Applying Artificial Neural Network Model Research Articles

Design of Miniaturized 90-Degree Hybrid Coupler with Wide Rejection Band Using Neural Network.

In this paper, a 3 dB 90-degree hybrid coupler with size reduction and harmonics rejection was designed. In the proposed coupler structure, four simple low-pass filters (LPFs) were applied. An artificial neural network (ANN) was used to determine the dimensions of the applied LPFs based on EM simulation data. The applied ANN model could also provide the desired LPF parameters, including the cut-off frequency (fc), bandwidth (BW), and insertion loss (IL). Designing an applied LPF involves complex mathematical calculations and simulations to optimize parameters. However, by utilizing neural networks, the design process can be significantly streamlined and automated. Neural networks have the ability to learn complex patterns and relationships within data, making them well suited for optimizing the performance of applied components. The proposed 90-degree hybrid coupler works correctly at 1800 MHz and has a small size of 16.6 mm × 15.15 mm, which provides a 73% size reduction compared to a normal 1800 MHz coupler. The designed coupler not only decreases the circuit size but also provides a wide rejection band from 4.8 GHz to 11.2 GHz, which suppresses the second to sixth harmonics. The insertion loss parameter of this 90-degree hybrid coupler is less than 0.1 dB at the working frequency, which shows the superior performance of the proposed coupler.

Reduced Sodium in White Brined Cheese Production: Artificial Neural Network Modeling for the Prediction of Specific Properties of Brine and Cheese during Storage

Background: White brined cheese is one of the most frequently consumed cheeses that is accepted among a large group of consumers, which is largely related to its unique sensory properties, which are characterized by specific technological processes including ripening in the brine. Thus, white brined cheese contains a high amount of NaCl, and frequent consumption might lead to excessive sodium intake, which nowadays, presents a global problem. Consequently, food industries have developed reduced sodium products by substitutional salts. Furthermore, various studies have indicated that increased sodium intake via the diet can be associated with cardiovascular diseases, a risk of digestive system cancer, obesity, and other conditions. Calcium salts (citrate and lactate) are safe for human health and can be added to various foods according to the quantum satis rule. The present study aimed to partially replace NaCl with Ca-citrate and Ca-lactate in proportions of 25% and 50%. Additionally, the study presents the possibility of applying Artificial Neural Network (ANN) models for the prediction of some brine and cheese properties. Methods: White brined cheese with substitutional salts in brine (25% and 50% Ca-citrate and 25% and 50% Ca-lactate) were produced and compared to the control cheese ripened in conventionally applied NaCl brine. The acidity, total dissolved solids, salt amount, conductivity, color, and textural and sensory properties were determined over the 28 days of cold storage. Results: The substitution of NaCl with Ca-citrate and Ca-lactate is promising for sodium reduction in white brined cheese, whereby the physical and chemical properties remain acceptable. The best sensory score gain occurred with a substitution of 25% NaCl with Ca-citrate. Furthermore, ANN models can be employed to predict brine and cheese properties during storage.

Artificial neural network-based optimization of baffle geometries for maximized heat transfer efficiency in microchannel heat sinks

Microchannel heat sinks (MCHSs) are widely utilized in various industries, including electronics, power systems, and aerospace, to dissipate heat generated by high-power components effectively. The efficient cooling of these devices is crucial for maintaining their optimal performance, reliability, and longevity. This research aimed to augment the overall efficiency of the MCHS by utilizing 25 baffles with varying geometric parameters. The aim was to identify the optimum geometry for these baffles using an Artificial Neural Network (ANN) model. The ANN model was applied to propose two optimal designs for the baffles, specifically targeting the maximum Nusselt number and overall performance of the MCHS. It was noticed that the vertical pitch of the baffles "d" had the most substantial influence on the device's behavior among the investigated geometric parameters. The higher value of "d" allowed the fluid to spread over the entire space of the heat sink rather than being confined to a specific area. This spreading effect enabled the fluid to contact all the baffles and channel walls, facilitating efficient heat transfer. The applied ANN model with R2 values of about 0.98 and lower values of MAE and RMSE successfully fitted the data. The findings of this study demonstrated that applying the specified input variables (horizontal pitch of 8.496 mm, vertical pitch of 5 mm, and attack angle of 210. 560°), as defined in the optimal overall design, in manufacturing of the baffles resulted in a remarkable enhancement of about 46% in the overall performance of the MCHS in comparison with the design proposed in the reference paper. The heat transfer improvement of the MCHS with baffles were due to the vortices in the baffles bring about chaotic advection and could greatly enhance the convective fluid mixing.

