Application Of ANN Research Articles

Prediction of Thermal Coal Ash Behavior of South African Coals: Comparative Applications of ANN, GPR, and SVR

Prediction of Thermal Coal Ash Behavior of South African Coals: Comparative Applications of ANN, GPR, and SVR

Application of ANN and FELA for Predicting Bearing Capacity of Shell Foundations on Sand

Application of ANN and FELA for Predicting Bearing Capacity of Shell Foundations on Sand

Application of ANN and traditional ML algorithms in modelling compost production under different climatic conditions

Application of ANN and traditional ML algorithms in modelling compost production under different climatic conditions

Weather-Based Statistical and Neural Network Tools for Forecasting Rice Yields in Major Growing Districts of Karnataka

Two multivariate models were compared to assess their yield predictability based on long-term (1980–2021) rice yield and weather datasets over eleven districts of Karnataka. Simple multiple linear regression (SMLR) and artificial neural network models (ANN) were calibrated (1980–2019 data) and validated (2019–2020 data), and yields were forecasted (2021). An intercomparison of the models revealed better yield predictability with ANN, as the observed deviations were smaller (−37.1 to 21.3%, 4% mean deviation) compared to SMLR (−2.5 to 35.0%, 16% mean deviation). Further, district-wise yield forecasting using ANN indicated an underestimation of yield, with higher errors in Mysuru (−0.2%), Uttara Kannada (−1.5%), Hassan (−0.1%), Ballari (−1.5%), and Belagavi (−15.3%) and overestimations in the remaining districts (0.0 to 4.2%) in 2018. Likewise, in 2019 the yields were underestimated in Kodagu (−0.6%), Shivamogga (−0.1%), Davanagere (−0.7%), Hassan (−0.2%), Ballari (−5.1%), and Belagavi (−10.8%) and overestimated for the other five districts (0.0 to 4.8%). Such model yield underestimations are related to the farmers’ yield improvement practices carried out under adverse weather conditions, which were not considered by the model while forecasting. As the deviations are in an acceptable range, they prove the better applicability of ANN for yield forecasting and crop management planning in addition to its use for regional agricultural policy making.

Application of Multiple Back-Propagation ANN for Predicting Finish Surface Roughness Produced by Vereco CNC Cylindrical Grinding Machine

Large industrial operations, such as those in manufacturing, marine, and oil & gas, rely on cylindrical grinding as one of their most critical machining processes. One of the difficulties encountered throughout the cylindrical grinding process is anticipating the workpiece’s exact surface roughness after the process. Due to the process’s inability to predict the exact surface roughness, the processing time and quality produced are difficult to control. Several independent variables that are immediately quantifiable are used to build a data set for the training procedure in this study. Predicting the ultimate surface roughness generated by the cylindrical grinding process is critical for optimizing production time, quality, efficiency, and customer satisfaction. An artificial neural network with multiple backpropagation algorithms is applied. Through the learning process, the best combination of learning is obtained, namely: a learning rate of 0.057 and a momentum of 0.434 with one hidden layer in which there are 10 hidden nodes. This combination is believed to be the best training combination to produce the minimum error between the target and the true value. The root mean squared error of the test calculation was 0.0436 with a prediction accuracy of 95.64%. This set of experiment results produces predictive results through the validation process and succeeds in predicting the finish surface roughness with promising results (accuracy in the range of 94.683-97.661%).

Artificial Neural Network for Fault Diagnosis of Solar Photovoltaic Systems: A Survey

Solar energy is one of the most important renewable energy sources. Photovoltaic (PV) systems, as the most crucial conversion medium for solar energy, have been widely used in recent decades. For PV systems, faults that occur during operation need to be diagnosed and dealt with in a timely manner to ensure the reliability and efficiency of energy conversion. Therefore, an effective fault diagnosis method is essential. Artificial neural networks, a pivotal technique of artificial intelligence, have been developed and applied in many fields including the fault diagnosis of PV systems, due to their strong self-learning ability, good generalization performance, and high fault tolerance. This study reviews the recent research progress of ANN in PV system fault diagnosis. Different widely used ANN models, including MLP, PNN, RBF, CNN, and SAE, are discussed. Moreover, the input attributes of ANN models, the types of faults, and the diagnostic performance of ANN models are surveyed. Finally, the main challenges and development trends of ANN applied to the fault diagnosis of PV systems are outlined. This work can be used as a reference to study the application of ANN in the field of PV system fault diagnosis.

