Two-layer Feed-forward Artificial Neural Network Research Articles

Quantitative monitoring of icing on CFRP laminate with guided wave combining forward modeling and inverse characterization

Aircraft icing is one of the critical factors for flight safety. Timely and accurate monitoring of in-flight ice accretion is essential for flight systems to take immediate and effective action to significantly improve the efficiency of subsequent de-icing processes and ensure flight safety. Since carbon fiber reinforced polymer (CFRP) are now widely used in the aviation industry, the study of ice accretion on the surfaces of CFRP with anisotropic properties is of great importance. In this work, a three-dimensional frequency domain finite element model for icing CFRP laminate is first established, via which the mechanism of mode conversion of ultrasonic guided waves (UGWs) at different ice layer thicknesses is quantitatively analyzed. Then, the time-domain finite element simulation was performed, from which the time–frequency domain spectrogram of the acquired UGW signal is obtained by short-time Fourier transform (STFT). The extracted frequency–time-of-flight (ToF) curve of the signal via STFT shows high agreement with the theoretical results, which confirms the influence of icing on UGW propagation. Finally, numerous experiments with bonded lead zirconate titanate wafers to excite and acquire UGWs are conducted with different ice layer thicknesses and lengths on CFRP. The extracted frequency–ToF curves of UGW signals are used as input to a two-layer feedforward artificial neural network (ANN) to accurately evaluate the length and thickness of the ice layer. The high reliability of this method with ANN is confirmed, which shows the potential of the proposed UGW-based method for quantitative monitoring of icing length and thickness on aircraft CFRP laminate.

Artificial neural network-based prediction model of elastic floor response spectra incorporating dynamic primary-secondary structure interaction

The evaluation of the Floor Response Spectrum (FRS) holds paramount significance in assessing the seismic behavior of secondary structures. Precise FRS prediction empowers engineers to make informed decisions concerning structural design, retrofitting, and safety precautions. This study aims to scrutinize the impact of dynamic interaction between primary and secondary structures on FRS. Both the elastic primary structure (PS) and elastic secondary structure (SS) employ a single-degree-of-freedom (SDOF) system. Governing motion equations for both coupled (with dynamic interaction) and uncoupled (without dynamic interaction) systems are formulated and solved numerically. The study investigates how variations in the vibration period of PS (Tp), tuning ratio (Tr), mass ratio (μ), and damping ratio (ξs) of SS influence FRS. The FRS impact remains minimal at μ = 0.001 (0.1%); however, with increasing mass ratio, PS-SS dynamic interaction significantly affects SS's spectral acceleration response. Coupled analysis is crucial only for secondary structures tuned to the primary structure's vibration period (0.8≤Tr≤1.2). This study utilizes two-layer feed-forward Artificial Neural Networks (ANNs) for FRS prediction. The Levenberg-Marquardt (LM) backpropagation (BP) algorithm trains the network using a comprehensive dataset. In summary, it is evident that the ANNs, once trained, enable accurate prediction of the FRS, exhibiting a R2 of 99%. Additionally, a design expression is formulated utilizing the ANN model and subsequently compared with the existing formulation.

Vis-NIR and SWIR hyperspectral imaging method to detect bruises in pomegranate fruit.

Fresh pomegranate fruit is susceptible to bruising, a common type of mechanical damage during harvest and at all stages of postharvest handling. Accurate and early detection of such damages in pomegranate fruit plays an important role in fruit grading. This study investigated the detection of bruises in fresh pomegranate fruit using hyperspectral imaging technique. A total of 90 sample of pomegranate fruit were divided into three groups of 30 samples, each representing purposefully induced pre-scanning bruise by dropping samples from 100 cm and 60 cm height on a metal surface. The control has no pre-scanning bruise (no drop). Two hyperspectral imaging setups were examined: visible and near infrared (400 to 1000 nm) and short wavelength infrared (1000 to 2500 nm). Region of interest (ROI) averaged reflectance spectra was implemented to reduce the image data. For all hypercubes a principal components analysis (PCA) based background removal were done prior to segmenting the region of interest (ROI) using the Evince® multi-variate analysis software 2.4.0. Then the average spectrum of the ROI of each sample was computed and transferred to the MATLAB 2022a (The MathWorks, Inc., Mass., USA) for classification. A two-layer feed-forward artificial neural network (ANN) is used for classification. The accuracy of bruise severity classification ranged from 80 to 96.7%. When samples from both bruise severity (Bruise damage induced from a 100cm and 60 cm drop heights respectively) cases were merged, class recognition accuracy were 88.9% and 74.4% for the SWIR and Vis-NIR, respectively. This study implemented the method of selecting out informative bands and disregarding the redundant ones to decreases the data size and dimension. The study developed a more compact classification model by the data dimensionality reduction method. This study demonstrated the potential of using hyperspectral imaging technology in sensing and classification of bruise severity in pomegranate fruit. This work provides the foundation to build a compact and fast multispectral imaging-based device for practical farm and packhouse applications.