Development of ANN Models for Prediction of Physical and Chemical Characteristics of Oil-in-Aqueous Plant Extract Emulsions Using Near-Infrared Spectroscopy

The potential of applying Artificial Neural Network (ANN) models based on near-infrared (NIR) spectra for the characterization of physical and chemical features of oil-in-aqueous oregano/rosemary extract emulsions was explored in this work. Emulsions were prepared using a batch emulsification process, with pea protein as the emulsifier. NIR spectral data were connected to the results of the analysis of physical and chemical properties of the emulsions (zeta potential, Feret droplet diameter, total polyphenolic content, and antioxidant capacity) with the final aim of quantitative prediction of the physical and chemical features. For that purpose, robust non-linear multivariate analysis (Artificial Neural Network modeling) was applied. The spectra themselves were preprocessed using several approaches (raw spectra, Savitzky–Golay smoothing, standard normal variate, and multiplicative scatter corrections) after which the impact of NIR spectral preprocessing on the ANN model’s efficiency was evaluated. The results show that NIR spectroscopy integrated with ANN computation can be employed to quantitatively predict the physical and chemical properties of oil-in-plant extract emulsions (R2 > 0.9).

Modeling wastewater treatment plant (WWTP) performance using artificial neural networks: Case of Adana (Seyhan)

In this study, performance estimation of biological wastewater treatment plants (WWTP) was made by applying Artificial Neural Network (ANN) techniques. As material, 355-day data from Adana Metropolitan Municipality Seyhan wastewater treatment plant for 2021 were used. Of the data used, 240 were evaluated as training data and 115 as test data. In the establishment of the ANN model, the daily chemical oxygen demand (COD), daily water flow (Qw) and daily suspended solids (SS) parameters at the entrance of the WWTP were used as input parameters. The daily biological oxygen demand (BOD) parameter was determined as the output parameter. In the study, feed forward back propagation ANN model (FFBPANN) was used to estimate the daily BOD amounts at the entrance of the WWTP. In the statistical analysis, the correlation (R2) values of the input parameters with BOD were found to be 0.906 for COD, 0.294 for Qw and 0.605 for SS. The R2 value was determined as 0.891, the MAE value was 10.32% and the RMSE value was 722.21 in the network structures where the best results were obtained for the test and training data (in the 4-4-1 ANN model). As a result of the study, it was concluded that the ANN model was successful in estimating the BODs of the WWTPs in obtaining reliable and realistic results, and that effective analyzes with the simulation of their nonlinear behavior could be used as a good performance evaluation tool in terms of reducing operating costs.

Critical points and stability analysis in MHD radiative non‐Newtonian nanoliquid transport phenomena with artificial neural network prediction

