scholarly journals Application of artificial intelligence to estimate phycocyanin pigment concentration using water quality data: a comparative study

2019 ◽  
Vol 9 (7) ◽  
Author(s):  
Salim Heddam ◽  
Hadi Sanikhani ◽  
Ozgur Kisi
Keyword(s):  
Artificial Intelligence ◽  
Water Quality ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Test Phase ◽  
Pigment Concentration ◽  
Quality Data ◽  
Subtractive Clustering ◽  
Inference System ◽  
Neuro Fuzzy

Abstract In the present investigation, the usefulness and capabilities of four artificial intelligence (AI) models, namely feedforward neural networks (FFNNs), gene expression programming (GEP), adaptive neuro-fuzzy inference system with grid partition (ANFIS-GP) and adaptive neuro-fuzzy inference system with subtractive clustering (ANFIS-SC), were investigated in an attempt to evaluate their predictive ability of the phycocyanin pigment concentration (PC) using data from two stations operated by the United States Geological Survey (USGS). Four water quality parameters, namely temperature, pH, specific conductance and dissolved oxygen, were utilized for PC concentration estimation. The four models were evaluated using root mean square errors (RMSEs), mean absolute errors (MAEs) and correlation coefficient (R). The results showed that the ANFIS-SC provided more accurate predictions in comparison with ANFIS-GP, GEP and FFNN for both stations. For USGS 06892350 station, the R, RMSE and MAE values in the test phase for ANFIS-SC were 0.955, 0.205 μg/L and 0.148 μg/L, respectively. Similarly, for USGS 14211720 station, the R, RMSE and MAE values in the test phase for ANFIS-SC, respectively, were 0.950, 0.050 μg/L and 0.031 μg/L. Also, using several combinations of the input variables, the results showed that the ANFIS-SC having only temperature and pH as inputs provided good accuracy, with R, RMSE and MAE values in the test phase, respectively, equal to 0.917, 0.275 μg/L and 0.200 μg/L for USGS 06892350 station. This study proved that artificial intelligence models are good and powerful tools for predicting PC concentration using only water quality variables as predictors.

Classification of EEG Signals for Motor Imagery based on Mutual Information and Adaptive Neuro Fuzzy Inference System

2016 ◽  
Vol 5 (4) ◽  
pp. 64-82 ◽  
Author(s):  
Shereen A. El-aal ◽  
Rabie A. Ramadan ◽  
Neveen I. Ghali
Keyword(s):  
Mutual Information ◽  
Motor Imagery ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Subtractive Clustering ◽  
Imagery Task ◽  
Eeg Signals ◽  
Inference System ◽  
Neuro Fuzzy ◽  
Maximal Relevance

Electroencephalogram (EEG) signals based Brain Computer Interface (BCI) is employed to help disabled people to interact better with the environment. EEG signals are recorded through BCI system to translate it to control commands. There are a large body of literature targeting EEG feature extraction and classification for Motor Imagery tasks. Motor imagery task have several features can be extracted to use in classification. However, using more features consume running time and using irrelevant and redundant features affect the performance of the used classifier. This paper is dedicated to extracting the best feature vector for motor imagery task. This work suggests two feature selection methods based on Mutual Information (MI) including Minimum Redundancy Maximal Relevance (MRMR) and maximal Relevance (MaxRel). Adaptive Neuro Fuzzy Inference System (ANFIS) classifier with Subtractive clustering method is utilized for EEG signals classifications. The suggested methods are applied to BCI Competition III dataset IVa and IVb and BCI Competition II dataset III.

scholarly journals Modeling river water quality parameters using modified adaptive neuro fuzzy inference system

2019 ◽  
Vol 12 (1) ◽  
pp. 45-54 ◽  
Author(s):  
Armin Azad ◽  
Hojat Karami ◽  
Saeed Farzin ◽  
Sayed-Farhad Mousavi ◽  
Ozgur Kisi
Keyword(s):  
Water Quality ◽  
River Water ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Quality Parameters ◽  
River Water Quality ◽  
Water Quality Parameters ◽  
Inference System ◽  
Neuro Fuzzy

scholarly journals Hybrid Artificial Intelligence Approaches for Predicting Buckling Damage of Steel Columns Under Axial Compression

Materials ◽  
2019 ◽  
Vol 12 (10) ◽  
pp. 1670 ◽  
Author(s):  
Lu Minh Le ◽  
Hai-Bang Ly ◽  
Binh Thai Pham ◽  
Vuong Minh Le ◽  
Tuan Anh Pham ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Fuzzy Inference System ◽  
Prediction Models ◽  
Fuzzy Inference ◽  
Buckling Load ◽  
Coefficient Of Determination ◽  
Inference System ◽  
Steel Columns ◽  
Neuro Fuzzy ◽  
Hybrid Artificial Intelligence

