Artificial Neural Network Model Research Articles

Predicting and Analyzing Electric Bicycle Adoption to Enhance Urban Mobility in Belgrade Using ANN Models

The development of alternative environmentally friendly modes of transportation is becoming an increasingly promising solution in traffic-congested and polluted urban areas. E-bikes, as one of them, are recognized as an ecologically sustainable means of transportation that has significant potential to replace motorized modes of transportation that can improve urban mobility. Relying on artificial intelligence and considering an ecological approach when considering the acceptability of e-bikes by setting a direct question for users influences the development of an innovative way of understanding and evaluating the use of more sustainable modes of transportation. In this regard, this study aims to elucidate the main variables influencing the acceptability of e-bike use using artificial neural network (ANN) models—multilayer perceptron (MLP) and radial basis function (RBF). For training and testing the models, data from a random sample obtained through an online questionnaire, which was answered by 626 residents of Belgrade (Serbia), were used. A multilayer perceptron with nine and seven neurons in two hidden layers, respectively, hyperbolic tangent activation function in the hidden layer and identity function in the output layer, gave better results than the radial basis function model. With an accuracy of 89%, a precision of 83%, a recall of 79%, and an area under the receiver operating characteristic (ROC) curve of 0.927, the multilayer perceptron model recognized the influential variables in predicting acceptability. The results of the model indicate that the mileage traveled, the frequency of motorcycle use, the respondents’ awareness of the pollution in Belgrade, and the age of the respondents have the greatest influence on the acceptability of using e-bikes. In addition to majority acceptability (69.8%), the results obtained by the model can represent a useful basis for decision-makers when defining strategies for the development and application of e-bikes while reducing traffic congestion and environmental pollution in Belgrade.

Experimental and ANN modeling of kerosene fuel desulfurization using a manganese oxide-tin oxide catalyst

This research pioneered the use of a nanocatalyst composed of manganese oxide (MnO2) and stannic oxide (SnO2) to effectively remove dibenzothiophene (DBT) from kerosene fuel through the catalytic oxidative desulfurization (ODS) process, using hydrogen peroxide (H2O2) as the oxidant. Impregnating SnO2 with varying amounts of MnO2 was used to manufacture the catalyst. The oxidation experiment ran in a batch reactor with varying reaction times and temperatures to determine optimal conditions. High MnO2 dispersion over SnO2 was shown by catalyst characterization data. Under optimal operating parameters (catalyst type: 5 % MnO2/SnO2, reaction temperature: 75 °C, and reaction duration: 100 min), the results demonstrated a maximum DBT removal efficiency of 82.84 % from kerosene fuel. This research also provides the construction of Artificial Neural Network (ANN) model to simulate the upgrading of kerosene fuel via desulfurization process. There has been a growing trend toward the diversified use of ANN to represent steady state systems in chemical engineering. MATLAB's code was employed for matching the experimental data to the artificial neural network (ANN) model. The resulted data showed significant agreement between the experimental and predicted outcomes, with regression coefficients (R2) of 0.99902, 0.99986, and 0.99961 and mean square errors (MSE) of 0.266, 0.272, and 0.104 for 0 % MnO2/SnO2, 1 % MnO2/SnO2, and 5 % MnO2/SnO2 respectively. This interactive model provided a solid foundation for understanding the novel behavior of the oxidation process.

Predicting and analysis of urban wastewater quantity to be treated by using Neural Network and ODE model: A case study

Wastewater data, including Population of the Municipality Served by the Wastewater Treatment Plants (WWTPs), Capacity, Number of WWTPs, Amount of Daily Wastewater Discharged per Person and Wastewater Treated in WWTPs (y(t)) obtained from Turkish Statistical Institute (TUIK) for Kayseri province for 2003-2020 were mathematically modelled and analysed with Artificial Neural Network (ANN) and Differential equations. Firstly, the data were augmented through ARIMA since TUIK data numbers are insufficient to ANN training and later the data were normalized. The augmented normalized data was trained with ANN twice, thus the effect of other variables on the y(t) variable was shown and mathematical ANN activation functions in the form of a tangent hyperbolic function was proposed for this variable. Then, arbitrary parameters used for a linear system consisting of differential equations representing the five variables mentioned above were estimated using normalized original data and thus the ODE (ordinary differential equation) model was proposed. Two ANN models and ODE model were evaluated on normalized real TUIK data and the performances of these three models were compared. Among these mathematical models, the model that gave the minimum MSE (mean squared error) has been determined as the ODE model. Finally, future predictions were made for the y(t) variable with the ODE model.

