Artificial Neural Network Tool Research Articles

Fundamental Frequency Prediction of Historic Masonry Towers Based on Artificial Neural Networks

ABSTRACT Historic masonry towers, with their brittle materials, slenderness, and distinctive shapes, are highly susceptible to lateral excitations. The urgency of preserving surviving ones in earthquake-prone regions has become apparent. There is a prioritization of identifying and reinforcing the most vulnerable masonry towers. Predictions are based on the earthquake spectrum specific to each region, effectively alerting to the seismic vulnerabilities of towers constructed within those areas. Rather than formulating relationships based on known geometrical parameters, this study relies on an Artificial Neural Network (ANN)-based model to promptly estimate the fundamental frequency of masonry towers. Measurements taken from 19 actual masonry towers are utilized for training the networks. Various distinct tower parameters as well as their combinations are considered. Only geometrical information is taken into account while material properties are not considered. The performances of ANNs are directly compared to some empirical equations. The ANN-based techniques are evaluated by testing with 20 different towers that are not considered in the training process. The proposed ANN tool demonstrates practicality and robustness when estimating the lowest frequency of masonry towers based only on geometrical information. The slenderness ratio that is rarely considered in existing equations remarkably enhances the accuracy of fundamental frequency anticipation.

Development of red mud based sintered artificial aggregates with various industrial wastes

Red mud (RM) has drawn a lot of attention in the search for potential uses in the production of sintered artificial aggregate from industrial waste products. The main objective of the study is to produce an RM-based sintered artificial aggregate (SAA), with several blends (binary, ternary, and quaternary) using various industrial wastes. This study includes assessing the mechanical and physical properties of SAA as well as the sintering parameters in order to determine the appropriate material mix ratio. To achieve these objectives, a comprehensive experimental approach was adopted. A total of 35 different mixtures were formulated by incorporating various industrial wastes as binders and sintering additives. The green pellets were preheated at 105 °C for 24 h, and consecutively sintered at different temperatures, namely 700 °C, 900 °C, 1100 °C, and 1150 °C with a duration of 30 min. A compressive strength test was performed in order to find the mechanical property of SAA similarly water absorption and bulk density tests were conducted to find the physical properties of SAA. To characterize the SAA, scanning electron microscope analysis (SEM), X-ray diffraction (XRD) and energy dispersive x-ray analysis were conducted, and also data analysis was performed using Artificial Neural Network (ANN) tools, yielding accurate predictions. Successfully best compressive strength low water absorption SAA was produced. The best material weight mix ratio for the production of SAA was identified as (A18) RM: Fly Ash: Waste Glass Powder; 78:10:12. Out of all blends the ternary blend (A18) SAA exhibited impressive properties after 30 min of sintering at 1150 °C: high compressive strength of 22.92 MPa, water absorption of 4.26%, and bulk density of 1296.12 kg m−13. This was made possible by the high amount of Al2O3, SiO2, in the combination of fly ash, and waste glass powder with RM. SEM and XRD analysis also confirmed that the (A18) SAA achieved the best compressive strength, and low water absorption due to turning the surface and core area into a solid, reduced internal pores and formed quartz, and hematite phases. The findings of this study serve as a foundation for future work and pave the way for the development of sustainable construction materials.

Artificial neural network-based modeling of Malachite green adsorption onto baru fruit endocarp: insights into equilibrium, kinetic, and thermodynamic behavior

In this study, artificial neural network (ANN) tools were employed to forecast the adsorption capacity of Malachite green (MG) by baru fruit endocarp waste (B@FE) under diverse conditions, including pH, adsorbent dosage, initial dye concentration, contact time, and temperature. Enhanced adsorption efficiency was notably observed under alkaline pH conditions (pH 10). Kinetic analysis indicated that the adsorption process closely followed a pseudo-second-order model, while equilibrium studies revealed the Langmuir isotherm as the most suitable model, estimating a maximum adsorption capacity of 57.85 mg g−1. Furthermore, the chemical adsorption of MG by B@FE was confirmed using the Dubinin–Radushkevich isotherm. Thermodynamic analysis suggested that the adsorption is spontaneous and endothermic. Various ANN architectures were explored, employing different activation functions such as identity, logistic, tanh, and exponential. Based on evaluation metrics like the coefficient of determination (R 2) and root mean square error (RMSE), the optimal network configuration was identified as a 5–11-1 architecture, consisting of five input neurons, eleven hidden neurons, and one output neuron. Notably, the logistic activation function was applied in both the hidden and output layers for this configuration. This study highlights the efficacy of B@FE as an efficient adsorbent for MG removal from aqueous solutions and demonstrates the potential of ANN models in predicting adsorption behavior across varying environmental conditions, emphasizing their utility in this field.

