Calibration Model of a Low-Cost Air Quality Sensor Using an Adaptive Neuro-Fuzzy Inference System

Conventional air quality monitoring systems, such as gas analysers, are commonly used in many developed and developing countries to monitor air quality. However, these techniques have high costs associated with both installation and maintenance. One possible solution to complement these techniques is the application of low-cost air quality sensors (LAQSs), which have the potential to give higher spatial and temporal data of gas pollutants with high precision and accuracy. In this paper, we present DiracSense, a custom-made LAQS that monitors the gas pollutants ozone (O3), nitrogen dioxide (NO2), and carbon monoxide (CO). The aim of this study is to investigate its performance based on laboratory calibration and field experiments. Several model calibrations were developed to improve the accuracy and performance of the LAQS. Laboratory calibrations were carried out to determine the zero offset and sensitivities of each sensor. The results showed that the sensor performed with a highly linear correlation with the reference instrument with a response-time range from 0.5 to 1.7 min. The performance of several calibration models including a calibrated simple equation and supervised learning algorithms (adaptive neuro-fuzzy inference system or ANFIS and the multilayer feed-forward perceptron or MLP) were compared. The field calibration focused on O3 measurements due to the lack of a reference instrument for CO and NO2. Combinations of inputs were evaluated during the development of the supervised learning algorithm. The validation results demonstrated that the ANFIS model with four inputs (WE OX, AE OX, T, and NO2) had the lowest error in terms of statistical performance and the highest correlation coefficients with respect to the reference instrument (0.8 < r < 0.95). These results suggest that the ANFIS model is promising as a calibration tool since it has the capability to improve the accuracy and performance of the low-cost electrochemical sensor.

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Classification of Gait Patterns in Patients with Neurodegenerative Disease Using Adaptive Neuro-Fuzzy Inference System

Computational and Mathematical Methods in Medicine ◽

10.1155/2018/9831252 ◽

2018 ◽

Vol 2018 ◽

pp. 1-8 ◽

Cited By ~ 9

Author(s):

Qiang Ye ◽

Yi Xia ◽

Zhiming Yao

Keyword(s):

Motor Neurons ◽

Fuzzy Inference System ◽

Fuzzy Inference ◽

Pso Algorithm ◽

Anfis Model ◽

Inference System ◽

Gait Dynamics ◽

Double Support ◽

Adaptive Capabilities ◽

Neuro Fuzzy

A common feature that is typical of the patients with neurodegenerative (ND) disease is the impairment of motor function, which can interrupt the pathway from cerebrum to the muscle and thus cause movement disorders. For patients with amyotrophic lateral sclerosis disease (ALS), the impairment is caused by the loss of motor neurons. While for patients with Parkinson’s disease (PD) and Huntington’s disease (HD), it is related to the basal ganglia dysfunction. Previously studies have demonstrated the usage of gait analysis in characterizing the ND patients for the purpose of disease management. However, most studies focus on extracting characteristic features that can differentiate ND gait from normal gait. Few studies have demonstrated the feasibility of modelling the nonlinear gait dynamics in characterizing the ND gait. Therefore, in this study, a novel approach based on an adaptive neuro-fuzzy inference system (ANFIS) is presented for identification of the gait of patients with ND disease. The proposed ANFIS model combines neural network adaptive capabilities and the fuzzy logic qualitative approach. Gait dynamics such as stride intervals, stance intervals, and double support intervals were used as the input variables to the model. The particle swarm optimization (PSO) algorithm was utilized to learn the parameters of the ANFIS model. The performance of the system was evaluated in terms of sensitivity, specificity, and accuracy using the leave-one-out cross-validation method. The competitive classification results on a dataset of 13 ALS patients, 15 PD patients, 20 HD patients, and 16 healthy control subjects indicated the effectiveness of our approach in representing the gait characteristics of ND patients.

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Energy Consumption, Economic Growth, and CO2 Emissions in G20 Countries: Application of Adaptive Neuro-Fuzzy Inference System

Energies ◽

10.3390/en11102771 ◽

2018 ◽

Vol 11 (10) ◽

pp. 2771 ◽

Cited By ~ 12

Author(s):

Abbas Mardani ◽

Dalia Streimikiene ◽

Mehrbakhsh Nilashi ◽

Daniel Arias Aranda ◽

Nanthakumar Loganathan ◽

...

