Multilayer Artificial Neural Network Research Articles

An integrated geomechanical and petrophysical multiparameter approach for gas reservoir evaluation

Integrating petrophysical and geomechanical parameters is an efficient approach to evaluating shale gas reservoir potential. The high cost of corings and their limited number, coupled with time-intensive investigation, led researchers to use this alternative combination approach. In the Jiaoshiba area, from single-pilot well core data and log measurements, petrophysical and geomechanical parameters such as shale volume, total organic carbon, gas content, as well as pore pressure, stress components, and mineral brittleness were first estimated using established methods. In the second phase, based on logging curves, the reservoir electro-facies (EF) classification was performed using the unsupervised multi-resolution graph-based clustering method on a series of twenty wells, identifying five EF with different intrinsic characteristics. Unsupervised analyses were developed using the multilayer artificial neural network while incorporating the K-nearest neighbors and graphical classification algorithms. The results from the first and second phases indicate reservoir richness in organic matter, with the best reservoir exhibited by EF2 and EF3. In addition, effective stress components (SV, SH, and Sh) evaluation shows a normal stress regime with hydraulic fracture systems perpendicular to the minimum horizontal stress at each measured depth of the reservoir (Sv > SH > Sh). This research workflow can efficiently evaluate shale reservoirs with a realistic approach for identifying favorable fracturing positions while reducing errors due to human interference.

Emerging Vanadium-Doped Cobalt Chloride Carbonate Hydroxide for Flexible Electrochromic Micro-Supercapacitor: Charged-State Prediction from RGB Input by ANN Model.

Multifunctional devices integrated with electrochromic and supercapacitance properties are fascinating because of their extensive usage in modern electronic applications. In this work, vanadium-doped cobalt chloride carbonate hydroxide hydrate nanostructures (V-C3H NSs) are successfully synthesized and show unique electrochromic and supercapacitor properties. The V-C3H NSs material exhibits a high specific capacitance of 1219.9Fg-1 at 1mVs-1 with a capacitance retention of 100% over 30000 CV cycles. The electrochromic performance of the V-C3H NSs material is confirmed through in situ spectroelectrochemical measurements, where the switching time, coloration efficiency (CE), and optical modulation (∆T) are found to be 15.7 and 18.8s, 65.85cm2C-1 and 69%, respectively. A coupled multilayer artificial neural network (ANN) model is framed to predict potential and current from red (R), green (G), and blue (B) color values. The optimized V-C3H NSs are used as the active materials in the fabrication of flexible/wearable electrochromic micro-supercapacitor devices (FEMSDs) through a cost-effective mask-assisted vacuum filtration method. The fabricated FEMSD exhibits an areal capacitance of 47.15mFcm-2 at 1mVs-1 and offers a maximum areal energy and power density of 104.78Whcm-2 and 0.04mWcm-2, respectively. This material's interesting energy storage and electrochromic properties are promising in multifunctional electrochromic energy storage applications.

Spatial Air Quality Assessment in Peninsular Malaysia: Insights from Environmetric Analyses and Artificial Neural Networks

The research investigated the spatial distribution of air quality in Peninsular Malaysia to offer crucial insights for mitigating air quality degradation. Utilizing historical data obtained from the Malaysian Department of Environment, eleven years' worth (2011-2021) of daily readings of PM10, O3 , SO2 , NO2 , and CO from thirty-four monitoring stations were subjected to environmetric techniques and Artificial Neural Network analysis. HACA successfully classified seven stations into the High Pollution Cluster (HPC), seven stations into the Moderate Pollution Cluster (MPC), and twenty stations into the Low Pollution Cluster (LPC). The discriminant analysis demonstrated a correct assignment rate of 91.87%, suggesting that all five parameters were able to discriminate with a significance level of p<0.0001. Principal Component Analysis (PCA) revealed two Variance Factors (VFs) across all clusters, with cumulative variances of 69.43% (HPC), 82.32% (MPC), and 62.16% (LPC) respectively. Furthermore, employing a Multi-Layer Perceptron Feed-Forward Artificial Neural Network (MLP-FF-ANN) model to predict API readings yielded a significant and strong correlation, as indicated by R2 =0.7774 and RMSE=9.9048. These findings hold significant potential in informing the adoption of effective preventative and management strategies.