Application of artificial neural networks and Langmuir and Freundlich isotherm models to the removal of textile dye using biosorbents: A comparative study among methodologies

AbstractAdsorption isotherms are valuable tools for describing the interaction between adsorbate and adsorbent since they demonstrate the equilibrium relationship. The Langmuir and Freundlich models are the most commonly used isotherm models to describe these relationships; still, they cannot consistently deliver efficient results due to the assumptions of the model not predicting more complex situations as occurs in biosorption. Artificial neural networks (ANN) are a set of algorithms modelled loosely after the human brain and are designed to recognize patterns. The ANN tool can overcome problems isotherm models have in describing the interactions mentioned and help define the best conditions for a given adsorption process. This paper reports the application of ANNs for predicting the removal efficiency of textile dye Neolan Black WA (Acid Black 52) using orange peel and sugarcane bagasse as biosorbents. The Freundlich, Langmuir, pseudo‐first‐order, and pseudo‐second‐order models were applied and compared to the ANN model. The parameters evaluated were initial dye concentration (10–600 mg/L), final dye concentration (0–83.44 mg/L), biosorbent mass (1.5 g), pH (2), and contact time of dye (0.167–24 h). Two classes of ANNs, Elman and feed‐forward networks, were tested with a mean square error of 0.0212 and 0.7274 for the isotherm and kinetics, respectively. Compared to the conventional isotherm and kinetic models, the Elman network predicted the amount adsorbed by the biosorbents with higher precision, acquiring a determination coefficient of 0.9998 and a mean square error of 8.75 × 10−5.

Numerical paradigm to explore the chemically reacting Williamson nanofluid flow with the influence of bioconvection effects using neural networks

This research focuses on using a type of advanced computer program called artificial neural networks (ANNs) using the Levenberg–Marquardt backpropagation technique (LMBPT-NNs) to stimulate the behavior of bioconvective effects on heat and mass transport in non-Newtonian chemically Williamson nanofluid through stretched surface (BEHMT-NNCRWNF-SS). Williamson nanofluid flow refers to specially engineered fluids with nanoparticles dispersed within them and moving when subjected to external forces. Understanding their flow behavior is crucial to effectively use them in various applications, especially in the field of heat and mass transfer and fluid dynamics. Before being solved numerically, a couple of governing partial differential equations are transformed into ordinary differential equations through suitable similarity functions. The finite difference method (FDM) (Lobatto IIIA) is applied for the solution of a nonlinear nanofluidic system by selecting different collocation points. These collocation points play a crucial role in approximating the solution and ensuring accurate results. To address various fluidic problems, it's important to use the appropriate results of FDM to create a reference dataset for the LMBPT-NNs. Statistical analysis should then be performed on the training, testing, and validation of the reference datasets to obtain the optimal scheme for different fluidic flow issues.. The precision of the LMBPT-NNs is checked through ANN tools like mean square error (MSE), regression analysis, curve fitness, and histogram error.

Sensitivity Analysis Based on Physical Properties to Permeability Coefficient of Cohesive Soil Using Artificial Neural Network

Permeability is the ability of a soil to allow liquids to pass through. Of course the soil has a physical characteristic that can be known by laboratory testing. This study aims to determine the physical properties that most affect the coefficient of cohesive soil permeability using the Artificial Neural Network (ANN) tool, the results obtained will later be matched with actual conditions according to the context of engineering geology. The research method begins with an influence or sensitivity analysis using ANN which will produce a correlation coefficient (R). Then, these results will be compared with the influence analysis based on the value of the coefficient of determination (R2). After that, accuracy and error tests will be carried out using the Mean Absolute Percentage Error (MAPE), the highest accuracy values is categorized as the most influential physical property of the 7 physical property parameters, namely liquid limit, plastic limit, plasticity index, %sand, %fines, %silt, and %clay. Based on the result of the analysis, %fines is the parameter that most influences permeability and is able to make very strong predictions with an R value using an ANN of 0.9941875, an R2 value of 0.6336, an accuracy of 99.6962%, and a MAPE of 0.3038%. These results are compared with the existing empirical equations with an accuracy of 96.4393% and MAPE of 3.5607%. It can be concluded that ANN is more effective and optimal in making predictions. In this case, in the context of engineering geology, the more %fines, the smaller the permeability coefficient of the soil.

