Artificial Neural Network Classifier Research Articles

Predicting financial distress in TSX-listed firms using machine learning algorithms

IntroductionThis study investigates the application of machine learning (ML) algorithms, a subset of artificial intelligence (AI), to predict financial distress in companies. Given the critical need for reliable financial health indicators, this research evaluates the predictive capabilities of various ML techniques on firm-level financial data.MethodsThe dataset comprises financial ratios and firm-specific variables from 464 firms listed on the TSX. Multiple ML models were tested, including decision trees, random forests, support vector machines (SVM), and artificial neural networks (ANN). Recursive feature elimination with cross-validation (RFECV) and bootstrapped CART were also employed to enhance model stability and feature selection.ResultsThe findings highlight key predictors of financial distress, such as revenue growth, dividend growth, cash-to-current liabilities, and gross profit margins. Among the models tested, the ANN classifier achieved the highest accuracy at 98%, outperforming other algorithms.DiscussionThe results suggest that ANN provides a robust and reliable method for financial distress prediction. The use of RFECV and bootstrapped CART contributes to the model’s stability, underscoring the potential of ML tools in financial health monitoring. These insights carry valuable implications for auditors, regulators, and company management in enhancing practices around financial oversight and fraud detection.

Advanced machine learning artificial neural network classifier for lithology identification using Bayesian optimization

Identifying lithology is crucial for geological exploration, and the adoption of artificial intelligence is progressively becoming a refined approach to automate this process. A key feature of this strategy is leveraging population search algorithms to fine-tune hyperparameters, thus boosting prediction accuracy. Notably, Bayesian optimization has been applied for the first time to select the most effective learning parameters for artificial neural network classifiers used for lithology identification. This technique utilizes the capability of Bayesian optimization to utilize past classification outcomes to enhance the lithology models performance based on physical parameters calculated from well log data. In a comparison of artificial neural network architectures, the Bayesian-optimized artificial neural network (BOANN) demonstrably achieved the superior classification accuracy in validation and significantly outperformed a non-optimized wide, bilayer, and tri-layer network configurations, indicating that incorporating Bayesian optimization can significantly advance lithofacies recognition, thus offering a more accurate and intelligent solution for identifying lithology.

Preventing mislabeling of organic white button mushrooms (Agaricus bisporus) combining NMR-based foodomics, statistical, and machine learning approach

Organic foods are among the most susceptible to fraud and mislabeling since the differentiation between organic and conventionally grown food relies on a paper-trail-based system. This study aimed to develop a differentiation model that combines nuclear magnetic resonance (NMR), statistical approach (principal component analysis – PCA and partial least square discriminant analysis – PLS-DA), and classification artificial neural network (cANN). The model was tested for hydrophilic and lipophilic extracts of Agaricus bisporus. As linear techniques, the PCA and PLS-DA analyses and cANN as a non-linear classification tool successfully discriminated organic from conventional samples regarding their NMR data. PLS-DA revealed higher similarity among the hydrophilic samples within the organic class and among the lipophilic samples within the conventional class. Both applied approaches demonstrated high statistical quality, but a higher level of classification confidence in the case of lipophilic extracts. The metabolites responsible for discrimination and observed (dis)similarities between classes were considered according to cultivation specificities.

Domain‐independent adaptive histogram‐based features for pomegranate fruit and leaf diseases classification

AbstractDisease identification for fruits and leaves in the field of agriculture is important for estimating production, crop yield, and earnings for farmers. In the specific case of pomegranates, this is challenging because of the wide range of possible diseases and their effects on the plant and the crop. This study presents an adaptive histogram‐based method for solving this problem. Our method describe is domain independent in the sense that it can be easily and efficiently adapted to other similar smart agriculture tasks. The approach explores colour spaces, namely, Red, Green, and Blue along with Grey. The histograms of colour spaces and grey space are analysed based on the notion that as the disease changes, the colour also changes. The proximity between the histograms of grey images with individual colour spaces is estimated to find the closeness of images. Since the grey image is the average of colour spaces (R, G, and B), it can be considered a reference image. For estimating the distance between grey and colour spaces, the proposed approach uses a Chi‐Square distance measure. Further, the method uses an Artificial Neural Network for classification. The effectiveness of our approach is demonstrated by testing on a dataset of fruit and leaf images affected by different diseases. The results show that the method outperforms existing techniques in terms of average classification rate.

