Artificial Neural Network Analysis Research Articles

Designing a set of criteria for evaluating artificial neural networks trained with physics-based data to replicate molecular dynamics and other particle method trajectories

This article presents an in-depth analysis and evaluation of artificial neural networks (ANNs) when applied to replicate trajectories in molecular dynamics (MD) simulations or other particle methods. This study focuses on several architectures—feedforward neural networks (FNNs), convolutional neural networks (CNNs), recurrent neural networks (RNNs), time convolutions (TCs), self-attention (SA), graph neural networks (GNNs), neural ordinary differential equation (ODENets), and an example of physics-informed machine learning (PIML) model—assessing their effectiveness and limitations in understanding and replicating the underlying physics of particle systems. Through this analysis, this paper introduces a comprehensive set of criteria designed to evaluate the capability of ANNs in this context. These criteria include the minimization of losses, the permutability of particle indices, the ability to predict trajectories recursively, the conservation of particles, the model’s handling of boundary conditions, and its scalability. Each network type is systematically examined to determine its strengths and weaknesses in adhering to these criteria. While, predictably, none of the networks fully meets all criteria, this study extends beyond the simple conclusion that only by integrating physics-based models into ANNs is it possible to fully replicate complex particle trajectories. Instead, it probes and delineates the extent to which various neural networks can “understand” and interpret aspects of the underlying physics, with each criterion targeting a distinct aspect of this understanding.

The impact of unmet treatment need on oral health related quality of life: a questionnaire survey

BackgroundBased on the present global burden of oral diseases, unmet dental needs affect a more significant population worldwide. It is characterised by the need for dental care but receiving delayed or no care. The contributing factors include lack of knowledge about oral health, its consequences, and the availability of dental services. We need to find out the scale of the problem of unmet dental needs for the south Indian population. Therefore, the objective was to determine the relationship between the presence of oral disease and the quality of life-related to oral health using the OHIP-14 tool.MethodsThe unmet dental requirements of the south Indian population were determined using a cross-sectional questionnaire survey. Close-ended questions were used to obtain data from two investigators trained to record the answers from the patients. The data was collected using the OHIP-14 questionnaire, which consists of 14 items divided into seven domains with two questions each. Physical pain, psychological impairment, physical disability, psychological disability, social disability, and disability were all considered. An additional analysis of artificial neural network (ANN) was done.ResultsThe response rate was 100 per cent. N = 1029 people replied to the questionnaire about their unmet dental needs. N = 497 (48.3%) were men, whereas N = 532 (51.7%) were women. The average age was 31.7811.72. As their current occupation, most of the included subjects (60.1%) were students. The respondents had no known personal habits and a mixed diet (94.93%). The average BMI was 24.022.59 (14-30.9). OHIP was present in 62.3% of the population. The average OHIP-14 severity score was 10.97. (8.54). The severity and degree of unmet dental need were substantial (p0.01) due to pain in the mouth/teeth/gums, malocclusion, and gum bleeding. The most common OHIP-14 domains affected by unmet oral needs were psychological discomfort, psychological limitation, social limitation, and feeling handicapped. The analysis of ANN revealed that high OHIP scores were primarily attributed to dental caries, poor oral health, and dental aesthetics.ConclusionThe severity and degree of unmet dental needs were significant among the south Indian population. The most common oral health status that impacted OHIP-14 domains were pain, malocclusion, and bleeding gums. These patients were significantly impacted by psychological discomfort and social limitations and felt handicapped.