Taguchi method and neural network for efficient β‐ketoenamine synthesis in deionized water

AbstractThe optimization of performance parameters, in particular the yield of the synthesis reaction of β‐enaminones in demineralized water, is crucial to improve their efficiency and accuracy. In this report, we investigate the optimization of the synthesis of β‐ketoenamines in deionized water by controlling several parameters such as reaction time, temperature, amine equivalent, acid percentage, and stirring rate. An orthogonal L16 (45) network was created using Taguchi's approach, allowing for the best possible parameters. To forecast the contribution of each parameter, analysis of variance (ANOVA) techniques are also used. Multiple linear and nonlinear regression (MLR, MNLR) and multilayer perception artificial neural network (MLP‐ANN) predictive models were developed. Analysis of the results led to optimized design parameters, with time = 6 h, temperature = 25°C, amine equivalent = 1.5, acid percentage = 20%, and stirring rate = 1000 rpm, leading to a maximum yield of 63%. ANOVA analysis revealed that temperature, stirring rate, acid percentage, and time are the parameters with the greatest influence. The least sensitive parameter is the amine equivalent. The two main interactions are temperature * acid % and amine equivalent * rpm. The MLP‐ANN predictions are in good agreement with the experimental values, resulting in a higher R2 compared to the quadratic regression model and the MLR model. By using molecular docking studies, the produced compounds' biological activity was investigated. Some of the synthesized compounds appear to be interesting and could be used for therapeutic applications. The results of this study give us insight into the gentle, cost‐effective, and biologically active synthesis of β‐enaminones in deionized water.

2:1 Multiplexer, 1:2 De-multiplexer,2:4 Decoder and 4:2 Encoder Circuit Design with CMOS Technology Implementing with Artificial Neural Network with Verilog HDL Code for Output

The goal of this project is to create a De-multiplexer gate using a complementary metal oxide semiconductor (CMOS) and an artificial neural network.When designing any COMS circuit, we always keep in mind that the lowest possible cost should be the aim.In this work, the circuit was designed using a multilayer artificial neural network. We utilize weights to alter the value and treat negative values as inverters and neurons as transistors in our work.We are also developing Verilog-HdL code to easily apply the De-multiplexer for experimenting with an artificial neural network and set weights to acquire the desired results. The purpose of this project is to use an artificial neural network and a complementary metal oxide semiconductor (CMOS) to develop a decoder gate.We constantly keep in mind that the goal should be to construct a COMS circuit at the lowest feasible cost.An artificial neural network with multiple layers was used to create the circuit in this work. In our study, we treat negative values as inverters and neurons as transistors, and we modify the value using weights.Additionally, we are creating Verilog-HdL code that will make it simple to use the Decoder to experiment with artificial neural networks and adjust weights to get the desired outcomes. The purpose of this project is to use an artificial neural network and a complementary metal oxide semiconductor (CMOS) to develop a decoder gate.We constantly keep in mind that the goal should be to construct a COMS circuit at the lowest feasible cost.An artificial neural network with multiple layers was used to create the circuit in this work. In our study, we treat negative values as inverters and neurons as transistors, and we modify the value using weights.Additionally, we are creating Verilog-HdL code that will make it simple to use the Decoder to experiment with artificial neural networks and adjust weights to get the desired outcomes. The purpose of this research is to use an artificial neural network and a complementary metal oxide semiconductor (CMOS) to produce a multiplexer gate.We always keep in mind that the lowest feasible cost should be the goal while designing any COMS circuit.This work used a multilayer artificial neural network to design the circuit. In our work, we treat neurons as transistors and negative values as inverters, and we use weights to adjust the value.In order to effortlessly apply the Multiplexer for experimenting with an artificial neural network and set weights to obtain the desired results, we are also building Verilog-HdL code.Keywords: Multiplexer, CMOS,ANN,Verilog-HDL. Keywords: Multiplexer De-multiplexer, Decoder, Encoder Verilog-HDL, CMOS, ANN, Verilog HDL

Organic-Inorganic Hybrid Synaptic Transistors: Methyl-Silsesquioxanes-Based Electric Double Layer for Enhanced Synaptic Functionality and CMOS Compatibility.

