Performance Of Artificial Neural Network Research Articles

Artificial neural network detection of pancreatic cancer from proton (1H) magnetic resonance spectroscopy patterns of plasma metabolites

BackgroundRoutine screening to detect silent but deadly cancers such as pancreatic ductal adenocarcinoma (PDAC) can significantly improve survival, creating an important need for a convenient screening test. High-resolution proton (1H) magnetic resonance spectroscopy (MRS) of plasma identifies circulating metabolites that can allow detection of cancers such as PDAC that have highly dysregulated metabolism.MethodsWe first acquired 1H MR spectra of human plasma samples classified as normal, benign pancreatic disease and malignant (PDAC). We next trained a system of artificial neural networks (ANNs) to process and discriminate these three classes using the full spectrum range and resolution of the acquired spectral data. We then identified and ranked spectral regions that played a salient role in the discrimination to provide interpretability of the results. We tested the accuracy of the ANN performance using blinded plasma samples.ResultsWe show that our ANN approach yields, in a cross validation-based training of 170 samples, a sensitivity and a specificity of 100% for malignant versus non-malignant (normal and disease combined) discrimination. The trained ANNs achieve a sensitivity and specificity of 87.5% and 93.1% respectively (AUC: ROC = 0.931, P-R = 0.854), with 45 blinded plasma samples. Further, we show that the salient spectral regions of the ANN discrimination correspond to metabolites of known importance for their role in cancers.ConclusionsOur results demonstrate that the ANN approach presented here can identify PDAC from 1H MR plasma spectra to provide a convenient plasma-based assay for population-level screening of PDAC. The ANN approach can be suitably expanded to detect other cancers with metabolic dysregulation.

Comparing prediction accuracy for 30-day readmission following primary total knee arthroplasty: the ACS-NSQIP risk calculator versus a novel artificial neural network model

BackgroundUnplanned readmission, a measure of surgical quality, occurs after 4.8% of primary total knee arthroplasties (TKA). Although the prediction of individualized readmission risk may inform appropriate preoperative interventions, current predictive models, such as the American College of Surgeons National Surgical Quality Improvement Program (ACS-NSQIP) surgical risk calculator (SRC), have limited utility. This study aims to compare the predictive accuracy of the SRC with a novel artificial neural network (ANN) algorithm for 30-day readmission after primary TKA, using the same set of clinical variables from a large national database.MethodsPatients undergoing primary TKA between 2013 and 2020 were identified from the ACS-NSQIP database and randomly stratified into training and validation cohorts. The ANN was developed using data from the training cohort with fivefold cross-validation performed five times. ANN and SRC performance were subsequently evaluated in the distinct validation cohort, and predictive performance was compared on the basis of discrimination, calibration, accuracy, and clinical utility.ResultsThe overall cohort consisted of 365,394 patients (trainingN = 362,559; validationN = 2835), with 11,392 (3.1%) readmitted within 30 days. While the ANN demonstrated good discrimination and calibration (area under the curve (AUC)ANN = 0.72, slope = 1.32, intercept = −0.09) in the validation cohort, the SRC demonstrated poor discrimination (AUCSRC = 0.55) and underestimated readmission risk (slope = −0.21, intercept = 0.04). Although both models possessed similar accuracy (Brier score: ANN = 0.03; SRC = 0.02), only the ANN demonstrated a higher net benefit than intervening in all or no patients on the decision curve analysis. The strongest predictors of readmission were body mass index (> 33.5 kg/m2), age (> 69 years), and male sex.ConclusionsThis study demonstrates the superior predictive ability and potential clinical utility of the ANN over the conventional SRC when constrained to the same variables. By identifying the most important predictors of readmission following TKA, our findings may assist in the development of novel clinical decision support tools, potentially improving preoperative counseling and postoperative monitoring practices in at-risk patients.

Brain-inspired wiring economics for artificial neural networks.

