Deep Neural Network Architecture Research Articles

Accelerated Gradient Approach For Deep Neural Network-Based Adaptive Control of Unknown Nonlinear Systems.

Recent connections in the adaptive control literature to continuous-time analogs of Nesterov's accelerated gradient method have led to the development of new real-time adaptation laws based on accelerated gradient methods. However, previous results assume that the system's uncertainties are linear-in-the-parameters (LIP). To compensate for non-LIP uncertainties, our preliminary results developed a neural network (NN)-based accelerated gradient adaptive controller to achieve trajectory tracking for nonlinear systems; however, the development and analysis only considered single-hidden-layer NNs. In this article, a generalized deep NN (DNN) architecture with an arbitrary number of hidden layers is considered, and a new DNN-based accelerated gradient adaptation scheme is developed to generate estimates of all the DNN weights in real-time. A nonsmooth Lyapunov-based analysis is used to guarantee the developed accelerated gradient-based DNN adaptation design achieves global asymptotic tracking error convergence for general nonlinear control affine systems subject to unknown (non-LIP) drift dynamics and exogenous disturbances. A comprehensive set of simulation studies are conducted on a two-state nonlinear system, a robotic manipulator, and a complex 20-D nonlinear system to demonstrate the improved performance of the developed method. Our simulation studies demonstrate enhanced tracking and function approximation performance from both DNN architectures and accelerated gradient adaptation.

An Ensemble Learning Framework for Anomaly Detection of Important Geographical Entities

Abstract. Due to the complex landforms and the limited resolution of remote sensing imagery, it is difficult to avoid the problem of incorrectly capturing geographical entities, such as buildings. Therefore, anomaly detection of important geographical entities is of great significance to ensure the authenticity and accuracy of geographical entity data. In this paper, we propose an ensemble learning framework for anomaly detection of geographical entity by aggregating the predicted labels generated by multiple deep learning models. In detail, we explore multiple change detection and semantic segmentation model and fully utilize the advantages of various deep learning neural network architectures. The proposed anomaly detection strategy of buildings has been performed on two benchmark datasets, including WHU Building change detection dataset and LEVIR building change detection dataset, the experimental results prove that the proposed method can achieve a more robust and better performance than using single change detection model in terms of quantitative performance and visual performance.

Discrete structural design synthesis: a hierarchical-inspired deep reinforcement learning approach considering topological and parametric actions

Abstract Structural design synthesis considering discrete elements can be formulated as a sequential decision process solved using deep reinforcement learning (DRL), shown in prior work to naturally accommodate discrete actions and efficiently provide adept solutions to a variety of problems. By modeling structural design synthesis as a Markov decision process (MDP), the states correspond to specific structural designs, the actions correspond to specific design alterations, and the rewards are related to the improvement in the altered design's performance with respect to the design objective and specified constraints. Here, the MDP action definition is extended by integrating parametric design grammars that further enable the design agent to not only alter a given structural design's topology, but also its element parameters. In considering topological and parametric actions, both the dimensionality of the state and action space and the diversity of the action types available to the agent in each state significantly increase, making the overall MDP learning task more challenging. Hence, this paper also addresses discrete design synthesis problems with large state and action spaces by significantly extending the framework. A hierarchical-inspired deep neural network architecture is developed to equip the agent to learn the type of action, topological or parametric, to apply, thus reducing the complexity of possible action choices in a given state. This extended framework is applied to the design synthesis of planar structures considering both discrete elements and cross-sectional areas, and it is observed to adeptly learn policies that synthesize high performing design solutions.

Neural Technologies for Objects Classification with Mobile Applications

This paper is related to the study of the features of the neural technologies’ application, in particular, ResNet neural networks for the classification of objects in photographs. The work aims to increase the accuracy of recognition and classification of objects in photographs by using various models of the ResNet neural network. The paper analyzes the features of the application of the corresponding models in comparison with other architectures of deep neural networks and evaluates their efficiency and accuracy in the classification of objects in photographs. The process of data formation for training neural networks, their processing and sorting is described. A web application and a mobile application for recognizing and classifying objects in a photo were also developed. A system for classifying objects, in particular airplanes in photographs, was developed using neural network technologies. It gives a recognition and classification accuracy of about 95%. Research results of ResNet models are of great practical importance, as they can improve the classification accuracy of various images. Features of ResNet, such as the use of skip connections or residual connections, make it effective in the relevant tasks. The results of the study will help to implement ResNet in various fields, including medicine, automatic pattern recognition and other areas where the classification of objects in photographs is an important task.