Predictive modeling of sustainable recycled materials for stone column construction

Climate change due to carbon dioxide emissions is one of the most significant global challenges. Sustainable recycled materials are recognized as a critical factor in mitigating climate change, representing a fundamental strategy for reducing gas emissions and preserving the environment. Promising sustainable recycled materials in construction, such as crushed asphalt, crushed concrete, crushed ceramic, and treated concrete offer numerous advantages, including reduced carbon dioxide (CO2)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${\ ext{(CO}}_{2} )$$\\end{document} emissions, cost-effectiveness, increased strength, and enhanced mechanical properties. In this study, various machine learning techniques, including the support vector machine (SVM), multiple linear regression (MLR), gaussian process regression (GPR), and artificial neural network (ANN), are used to assess the effectiveness of sustainable recycled materials within stone columns. The MLR model was specifically utilized to predict the ultimate stress equation for these materials within stone columns. Forty-five constructed samples were used in developing the models. The effectiveness of each model was evaluated using various statistical assessment measures, including mean absolute error (MAE), absolute fraction of variation (R2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${\ ext{R}}^{2}$$\\end{document}), and root mean square error (RMSE). The predictive model’s performance was validated using the k-fold cross-validation method. The ANN model with an R2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${\ ext{R}}^{2}$$\\end{document} value of 0.962 and an RMSE value of 7.268 kPa, performed better compared to GPR, SVM and MLR models. In summary, the results of the study indicate that the MLR model, utilizing the identified input parameters, can accurately predict the ultimate stress for different sustainable recycled materials. Implementing such technologies within the construction sector can expedite and reduce the cost of assessing material characteristics and the influence of input parameters.

Artificial Neural Network Model for Predicting Carrot Root Yield Loss in Relation to Mechanical Heading

Modelling and predicting agricultural production processes have high cognitive and practical values. Plant growth, the threat of pathogens and pests, and the structure of agricultural machinery treatments are mostly non-linear, measurable processes that can be described mathematically. In this paper, a multiple regression analysis was carried out in the first step to check the non-linearity of the data and yielded a coefficient of determination of R2 = 0.9741 and the coefficient of determination corrected for degrees of freedom was R2adj = 0.9739. An artificial neural network model, called CH-NET, is then presented to predict the yield loss of carrot roots by leaving root mass in the field during harvest at the mechanical heading stage. The proposed network model has an architecture consisting of an input layer, three hidden layers with 12 neurons each, and an output layer with one neuron. Twelve input criteria were defined for the analysis and testing of the network, eight of which related to carrot root parameters and four to the heading machine. The training, testing, and validation database of the CH-NET network consisted of the results of field trials and tests of the operation of the patented (P.242097) root heading machine. The proposed CH-NET neural network model achieved global error (GE) values of 0.0931 t·ha−1 for predicting carrot root yield losses for all twelve criteria adopted. However, when the number of criteria is reduced to eight, the error increased to 0.0991 t·ha−1. That is, the prediction was realised with an accuracy of 90.69%. The developed CH-NET model allows the prediction of economic losses associated with root mass left in the field or contamination of the raw material with undercut leaves. The simulations carried out showed that minimum root losses (0.263 t·ha−1) occur at an average root head projection height of 38 mm and a heading height of 20 mm from the ridge surface.

A data-driven ductile fracture criterion for high-speed impact

Data-driven methods based on machine learning (ML) models offer new approaches for characterizing the fracture behavior of advanced elastoplastic materials. In this paper, a ML-based data-driven ductile fracture criterion is proposed to characterize the fracture behavior of elastoplastic materials under high-speed impact loading conditions. To reduce the required training dataset and enhance the predictability capability, several assumptions are used. Firstly, utilizing the decoupled assumption, two separate artificial neural network (ANN) models are employed to establish the fundamental fracture model and characterize the strain rate effect of ductile fracture behavior, respectively. In addition, the enhanced method with a logarithmic function is introduced to improve predictability capability of the proposed data-driven criterion under unknown high strain rates. To establish a complete numerical implementation framework, an enhanced rate-dependent data-driven constitutive model and a compatible numerical implementation algorithm are additionally introduced. Eventually, to assess the applicability of the proposed data-driven fracture criterion, numerical simulations of notched specimens and ballistic impact conditions of Ti-6Al-4V material are conducted, respectively. These investigation results demonstrate the effectiveness of the proposed data-driven ductile fracture criterion.