Multi-Dimensional Feature Combination Method for Continuous Blood Pressure Measurement Based on Wrist PPG Sensor.

The cuff-less blood pressure (BP) monitoring method based on photoplethysmo- gram (PPG) makes it possible for long-term BP monitoring to prevent and treat cardiovascular and cerebrovascular events. In this paper, a portable BP prediction system based on feature combination and artificial neural network (ANN) is implemented. The robustness of the model is improved from three aspects. Firstly, an adaptive peak extraction algorithm was used to improve the accuracy of peaks and troughs detection. Secondly, multi-dimensional features were extracted and fused, including three groups of PPG-based features and one group of demographics-based features. Finally, a two-layer feedforward artificial neural networks algorithm was used for regression. Thirty-three subjects distributed in the three BP groups were recruited. The proposed method passed the European Society of Hypertension International Protocol revision 2010 (ESP-IP2). Experimental results show that the proposed method exhibits good accuracy for a diverse population with an estimation error of -0.07 ± 4.47 mmHg for SBP and 0.00 ± 3.61 mmHg for DBP. Moreover, the model tracked the BP of two subjects for half a month, laying the foundation work for daily BP monitoring. This work will contribute to the long-term wellness management and rehabilitation process, enabling timely detection and improvement of the user's physical health.

Surface roughness prediction applying artificial neural network at micro machining

Micro machining forms an integral part of modern manufacturing technology. In order to satisfy the most important demands, like cost reduction, quality improvement, and production efficiency, this process has to be implemented into the framework of Industry 4.0, too. Defect reduction of products can be realized by beneficial trends of Industry 4.0, such as application of sensors driven AI based predictive models. Surface roughness is one of the main indicators, which can be used for evaluation of the machined surface. The aim of this research is to develop a predictive surface roughness model in the case of micro milling of hard materials. Most important scientific knowledge related to micro milling, and application possibilities of AI based predictive models in the case of chip removal processes were summarised. Micro milling experiments on hot work tool steel (AISI H13) were carried out in order to determine the effect of different cutting parameters on surface roughness. The effects of the cutting parameters were investigated by the analysis of variance (ANOVA). Two-layer feedforward artificial neural network (ANN) based model was trained, validated and tested by means of experimental results. It was found that the trained model has an average MSE, RMSE and R values of 0.00063850, 0.019116835 and 0.9948, while the additional validation test provided 0.00038148, 0.019531513 and 0.8545, respectively, which are statistically significant. The developed model can effectively predict the surface roughness under the investigated circumstances.

Neural Network Modeling of the Solar Photovoltaic Thermal Module Performance

The design of photovoltaic thermal modules (PVT modules) features a nonuniform distribution of coolant temperature in the channel, and the solar cells (SC) that are in thermal contact with the PVT module channel are under different temperature conditions. The nonuniform distribution of the SC temperature causes undesirable effects that are complex and nonlinear, such as a drop of generated power and SC damage resulting from the occurrence of hot spots. The aim of the work is to develop a mathematical model for modeling the PVT module thermal and electrical performance based on a feedforward artificial neural network (FNN). A two-layer FNN with sigmoid hidden neurons and linear output neurons has been developed. The input layer is made up of the ambient temperature, coolant flowrate and environmental variables (the total insolation). The output layer represents the PVT module thermal and electrical efficiencies. The developed FNN was trained and adapted on the basis of modeling and an experimental database on the PVT module input and output parameters. The developed FNN was trained using the Levenberg–Marquardt algorithm. The mean absolute error achieved during the training is in the range from –0.319 to 0.448 for electrical efficiency and from –0.129 to 0.198 for thermal efficiency. The r.m.s error is 0.0678 for electrical efficiency and 0.0247 for thermal efficiency; the training time is 15 s or longer. An effective model has been developed that implements a new approach to modeling the performance of PVT modules based on artificial neural network algorithms with fairly close values of thermal and electrical efficiencies.