205 Detection of anemic sheep using ocular conjunctiva images and deep learning technology

Abstract In sheep, severe anemia often results from gastrointestinal nematodes infections, commonly caused by Haemonchus contortus, a blood-sucking nematode. The objective of the study was to develop a model to predict packed cell volume (PCV) in sheep through ocular conjunctiva images as a real-time anemia diagnosis approach. We collected 3,291 ocular conjunctiva images from 392 sheep on three different farms using an iPhone 8 (12-megapixel). To identify the region of interest in the images (ocular conjunctiva), we annotated 480 images using the Segment Anything Model (SAM). Subsequently, we employed an image segmentation algorithm based on U-net, utilizing the original images and annotations obtained from SAM. We then cropped the segmented images to retain only the region containing the ocular conjunctiva. These cropped and segmented images were used as input data, with PCV as the target variable, in both classification and regression models, while exploring three different deep neural network (DNN) architectures: VGG19, Inception v3, and Xception. We set a threshold of 27% (anemic < 27%, non-anemic ≥ 27%) to convert PCV into a binary variable for classification models. The dataset was split in train, validation and test sets using random by sheep strategy to perform the analysis of the segmentation and prediction models. The segmentation was tested using intersection over union (IoU) as an evaluation metric. To assess and compare predictive quality in the test set, we calculated accuracy, precision, recall, and F1 score for classification models, and R2 for regression models. The U-net segmentation model demonstrated good localization accuracy and reliable segmentation ability, with an average IoU of 0.93, 0.84 and 0.68 in the train, validation and test set, respectively. VGG19 outperformed the other models in classifying individuals as anemic or non-anemic, achieving an F1 score of 0.60, indicating its moderate ability to distinguish between these two classes. For regression, Xception provided the best performance with an R2 of 0.29, suggesting that 29% of the variance in PCV can be explained by the model’s predictions. This innovative approach not only expands the possibilities for integrated high-throughput phenotyping through computer vision but also aids in identifying anemic animals. The results suggest that using ocular conjunctiva images and DNN technology can contribute to support farm-level management decisions, and potentially reduce economic losses due to parasitic infections like H. contortus. Furthermore, this approach reduces the cost of blood tests for anemia identification while allowing for real-time anemia diagnosis.

Cushion gas effects on hydrogen storage in porous rocks: Insights from reservoir simulation and deep learning

Underground hydrogen (H2) storage (UHS) is crucial for the H2 economy, offering scalable and long-term solutions vital for reliable and sustainable H2-based energy systems. Cushion gas is a common component in UHS designs, but its impact on H2 storage performance is not yet fully understood. This study bridges this research gap by systematically investigating the effects of various cushion gas scenarios on UHS performance in porous rocks, specifically saline aquifers and depleted gas reservoirs, using reservoir simulations and deep learning. Firstly, we conducted 8,000 multiphase compositional reservoir simulations for UHS operations in two types of storage formations, considering four cushion gas scenarios: no cushion gas, methane (CH4), nitrogen (N2), and carbon dioxide (CO2). These simulations cover a wide range of geological and operational parameters relevant to practical UHS projects. From these simulations, we derived key insights into the physical mechanisms governing UHS processes and proposed critical storage performance metrics, including H2 withdrawal efficiency, produced H2 purity, produced gas–water ratio, and well injectivity. Then, we developed a unified reduced-order model (ROM) using a deep neural network (DNN) based on the comprehensive simulation results. The DNN architecture, with an input layer, five hidden layers, and an output layer, takes 12 geological and operational parameters as inputs, and forecasts the four performance metrics. The ROM accurately predicts the cyclic evolution of performance metrics and is over 5,000,000 times faster than traditional physics-based simulations, allowing for thorough uncertainty quantification of UHS performance prediction under various geological and operational conditions. Key findings of this study include: (a) UHS in porous rocks is technically promising, with improving storage performance over cycles; (b) UHS in saline aquifers has higher withdrawal efficiency and purity but lower gas–water ratio and injectivity compared to depleted gas reservoirs; (c) In saline aquifers, cushion gas reduces withdrawal efficiency and purity but significantly enhances gas–water ratio and injectivity, making it particularly beneficial in scenarios with high water production risks; (d) In depleted gas reservoirs, cushion gas is less important under the current operational conditions, as it barely affects withdrawal efficiency and purity, and these reservoirs inherently exhibit high gas–water ratio and injectivity due to initial/leftover native gas (mostly CH4); (e) The cushion gas type slightly affects storage performance, with N2 slightly outperforming CH4 and CO2 in withdrawal efficiency and purity, CO2 being better for gas–water ratio, and CH4 for injectivity. The findings of this study support cushion gas designs in future UHS projects in both saline aquifers and depleted gas reservoirs. Moreover, the developed ROM offers a highly efficient tool for conducting extensive sensitivity analysis and uncertainty quantification, optimizing operational conditions, and facilitating the screening of potential field sites for future UHS projects.

