Artificial Neural Network Model For Prediction Research Articles

Analysis of Enterprise Human Resources Demand Forecast Model Based on SOM Neural Network.

Human resource planning is the prerequisite of human resource management, and the basic work of human resource planning is to predict human resource demand. Scientific and reasonable human resource demand forecasting results can provide important data support for enterprise human resource planning and strategic decision-making so that human resources management can play a better role in the realization of corporate goals. Because human resource demand is affected by many factors, there is a high degree of nonlinearity and uncertainty between each factor and personnel demand, as well as the incompleteness and inaccuracy of corporate human resource data. In this paper, the self-organizing feature mapping (SOM) artificial neural network prediction model is selected as the prediction model, and the input and output process of sample data is converted into the optimal solution process of the nonlinear function. In the application of the model, the human resource demand prediction index system is used as the input of the SOM neural network and the total number of employees in the enterprise is used as the output so that the problem of nonlinear fitting between human resource demand-influencing factors and human resource demand can be solved. Finally, through the empirical analysis of the enterprise, the model forecasting process is explained and the human resource demand forecast is realized.

Predictive analytics of bridge safety for intelligent transportation system using ensemble model

Bridges are the main links for many transportation systems. Many of the bridges built are subjected to deterioration but are still in use. Factors like heavy weights imposed by vehicles, pressure of water and depreciation effect the lifetime of the bridges. This may lead to various disasters endangering the lives of people. Hence continuous monitoring of bridges using real time bridge data is required to predict bridge safety and alert user passing by the bridge. Existing bridge safety monitoring systems are either Sensor based or uses specialized equipment to monitor safety using frequencies of vibration. Such systems are expensive and have low performance. Traditional prediction algorithms from data mining and regression modeling can help in correlation between input parameters and bridge condition using historical data. They have difficulty either in scaling of data or may not adapt to dynamic changes in input data. Mobile app based system that uses ensemble of both Artificial Neural Network (ANN) and Gaussian Process Regression (GPR) is proposed in this paper to predict bridge safety. This paper signifies on the structural health monitoring of the bridge and the potential advantage of integrating Artificial Intelligence based predictive analytics into IoT sensors to assess the bridge safety. Three different experiments based on ANN, GPR and ensemble of ANN,GPR are made. In the first step, ANN is trained and validated on two datasets with different scenario (Indian and U.S.A). Error reported from this best trained network is used by GPR for statistical analysis of the error distributions. This combined system based mobile app is evaluated using NRMSE, COD and cross entropy loss measures. Test results proved that, the third hyper-parameter values of ensemble model yielded a good result with less error rate than ANN and GPR predictive models.

Implementation of Artificial Neural Network to Predict the Permeability and Solubility Models of Gypseous Soil

The objective of the present study is to make a database that describes the leaching-permeability behavior of collapsible gypseous soil. The data will be implemented to develop ANN prediction models for predicting the saturated coefficient of permeability and percentage of solubility by weight. The complex soil behavior and tedious and time consume in soil testing have driven researchers to use Artificial Neural Network (ANN) as tool for prediction. The objectives of the study were to investigate leaching-permeability behavior of collapsible gypseous soils and to develop ANN models for estimating the saturated coefficient of permeability and solubility of the soils. The MATLAB R2015a software was used to predict the saturated coefficient of permeability and the solubility percentage by weight of gypseous soils. The dataset used in this work included (513) records of experimental measurements extracted from leaching-permeability tests conducted on gypseous soil samples taken from Baher Al-Najaf in Iraq. Four input variables were investigated to have the most important influence on the permeability and solubility percentage by weight. According to the achieved statistical analysis, the ANNs model have a reliable capability to find out the predictions with a high-level of accuracy. The gypseous soils exhibited a high rate of dissolution of soluble minerals content, which caused increase in the coefficient of permeability as the soil samples reach the state of long-term full saturation.