Application of Artificial Neural Networks in Predicting the Thermal Performance of Heat Pipes

The loss of energy by heat is a common problem in almost all areas of industry, and heat pipes are essential to increase efficiency and reduce energy waste. However, in many cases, they have complex theoretical equations with high percentages of error, limiting their development and causing dependence on empirical methods that generate a waste of time and material, resulting in significant expenses and reducing the viability of their use. Thus, Artificial Neural Networks (ANNs) can be an excellent option to facilitate the construction and development of heat pipes without knowledge of the complex theory behind the problem. This investigation uses experimental data from previous studies to evaluate the ability of three different ANNs to predict the thermal performance of heat pipes with different capillary structures, each of them in various configurations of the slope, filling ratio, and heat load. The goal is to investigate results in as many different scenarios as possible to clearly understand the networks’ capacity for modeling heat pipes and their operating parameters. We chose two classic ANNs (the most used, Multilayer Perceptron (MLP) network, and the Radial Basis Function (RBF) network) and the Extreme Learning Machine (ELM), which has not yet been applied to heat pipes studies. The ELM is an Unorganized Machine with a fast training process and a simple codification. The ANN results were very close to the experimental ones, showing that ANNs can successfully simulate the thermal performance of heat pipes. Based on the RMSE (error metric being reduced during the training step), the ELM presented the best results (RMSE = 0.384), followed by MLP (RMSE = 0.409), proving their capacity to generalize the problem. These results show the importance of applying different ANNs to evaluate the system deeply. Using ANNs in developing heat pipes is an excellent option for accelerating and improving the project phase, reducing material loss, time, and other resources.

Modeling the occupancy rate and nights spent at tourism accommodations of Spain using autoregressive deep learning networks

Purpose The purpose of this paper is to autoregressively model the net occupancy rate of beds and bedrooms in hotels and similar accommodations and the nights spent at these accommodations of Spain for the period of 1990–2023 using monthly data. Design/methodology/approach The monthly occupancy rate of hotels and the total number of hotel nights data of Spain for the 1990M01–2023M09 range is considered. An autoregressive deep learning network is developed for the modeling of both metrics. Moreover, the results of the proposed autoregressive deep learning method are compared to those of a classical artificial neural network. Findings The actual occupancy rate, total night data and the deep learning model results are compared showing the accuracy of the developed model. Moreover, the R2, mean absolute error, root mean square error and mean absolute percentage error of the models are calculated further demonstrating the high performance of the developed model. The R2 values higher than 0.9 are achieved for both occupancy rate and total number of hotel nights data. Practical implications The modeling results given in this paper demonstrate that the previous values of the net occupancy rate and the total number of nights can be used as inputs of a deep learning network model by which accurate forecasts can be made for the future values of the occupancy rate and the total number of hotel nights. This modeling approach possesses importance from the practical viewpoint considering that the accurate planning and forecast of the net occupancy rate and the total number of nights affect the tourism income. Originality/value This study differs from existing literature by attempting to model the occupancy rate and the total number of hotel nights data autoregressively using deep learning networks.

Early monitoring of drought stress in safflower (Carthamus tinctorius L.) using hyperspectral imaging: A comparison of machine learning tools and feature selection approaches

Early detection of drought stress is essential for preventing permanent plant damage and minimizing yield loss. This study utilized hyperspectral imaging at the leaf level to visualize drought stress in safflower plants (Carthamus tinctorius L.). Three safflower genotypes, Palenus, A82, and IL-111, were cultivated under three irrigation levels. Stress conditions were simulated by depleting 50%, 70%, and 90% of soil water content, representing unstressed (US), mild stress (MS), and severe stress (SS) conditions, respectively. Hyperspectral images of leaf samples were captured before any visible signs of water scarcity emerged. Classification analysis was performed using the full mean spectral data with partial least squares discriminant analysis, soft independent modeling of class analogy (SIMCA), support vector machines, and artificial neural network (ANN) classifiers. Feature selection methods were applied to extract the most informative wavebands, and ANN was used to build predictive models. Spatial analysis involved pixel-wise classification using both unsupervised (k-means clustering) and supervised (best classifiers) approaches. ANN outperformed other classifiers using the entire spectral data, effectively distinguishing US, MS, and SS classes in the Palenus, A82, and IL-111 genotypes, achieving F1-scores of 92.22%, 96.01%, and 96.47%, respectively. Among the feature selection methods, SIMCA-based features excelled in monitoring stress conditions in the Palenus and A82 genotypes. In supervised spatial analysis, ANN models clearly depicted the progression of stress in leaves across different genotypes. This study demonstrates the potential of hyperspectral imaging to differentiate various levels of drought stress in safflower, an important oilseed crop.