Enhanced accuracy through machine learning-based simultaneous evaluation: a case study of RBS analysis of multinary materials

We address the high accuracy and precision demands for analyzing large in situ or in operando spectral data sets. A dual-input artificial neural network (ANN) algorithm enables the compositional and depth-sensitive analysis of multinary materials by simultaneously evaluating spectra collected under multiple experimental conditions. To validate the developed algorithm, a case study was conducted analyzing complex Rutherford backscattering spectrometry (RBS) spectra collected in two scattering geometries. The dual-input ANN analysis excelled in providing a systematic analysis and precise results, showcasing its robustness in handling complex data and minimizing user bias. A comprehensive comparison with human supervision analysis and conventional single-input ANN analysis revealed a reduced susceptibility of the dual-input ANN analysis to inaccurately known setup parameters, a common challenge in material characterization. The developed multi-input approach can be extended to a wide range of analytical techniques, in which the combined analysis of measurements performed under different experimental conditions is beneficial for disentangling details of the material properties.

Predictive modeling of copper (II) adsorption from aqueous solutions by sawdust: a comparative analysis of adaptive neuro-fuzzy interference system (ANFIS) and artificial neural network (ANN) approaches

Heavy metal ions are considered to be the most prevalent and toxic water contaminants. The objective of thois work was to investigate the effectiveness of employing the adsorption technique in a laboratory-size reactor to remove copper (II) ions from an aqueous medium. An adaptive neuro-fuzzy inference system (ANFIS) and a feed-forward artificial neural network (ANN) were used in this study. Four operational factors were chosen to examine their influence on the adsorption study: pH, contact duration, initial Cu (II) ions concentration, and adsorbent dosage. Using sawdust from wood, prediction models of copper (II) ions adsorption were optimized, created, and developed using the ANN and ANFIS models for tests. The result indicates that the determination coefficient for copper (II) metal ions in the training dataset was 0.987. Additionally, the ANFIS model’s R 2 value for both pollutants was 0.992. The findings demonstrate that the models presented a promising predictive approach that can be applied to successfully and accurately anticipate the simultaneous elimination of copper (II) and dye from the aqueous solution.

Detection and diagnosis of bacterial pathogens in blood using laser-induced breakdown spectroscopy

The ability to rapidly and accurately detect and identify pathogenic bacteria in clinically-obtained blood specimens with laser-induced breakdown spectroscopy (LIBS) is evaluated. Samples of blood obtained from five patients in a local hospital were confirmed to be negative for the presence of bacteria by the pathology department and were then tested with LIBS. Specimens of blood were tested as obtained from the hospital with no other alteration as control samples and were also intentionally spiked with known aliquots of Escherichia coli, Staphylococcus aureus, Enterobacter cloacae, and Pseudomonas aeruginosa to simulate blood infections. LIBS spectra were acquired from blood deposited on nitrocellulose filters. The intensities of 15 emission lines measured in the spectra and 92 ratios of those line intensities were used as 107 independent variables in a partial least squares discriminant analysis (PLS-DA) to discriminate between sterile control samples and those spiked with bacteria. In addition, the entire LIBS spectrum from 200 nm – 590 nm was input into an artificial neural network analysis with principal component analysis pre-processing (PCA-ANN) to diagnose the bacterial species once detected.The PLS-DA test possessed a 96.3% sensitivity and a 98.6% specificity for the detection of pathogenic bacteria in blood when 776 spectra from 26 filters were tested by removing one entire filter at a time from the model and testing each spectrum individually. When all the spectra obtained from a filter were averaged to enhance the signal to noise of the spectrum, 19 of 19 filters of infected blood tested positive and 7 of 7 filters with sterile blood tested negative, yielding 100% sensitivity and 100% specificity. The PCA-ANN test performed on the entire LIBS spectrum possessed a 100% sensitivity and 100% specificity when using 80% of the data to build a model and withholding 20% for cross-validation testing. The same PCA-ANN performed on each of the 19 filters individually, using the other 18 filters to build the model, possessed an average sensitivity of 85.5%, an average specificity of 95.0%, and a classification accuracy of 92.5%. These results indicate the potential usefulness of LIBS for detecting and diagnosing blood infections in a clinical setting.

