Error ◽

Fuzzy Inference ◽

The United States ◽

Coefficient Of Determination ◽

Improve Performance ◽

Inference System ◽

Squared Error ◽

Neuro Fuzzy

A model of Adaptive Neuro-Fuzzy Inference System (ANFIS) trained with an evolutionary algorithm, namely Genetic Algorithm (GA) is presented in this paper. Further, the model is tested on the NASDAQ stock market indices which is among the most widely followed indices in the United States. Empirical results show that by determining the parameters of ANFIS (premise and consequent parameters) using GA, we can improve performance in terms of Mean Squared Error (MSE), Root Mean Squared Error (RMSE), coefficient of determination (R-Squared) in comparison with using solely ANFIS.

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Artificial Intelligence Based Modelling of Adsorption Water Desalination System

Mathematics ◽

10.3390/math9141674 ◽

2021 ◽

Vol 9 (14) ◽

pp. 1674

Author(s):

Hesham Alhumade ◽

Hegazy Rezk ◽

Abdulrahim A. Al-Zahrani ◽

Sharif F. Zaman ◽

Ahmed Askalany

Keyword(s):

Artificial Intelligence ◽

Fuzzy Inference ◽

Research Work ◽

Water Desalination ◽

Coefficient Of Performance ◽

Coefficient Of Determination ◽

Cooling Power ◽

Anfis Model ◽

Inference System ◽

Output Performance

The main target of this research work is to model the output performance of adsorption water desalination system (AWDS) in terms of switching and cycle time using artificial intelligence. The output performance of the ADC system is expressed by the specific daily water production (SDWP), the coefficient of performance (COP), and specific cooling power (SCP). A robust Adaptive Network-based Fuzzy Inference System (ANFIS) model of SDWP, COP, and SCP was built using the measured data. To demonstrate the superiority of the suggested ANFIS model, the model results were compared with those achieved by Analysis of Variance (ANOVA) based on the maximum coefficient of determination and minimum error between measured and estimated data in addition to the mean square error (MSE). Applying ANOVA, the average coefficient-of-determination values were 0.8872 and 0.8223, respectively, for training and testing. These values are increased to 1.0 and 0.9673, respectively, for training and testing thanks to ANFIS based modeling. In addition, ANFIS modelling decreased the RMSE value of all datasets by 83% compared with ANOVA. In sum, the main findings confirmed the superiority of ANFIS modeling of the output performance of adsorption water desalination system compared with ANOVA.

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Prediction of Epidemic Peak and Infected Cases for COVID-19 Disease in Malaysia, 2020

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph17114076 ◽

2020 ◽

Vol 17 (11) ◽

pp. 4076 ◽

Cited By ~ 1

Author(s):

Abdallah Alsayed ◽

Hayder Sadir ◽

Raja Kamil ◽

Hasan Sari

Keyword(s):

Infection Rate ◽

Fuzzy Inference ◽

Basic Reproductive Number ◽

Coefficient Of Determination ◽

Reproductive Number ◽

Percentage Error ◽

Health Providers ◽

Anfis Model ◽

Inference System ◽

The Government

The coronavirus COVID-19 has recently started to spread rapidly in Malaysia. The number of total infected cases has increased to 3662 on 05 April 2020, leading to the country being placed under lockdown. As the main public concern is whether the current situation will continue for the next few months, this study aims to predict the epidemic peak using the Susceptible–Exposed–Infectious–Recovered (SEIR) model, with incorporation of the mortality cases. The infection rate was estimated using the Genetic Algorithm (GA), while the Adaptive Neuro-Fuzzy Inference System (ANFIS) model was used to provide short-time forecasting of the number of infected cases. The results show that the estimated infection rate is 0.228 ± 0.013, while the basic reproductive number is 2.28 ± 0.13. The epidemic peak of COVID-19 in Malaysia could be reached on 26 July 2020, with an uncertain period of 30 days (12 July–11 August). Possible interventions by the government to reduce the infection rate by 25% over two or three months would delay the epidemic peak by 30 and 46 days, respectively. The forecasting results using the ANFIS model show a low Normalized Root Mean Square Error (NRMSE) of 0.041; a low Mean Absolute Percentage Error (MAPE) of 2.45%; and a high coefficient of determination (R2) of 0.9964. The results also show that an intervention has a great effect on delaying the epidemic peak and a longer intervention period would reduce the epidemic size at the peak. The study provides important information for public health providers and the government to control the COVID-19 epidemic.

