Simple Artificial Neural Network Research Articles

Neurosymbolic artificial intelligence (NSAI) based algorithm for predicting the impact strength of additive manufactured polylactic acid (PLA) specimens

In this study, we introduce application of Neurosymbolic Artificial Intelligence (NSAI) for predicting the impact strength of additive manufactured polylactic acid (PLA) components, representing the first-ever use of NSAI in the domain of additive manufacturing. The NSAI model amalgamates the advantages of neural networks and symbolic AI, offering a more robust and accurate prediction than traditional machine learning techniques. Experimental data was collected and synthetically augmented to 1000 data points, enhancing the model’s precision. The Neurosymbolic model was developed using a neural network architecture comprising input, two hidden layers, and an output layer, followed by a decision tree regressor representing the symbolic component. The model’s performance was benchmarked against a Simple Artificial Neural Network (ANN) model by assessing mean squared error (MSE) and R-squared (R2) values for both training and validation datasets. The results reveal that the Neurosymbolic model surpasses the Simple ANN model, attaining lower MSE and higher R2 values for both training and validation sets. This innovative application of the Neurosymbolic approach in estimating the impact strength of additive manufactured PLA components underscores its potential for optimizing the additive manufacturing process. Future research could investigate further refinements to the Neurosymbolic model, extend its application to other materials and additive manufacturing processes, and incorporate real-time monitoring and control for enhanced process optimization.

Prediction of Wear Rate in Al/SiC Metal Matrix Composites Using a Neurosymbolic Artificial Intelligence (NSAI)-Based Algorithm

This research paper delves into an innovative utilization of neurosymbolic programming for forecasting wear rates in aluminum-silicon carbide (Al/SiC) metal matrix composites (MMCs). The study scrutinizes compositional transformations in MMCs with various weight percentages of SiC (0%, 3%, and 5%), employing comprehensive spectroscopic analysis. The effect of SiC integration on the compositional distribution and ratio of elements within the composite is meticulously examined. In a novel move for this field of research, the study introduces and applies neurosymbolic programming as a novel computational modeling approach. The performance of this cutting-edge methodology is compared to a traditional simple artificial neural network (ANN). The neurosymbolic algorithm exhibits superior performance, providing lower mean squared error (MSE) values and higher R-squared (R2) values across both training and validation datasets. This highlights its potential for delivering more precise and resilient predictions, marking a significant development in the field. Despite the promising results, the study recognizes that the performance of the model might vary based on specific characteristics of the composite material and operational conditions. Thus, it encourages future studies to authenticate and expand these innovative findings across a wider spectrum of materials and conditions. This research represents a substantial advancement towards a more profound understanding of wear rates in Al/SiC MMCs and emphasizes the potential of the novel neurosymbolic programming in predictive modeling of complex material systems.

A Neural-Network-Optimized Hydrogen Peroxide Pairwise Additive Model for Classical Simulations.

We have developed an all-atom pairwise additive model for hydrogen peroxide using an optimization procedure based on artificial neural networks (ANNs). The model is based on experimental molecular geometry and includes a dihedral potential that hinders the cis-type configuration and allows for crossing the trans one, defined between the planes that have the two oxygen atoms and each hydrogen. The model's parametrization is achieved by training simple ANNs to minimize a target function that measures the differences between various thermodynamic and transport properties and the corresponding experimental values. Finally, we evaluated a range of properties for the optimized model and its mixtures with SPC/E water, including bulk-liquid properties (density, thermal expansion coefficient, adiabatic compressibility, etc.) and properties of systems at equilibrium (vapor and liquid density, vapor pressure and composition, surface tension, etc.). Overall, we obtained good agreement with experimental data.

A simple ANN-MLP model for estimating 60-GHz PDP inside public and private vehicles

Radio wave propagation in an intra-vehicular (IV) environment is markedly different from other well-studied indoor scenarios, such as an office or a factory floor. While millimetre wave (mmWave)-based intra-vehicular communications promise large bandwidth and can achieve ultra-high data rates with lower latency, exploiting the advantages of mmWave communications largely relies on adequately characterising the propagation channel. Channel characterisation is most accurately done through an extensive channel sounding, but due to hardware and environmental constraints, it is impractical to test channel conditions for all possible transmitter and receiver locations. Artificial neural network (ANN)-based channel sounding can overcome this impediment by learning and estimating the channel parameters from the channel environment. We estimate the power delay profile in intra-vehicular public and private vehicle scenarios with a high accuracy using a simple feedforward multi-layer perception-based ANN model. Such artificially generated models can help extrapolate other relevant scenarios for which measurement data are unavailable. The proposed model efficiently matches the taped delay line samples obtained from real-world data, as shown by goodness-of-fit parameters and confusion matrices.