Empowering saving energy at home through serious games on thermostat interfaces

The residential Heating Ventilation and Air-Conditioning (HVAC) system use around 3/5 of the total energy consumption. Connected thermostats optimize the HVAC operation; however, householders have personality traits that lead into behavioral and usability problems toward the thermostat’s interface usage. Thus, a serious game applied in the thermostat interface can balance entertainment and education. Therefore, thermostat interfaces must address strategies that reduce energy without losing thermal comfort. This paper proposed an interactive interface type and a predicted interface type based on an HVAC strategy and a Natural Ventilation strategy. These strategies measured the impact of adaptive thermal comfort, energy consumption, and costs. Hence, twelve energy models located in California (Concord, Riverside, Los Angeles, and San Diego) were simulated using EnergyPlus™ through LadybugTools. The first interactive interface included Serious Game elements, so the householder interacted with the date, location, and setpoint. The second interface predicted the energy consumption and thermal comfort during winter and summer in Concord by a two-layer feed-forward Artificial Neural Network structure. The proposed structure decreases the energy consumption by at least 62% without losing thermal comfort.

Probabilistic flutter analysis of a long-span bridge in typhoon-prone regions considering climate change and structural deterioration

In this paper, a long-term probabilistic flutter analysis of long-span bridges, with consideration of the time-variant probability density function (PDF) of annual maximum wind speed caused by climate change and the deterioration effects of dynamic properties obtained by field monitoring data, is investigated. Estimated uncertainty, which occurs in probabilistic flutter analysis, is quantified by the generalized density evolution equation (GDEE). A suspension bridge, with the center span of 1650 m, is chosen as application example. Long-term deterioration and inter-seasonal varying characteristics of modal frequencies and damping ratios are discussed. An implicit formulation among radius to maximum winds Rmax, central pressure deficit Δp, latitude ψ and sea surface temperature SST is set up by training a two-layer feed-forward artificial neural network (ANN) with historical records, and then the full-track typhoon simulation is conducted based on Vickery’s empirical model. Lastly, long-term probabilistic flutter analysis is conducted in conjunction with three prospective climate change scenarios, RCP2.6, RCP4.5 and RCP8.5, showing that the likelihood of annual flutter failure will increase greatly mainly due to higher annual maximum wind speed in a warming climate.

Long-Term Rainfall Information Forecast by Utilizing Constrained Amount of Observation through Artificial Neural Network Approach

Estimating models are becoming increasingly crucial in highlighting the nonlinear connections of the massive level of rough information and chaotic components. The study demonstrates a modern approach utilizing a created artificial neural network (ANN) method that may be an alternative strategy to conventional factual procedures for advancing rainfall estimate execution. A case study was presented for Turkey’s Düzce and Bolu neighboring territories located on the Black Sea’s southern coast. This study’s primary aim is to create an ANN model unique in the field to generate satisfactory results even with limited data. The proposed technique is being used to estimate rainfall and make predictions regarding future precipitation. Bolu daily average rainfall by month data and a limited number of Düzce rainfall data were used. Missing forecasts and potential rainfall projections will be examined in the fundamental research. This research further focuses on ANN computational concepts and develops a neural network for rainfall time series forecasting. The emphasis of this study was a feed-forward backpropagation network. The Levenberg–Marquardt algorithm (LMA) was implemented for training a two-layer feed-forward ANN for the missing rainfall data prediction part of this research. The inaccessible rainfall parameters for Düzce were determined for the years 1995 to 2009. From 2010 to 2020, a two-layer feed-forward ANN was trained using the gradient descent algorithm to forecast daily average rainfall data by month. The findings reported in this study guide researchers interested in implementing the ANN forecast model for an extended period of missing rainfall data.