Biodegradation of ciprofloxacin using machine learning tools: Kinetics and modelling

Recently, the rampant administration of antibiotics and their synthetic organic constitutes have exacerbated adverse effects on ecosystems, affecting the health of animals, plants, and humans by promoting the emergence of extreme multidrug-resistant bacteria (XDR), antibiotic resistance bacterial variants (ARB), and genes (ARGs). The constraints, such as high costs, by-product formation, etc., associated with the physico-chemical treatment process limit their efficacy in achieving efficient wastewater remediation. Biodegradation is a cost-effective, energy-saving, sustainable alternative for removing emerging organic pollutants from environmental matrices. In view of the same, the current study aims to explore the biodegradation of ciprofloxacin using microbial consortia via metabolic pathways. The optimal parameters for biodegradation were assessed by employing machine learning tools, viz. Artificial Neural Network (ANN) and statistical optimization tool (Response Surface Methodology, RSM) using the Box-Behnken design (BBD). Under optimal culture conditions, the designed bacterial consortia degraded ciprofloxacin with 95.5% efficiency, aligning with model prediction results, i.e., 95.20% (RSM) and 94.53% (ANN), respectively. Thus, befitting amendments to the biodegradation process can augment efficiency and lead to a greener solution for antibiotic degradation from aqueous media.

Adsorptive removal of Cu(II) ions from aqueous solution using Teff (Eragrostis tef) hay based magnetized biocarbon: RSM-GA, ANN based optimization and kinetics aspects

Abstract Teff (Eragrostis tef) is known as a staple grain crop which grown as edible seeds, remarkably in Ethiopia. However, after removal of its seeds, the hay obtained from this crop, are considered as agro-waste. Such a lignocellulosic agro-waste can be potentially exploited to prepare activated carbon to treat the harmful industrial effluents for detoxification. In this study, Teff hay (TH) was used as a precursor for preparing the activated carbon. The using H2SO4, chemical activation was carried out followed by carbonaceous process was undertaken to prepared activated carbon (AC) by pyrolysis. The prepared AC was modified as magnetized AC and characterized by various characterization methods. Further, as a bio-absorbent, its potency for adsorptive removal of Cu(II) ions was ascertained. Notably, the most studied and important process factors for adsorption, such as, initial concentration of metal ion, adsorbent dosage, pH, and contact time were subjected to optimization using response surface method (RSM). Further, to ensure the statistical optimization of aforementioned factors, the non-statistical techniques, genetic algorithm (GA) and artificial neural network (ANN) tools have been executed. In addition, the use of prepared Teff-hay based magnetized AC for the removal of Cu(II) by adsorption process was evaluated through different isotherms and kinetic approaches.

Using ANN for well type identifying and increasing production from Sa’di formation of Halfaya oil field – Iraq

Abstract The current study focuses on utilizing artificial intelligence (AI) techniques to identify the optimal locations of production wells and types for achieving the production company’s primary objective, which is to increase oil production from the Sa’di carbonate reservoir of the Halfaya oil field in southeast Iraq, with the determination of the optimal scenario of various designs for production wells, which include vertical, horizontal, multi-horizontal, and fishbone lateral wells, for all reservoir production layers. Artificial neural network tool was used to identify the optimal locations for obtaining the highest production from the reservoir layers and the optimal well type. For layer SB1, the average daily production is 291.544 STB/D with the horizontal well, 441.82 STB/D with the multilateral well, and 1298.461 STB/D with the fishbone well type. Also, for the SB2 layer: 197.966, 336.9834, and 924.554 STB/D, and for the SB3 layer: 333.641, 546.6364, and 1187.159 STB/D for the same well type sequence. The cumulative production for each formation layer is 22.440 MMSTB from the horizontal well, 59.05 MMSTB from the multilateral well, and 84.895 MMSTB from the fishbone well types for the SB1 layer; 48.06, 70.1094, and 160.254 MMSTB for SB2; and 75.2764, 111.7325, and 213.1291 MMSTB for SB3 for the same well types.