НЕЙРОСЕТЕВОЙ ПОДХОД К КОГНИТИВНОМУ МОДЕЛИРОВАНИЮ

Some features of cognitive modeling are presented, including the prerequisites for a cognitive approach to solving complex problems. Cognitive modeling involves the use of various artificial neural networks, including convolutional neural networks. The classification of artificial neural networks according to various characteristics is given. The features of self-organizing neural networks and networks using deep learning methods are considered. The artificial neural network, which is a three-layer unidirectional direct propagation network, the interface of a computer program used to approximate functions using the specified neural network, as well as the solution of the image recognition problem using an artificial convolutional neural network, in which the neural network parameters are adjusted for each recognizable image fragment in order to adaptively filter the image, are considered in detail. The analysis of images in video surveillance systems in order to detect fires allows them to be detected at an early stage and, thus, prevent the fire propogation.

Dynamic robustness evaluation for automated model selection in operation

Context:The increasing use of artificial neural network (ANN) classifiers in systems, especially safety-critical systems (SCSs), requires ensuring their robustness against out-of-distribution (OOD) shifts in operation, which are changes in the underlying data distribution from the data training the classifier. However, measuring the robustness of classifiers in operation with only unlabeled data is challenging. Additionally, machine learning engineers may need to compare different models or versions of the same model and switch to an optimal version based on their robustness. Objective:This paper explores the problem of dynamic robustness evaluation for automated model selection. We aim to find efficient and effective metrics for evaluating and comparing the robustness of multiple ANN classifiers using unlabeled operational data. Methods:To quantitatively measure the differences between the model outputs and assess robustness under OOD shifts using unlabeled data, we choose distance-based metrics. An empirical comparison of five such metrics, suitable for higher-dimensional data like images, is performed. The selected metrics include Wasserstein distance (WD), maximum mean discrepancy (MMD), Hellinger distance (HL), Kolmogorov–Smirnov statistic (KS), and Kullback–Leibler divergence (KL), known for their efficacy in quantifying distribution differences. We evaluate these metrics on 20 state-of-the-art models (ten CIFAR10-based models, five CIFAR100-based models, and five ImageNet-based models) from a widely used robustness benchmark (RobustBench) using data perturbed with various types and magnitudes of corruptions to mimic real-world OOD shifts. Results:Our findings reveal that the WD metric outperforms others when ranking multiple ANN models for CIFAR10- and CIFAR100-based models, while the KS metric demonstrates superior performance for ImageNet-based models. MMD can be used as a reliable second option for both datasets. Conclusion:This study highlights the effectiveness of distance-based metrics in ranking models’ robustness for automated model selection. It also emphasizes the significance of advancing research in dynamic robustness evaluation.

Towards imagined speech: Identification of brain states from EEG signals for BCI-based communication systems

BackgroundThe electroencephalogram (EEG) based brain-computer interface (BCI) system employing imagined speech serves as a mechanism for decoding EEG signals to facilitate control over external devices or communication with the external world at the moment the user desires. To effectively deploy such BCIs, it is imperative to accurately discern various brain states from continuous EEG signals when users initiate word imagination. New methodThis study involved the acquisition of EEG signals from 15 subjects engaged in four states: resting, listening, imagined speech, and actual speech, each involving a predefined set of 10 words. The EEG signals underwent preprocessing, segmentation, spatio-temporal and spectral analysis of each state, and functional connectivity analysis using the phase locking value (PLV) method. Subsequently, five features were extracted from the frequency and time-frequency domains. Classification tasks were performed using four machine learning algorithms in both pair-wise and multiclass scenarios, considering subject-dependent and subject-independent data. ResultsIn the subject-dependent scenario, the random forest (RF) classifier achieved a maximum accuracy of 94.60 % for pairwise classification, while the artificial neural network (ANN) classifier achieved a maximum accuracy of 66.92 % for multiclass classification. In the subject-independent scenario, the random forest (RF) classifier achieved maximum accuracies of 81.02 % for pairwise classification and 55.58 % for multiclass classification. Moreover, EEG signals were classified based on frequency bands and brain lobes, revealing that the theta (θ) and delta (δ) bands, as well as the frontal and temporal lobes, are sufficient for distinguishing between brain states. ConclusionThe findings promise to develop a system capable of automatically segmenting imagined speech segments from continuous EEG signals.