Electrical double-layer (EDL) synaptic transistors based on organic materials exhibit low thermal and chemical stability and are thus incompatible with complementary metal oxide semiconductor (CMOS) processes involving high-temperature operations. This paper proposes organic-inorganic hybrid synaptic transistors using methyl silsesquioxane (MSQ) as the electrolyte. MSQ, derived from the combination of inorganic silsesquioxanes and the organic methyl (-CH3) group, exhibits exceptional thermal and chemical stability, thus ensuring compatibility with CMOS processes. We fabricated Al/MSQ electrolyte/Pt capacitors, exhibiting a substantial capacitance of 1.89 µF/cm2 at 10 Hz. MSQ-based EDL synaptic transistors demonstrated various synaptic behaviors, such as excitatory post-synaptic current, paired-pulse facilitation, signal pass filtering, and spike-number-dependent plasticity. Additionally, we validated synaptic functions such as information storage and synapse weight adjustment, simulating brain synaptic operations through potentiation and depression. Notably, these synaptic operations demonstrated stability over five continuous operation cycles. Lastly, we trained a multi-layer artificial deep neural network (DNN) using a handwritten Modified National Institute of Standards and Technology image dataset. The DNN achieved an impressive recognition rate of 92.28%. The prepared MSQ-based EDL synaptic transistors, with excellent thermal/chemical stability, synaptic functionality, and compatibility with CMOS processes, harbor tremendous potential as materials for next-generation artificial synapse components.

Implementation of an Artificial Neural Network Based on the Backpropagation Algorithm in Forecasting the Closing Price of the Jakarta Composite Index (IHSG)

Investing is highly common in Indonesia. Continuous investment activities carried out by the community will increase economic activity and employment opportunities, increase national income, and increase the level of prosperity of the community. In carrying out share buying and selling transactions, there is a means for companies to obtain funds from official financiers or investors, which is called the capital market. One of the indices issued by the IDX is the Jakarta Composite Index (IHSG). Statistics can be used to help investors, the government, or related institutions to predict the value of the IHSG. One method that can be used to predict data is an Artificial Neural Network (ANN). Backpropagation method is a multi-layer ANN method that works in a supervised learning. The idea of the Backpropagation algorithm is that the input of the neural network is evaluated against the desired output results of the research showed that the BP (4,6,1) model produced an RMSE value of 28,24024 and a MAPE value of 0.00342%. Based on the results of this research, an Artificial Neural Network model based on the Backpropagation Algorithm can be applied to predict the IHSG Closing Price value.

The Comparison of the Multi-Layer Artificial Neural Network Training Methods in Terms of the Predictive Quality of the Coefficient of Friction of 1.0338 (DC04) Steel Sheet.

Friction is one of the main phenomena accompanying sheet metal forming methods, affecting the surface quality of products and the formability of the sheet metal. The most basic and cheapest way to reduce friction is to use lubricants, which should ensure the highest lubrication efficiency and at the same time be environmentally friendly. Due to the trend towards sustainable production, vegetable oils have been used in research as an alternative to petroleum-based lubricants. The analysis of friction in sheet metal forming requires an appropriate tribotester simulating the friction conditions in a specific area of the sheet metal being formed. Research has used a special strip drawing tribometer, enabling the determination the value of the coefficient of friction in the blankholder zone in the deep drawing process. Quantitative analysis of the friction phenomenon is necessary at the stage of designing the technological process and selecting technological parameters, including blankholder pressure. This article presents the results of friction testing of 1.0338 (DC04) steel sheets using a strip drawing test. The experimental tests involved pulling a strip of sheet metal between two countersamples with a rounded surface. The tests were carried out on countersamples with different levels of roughness for the range of contact pressures occurring in the blankholder zone in the deep drawing process (1.7-5 MPa). The values of the coefficient of friction determined under dry friction conditions were compared with the results for edible (corn, sunflower and rapeseed) and non-edible (Moringa, Karanja) vegetable lubricants. The tested oils are the most commonly used vegetable-based biolubricants in metal forming operations. Multi-layer artificial neural networks were used to determine the relationship between the value of the contact pressure, the roughness of the countersamples, the oil viscosity and density, and the value of the coefficient of friction. Rapeseed oil provided the best lubrication efficiency during friction testing for all of the tested samples, with an average surface roughness of Sa 0.44-1.34 μm. At the same time, as the roughness of the countersamples increased, a decrease in lubrication efficiency was observed. The lowest root mean squared error value was observed for the MLP-4-8-1 network trained with the quasi-Newton algorithm. Most of the analysed networks with different architectures trained using the various algorithms showed that the kinematic viscosity of the oil was the most important aspect in assessing the friction of the sheets tested. The influence of kinematic viscosity on the value of the coefficient of friction is strongly dependent on the surface roughness of the countersamples.