Application of ANN Weighted by Optimization Algorithms to Predict the Color Coordinates of Cellulosic Fabric in Dyeing with Binary Mix of Natural Dyes

Cotton is one of the most important fibers used in the textile industry. The dyeing of cotton with synthetic anionic dyes consumes large amounts of salt and alkali, which makes it a challenge for the environment. Furthermore, the relatively high percentage of synthetic dyes remaining in the dyebath is a potential threat for the environment and human health. The application of plant-derived natural dyes has recently been considered as a promising approach to overcome this problem. Optimization of the dyeing process and prediction of the values of the color coordinates of dyed textiles have always been among the most pronounced challenges in the textile industry, especially when a mixture of dyes or mordants is used. In this study, alum was used for mordanting of cotton and two natural dyes—namely, weld and madder—were used for the dyeing. The samples were dyed with various combinations of mordant, weld, and madder for the weight of the fabric and statistical analysis revealed that all three mentioned parameters were effective in determining the color coordinates. To determine the best model to predict the color coordinates of cotton fabrics, the regression method and ANN models weighted with back-propagation (BP) and optimization algorithms, such as the genetic algorithm, particle swarm optimization, gray wolf optimization, FMINCON (a built-in function of MATLAB software) and a combination of particle swarm optimization and FMINCON (PSO-FMIN), were employed and compared based on the mean squared error (MSE). The obtained results revealed that using the PSO-FMIN algorithm for ANN weighting led to higher accuracy in the prediction of color coordinates. The MSEs obtained for ANN outputs and the corresponding actual values reached 2.02, 1.68 and 1.39 for the l*, a* and b* coordinates, which were 44%, 23% and 26% better than the result obtained with BP, respectively.

Effect of complex nutrients, amino acids, vitamins on Ni(II) biosorption from aqueous solution by Ni(II) resistant Saccharomyces cerevisiae AJ208

The paper aims to establish and enhance the microorganism’s successful growth, proper activity, and biosorption potency for Ni(II) biosorption from an aqueous solution using 5,000 mg/l Ni(II) resistant Saccharomyces cerevisiae AJ208. Complex nutrients, amino acids, and vitamins were added to the specifically optimized fermentation media as essential growth factors. Amino acids such as L-cysteine (0.0002 g/ml), L-Proline (0.0002 g/ml), L-Lysine (0.0002 g/ml), L-tryptophan (0.0001 g/ml) and L-Histidine (0.0003 g/ml) led to an increase of more than 87% biosorption. Vitamins such as, Ascorbic acids (0.01 × 10−8 g/ml), folic acids (0.01 × 10−8 g/ml), pyridoxine-HCl (0.01 × 10−8 g/ml),Thiamin-HCl (0.05 × 10−8 g/ml) promotes biosorption more than 91%. The Ni(II) bio-removal increased with complex nutrients like soybean meal, malt extract, and yeast extract at the concentration of 0.03, 0.4, 0.05 in g/ml, and nickel removal reached more than 85%. The multiple linear regression (MLR) and ANN application of the experimental data have predicted Ni(II) percentage removal well. This adsorption shows that the proposed Ni(II) removal process using complex nutrients is environmentally friendly and economically feasible. Novelty statement: This study evaluates a cost-effective approach to bioremediation of Ni(II) by using complex nutrients as a growth factor. Media enriched with complex nutrients is cheap than chemical media. Ni(II) Removal significant increased up to 87%, 88.34%, 96% with soybean meal, L-proline, and L-ascorbic acids at 3,000 mg/l initial Ni(II) concentration using newly developed 5,000 mg/l Ni(II) resistant Saccharomyces cerevisiae AJ208 and their NCBI accession number: MZ027228 (AJ208 ITS 1) and MZ027229 (AJ208 ITS 2).