Simple method for detecting sleep episodes in rats ECoG using machine learning

In this paper we propose a new method for the automatic recognition of the state of behavioral sleep (BS) and waking state (WS) in freely moving rats using their electrocorticographic (ECoG) data. Three-channels ECoG signals were recorded from frontal left, frontal right and occipital right cortical areas. We employed a simple artificial neural network (ANN), in which the mean values and standard deviations of ECoG signals from two or three channels were used as inputs for the ANN. Results of wavelet-based recognition of BS/WS in the same data were used to train the ANN and evaluate correctness of our classifier. We tested different combinations of ECoG channels for detecting BS/WS.Our results showed that the accuracy of ANN classification did not depend on ECoG-channel. For any ECoG-channel, networks were trained on one rat and applied to another rat with an accuracy of at least 80 %. Itis important that we used a very simple network topology to achieve a relatively high accuracy of classification. Our classifier was based on a simple linear combination of input signals with some weights, and these weights could be replaced by the averaged weights of all trained ANNs without decreases in classification accuracy. In all, we introduce a new sleep recognition method that does not require additional network training. It is enough to know the coefficients and the equations suggested in this paper. The proposed method showed very fast performance and simple computations, therefore it could be used in real time experiments. It might be of high demand in preclinical studies in rodents that require vigilance control or monitoring of sleep-wake patterns.

Practical ANN prediction models for the axial capacity of square CFST columns

In this study, two machine-learning algorithms based on the artificial neural network (ANN) model are proposed to estimate the ultimate compressive strength of square concrete-filled steel tubular columns. The development of such prognostic models is achievable since an extensive set of experimental tests exist for these members. The models are developed to use the simplest possible network architecture but attain very high accuracy. A total dataset of 1022 specimens with 685 stub columns and 337 slender columns subjected to pure axial compression is collected from the available literature. This is significant for the development of the initial model considering that for this field it falls under the scope of big data analysis. The ANN models are validated by comparison with experimental results. The validation study has shown the superiority of surrogate models over the Eurocode 4 design code. The empirical equation derived from the best-tuned Bayesian regularization algorithm shows a better agreement with the experimental results than those obtained by the Levenberg–Marquardt algorithm, and Eurocode 4 design code. A similar conclusion applies to stub and slender columns independently. The Bayesian regularization-based model is negligibly slower than the one developed on the Levenberg–Marquardt algorithm but gives a better generalization even with simplified ANN. Generally, besides its high accuracy, one of the key benefits of the presented ANN model is its applicability to a broader range of columns than Eurocode 4 and other studies.

MONITORING PERKEMBANGAN ANAK USIA DINI DENGAN METODE PERCEPTRON PADA DI TK KASIH BUNDA BATANGHARI

Monitoring the development of early childhood is very important, especially for children aged 4-6 years, because at that time it was a golden age that required more attention in order to stimulate child development to the maximum. This research is expected to be able to help educators to determine the development that occurs in students. The method used in this research is the perceptron method. The perceptron method is a simple Artificial Neural Network (ANN) method that uses a training algorithm to classify linearly, while the input data that becomes the criteria for this research are Moral Religious Values (MRV), Physical Motoric (PM), Cognitive, Social Emotional, Language, and Art. While the output of the data that has been processed is in the form of selected development indicators, this indicator is divided into three, namely Very Good Development [1 1], Evolves According to Expectations [1 0], and Begins to Develop [0 1]. After analyzing the monitoring of early childhood development by applying the perceptron method and the processing it using the Matlab R2009a application, the research concluded that the perceptron method was proven to be able to determine the target output and the actual appropriate ouput reached 68%.