Analysis of Coefficient of Friction of Deep-Drawing-Quality Steel Sheets Using Multi-Layer Neural Networks

This article presents the results of an analysis of the influence of friction process parameters on the coefficient of friction of steel sheets 1.0347 (DC03), 1.0338 (DC04) and 1.0312 (DC05). A special tribometer was designed and manufactured in order to simulate the friction phenomenon occurring in the blankholder area in deep drawing operations. Lubricant was supplied to the contact zone under pressure. The value of the coefficient of friction was determined under various contact pressures and lubrication conditions. Multi-layer artificial neural networks (ANNs) were used to predict the value of the coefficient of friction. The input parameters considered were the kinematic viscosity of lubricants, contact pressure, lubricant pressure, selected mechanical properties and basic surface roughness parameters of sheet metals. The value of the coefficient of friction of 1.0312 steel sheets was predicted based on the results of friction tests on 1.0347 and 1.0338 steel sheets. Many ANN models were built to find a neural network that will provide the best prediction performance. It was found that to ensure a high performance of ANN prediction, it is necessary to simultaneously take into account all the considered roughness parameters (Sa, Ssk and Sku). The predictive performance of the ‘best’ network was greater than R2 = 0.98. The lubricant pressure had the greatest impact on the coefficient of friction. Increasing the value of this parameter reduces the value of the coefficient of friction. However, the greater the contact pressure, the smaller the beneficial effect of pressure-assisted lubrication. The third parameter of the friction process, the kinematic viscosity of the oil, exhibited the smallest impact on the coefficient of friction.

A hybrid ANN-MILP model for agile recovery production planning for PPE products under sharp demands

Today, supply chains (SCs) have been struggling with a new type of disruption known as outbreaks such as epidemics or pandemics. This type of disruption has features such as long-term and uncertain lifespan and leads to severe fluctuations in product demand. This paper elaborates on a hybrid approach based on artificial neural networks (ANN) and mathematical programming techniques to efficiently deal with this type of disruption in the production planning of SCs. In the first phase of the hybrid approach, a multi-layer perceptron ANN model with an optimised structure is developed to efficiently predict the demand and its peak points. In the second phase, the predicted demands are considered as input in a new multiobjective agile recovery production planning model. The proposed model minimises total costs and delivery times and maximises responsiveness. A real case study in Iran is conducted to verify and validate the proposed hybrid approach. The prediction error of the ANN method is about 1 percent. According to the predicted demand, optimal decisions are determined by the proposed model. The impact of under-estimation and over-estimation of demand is evaluated in terms of total costs, delivery time, responsiveness and shortage costs in the SCs.

Optimizing Quality and Shelf-Life Extension of Bor-Thekera (Garcinia pedunculata) Juice: A Thermosonication Approach with Artificial Neural Network Modeling

This study investigated the quality characteristics of pasteurized and thermosonicated bor-thekera (Garcinia pedunculata) juices (TSBTJs) during storage at 4 °C for 30 days. Various parameters, including pH, titratable acidity (TA), total soluble content (TSSs), antioxidant activity (AA), total phenolic content (TPC), total flavonoid content (TFC), ascorbic acid content (AAC), cloudiness (CI) and browning indexes (BI), and microbial activity, were analyzed at regular intervals and compared with the quality parameters of fresh bor-thekera juice (FBTJ). A multi-layer artificial neural network (ANN) was employed to model and optimize the ultrasound-assisted extraction of bor-thekera juice. The impacts of storage time, treatment time, and treatment temperature on the quality attributes were also explored. The TSBTJ demonstrated the maximum retention of nutritional attributes compared with the pasteurized bor-thekera juice (PBTJ). Additionally, the TSBTJ exhibited satisfactory results for microbiological activity, while the PBTJ showed the highest level of microbial inactivation. The designed ANN exhibited low mean squared error values and high R2 values for the training, testing, validation, and overall datasets, indicating a strong relationship between the actual and predicted results. The optimal extraction parameters generated by the ANN included a treatment time of 30 min, a frequency of 44 kHz, and a temperature of 40 °C. In conclusion, thermosonicated juices, particularly the TSBTJ, demonstrated enhanced nutritional characteristics, positioning them as valuable reservoirs of bioactive components suitable for incorporation in the food and pharmaceutical industries. The study underscores the efficacy of ANN as a predictive tool for assessing bor-thekera juice extraction efficiency. Moreover, the use of thermosonication emerged as a promising alternative to traditional thermal pasteurization methods for bor-thekera juice preservation, mitigating quality deterioration while augmenting the functional attributes of the juice.