Tailoring Atomoxetine Release Rate from DLP 3D-Printed Tablets Using Artificial Neural Networks: Influence of Tablet Thickness and Drug Loading

Various three-dimensional printing (3DP) technologies have been investigated so far in relation to their potential to produce customizable medicines and medical devices. The aim of this study was to examine the possibility of tailoring drug release rates from immediate to prolonged release by varying the tablet thickness and the drug loading, as well as to develop artificial neural network (ANN) predictive models for atomoxetine (ATH) release rate from DLP 3D-printed tablets. Photoreactive mixtures were comprised of poly(ethylene glycol) diacrylate (PEGDA) and poly(ethylene glycol) 400 in a constant ratio of 3:1, water, photoinitiator and ATH as a model drug whose content was varied from 5% to 20% (w/w). Designed 3D models of cylindrical shape tablets were of constant diameter, but different thickness. A series of tablets with doses ranging from 2.06 mg to 37.48 mg, exhibiting immediate- and modified-release profiles were successfully fabricated, confirming the potential of this technology in manufacturing dosage forms on demand, with the possibility to adjust the dose and release behavior by varying drug loading and dimensions of tablets. DSC (differential scanning calorimetry), XRPD (X-ray powder diffraction) and microscopic analysis showed that ATH remained in a crystalline form in tablets, while FTIR spectroscopy confirmed that no interactions occurred between ATH and polymers.

Dynamic characterization of recycled glass-recycled concrete blends using experimental analysis and artificial neural network modeling

This study investigates the deformation properties of recycled concrete aggregate (RCA) when blended with up to 70% of recycled glass (RG) for pavement base applications. A multi-stage repeated load triaxial (RLT) testing procedure was proposed and utilized for evaluating the permanent deformation behavior of RCA/RG blends. The resilient modulus (Mr) of the blends was examined by performing RLT test in different stress combinations using a proposed testing protocol. The shear strength response of the blends was also investigated. Shakedown theory was utilized to classify the permanent deformation behavior of the blends. Except for the RCA30/RG70 blend, all other blends exhibited either Range A or Range B response in the investigated stress levels. There was an increase in the permanent strain and a decrease in the Mr of blends as the RG content increased. The shear response of the blends exhibited a strain-hardening behavior in the post-peak zone when the RG content was more than 10%. Artificial neural network (ANN) models were developed for predicting the deformation properties of the blends and examining the effect of test variables on the deformation properties. The developed ANN models for prediction of permanent strain and Mr were converted to practical equations for pre-design purposes. Results of numerical modeling indicated that ANNs were robust for predicting the deformation properties as well as identifying the impact of input variables on the deformation properties of RCA/RG blends.

Improvement the protection performance of lanolin based temporary coating using benzotriazole and cerium (III) nitrate: Combined experimental and computational analysis

Lanolin as a natural and environmental friendly material, as temporary coating, has been used for corrosion inhibition of steel against aggressive media. The protection efficiency could further have enhanced by the addition of corrosion inhibitors. In this study, effect of the presence of benzotriazole and Cerium (III) nitrate inhibitors loaded into the lanolin solution on the corrosion resistance of an eco-friendly lanolin coating was investigated. The effective impact of corrosion inhibitors on the structure of the lanolin coating was revealed by Fourier Transform Infrared Spectroscopy (FT-IR). The addition of inhibitors into the lanolin film increases the hydrophobicity of the coating surface which was connected to the cross-linked formation. The protection performance and active protection of the fabricated coatings were investigated by electrochemical impedance spectroscopy (EIS) and salt spray testing. The results revealed the positive effect of inhibitors doped in LA coating, which provided a charge transfer of approximately 26 time higher than the neat LA coating. Computational analysis methods like artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) were used to predict the corrosion behavior of fabricated lanolin coatings. The outcome of these methods demonstrated high accuracy for predictability of the models. In order to measure the validity and Performance analysis of constructed models various criteria were utilized such as MSE, MAE and R2, the result revealing that ANFIS model was more powerful compared to the ANN model in prediction of Eocp and Log (|Z|0.01 Hz). The effect of input parameters was investigated and also the by applying the ANN- genetic algorithm (GA) the optimized conditions were achieved.

Dynamic Modeling Using Artificial Neural Network of Bacillus Velezensis Broth Cross-Flow Microfiltration Enhanced by Air-Sparging and Turbulence Promoter.