Agarwood oil quality identification using artificial neural network modelling for five grades

Agarwood (Aquilaria Malaccensis) oil stands out as one of the most valuable and highly sought-after oils with a hefty price tag due to its widespread use of fragrances, incense, perfumes, ceremonial practices, medicinal applications and as a symbol of luxury. However, nowadays the conventional method that rely on color alone has its limitations as it yields varying results depending on individual panelists' experiences. Hence, the quality identification system of Agarwood oil using its chemical compounds had been proposed in this study to enhance the precision of the Agarwood oil grades thus addressing the shortcomings of traditional methods. This study indicates that the primary chemical compounds of Agarwood oil encompass ɤ-Eudesmol, ar-curcumene, β-dihydroagarofuran, ϒ-cadinene, α-agarofuran, allo-aromadendrene epoxide, valerianol, α-guaiene, 10-epi-ɤ-eudesmol, β-agarofuran and dihydrocollumellarin. This study employed artificial neural network analysis with the implementation of Levenberg-Marquardt algorithm to identify the Agarwood oil grades. The study's findings revealed that this modeling system of five grades got 100% accuracies with mean square error of 0.14338×10-08. Notably, this lowest mean square error (MSE) value falls within the best hidden neuron 3. These study outcomes play a pivotal role in highlighting the Levenberg Marquardt- artificial neural network (LM-ANN) modeling that contribute to the successful of Agarwood oil quality identification using its chemical compounds.

Artificial neural network analysis of factors affecting functional independence recovery in patients with lumbar stenosis after neurosurgery treatment: An observational cohort study

Background and aimLumbar spinal stenosis (LSS) is a leading cause of low back pain and lower limbs pain often associated with functional impairment which entails the loss or the impairment of independence in older adults. Conservative treatment is effective in a small percentage of patients, while a significant percentage undergo surgery, even if often without a complete resolution of clinical symptoms and motor deficits. The aim of the study is to identify clinical and demographic prognostic factors characterising the patients who would benefit most from surgical treatment in relation to the functional independence recovery using an innovative approach based on an artificial neural network. MethodsAdult patients with LSS and indication of neurosurgical treatment were enrolled in the study. Clinical evaluation was performed in the preoperative-phase (into the 48 h before surgery) and after two months. Clinical battery investigated the motor, functional, cognitive, behavioural, and pain status. Demographics and clinical characteristics were analysed via Artificial Neural Network (ANN) using 24 input variables, 2 hidden layers and a single final output layer to predict the outcome. ANN results were compared with those of a multiple linear regression. Results108 patients were included in the study and 90 of them [66.5 ± 12.8 years; 27.8 % F] were submitted to surgery treatment and completed longitudinal evaluation. Statistically significant improvement was recorded in all clinical scales comparing pre- and post-surgery. The ANN results showed a prediction ability up to 81 %. Disability, functional limitations, and pain concerning clinical assessment and stature, onset and age about demographic characteristics are the main variables impacting on surgical outcome. ConclusionsANN can support clinical decision making, using clinical and demographic characteristics of patients with LSS identifying the characteristics of those who might benefit more from the surgical treatment in terms of global functional recovery.

Comparative Analysis of Ensemble Learning Techniques and ANN for Prediction of Compressive Strength of Concrete Mix

Abstract: In this study, non-linear machine learning regressors and an artificial neural network (ANN) were modeled to predict the compressive strength of concrete mixes. A dataset of 1030 mix design data points with a wide range of compressive strength varying from M10 to M80 is taken into account. The data set comprised of 8 input parameters namely- cement, water, fine & coarse aggregate, flyash, slag, superplasticizer, and age. The paper also analyzes effects on the prediction of compressive strength due to waiving of the parameters with lesser importance factors. To improve the performances of models by reducing the variance, biases, and errors, K-fold cross-validation was examined. To examine the performances of the models, metrics such as coefficient of determination (R2) and mean square error (MSE) were employed. Most of the models can predict the compressive strength with good accuracy. Out of the 7-machine learning regressors and ANN models employed, Gradient Boosting Regressor yielded the best prediction accuracy of 0.9561 with the lowest MSE value of 0.0513.