Efficiency-House Optimization to Widen the Operation Range of the Double-Suction Centrifugal Pump

Most pumping machineries have a problem of obtaining a higher efficiency over a wide range of operating conditions. To solve that problem, an optimization strategy has been designed to widen the high-efficiency range of the double-suction centrifugal pump at design (Qd) and nondesign flow conditions. An orthogonal experimental scheme is therefore designed with the impeller hub and shroud angles as the decision variables. Then, the “efficiency-house” theory is introduced to convert the multiple objectives into a single optimization target. A two-layer feedforward artificial neural network (ANN) and the Kriging model were combine based on a hybrid approximate model and solved with swarm intelligence for global best parameters that would maximize the pump efficiency. The pump performance is predicted using three-dimensional Reynolds-averaged Navier–Stokes equations which is validated by the experimental test. With ANN, Kriging, and a hybrid approximate model, an optimization strategy is built to widen the high-efficiency range of the double-suction centrifugal pump at overload conditions by 1.63%, 1.95%, and 4.94% for flow conditions 0.8Qd, 1.0Qd, and 1.2Qd, respectively. A higher fitting accuracy is achieved for the hybrid approximation model compared with the single approximation model. A complete optimization platform based on efficiency-house and the hybrid approximation model is built to optimize the model double-suction centrifugal pump, and the results are satisfactory.

Determination of Heavy Metal Soil Contaminants Based on Photoacoustic Spectroscopy

Heavy metal (HM) contamination in soil is a threat to human health and environmental safety. This paper presents an infrared photoacoustic spectroscopic non-destructive testing for HM evaluation by a robust customized photoacoustic spectrometric system. Cadmium (Cd) was selected as the target contamination which was blended in soil samples. A two-layer feed-forward artificial neural network (ANN) model was developed for HM concentration estimation. The results show that the standard normal variate and continuum removal methods are the best preprocessing algorithms regarding the maximal correction coefficient (R2 > 0.90) and the minimal root mean square error criteria. The correction coefficient analysis shows a better prediction performance (R2 > 0.95) for a HM concentration estimation with the two preprocessing methods. In conclusion, the prediction accuracy can be significantly improved by implementing specific preprocessing and training methods. The ANN model-based infrared photoacoustic spectroscopic method has a future potential for the featureless HM contamination estimation in soil.

Classification of drug-induced hERG potassium-channel block from electrocardiographic T-wave features using artificial neural networks.

Human ether-à-go-go-related gene (hERG) potassium-channel block represents a harmful side effect of drug therapy that may cause torsade de pointes (TdP). Analysis of ventricular repolarization through electrocardiographic T-wave features represents a noninvasive way to accurately evaluate the TdP risk in drug-safety studies. This study proposes an artificial neural network (ANN) for noninvasive electrocardiography-based classification of the hERG potassium-channel block. The data were taken from the "ECG Effects of Ranolazine, Dofetilide, Verapamil, and Quinidine in Healthy Subjects" Physionet database; they consisted of median vector magnitude (VM) beats of 22 healthy subjects receiving a single 500μg dose of dofetilide. Fourteen VM beats were considered for each subject, relative to time-points ranging from 0.5hr before to 14.0hr after dofetilide administration. For each VM, changes in two indexes accounting for the early and the late phases of repolarization, ΔERD30% and ΔTS/A , respectively, were computed as difference between values at each postdose time-point and the predose time-point. Thus, the dataset contained 286 ΔERD30% -ΔTS/A pairs, partitioned into training, validation, and test sets (114, 29, and 143 pairs, respectively) and used as inputs of a two-layer feedforward ANN with two target classes: high block (HB) and low block (LB). Optimal ANN (OANN) was identified using the training and validation sets and tested on the test set. Test set area under the receiver operating characteristic was 0.91; sensitivity, specificity, accuracy, and precision were 0.93, 0.83, 0.92, and 0.96, respectively. OANN represents a reliable tool for noninvasive assessment of the hERG potassium-channel block.

Artificial neural networks can predict trauma volume and acuity regardless of center size and geography: A multicenter study.

Trauma has long been considered unpredictable. Artificial neural networks (ANN) have recently shown the ability to predict admission volume, acuity, and operative needs at a single trauma center with very high reliability. This model has not been tested in a multicenter model with differing climate and geography. We hypothesize that an ANN can accurately predict trauma admission volume, penetrating trauma admissions, and mean Injury Severity Score (ISS) with a high degree of reliability across multiple trauma centers. Three years of admission data were collected from five geographically distinct US Level I trauma centers. Patients with incomplete data, pediatric patients, and primary thermal injuries were excluded. Daily number of traumas, number of penetrating cases, and mean ISS were tabulated from each center along with National Oceanic and Atmospheric Administration data from local airports. We trained a single two-layer feed-forward ANN on a random majority (70%) partitioning of data from all centers using Bayesian Regularization and minimizing mean squared error. Pearson's product-moment correlation coefficient was calculated for each partition, each trauma center, and for high- and low-volume days (>1 standard deviation above or below mean total number of traumas). There were 5,410 days included. There were 43,380 traumas, including 4,982 penetrating traumas. The mean ISS was 11.78 (SD = 6.12). On the training partition, we achieved R = 0.8733. On the testing partition (new data to the model), we achieved R = 0.8732, with a combined R = 0.8732. For high- and low-volume days, we achieved R = 0.8934 and R = 0.7963, respectively. An ANN successfully predicted trauma volumes and acuity across multiple trauma centers with very high levels of reliability. The correlation was highest during periods of peak volume. This can potentially provide a framework for determining resource allocation at both the trauma system level and the individual hospital level. Care Management, level IV.