Cross-flow microfiltration is a broadly accepted technique for separation of microbial biomass after the cultivation process. However, membrane fouling emerges as the main problem affecting permeate flux decline and separation process efficiency. Hydrodynamic methods, such as turbulence promoters and air sparging, were tested to improve permeate flux during microfiltration. In this study, a non-recurrent feed-forward artificial neural network (ANN) with one hidden layer was examined as a tool for microfiltration modeling using Bacillus velezensis cultivation broth as the feed mixture, while the Kenics static mixer and two-phase flow, as well as their combination, were used to improve permeate flux in microfiltration experiments. The results of this study have confirmed successful application of the ANN model for prediction of permeate flux during microfiltration of Bacillus velezensis cultivation broth with a coefficient of determination of 99.23% and absolute relative error less than 20% for over 95% of the predicted data. The optimal ANN topology was 5-13-1, trained by the Levenberg–Marquardt training algorithm and with hyperbolic sigmoid transfer function between the input and the hidden layer.

Artificial neural network based prediction of postthrombolysis intracerebral hemorrhage and death

Despite the salient benefits of the intravenous tissue plasminogen activator (tPA), symptomatic intracerebral hemorrhage (sICH) remains a frequent complication and constitutes a major concern when treating acute ischemic stroke (AIS). This study explored the use of artificial neural network (ANN)-based models to predict sICH and 3-month mortality for patients with AIS receiving tPA. We developed ANN models based on evaluation of the predictive value of pre-treatment parameters associated with sICH and mortality in a cohort of 331 patients between 2009 and 2018. The ANN models were generated using eight clinical inputs and two outputs. The generalizability of the model was validated using fivefold cross-validation. The performance of each model was assessed according to the accuracy, precision, sensitivity, specificity, and area under the receiver operating characteristic curve (AUC). After adequate training, the ANN predictive model AUC for sICH was 0.941, with accuracy, sensitivity, and specificity of 91.0%, 85.7%, and 92.5%, respectively. The predictive model AUC for 3-month mortality was 0.976, with accuracy, sensitivity, and specificity of 95.2%, 94.4%, and 95.5%, respectively. The generated ANN-based models exhibited high predictive performance and reliability for predicting sICH and 3-month mortality after thrombolysis; thus, its clinical application to assist decision-making when administering tPA is envisaged.

Prediction and control of surface roughness for the milling of Al/SiC metal matrix composites based on neural networks

In recent years, there has been a significant increase in the utilization of Al/SiC particulate composite materials in engineering fields, and the demand for accurate machining of such composite materials has grown accordingly. In this paper, a feed-forward multi-layered artificial neural network (ANN) roughness prediction model, using the Levenberg-Marquardt backpropagation training algorithm, is proposed to investigate the mathematical relationship between cutting parameters and average surface roughness during milling Al/SiC particulate composite materials. Milling experiments were conducted on a computer numerical control (CNC) milling machine with polycrystalline diamond (PCD) tools to acquire data for training the ANN roughness prediction model. Four cutting parameters were considered in these experiments: cutting speed, depth of cut, feed rate, and volume fraction of SiC. These parameters were also used as inputs for the ANN roughness prediction model. The output of the model was the average surface roughness of the machined workpiece. A successfully trained ANN roughness prediction model could predict the corresponding average surface roughness based on given cutting parameters, with a 2.08% mean relative error. Moreover, a roughness control model that could accurately determine the corresponding cutting parameters for a specific desired roughness with a 2.91% mean relative error was developed based on the ANN roughness prediction model. Finally, a more reliable and readable analysis of the influence of each parameter on roughness or the interaction between different parameters was conducted with the help of the ANN prediction model.

FastTTPS: fast approach for video transcoding time prediction and scheduling for HTTP adaptive streaming videos

HTTP adaptive streaming of video content becomes an integrated part of the Internet and dominates other streaming protocols and solutions. The duration of creating video content for adaptive streaming ranges from seconds or up to several hours or days, due to the plethora of video transcoding parameters and video source types. Although, the computing resources of different transcoding platforms and services constantly increase, accurate and fast transcoding time prediction and scheduling is still crucial. We propose in this paper a novel method called fast video transcoding time prediction and scheduling (FastTTPS) of x264 encoded videos based on three phases: (i) transcoding data engineering, (ii) transcoding time prediction, and (iii) transcoding scheduling. The first phase is responsible for video sequence selection, segmentation and feature data collection required for predicting the transcoding time. The second phase develops an artificial neural network (ANN) model for segment transcoding time prediction based on transcoding parameters and derived video complexity features. The third phase compares a number of parallel schedulers to map the predicted transcoding segments on the underlying high-performance computing resources. Experimental results show that our predictive ANN model minimizes the transcoding mean absolute error (MAE) and mean square error (MSE) by up to 1.7 and 26.8, respectively. In terms of scheduling, our method reduces the transcoding time by up to 38% using a Max–Min algorithm compared to the actual transcoding time without prediction information.