Evaluations of artificial intelligence and machine learning in neurodiagnostics.

This paper evaluates the ethical implications of utilizing artificial intelligence (AI) algorithms in neurological diagnostic examinations. Applications of AI technology have been utilized to aid in the determination of pharmacological dosages of gadolinium for brain lesion detection, localization of seizure foci, and the characterization of large vessel occlusion in ischemic stroke patients. Multiple subtypes of AI- machine learning (AI/ML) algorithms are analyzed as AI-assisted neurology utilizes supervised, unsupervised, artificial neural network (ANN), and deep neural network (DNN) learning models. As ANN and DNN analyses can be applied to data with an unknown clinical diagnosis, these algorithms are evaluated according to Bayesian statistical analyses. Bayesian neural network analyses are incorporated as these algorithms indicate that the predictive accuracy and model performance are dependent upon accurate configurations of the model's hyperparameters and neural inputs. Thus, mathematical evaluations of AI algorithms are comprehensively explored to examine their clinical utility as underperformance of AI/ML models may have deleterious consequences that affect patient outcomes due to misdiagnosis and false-negative test results.

Floating-Point Quantization Analysis of Multi-Layer Perceptron Artificial Neural Networks

AbstractThe impact of quantization in Multi-Layer Perceptron (MLP) Artificial Neural Networks (ANNs) is presented in this paper. In this architecture, the constant increase in size and the demand to decrease bit precision are two factors that contribute to the significant enlargement of quantization errors. We introduce an analytical tool that models the propagation of Quantization Noise Power (QNP) in floating-point MLP ANNs. Contrary to the state-of-the-art approach, which compares the exact and quantized data experimentally, the proposed algorithm can predict the QNP theoretically when the effect of operation quantization and Coefficient Quantization Error (CQE) are considered. This supports decisions in determining the required precision during the hardware design. The algorithm is flexible in handling MLP ANNs of user-defined parameters, such as size and type of activation function. Additionally, a simulation environment is built that can perform each operation on an adjustable bit precision. The accuracy of the QNP calculation is verified with two publicly available benchmarked datasets, using the default precision simulation environment as a reference.

Artificial neural network (ANN) analysis on thermophysical properties of magnetohydrodynamics flow with radiation in an arc-shaped enclosure with a rotating cylinder

The objective of this research is to examine the thermophysical features of magnetic parameter (Ha) and time step (τ) in a lid-driven cavity using a water-based Al2O3 nanofluid and the efficacy of ANN models in accurately predicting the average heat transfer rate. The Galerkin weighted residual approach is used to solve a set of dimensionless nonlinear governing equations. The Levenberg-Marquardt back propagation technique is used for training ANN using sparse simulated data. The findings of the investigation about the flow and thermal fields are shown. Furthermore, a comparative study and prediction have been conducted on the impact of manipulating factors on the average Nusselt number derived from the numerical heat transfer analysis. The findings of the research indicate that, in the absence of magnetohydrodynamics, a rise in the Hartmann number resulted in a drop in both the fluid velocity profile and magnitude. Conversely, it was observed that the temperature and Nusselt number exhibited an increase under these conditions. The mean temperature of the fluid rises as the Hartmann number drops, reaching a peak value of 0.114 when Ha = 0. The scenario where Ha = 0, representing the lack of magnetohydrodynamics, shows the highest average Nusselt number, whereas the instance with Ha = 45 presents the lowest Nusselt number. The ANN model has a high level of accuracy, as seen by an MSE value of 0.00069 and a MAE value of 0.0175, resulting in a 99% accuracy rate.