CADefender: Detection of unknown malicious AutoLISP computer-aided design files using designated feature extraction and machine learning methods

Computer-aided design (CAD) files are used to create digital designs for various structures – from the smallest chips in the high-tech industry to large-scale buildings and bridges in the civil engineering space. We found that most exploits and malicious payloads are deployed through Auto List Processing (AutoLISP) source code (LSP) or Fast Load AutoLISP (FAS) files, which are non-executable files (NEFs) containing scripts in the AutoLISP language that are native to AutoCAD; While antivirus software is capable of detecting many malicious CAD files, the potential to improve protection by using a dedicated machine learning (ML) based detection solution remains, especially against unknown and sophisticated CAD malware. In this study, we are the first to propose designated feature extraction methods and a robust framework aimed at the detection of known and unknown AutoLISP malware using ML algorithms. To accomplish this, we examined the structure, functionality, and ecosystems of AutoLISP files and collected the largest known representative collection of LSP files consisting of 6418 malicious and benign files (labeled and verified). We then explored the use of two novel static-analysis-based feature extraction methods (knowledge-based and structural) designated for LSP files to extract a discriminative set of informative features, which can subsequently be used by ML models to detect malicious LSP files. These two feature extraction methods serve as the basis of the proposed detection framework, whose performance we comprehensively compare to both widely used antiviruses and baseline ML models based on existing feature extraction methods, including MinHash, Bidirectional Encoder Representations from Transformers (BERT), and n-gram. Our results highlight our methods' contributions to the detection of unknown AutoLISP malware and demonstrate their ability to outperform existing methods. The best performance in the task of unknown malicious LSP file detection was obtained by the Artificial Neural Networks (ANN) model trained on 100 knowledge-based features, which obtained a true positive rate (TPR) of 99.49% with a false positive rate (FPR) of 0.57%. Our framework's role in explainability is also highlighted, as we also present the prominent features that contribute most to the model's detection capabilities; this information can be used for explainability purposes. We conclude by evaluating the proposed framework's ability to detect a malicious file from an unknown AutoLISP malware family and by evaluating our framework on an additional independent test set that originated from another source, scenarios that are often faced by malware detection solutions.

Ultimate strength assessment of randomly pitted stiffened panels considering stiffener corrosion

Stiffened panels are renowned for their exceptional load-bearing capabilities due to their high strength-to-weight ratio and are extensively utilized in ship and marine structures. Nevertheless, they are susceptible to failure under compression, particularly in marine environments where corrosion leads to material and structural degradation. Stiffener corrosion was not involved adequately in current studies, with an unclear synergistic effect of corrosion damage with structural configurations. This paper introduces a numerical modeling approach to develop finite element (FE) models of panels with random pitting corrosion, considering both corrosion on the plating and stiffeners. These FE models are validated against existing experiments and empirical formulae and employed to investigate the effect of random pitting damage on the ultimate strength of panels. The study examines the impact of various structural parameters, such as plate aspect ratio, plate slenderness ratio, stiffener slenderness ratio, and the height-to-thickness ratio of stiffener web, on ultimate strength under different levels of pitting damage. Additionally, the load-bearing behavior and failure modes of damaged panels are analyzed. An artificial neural network (ANN) is developed to predict the ultimate strength of pitted panels. The findings indicate that the ultimate strength of panels with stiffener corrosion in the form of random pitting corrosion leads to a more significant reduction by about 17.7% than that without stiffener corrosion, potentially altering the failure mode. The proposed ANN model accurately predicts the ultimate strength of pitted panels with and without stiffener corrosion, with a mean relative error of approximately 1.1% compared to FE analysis results.