The Influence of Seasonal Meteorology on Vehicle Exhaust PM2.5 in the State of California: A Hybrid Approach Based on Artificial Neural Network and Spatial Analysis

This study aims to develop a hybrid approach based on backpropagation artificial neural network (ANN) and spatial analysis techniques to predict particulate matter of size 2.5 µm (PM2.5) from vehicle exhaust emissions in the State of California using aerosol optical depth (AOD) and several meteorological indicators (relative humidity, temperature, precipitation, and wind speed). The PM2.5 data were generated using the Motor Vehicle Emission Simulator (MOVES). The measured meteorological variables and AOD were obtained from the California Irrigation Management Information System (CIMIS) and NASA’s Moderate Resolution Spectroradiometer (MODIS), respectively. The data were resampled to a seasonal format and downscaled over grids of 10 by 10 to 150 by 150. Coefficient of determination (R2), mean absolute percentage error (MAPE), and root mean square error (RMSE) were used to assess the quality of the ANN prediction model. The model peaked at winter seasons with R2 = 0.984, RMSE = 0.027, and MAPE = 25.311, whereas it had the lowest performance in summer with R2 = 0.920, RMSE = 0.057, and MAPE = 65.214. These results indicate that the ANN model can reasonably predict the PM2.5 mass and can be used to forecast future trends.

Residual Strength Prediction of Aluminum Panels with Multiple Site Damage Using Artificial Neural Networks.

Multiple site damage (MSD) cracks are small fatigue cracks that may accumulate at the sides of highly loaded holes in aging aircraft structures. The presence of MSD cracks can drastically reduce the residual strength of fuselage panels. In this paper, artificial neural networks (ANN) modeling is used for predicting the residual strength of aluminum panels with MSD cracks. Experimental data that include 147 unique configurations of aluminum panels with MSD cracks are used. The experimental dataset includes three different aluminum alloys (2024-T3, 2524-T3, and 7075-T6), four different test panel configurations (unstiffened, stiffened, stiffened with a broken middle stiffener, and bolted lap-joints), many different panel widths and thicknesses, and the sizes of the lead and MSD cracks. The results presented in this paper demonstrate that a single ANN model can predict the residual strength for all materials and configurations with high accuracy. Specifically, the overall mean absolute error for the ANN model predictions is 3.82%. Furthermore, the ANN model residual strength predictions are compared to those obtained using the most accurate semi-analytical and computational approaches from the literature. The ANN model predictions are found to be at the same accuracy level of these approaches, and they even outperform the other approaches for many configurations.

Comprehensive performance assessment of a solid desiccant wheel using an artificial neural network approach

The design and operational parameters of a solid desiccant wheel (DW), as the heart of a desiccant system, significantly affect the performance of systems in which it is applied. A general and fast model to predict the DW operation is needed to allow investigation of desiccant systems under various working conditions. This paper presents a comprehensive study of the performance prediction of a DW using an artificial neural network (ANN) technique. Comprehensive design parameters, namely rotational speed, channel length, hydraulic diameter, process/regeneration section area ratio, desiccant layer thickness, and the purge angle, are applied as inputs to the ANN model. Moreover, the inlet process and regeneration air temperatures, humidity ratios, and velocities are considered as inputs to confirm that the model outcomes are applicable when various DW parameters are taken into account. Here, the network outputs are the process, the purge, and the regeneration air temperatures and humidity ratios at the DW outlet. This generality in the DW ANN model that contains most of the attributed parameters as inputs and predicts various outputs has not been provided in previous works. A data bank for training the network is generated through transient equations for a wide variation range of DW associated parameters. Several ANN structures are trained in MATLAB to study their prediction accuracy and eventually to find the optimum. The best architecture, which has four hidden layers and 50 neurons, yields a mean relative percentage and a mean square error of 0.54% and 5.9 × 10−5, respectively. This structure can successfully predict DW behavior. Comparing the ANN model predictions and experimental data indicates that the selected structure permits reasonably accurate predictions of the performance of a DW.