Investing in our planet: Examining retail investors' preference for green bond investment

AbstractThe green bond has emerged as an important financial instrument to advance environmentally friendly projects. While institutional investors have shown ample interest in green bonds, retail investors have lagged in their adoption. This study intends to examine the determinants of retail investors' attitude and intention toward green bond investment, utilizing the theory of planned behavior as the fundamental model and incorporating several context‐specific factors. Data from 506 Indian retail investors, representing the majority of Indian states and union territories (UTs), were collected through multi‐stage stratified random sampling. The PLS‐SEM method, coupled with artificial neural network (ANN) analysis and fuzzy set qualitative comparative analysis (fsQCA), was employed to test the hypothesized relationships, ensure the robustness of outcomes, and derive important practical insights. The findings suggest that intrinsic factors (perceived behavioral control and attitude) are superior predictors of investors' behavioral intentions relative to external factors (government policy support and social influence). Further, for green bonds to receive favorable evaluations from investors, they should display adequate financial cum environmental performance. Investors' attitude is significantly influenced by issuers' ratings and their willingness to pay premiums. Environmental concerns and perceived risk also influence investors' attitude toward green bonds, albeit with relatively lower strength. This study holds significance as it offers crucial implications for researchers, market participants, policymakers, and regulators involved in the development of the green bond market.

Multi-Objective Optimization and Prediction of Responses Using GRA and ANN in Micro-EDM of Ti6Al4V Using Different Tool Materials

An experimental investigation was conducted to evaluate the machinability of a titanium alloy (Ti6Al4V) using copper (Cu), tungsten carbide (WC), and graphite (C) tools. Voltage (V), capacitance (pF), pulse-on time ([Formula: see text][Formula: see text]), and pulse-off time ([Formula: see text][Formula: see text]) were considered as the input machining parameters, whereas the material removal rate (MRR) and tool wear rate (TWR) were considered as the output machining parameters. A Taguchi L[Formula: see text] orthogonal array and gray relational analysis (GRA) were utilized to design and optimize the machining parameters for both responses. Artificial neural network (ANN) analysis was performed to predict the experimental outcomes. Scanning electron microscopy (SEM) and energy-dispersive spectroscopy (EDS) were used to assess the surface morphology and determine the elemental composition of the machined surface. The results indicated that the optimum machining conditions for the copper tool were 150 V, 1000 pF, 15 [Formula: see text]s ([Formula: see text][Formula: see text]), and 15 [Formula: see text]s ([Formula: see text][Formula: see text]). However, the optimal machining conditions for the WC were 200 V, 100 pF, 25 [Formula: see text]s ([Formula: see text][Formula: see text]), and 10 [Formula: see text]s ([Formula: see text][Formula: see text]), and the optimal conditions for the C were 200 V, 1000 pF, 20 [Formula: see text]s ([Formula: see text][Formula: see text]), and 25 [Formula: see text]s ([Formula: see text][Formula: see text]), respectively. The highest MRR achieved using the WC tool was 9.4510 mg/s, whereas the TWR of the Cu, WC, and C tools were 1.1039 mg/s, 1.0307 mg/s, and 1.2796 mg/s, respectively. The results showed that machining with the graphite tool had a higher TWR than machining with the Cu and WC tools.

Comparative study of time-series forecasting models for wind power generation in Gujarat, India

The rapid rate of transformation in the power sector of India has placed a significant emphasis on robust grids and distributed generation units. The observable shift in the energy sector, especially in wind and solar energy, also requires smooth integration of Distributed Generation units with the existing power grid. Precise wind power generation forecast, therefore, becomes an important and complex task for the strategic planning and management of the systems. We, thus, aim towards a system that can actually provide precise wind power forecasts by applying machine learning techniques. This work proposes a comparative and comprehensive analysis of Artificial Neural Network (ANN), Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and Autoregressive Integrated Moving Average (ARIMA) model. The experimentations and modelling are performed considering meteorological and historical power generation data. The study is concentrated in Kutch, Gujarat and is validated on the data collected from the Central Electricity Authority (CEA), India for power generation data and weather data collected from regional weather centres. The findings show that ARIMA outperforms the other models for non-linear data in multivariate analysis, with a MAPE score of less than 5.5 on the prediction dataset.