Deep Learning Approaches for Automated Waste Classification and Sorting

Abstract: Effective waste management is critical for environmental sustainability in modern societies. Manual classification and sorting of waste streams is labour-intensive, error-prone, and poses health hazards. Recent advancements in deep neural networks provide new opportunities for automated waste characterization and segregation. This work develops a deep convolutional neural network architecture for classifying various materials commonly found in municipal solid waste. The model achieves over 93% accuracy in categorizing plastics, paper, metals, and glass items from images. The system is designed for edge deployment on low-cost hardware at waste sorting facilities. We implement a closed-loop simulation environment using robotic arms and conveyor belts to demonstrate automated segregation based on predicted waste categories. Results highlight viability for improved waste processing via integration of deep learning-enabled automation. Broader adoption could lead to reduced costs and environmental impact while increasing recycling rates

Emotion recognition with reduced channels using CWT based EEG feature representation and a CNN classifier

Objective. Although emotion recognition has been studied for decades, a more accurate classification method that requires less computing is still needed. At present, in many studies, EEG features are extracted from all channels to recognize emotional states, however, there is a lack of an efficient feature domain that improves classification performance and reduces the number of EEG channels. Approach. In this study, a continuous wavelet transform (CWT)-based feature representation of multi-channel EEG data is proposed for automatic emotion recognition. In the proposed feature, the time-frequency domain information is preserved by using CWT coefficients. For a particular EEG channel, each CWT coefficient is mapped into a strength-to-entropy component ratio to obtain a 2D representation. Finally, a 2D feature matrix, namely CEF2D, is created by concatenating these representations from different channels and fed into a deep convolutional neural network architecture. Based on the CWT domain energy-to-entropy ratio, effective channel and CWT scale selection schemes are also proposed to reduce computational complexity. Main results. Compared with previous studies, the results of this study show that valence and arousal classification accuracy has improved in both 3-class and 2-class cases. For the 2-class problem, the average accuracies obtained for valence and arousal dimensions are 98.83% and 98.95%, respectively, and for the 3-class, the accuracies are 98.25% and 98.68%, respectively. Significance. Our findings show that the entropy-based feature of EEG data in the CWT domain is effective for emotion recognition. Utilizing the proposed feature domain, an effective channel selection method can reduce computational complexity.

On the Sea Surface Temperature Forecasting Problem with Deep Dilation-Erosion-Linear Models

The sea surface temperature (SST) is considered an important measure for detecting changes in climate and marine ecosystems. So, its forecasting is essential for supporting governmental strategies to avoid side effects on the global population. In this paper, we analyze the SST time series and suggest that a combination between a linear component and a nonlinear component with long-term dependency can better represent it. Based on this assumption, we propose a deep neural network architecture with dilation-erosion-linear (DEL) processing units to deal with this particular kind of time series. An empirical analysis is performed in this work using three SST time series, where we explore three statistical measures. The experimental results demonstrate that the proposed model outperformed recent and classical literature forecasting techniques according to well-known performance metrics.

Physics informed machine learning based applications for the stability analysis of breakwaters

ABSTRACT One of the key aspects in designing and stability analysis of breakwater structures is predicting the stability number of their armour blocks. This study presents a novel approach called physics informed deep neural network, for the stability analysis of rubble-mound breakwaters. The present work makes two main contributions. Firstly, it proposes a method for creating hybrid combinations of theoretical models or physical models and deep neural network architectures, leveraging the advantages of both physics and data. This framework incorporates the output of physics-based simulations and observational features into a hybrid modelling setup. Secondly, the framework employs physics-based loss functions in the learning objective of these deep neural networks, which not only demonstrate lower errors on the training set but also adhere to the established physical relations. The proposed study may have the potential to address the existing limitations in this field and provide better accuracy in estimating the stability number.