Wiring patterns of brain networks embody a trade-off between information transmission, geometric constraints, and metabolic cost, all of which must be balanced to meet functional needs. Geometry and wiring economy are crucial in the development of brains, but their impact on artificial neural networks (ANNs) remains little understood. Here, we adopt a wiring cost-controlled training framework that simultaneously optimizes wiring efficiency and task performance during structural evolution of sparse ANNs whose nodes are located at arbitrary but fixed positions. We show that wiring cost control improves performance across a wide range of tasks, ANN architectures and training methods, and can promote task-specific structural modules. An optimal wiring cost range provides both enhanced predictive performance and high values of topological properties, such as modularity and clustering, which are observed in real brain networks and known to improve robustness, interpretability, and performance of ANNs. In addition, ANNs trained using wiring cost can emulate the connection distance distribution observed in the brains of real organisms (such as Ciona intestinalis and Caenorhabditis elegans), especially when achieving high task performance, offering insights into biological organizing principles. Our results shed light on the relationship between topology and task specialization of ANNs trained within biophysical constraints, and their geometric resemblance to real neuronal-level brain maps.

Prediction of load-bearing capacity of FRP-steel composite tubed concrete columns: using explainable machine learning model with limited data

The complex interaction mechanism between the FRP jacket, steel tube, and confined concrete in the FRP-steel composite tubed concrete (F-STC) column makes the prediction of its mechanical behaviour a challenging task. This paper specifically investigates the application of machine learning models to predict the load-bearing capacity of F-STC columns. Since relatively few experimental works are performed on F-STC columns, the database established based on the test results from previous research contains only 69 data samples. In order to circumvent the training difficulties caused by the relatively small database, the application of the Gaussian process regression (GPR) model is trialled in this study. To make the predictions of the GPR model explainable, the Shapley additive explanations (SHAP) approach is incorporated with the GPR model in this study. Besides, four contrasting prediction models based on artificial neural network (ANN), support vector regression (SVR), decision tree (DT), and random forest (RF), are also proposed. The k-fold validation and test results indicate that the GPR model provides strong potential in predicting the load-bearing capacity of F-STC columns with high prediction accuracy and generalisation capability. Besides, 95% and 99% confidence intervals obtained by the GPR model are provided to show the uncertainty of the prediction results. Furthermore, the effect of the database size on the prediction performance of GPR, ANN, and SVR models is further examined by gradually reducing the number of data samples, and the comparisons illustrate the superiority of the GPR model.

Evaluating Machine Learning and Deep Learning models for predicting Wind Turbine power output from environmental factors.

This study presents a comprehensive comparative analysis of Machine Learning (ML) and Deep Learning (DL) models for predicting Wind Turbine (WT) power output based on environmental variables such as temperature, humidity, wind speed, and wind direction. Along with Artificial Neural Network (ANN), Long Short-Term Memory (LSTM), Recurrent Neural Network (RNN), and Convolutional Neural Network (CNN), the following ML models were looked at: Linear Regression (LR), Support Vector Regressor (SVR), Random Forest (RF), Extra Trees (ET), Adaptive Boosting (AdaBoost), Categorical Boosting (CatBoost), Extreme Gradient Boosting (XGBoost), and Light Gradient Boosting Machine (LightGBM). Using a dataset of 40,000 observations, the models were assessed based on R-squared, Mean Absolute Error (MAE), and Root Mean Square Error (RMSE). ET achieved the highest performance among ML models, with an R-squared value of 0.7231 and a RMSE of 0.1512. Among DL models, ANN demonstrated the best performance, achieving an R-squared value of 0.7248 and a RMSE of 0.1516. The results show that DL models, especially ANN, did slightly better than the best ML models. This means that they are better at modeling non-linear dependencies in multivariate data. Preprocessing techniques, including feature scaling and parameter tuning, improved model performance by enhancing data consistency and optimizing hyperparameters. When compared to previous benchmarks, the performance of both ANN and ET demonstrates significant predictive accuracy gains in WT power output forecasting. This study's novelty lies in directly comparing a diverse range of ML and DL algorithms while highlighting the potential of advanced computational approaches for renewable energy optimization.