Prediction of Cumulative Biomethane Yield Using Artificial Neural Network - Case Study of an Industrial Biogas Plant

There are many facets to the applications of Artificial Intelligence (AI) in the energy sector however, this research focuses on the utilization of Artificial Neural Networks (ANN) as parts of AI technique to simulate and model the operating performance of an industrial biogas plant data set. In this study, eight (8) model network architectures were developed using the ANN tool of MATLAB 2016a version and it was found that the best result was obtained based on the model performance evaluation metrics used such as Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), Mean Absolute Deviation (MAD) and Determination Coefficient (R2) was as a result of the combination of two activation functions namely: tansig and logsig. The model, that produced the best result was a result of the architecture that contains 2 hidden neurons and the training algorithm of Scaled Conjugate Gradient (SCG). It was also observed that the ANN-predicted network diagram is better than the observed.

Building MLR, ANN and FL models to predict the strength of problematic clayey soil stabilized with a combination of nano lime and nano pozzolan of natural sources for pavement construction

The current study aims at predicting the strength of the problematic clayey soils treated with combinations of pozzolan of natural sources and lime powder when added as soil additives at a nano scale. Multiple linear regression (MLR), artificial neural networks (ANN) and fuzzy logic (FL) tools were employed in the analytical study. The variables of the present study include the following: nano pozzoaln of natural source (NNP) content, nano lime content (NL), median particle size of NNP, active silica content of NNP (SiO2active), Initial liquid limit (ILL) and initial plastic limit (IPL) of the investigated soils. NNP was added at five percentages, i.e. 0%, 0.5%, 1%, 1.5% and 2%, while NL was added at five percentages, i.e. 0%, 0.3%, 0.6%, 0.9% and 1.2%. Three median particle sizes namely 50, 100 and 500 nm size were studied. Based on the different investigated soils and combinations, 120 soil mixtures were prepared and tested. California bearing ratio (CBR) and plasticity index (PI) were particularly examined. CBR tests were conducted at a soaked condition on specimens compacted to a maximum dry density (MDD) at the optimum moisture content (OMC). PI values were obtained following the Atterberg limits test. Based on the results of the performance criteria of the developed predictive models, it can be concluded that the CBR and PI of the expansive clayey soils can be effectively predicted using ANN and FL techniques. The results obtained by MLR were far from those obtained by both ANN & FL. In addition, ANN tool was slightly more accurate than FL as far as prediction of CBR and PI is concerned. The higher capability of ANN & FL models in predicting CBR & PI values, which generally obtained through time-consuming and expensive tests, could be useful for geotechnical engineers to assess or design a new pavement project. Further, it is recommended to do a re-evaluation of the current study in future, particularly when more data is available in the literature.

An artificial-intelligence-based petrophysical property predictor for compositional volatile oil reservoir using three-phase production data

When considering multiphase flow scenarios, the interpretation of petrophysical properties poses significant challenges for production forecasts and reservoir modeling. The findings of the numerical modeling were therefore subject to uncertainty because characteristics like relative permeability and capillary pressure curve were hardly ever bound by interpretations. The uncertainty may result in inaccurate predictions of reservoir performance and skewed perceptions of the reservoir. Due to the difficulty in directly interpreting such property from the available field data and the expensive cost of coring, analyses or experimental measurements to determine relative permeability and capillary pressure were infrequently carried out. Such a gap would be filled by a straightforward yet rigorous method. In this study, we develop production projections for a wide range of three-phase compositional volatile oil reservoirs. Then, we used an artificial neural network to figure out how petrophysical characteristics and production data relate to one another. The artificial neural network model was adjusted, and the final trained model was tested blindly to determine how well it predicted permeability, multiphase relative permeability, and capillary pressure data. For the testing scenarios, consistency is seen between the predicted values and the original ones, despite some mispredictions being present. To provide production projections that can be compared to those from the reservoir model that include the initial petrophysical characteristic, the anticipated properties are then propagated into reservoir models. The comparison findings show that for 65/59/34 out of 74 testing scenarios, the reservoir model with artificial neural network-predicted features can anticipate oil/gas/water output with < 20% inaccuracy. With the developed artificial neural network tool, the reservoir engineers can evaluate the three-phase relative permeability surface from rate-transient data conveniently improving the accuracy of the relative permeability data implemented by history matching or from core experiments which sometimes are extremely expensive. The findings of this study can help for a better understanding of the relationships between three-phase rate-transient data and the relative permeability surface as well as the horizontal/vertical permeability.