Automatic classification of dog barking using deep learning

Barking and other dog vocalizations have acoustic properties related to emotions, physiological reactions, attitudes, or some particular internal states. In the field of intelligent audio analysis, researchers use methods based on signal processing and machine learning to analyze the digitized acoustic signals’ properties and obtain relevant information. The present work describes a method to classify the identity, breed, age, sex, and context associated with each bark. This information can support the decisions of people who regularly interact with animals, such as dog trainers, veterinarians, rescuers, police, people with visual impairment. Our approach uses deep neural networks to generate trained models for each classification task. We worked with 19,643 barks recorded from 113 dogs of different breeds, ages and sexes. Our methodology consists of three stages. First, the pre-processing stage prepares the data and transforms it into the appropriate format for each classification model. Second, the characterization stage evaluates different representation models to identify the most suitable for each task. Third, the classification stage trains each classification model and selects the best hyperparameters. After tuning and training each model, we evaluated its performance. We analyzed the most relevant features extracted from the audio and the most appropriate deep neural network architecture for that feature type. Even if the application of our method is not ready for being used in ethological practice, our evaluation showed an outstanding performance of the proposed method, surpassing previous research results on this topic, providing the basis for further technological development.

Visceral adipose tissue reduction measured by deep neural network architecture improved reflux esophagitis endoscopic grade.

Visceral obesity is a risk factor for reflux esophagitis (RE). We investigated the risk of RE according to visceral adipose tissue (VAT) measured by deep neural network architecture using computed tomography and evaluated the longitudinal association between abdominal adipose tissue changes and the disease course of RE. Individuals receiving health checkups who underwent esophagogastroduodenoscopy (EGD) and abdominal computed tomography (CT) at Seoul National University Healthcare System Gangnam Center between 2015 and 2016 were included. Visceral and subcutaneous adipose tissue areas and volumes were measured using a deep neural network architecture and CT. The association between the abdominal adipose tissue area and volume and the risk of RE was evaluated. Participants who underwent follow-up EGD and abdominal CT were selected; the effects of changes in abdominal adipose tissue area and volume on RE endoscopic grade were investigated using Cox proportional hazards regression. We enrolled 6570 patients who underwent EGD and abdomen CT on the same day. RE was associated with male sex, hypertension, diabetes, excessive alcohol intake, current smoking status, and levels of physical activity. The VAT area and volume increased the risk of RE dose-dependently. A decreasing VAT volume was significantly associated with improvement in RE endoscopic grade (HR:3.22, 95%CI:1.82-5.71). Changes in subcutaneous adipose tissue volume and the disease course of RE were not significantly correlated. Visceral obesity is strongly associated with RE. VAT volume reduction was prospectively associated with improvement in RE endoscopic grade dose-dependently. Visceral obesity is a potential target for RE treatment.

Analyzing to discover origins of CNNs and ViT architectures in medical images

In this paper, we introduce in-depth the analysis of CNNs and ViT architectures in medical images, with the goal of providing insights into subsequent research direction. In particular, the origins of deep neural networks should be explainable for medical images, but there has been a paucity of studies on such explainability in the aspect of deep neural network architectures. Therefore, we investigate the origin of model performance, which is the clue to explaining deep neural networks, focusing on the two most relevant architectures, such as CNNs and ViT. We give four analyses, including (1) robustness in a noisy environment, (2) consistency in translation invariance property, (3) visual recognition with obstructed images, and (4) acquired features from shape or texture so that we compare origins of CNNs and ViT that cause the differences of visual recognition performance. Furthermore, the discrepancies between medical and generic images are explored regarding such analyses. We discover that medical images, unlike generic ones, exhibit class-sensitive. Finally, we propose a straightforward ensemble method based on our analyses, demonstrating that our findings can help build follow-up studies. Our analysis code will be publicly available.