Enhancing hydraulic fracturing efficiency through machine learning

Hydraulic fracturing (HF) is a technique employed in the oil and gas industry to extract hydrocarbon resources from shale formations and low-permeability rocks. This process involves the creation of new fractures and the extension of existing ones within the rock by injecting a high-pressure fracturing fluid, which enhances the flow of hydrocarbons. To assess the efficiency and safety of HF, various factors such as fracture orientation, geometry, length, distribution, and stimulated reservoir volume are analyzed. In the complex field of HF, the accurate assessment of fracture parameters is crucial for optimizing operational efficiency and ensuring environmental safety. This study investigates the role of machine learning (ML) methodologies in enhancing the precision of these assessments, with a particular focus on fracture width a critical factor in the effectiveness of HF. By utilizing a comprehensive dataset from hydrocarbon fields, in this study, the application of several advanced ML models was investigated, including Artificial Neural Networks (ANNs), Random Forest (RF), and K-Nearest Neighbors (KNNs). These models were specifically employed to predict and assess fracture width based on a variety of geological and operational parameters: geometric factors (γ), Shear Modulus (G), Poisson’s ratio (ν), viscosity of the fracturing fluid (µ in centipoise), crack height (h), fluid efficiency (η), injection time (t), and crack length (x). This study evaluated the performance of ANN, RF, and KNN models, achieving accuracies of 0.978, 0.979, and 0.893, respectively, which underscores their strong predictive modeling capabilities. The results were meticulously documented for each methodology. The RMSE values for ANN, KNN, and RF were 7.552, 15.711, and 6.194, respectively. Notably, the RF approach demonstrated superior performance with an RMSE of 6.194, establishing it as the most accurate method. This study highlights the transformative potential of ML in the pre-stimulation planning phase of HF, enabling enhanced real-time decision-making and operational optimization, which ultimately results in more effective and efficient fracturing operations.

Advancements in Predictive Maintenance: A Bibliometric Review of Diagnostic Models Using Machine Learning Techniques

This bibliometric review investigates the advancements in machine learning techniques for predictive maintenance, focusing on the use of Artificial Neural Networks (ANNs) and Support Vector Machines (SVMs) for fault detection in wheelset axle bearings. Using data from Scopus and Web of Science, the review analyses key trends, influential publications, and significant contributions to the field from 2000 to 2024. The findings highlight the performance of ANNs in handling large datasets and modelling complex, non-linear relationships, as well as the high accuracy of SVMs in fault classification tasks, particularly with small-to-medium-sized datasets. However, the study also identifies several limitations, including the dependency on high-quality data, significant computational resource requirements, limited model adaptability, interpretability challenges, and practical implementation complexities. This review provides valuable insights for researchers and engineers, guiding the selection of appropriate diagnostic models and highlighting opportunities for future research. Addressing the identified limitations is crucial for the broader adoption and effectiveness of machine learning-based predictive maintenance strategies across various industrial contexts.

Ann-based predictive model of geometrical deviations in dry turning of AA7075 (Al-Zn) alloy

This work presents the use of a shallow feedforward artificial neural network (ANN) to develop a prediction model for geometrical deviations in the dry turning of the AA7075 (Al-Zn) alloy. The study focuses on the influence of cutting speed and feed on the arithmetic mean roughness, straightness, and circular runout of cylindrical specimens. The main novelty of this ANN-based model compared to traditional models lies in the simultaneous consideration of geometrical variables at macro and micro scales. The analysis showed that feed was the most influential variable, particularly at higher values, whereas cutting speed had a lesser impact. For all three analysed output variables, the optimal results were achieved by combining low feed and high cutting speed values. The proposed ANN model showed a reasonable adjusted R2 value for all the variables, ranging from 0.87 to 0.97. The ANN performance was compared with other regression models, providing a better fit to the experimental data for all the output variables analysed. Testing of the ANN on additional data not included in the training and validation set confirmed its practical usefulness for predicting geometrical deviations under the studied cutting conditions.

