Applied Sciences

Research

14 pages, 293 KiB

Open AccessArticle

Refining Filter Global Feature Weighting for Fully Unsupervised Clustering

by Fabian Galis, Darian M. Onchis and Codruta Istin

Appl. Sci. 2025, 15(16), 9072; https://doi.org/10.3390/app15169072 - 18 Aug 2025

Viewed by 182

In the context of unsupervised learning, effective clustering plays a vital role in revealing patterns and insights from unlabeled data. However, the success of clustering algorithms often depends on the relevance and contribution of features, which can differ between various datasets. This paper [...] Read more.

In the context of unsupervised learning, effective clustering plays a vital role in revealing patterns and insights from unlabeled data. However, the success of clustering algorithms often depends on the relevance and contribution of features, which can differ between various datasets. This paper explores feature weighting for clustering and presents new weighting strategies, including methods based on SHAP (SHapley Additive exPlanations), a technique commonly used for providing explainability in various supervised machine learning tasks. By taking advantage of SHAP values in a way other than just to gain explainability, we use them to weight features and ultimately improve the clustering process itself in unsupervised scenarios. Our empirical evaluations across five benchmark datasets and clustering methods demonstrate that feature weighting based on SHAP can enhance unsupervised clustering quality, achieving up to a 22.69% improvement over other weighting methods (from 0.586 to 0.719 in terms of the Adjusted Rand Index). Additionally, these situations where the weighted data boosts the results are highlighted and thoroughly explored, offering insight for practical applications. Full article

(This article belongs to the Special Issue Innovations in Artificial Neural Network Applications)

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22 pages, 13186 KiB

Open AccessArticle

Detection of Steel Reinforcement in Concrete Using Active Microwave Thermography and Neural Network-Based Analysis

by Barbara Szymanik, Maja Kocoń, Sam Ang Keo, Franck Brachelet and Didier Defer

Appl. Sci. 2025, 15(15), 8419; https://doi.org/10.3390/app15158419 - 29 Jul 2025

Viewed by 346

Abstract

Non-destructive evaluation of reinforced concrete structures is essential for effective maintenance and safety assessments. This study explores the combined use of active microwave thermography and deep learning to detect and localize steel reinforcement within concrete elements. Numerical simulations were developed to model the [...] Read more.

Non-destructive evaluation of reinforced concrete structures is essential for effective maintenance and safety assessments. This study explores the combined use of active microwave thermography and deep learning to detect and localize steel reinforcement within concrete elements. Numerical simulations were developed to model the thermal response of reinforced concrete subjected to microwave excitation, generating synthetic thermal images representing the surface temperature patterns of reinforced concrete, influenced by subsurface steel reinforcement. These images served as training data for a deep neural network designed to identify and localize rebar positions based on thermal patterns. The model was trained exclusively on simulation data and subsequently validated using experimental measurements obtained from large-format concrete slabs incorporating a structured layout of embedded steel reinforcement bars. Surface temperature distributions obtained through infrared imaging were compared with model predictions to evaluate detection accuracy. The results demonstrate that the proposed method can successfully identify the presence and approximate location of internal reinforcement without damaging the concrete surface. This approach introduces a new pathway for contactless, automated inspection using a combination of physical modeling and data-driven analysis. While the current work focuses on rebar detection and localization, the methodology lays the foundation for broader applications in non-destructive testing of concrete infrastructure. Full article

(This article belongs to the Special Issue Innovations in Artificial Neural Network Applications)

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16 pages, 1170 KiB

Open AccessArticle

LoRA-Tuned Multimodal RAG System for Technical Manual QA: A Case Study on Hyundai Staria

by Yerin Nam, Hansun Choi, Jonggeun Choi and Hyukjin Kwon

Appl. Sci. 2025, 15(15), 8387; https://doi.org/10.3390/app15158387 - 29 Jul 2025

Viewed by 461

Abstract

This study develops a domain-adaptive multimodal RAG (Retrieval-Augmented Generation) system to improve the accuracy and efficiency of technical question answering based on large-scale structured manuals. Using Hyundai Staria maintenance documents as a case study, we extracted text and images from PDF manuals and [...] Read more.