In the present framework, flow and thermal transport behavior of non‐Newtonian viscoelastic fluid induced by stretching/shrinking of the horizontal sheet under the influence of Lorentz force, volumetric heat source/sink, and radiation (assuming optically thick medium) has been investigated. Multiple solutions (Branches) have been predicted numerically using Lie symmetry transformation and Runge Kutta Dormand Prince (RKDP) algorithm‐based Shooting method for the different controlling parameters, stretching/shrinking , and suction with substantial impact with UCM parameter, . Some of the results have also been compared with MATLAB built‐in solvers to validate our in‐house code. The deviations in critical (turning) points have been noticed for different values of and . The temporal stability analysis is performed for these parameters , and the upper (lower) branch is only physically feasible (infeasible). The combined effect on Nusselt number and skin‐friction coefficient is also depicted in the form of contour plots. The outcomes from artificial neural network (ANN) using Levenberg‐Marquardt algorithm give a preferred estimate for current numerical simulation. In ANN prediction, the optimal number of neurons in the hidden layer is used, having minimum value of RMSE and RMRE with maximum for different cases of non Newtonian stretching/shrinking sheet. Finally, the results conclude that the applied ANN model can accurately predict the output results.

Data-driven performance analysis of a residential building applying artificial neural network (ANN) and multi-objective genetic algorithm (GA)

Residential buildings account for nearly 60% of electrical energy consumption in hot climates as in Kuwait. As a result, this study proposes a reliable multi-objective model to obtain the optimal design for a typical residential Kuwaiti building by integrating an Artificial Neural Network (ANN) model with Genetic Algorithm (GA) optimization method. The ANN model was investigated and verified using the results of building performance simulations applying EnergyPlus software. The effects of sample size of dataset on performance of ANN were evaluated. The final optimal building design was optimized using the GA method after ensuring the convergence of the final ANN model. Several design and operation parameters were considered as decision variables, while cooling energy consumption, discomfort hours, and equivalent carbon emissions were selected as objective functions. In addition, sensitivity analysis was conducted to evaluate the impacts of decision variables on objective functions. The sensitivity results indicated that insulation highly affect energy consumption and carbon emission, while cooling setpoint played a key role in discomfort hours. Furthermore, Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method was applied for decision-making among Pareto optimal solutions. The results showed that the optimal solution suggested by TOPSIS methods using ANN-based model provided a substantial reduction in energy consumption, discomfort hours, and carbon emission up to 39.3%, 62.8%, and 40.5% compared with the base case, respectively. It is recommended that further research be undertaken in considering uncertainty parameters on optimization process and applying the developed framework for building in different climate.

Developing application techniques of kinematics and simulation model for InMoov robot

In this work, the direct and inverse kinematic analysis of both robot arms are investigated based on the analytical and informational representation. The results of the study will be used to provide the functionality of gesturing by a robot in sign language, both Kazakh and other languages, used in educational systems, especially in children's institutions and societies for deaf people. A simulation model of the movement of the robot's arms in the workspace has been studied and built. The developed model will be further implemented and used as mathematical and information support for the created robot. The developed library contains implementations of forward kinematics and iterative algorithms for inverse kinematics. The InMoov robot is a platform widely used in research tasks, supported by the MyRobotLab package. A direct kinematic model for the left and right hands of the robot has been studied. Based on the Python programming language, the working space for robot manipulators was calculated, using the matpilotlib library, an iteration method algorithm was developed to find the probable path of robot manipulators in space. A model of a structured artificial neural network (ANN) is proposed, which is used to find a solution to the inverse kinematics of the InMoov robot with six degrees of freedom (4-dof). The applied ANN model is a multilayer perceptron neural network (MLPNN) in which the learning rule of the Adam-a gradient diskend type is applied. To solve this problem, the problem of finding the best ANN configuration was studied. It has been established that a multilayer parseptron neural network gives the minimum mean square error. The regression coefficient analysis, which shows a 95.6 % fit of all communication variables, is acceptable for obtaining the inverse kinematics of the InMoov robot.