Machine learning, conventional and statistical physics modeling of 2,4-Dichlorophenoxyacetic acid (2,4-D) herbicide removal using biochar prepared from Vateria indica fruit biomass

The adsorption properties of 2,4-Dichlorophenoxyacetic acid (2,4-D) onto biochar, obtained through HCl-assisted hydrothermal carbonization process of Vateria indica fruits (VI-BC), were extensively studied using traditional and statistical physics approaches. The traditional adsorption investigations encompassed kinetics, equilibrium, and thermodynamics studies. Subsequently, the Hill statistical physics model was employed to interpret the mechanism. Also, artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) machine learning tools were successfully employed to model the adsorption data wherein both models had high prediction potential (R2 > 0.99). The outcomes demonstrated that the produced VI-BC exhibited remarkable adsorptive traits, having a considerable specific surface area (111.54 m2/g), pore size (5.89 nm), a variety of functional groups, and appropriate attributes for efficiently adsorbing 2,4-D. For 10 mg/L 2,4-D, at pH 2.0 and with 0.3 g/L dose, an impressive 91.67% adsorption efficiency was achieved within a 120-min. Pseudo-second-order model aptly depicted the kinetic behavior of 2,4-D adsorption, while the Freundlich model provided a more accurate representation of the isotherms. 2,4-D maximum adsorption capacity stood at 131.39 mg/g at 303 K. The Hill statistical physics model elucidated that the adsorption primarily occurred via physisorption mechanisms, involving electrostatic attractions, π-π conjugation, and pore filling. This conclusion was further substantiated by post-adsorption characterization of the VI-BC. Thermodynamic analysis indicated that the interactions between VI-BC and 2,4-D were favorable, spontaneous, and exothermic. The calculated low energy of adsorption (1.255 kJ/mol) and ΔH° value (−20.49 kJ/mol) further supported physisorption as the dominant mechanism. In summary, this study underscores the significant potential of the newly developed biochar as a promising alternative material for efficiently removing the 2,4-D herbicide from polluted environments.

Nutrient sequestration and lipid production potential of Chlorella vulgaris under pharmaceutical wastewater treatment: experimental, optimization, and prediction modeling studies.

The efficient management and treatment of pharmaceutical industry wastewater (PIWW) have become a serious environmental issue due to its high toxicity. To overcome this problem, the present study deals with the phycoremediation of PIWW using Chlorella vulgaris microalga isolated from the Ganga River at Haridwar, India. For this, response surface methodology (RSM) and artificial neural network (ANN) tools were used to identify the best reduction of total phosphorus (TP) and total Kjeldahl's nitrogen (TKN) based pollutants along with the lipid production efficiency of C. vulgaris. Three different concentrations of pharmaceutical wastewater (0, 50, and 100%), operating temperatures (20, 25, and 30 °C), and light intensity (2000, 3000, and 4000 lx) were used to design the phycoremediation experiments having 6:18 h of dark/light period and reactor functional volume of 15L. Findings revealed that C. vulgaris was good enough to remove maximum TP (90.35%), TKN (83.55%) along 20.88% of lipid yield at 25.62 °C temperature, 60.73% PIWW concentration, and 4000 lx of light intensity, respectively. Based on the model performance and validation results, ANN showed more accuracy as compared to the RSM tool. Therefore, the findings of this study showed that C. vulgaris is capable of treating PIWW efficiently along with significant production of lipid content which can further be used in various applications including biofuel production.