New forest fire assessment model based on artificial neural network and analytic hierarchy process or fuzzy-analytic hierarchy process methodology for fire vulnerability map

Forest fires significantly disrupt global ecosystems. Many forecasting techniques predict fire activity and allocate prevention resources, but various factors are missing from the assessments, and multi-criteria decision approaches alone are insufficient. This work introduced a novel methodology combining artificial neural networks (ANN) with the analytical hierarchy process (AHP), fuzzy AHP multi-criteria methods, and spatial data to detect potential forest fire vulnerability areas using twenty variables. The results from AHP or fuzzy AHP, were processed using a multilayer perceptron with a backpropagation algorithm. The final ANN model has two objectives: first, to create a forest fire vulnerability with four classes (low, moderate, high, and very high), and second, to classify burned area sizes in regions highly or very highly vulnerable to fire. Evaluation metrics were also applied for validation. The fire model was tested using both literature review data and in situ observations. Comparative analysis showed that the burned area size model performed better than other machine learning methods, achieving an accuracy score of 89%. Meanwhile, the fire vulnerability model scored 82%. The study addresses the problem of prediction and provides an algorithm for classifying fire risk based on historical data in the Czech Republic, offering a master model for future forest management. Therefore, the ANN model, once trained, validated, and tested, does not require resetting and is effective for estimating forest fire vulnerability. Moreover, it produces results quickly, providing rapid insights into complex forest ecosystems, saving time and enhancing understanding for decision-makers.

Optimization of reed-based polyol production driven by response surface methodology and machine learning: Process modeling and hydroxyl value prediction

In pursuit of sustainable development goals, the transformation of biomass resources into high-value chemicals has become a focal point within the academic community. Despite being a biomass resource, reed (Phragmites australis) has not yet fully realized its potential in the production of bio-based polyols. This study utilized Response Surface Methodology (RSM) and an Artificial Neural Network (ANN) enhanced by a Genetic Algorithm (GA) to model and optimize the liquefaction process of reeds. Furthermore, this study employed a pre-existing predictive model to estimate the hydroxyl value of reed-derived liquefaction products, thereby aiming to curtail expenses during the biomass chemical development phase. The results demonstrated that both the RSM and GA-ANN models accurately predict bio-polyol yield, with the RSM model outperforming the ANN model. Based on optimal conditions predicted by the RSM model, the best experimental process parameters were established: raw material particle size 2–12 mesh, solid-liquid ratio 5:1, glycerol content 32.5 %, catalyst content 3.6 %, temperature 169 °C, and reaction time 58 minutes. Under these conditions, the polyol yield reached 89.145 %, with a relative deviation of 2.366 %, and the bio-polyol exhibited a viscosity of 0.51 Pa·s. The predicted hydroxyl value for the bio-based polyol was found to be 399.19 mg KOH/g. The liquefied product is rich in hydroxyl groups, indicating its potential application value in preparing bio-based polymer materials. This study introduces innovative approaches to the economical production of bio-based polyols at the laboratory scale, which may pave the way for their broader industrial implementation.

Device packaging and integration optimization based on neural network method: Effect of microchannel structure on heat sink performance

With the continuous improvement of power density of electronic device packaging, microchannel structure based on advanced packaging integration technology has become one of the hot topics in thermal management research. In this paper, two heat sink models are numerically simulated: rectangular microchannel and microchannel-pinfin hybrid. The effects of microchannel structure, inlet/outlet arrangement and pinfin design on heat sink performance were studied. The heat sink performance is evaluated by the maximum temperature at hotspot area, the total thermal resistance and the mean absolute temperature deviation (MATD). The research results show that at the same Reynolds number, the hybrid structure with pinfin has better heat sink performance. Compared with the rectangular microchannel structure, the maximum temperature at the hotspot area is reduced by 20.45 K and the total thermal resistance is reduced by 5.9 %. Compared with the three inlet/outlet arrangements of I-type, U-type and Z-type, the I-type hybrid structure has the best heat sink performance. The average absolute temperature deviation is about 10 K at high Reynolds number, and the temperature distribution is more uniform. In addition, the heat sink performance is improved by increasing the pinfin size to expand the heat transfer area. This paper uses simulation results to build an artificial neural network model in order to quickly and accurately predict numerical results under different parameter conditions. The network model has good accuracy and robustness, with a mean absolute error of 0.376 %. The prediction model takes heat source power, Reynolds number and pinfin structural parameters as input. Rapidly predicting the performance of heat sinks with different structural parameters under different boundary conditions will effectively save experimental and computational costs. This research provides new ideas and tools for optimizing heat sink design, and helps improve the application of microchannel cooling technology in electronic devices.