Estimation and prediction of Jatropha cultivation areas in China and India

The utilization of second-generation biofuel has been known as one of the sustainable solutions for energy challenges worldwide. Jatropha curcas L. (JCL), as a non-edible feedstock with high oil content fruits, has a promising potential for biodiesel production. JCL can grow in semi-arid and wasteland areas and has attracted many interests in recent years. Making efficient decisions for the production of JCL biofuels requires accurate forecasting of JCL cultivation areas. The present study develops an artificial neural network (ANN) model to predict the cultivation areas for JCL cultivation in China and India. Different structures of the ANN model are investigated for the ANN model, and the most appropriate one is determined. Eight input parameters were selected according to their impact on JCL development, including CO2 emission, Gross Domestic Product (GDP), oil price, fossil fuel energy consumption, renewable energy consumption, total energy use, population, and value-added agriculture. The mean absolute percentage error (MAPE) was used to validate the ANN models. The acquired results illustrate that the precision accuracy of the applied ANN model is 99.8%. Also, the results show that the JCL cultivation areas have increased by about 20% in China and have decreased by about 5% in India from 2010 to 2015. Finally, the achieved results from the ANN model are compared with different regression models to show the advantages of the ANN model.

Application of artificial intelligence methods for monsoonal river classification in Selangor river basin, Malaysia.

Rivers in Malaysia are classified based on water quality index (WQI) that comprises of six parameters, namely, ammoniacal nitrogen (AN), biochemical oxygen demand (BOD), chemical oxygen demand (COD), dissolved oxygen (DO), pH, and suspended solids (SS). Due to its tropical climate, the impact of seasonal monsoons on river quality is significant, with the increased occurrence of extreme precipitation events; however, there has been little discussion on the application of artificial intelligence models for monsoonal river classification. In light of these, this study had applied artificial neural network (ANN) and support vector machine (SVM) models for monsoonal (dry and wet seasons) river classification using three of the water quality parameters to minimise the cost of river monitoring and associated errors in WQI computation. A structured trial-and-error approach was applied on input parameter selection and hyperparameter optimisation for both models. Accuracy, sensitivity, and precision were selected as the performance criteria. For dry season, BOD-DO-pH was selected as the optimum input combination by both ANN and SVM models, with testing accuracy of 88.7% and 82.1%, respectively. As for wet season, the optimum input combinations of ANN and SVM models were BOD-pH-SS and BOD-DO-pH with testing accuracy of 89.5% and 88.0%, respectively. As a result, both optimised ANN and SVM models have proven their prediction capacities for river classification, which may be deployed as effective and reliable tools in tropical regions. Notably, better learning and higher capacity of the ANN model for dataset characteristics extraction generated better predictability and generalisability than SVM model under imbalanced dataset.

Towards Engineered Hydrochars: Application of Artificial Neural Networks in the Hydrothermal Carbonization of Sewage Sludge

Sewage sludge hydrochars (SSHs), which are produced by hydrothermal carbonization (HTC), offer a high calorific value to be applied as a biofuel. However, HTC is a complex processand the properties of the resulting product depend heavily on the process conditions and feedstock composition. In this work, we have applied artificial neural networks (ANNs) to contribute to the production of tailored SSHs for a specific application and with optimum properties. We collected data from the published literature covering the years 2014–2021, which was then fed into different ANN models where the input data (HTC temperature, process time, and the elemental content of hydrochars) were used to predict output parameters (higher heating value, (HHV) and solid yield (%)). The proposed ANN models were successful in accurately predicting both HHV and contents of C and H. While the model NN1 (based on C, H, O content) exhibited HHV predicting performance with R2 = 0.974, another model, NN2, was also able to predict HHV with R2 = 0.936 using only C and H as input. Moreover, the inverse model of NN3 (based on H, O content, and HHV) could predict C content with an R2 of 0.939.