Co-pyrolysis and co-combustion of orange peel and biomass blends: Kinetics, thermodynamic, and ANN application

Co-pyrolysis and co-combustion of orange peel, oil palm shell, and empty fruit bunch biomass with and without blends were investigated in a thermogravimetric analyzer (TGA) at 5, 10, 15, and 20 °C/min heating rate. The kinetic parameters were calculated using the model-free and model-fitting methods. Results showed that blending altered the thermogravimetry (TG) and derivative thermogravimetry (DTG) profiles and indicated the existence of multiple reactions. Biomass blends showed a strong synergetic effect, specifically during the char formation. The average activation energy was a function of conversion degree, and the values for the biomass blends were 82–120 kJ/mol compared to their parent biomass (64–111 kJ/mol). The trained artificial neural network model predicted the TG profiles of biomass blends with high R2 values (≥0.9), but the prediction accuracy was relatively poor for DTG profiles (≤0.8). Overall, mixing orange peel waste and oil palm biomass altered the thermo-chemical behavior and kinetics, which could develop a new dimension for future studies.

Optimization of operational parameters of pneumatic system for ballast tank sediment reduction with experimental and ANN applications

The studies show that ballast tank sediments pose several problems, including the continuing risk of invasive and pathogenic species transfer. Moreover, Regulation B.5 of The IMO's BWM convention states “ships should be designed and constructed with a view to minimize the uptake and undesirable entrapment of Sediments, facilitate removal of Sediments”. The purpose of the study is to investigate the effectiveness of the pneumatic system for reducing the amount of accumulated sediment under different operating conditions and determine an optimal operating condition for the pneumatic system. The experimental study has demonstrated that there are complex relationships and interactions among initial air pressure, run number, and run time in terms of reducing sediment accumulation. The overall results of this study show that sediment reduction occurs at rates ranging from ∼4% to ∼29% in sets completed under different operating conditions. Even though the total amount of the sediment was reduced in all conditions at different rates, it is figured out that the sediment amount increased in some locations within the tank. However, when operated under optimal conditions suggested by the ANN application, the sediment reduction occurred at a rate of ∼29% and there was no local increase detected throughout the tank model.

A comparative optimization and modeling of ammonia–nitrogen adsorption from abattoir wastewater using a novel iron-functionalized crab shell

High-grade adsorbent (Fe–CS) was successfully synthesized from waste crab shells (CS) for the adsorptive removal of ammonia–nitrogen (A–N) from abattoir wastewater. Fe–CS was analyzed using Fourier transform infrared spectroscopy, x-ray diffraction, Scanning electron microscopy, and Thermogravimetric analysis. The characterization results indicated that Fe–CS possessed important qualities required for surface-driven chelation. The analysis of variance result showed that Fe–CS dosage (with f–value of 284.5) was the most effective process parameter of influence during A-N adsorption. The regression coefficients obtained from the process modeling illustrated the applicability of RSM (R2 = 0.9799), ANN (R2 = 0.9025), and ANFIS (R2 = 0.9998) in predicting the A–N adsorptive removal, while the comparative statistical analysis established the superiority of ANFIS model over ANN and RSMs’ data prediction accuracy. The optimization result further demonstrated that ANFIS–GA predicted an optimum removal efficiency of 92.60% at pH of 6.5, a dosage of 2.2 g, A–N conc. of 18.8 mg/L, Temp. of 317 K, and adsorption time of 156 min. The mechanistic plot obtained from Weber–Morris model depicted that three regions were involved in the adsorption process. Boyd model parameters revealed that intraparticle diffusion controlled the process at low A–N concentration (A–N concentration ≤ 15 mg/L), while film diffusion dominated the adsorption process at concentrations higher than 15 mg/L. The average Gibbs free energy value (- Delta G^{0} = 3.08212 kJ/mol), enthalpy (Delta H^{0} = 4.1150 kJ/mol), activation energy (E_{A} = 3.7454 kJ/mol) and entropy (Delta S^{0} = 22.9710 J/mol K) obtained from thermodynamic studies confirmed the spontaneous, endothermic, favorable and physical nature of the process.