Forecasting the Nexus and Impact of News Sentiment on NYSE, Gold Prices, and WTI Oil Using The Neural Network Approach

The study aims to determine the impact of news sentiment during COVID-19 and the nexus between the NYSE, Gold prices, and WTI oil prices. For this purpose, we use hourly data of news sentiment and commodity prices hourly data from February 10, 2020, hours 1000 to March 06, 2020 hours 1600 hours, with the help of a multilayer artificial neural network. Further, We use the connection weight approach to observe the relative importance of input variables that help predict output variables. The results suggest that all the indexes are interconnected, where the increase/decrease of any index impacts the other commodity index. Further, the news index remains the second highest predictor for gold price and WTI oil price indexes. In addition, the news index also seems to be an essential predictor of these commodities.
 Keywords: NYSE, WTI Oil, Gold, Covid-19, Neural Network, Connection Weight

Modelling and forecasting India's electricity consumption using artificial neural networks

AbstractPrecise electricity forecasting is a pertinent challenge in effectively controlling the supply and demand of power. This is due to the inherent volatility of electricity, which cannot be stored and must be utilised promptly. Thus, this study develops a framework integrating canonical cointegrating regressions (CCR), time series artificial neural network (ANN) and a multilayer perceptron ANN model for analysing and projecting India's gross electricity consumption to 2030. Annual data for the years 1961–2020 have been collected for variables like gross domestic product (GDP), population, inflation GDP deflator (annual %), annual average temperature and electricity consumption. The study was conducted in three phases. In the first phase of the study, the CCR method was used to check the significance of the selected variables. In the second phase, the projected values of independent variables (GDP, population, inflation GDP deflator [annual %] and annual average temperature) were predicted using the time series ANN model. Finally, a multilayer perceptron ANN model with independent variables was used to forecast the gross electricity consumption in India by 2030. The result shows that the electricity consumption in India will increase by around 50% in the next 10 years, reaching over 1800 TWh in 2030. The proposed approach can be utilised to effectively implement energy policies, as an accurate prediction of energy consumption can help capture future demand.

Enhancing Solar Radiation Forecasting in Diverse Moroccan Climate Zones: A Comparative Study of Machine Learning Models with Sugeno Integral Aggregation

Hourly solar radiation (SR) forecasting is a vital stage in the efficient deployment of solar energy management systems. Single and hybrid machine learning (ML) models have been predominantly applied for precise hourly SR predictions based on the pattern recognition of historical heterogeneous weather data. However, the integration of ML models has not been fully investigated in terms of overcoming irregularities in weather data that may degrade the forecasting accuracy. This study investigated a strategy that highlights interactions that may exist between aggregated prediction values. In the first investigation stage, a comparative analysis was conducted utilizing three different ML models including support vector machine (SVM) regression, long short-term memory (LSTM), and multilayer artificial neural networks (MLANN) to provide insights into their relative strengths and weaknesses for SR forecasting. The comparison showed the proposed LSTM model had the greatest contribution to the overall prediction of six different SR profiles from numerous sites in Morocco. To validate the stability of the proposed LSTM, Taylor diagrams, violin plots, and Kruskal–Wallis (KW) tests were also utilized to determine the robustness of the model’s performance. Secondly, the analysis found coupling the models outputs with aggregation techniques can significantly improve the forecasting accuracy. Accordingly, a novel aggerated model that integrates the forecasting outputs of LSTM, SVM, MLANN with Sugeno λ-measure and Sugeno integral named (SLSM) was proposed. The proposed SLSM provides spatially and temporary interactions of information that are characterized by uncertainty, emphasizing the importance of the aggregation function in mitigating irregularities associated with SR data and achieving an hourly time scale forecasting accuracy with improvement of 11.7 W/m2.