Advanced facial recognition with LBP-URIGL hybrid descriptors

AbstractFacial recognition technology is transformative in security and human-machine interaction, reshaping societal interactions. Robust descriptors, essential for high precision in machine learning tasks like recognition and recall, are integral to this transformation. This paper presents a hybrid model enhancing local binary pattern descriptors for facial representation. By integrating rotation-invariant local binary pattern with uniform rotation-invariant grey-level co-occurrence, employing linear discriminant analysis for feature space optimization, and utilizing an artificial neural network for classification, the model achieves exceptional accuracy rates of 100% for Olivetti Research Laboratory, 99.98% for Maastricht University Computer Vision Test, and 99.17% for Extended Yale B, surpassing traditional methods significantly.

Neural network-enhanced internal leakage analysis for efficient fault detection in heavy machinery hydraulic actuator cylinders

This work presents a method for detecting internal leakage faults in hydraulic actuator cylinders using signal analysis and a supervised artificial neural network classifier. An artificial leakage is introduced into the hydraulic cylinder actuator, and a signal-based fault detection method is employed to process and transform the signals for internal leakage detection. The analysis focuses on extracting features from the pressure signal, particularly the peaks, which include information such as location, height, and width. After the neural network has undergone the training process, it is deployed for the purpose of categorizing fault levels into three distinct categories: “a healthy system,”“a system with a low fault,” and “a system with a high fault.” The proposed technique utilizes pressure difference signals from the cylinder chambers and extracts features from the peak signals to reduce dimensionality. The extracted features are then used for supervised training of an artificial neural network using a feed-forward algorithm. The trained network is capable of predicting the leakage class of unknown datasets. This method offers advantages such as reduced computational cost through feature extraction and dimensionality reduction, and it is capable of detecting multiple leakage classes. This study introduces a notably efficient approach, predicated on artificial neural networks, for the identification of internal leakage faults in hydraulic cylinders, with specific emphasis on faults arising from component wear and seal damage. The practical value of this research lies in its potential to significantly improve the reliability and efficiency of hydraulic systems used in heavy machinery. By utilizing neural networks for internal leakage detection, this research addresses a critical issue that can lead to costly downtime and maintenance in industrial operations.

Texture-Based Classification of Fetal Growth Restriction From Intrauterine Neurosonographic Image.

Fetal growth restriction (FGR) is a condition where fetuses fail to reach their genetic potential for growth, posing a significant health challenge for newborns. The aim of this research was to explore the efficacy of texture-based analysis of neurosonographic images in identifying FGR in fetuses, which may provide a promising tool for early assessment of FGR. A retrospective analysis collected 100 intrauterine neurosonographic images from 50 FGR and 50 gestational age-appropriate fetuses. Using MaZda software, approximately 300 texture features were extracted from occipital white matter (OWM) and cerebellum of intrauterine neurosonographic images, respectively. Then 10 optimal features were separately selected by 3 algorithms, including the Fisher coefficient method, the method of minimizing classification error probability and average correlation coefficients, and the mutual information coefficient method. Further, the 10 statistically most significant features were selected from these sets to form the mixed feature set. After nonlinear discriminant analysis was performed to reduce feature dimensionality, the artificial neural network (ANN) classifier was conducted, respectively. For OWM and cerebellum, a total of 11 and 14 statistically significant features were selected. When the mixed feature sets of OWM and cerebellum were applied to ANN classifier, classification accuracy were 90.00% (κ = 0.800; P < .001) and 93.00% (κ = 0.860; P < .001), and the receiver operating characteristic curve for identifying FGR showed an area under the curve of 0.82 and 0.87. Texture analysis of fetal intrauterine neurosonographic images is a feasible and noninvasive strategy for evaluating FGR fetuses.