A distortion-agnostic video quality metric based on multi-scale spatio-temporal structural information

Considering the high sensitivity of human visual perception towards the structural information, a general purpose no-reference video quality assessment (NR-VQA) metric based on local binary patterns (LBP) is proposed. In the proposed metric, 2D LBP method is extended to third dimension using LBP-TOP (LBP from Three Orthogonal Planes) for extracting the spatio-temporal structural information. Further, spatio-temporal structural information is evaluated for multi-resolution local pixel windows to incorporate the human vision system (HVS) characteristic of multi-scale perception. Since LBP only encodes the sign information of local pixel differences, and ignores the degree of variations, a second set of feature based on the magnitude information of local pixel differences is extracted. The magnitude information based LBP-TOP feature set is accumulated on the sign information based LBP-TOP feature set such that the resultant feature set has more spatio-temporal structural information with same feature vector length. To emulate the HVS mechanism of quality feature mapping to a scalar quantity, a simple two-layer feed-forward artificial neural network (ANN) of single hidden layer with four neurons is proposed. The performance of the proposed metric is validated on LIVE, CSIQ, and IVP video quality datasets and consistent performance is observed. Further, the performance of proposed metric is compared with the existing state-of-the-art NR-VQA and other IQA/VQA metrics and have exhibited better performance than the others. The proposed metric is highly consistent in performance with low computational complexity and thus suitable for real-time applications.

Application of neural networks to lighting systems

This paper will continue application of neural networks to the concept of environmental quality management technology described elsewhere. Human preferences are different and may vary depending on type of the work or psycho-physical conditions of workers. This paper deals with application of artificial neural networks (ANNs) to control of general lighting systems to provide personal (individual) illuminance on worktables. Two-layer feedforward ANN is used to identify and model the system. Introduction of ANN to model illumination systems opens a new possibility of individual control systems in which specific areas have different set of requirements than remaining areas of a lighting system.

Prediction of rupture risk in anterior communicating artery aneurysms with a feed-forward artificial neural network.

Anterior communicating artery (ACOM) aneurysms are the most common intracranial aneurysms, and predicting their rupture risk is challenging. We aimed to predict this risk using a two-layer feed-forward artificial neural network (ANN). 594 ACOM aneurysms, 54 unruptured and 540 ruptured, were reviewed. A two-layer feed-forward ANN was designed for ACOM aneurysm rupture-risk analysis. To improve ANN efficiency, an adaptive synthetic (ADASYN) sampling approach was applied to generate more synthetic data for unruptured aneurysms. Seventeen parameters (13 morphological parameters of ACOM aneurysm measured from these patients' CT angiography (CTA) images, two demographic factors, and hypertension and smoking histories) were adopted as ANN input. Age, vessel size, aneurysm height, perpendicular height, aneurysm neck size, aspect ratio, size ratio, aneurysm angle, vessel angle, aneurysm projection, A1 segment configuration, aneurysm lobulations and hypertension were significantly different between the ruptured and unruptured groups. Areas under the ROC curve for training, validating, testing and overall data sets were 0.953, 0.937, 0.928 and 0.950, respectively. Overall prediction accuracy for raw 594 samples was 94.8 %. This ANN presents good performance and offers a valuable tool for prediction of rupture risk in ACOM aneurysms, which may facilitate management of unruptured ACOM aneurysms. • A feed-forward ANN was designed for the prediction of rupture risk in ACOM aneurysms. • Two demographic parameters, 13 morphological aneurysm parameters, and hypertension/smoking history were acquired. • An ADASYN sampling approach was used to improve ANN quality. • Overall prediction accuracy of 94.8 % for the raw samples was achieved.