Development of ANN model for predicting mechanical properties of 3D printed PEEK polymer using FDM and optimization of process parameters for better mechanical properties

Additive manufacturing (AM) has evolved from a proven technology to a rapid prototyping tool with great potential. This technology is widely used in many industries such as automotive, aerospace, and medical; fused deposition modeling (FDM) is a popular 3D printing technique for producing PEEK (polyether ether ketone) parts, including implant prosthetic teeth. In this study, artificial neural network (ANN) modeling, parametric optimization, and experimental examination of PEEK 3D printing were conducted to enhance 3D printing processes. In this study, four critical process factors (infill density, layer height, printing speed, and infill pattern) influence the surface roughness, mechanical strength, and elastic modulus of the printed samples. Utilizing a 4–12–3 network design, this study demonstrates that an ANN model with an average error of less than 5% is optimal for all three responses. Furthermore, the study employed a teaching and learning based optimization algorithm (TLBO) and a non-dominated sorting genetic algorithm (NSGA) to optimize the printing process to obtain improved mechanical properties. The findings highlight TLBO’s ability to minimize surface roughness to 6.01 μm and NSGA’s capability to maximize the elastic modulus to 1253.35 MPa and ultimate tensile strength to 65.55 MPa. Microstructural studies supported the results obtained through parametric analysis and optimization.

Evaluating the impact of industrial wastes on the compressive strength of concrete using closed-form machine learning algorithms

Industrial wastes have found great use in the built environment due to the role they play in the sustainable infrastructure development especially in green concrete production. In this research investigation, the impact of wastes from the industry on the compressive strength of concrete incorporating fly ash (FA) and silica fume (SF) as additional components alongside traditional concrete mixes has been studied through the application of machine learning (ML). A green concrete database comprising 330 concrete mix data points has been collected and modelled to estimate the unconfined compressive strength behaviour. Considering the concerning environmental ramifications associated with concrete production and its utilization in construction activities, there is a pressing need to perform predictive model exercise. Furthermore, given the prevalent reliance of concrete production professionals on laboratory experiments, it is imperative to propose smart equations aimed at diminishing this dependency. These equations should be applicable for use in the design, construction, and performance assessment of concrete infrastructure, thereby reflecting the multi-objective nature of this research endeavour. It has been proposed by previous research works that the addition of FA and SF in concrete has a reduction impact on the environmental influence indicators due to reduced cement use. The artificial neural network (ANN) and the M5P models were applied in this exercise to predict the compressive strength of FA- and SF-mixed concrete also considering the impact of water reducing agent in the concrete. A sensitivity analysis was also conducted to determine the impact of the concrete components on the strength of the concrete. At the end, closed-form equations were proposed by the ANN and M5P with performance indices which outperformed previous models conducted on the same database size. The result of the sensitivity analysis showed that FA is most impactful of all the studied components thereby emphasizing the importance of adding industrial wastes in concrete production for improved mechanical properties and reduced carbon footprint in the concrete construction activities. Also, the M5P and ANN models with R2 of 0.99 showed a potential for use as decisive models to predict the compressive strength of FA- and SF-mixed concrete.

Multi-omics analysis and response prediction of PD-1 monoclonal antibody containing regimens in patients with relapsed/refractory diffuse large B-cell lymphoma