This study develops a domain-adaptive multimodal RAG (Retrieval-Augmented Generation) system to improve the accuracy and efficiency of technical question answering based on large-scale structured manuals. Using Hyundai Staria maintenance documents as a case study, we extracted text and images from PDF manuals and constructed QA, RAG, and Multi-Turn datasets to reflect realistic troubleshooting scenarios. To overcome limitations of baseline RAG models, we proposed an enhanced architecture that incorporates sentence-level similarity annotations and parameter-efficient fine-tuning via LoRA (Low-Rank Adaptation) using the bLLossom-8B language model and BAAI-bge-m3 embedding model. Experimental results show that the proposed system achieved improvements of 3.0%p in BERTScore, 3.0%p in cosine similarity, and 18.0%p in ROUGE-L compared to existing RAG systems, with notable gains in image-guided response accuracy. A qualitative evaluation by 20 domain experts yielded an average satisfaction score of 4.4 out of 5. This study presents a practical and extensible AI framework for multimodal document understanding, with broad applicability across automotive, industrial, and defense-related technical documentation. Full article

(This article belongs to the Special Issue Innovations in Artificial Neural Network Applications)

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22 pages, 5154 KiB

Open AccessArticle

BCS_YOLO: Research on Corn Leaf Disease and Pest Detection Based on YOLOv11n

by Shengnan Hao, Erjian Gao, Zhanlin Ji and Ivan Ganchev

Appl. Sci. 2025, 15(15), 8231; https://doi.org/10.3390/app15158231 - 24 Jul 2025

Viewed by 374

Abstract

Frequent corn leaf diseases and pests pose serious threats to agricultural production. Traditional manual detection methods suffer from significant limitations in both performance and efficiency. To address this, the present paper proposes a novel biotic condition screening (BCS) model for the detection of [...] Read more.

Frequent corn leaf diseases and pests pose serious threats to agricultural production. Traditional manual detection methods suffer from significant limitations in both performance and efficiency. To address this, the present paper proposes a novel biotic condition screening (BCS) model for the detection of corn leaf diseases and pests, called BCS_YOLO, based on the You Only Look Once version 11n (YOLOv11n). The proposed model enables accurate detection and classification of various corn leaf pathologies and pest infestations under challenging agricultural field conditions. It achieves this thanks to three key newly designed modules—a Self-Perception Coordinated Global Attention (SPCGA) module, a High/Low-Frequency Feature Enhancement (HLFFE) module, and a Local Attention Enhancement (LAE) module. The SPCGA module improves the model’s ability to perceive fine-grained targets by fusing multiple attention mechanisms. The HLFFE module adopts a frequency domain separation strategy to strengthen edge delineation and structural detail representation in affected areas. The LAE module effectively improves the model’s discrimination ability between targets and backgrounds through local importance calculation and intensity adjustment mechanisms. Conducted experiments show that BCS_YOLO achieves 78.4%, 73.7%, 76.0%, and 82.0% in

p r e c i s i o n

, recall,

F 1 s c o r e

, and

m A P @ 50

, respectively, representing corresponding improvements of 3.0%, 3.3%, 3.2%, and 4.6% compared to the baseline model (YOLOv11n), while also outperforming the mainstream object detection models. In summary, the proposed BCS_YOLO model provides a practical and scalable solution for efficient detection of corn leaf diseases and pests in complex smart-agriculture scenarios, demonstrating significant theoretical and application value. Full article

(This article belongs to the Special Issue Innovations in Artificial Neural Network Applications)

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22 pages, 868 KiB

Open AccessArticle

Enhancing Security of Error Correction in Quantum Key Distribution Using Tree Parity Machine Update Rule Randomization

by Bartłomiej Gdowski, Miralem Mehic and Marcin Niemiec

Appl. Sci. 2025, 15(14), 7958; https://doi.org/10.3390/app15147958 - 17 Jul 2025

Viewed by 380

Abstract

This paper presents a novel approach to enhancing the security of error correction in quantum key distribution by introducing randomization into the update rule of Tree Parity Machines. Two dynamic update algorithms—dynamic_rows and dynamic_matrix—are proposed and tested. These algorithms select the update rule [...] Read more.