Blackbox modeling of central heating and cooling plant equipment performance

The current article presents an analysis conducted upon the sensor data gathered from the distribution control system of a central heating and cooling plant in Ottawa, Canada. After observing that the performance of four boilers and five chillers of this plant vary substantially in time under steady-state conditions, data-driven models were developed to explain this variability from the archived sensor data. By employing a forward stepwise regression and a repeated random sub-sampling cross-validation approach, two-layer feed-forward artificial neural network models with 7 to 15 hidden-nodes were selected for each boiler and chiller. The selected boiler models could explain 84% to 95% of the variability in a boiler's efficiency, and the selected chiller models could explain 65% to 94% of the variability in a chiller's coefficient of performance. Among studied nine variables, the most informative ones to predict a boiler's efficiency were identified as follows: flue gas O2 concentration, pressure, part-load ratio, forced draft fan state, and return water flow rate. Unlike boilers, all four studied variables were found useful in predicting a chiller's coefficient of performance. These four variables were the return water flow rate, part-load ratio, outdoor temperature, and return water temperature. A residual analysis was conducted to verify the appropriateness of the selected models to the datasets. In addition, potential use cases for the selected models were discussed with illustrative examples.

Feature selection for surface defect classification of extruded aluminum profiles

This research investigates detection and classification of two types of the surface defects in extruded aluminium profiles; blisters and scratches. An experimental system is used to capture images and appropriate statistical features from a novel technique based on gradient-only co-occurrence matrices (GOCM) are proposed to detect and classify three distinct classes; non-defective, blisters and scratches. The developed methodology makes use of the Sobel edge detector to obtain the gradient magnitude of the image (GOCM). A comparison is made between the statistical features extracted from the original image (GLCM) and those extracted from the gradient magnitude (GOCM). This paper describes in detail every step of the image processing with example pictures illustrating the methodology. The features extracted from the image processing are classified by a two-layer feed-forward artificial neural network. The artificial neural network training is tested using different combinations of statistical features with different topologies. Features are compared individually and grouped. Results are discussed, achieving up to 98.6 % total testing accuracy.

Prediction of thermal conductivity of alumina water-based nanofluids by artificial neural networks

The aims of the present study are to develop and validate an artificial neural network (ANN) approach to estimate the thermal conductivity ratio (TCR) of alumina water-based nanofluids as a function of temperature, volume fraction and diameter of the nanoparticle. The ANN parameters are adjusted by back propagation learning algorithm using 285 collected experimental data sets from various literatures. Statistical accuracy analysis confirms that a two-layer feed forward ANN model with fourteen hidden neurons is the best architecture for modeling the considered task. The developed ANN approach has predicted the experimental data with the absolute average relative deviation (AARD%) of 1.27%, mean square error (MSE) of 4.73×10−4 and regression coefficient (R2) of 0.971875. Comparison of predictive capability of the proposed technique with some recommended correlations in the literatures confirmed that the ANN model is more superior to other published works and therefore can be considered as a practical tool for estimation of the thermal conductivity ratio of alumina water-based nanofluids.

Viscosity of ionic liquids: an extensive database and a new group contribution model based on a feed-forward artificial neural network.

A knowledge of various thermophysical (in particular transport) properties of ionic liquids (ILs) is crucial from the point of view of potential applications of these fluids in chemical and related industries. In this work, over 13 000 data points of temperature- and pressure-dependent viscosity of 1484 ILs were retrieved from more than 450 research papers published in the open literature in the last three decades. The data were critically revised and then used to develop and test a new model allowing in silico predictions of the viscosities of ILs on the basis of the chemical structures of their cations and anions. The model employs a two-layer feed-forward artificial neural network (FFANN) strategy to represent the relationship between the viscosity and the input variables: temperature, pressure, and group contributions (GCs). In total, the resulting GC-FFANN model employs 242 GC-type molecular descriptors that are capable of accurately representing the viscosity behavior of ILs composed of 901 distinct ions. The neural network training, validation, and testing processes, involving 90, 5, and 5% of the whole data pool, respectively, gave mean square errors of 0.0334, 0.0595, and 0.0603 log units, corresponding to squared correlation coefficients of 0.986, 0.973, and 0.972 and overall relative deviations at the level of 11.1, 13.8, and 14.7%, respectively. The results calculated in this work were shown be more accurate than those obtained with the best current GC model for viscosity of ILs described in the literature.