Photonic Synapses for Image Recognition and High Density Integration of Simplified Artificial Neural Networks

AbstractWith the rapid development of artificial intelligence (AI), there is an urgent need for developing a biological sensory perception system that can simulate the human brain for information processing. Inspired by the biological vision system, photo‐responsive photonic synapses are ideal devices for constructing photosensitive artificial neural networks for neuromorphic computing tasks. This paper reports a stable photonic synaptic device in an array layout with adjustable synaptic plasticity under ultraviolet light pulses. Since the heterojunction has a photoconductivity effect and the trap layer provides superior charge carrier trapping capability, optical sensing, memory, and neuromorphic computing are integrated into a single device. Meanwhile, supervised learning of handwritten digitals is achieved by exploiting the multistate conductance by photoelectric co‐modulation and the specific decay law. The recognition rate reaches 90.6% and hardly changes with time. Additionally, the device can simplify the artificial neural network (ANN) and reduce its size to 3.78% of the original network while retaining strong fault tolerance and learning ability. The photonic artificial synapses based on ultraviolet light modulation provide a novel and effective approach for photosensory ANNs to perform in situ computation.

Better decision-making strategy with target seeking approach of humanoids using hybridized SOARANN-fuzzy technique

The prediction and decision-making features of the artificial neural network have been applied to several robotic related research works. Humanoid robot navigation is a challenging task for the researchers with best decision making. Simple artificial neural network (ANN) is the connection models which are poor in multistep decision making with long term planning and logical reasoning. So, in this study artificial neural network is improved in long term cognitive planning by SOAR (State Operator and Result) with powerful feature prediction through ANN (SANN) and hybridized with Fuzzy system for generation effective turning angle value, which will guide the humanoids to reach target location with better decision target seeking ability. An intelligent data fusion model is embedded between SOAR and ANN for converting the logical sequence information of SOAR to probabilistic vectors in multilayer ANNs. The obstacle distances are the input parameters to the SANN model and turning angle (TA) is the output from SANN. The obstacle distances along with the TA obtained from SANN is fed to the fuzzy system as input value and effective turning angle (ETA) is obtained to guide the humanoids towards reaching target. The proposed novel technique and ANN is used by both single and multiple humanoids in both simulation and experimental platforms. The novelty of the research focuses on the development of the new SOAR-ANN technique with better decision strategy for effective turning angle value, to obtain global optimal path by avoiding trapping in local optima and dead ends. The simulation and experimental results in terms of path length travelled and time spent to reach target are obtained from both ANN and novel SANN-Fuzzy technique. When the developed novel technique is compared with standalone ANN method during single humanoid navigation then, a significant improvement of 2–5% and 14–15% is observed in related to trajectory path span and trajectory time spent respectively. Similarly, during multiple humanoid navigation percentage improvement of 5–9% and 14–18% is obtained for humanoids in terms of navigational parameters respectively. The results clearly shows the developed technique requires less time and travels less path to reach target as compared to ANN and the percentage of error is also quite low. Statistical analysis is performed from the results of both the techniques and reflects the effectiveness of the proposed technique in comparison to ANN. Further the developed technique is compared against another existing methodologies like Fuzzy-ANN and Boundary Node Approach and, significant enhancements of 5.64% and 9.72% is attained in terms of track span respectively.

Estimation of the Full-sky Power Spectrum between Intermediate and Large Angular Scales from Partial-sky CMB Anisotropies Using an Artificial Neural Network

Reliable extraction of cosmological information from observed cosmic microwave background (CMB) maps may require removal of strongly foreground-contaminated regions from the analysis. In this paper, we employ an artificial neural network (ANN) to predict the full-sky CMB angular power spectrum between intermediate and large angular scales from the partial-sky spectrum obtained from a masked CMB temperature anisotropy map. We use a simple ANN architecture with one hidden layer containing 895 neurons. Using 1.2 × 105 training samples of full-sky and corresponding partial-sky CMB angular power spectra at HEALPix pixel resolution parameter N side = 256, we show that the spectrum predicted by our ANN agrees well with the target spectrum at each realization for the multipole range 2 ≤ l ≤ 512. The predicted spectra are statistically unbiased, and they preserve the cosmic variance accurately. Statistically, the differences between the mean predicted and underlying theoretical spectra are within approximately 3σ. Moreover, the probability densities obtained from predicted angular power spectra agree very well with those obtained from “actual” full-sky CMB angular power spectra for each multipole. Interestingly, our work shows that the significant correlations in input cut-sky spectra due to mode–mode coupling introduced on the partial sky are effectively removed, since the ANN learns the hidden pattern between the partial- and full-sky spectra preserving all of the statistical properties. The excellent agreement of statistical properties between the predicted and the ground truth demonstrates the importance of using artificial intelligence systems in cosmological analysis more widely.