This paper presents a novel approach to enhancing the security of error correction in quantum key distribution by introducing randomization into the update rule of Tree Parity Machines. Two dynamic update algorithms—dynamic_rows and dynamic_matrix—are proposed and tested. These algorithms select the update rule quasi-randomly based on the input vector, reducing the effectiveness of synchronization-based attacks. A series of simulations were conducted to evaluate the security implications under various configurations, including different values of K, N, and L parameters of neural networks. The results demonstrate that the proposed dynamic algorithms can significantly reduce the attacker’s synchronization success rate without requiring additional communication overhead. Both proposed solutions outperformed hebbian, an update rule-based synchronization method utilizing the percentage of attackers synchronization. It has also been shown that when the attacker chooses their update rule randomly, the dynamic approaches work better compared to random walk rule-based synchronization, and that in most cases it is more profitable to use dynamic update rules when an attacker is using random walk. This study contributes to improving QKD’s robustness by introducing adaptive neural-based error correction mechanisms. Full article

(This article belongs to the Special Issue Innovations in Artificial Neural Network Applications)

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35 pages, 11934 KiB

Open AccessArticle

A Data-Driven Approach for Generating Synthetic Load Profiles with GANs

by Tsvetelina Kaneva, Irena Valova, Katerina Gabrovska-Evstatieva and Boris Evstatiev

Appl. Sci. 2025, 15(14), 7835; https://doi.org/10.3390/app15147835 - 13 Jul 2025

Viewed by 441

Abstract

The generation of realistic electrical load profiles is essential for advancing smart grid analytics, demand forecasting, and privacy-preserving data sharing. Traditional approaches often rely on large, high-resolution datasets and complex recurrent neural architectures, which can be unstable or ineffective when training data are [...] Read more.

The generation of realistic electrical load profiles is essential for advancing smart grid analytics, demand forecasting, and privacy-preserving data sharing. Traditional approaches often rely on large, high-resolution datasets and complex recurrent neural architectures, which can be unstable or ineffective when training data are limited. This paper proposes a data-driven framework based on a lightweight 1D Convolutional Wasserstein GAN with Gradient Penalty (Conv1D-WGAN-GP) for generating high-fidelity synthetic 24 h load profiles. The model is specifically designed to operate on small- to medium-sized datasets, where recurrent models often fail due to overfitting or training instability. The approach leverages the ability of Conv1D layers to capture localized temporal patterns while remaining compact and stable during training. We benchmark the proposed model against vanilla GAN, WGAN-GP, and Conv1D-GAN across four datasets with varying consumption patterns and sizes, including industrial, agricultural, and residential domains. Quantitative evaluations using statistical divergence measures, Real-vs-Synthetic Distinguishability Score, and visual similarity confirm that Conv1D-WGAN-GP consistently outperforms baselines, particularly in low-data scenarios. This demonstrates its robustness, generalization capability, and suitability for privacy-sensitive energy modeling applications where access to large datasets is constrained. Full article

(This article belongs to the Special Issue Innovations in Artificial Neural Network Applications)

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32 pages, 1517 KiB

Open AccessArticle

A Proposed Deep Learning Framework for Air Quality Forecasts, Combining Localized Particle Concentration Measurements and Meteorological Data

by Maria X. Psaropa, Sotirios Kontogiannis, Christos J. Lolis, Nikolaos Hatzianastassiou and Christos Pikridas

Appl. Sci. 2025, 15(13), 7432; https://doi.org/10.3390/app15137432 - 2 Jul 2025

Viewed by 390

Abstract

Air pollution in urban areas has increased significantly over the past few years due to industrialization and population increase. Therefore, accurate predictions are needed to minimize their impact. This paper presents a neural network-based examination for forecasting Air Quality Index (AQI) values, employing [...] Read more.

Air pollution in urban areas has increased significantly over the past few years due to industrialization and population increase. Therefore, accurate predictions are needed to minimize their impact. This paper presents a neural network-based examination for forecasting Air Quality Index (AQI) values, employing two different models: a variable-depth neural network (NN) called slideNN, and a Gated Recurrent Unit (GRU) model. Both models used past particulate matter measurements alongside local meteorological data as inputs. The slideNN variable-depth architecture consists of a set of independent neural network models, referred to as strands. Similarly, the GRU model comprises a set of independent GRU models with varying numbers of cells. Finally, both models were combined to provide a hybrid cloud-based model. This research examined the practical application of multi-strand neural networks and multi-cell recurrent neural networks in air quality forecasting, offering a hands-on case study and model evaluation for the city of Ioannina, Greece. Experimental results show that the GRU model consistently outperforms the slideNN model in terms of forecasting losses. In contrast, the hybrid GRU-NN model outperforms both GRU and slideNN, capturing additional localized information that can be exploited by combining particle concentration and microclimate monitoring services. Full article