Simultaneously maximizing microalgal biomass and lipid productivities by machine learning driven modeling, global sensitivity analysis and multi-objective optimization for sustainable biodiesel production

Simultaneous enhancement in the productivities of microalgal biomass and lipids that are inversely correlated to each other has been a long-standing challenge before the scientists engaged in algal biodiesel research and innovation. This study, develops an Artificial Neural Network (ANN) model and uses multi-objective optimization approach to determine the process parameters, namely, light intensity, CO2%, air flow rate, and C/N ratio to maximize biomass and lipid productivities of microalga, Chlorella sorokiniana, simultaneously. Experimental data based on central composite design (CCD) were used to train a feed-forward multilayer ANN with four critical parameters. The global sensitivity analysis was performed on the trained ANN model using Sobol's method to assess the relative significance of the four process variables. Since the objectives of maximizing biomass and lipid productivities conflict with each other, multi-objective optimization approach was used for deriving the optimal process parameters, experimental validation of which resulted in approximately 3 times increase in biomass productivity and 7 times increase in lipid productivity. On transesterification of the lipid, the biodiesel product with saturated to unsaturated fatty acids ratio of 48:52 conformed very well to the international standards, ASTM D6751 and EN14214, thereby making it an environment friendly green biofuel. Thus, the present study showcases the successful application of machine learning tool in analysing the global sensitivity analysis of process variables, and employ it for multi-objective optimization towards achieving maximum microalgal biomass and lipid productivities concomitantly for potentially sustainable production of biodiesel.

Robust Prediction of Healthcare Inflation Rate With Statistical and AI Methods in Iran

The expected healthcare (HC) inflation rate (IR) (HCIR) is an important variable for all economic agents within HC systems. In recent years, during the COVID‐19 pandemic, Iran has experienced a high HCIR in its health system. In this context, a robust approximation of HCIR will be a helpful tool for health authorities and other decision makers. Using monthly time series data of HCIR in Iran, we developed various forecasting techniques based on classical smoothing methods, decomposition ETS (error, trend, and seasonality) approaches, autoregressive (AR) integrated moving average (ARIMA), seasonal ARIMA (SARIMA), and a multilayer nonlinear AR artificial neural network (NARANN) with several training algorithms including Bayesian regularization (BR), Levenberg–Marquardt (LM), scaled conjugate gradient (SCG), Broyden–Fletcher–Goldfarb–Shanno (BFGS) quasi‐Newton, conjugate gradient with Powell–Beale restarts (CGB), conjugate gradient with Fletcher–Reeves updates (CGF), and resilient propagation (RPROP) algorithms. Initially, based upon various criteria and possible combinations, we selected the superior model for each method separately. After that, the best model in each category is involved in 6‐ and 12‐multi‐step‐ahead prediction. In this stage, several error criteria are calculated. According to our findings, in a six‐step forecasting window, the Holt–Winters with a multiplicative seasonal pattern and SARIMA showed less bias, though compared to other alternatives like NARANN‐lm/br, the difference was relatively small. In the next process, by doubling the forecasting window, it is observed that artificial neural network (ANN) (i.e., Bayesian NARANN) strictly outperformed other models. As a result, in shorter steps, the Holt–Winters method can provide a better prediction, while in longer windows, Bayesian NARANN can be implemented vigorously for the prediction. Finally, we used 10 models to predict the future trend of HCIR in Iran till the end of July 2024.

Predicting Rheological Properties of Wheat Dough from Flour Properties Using NIR Coupled with Artificial Neural Network

Highlights A multi-layered perceptron type artificial neural network (ANN) was developed to predict the farinograph properties of wheat dough. ANN models with two to four hidden layers were developed for each Farinograph response, i.e., water absorption, dough development time, and dough stability. Permutation importance and Shapley values analysis emphasized the significance of protein content in model prediction. The developed model would serve as a decision support tool for flour mill and bakery managers and would assist in adapting flour mill settings and modifying processing parameters in bakeries. Abstract. Farinograph analysis serves as the standard for determining the baking quality of wheat flour. It measures the water absorption (WA), dough development time (DDT), and stability (DS) of wheat dough. These characteristics depend on wheat flour properties such as protein content, moisture, ash, and falling number. Additionally, farinograph analysis is costly, time-consuming, and requires skilled personnel. Therefore, an artificial neural network (ANN) model was developed to predict the farinograph properties of wheat dough as a function of flour properties. The models were developed using data from 192 wheat samples. Multi-layer perceptron-type feed-forward ANN models with increasing complexity were developed for each response variable, i.e., ANN-WA, ANN-DDT, and ANN-DS, and model success was evaluated via mean squared error (MSE) and correlation coefficient (r). The optimal models had two to four hidden layers, each with five to sixty neurons, and exhibited the lowest MSE and highest r values. In terms of predictive performance, the models ANN-WA and ANN-DDT (r = 0.79) demonstrated superior performance when compared with ANN-DS (r = 0.63). A feature importance analysis was conducted to provide insight on variable contributions, underscoring the significance of flour protein content in the model’s prediction. The study explored the applicability of data-driven ANN models in predicting the rheological characteristics of dough. The developed models could serve as a decision support tool and aid millers in adjusting mill settings and bakers in modifying dough mixing based on dough rheology. Keywords: Artificial neural network, Dough rheology, Farinograph analysis, Milling, NIR.