An AI-Based Approach for Developing a Recommendation System for Underground Mining Methods Pre-Selection

Selecting the most appropriate mining method to recover mineral resources is a critical decision-making task in mining project development. This study introduces an artificial intelligence-based mining methods recommendation system (AI-MMRS) for the pre-selection of underground mining methods. The study integrates and evaluates the capability of two approaches for mining methods selection (MMS): the memory-based collaborative filtering (CF) approach aided by the UBC-MMS system to predict the top-3 relevant mining methods and supervised machine learning (ML) classification algorithms to enhance the effectiveness and novelty of the AI-MMRS, addressing the limitations of the CF approach. The results reveal that the memory-based CF approach achieves an accuracy ranging from 81.8% to 87.9%. Among the classification algorithms, artificial neural network (ANN) and k-nearest neighbors (KNN) classifiers perform the best, with accuracy levels of 66.7% and 63.6%, respectively. These findings demonstrate the effectiveness and viability of both approaches in MMS, acknowledging their limitations and the need for continuous training and optimization. The proposed AI-MMRS for the pre-selection stage supplemented by the direct involvement of mining professionals in later stages of MMS, has the potential to significantly aid in the MMS decision-making, providing data-driven and experience-based recommendations following the ongoing evolution of mining practices.

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.

Detection of Alcoholic EEG signal using LASSO regression with metaheuristics algorithms based LSTM and enhanced artificial neural network classification algorithms

The world has a higher count of death rates as a result of Alcohol consumption. Identification is possible because Alcoholic EEG waves have a certain behavior that is totally different compared to the non-alcoholic individual. The available approaches take longer to provide the feedback because they analyze the data manually. For this reason, in the present paper we propose a novel approach applied to detect alcoholic EEG signals automatically by using deep learning methods. Our strategy has advantages as far as fast detection is concerned; hence people can help immediately when there is a need. The potential for a significant decrease in deaths from alcohol poisoning and improvement to public health is presented by this advancement. In order to create clusters and classify the alcoholic EEG signals, this research uses a cascaded process. To begin with, an initial clustering and feature extraction is done by LASSO regression. After that, a variety of meta-heuristics algorithms like Particle Swarm Optimization (PSO), Binary Coding Harmony Search (BCHS) as well as Binary Dragonfly Algorithm (BDA) are employed for feature minimization. When this method is used, normal and alcoholic EEG signals may be differentiated using non-linear features. PSO, BCHS, and BDA features allow for estimation of statistical parameters through t-test, Friedman statistic test, Mann-Whitney U test, and Z-Score with corresponding p-values for alcoholic EEG signals. Lastly, classification is done by the use of support vector machines (SVM) (including linear, polynomial, and Gaussian kernels), random forests, artificial neural networks (ANN), enhanced artificial neural networks (EANN), and LSTM models. Results showed that LASSO regression with BDA-based EANN proposed classifier have a classification accuracy of 99.59%, indicating that our method is highly accurate at classifying alcoholic EEG signals.

Leveraging In Silico Structure-Activity Models to Predict Acute Honey Bee (Apis mellifera) Toxicity for Agrochemicals.

In the realm of crop protection products, ensuring the safety of pollinators stands as a pivotal aspect of advancing sustainable solutions. Extensive research has been dedicated to this crucial topic as well as new approach methodologies in toxicity testing. Hence, within the agricultural and chemical industries, prioritizing pollinator safety remains a constant objective during the development of predictive tools. One of these tools includes computational models like quantitative structure-activity relationships (QSARs) that are valuable in predicting the toxicity of chemicals. This research uses bee toxicity data to develop artificial neural network classification models for predicting honey bee acute toxicity. Bee toxicity data from 1542 compounds were used to develop models; the sensitivity and specificity of the best model were 0.90 and 0.91, respectively. These in silico models can aid in the discovery of next-generation crop protection products. These tools can guide the screening and selection of next-generation crop protection molecules with high margins of safety to pollinators, and candidates with favorable sustainability profiles can be identified at the early discovery stage as precursors to in vivo data generation.