(This article belongs to the Special Issue Innovations in Artificial Neural Network Applications)

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28 pages, 5257 KiB

Open AccessArticle

Comparative Evaluation of Sequential Neural Network (GRU, LSTM, Transformer) Within Siamese Networks for Enhanced Job–Candidate Matching in Applied Recruitment Systems

by Mateusz Łępicki, Tomasz Latkowski, Izabella Antoniuk, Michał Bukowski, Bartosz Świderski, Grzegorz Baranik, Bogusz Nowak, Robert Zakowicz, Łukasz Dobrakowski, Bogdan Act and Jarosław Kurek

Appl. Sci. 2025, 15(11), 5988; https://doi.org/10.3390/app15115988 - 26 May 2025

Viewed by 952

Abstract

Job–candidate matching is pivotal in recruitment, yet traditional manual or keyword-based methods can be laborious and prone to missing qualified candidates. In this study, we introduce the first Siamese framework that systematically contrasts GRU, LSTM, and Transformer sequential heads on top of a [...] Read more.

Job–candidate matching is pivotal in recruitment, yet traditional manual or keyword-based methods can be laborious and prone to missing qualified candidates. In this study, we introduce the first Siamese framework that systematically contrasts GRU, LSTM, and Transformer sequential heads on top of a multilingual Sentence Transformer backbone, which is trained end-to-end with triplet loss on real-world recruitment data. This combination captures both long-range dependencies across document segments and global semantics, representing a substantial advance over approaches that rely solely on static embeddings. We compare the three heads using ranking metrics such as Top-K accuracy and Mean Reciprocal Rank (MRR). The Transformer-based model yields the best overall performance, with an MRR of 0.979 and a Top-100 accuracy of 87.20% on the test set. Visualization of learned embeddings (t-SNE) shows that self-attention more effectively clusters matching texts and separates them from irrelevant ones. These findings underscore the potential of combining multilingual base embeddings with specialized sequential layers to reduce manual screening efforts and improve recruitment efficiency. Full article

(This article belongs to the Special Issue Innovations in Artificial Neural Network Applications)

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16 pages, 7005 KiB

Open AccessArticle

Digitization of Medical Device Displays Using Deep Learning Models: A Comparative Study

by Pedro Ferreira, Pedro Lobo, Filipa Reis, João L. Vilaça and Pedro Morais

Appl. Sci. 2025, 15(10), 5436; https://doi.org/10.3390/app15105436 - 13 May 2025

Viewed by 552

Abstract

With the growing number of patients living with chronic conditions, there is an increasing need for efficient systems that can automatically capture and convert medical device readings into digital data, particularly in home-based care settings. However, most home-based medical devices are closed systems [...] Read more.

With the growing number of patients living with chronic conditions, there is an increasing need for efficient systems that can automatically capture and convert medical device readings into digital data, particularly in home-based care settings. However, most home-based medical devices are closed systems that do not support straightforward automatic data export and often require complex connections to access or transmit patient information. Since most of these devices display clinical information on a screen, this research explores how a standard smartphone camera, combined with artificial intelligence, can be used to automatically extract the displayed data in a simple and non-intrusive way. In particular, this study provides a comparative analysis of several You Only Look Once (YOLO) and Single Shot MultiBox Detector (SSD) models to evaluate their effectiveness in detecting and recognizing the readings on medical device displays. In addition to these comparisons, we also explore a hybrid approach that combines the YOLOv8l model for object detection with a Convolutional Neural Network (CNN) for classification. Several iterations of the aforementioned models were tested, using image resolutions of 320 × 320 and 640 × 640. The performance was assessed using metrics such as precision, recall, mean average precision at 0.5 Intersection over Union (mAP@50), and frames per second (FPS). The results show that YOLOv8l (640) achieved the highest mAP@50 of 0.979, but at a lower inference speed (13.20 FPS), while YOLOv8n (320) offered the fastest inference (129.79 FPS) with a reduction in mean average precision (0.786). Combining YOLOv8l with a CNN classifier resulted in a slight reduction in overall accuracy (0.96) when compared to the standalone model (0.98). While the results are promising, the study acknowledges certain limitations, including dataset-specific biases, controlled acquisition settings, and challenges in adapting to real-world scenarios. Nevertheless, the comparative analysis offers valuable insights into the trade-off between inference time and accuracy, helping guide the selection of the most suitable model based on the specific demands of the intended scanning application. Full article