Prediction of crippling load of I-shaped steel columns by using soft computing techniques

This study is primarily aimed at creating three machine learning models: artificial neural network (ANN), random forest (RF), and k-nearest neighbour (KNN), so as to predict the crippling load (CL) of I-shaped steel columns. Five input parameters, namely length of column (L), width of flange (bf), flange thickness (tf), web thickness (tw) and height of column (H), are used to compute the crippling load (CL). A range of performance indicators, including the coefficient of determination (R2), variance account factor (VAF), a-10 index, root mean square error (RMSE), mean absolute error (MAE) and mean absolute deviation (MAD), are used to assess the effectiveness of the established machine learning models. The results show that all of the three ML (machine learning) models can accurately predict the crippling load, but the performance of ANN is superior: it delivers the highest value of R2 = 0.998 and the lowest value of RMSE = 0.008 in the training phase, as well as the highest value of R2 = 0.996 and the smaller value of RMSE = 0.012 in the testing phase. Additional methods, including rank analysis, reliability analysis, regression plot, Taylor diagram and error matrix plot, are employed to assess the models’ performance. The reliability index (β) of the models is calculated by using the first-order second moment (FOSM) technique, and the result is compared with the actual value. Additionally, sensitivity analysis is performed to check the impact of the input variables on the output (CL), finding that bf has the greatest impact on the crippling load, followed by tf, tw, H and L, in that order. This study demonstrates that ML techniques are useful for developing a reliable numerical tool for measuring the crippling load of I-shaped steel columns. It is found that the proposed techniques can also be used to predict other kinds of failures as well as different kinds of perforated columns.

A General DNA-Like Hybrid Symbiosis Framework: An EEG Cognitive Recognition Method.

In electroencephalogram (EEG) cognitive recognition research, the combined use of artificial neural networks (ANNs) and spiking neural networks (SNNs) plays an important role to realize different categories of recognition tasks. However, most of the existing studies focus on the unidirectional interaction between an ANN and a SNN, which may be overly dependent on the performance of ANNs or SNNs. Inspired by the symbiosis phenomenon in nature, in this study, we propose a general DNA-like Hybrid Symbiosis (DNA-HS) framework, which enables mutual learning between the ANN and the SNN generated by this ANN through parametric genetic algorithm and bidirectional interaction mechanism to enhance the optimization ability of the model parameters, resulting in a significant improvement of the performance of the DNA-HS framework in all aspects. By comparing with seven typical EEG cognitive recognition models, the performance of the seven hybrid network frameworks constructed using this method on different EEG-based cognitive recognition tasks are all improved to different degrees, verifying the effectiveness of the proposed method. This unified hybrid network framework similar to the DNA structure is expected to open up a new approach and form a new research paradigm for EEG-based cognitive recognition task.

Predictive Modelling for Sensitive Social Media Contents Using Entropy-FlowSort and Artificial Neural Networks Initialized by Large Language Models

This work offers an integrated methodological framework that integrates the capabilities of large language models (LLMs), rules-based reasoning, multi-criteria sorting, and artificial neural networks (ANN) in developing a predictive model for classifying the intensity of sensitive social media contents. The current literature lacks a holistic consideration of multiple attributes in evaluating social media contents, and the proposed framework intends to bridge such a gap. Three actions constitute the development of the framework. First, LLMs (i.e., GPT4) evaluate the social media contents under a predefined set of attributes, leveraging the power of LLMs in content analytics. Second, rules-based reasoning and multi-criteria sorting (i.e., entropy-FlowSort) determine the categories of social media contents. Lastly, the two previous actions produced a complete dataset that can be used to train a predictive model using ANN to classify sensitive social media contents. With 1100 randomly extracted social media contents and the predefined categories of violations against community standards set by Facebook, the proposed integrated methodology produces an ANN-based classification model with 86.36% prediction accuracy. Comparative analysis using Decision Trees, k-nearest neighbors, Linear Discriminant Analysis, Random Forest, and Naive Bayes classification yields the highest performance of ANN. The predictive model can be used as a decision-support tool to design moderation actions on social media contents.

Exploring artificial neural networks for the forward kinematics of a SCARA robotic manipulator using varied datasets and training optimizers