Mortality Prediction in ICU Using Artificial Neural Networks

Abstract: Intensive Care Unit (ICU) death rate prediction supported artificial neural network, patients WHO have important unwellness and injury can get admitted to the unit. The dying rate for sufferers admitted to the unit can vary from Associate in Nursing underlying illness with a dying rate as low as one in twenty sufferers admitted for non-compulsory surgical operation and as excessive as one in 4 sufferers with metabolic process diseases. Artificial Neural Networks facilitate health care management selections, improve care and cut back value at same time by exploiting the applications of ANN within the care department. It helps to alter, minimize errors and predict additional correct diagnoses for the diseases or injuries that may cut back patients' risk of hospitalization supported given knowledge. Associate in Nursing empiric study was conducted with 4000+ adult patients from 100+ ICUs. The patients with ≥1 organ failure are forty sixth. Patients without infection receiving antibiotics stand for an hour. There have been 500+ deaths and 183 discharges from the unit. In 1627 patients admitted among twenty four h of the study day, the standardized mortality quantitative relation was zero.67. We have a tendency to develop our model exploitation of a synthetic Neural Network to predict the death rate of sick patients supported by their diseases, injuries, and medical records. This can additionally facilitate managing the unit beds in hospitals throughout the Associate in Nursing emergency.

Application of Random Forest and Multi-layer Perceptron ANNS in Estimating the Axial Compression Capacity of Concrete-Filled Steel Tubes

Application of Random Forest and Multi-layer Perceptron ANNS in Estimating the Axial Compression Capacity of Concrete-Filled Steel Tubes

Enhanced Network Security for Improved Trustworthiness of VoIP Applications via Cuckoo Search and Machine Learning

Objectives: To offer network design for secure Voice over Internet Protocol (VoIP) services with improved Quality of Services (QoS) parameters. Methods: The network area is created with required number of nodes. INTRA-SR process is employed for tracing of the route from the source node and INTER-SR is involved to reach a destination node. Cuckoo Search (CS) based optimization followed for the broadcasted voice packets. The Machine learning classifiers as SVM and ANN applied to decrease the instances of loss of voice packets. Simulation work is performed by using MATLAB 2018 and results obtained plotted in graphs using MS-Excel. Findings: The proposed design evaluated by incorporating CS algorithm to minimize the packet drops. SVM and ANN hybrid used to locate secure routing path. The QoS for throughput, latency and jitter are observed. The results exhibited higher average throughput of 98.8% irrespective of the attack instances. Lower average latency and jitter of 1.9s and 2.51ms are also exhibited by the proposed work. Similarly, latency work employing Ant Colony Optimization (ACO) with multiplex and multicasting (MM) is 2.66s that get increased to 3.16s due to attack. Novelty/Applications: The proposed algorithm significantly enhanced by deployment of highly protected network design with QoS and security. Application of ANN and SVM shown the improvement in performance for VoIP services. In addition to the regression analysis validates the results by applying other optimization algorithms. Keywords: Voice over Internet Protocol; Cuckoo Search; Support Vector Machine; Artificial Neural Network; Machine Learning

Application of ANN and prediction of drying behavior of mushroom drying in side hybrid greenhouse solar dryer: An experimental validation

The newly developed Heat Exchanger Evacuated Tube Assisted Drying System (HE-ETADS) is fabricated in roof of campus of MITS, Gwalior, India. Heat transfer analysis of developed drying system is carried out by drying mushroom inside it. The heat transfer coefficient plays a significant role in drying method. The study emphasizes on determining the convective as well as evaporative heat transfer coefficients (CHTC & EHTC) and the determination of best drying rate model fit for mushroom drying inside novel drying system. The artificial neural network is developed for predicting the CHTC & EHTC. Value of CHTC & EHTC varies from 2.10 to 3.43 and 25.68 to 49.85 W/m2°C respectively. The developed ANN model helps in predicting the heat transfer coefficient once trained using input factors like solar radiation, relative humidity, environmental temperature and time. The value of R2 for the developed ANN model is 0.99, which shows that model predicts the value very close to the calculated value of heat transfer coefficients. Drying kinetics of mushroom is tried to fit in nine drying rate models. The Midili-Kucuk model shows the better fit among the other models.