(This article belongs to the Special Issue Innovations in Artificial Neural Network Applications)

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18 pages, 7263 KiB

Open AccessArticle

Investigating the Machining Behavior of the Additively Manufactured Polymer-Based Composite Using Adaptive Neuro-Fuzzy Learning

by Anastasios Tzotzis, Dumitru Nedelcu, Simona-Nicoleta Mazurchevici and Panagiotis Kyratsis

Appl. Sci. 2025, 15(10), 5373; https://doi.org/10.3390/app15105373 - 12 May 2025

Viewed by 584

Abstract

This study presents an experimental and computational investigation into the machinability of additively manufactured (AM) fiber-reinforced PETG during external CNC turning. A series of machining trials were conducted under dry conditions, with cutting speed (Vc), feed (f), and depth-of-cut [...] Read more.

This study presents an experimental and computational investigation into the machinability of additively manufactured (AM) fiber-reinforced PETG during external CNC turning. A series of machining trials were conducted under dry conditions, with cutting speed (Vc), feed (f), and depth-of-cut (ap) as the primary input parameters. The corresponding surface roughness (Ra) and tool-tip temperature (T) were recorded as key output responses. An Adaptive Neuro-Fuzzy Inference System (ANFIS) was developed to model the process behavior, utilizing a 3–3–3 architecture with triangular membership functions. The resulting models demonstrated high predictive accuracy across training, testing, and validation datasets. Experimental results revealed that elevated feed rates and depth-of-cut significantly increase surface roughness, while combinations of high cutting speed and feed contribute to elevated tool temperatures. Multi-objective optimization using the Non-Dominated Sorting Genetic Algorithm 2 (NSGA-II) algorithm was employed to minimize both Ra and T simultaneously. The Pareto-optimal front indicated that optimal performance could be achieved within the range of 100–200 m/min for Vc, 0.054–0.059 mm/rev for f, and 0.512–0.516 mm for ap. The outcomes of this research provide valuable insights into the machinability of reinforced polymer-based AM components and establish a robust framework for predictive modeling and process optimization. Full article

(This article belongs to the Special Issue Innovations in Artificial Neural Network Applications)

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16 pages, 426 KiB

Open AccessArticle

AI-Driven Consensus: Modeling Multi-Agent Networks with Long-Range Interactions Through Path-Laplacian Matrices

by Yusef Ahsini, Belén Reverte and J. Alberto Conejero

Appl. Sci. 2025, 15(9), 5064; https://doi.org/10.3390/app15095064 - 2 May 2025

Viewed by 563

Abstract

Extended connectivity in graphs can be analyzed through k-path Laplacian matrices, which permit the capture of long-range interactions in various real-world networked systems such as social, transportation, and multi-agent networks. In this work, we present several alternative methods based on machine learning [...] Read more.

Extended connectivity in graphs can be analyzed through k-path Laplacian matrices, which permit the capture of long-range interactions in various real-world networked systems such as social, transportation, and multi-agent networks. In this work, we present several alternative methods based on machine learning methods (LSTM, xLSTM, Transformer, XGBoost, and ConvLSTM) to predict the final consensus value based on directed networks (Erdös–Renyi, Watts–Strogatz, and Barabási–Albert) and on the initial state. We highlight how different k-hop interactions affect the performance of the tested methods. This framework opens new avenues for analyzing multi-scale diffusion processes in large-scale, complex networks. Full article

(This article belongs to the Special Issue Innovations in Artificial Neural Network Applications)

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17 pages, 2295 KiB

Open AccessArticle

Quantum Neural Networks Approach for Water Discharge Forecast

by Liu Zhen and Alina Bărbulescu

Appl. Sci. 2025, 15(8), 4119; https://doi.org/10.3390/app15084119 - 9 Apr 2025

Cited by 2 | Viewed by 1008

Abstract

Predicting the river discharge is essential for preparing effective measures against flood hazards or managing hydrological droughts. Despite mathematical modeling advancements, most algorithms have failed to capture the extreme values (especially the highest ones). In this article, we proposed a quantum neural networks [...] Read more.