Abstract Although analytical methods are traditionally employed, the solution to the Forward Kinematics (FK) problem for Selective Compliance Assembly Robot Arm (SCARA) manipulator robots can prove intricate and computationally demanding. Recognizing this challenge, this study endeavors to introduce an intelligent approach by leveraging Artificial Neural Networks (ANNs) to address the FK problem specifically tailored for a four-degree-of-freedom (4-DoF) SCARA robot. To train the ANNs, we employ three distinct datasets, one with a fixed step size, one with a random step size, and one based on a sinusoidal signal. Moreover, the objective is to scrutinize the ANNs performance under the influence of three distinct training algorithms: Levenberg-Marquardt (LM), Bayesian Regularization (BR), and Scaled Conjugate Gradient (SCG). Through a systematic comparison of various ANN models, diverse training algorithms, and the three chosen datasets, the investigation reveals that optimal Mean Squared Error (MSE) results are achieved with random step size datasets for models with two hidden layers using the LM algorithm (MSE = 8.6099e-05). For the BR algorithm, the best MSE (8.0535e-05) was obtained with sinusoidal datasets and three hidden layers. For the SCG algorithm, the optimal MSE (1.1144e-04) was achieved with fixed step size datasets and one hidden layer. The accuracy of the ANN model is significantly influenced by the dataset, the choice of training optimizer, and the configuration of hidden layers. Consequently, further research could explore hybrid approaches that integrate evolutionary algorithms to leverage their respective strengths and improve overall ANN model performance.

Artificial Neural Networks to Predict Metabolic Syndrome without Invasive Methods in Adolescents.

Background/Objectives: The prevalence of metabolic syndrome (MetS) is increasing worldwide, and an increasing number of cases are diagnosed in younger age groups. This study aimed to propose predictive models based on demographic, anthropometric, and non-invasive clinical variables to predict MetS in adolescents. Methods: A total of 2064 adolescents aged 18-19 from São Luís-Maranhão, Brazil were enrolled. Demographic, anthropometric, and clinical variables were considered, and three criteria for diagnosing MetS were employed: Cook et al., De Ferranti et al. and the International Diabetes Federation (IDF). A feed-forward artificial neural network (ANN) was trained to predict MetS. Accuracy, sensitivity, and specificity were calculated to assess the ANN's performance. The ROC curve was constructed, and the area under the curve was analyzed to assess the discriminatory power of the networks. Results: The prevalence of MetS in adolescents ranged from 5.7% to 12.3%. The ANN that used the Cook et al. criterion performed best in predicting MetS. ANN 5, which included age, sex, waist circumference, weight, and systolic and diastolic blood pressure, showed the best performance and discriminatory power (sensitivity, 89.8%; accuracy, 86.8%). ANN 3 considered the same variables, except for weight, and exhibited good sensitivity (89.0%) and accuracy (87.0%). Conclusions: Using non-invasive measures allows for predicting MetS in adolescents, thereby guiding the flow of care in primary healthcare and optimizing the management of public resources.

Handwritten Character Recognition using Enhanced Artificial Neural Network

This study addresses the concerns regarding the performance of Handwritten Character Recognition (HCR) systems, focusing on the classification stage. It is widely acknowledged that the development of the classification model significantly impacts the overall performance of HCR. The problems identified specifically pertain to the classification model, particularly in the context of the Artificial Neural Network (ANN) learning problem, leading to low accuracy in recognizing handwritten characters. The objective of this study is to improve and refine the ANN classification model to achieve better HCR. To achieve this goal, this study proposed a hybrid Flower Pollination Algorithm with Artificial Neural Network (FPA-ANN) classification model for HCR. The FPA is one of the metaheuristic approaches is utilized as an optimization technique to enhance the performance of ANN, particularly by optimizing the network training process of ANN. The experimentation phase involves using the National Institute of Standards and Technology (NIST) handwritten character database. Finally, the proposed FPA-ANN classification model is analyzed based on generated confusion matrix and evaluated performance of the classification model in terms of precision, sensitivity, specificity, F-score and accuracy.

Comparative analysis of HEC-HMS and machine learning models for rainfall-runoff prediction in the upper Baro watershed, Ethiopia