Taxonomic Analysis of Asteroids with Artificial Neural Networks

We study the surface composition of asteroids with visible and/or infrared spectroscopy. For example, asteroid taxonomy is based on the spectral features or multiple color indices in visible and near-infrared wavelengths. The composition of asteroids gives key information to understand their origin and evolution. However, we lack compositional information for faint asteroids due to the limits of ground-based observational instruments. In the near future, the Chinese Space Survey Telescope (CSST) will provide multiple colors and spectroscopic data for asteroids of apparent magnitude brighter than 25 and 23 mag, respectively. With the aim of analyzing the CSST spectroscopic data, we applied an algorithm using artificial neural networks (ANNs) to establish a preliminary classification model for asteroid taxonomy according to the design of the survey module of CSST. Using the SMASS II spectra and the Bus–Binzel taxonomic system, our ANN classification tool composed of five individual ANNs is constructed, and the accuracy of this classification system is higher than 92%. As the first application of our ANN tool, 64 spectra of 42 asteroids obtained by us in 2006 and 2007 with the 2.16 m telescope in the Xinglong station (Observatory Code 327) of National Astronomical Observatory of China are analyzed. The predicted labels of these spectra using our ANN tool are found to be reasonable when compared to their known taxonomic labels. Considering its accuracy and stability, our ANN tool can be applied to analyze CSST asteroid spectra in the future.

Parameter Estimation for Open Clusters using an Artificial Neural Network with a QuadTree-based Feature Extractor

With the unprecedented increase in the number of known star clusters, quick and modern tools are needed for their analysis. In this work, we develop an artificial neural network (ANN) trained on synthetic clusters to estimate the age, metallicity, extinction, and distance of Gaia open clusters. We implement a novel technique to extract features from the color–magnitude diagram of clusters by means of the QuadTree tool, and we adopt a multiband approach. We obtain reliable parameters for ∼5400 clusters. We demonstrate the effectiveness of our methodology in accurately determining crucial parameters of Gaia open clusters by performing a comprehensive scientific validation. In particular, with our analysis we have been able to reproduce the Galactic metallicity gradient as it is observed by high-resolution spectroscopic surveys. This demonstrates that our method reliably extracts information on metallicity from color–magnitude diagrams (CMDs) of stellar clusters. For the sample of clusters studied, we find an intriguing systematic older age compared to previous analyses present in the literature. This work introduces a novel approach to feature extraction using a QuadTree algorithm, effectively tracing sequences in CMDs despite photometric errors and outliers. The adoption of ANNs, rather than convolutional neural networks, maintains the full positional information and improves performance, while also demonstrating the potential for deriving cluster parameters from simultaneous analysis of multiple photometric bands, beneficial for upcoming telescopes like the Vera Rubin Observatory. The implementation of ANN tools with robust isochrone fit techniques could provide further improvements in the quest for open cluster parameters.

Acoustic absorption of 3D printed glycol‐modified polyethylene terephthalate composites with organically modified montmorillonite and short carbon fibers: Experimentation and ANN based predictive strategy

AbstractIn this article, the acoustic properties of 3D printed glycol‐modified polyethylene terephthalate (PETG) reinforced with organically modified montmorillonite (OMMT) nanoclay/short carbon fiber (SCF) nanocomposites are experimentally investigated using ASTM E1050‐08 standard. To this end, the sound absorption coefficient (SAC) of different PETG composites was calculated with the aid of two microphone impedance tube, working in the frequency range of 50–6300 Hz. The effect of different weight percentages (wt%) of OMMT nanoclay, SCFs, and 3D printing infill density on the acoustic behavior of PETG nanocomposites is studied. The experimental results reveal that higher wt% of OMMT nanoclay and SCFs has a beneficial effect on sound absorption. Further, the trend of variation of SAC is justified with morphological studies. Also, an artificial neural network (ANN) based prediction methodology to predict SAC is developed using the datasets obtained from the experimentation. Levenberg–Marquardt backward propagation algorithm with 20 neurons trains the ANN model. Using the trained ANN model, the acoustic properties of PETG/OMMT/SCF nanocomposites with different operating frequencies, infill density and wt% of reinforcements are predicted with less than 5% average error. This can be beneficial in eliminating the fabrication and experimentation costs incurred while assessing the acoustic properties of the PETG composites.Highlights PETG/OMMT nanoclay/SCF composite filaments are fabricated. Acoustic absorption of 3D printed PETG composites are experimentally studied. ANN based predictive tool is developed using experimental data. ANN tool predicts the sound absorption and reduces experimentation cost. Microstructural studies justify the trend of sound absorption.