Klasifikasi Kendaraan Berbasis Suara Di Lalu Lintas: Implementasi TinyML

Driving is deeply embedded in Indonesian culture. All Indonesian citizens (WNI) who meet the qualifications can obtain a driver's license (SIM). As a result, the number of drivers in Indonesia is relatively significant, resulting in a high rate of traffic accidents. One of the reasons leading to the rising number of road accidents is hearing loss. This study focuses on building a tool that can classify vehicles based on the audio of the vehicle while it is running in order to assist drivers with hearing impairments. This study employs the Arduino Nano 33 BLE Sense, which includes an embedded microphone, and the Edge Impulse open platform. A simple artificial neural network model with four 1-D convolution layers and one hidden layer was utilized. The dataset was obtained manually using the YouTube open platform and was captured directly on the Edge Impulse platform using the Arduino Nano's microphone. The model training procedure was repeated 80 epochs. During training and testing, the model is regarded to be accurate, with accuracy scores of 96.3% and 90.9%, respectively. With these adequate results, the model is judged eligible for implementation, despite the fact that it still requires further refinement.

Fet-Net Algorithm for Automatic Detection of Fetal Orientation in Fetal MRI

Identifying fetal orientation is essential for determining the mode of delivery and for sequence planning in fetal magnetic resonance imaging (MRI). This manuscript describes a deep learning algorithm named Fet-Net, composed of convolutional neural networks (CNNs), which allows for the automatic detection of fetal orientation from a two-dimensional (2D) MRI slice. The architecture consists of four convolutional layers, which feed into a simple artificial neural network. Compared with eleven other prominent CNNs (different versions of ResNet, VGG, Xception, and Inception), Fet-Net has fewer architectural layers and parameters. From 144 3D MRI datasets indicative of vertex, breech, oblique and transverse fetal orientations, 6120 2D MRI slices were extracted to train, validate and test Fet-Net. Despite its simpler architecture, Fet-Net demonstrated an average accuracy and F1 score of 97.68% and a loss of 0.06828 on the 6120 2D MRI slices during a 5-fold cross-validation experiment. This architecture outperformed all eleven prominent architectures (p < 0.05). An ablation study proved each component's statistical significance and contribution to Fet-Net's performance. Fet-Net demonstrated robustness in classification accuracy even when noise was introduced to the images, outperforming eight of the 11 prominent architectures. Fet-Net's ability to automatically detect fetal orientation can profoundly decrease the time required for fetal MRI acquisition.

A simple neural network for predicting operable window status

A simple artificial neural network (ANN) is globally developed and broadly adopted in the building engineering field from many perspectives. It is a powerful tool to help engineer or predict future statements. There are many methods to introduce natural ventilation interior, and the simplest way is to conduct the airflow through windows. The general passive ventilation is practically in Spring and Autumn. This study aims to forecast the trends of indoor temperature and schedule the operation status of operable windows by using time-series differential data set. The building simulation has conducted during the transition periods to create training and testing data. A single-layer artificial neural network has been developed and performs training using the Levenberg-Marquardt algorithm. Additionally, the simulations have investigated in different seasons and places to validate the ANN model and find the best ANN model. From the result, the best trained ANN model is the training data that created covers the spring and autumn seasons with one hidden layer and 25 nodes. The training performances present in terms of MSE and R-values are 0.0507 and 88.25%, respectively. Finally, the best ANN model that has been built from a location is applicable and adapted to another location efficiently.

Advanced Self Driving Car Using Machine Learning

There are surprisingly innovative approaches to develop AI systems for self-driving cars, many of which require advanced and high-end hardware. However, this paper proposes a simple AI-based system with low hardware requirements. The proposed system comprises a simple three-layer fully connected neural network that can interpret images captured by a forward-facing QVGA camera and generate corresponding steering commands. When presented with an input image, the neural network selects one of four available commands: forward, left, right, or stop. Surprisingly, the system successfully learns to navigate the road and stay within its lane using only a limited amount of training data (just 250 images). Notably, the system acquires knowledge of crucial road features solely based on the steering angle provided by the human driver, without explicit training for road line detection. In comparison to more intricate approaches like Nvidia's convolutional neural network-based lane detection and management, this method demonstrates remarkable robustness and cost-effectiveness. The aim is to showcase that this approach can lead to enhanced performance and reduced hardware requirements, thereby facilitating the development of more accessible and affordable self-driving vehicles. The described paper highlights that a simple artificial neural network, like the one discussed, is sufficient for accomplishing relatively complex tasks such as lane keeping.