ABSTRACT Accurate streamflow simulation is crucial for effective hydrological management, especially in regions like the upper Baro watershed, Ethiopia, where data scarcity challenges conventional modeling approaches. This study evaluates the efficacy of three hydrological models: the Hydrologic Engineering Center's Hydrologic Modeling System (HEC-HMS), artificial neural network (ANN), and support vector regression (SVR) in predicting runoff. Using data from 2000 to 2016, the analysis focused on various performance metrics such as the Nash–Sutcliffe efficiency (NSE), root mean square error (RMSE), and coefficient of determination (R2). The results indicated that the ANN model significantly outperformed the others, achieving an NSE of 0.98, RMSE of 24 m3/s, and R2 of 0.99. In comparison, the HEC-HMS model yielded an NSE of 0.85, RMSE of 113.4 m3/s, and R2 of 0.89, while the SVR model displayed an NSE of 0.97, RMSE of 27 m3/s, and R2 of 0.99. These findings highlight the superior performance of ANN in regions with limited hydrological data, suggesting its potential as a reliable alternative to traditional physical models. By demonstrating the efficacy of machine learning models, this research facilitates the way for innovative approaches to water resource management, offering valuable insights for policymakers and practitioners.

Comparative analysis of machine learning models for predicting PM2.5 concentrations using meteorological and chemical indicators

Air pollution significantly impacts human health, causing numerous premature deaths, particularly with the rise in PM2.5 concentrations. Therefore, comparing different machine learning (ML) models for predicting PM2.5 concentration is crucial. This research focuses on six ML models: Linear Regression (LR), Regression Tree (RT), Support Vector Machine (SVM), Ensemble Regression (ERT), Gaussian Process Regression (GPR), and Artificial Neural Networks (ANN). Trained on six years of data (July 2015–December 2021) with optimized hyperparameters, the models consider eight meteorological and chemical indicators as PM2.5 predictors, including temperature, relative humidity, air pressure, O3, SO2, NO2, dew point, and wind speed. Model efficiency is assessed using Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Correlation Coefficient (R), and Coefficient of Determination (R2) values. The models achieve R2 and RMSE values as follows: LR (0.72, 13.52), RT (0.8, 12.156), SVM (0.82, 10.28), ERT (0.81, 11.87), GPR (0.94, 7.65), and ANN (0.99, 2.36). These metrics indicate the superior performance of ANN, with its R2 value approaching 1 and the lowest RMSE compared to other models. The results highlight the effectiveness of ANN, particularly the model with three hidden layers, in predicting PM2.5 concentration. Utilizing ML models for this purpose is crucial for understanding and mitigating the impacts on human health and the environment, with ANN emerging as a promising tool for various investigations.

Machine learning and numerical approaches for vibration behavior prediction of rotating nanobeams in complex environments

This work scrutinized the vibration behavior of nanoscale beam structures with rotating motion according to the nonlocal stress-strain gradient theory (NSSGT). The surface layer energy, rotary inertia factor, and thickness-dependent scale impacts are considered in the mathematical simulation. Additionally, parametric examinations are exploited to comprehend the impressions of varying environmental fields and follower and axial forces. Adopting Hamilton’s principle, the dynamic equation of nanoscale beams with rotation is achieved. The eigenvalue analysis is accomplished with the aid of the Galerkin decomposition scheme, and dynamic characteristics are recognized. Besides, machine learning (ML)-based approaches, viz., artificial neural network (ANN) and Gaussian support vector machine (GSVM) techniques, are exploited to predict the vibration frequencies. Comparison analyses with existing scientific reports in the open technical literature are conducted to guarantee the correctness of the deduced outcomes. The performance and correctness of ANN and GSVM techniques are verified by assessing the performance parameters of ML approaches. The numerical technique outcomes are consistent with those of ML-assisted models. Furthermore, the proposed ML prediction models are perceived to have excellent performance (viz., low mean square error and high determination coefficient) and superior computational time and cost-effectiveness. It is deduced that the scale-dependency in the thickness direction improves the vibration frequency. Moreover, the vibration frequencies are decreased/increased with increasing nanobeam thickness/length by considering the surface layer energy. The current research outcomes can be valuable in designing next-generation high-speed rotating nanodevices, inspiring further innovation and development in the field.