Perceptions of how occupants adopt water conservation behaviors under psychosocial processes: A complementary dual-stage SEM-ANN perspective

This study delved into socio-psychological determinants driving residential water curtailment actions, an area relatively overlooked in the existing literature. The theory of planned behavior was innovatively expanded with moral norms, perceived expectations, self-identity, and risk-related components. The proposed framework was empirically examined by conducting an online self-administered survey of 343 citizens residing in Isfahan, Iran. What sets this research apart is introducing a new multi-analytical hybrid approach integrating linear and non-linear techniques to leverage their respective strengths. Artificial neural network (ANN), coupled with Sobol sensitivity analysis, was employed to robustly validate structural equation modeling (SEM) results regarding the framework's explanatory power and predictors' ranking. Through providing reliable performance, the ANN analysis confirmed SEM findings, revealing that the significant influences of moral norms and self-identity on intention are mediated by attitude and perceived behavioral control. Moreover, self-identity and attitude were the strongest direct predictors of behavior and intention. Several policy recommendations were proposed, with the following highlighted as crucial: (1) fostering favorable water conservation attitudes, which entails targeting individuals' moral considerations and inducing self-reward feelings within the community; (2) cultivating a sense of water conservation identity among citizens; and (3) boosting individuals’ self-confidence and facilitating their engagement in water conservation behaviors.

Embodimetrics: A Principal Component Analysis Study of the Combined Assessment of Cardiac, Cognitive and Mobility Parameters.

There is a growing body of literature investigating the relationship between the frequency domain analysis of heart rate variability (HRV) and cognitive Stroop task performance. We proposed a combined assessment integrating trunk mobility in 72 healthy women to investigate the relationship between cognitive, cardiac, and motor variables using principal component analysis (PCA). Additionally, we assessed changes in the relationships among these variables after a two-month intervention aimed at improving the perception-action link. At baseline, PCA correctly identified three components: one related to cardiac variables, one to trunk motion, and one to Stroop task performance. After the intervention, only two components were found, with trunk symmetry and range of motion, accuracy, time to complete the Stroop task, and low-frequency heart rate variability aggregated into a single component using PCA. Artificial neural network analysis confirmed the effects of both HRV and motor behavior on cognitive Stroop task performance. This analysis suggested that this protocol was effective in investigating embodied cognition, and we defined this approach as "embodimetrics".

Comparative Analysis of Linear Models and Artificial Neural Networks for Sugar Price Prediction

Sugar is an important commodity that is used beyond the food industry. It can be produced from sugarcane and sugar beet, depending on the region. Prices worldwide differ due to high volatility, making it difficult to estimate their forecast. Thus, the present work aims to predict the prices of kilograms of sugar from four databases: the European Union, the United States, Brazil, and the world. To achieve this, linear methods from the Box and Jenkins family were employed, together with classic and new approaches of artificial neural networks: the feedforward Multilayer Perceptron and extreme learning machines, and the recurrent proposals Elman Network, Jordan Network, and Echo State Networks considering two reservoir designs. As performance metrics, the MAE and MSE were addressed. The results indicated that the neural models were more accurate than linear ones. In addition, the MLP and the Elman networks stood out as the winners.