A Comparative Study of Future Stock Price Prediction Through Artificial Neural Network and ARIMA Modelling

Stock price estimation and prediction have been the popular topics for ages for various market participants, including investors, traders, financial analysts and researchers. Understanding the expected direction of stock prices can provide insights into broader market trends and sentiments. Different methods have been tried to find out future predictions with the least degree of error. Autoregressive integrated moving average (ARIMA) and artificial neural networks (ANN) are two popular methods for time series forecasting, including stock price prediction. This paper attempts to forecast stock prices with more accuracy by using ARIMA and ANN tool modelling with secondary data NIFTY 50 of the National Stock Exchange from December 2005 to July 2019. Stock price prediction through ARIMA and ANN was calculated and compared. Results obtained revealed that the ANN method of forecasting stock prices has more accuracy as compared to ARIMA modelling. Future studies can be based on different sector-based data across different sectors and stock markets.

Machine Learning-Based Energy Consumption Estimation of Wastewater Treatment Plants in Greece

Amidst a global discourse on energy resources, it is imperative to provide decision makers with a comprehensive overview of energy consumption (EC) associated with various projects, particularly wastewater treatment plants (WWTPs). Ensuring compliance with stringent effluent quality criteria in the treatment of municipal wastewater necessitates a substantial EC, representing a predominant factor contributing to the operational expenses incurred by WWTP. Machine learning (ML) techniques can contribute to the estimation of the WWTPs’ EC, which requires efficient and accurate data. This article uses data from several municipal WWTP projects in Greece, which are examined in order to produce EC estimation models. Data were first statistically analyzed, according to the context of project attributes and the context of EC, and correlation analysis identified the appropriate predictive project variables. Then, the attribute selection function in Waikato Environment for Knowledge Analysis 3.8.4 (WEKA 3.8.4) software emphasized the most effective subset of variables. The extracted variables from the combination of the correlation analysis and the WEKA attribute function were used as input neurons for the construction of neural network (NN) models, in the Fast Artificial Neural Network Tool 1.2 (FANN Tool 1.2). The optimum NN model resulted in a mean squared error (MSE) of 8.99899 × 10−5 and was based on treatment capacity, flow rate, influent load, and served population as its inputs. Notably, the research highlights the potential generalizability of these models in Greece and beyond the Greek context, offering valuable tools for stakeholders to inform decision making, allocate resources efficiently, and improve energy-efficient designs, resulting in cost savings and sustainability benefits.

Modeling and optimization using artificial neural network and genetic algorithm of self‐propelled machine reach envelope

AbstractSelf‐propelled machinery, which is a crucial component of agricultural mechanization, is essential for enhancing production. Few farming operations involve the operation of riding‐type self‐propelled machinery for a prolonged period of time (6–8 h/day) and in a fluctuating weather condition in field condition. To alleviate the operator's workload, an ergonomically designed self‐propelled reach envelope was developed using various configuration simulator systems. To model the process and achieve the desired output for the development of simulator of self‐propelled machinery, artificial neural network (ANN) technique was employed. The simulator was tested using a range of control lever positions (0–23) and engine speeds (1600 and 2000 rpm). The study investigated how these process parameters influence the oxygen consumption rate (OCR) of female farm workers and determined the optimal operating parameters corresponding to the 100% OCR of female agricultural workers. Genetic algorithm (GA) was used along with the developed ANN model to forecast the OCR of agricultural workers. The experimental mean ± SD values were found 0.79 ± 0.04 L/min and predicated ANN values were found to be 0.776 ± 0.06 L/min for OCR, respectively. It is evident that not all operating parameters have the same effect on OCR. The training, testing, validation, and overall data sets employed in the ANN model and yielded correlation coefficients (R2) of 0.50889, 0.44229, 0.39029, and 0.48323, respectively. The goodness of fit was another crucial factor in evaluating the ANN tools. The ANN model demonstrated the lowest mean‐square error value that indicates higher precision and predictive power. The results satisfy the most suitable optimal parameters for the OCR of female workers operating the self‐propelled machinery simulator.