Modeling relational responding with artificial neural networks

Relational responding refers to behavior that conforms to a rule for com- paring stimuli. Lazareva et al. (2014) trained pigeons to choose either the smaller or the larger of two circles, using 1–3 pairs of circles for training and 17–19 new pairs for testing. The pigeons showed relational responding on some test pairs and systematic failures on others. We present a simple artificial neural network model that reproduces the animals’ behavior well, similarly to Lazareva et al.’s (2014) statistical model based on stimulus features and stimulus relationships. We analyze how the network model gener- alizes from training to test stimuli, and show that it can reconcile contrasting ideas about relational responding from the seminal works by Köhler (1929, 1918/1938, 1924), positing that animals can learn relational rules such as “choose the larger stimulus,” and Spence (1937), positing that relational re- sponding can be explained based on stimulus generalization.

Quantitative analysis of ToF-SIMS data of a two organic compound mixture using an autoencoder and simple artificial neural networks.

Matrix effects cause a nonlinear relationship between ion intensities and concentrations in mass spectrometry, including time-of-flight secondary ion mass spectrometry (ToF-SIMS). Here, two artificial neural network (ANN)-based methods, autoencoder-based and simple ANN methods, were employed for the quantitative and qualitative analyses of a two organic compound mixture via ToF-SIMS. The multilayer model sample contained a mixture of Irganox 1010 and Fmoc-pentafluoro-L-phenylalanine (Fmoc-PFLPA). The sample's positive and negative ion depth profiles were collected through ToF-SIMS. ToF-SIMS-derived cross-sectional image datasets were analyzed using three unsupervised methods, namely principal component analysis (PCA), multivariate curve resolution (MCR), and use of a sparse autoencoder (SAE). The supervised simple ANN method was optimized based on the spectra and validated by predicting the test dataset ratios of Irganox 1010. The results obtained using the SAE demonstrated linear calibration curves and appropriate material distribution images. The Irganox 1010 and Fmoc-PFLPA positive and negative ion datasets exhibited >0.97 correlation coefficients. The PCA and MCR results demonstrated lower linearity than that of SAE. Moreover, SAE weights indicated the ions important for each organic compound. The simple ANN method accurately predicted the ratios in the test dataset and indicated the important ions. Both the supervised and unsupervised methods based on ANN, which were employed in regulating nonlinear relationships, were effective in the quantitative and qualitative analyses of the ToF-SIMS data of the two organic compound mixtures. Regarding qualitative analysis, both ANN-based methods indicated specific ions from the molecules in the sample.

Application of Artificial Neural Network to Nucleic Acid Analysis: Accurate Discrimination for Untypical Real-Time Fluorescence Curves With High Specificity and Sensitivity

Abstract As a division of polymerase chain reaction (PCR), convective PCR (CPCR) is able to achieve highly efficient thermal cycling based on free thermal convection with pseudo-isothermal heating, which could be beneficial to point-of-care (POC) nucleic acid analysis. Similar to traditional PCR or isothermal amplification, due to a couple of issues, e.g., reagent, primer design, reactor, reaction dynamics, amplification status, temperature and heating condition, and other reasons, in some cases of CPCR tests, untypical real-time fluorescence curves with positive or negative tests will show up. Especially, when parts of the characteristics between untypical low-positive and negative tests are mixed together, it is difficult to discriminate between them using traditional cycle threshold (Ct) value method. To handle this issue which may occur in CPCR, traditional PCR or isothermal amplification, as an example, instead of using complicated mathematical modeling and signal processing strategy, an artificial intelligence (AI) classification method with artificial neural network (ANN) modeling is developed to improve the accuracy of nucleic acid detection. It has been proven that both the detection specificity and sensitivity can be significantly improved even with a simple ANN model. It can be estimated that the developed method based on AI modeling can be adopted to solve similar problem with PCR or isothermal amplification methods.