Predicting the river discharge is essential for preparing effective measures against flood hazards or managing hydrological droughts. Despite mathematical modeling advancements, most algorithms have failed to capture the extreme values (especially the highest ones). In this article, we proposed a quantum neural networks (QNNs) approach for forecasting the river discharge in three scenarios. The algorithm was applied to the raw data series and the series without aberrant values. Comparisons with the results obtained on the same series by other neural networks (LSTM, BPNN, ELM, CNN-LSTM, SSA-BP, and PSO-ELM) emphasized the best performance of the present approach. The lower error between the recorded values and the predicted ones in the evaluation of maxima compared to the case of the competitors mentioned shows that the algorithm best fits the extremes. The most significant mean standard errors (MSEs) and mean absolute errors (MAEs) were 26.9424 and 4.8914, respectively, and the lowest R² was 84.36%, indicating the good performances of the algorithm. Full article

(This article belongs to the Special Issue Innovations in Artificial Neural Network Applications)

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30 pages, 4869 KiB

Open AccessArticle

Neural Network Method of Controllers’ Parametric Optimization with Variable Structure and Semi-Permanent Integration Based on the Computation of Second-Order Sensitivity Functions

by Serhii Vladov, Lukasz Scislo, Nina Szczepanik-Ścisło, Anatoliy Sachenko and Victoria Vysotska

Appl. Sci. 2025, 15(5), 2586; https://doi.org/10.3390/app15052586 - 27 Feb 2025

Cited by 1 | Viewed by 734

Abstract

This article presents a method for researching processes in automatic control systems based on the operator approach for modelling the control object and the controller. Within the method framework, a system of equations has been developed that describes the relations between the control [...] Read more.

This article presents a method for researching processes in automatic control systems based on the operator approach for modelling the control object and the controller. Within the method framework, a system of equations has been developed that describes the relations between the control error, the reference and control action, the output coordinate and the controller and the control object operators. The traditional PI controller modification, including a switching function for adaptation to operating conditions, allows for the system’s effective control in real time. The controller optimization algorithm is based on a functional expression with weighting coefficients that take into account control errors and the control action. To train the neural network through implementing the proposed method, a multilayer architecture was used, including nonlinear activation functions and a dynamic training rate, which ensure high accuracy and accelerated convergence. The TV3-117 turboshaft engine was chosen as the research object, which allows the method to be demonstrated in practical applications in aviation technology. The experimental results showed a significant improvement in control characteristics, including a reduction in the gas-generator rotor speed parameter transient time to ≈1, which is two times faster than the traditional method, where the transient process reaches ≈0.5. The model achieved a maximum accuracy of 0.993 with 160 training epochs, minimizing the error function to 0.005. In comparison with similar approaches, the proposed method demonstrated better results in accuracy and training speed, which was confirmed by a reduction in the number of iterations by 1.36 times and an improvement in the mean square error by 1.86–6.02 times. Full article

(This article belongs to the Special Issue Innovations in Artificial Neural Network Applications)

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12 pages, 2340 KiB

Open AccessArticle

Tensor Decomposition Through Neural Architectures

by Chady Ghnatios and Francisco Chinesta

Appl. Sci. 2025, 15(4), 1949; https://doi.org/10.3390/app15041949 - 13 Feb 2025

Viewed by 1107

Abstract

Machine learning (ML) technologies are currently widely used in many domains of science and technology, to discover models that transform input data into output data. The main advantages of such a procedure are the generality and simplicity of the learning process, while their [...] Read more.

Machine learning (ML) technologies are currently widely used in many domains of science and technology, to discover models that transform input data into output data. The main advantages of such a procedure are the generality and simplicity of the learning process, while their weaknesses remain the required amount of data needed to perform the training and the recurrent difficulties to explain the involved rationale. At present, a panoply of ML techniques exist, and the selection of a method or another depends, in general, on the type and amount of data being considered. This paper proposes a procedure which provides not a field or an image as an output, but its singular value decomposition (SVD), or an SVD-like decomposition, while injecting as input data scalars or the SVD decomposition of an input field. The result is a tensor-to-tensor decomposition, without the need for the full fields, or an input to an output SVD-like decomposition. The proposed method works for the non-hyper-parallepipedic domain, and for any space dimensionality. The results show the ability of the proposed architecture to link the input filed and output field, without requiring access to full space reconstruction. Full article

(This article belongs to the Special Issue Innovations in Artificial Neural Network Applications)

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