Keyword(s):

Economic Growth ◽

Energy Consumption ◽

Co2 Emissions ◽

Fuzzy Inference System ◽

Global Economy ◽

Prediction Models ◽

Fuzzy Inference ◽

Anfis Model ◽

Inference System ◽

Neuro Fuzzy

Understanding the relationships among CO2 emissions, energy consumption, and economic growth helps nations to develop energy sources and formulate energy policies in order to enhance sustainable development. The present research is aimed at developing a novel efficient model for analyzing the relationships amongst the three aforementioned indicators in G20 countries using an adaptive neuro-fuzzy inference system (ANFIS) model in the period from 1962 to 2016. In this regard, the ANFIS model has been used with prediction models using real data to predict CO2 emissions based on two important input indicators, energy consumption and economic growth. This study made use of the fuzzy rules through ANFIS to generalize the relationships of the input and output indicators in order to make a prediction of CO2 emissions. The experimental findings on a real-world dataset of World Development Indicators (WDI) revealed that the proposed model efficiently predicted the CO2 emissions based on energy consumption and economic growth. The direction of the interrelationship is highly important from the economic and energy policy-making perspectives for this international forum, as G20 countries are primarily focused on the governance of the global economy.

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Development of an adaptive neuro-fuzzy inference system (ANFIS) model to predict sea surface temperature (SST)

Oceanological and Hydrobiological Studies ◽

10.1515/ohs-2020-0031 ◽

2020 ◽

Vol 49 (4) ◽

pp. 354-373

Author(s):

Semih Kale

Keyword(s):

Surface Temperature ◽

Sea Surface Temperature ◽

Mean Square Error ◽

Fuzzy Inference System ◽

Fuzzy Inference ◽

Sea Surface ◽

Mean Square ◽

Anfis Model ◽

Inference System ◽

Neuro Fuzzy

Abstract An accurate estimation of the sea surface temperature (SST) is of great importance. Therefore, the objective of this work was to develop an adaptive neuro-fuzzy inference system (ANFIS) model to predict SST in the Çanakkale Strait. The observed monthly air temperature, evaporation and precipitation data from the Çanakkale meteorological observation station were used as input data. The Takagi–Sugeno fuzzy inference system was applied. The grid partition method (ANFIS-GP) and the subtractive clustering partitioning method (ANFIS-SC) were used with Gaussian membership functions to generate the fuzzy inference system. Six performance evaluation criteria were used to evaluate the developed SST prediction models, including mean square error (MSE), root mean square error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE), Nash-Sutcliffe efficiency (NSE) and correlation of determination (R2). The dataset was randomly divided into training and testing datasets for the machine learning process. Training data accounted for 75% of the dataset, while 25% of the dataset was allocated for testing in ANFIS. The hybrid algorithm was selected as a training algorithm for the ANFIS. Simulation results revealed that the ANFIS-SC4 model provided a higher correlation coefficient of 0.96 between the observed and predicted SST values. The results of this study suggest that the developed ANFIS model can be applied for predicting sea surface temperature around the world.

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Prediction of vitrinite reflectance values using machine learning techniques: a new approach

Journal of Petroleum Exploration and Production Technology ◽

10.1007/s13202-020-01043-8 ◽

2020 ◽

Author(s):

Zahra Sadeghtabaghi ◽

Mohsen Talebkeikhah ◽

Ahmad Reza Rabbani

Keyword(s):

Machine Learning ◽

Decision Tree ◽

Fuzzy Inference System ◽

Fuzzy Inference ◽

Low Cost ◽

Vitrinite Reflectance ◽

Empirical Equations ◽

Inference System ◽

Neuro Fuzzy ◽

Rock Eval

AbstractVitrinite reflectance (VR) is considered the most used maturity indicator of source rocks. Although vitrinite reflectance is an acceptable parameter for maturity and is widely used, it is sometimes difficult to measure. Furthermore, Rock-Eval pyrolysis is a current technique for geochemical investigations and evaluating source rock by their quality and quantity of organic matter, which provide low cost, quick, and valid information. Predicting vitrinite reflectance by using a quick and straightforward method like Rock-Eval pyrolysis results in determining accurate and reliable values of VR with consuming low cost and time. Previous studies used empirical equations for vitrinite reflectance prediction by the Tmax data, which was accompanied by poor results. Therefore, finding a way for precise vitrinite reflectance prediction by Rock-Eval data seems useful. For this aim, vitrinite reflectance values are predicted by 15 distinct machine learning models of the decision tree, random forest, support vector machine, group method of data handling, radial basis function, multilayer perceptron, adaptive neuro-fuzzy inference system, and multilayer perceptron and adaptive neuro-fuzzy inference system, which are coupled with evolutionary optimization methods such as grasshopper optimization algorithm, bat algorithm, particle swarm optimization, and genetic algorithm, with four inputs of Rock-Eval pyrolysis parameters of Tmax, S1/TOC, HI, and depth for the first time. Statistical evaluations indicate that the decision tree is the most precise model for VR prediction, which can estimate vitrinite reflectance precisely. The comparison between the decision tree and previous proposed empirical equations indicates that the machine learning method performs much more accurately.

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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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