This study aims to investigate the prediction of critical buckling load of steel columns using two hybrid Artificial Intelligence (AI) models such as Adaptive Neuro-Fuzzy Inference System optimized by Genetic Algorithm (ANFIS-GA) and Adaptive Neuro-Fuzzy Inference System optimized by Particle Swarm Optimization (ANFIS-PSO). For this purpose, a total number of 57 experimental buckling tests of novel high strength steel Y-section columns were collected from the available literature to generate the dataset for training and validating the two proposed AI models. Quality assessment criteria such as coefficient of determination (R2), Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) were used to validate and evaluate the performance of the prediction models. Results showed that both ANFIS-GA and ANFIS-PSO had a strong ability in predicting the buckling load of steel columns, but ANFIS-PSO (R2 = 0.929, RMSE = 60.522 and MAE = 44.044) was slightly better than ANFIS-GA (R2 = 0.916, RMSE = 65.371 and MAE = 48.588). The two models were also robust even with the presence of input variability, as investigated via Monte Carlo simulations. This study showed that the hybrid AI techniques could help constructing an efficient numerical tool for buckling analysis.

scholarly journals Detection of waterborne bacteria using Adaptive Neuro-Fuzzy Inference System

2020 ◽  
Vol 158 ◽  
pp. 05002
Author(s):  
Farhan Mohammad Khan ◽  
Smriti Sridhar ◽  
Rajiv Gupta
Keyword(s):  
Water Quality ◽  
Membership Function ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Quality Analysis ◽  
Hybrid Technique ◽  
Membership Functions ◽  
Inference System ◽  
Neuro Fuzzy ◽  
Soft Computing Techniques

The detection of waterborne bacteria is crucial to prevent health risks. Current research uses soft computing techniques based on Artificial Neural Networks (ANN) for the detection of bacterial pollution in water. The limitation of only relying on sensor-based water quality analysis for detection can be prone to human errors. Hence, there is a need to automate the process of real-time bacterial monitoring for minimizing the error, as mentioned above. To address this issue, we implement an automated process of water-borne bacterial detection using a hybrid technique called Adaptive Neuro-fuzzy Inference System (ANFIS), that integrates the advantage of learning in an ANN and a set of fuzzy if-then rules with appropriate membership functions. The experimental data as the input to the ANFIS model is obtained from the open-sourced dataset of government of India data platform, having 1992 experimental laboratory results from the years 2003-2014. We have included the following water quality parameters: Temperature, Dissolved Oxygen (DO), pH, Electrical conductivity, Biochemical oxygen demand (BOD) as the significant factors in the detection and existence of bacteria. The membership function changes automatically with every iteration during training of the system. The goal of the study is to compare the results obtained from the three membership functions of ANFIS- Triangle, Trapezoidal, and Bell-shaped with 35 = 243 fuzzy set rules. The results show that ANFIS with generalized bell-shaped membership function is best with its average error 0.00619 at epoch 100.

scholarly journals Effective Electricity Theft Detection in Power Distribution Grids Using an Adaptive Neuro Fuzzy Inference System

Energies ◽  
2020 ◽  
Vol 13 (12) ◽  
pp. 3110
Author(s):  
Konstantinos V. Blazakis ◽  
Theodoros N. Kapetanakis ◽  
George S. Stavrakakis
Keyword(s):  
Artificial Intelligence ◽  
Electric Power ◽  
Power Distribution ◽  
Fuzzy Inference System ◽  
Distribution System ◽  
Fuzzy Inference ◽  
Electrical Power ◽  
Electrical Power System ◽  
Inference System ◽  
Neuro Fuzzy

Electric power grids are a crucial infrastructure for the proper operation of any country and must be preserved from various threats. Detection of illegal electricity power consumption is a crucial issue for distribution system operators (DSOs). Minimizing non-technical losses is a challenging task for the smooth operation of electrical power system in order to increase electricity provider’s and nation’s revenue and to enhance the reliability of electrical power grid. The widespread popularity of smart meters enables a large volume of electricity consumption data to be collected and new artificial intelligence technologies could be applied to take advantage of these data to solve the problem of power theft more efficiently. In this study, a robust artificial intelligence algorithm adaptive neuro fuzzy inference system (ANFIS)—with many applications in many various areas—is presented in brief and applied to achieve more effective detection of electric power theft. To the best of our knowledge, there are no studies yet that involve the application of ANFIS for the detection of power theft. The proposed technique is shown that if applied properly it could achieve very high success rates in various cases of fraudulent activities originating from unauthorized energy usage.

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