Prediction and influence of the mass proportion of trichromatic colourants and acrylic substrate on the optical and thermal performance of external wall coatings: An artificial neural network approach

Colourants are the main components for the colour control of acrylic coatings for the external walls of buildings. Since the different mass proportions of cyan, magenta, and yellow colourants, the colour of the coating changes after mixing, which also affects the optical property and thermal performance of the coating. In this paper, an artificial neural network model is established, the BP algorithm is used for calculation, and the model is trained and verified by making and testing experimental samples to accurately predict the solar reflectivity by the mass proportions of trichromatic colourants and acrylic coating substrate. The mean square error is 0.0027. Meanwhile, the influence weights of the mass proportions of cyan, magenta, and yellow colourants and the acrylic coating substrate on the solar reflectivity are calculated by the model, which are 0.2863, 0.2209, and 0.4928, respectively. The increase in cyan colourant has a greater influence on the decrease in reflectivity of the blended coating. The outer surfaces of 11 samples were tested outdoors. Compared with the original solution of the acrylic coating, the maximum temperature difference of the mixed coating reached 16.03 °C. The highest temperature of the coating with the highest proportion of cyan colourant and the acrylic substrate is 10.61 °C higher than that of the coating with the lowest proportion. 1 There are only a few studies on the application of ANN in coating colour, and this is the first time that an artificial neural network model has been used to predict the coating reflectivity based on the mass proportion of trichromatic colourants to the acrylic substrate. 2 Based on the preparation and spectrum measurement of a large number of coating samples with different proportions of colourants and substrates, a cross-study of the material composition and optical properties has been carried out. 3 The influence of the different mass proportions of the trichromatic colourants to the substrate on the reflectivity of the coloured acrylic coating has been studied. The increase in the cyan colourant has the maximum impact on the decrease in reflectivity. The acrylic coating having the lowest proportion of the cyan colourant to the substrate shows a different temperature performance than that with highest proportion of the cyan colourant to the substrate.

Application of ANN, RSM on engine response prediction using lemongrass biomaterial blends

By framing the design of experimental combinations in RSM CCD technique with specified influencing variable ranges, the viability of employing biodiesel generated from lemongrass in a CRDI engine was investigated. To check the accuracy, observed experimental data is supplied into prediction tools such as ANN and RSM. It is concluded that sophisticated neural networks and response surface methodologies can be successfully used for forecasting output responses within the ranges of experimentation based on experimental data. Grey analysis was used to find the best engine settings. In terms of emissions and performance in emergency scenarios, lemongrass fuel can be utilised as a partial replacement for diesel fuel in engines.

Investigation of Nettle Straw Pyrolysis: Multicomponent Kinetics Modeling, Gas Emissions and Ann Application

Investigation of Nettle Straw Pyrolysis: Multicomponent Kinetics Modeling, Gas Emissions and Ann Application

Rsm, Ann and Anfis Applications in Modelling Fermentable Sugar Production from Enzymatic Hydrolysis of Colocynthis Vulgaris Shrad Seeds Shell

Rsm, Ann and Anfis Applications in Modelling Fermentable Sugar Production from Enzymatic Hydrolysis of Colocynthis Vulgaris Shrad Seeds Shell

Hardware-Based Activation Function-Core for Neural Network Implementations

Today, embedded systems (ES) tend towards miniaturization and the carrying out of complex tasks in applications such as the Internet of Things, medical systems, telecommunications, among others. Currently, ES structures based on artificial intelligence using hardware neural networks (HNNs) are becoming more common. In the design of HNN, the activation function (AF) requires special attention due to its impact on the HNN performance. Therefore, implementing activation functions (AFs) with good performance, low power consumption, and reduced hardware resources is critical for HNNs. In light of this, this paper presents a hardware-based activation function-core (AFC) to implement an HNN. In addition, this work shows a design framework for the AFC that applies a piecewise polynomial approximation (PPA) technique. The designed AFC has a reconfigurable architecture with a wordlength-efficient decoder, i.e., reduced hardware resources are used to satisfy the desired accuracy. Experimental results show a better performance of the proposed AFC in terms of hardware resources and power consumption when it is compared with state of the art implementations. Finally, two case studies were implemented to corroborate the AFC performance in widely used ANN applications.