Data-Driven Models to Predict Hydrocarbon Production From Unconventional Reservoirs by Thermal Recovery

AbstractIn the numerical simulations of thermal recovery for unconventional resources, reservoir models involve complex multicomponent-multiphase flow in non-isothermal conditions, where spatial heterogeneity necessitates the huge number of discretized elements. Proxy modeling approaches have been applied to efficiently approximate solutions of reservoir simulations in such complex problems. In this study, we apply machine learning technologies to the thermal recovery of unconventional resources, for the efficient computation and prediction of hydrocarbon production. We develop data-driven models applying artificial neural network (ANN) to predict hydrocarbon productions under heterogeneous and unknown properties of unconventional reservoirs. We study two different thermal recovery methods—expanding solvent steam-assisted gravity drainage for bitumen and in-situ upgrading of oil shale. We obtain training datasets by running high-fidelity simulation models for these two problems. As training datasets of ANN models, diverse input and output data of phase and component productions are generated, by considering heterogeneity and uncertainty. In the bitumen reservoirs, diverse permeability anisotropies are considered as unknown properties. Similarly, in the oil shale reservoirs, diverse kerogen decomposition kinetics are considered. The performance of data-driven models is evaluated with respect to the position of the test dataset. When the test data is inside of the boundary of training datasets, the developed data-driven models based on ANN reliably predict the cumulative productions at the end of the recovery processes. However, when the test data is at the boundary of training datasets, physical insight plays a significant role to provide a reliable performance of data-driven models.

Damage detection of bottom-set gillnet using Artificial Neural Network

This paper presents a method for detecting damage of a bottom-set gillnet based on sensor fusion and the Artificial Neural Network (ANN) model. In this regard, time-domain numerical simulations for a 300-m-long bottom-set gillnet with an equivalent-drag-net model are extensively performed. Various wave conditions as well as numerous damaged scenarios are considered in the numerical simulations, and extensive data are collected for the training and testing of the ANN-based machine-learning scheme. In training, representative sea states, net-assembly accelerations, and location-buoy displacements are selected as the input variables. The back-propagation learning algorithm is employed for training to maximize the damage-detection performance. The output of the ANN model is the identification of the type and location of the damaged net. The damage-detection capability is significantly enhanced by employing the moving standard deviation and median filter. The well-trained ANN models are shown to accurately (96% correct) detect the damage of the net even for the sea states not included in training. This study demonstrates that the real-time automatic monitoring of the underwater net systems can be designed by using the digital-twins technology with the applied ANN model and machine-learning scheme for the detection of damages and malfunctions.

Artificial neural network approach to predict the lightfastness of gravure prints on the plastic film

AbstractThe lightfastness of prints is an important property for assessing their print stability. The objective of this study is to determine the lightfastness rate of printed films due to long‐time exposure by applying artificial neural network (ANN). Package printing is gradually becoming extremely important because its color and quality increase the marketability of the product. Sometimes it has been observed that the initial print quality is bright and attractive. However, with time, it degrades the exposure of light, water, or other external parameters. Thus, it reduces the marketability if its color degrades before its expiration. Therefore, the lightfastness of prints may be considered for the authenticity or validity of the product. The plastic film substrate is chosen because it has extensive usage in food and other packaging industries. The samples printed in the gravure process are exposed in artificial lightfastness tester BGD 865/A Bench Xenon Test Chamber (B‐SUN) for assessing the lightfastness of prints. The ocean optics spectroradiometer (DH2000BAL) is used to measure the spectrophotometric properties of prints before and after exposure. The obtained reflectance spectra are modeled by applying an ANN technique that is proposed to predict the fading rate of the printed film. The optimal model gives excellent prediction with the minimum mean square error for each color and a correlation coefficient of 0.80 to 0.99. ANN model and a Regression model (assuming first‐order kinetic equation) are compared for predicting the lightfastness properties of prints. The results show that the ANN has better prediction capability than the regression model.