Aspects of Lighting and Color in Classifying Malignant Skin Cancer with Deep Learning

Malignant skin cancers are common in emerging countries, with excessive sun exposure and genetic predispositions being the main causes. Variations in lighting and color, resulting from the diversity of devices and lighting conditions during image capture, pose a challenge for automated diagnosis through digital images. Deep learning techniques emerge as promising solutions to improve the accuracy of identifying malignant skin lesions. This work aims to investigate the impact of lighting and color correction methods on automated skin cancer diagnosis using deep learning architectures, focusing on the relevance of these characteristics for accuracy in identifying malignant skin cancer. The developed methodology includes steps for hair removal, lighting, and color correction, defining the region of interest, and classification using deep neural network architectures. We employed deep learning techniques such as LCDPNet, LLNeRF, and DSN for lighting and color correction, which still need to be tested in this context. The results emphasize the importance of image preprocessing, especially in lighting and color adjustments, where the best results show an accuracy increase of between 3% and 4%. We observed that different deep neural network architectures react variably to lighting and color corrections. Some architectures are more sensitive to variations in these characteristics, while others are more robust. Advanced lighting and color correction can thus significantly improve the accuracy of malignant skin cancer diagnosis.

A Multi-Modal Egocentric Activity Recognition Approach towards Video Domain Generalization.

Egocentric activity recognition is a prominent computer vision task that is based on the use of wearable cameras. Since egocentric videos are captured through the perspective of the person wearing the camera, her/his body motions severely complicate the video content, imposing several challenges. In this work we propose a novel approach for domain-generalized egocentric human activity recognition. Typical approaches use a large amount of training data, aiming to cover all possible variants of each action. Moreover, several recent approaches have attempted to handle discrepancies between domains with a variety of costly and mostly unsupervised domain adaptation methods. In our approach we show that through simple manipulation of available source domain data and with minor involvement from the target domain, we are able to produce robust models, able to adequately predict human activity in egocentric video sequences. To this end, we introduce a novel three-stream deep neural network architecture combining elements of vision transformers and residual neural networks which are trained using multi-modal data. We evaluate the proposed approach using a challenging, egocentric video dataset and demonstrate its superiority over recent, state-of-the-art research works.

Improving the accuracy of diagnostic predictions for power transformers by employing a hybrid approach combining SMOTE and DNN

In power transformers, accurate diagnosis prediction plays a crucial role in ensuring the reliability and efficiency of electrical systems. This paper presents an enhanced diagnostic prediction approach for power transformers by combining the Synthetic Minority Over-sampling Technique (SMOTE) and Deep Neural Networks (DNN). Our approach addresses the challenge of imbalanced data in power transformer diagnosis prediction by leveraging SMOTE to generate synthetic samples and balance the dataset. By effectively handling rare events through the augmentation of minority class instances, our predictive model becomes more robust. Additionally, using deep neural networks enables the exploration of complex patterns and relationships within the transformer dataset. The DNN architecture integrates multiple layers of learnable features, enabling the prediction model to extract high-level representations and achieve improved accuracy and performance. To evaluate the effectiveness of our proposed approach, we conducted extensive experiments using real-world power transformer datasets. Comparative analysis of the results demonstrates that the combination of SMOTE and DNN outperforms traditional classification techniques applied to imbalanced datasets. The proposed model achieves a higher training accuracy of 98.22 % and a testing accuracy of 94.6 % and mitigates the risks associated with misdiagnosis, contributing to power systems' overall reliability and maintenance efficiency.

Mixup Mask Adaptation: Bridging the gap between input saliency and representations via attention mechanism in feature mixup

The inherent complexity and extensive architecture of deep neural networks often lead to overfitting, compromising their ability to generalize to new, unseen data. One of the regularization techniques, data augmentation, is now considered vital to alleviate this, and mixup, which blends pairs of images and labels, has proven effective in enhancing model generalization. Recently, incorporating saliency in mixups has shown performance gains by retaining salient regions in mixed results. While these methods have become mainstream at the input level, their applications at the feature level remain under-explored. Our observations indicate that outcomes from naive applications of input saliency-based methods did not consistently lead to enhancements in performance. In this paper, we attribute these observations primarily to two challenges: ‘Hard Boundary Issue’ and ‘Saliency Mismatch.’ The Hard Boundary Issue describes a situation where masks with distinct, sharp edges work well at the input level, but lead to unintended distortions in the deeper layers. The Saliency Mismatch points to the disparity between saliency masks generated from input images and the saliency of feature maps. To tackle these challenges, we present a novel method called ‘attention-based mixup mask adaptation’ (MMA). This approach employs an attention mechanism to effectively adapt mixup masks, which are designed to maximize saliency at the input level, for feature augmentation purposes. We reduce the Saliency Mismatch problem by incorporating the spatial significance of the feature map into the mixup mask. Additionally, we address the Hard Boundary Issue by applying softmax to smoothen the adjusted mixup mask. Through comprehensive experiments, we validate our observations and confirm the effectiveness of applying MMA to saliency-aware mixup approaches at the feature level, as evidenced by the performance improvements on multiple benchmarks and the robustness improvements against corruption and deformation.