Reservoir water balance simulation model utilizing machine learning algorithm

Developing water losses and reservoir final storage forecast has become an increasingly important task for reservoir operation. Accurate forecasts would lead to better monitoring of water quality and more efficient reservoir operation. Therefore, the flash flood and water crisis problems in Malaysia can be reduced. Artificial neural networks (ANN) models with radial basis function (RBF) have been determined for high efficiency and accuracy, especially in the dynamics system. In this study, the proposed ANN Prediction Model is being developed by using inflow, the release of dam, initial and final storage of the reservoir as input, whereas the water losses from the reservoir as output. All the data collected over 11 years (1997–2007) at Klang Gate reservoir has been used to develop and test model output. The results indicated that the proposed model could provide monthly forecasting with maximum root mean square error of ± 20.07%. The advantages of this ANN model are to provide information for water losses, final storage, and variation of water level for better reservoir operation.

Performance of Artificial Neural Networks (ANN) At Transportation Planning Model

This study based on Artificial Neural Networks (ANN) in modeling trip production, which represent the first stage at transportation planning. The study analyzed the performance of ANN based on data that collected for central business district (CBD) of the city of Nasiriyah by calculating the all trips (y) that produced from this sector. As results, The ANN model gave a high performance comparative with Statistical Techniques in predicting trips. The coefficient of determination R2 for the number of all trips (y) is (0.948 ) and (0.871) for ANN and Statistical Techniques approaches respectively which is considered as an acceptable relationship obtained; also the ANN prediction model is more accurate for prediction process.

Modeling and Development of a Novel Quality of Service Prediction Model for Global System for Mobile Communications Network using Artificial Neural Networks

Quality of service (QoS) performance evaluation is an essential indicator in determining the efficiency of services rendered by an industry. Comparison of some key performance indicators with standard threshold values has been a major approach for determining QoS of Global System for Mobile Communication (GSM) in Nigeria. This comparative approach, which usually involves human involvement is prone to error. Thus, an automatic artificial neural networks (ANN) predictive QoS model was developed in the study presented in this paper. In carrying out the study, five key performance indicators (KPIs) data were collected form the GSM operator used. The collected KPIs parameters were used to develop a mathematical model that was transformed into the proposed automatic QoS predicted model using ANN. The developed QoS prediction model when evaluated was found to be accuracy and performed favorably well when compared with the manual approach being used by the Nigerian Communications Commission. The developed automatic QoS prediction model for this study is thus suggested as a better replacement for the current manual method based on its accuracy and non-human involvement in predicting QoS of GSM network investigated.

Artificial neural networks improve LDCT lung cancer screening: a comparative validation study

BackgroundThis study proposes a prediction model for the automatic assessment of lung cancer risk based on an artificial neural network (ANN) with a data-driven approach to the low-dose computed tomography (LDCT) standardized structure report.MethodsThis comparative validation study analysed a prospective cohort from Chiayi Chang Gung Memorial Hospital, Taiwan. In total, 836 asymptomatic patients who had undergone LDCT scans between February 2017 and August 2018 were included, comprising 27 lung cancer cases and 809 controls. A derivation cohort of 602 participants (19 lung cancer cases and 583 controls) was collected to construct the ANN prediction model. A comparative validation of the ANN and Lung-RADS was conducted with a prospective cohort of 234 participants (8 lung cancer cases and 226 controls). The areas under the curves (AUCs) of the receiver operating characteristic (ROC) curves were used to compare the prediction models.ResultsAt the cut-off of category 3, the Lung-RADS had a sensitivity of 12.5%, specificity of 96.0%, positive predictive value of 10.0%, and negative predictive value of 96.9%. At its optimal cut-off value, the ANN had a sensitivity of 75.0%, specificity of 85.0%, positive predictive value of 15.0%, and negative predictive value of 99.0%. The area under the ROC curve was 0.764 for the Lung-RADS and 0.873 for the ANN (P = 0.01). The two most important predictors used by the ANN for predicting lung cancer were the documented sizes of partially solid nodules and ground-glass nodules.ConclusionsCompared to the Lung-RADS, the ANN provided better sensitivity for the detection of lung cancer in an Asian population. In addition, the ANN provided a more refined discriminative ability than the Lung-RADS for lung cancer risk stratification with population-specific demographic characteristics. When lung nodules are detected and documented in a standardized structured report, ANNs may better provide important insights for lung cancer prediction than conventional rule-based criteria.