An analysis of the influence of hand hole and ventilation hole design on compressive strength of corrugated fiberboard boxes by an artificial neural network model

This research aimed at developing a high‐performing corrugated fiberboard box compression strength prediction model and to analyze the influences of ventilation and hand hole designs for these containers on the box compression test (BCT) by applying artificial neural network (ANN) modeling. The input variables considered in this study are composed of nine parameters including box dimension as well as shapes, sizes, positions, and locations of ventilations and hand holes of a regular slotted container (RSC, FEFCO 0201). Back propagation algorithms (BPNs) of ANN models were developed from 970 BCT testing data points (single wall boards, C flute, 205/112/205 g/m2). Tested data was randomly broken into three groups for the model development as 80:10:10 for the training set, testing set, and validating set. According to the analysis performed, a BPN 9‐13‐1 model reflected the highest prediction performance with R2 = 0.97. According to the analysis, BCT was significantly affected by the hand hole location followed by the geometrical dimensions of the box (height, length, and width) and the ventilation factors (shape, number, and location) in that order. Hand holes at the top flaps caused a lower BCT reduction compared with those at the vertical locations of the box. Slight changes to the eliminated board area for both hand holes and ventilation (±5%) contributed to less BCT reduction compared with its locations and shapes. Interestingly, increasing the box height significantly increased the BCT, and this was found to be limited only to shorter boxes fabricated from a high stiffness corrugated board.

Empirical Mode Decomposition Couple with Artificial Neural Network for Water Level Prediction

Natural disaster brings massive destruction towards properties and human being and flood is one of them. In order for the government to take earlier action to reduce the damages, an accurate flood prediction is necessary. In Malaysia, Kelantan is categorized as a high flood risk area, thus this study focuses on Kelantan flood prediction. This study is to investigate the effect of decomposition for water level prediction by applying Artificial Neural Network (ANN) forecasting model. In this study, Empirical Mode Decomposition (EMD) is used as the decomposition method. The best Intrinsic Mode Function (IMF) for each input variable is selected using correlation-based selection method. The results showed that the performance of hybrid EMD and ANN is superior compared to other models, especially classic ANN model. The reason for this outcome is that through decomposition methods, ANN is able to capture more in-depth information of the Kelantan hydrological time series data. The resulting model provides new insights for government and hydrologist in Kelantan to have better prediction towards flood occurrence.

Application of Feed Forward Neural Network for Prediction of Optimum Coagulant Dose in Water Treatment Plant

Coagulation is anecessary process used mainly to reduce turbidity and natural organic matter in water treatment. The dosage of coagulant required is conventionally determined by carrying out jar tests which consume time and chemicals.In India, coagulant dose in a WTP remains constant during certain periods due to delay in jar testing, which may lead to under-dosing or over-dosing of coagulant. This research work is focused on applying artificial neural network (ANN) approach to predict coagulant dose in a WTP. Forty-eight months daily water testing data concerning inlet & outlet water turbidity and coagulant dose were obtained from the plant laboratory for ANN modelling. The appropriate architecture of feed forward neural network (FFNN) coagulant models were established with several steps of training and testing by applying various training algorithms vizLevenberg-Marquardt (LM) and Bayesian regularization (BR), resilient back propagation (RBP), one step secant(OSS),variants of conjugate gradient(CG) and modifications of gradient descent (GD) with evaluating coefficient of correlation (R) & mean square error (MSE). Further, best performed LM and BR training algorithm were used for development of four ANN models of FFNN for prediction of coagulant dose at WTP. FFNN coagulant model with BR training algorithm provided excellent estimates with network architecture (2-50-1) for coagulant dose with maximum value of R= 0.943 (training) and R = 0.945 (testing). Thus, ANN provided an effective diagnosing tool to understand the non-linear behavior of the coagulation process, and can be used as a valuable performance assessment tool for plant operators and decision makers.

Prediction of water quality index using artificial neural network and multiple linear regression modelling approach in Shivganga River basin, India