Thermosonication Processing of Purple Onion Juice (Allium cepa L.): Anticancer, Antibacterial, Antihypertensive, and Antidiabetic Effects

Onion (Allium cepa L.) juice is an important product used in gastronomy and food formulations. The first objective of this study was to optimize the content of bioactive compounds in purple onion juice (POJ) after the thermosonication process using response surface methodology (RSM) and artificial neural network (ANN) application models. Second, the anticancer, antibacterial, antihypertensive, and antidiabetic effects of POJ obtained after thermal pasteurization (P-POJ) or thermosonication (TS-POJ) were investigated after obtaining the ANN and RSM analysis reports. The optimization process for TS-POJ was carried out at 44 °C, for 13 min, with a 68% amplitude. The findings demonstrated that the angiotensin-converting enzyme (ACE) inhibition level was greater in TS-POJ samples than in the untreated control (C-POJ) sample (p > 0.05). C-POJ, TS-POJ, and P-POJ exhibited the inhibition of cell proliferation in vitro in a dose-dependent manner in lung (A549), cervical (HeLa), and colon cancer cells following 24 h incubation. Thermosonication or thermal pasteurization did not markedly affect the cell proliferation of the examined cancer cells compared to the untreated control group. While no antibacterial effect was observed with low concentrations of samples, they showed an antibacterial effect at pure concentrations (100%). The thermosonication treatment for processing purple onion juice was successful in this study’s results.

Experimental and ANN Analysis of Shearing Rate Effects on Coarse Sand Crushing

The present study analyzes laboratory experiments on how shearing rate affects the shear strength and crushability of natural coarse sand, employing artificial neural network (ANN) analysis. This study tested three different coarse sands obtained from the crushing of natural rocks: Black Virgin Tuff, weathered Zeolitic Tuff, and calcareous limestone. The behavior of crushed sand specimens with consistent grading, which passed through sieve #4 and were retained on sieve #8, was analyzed using a direct shear box. The specimens were subjected to varied normal loads and shearing speeds to examine their behavior at different relative densities. The test results were analyzed using ANN to investigate the significance of shearing rates on shearing strength parameters, specifically internal mobilized peak friction, the constant volume (residual) internal friction angle, and the consequence of shearing rate on the particle's breakage index. The selected normal (Gaussian) rate significantly affected both the shear strength parameters and breakage. The loading rate increased both shear strength parameters and particle breakage. Therefore, it's highly recommended to maintain secure sets of shear strength values and comprehensive test data for assessing parameters at typical strain rates, prioritizing using slower rates whenever possible. Doi: 10.28991/CEJ-2024-010-03-011 Full Text: PDF

Investigation of optimal machining Monel 400 superalloy considering carbon emissions using FEM, regression and ANN methods

In this study, the optimal machining parameters for the machining of Monel 400 superalloy considering carbon emission were investigated. Monel 400 stands out among nickel alloys with its resistance to high corrosive environments. First, the Johnson-Cook material model for Monel 400 was developed for FEM simulations. Then, FEM simulations were performed using seven different machining parameters, including cutting and geometric, and validated with experimental results in the literature. The obtained FEM results were trained and predicted by regression and Artificial Neural Network (ANN) analysis. With this study, the Johnson-Cook material model of Monel 400 superalloy and the optimal machining parameters considering carbon emission with different cutting and geometric machining parameters are provided to the literature. As a result, it was determined that the most effective machining parameters in terms of cutting force were 139.04% increase in depth of cut, 74.23% increase in feed and 9.57% decrease in side rake angle. In terms of carbon emission, the most effective machining parameters were found to be −338.9g reduction in depth of cut, −294.3g reduction in feed rate and −171.9g reduction in cutting speed. In terms of training and prediction of FEM results, it was observed that the regression model was 89.19% for cutting force data and 95.39% for carbon emission, while the ANN model was 98.081% for cutting force data and 99.978% for carbon emission. Optimal machining parameters were determined by ANN method. The optimal machining parameters (cutting speed, feed, cutting depth, side rake angle, back rake angle, lead angle and nose radius) of Monel 400 superalloy with the lowest cutting force and carbon emission were determined as 100 m/min, 0.15mm/rev, 0.375 mm, 0°, 5°, 0°, 0°, 0°, 0.4 mm and 100 m/min, 0.2mm/rev, 0.25 mm, −5°, 5°, 0°, 0.8 mm, respectively.