Artificial intelligence model with deep learning in nonalcoholic fatty liver disease diagnosis: genetic based artificial neural networks

Nonalcoholic fatty liver disease (NAFLD) is one of the most common causes of chronic liver disease in the world. The NAFLD spectrum includes simple steatosis, steatohepatitis, fibrosis, cirrhosis, and hepatocellular carcinoma (HCC). Genetic, nutritional factors, obesity, insulin resistance, gut microbiota are among the risk factors for NAFLD. The genetic variant Patatin-like phospholipase domain-containing protein 3 (PNPLA3) plays an important role in the development of a number of liver diseases ranging from steatosis, chronic hepatitis, cirrhosis and HCC. Due to the increase in the prevalence of NAFLD, new models are being developed with machine learning, deep learning, artificial neural network (ANN) algorithms in the field of artificial intelligence (AI) to determine low-cost, noninvasive diagnostic methods. Models developed with ANN from AI modules are important in order to examine biochemical and genomic information in detail in the diagnosis of NAFLD. The aim of this study is to develop a simple ANN model using biochemical and genotypic parameters in the diagnosis of NAFLD. A total of 300 patients followed up with the diagnosis of NAFLD and 100 controls were included in the study. The data set was divided into two as training and test set. Genotyping of PNPLA3 (CC, CG, GG) as genomic analysis was performed with real time PCR device. The algorithm used for the diagnosis of NAFLD was designed using age, body mass index (BMI), mean platelet volume (MPV), insulin resistance (IR), alanine aminotransferase (ALT), genotype PNPLA3 (CC, CG, GG) parameters. MLP Classifier algorithm from ANN was used in the development of the model. ANN algorithms are used in python programming language. Statistical analyzes were made in SPSS program. Percent accuracy, area under the ROC curve, confusion matrix, Positive (PPV) and Negative Predicted Value (NPV) values, precision, recall, and f1-score results were determined. The accuracy percentage was determined as 0.979 in the train set and 0.970 in the test set. The Log Loss value was set to 0.09. The developed neural network achieved an accuracy percentage of 97.0% during testing, with an area under the ROC curve value of 0.95. We think that the ANN model developed with genomic and biochemical parameters can be used as a cost-effective, noninvasive new predictive diagnostic model in clinical practice in the diagnosis of NAFLD.

Using Neural Network NO2-Predictions to Understand Air Quality Changes in Urban Areas—A Case Study in Hamburg

Due to the link between air pollutants and human health, reliable model estimates of hourly pollutant concentrations are of particular interest. Artificial neural networks (ANNs) are powerful modeling tools capable of reproducing the observed variations in pollutants with high accuracy. We present a simple ANN for the city of Hamburg that estimated the hourly NO2 concentration. The model was trained with a ten-year dataset (2007–2016), tested for the year 2017, and then applied to assess the efficiency of countermeasures against air pollution implemented since 2018. Using both meteorological data and describing the weekday dependent traffic variabilities as predictors, the model performed accurately and showed high consistency over the test data. This proved to be very efficient in detecting anomalies in the time series. The further the prediction was from the time of the training data, the more the modeled data deviated from the measured data. Using the model, we could detect changes in the time series that did not follow previous trends in the training data. The largest deviation occurred during the COVID-19 lockdown in 2020, when traffic volumes decreased significantly. Concluding our case study, the ANN based approach proved suitable for modeling the NO2 concentrations and allowed for the assessment of the efficiency of policy measures addressing air pollution.

Demonstration of a fast-training feed-forward machine learning algorithm for studying key optical properties of FBG and predicting precisely the output spectrum

In this article, we propose and demonstrate a generalized machine learning (ML) approach to analyse the various optical properties of the Fiber Bragg grating (FBGs), namely effective refractive index, bandwidth, reflectivity and wavelength. For this purpose, three commonly used variants of FBG, namely conventional, π phase-shifted and chirped ones are investigated and the reflected spectra of the aforementioned FBGs are predicted using ab initio artificial neural networks (ANNs). We implemented a simple and fast-training feed-forward ANN and established the efficacy of our model by predicting the output spectrum with minute details for unknown device parameters along with non-linear and complex behaviour of the spectrum. Thus, our proposed ANN model is capable of predicting various key optical properties and reproducing the exact spectrum accurately and quickly, providing a cost-effective solution for efficient and precise modelling.