Comparative Study of Utilising Neural Network and Response Surface Methodology for Flexible Pavement Maintenance Treatments

The use of Artificial Intelligence (AI) for the prediction of flexible pavement maintenance that is caused by distressing on the surface layer is crucial in the effort to increase the service life span of pavements as well as reduce government expenses. This study aimed to predict flexible pavement maintenance in tropical regions by using an Artificial Neural Network (ANN) and the Response Surface Methodology (RSM) for predicting models for pavement maintenance in the tropical region. However, to predict the performance of the treatment techniques for flexible pavements, we used critical criteria to choose our date from different sources to represent the situation of the current pavement. The effect of the distress condition on the flexible pavement surface performance was one of the criteria considered in our study. The data were chosen in this study for 288 sets of treatment techniques for flexible pavements. The input parameters used for the prediction were severity, density, road function, and Average Daily Traffic (ADT). The finding of regression models in (R2) values for the ANN prediction model is 0.93, while the (R2) values are (RSM) prediction model dependent on the full quadratic is 0.85. The results of two methods were compared for their predictive capabilities in terms of the coefficient of determination (𝑅2), the Mean Squared Error (MSE), and the Root Mean Square Error (RMSE), based on the dataset. The results showed that the prediction made utilizing ANN was very relevant to the goal in contrast to that made using the statistical program RSM based on different types of mathematical methods such as full quadratic, pure quadratic, interactions, and linear regression.

An efficient optimal neural network based on gravitational search algorithm in predicting the deformation of geogrid-reinforced soil structures

The deformation of a Geosynthetic reinforced soil (GRS) structure is a key factor in designing this type of retaining structures. On the other hand, the feasibility of artificial intelligence techniques in solving geotechnical engineering problems is underlined in literature. This paper is aimed to show the workability of two soft computing techniques in predicting the deformation of GRS structures. For this reason, first a relevant case study was modelled into ABAQUS, a finite element (FE) software. Then, the FE results (GRS deformations) were checked against the recorded deformations of the full-scale test. Subsequently, 166 finite element analyses were performed for dataset construction. Then, two predictive models of GRS deformations were constructed. For intelligent model construction, two artificial neural networks (ANN) were coupled with Gravitational Search Algorithm (GSA) and Particle Swarm Optimization (PSO), respectively. It was found that both GSA-based ANN and PSO-based ANN predictive models work good enough. However, the correlation coefficient (R) of 0.981 as well as the system error of 0.0101 for testing data suggest that the GSA-based ANN predictive model outperforms the PSO-based ANN model with R value of 0.973 and system error of 0.0127. Overall, findings recommend that the proposed models can be implemented in assessing the performance of geosynthetic reinforced soil structures.

Prediction of ice resistance for ice-going ships in level ice using artificial neural network technique

Ice resistance is affected by many parameters, i.e., ship geometries, ice properties and interaction properties. Over the years, various methods ranging from empirical, semi-empirical, numerical methods and various of their combinations were developed to calculate the ice resistance with certain degree of success. In this study, from a brand-new perspective, a data-driven approach for estimating ice resistance based on the artificial neural network was proposed. The artificial neural network (ANN) is one of the main tools for machine learning. It can accurately and efficiently correlate directly the inputs and outputs of complex and nonlinear systems, such as the ice resistance calculation system. This physical system involves several interaction phases and at least three groups of input parameters (i.e., ship, ice and interaction characteristics). Following the basic idea of ANN, we trained and built six ANN models based on datasets that were collected over past decades involving both model tests and full-scale measurements. The six different ANN models differ in the amount of input parameters. Based on comparative studies, among all the input parameters (i.e., 7 variables in total: ship length, ship breadth, ship draft, stem angle, ship speed, ice flexural strength and ice thickness), we found that the ship breadth, ice thickness and ship speed have the largest influence on the calculation of ice resistance. Afterwards, the 7-variable ANN model's prediction was compared with existing semi-empirical methods and measurements; and favorable agreement was achieved with fairly simple matrix form formulas (i.e., Eq. (19)). The formula offers another simple, yet reliable approach to calculate ice resistance. However, since the method is data driven, high quality data are always needed in improving the predicting capability of the relevant ANN model following the methodology outlined in this paper.