The present study has applied artificial neural network (ANN) and multiple linear regression (MLR) techniques to predict the fitness of groundwater quality for drinking from Shivganga River basin, located on the eastern slopes of the Western Ghat region of India. In view of this, thirty-four (34) representative groundwater samples have been collected and analyzed for major cations and anions during pre- and post-monsoon seasons of 2015. The physicochemical parameters such as pH, EC, TDS, TH, Ca, Mg, Na, K, Cl, HCO3, SO4, NO3 and PO4 were considered for computing water quality index (WQI). Analytical results confirmed that all the parameters are within acceptable range; however, EC, TDS, TH, Ca and Mg are exceeding the desirable limit of the WHO drinking standards. The groundwater suitability for drinking was ascertained by WQI method. The WQI value ranges from 25.75 to 129.07 and from 37.54 to 91.38 in pre- and post-monsoon seasons, respectively. Only one sample (DW5) shows 129.07 WQI value indicating poor quality for drinking due to input of domestic and agricultural waste. In the view of generating consistent and precise model for prediction of WQI-based groundwater quality, a Levenberg–Marquardt three-layer back propagation algorithm was used in ANN architecture. Further, MLR model is used to check the efficiency of ANN prediction. The results corroborated that predictions of ANN model are satisfactory and confirms consistently acceptable performance for both the seasons. The proposed ANN model may be useful in similar studies of groundwater quality prediction for drinking purpose.

Modification of landslide susceptibility mapping using optimized PSO-ANN technique

In the present study, we applied artificial neural network (ANN) optimized with particle swarm optimization (PSO) for the problem of landslide susceptibility mapping (LSM) prediction. Many studies have revealed that the ANN-based techniques are reliable methods for estimating the LSM. However, most ANN training models facing with major problems such as slow degree of learning system as well as being trapped in their local minima. Optimization algorithms (OA) such as PSO can improve performance results of ANN. Existing applications of PSO model to ANN training have not been used in area of landslide mapping, neither assess the optimal architecture of networks nor the influential factors affecting this problem. Hence, the present study focused on the application of a hybrid PSO-based ANN model (PSO-ANN) to the prediction of landslide susceptibility hazardous mapping. To prepare training and testing datasets for the ANN and PSO-ANN network models, large data collection (i.e., a database consists 168970 training datasets and 42243 testing datasets) were provided from an area of Layleh valley, located in Kermanshah, west of Iran. All the variables of PSO algorithm (e.g., in addition to the network parameter and network weights) were optimized to achieve the most reliable maps of landslide susceptibility. The input dataset includes elevation, slope aspect, slope degree, curvature, soil type, lithology, distance to road, distance to river, distance to fault, land use, stream power index (SPI) and topographic wetness index (TWI), where the output was taken landslide susceptibility value. The predicted results (e.g., from ANN, PSO-ANN) for both of datasets (e.g., training and testing) of the models were assessed based on two statistical indices namely, coefficient of determination (R2) and root-mean-squared error (RMSE). In this study, to evaluate the ability of all methods, color intensity rating (CER) based on the result of above indices was developed. Apart from CER, the total ranking system was also used to rank the obtained statistical indexes. As a result, both models presented good performance, however, according to the introduced ranking system, the PSO-ANN model could perform a better performance compared to ANN. According to R2 and RMSE values of (0.9717 and 0.1040) and (0.99131 and 0.0366) were found for training dataset and values of (0.9733 and 0.111) and (0.9899 and 0.0389) obtained for testing dataset, respectively, for the ANN and PSO-ANN approximation models, it can be resulted that PSO-ANN model showed higher reliability in estimating the LSM compared to the ANN.

QSPR modeling of decomposition temperature of energetic cocrystals using artificial neural network

The quantitative structure–property relationship for the decomposition temperature (Td) of energetic cocrystals was investigated. The artificial neural network (ANN) model was employed to predict the Td of cocrystals by using molecular descriptors achieved from Dragon software as input variables. The complete set of 30 cocrystals was randomly divided into a training set of 19, a test set of 6, and a validation set of 5 compounds. Average absolute relative deviations and correlation coefficient (R2) of the ANN model (for the whole dataset) were 1.94% and 0.9784, respectively, indicating satisfactory predictive ability and reliability of the model. Moreover, these data were analyzed by multiple linear regression (MLR) method which showed R2 = 0.7438. The result of MLR proves the necessity of applying ANN models for the prediction of the Td of cocrystals.