Smart solutions for dissolved oxygen control in semi-batch fermenters: A machine learning approach

The monitoring and measurement of dissolved oxygen (DO), plays a significant role in industrial effluent, and disposal of effluents has been a serious challenge to researchers. Aeration is the primary step in effluent treatment. The basic method for determining DO is direct measurement. This study has been carried out in effluent from the paper industry to estimate the performance of dissolved oxygen in a semi-batch lark hygiene fermenter and was oxygenated till saturation at 25 °C under real-time study. This study has been carried out using Multi-Layered Feed Forward Back Propagation Artificial Neural Network (ML-FFBPANN) and the experimental results were optimized with fourteen different machine learning algorithms such as Polak-Ribiere Conjugate Gradient (CGP), Conjugate Gradient with Powell/Beale Restarts (CGB), Bayesian Regularization (BR), Gradient Descent (GD), BFGS Quasi-Newton (BFG), GD with Momentum (GDM), Gaussian Discriminate Analysis(GDA), Fletcher-Powell Conjugate Gradient (CGF), Resilient Backpropagation (RP), Variable Learning Rate Gradient Descent (GDX), One Step Secant (OSS), Regression (R), Levenberg-Marquardt (LM), Scaled Conjugate Gradient(SCG). Parameters Root Mean Squared Error (RMSE) and the correlation coefficient (R-square) were used to evaluate the performance of each model and mathematical modeling for correlation co-efficient of all algorithms are also presented. LM and R are the best algorithms but the ML-FFBPANN-LM algorithm provided the best optimization results of maximum correlation co-efficient. LM algorithm produced a maximum correlation coefficient under training (0.99818), testing (0.99351), valediction (1.0), and an overall correlation coefficient be 0.9568. From the study, it can be concluded that the ML-FFBPANN with LM algorithm can be used in the aeration process in effluent treatment for analysis and prediction of process parameters.

UTILIZING DEEP LEARNING TECHNOLOGIES TO FORM PRICING MODELS

The work is devoted to evaluating pricing models for calculating the profitability of individual financial instruments, for example, such as stocks, using a multilayer generative-adversarial artificial neural network (GAN) and developing its own model based on the analysis. A huge amount of specially selected data is supplied to the inputs of the neural network, changing over time (dynamic). To improve the objectivity of the model, this work does not implement the arbitration capabilities of the markets. This is how one can analyze and explain variations and errors in pricing, as well as identify the key factors affecting asset prices. As the outcome, it was found out that the resulting asset pricing model exceeds all standard approaches for its assessment using the Sharpe ratio. It was shown that machine learning is an acceptable tool in term of investment forecasting, on the example of Russian Federation market.

SELECTING A COMPUTER NETWORK SIMULATOR FOR HIGHER EDUCATION

The work is devoted to evaluating pricing models for calculating the profitability of individual financial instruments, for example, such as stocks, using a multilayer generative-adversarial artificial neural network (GAN) and developing its own model based on the analysis. A huge amount of specially selected data is supplied to the inputs of the neural network, changing over time (dynamic). To improve the objectivity of the model, this work does not implement the arbitration capabilities of the markets. This is how one can analyze and explain variations and errors in pricing, as well as identify the key factors affecting asset prices. As the outcome, it was found out that the resulting asset pricing model exceeds all standard approaches for its assessment using the Sharpe ratio. It was shown that machine learning is an acceptable tool in term of investment forecasting, on the example of Russian Federation market.