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Simultaneous optimization of clustering and fuzzy IF-THEN rules parameters by the genetic algorithm in fuzzy inference system-based wave predictor models

Journal of Hydroinformatics ◽

10.2166/hydro.2017.045 ◽

2017 ◽

Vol 19 (3) ◽

pp. 385-404 ◽

Cited By ~ 2

Author(s):

Morteza Zanganeh

Keyword(s):

Genetic Algorithm ◽

Fuzzy Inference System ◽

Lake Michigan ◽

Fuzzy Inference ◽

Simultaneous Optimization ◽

Subtractive Clustering ◽

Generation Process ◽

Anfis Model ◽

Inference System ◽

Wave Parameters

Prediction of wave parameters is of great importance in the design of marine structures. In this paper, two shortcomings with the adaptive network-based fuzzy inference system (ANFIS) model for prediction of wave parameters are remedied by employing a genetic algorithm (GA). The first shortcoming in the ANFIS model goes back to its problem for automatic extraction of fuzzy IF-THEN rules and the second one is related to its gradient-based nature for tuning the antecedent and consequent parameters of fuzzy IF-THEN rules. To deal with these shortcomings, in this study a combined FIS and GA model is developed in which the capability of the GA as an evolutionary algorithm is used for simultaneous optimization of the subtractive clustering parameters and the antecedent and consequent parameters of fuzzy IF-THEN rules. Following the development of the combined model, this model is used to predict wave parameters, i.e., significant wave height and peak spectral period at Lake Michigan. The obtained results show that the developed model outperforms the ANFIS model and the Coastal Engineering Manual (CEM) method to estimate the function representing the generation process of the wind-driven waves.

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A Wavelet Based Solar Radiation Prediction in Nigeria Using Adaptive Neuro-Fuzzy Approach

Indonesian Journal of Electrical Engineering and Computer Science ◽

10.11591/ijeecs.v12.i3.pp907-915 ◽

2018 ◽

Vol 12 (3) ◽

pp. 907 ◽

Cited By ~ 1

Author(s):

Sani Salisu ◽

Mohd Wazir Mustafa ◽

Mamunu Mustapha

Keyword(s):

Solar Radiation ◽

Meteorological Data ◽

Hybrid Approach ◽

Model Performance ◽

Coefficient Of Determination ◽

Maximum Temperature ◽

Percentage Error ◽

Anfis Model ◽

Inference System ◽

Neuro Fuzzy

In this study, a hybrid approach combining an Adaptive Neuro-Fuzzy Inference System (ANFIS) and Wavelet Transform (WT) is examined for solar radiation prediction in Nigeria. Meteorological data obtained from NIMET Nigeria comprising of monthly mean minimum temperature, maximum temperature, relative humidity and sunshine hours were used as inputs to the model and monthly mean solar radiation was used as the model output. The data used was divided into two for training and testing, with 70% used during the training phase and 30% during the testing phase. The hybrid model performance is assessed using three statistical evaluators, Mean Absolute Percentage Error (MAPE), Root Mean Square Error (RMSE) and Coefficient of determination (R2). According to the results obtained, a very accurate prediction was achieved by the WT- ANFIS model by improving the value of (R2) by at least 14% and RMSE by at least 78% when compared with other existing models. And a MAPE of 2% is recorded using the proposed approach. The obtained results prove the developed WT-ANFIS model as an efficient tool for solar radiation prediction.

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Leveraging a Genetic Algorithm for the Optimal Placement of Distributed Generation and the Need for Energy Management Strategies Using a Fuzzy Inference System

Electronics ◽

10.3390/electronics10020172 ◽

2021 ◽

Vol 10 (2) ◽

pp. 172

Author(s):

Sunny Katyara ◽

Muhammad Fawad Shaikh ◽

Shoaib Shaikh ◽

Zahid Hussain Khand ◽

Lukasz Staszewski ◽

...

Keyword(s):

Genetic Algorithm ◽

Energy Management ◽

Fuzzy Inference System ◽

Distribution System ◽

Fuzzy Inference ◽

Management Strategies ◽

Test System ◽

Optimal Placement ◽

Inference System ◽

Simulated Environment

With the rising load demand and power losses, the equipment in the utility network often operates close to its marginal limits, creating a dire need for the installation of new Distributed Generators (DGs). Their proper placement is one of the prerequisites for fully achieving the benefits; otherwise, this may result in the worsening of their performance. This could even lead to further deterioration if an effective Energy Management System (EMS) is not installed. Firstly, addressing these issues, this research exploits a Genetic Algorithm (GA) for the proper placement of new DGs in a distribution system. This approach is based on the system losses, voltage profiles, and phase angle jump variations. Secondly, the energy management models are designed using a fuzzy inference system. The models are then analyzed under heavy loading and fault conditions. This research is conducted on a six bus radial test system in a simulated environment together with a real-time Power Hardware-In-the-Loop (PHIL) setup. It is concluded that the optimal placement of a 3.33 MVA synchronous DG is near the load center, and the robustness of the proposed EMS is proven by mitigating the distinct contingencies within the approximately 2.5 cycles of the operating period.

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