Patients with relapsed/refractory (r/r) diffuse large B-cell lymphoma (DLBCL) show varied responses to PD-1 monoclonal antibody (mAb) containing regimens. The mechanisms and predictive biomarkers for the efficacy of this regimen are unclear. This study retrospectively collected r/r DLBCL patients who received PD-1 mAb and rituximab regimens as salvage therapy. Clinical and genomic features were collected, and mechanisms were explored by multiplex immunofluorescence and digital spatial profiling. An artificial neural network (ANN) model was constructed to predict the response. Between October 16th, 2018 and May 4th, 2023, 50 r/r DLBCL patients were collected, 29 were response patients and 21 were non-response patients. CREBBP (p = 0.029) and TP53 (p = 0.015) alterations were statistically higher in non-response patients. Patients with PD-L1 CPS ≥ 5 were correlated with a longer overall survival (OS) than those with PD-L1 CPS < 5 (median OS: not reached vs. 9.7 months, hazard ratio [HR]: 3.8, 95% confidence interval [CI] 0.64–22.44, p = 0.016). Immune-related pathways were activated in response patients. The proportion and spatial organization of tumor-infiltrating immune cells affect the response. PD-L1 CPS level, age, and alterations of TP53, MYD88, CREBBP, EP300, GNA13 were used to build an ANN predictive model that showed high prediction efficiency (training set area under curve [AUC] of 0.97 and test set AUC of 0.94). The proportion and spatial distribution of tumor-infiltrating immune cells may be related to the function of immune-related pathways, thereby influencing the efficacy of PD-1 mAb containing regimens. The ANN predictive model showed potential value in predicting the responses of r/r DLBCL patients received PD-1 mAb and rituximab regimens.

A comparative study of RSM and ANN models for predicting spray drying conditions for encapsulation of Lactobacillus casei

AbstractBackground and ObjectivesThe aim of this study was to develop a wall material using pea protein isolate and pectin to optimize the encapsulation of Lactobacillus casei by spray drying. Response surface methodology (RSM) and artificial neural network (ANN) were used to analyze the effect of processing parameters.FindingsThe results showed that both RSM and ANN could be used to successfully characterize the experimental data, although ANN demonstrated greater predictive accuracy than RSM due to a higher R2 and lower mean square error (MSE).ConclusionANN was observed to show more suitability than RSM. The encapsulation efficiency (90.7%), yield (45.5%), and wettability (169 s) of spray‐dried probiotic powder obtained under optimal spray drying conditions (inlet air temperature (132°C); feed flow rate (9.5 mL/min) and pea protein isolate concentration (7.1%)) were observed to be not significantly different (p &lt; .05) from predicted values for all three parameters, demonstrating the validity of applied model.Significance and NoveltyIn this study, production technology of vegan base probiotic powder has been developed using mathematical modeling through the spray‐drying method. Therefore, this data can be useful for food processing industries to develop a high‐quality probiotic powder through spray drying.

Performance Modeling of the Fenton Process Used as a Single Unit for Treating Raw Textile Effluent

This study investigates the direct application of the Fenton Oxidation Process (FOP) to untreated textile wastewater, specifically from a woven fabric production facility. Under optimized conditions (pH 3, 0.7 g/L Fe+2, 2 mM H2O2), the process achieved significant removal efficiencies: 81% Chemical Oxygen Demand (COD), 80% Suspended Solids (SS), and 93% color removal. Artificial Neural Networks (ANN) and NARX-ANN were utilized in Matlab R2020a to model FOP performance, employing Levenberg-Marquardt (trainlm) and Scaled Conjugate Gradient (trainscg) algorithms. With a 9-20-3 network topology, the ANN model demonstrated superior predictive capability, achieving an R2 of 0.9843.

Deep deoxidation of water in a miniaturized annular rotating device: Experimental investigation and machine learning modeling

Conventional gas–liquid devices exhibit limitations, including inadequate dispersion efficiency and significant backmixing, leading to low conversion and large equipment volume. Thus, a miniaturized annular rotating device (m-ARD) was proposed based on microscale effects and rotating flow field to achieve efficient gas–liquid mass transfer and high conversion. Nitrogen-oxygenated water was selected as the experimental system to evaluate the performance of the m-ARD. The gas–liquid flow characteristics were investigated. The analysis focused on examining the impact of different factors on deoxygenation efficiency. The deoxygenation performance was optimized and compared with different devices. The study found that the m-ARD enables a continuous counter-current gas–liquid flow mode, with superior mixing performance and a narrow residence time distribution. The deoxygenated water with an oxygen concentration as low as 0.14 ppm can be produced, with a volumetric mass transfer coefficient (kLa) of 0.26 s−1, a theoretical stage value of 4.22, and a handling capacity of 30 mL/min under optimized experimental conditions. In addition, kLa was predicted using an artificial neural network model and showed good agreement with the simulated values. This work provides a promising reactor for chemical reactions that require high conversions.