Imitating the respiratory activity of the brain stem by using artificial neural networks: exploratory study on an animal model of lactic acidosis and proof of concept

Artificial neural networks (ANNs) are versatile tools capable of learning without prior knowledge. This study aims to evaluate whether ANN can calculate minute volume during spontaneous breathing after being trained using data from an animal model of metabolic acidosis. Data was collected from ten anesthetized, spontaneously breathing pigs divided randomly into two groups, one without dead space and the other with dead space at the beginning of the experiment. Each group underwent two equal sequences of pH lowering with pre-defined targets by continuous infusion of lactic acid. The inputs to ANNs were pH, ΔPaCO2 (variation of the arterial partial pressure of CO2), PaO2, and blood temperature which were sampled from the animal model. The output was the delta minute volume (ΔVM), (the change of minute volume as compared to the minute volume the animal had at the beginning of the experiment). The ANN performance was analyzed using mean squared error (MSE), linear regression, and the Bland-Altman (B-A) method. The animal experiment provided the necessary data to train the ANN. The best architecture of ANN had 17 intermediate neurons; the best performance of the finally trained ANN had a linear regression with R2 of 0.99, an MSE of 0.001 [L/min], a B-A analysis with bias ± standard deviation of 0.006 ± 0.039 [L/min]. ANNs can accurately estimate ΔVM using the same information that arrives at the respiratory centers. This performance makes them a promising component for the future development of closed-loop artificial ventilators.

Macro-Nutrient Prediction of Paddy Field Soil Using Artificial Neural Network and NIR Spectroscopy

Understanding soil fertility, influenced by macronutrients like nitrogen, phosphorus, and potassium, is essential for adaptive agriculture implementation based on various soil conditions. Near-infrared spectroscopy technology provides non-destructive, rapid soil property measurements without chemicals, applicable both in-field and in-laboratory. However, the wide NIR spectrum range and neural network complexities can hinder Artificial Neural Network (ANN) training and inference, leading to time and resource inefficiency, especially without sophisticated computing devices. This study examines data reduction methods to enhance ANN performance in predicting soil macronutrients using NIR spectra. Multiple Linear Regression (MLR) and Principal Component Analysis (PCA) were applied to select wavelengths from the 1000–2500 nm for ANN input, comparing their performance. About 237 NIR reflectance data from paddy soil were transformed into absorbance data. MLR used forward selection to identify wavelengths with correlations higher than 0.9, while PCA selected wavelengths corresponding to the loading factor peaks for each principal component. These selected wavelengths served as inputs for the ANN model. The ANN’s performance was assessed using correlation and determination coefficients, RMSE, RPD, and model consistency. For nitrogen, the PCA+ANN model with reflectance spectra performed better (RPD 2.4-4.8) than the MLR+ANN model (RPD 2.2-2.6) using fewer wavelengths (5-9 for PCA+ANN vs. 9-12 for MLR+ANN). For phosphorus estimation, the PCA+ANN model also excelled (RPD 2.3-7.0 vs. 2.3-2.4) with fewer wavelengths (4-7 vs. 7). For potassium estimation, the PCA+ANN model showed superior performance (RPD 4.3-9.5 vs. 4.2-4.4), using the same number of wavelengths (4-8 vs. 4-6).

Modeling Consumer Overall Acceptance for Traditional Spice-Based Ready-to-Drink Using Artificial Neural Network and Kansei Engineering

Measuring consumer acceptance of food products is challenging, primarily because the process is susceptible to bias. Several studies have reported that this challenge can be addressed through Kansei engineering through verbal and nonverbal response measurements. Therefore, this study aimed to predict consumer overall acceptance using artificial neural networks (ANN) and Kansei engineering. A total of 30 respondents participated in this study to test nine different samples of traditional spice-based ready-to-drink (RTD). The overall acceptance score and Kansei responses, including verbal and nonverbal, were then measured. Each sample was served cold in a 60-mL cup labeled with a three-digit random code, and the panelists were successfully presented with the nine drinks. All participants were asked to rank each Kansei word scale based on the intensity of their feelings during the assessment. The heart rate (HR) and skin temperature (ST) were also measured as nonverbal responses in real time. The results showed that Kansei's responses and respondent background best predicted overall acceptance. The optimal model architecture had ten input neurons, two hidden neurons, and one output neuron (10-2-1). The training, validation, and testing data showed that the performance of ANN was satisfactory, with a low error rate (RMSE) and a high coefficient of correlation value (R2). Based on the findings, the developed model could inspire and motivate further studies and development in industries to develop appropriate products for potential consumers, thereby revolutionizing the food industry.