Investigate the Application of Particle Swarm Optimization to Fine-Tune the Architecture and Parameters of Deep Convolutional Neural Networks for Enhanced Accuracy in Brain Tumor Detection from Medical Images

This study researches the use of Particle Swarm Optimization (PSO) to adjust the design and boundaries of Deep Convolutional Neural Networks (DCNNs) for improved precision in cerebrum growth recognition from clinical pictures. Different PSO calculation variants are investigated for division and grouping of MR pictures. A clever PSO-based strategy for picture gathering is presented, alongside a half and half K-Means/SBM-PSO approach for MR picture division. The proposed methods are assessed utilizing MR pictures from assorted sources, uncovering the adequacy of PSO in both picture division and advancing the K-Means grouping strategy. A half breed PSO approach is exhibited for characterizing MR pictures as typical or strange in light of the presence of mind growths, and for evaluating MR pictures as per the WHO characterization framework for cerebrum cancers. The exploratory outcomes demonstrate that the proposed techniques lead to more modest intra-bunch distances and bigger between-bunch distances, bringing about superior division results. Eminently, the reconciliation of PSO with K-Means shows improved strength and execution contrasted with individual methodologies. The reviewing discoveries recommend impediments in the ongoing X-ray approach for growth evaluating. Overall, this study highlights the potential of PSO in optimizing DCNN architectures for accurate brain tumor detection from medical images, emphasizing the effectiveness of hybrid PSO-K-Means models for improved segmentation and classification outcomes.

ECARRNet: An Efficient LSTM-Based Ensembled Deep Neural Network Architecture for Railway Fault Detection

Accidents due to defective railway lines and derailments are common disasters that are observed frequently in Southeast Asian countries. It is imperative to run proper diagnosis over the detection of such faults to prevent such accidents. However, manual detection of such faults periodically can be both time-consuming and costly. In this paper, we have proposed a Deep Learning (DL)-based algorithm for automatic fault detection in railway tracks, which we termed an Ensembled Convolutional Autoencoder ResNet-based Recurrent Neural Network (ECARRNet). We compared its output with existing DL techniques in the form of several pre-trained DL models to investigate railway tracks and determine whether they are defective or not while considering commonly prevalent faults such as—defects in rails and fasteners. Moreover, we manually collected the images from different railway tracks situated in Bangladesh and made our dataset. After comparing our proposed model with the existing models, we found that our proposed architecture has produced the highest accuracy among all the previously existing state-of-the-art (SOTA) architecture, with an accuracy of 93.28% on the full dataset. Additionally, we split our dataset into two parts having two different types of faults, which are fasteners and rails. We ran the models on those two separate datasets, obtaining accuracies of 98.59% and 92.06% on rail and fastener, respectively. Model explainability techniques like Grad-CAM and LIME were used to validate the result of the models, where our proposed model ECARRNet was seen to correctly classify and detect the regions of faulty railways effectively compared to the previously existing transfer learning models.

Aircraft Engine Remaining Useful Life Prediction using neural networks and real-life engine operational data

Aircraft Engine Remaining Useful Life is a key factor which strongly affects flight operations safety and flight operators business decisions. In the article authors decided to present the concept of engine remaining useful life prediction. Proposed method was created as a result of the analysis of the real turbofan engine operational data collected for a few years which was used as an input data for the deep neural network, in order to train, validate and test machine learning algorithms. Two architectures of deep neural networks were created: multilayered deep convolutional neural networks and a long short-term memory network with regression output. Both neural networks were trained, validated and tested on the same engine data and with a various network training options. Results were compared with the neural network performance metrics and figures presenting network prediction convergence. To present how the real-life engine dataset differs the results from the simulated data, both datasets were validated on the same neural network architectures. The main purpose of this article was to present the idea and method of how the artificial neural networks could be used to predict aircraft remaining useful life indicator on the real-life engine operational data not the simulated one.