Continuous and funnel-gate configurations of a permeable reactive barrier for reclamation of groundwater laden with tetracycline: experimental and simulation approaches

The current study investigates removing tetracycline from water using batch, column, and tank experiments with statistical modelling using ANN for continuous tests. An artificial neural network (ANN) using the Levenberg-Marquardt back-propagation (LMA) training algorithm is constructed to compare the effectiveness of Tetracycline removal from aqueous solution using the sorption technique with prepared adsorbent. Several characterization analyses XRD, FT-IR, and SEM are employed for prepared Brownmillerite (Ca2Fe2O5)–Na alginate beads. The operating conditions of batch tests involved, contact time (0.1–3 h), initial of tetracycline (Co) of (100–250 mg/L), pH (3–12), agitation speed (50–250) rpm and dosage of adsorbent (0.2–1.2 g/50 mL). The outcomes of experiments have demonstrated that the optimum conditions for the batch test to achieve the maximum adsorbent capacity (qmax =7.845 mg/g) are achieved at pH 7, contact time 1.5 h, adsorbent dose 1.2 g/50 mL, agitation speed of 200 rpm, and initial concentration of TC 100 mg/L. Minimum mean square error (MSE) values of 7.09E-04 for 30 hidden neurons and 0.0029 for 59 hidden neurons in the 1D and 2D systems are accomplished, respectively. The artificial neural network model has exhibited excellent performance with correlation coefficients exceeding 0.980 for the operating variables, demonstrating its accuracy and effectiveness in predicting the experimental outcomes. According to sensitivity analysis, the influential parameter in the column test (1D) is the flow rate (mL/min), with a relative importance of 32.769%. However, in the tank test (2D), time (day) is signified as an influential parameter with a relative importance of 31.207%.

Intelligent healthcare for cardiac patients utilizing neural networks, k-means clustering, and ad hoc routing

This paper introduced a comprehensive healthcare system designed to address the unique challenges faced by cardiac patients in economically and geographically constrained regions. The system operated in three phases, each contributing to the effective care of patients. In the initial phase, an artificial neural network model identified potential cardiac patients with an impressive accuracy of 90.16%, demonstrating its potential for early detection. The second phase employed k-means clustering to categorize patients into three groups based on the severity of their condition, facilitating precise prognosis and stratification. Finally, the system utilized an innovative ad hoc routing algorithm to securely transmit patient data to remote servers, enabling expert monitoring and consultation. The study's outcomes demonstrate the system's ability to accurately identify at-risk patients, appropriately categorize their condition, and efficiently route critical data. This holistic approach leverages cutting-edge technologies and methodologies to transform healthcare delivery in underserved areas. The novel system presents a promising avenue for enhancing cardiac care in regions with limited access to advanced healthcare services, ultimately improving patient outcomes and reducing disparities in cardiovascular healthcare.

A computer vision system and machine learning algorithms for prediction of physicochemical changes and classification of coated sweet cherry

The current research utilized visual characteristics obtained from RGB images and qualitative characteristics to investigate changes in surface defects, predict physical and chemical characteristics, and classify sweet cherries during storage. It was achieved with the help of ANN (Artificial Neural Network) and ANFIS (Adaptive Neuro-Fuzzy Inference System) models. The ANN used in this study was a Multilayer Perceptron (MLP) with SigmoidAxon and TanhAxon threshold functions, trained with the Momentum training function. Additionally, ANFIS with a Mamdani system and Triangle, Gauss, and Trapezoidal membership functions, was employed to predict sweet cherries' physical and chemical properties and their quality classification. Both models incorporate four algorithms. Additionally, the algorithms use color statistical features and color texture features combined with physical and chemical properties, including weight loss, firmness, titratable acidity, and total anthocyanin content. The image color and texture characteristics were used by ANN and ANFIS models to predict physical and chemical properties with high accuracy. ANN and ANFIS models accurately estimate sweet cherry quality grades in all four algorithms with over 90% accuracy. According to the findings, the ANN and ANFIS models have demonstrated satisfactory performance in the qualitative classification and prediction of sweet cherries' physical and chemical properties.