Cyclical Curriculum Learning.

Artificial neural networks (ANNs) are inspired by human learning. However, unlike human education, classical ANN does not use a curriculum. Curriculum learning (CL) refers to the process of ANN training in which samples are used in a meaningful order. When using CL, training begins with a subset of the dataset and new samples are added throughout the training, or training begins with the entire dataset and the number of samples used is reduced. With these changes in training dataset size, better results can be obtained with curriculum, anti-curriculum, or random-curriculum methods than the vanilla method. However, a generally efficient CL method for various architectures and datasets is not found. In this article, we propose cyclical CL (CCL), in which the data size used during training changes cyclically rather than simply increasing or decreasing. Instead of using only the vanilla method or only the curriculum method, using both methods cyclically like in CCL provides more successful results. We tested the method on 18 different datasets and 15 architectures in image and text classification tasks and obtained more successful results than no-CL and existing CL methods. We also have shown theoretically that it is less erroneous to apply CL and vanilla cyclically instead of using only CL or only the vanilla method. The code of the cyclical curriculum is available at https://github.com/CyclicalCurriculum/Cyclical-Curriculum.

Optimizing machine learning algorithms for diabetes data: A metaheuristic approach to balancing and tuning classifiers parameters

Diabetes mellitus poses a global health concern, prompting the development of machine learning algorithms designed to construct a model for the accurate classification of patients, enabling precise diagnoses and early-stage treatment. However, the efficacy of classifying diabetes patients through machine learning relies on datasets, often plagued by imbalance, leading to biased classification and inaccurate diagnoses. Previous research attempts, employing techniques like random sampling (under-sampling and oversampling) and the Synthetic Minority Oversampling Technique (SMOTE), have struggled to achieve optimally balanced datasets. Additionally, setting the best parameters for machine learning classifiers remains a challenging task. To address these issues, this research focuses on devising a methodological metaheuristic optimization, a machine learning algorithm tailored for diabetes data balancing, and classifier hyperparameter tuning. Leveraging Particle Swarm Optimization (PSO) algorithm for diabetes data balancing and a genetic algorithm to select the optimal architecture for various machine learning classifiers. The study compares the performance of the proposed metaheuristic data balancer and classifier architecture parameter tuner using classification metrics (F1 score, Average Precision–Recall (APR), AUC, and accuracy). The PSO balanced dataset emerges as the most effective in classifying diabetes, with an Average Percentage Improvement (API) in classification performance metrics: 20.78% accuracy, 16.79% area under the curve for receiver operating characteristics, and a significant 32.78% enhancement in APR. Moreover, the XGBOOST classifier trained with a genetic algorithm demonstrates minimal computational training time for the Centre for Disease Control and Prevention (CDC) diabetes dataset compared to the artificial neural network and random forest classifier. Notably, the imbalanced CDC diabetes dataset exhibits the least APR compared to random under-sampling and the PSO data balancing technique.

The development of machine learning approaches in two-dimensional NMR data interpretation for metabolomics applications

Metabolomics has been widely applied in human diseases and environmental science to study the systematic changes of metabolites over diverse types of stimuli. NMR-based metabolomics has been widely used, but the peak overlap problems in the one-dimensional (1D) NMR spectrum could limit the accuracy of quantitative analysis for metabolomics applications. Two-dimensional (2D) NMR has been applied to solve the 1D NMR overlap problem, but the data processing is still challenging. In this study, we built an automatic approach to process the 2D NMR data for quantitative applications using machine learning approaches. Partial least square discriminant analysis (PLS-DA), artificial neural network classification (ANN-DA), gradient boosted trees classification (XGBoost-DA), and artificial deep learning neural network classification (ANNDL-DA) were applied in combination with an automatic peak selection approach. Standard mixtures, sea anemone extracts, and mouse fecal samples were tested to demonstrate the approach. Our results showed that ANN-DA and ANNDL-DA have high accuracy in selecting 2D NMR peaks (around 90 %), which have a high potential application in 2D NMR-based metabolomics quantitively study, while PLS-DA and XGBoost-DA showed limitations in either data variation or overfitting. Our study built an automatic approach to applying 2D NMR data to routine quantitative analysis in metabolomics.