Sign in to use this feature.

Years

Between: -

Subjects

remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline

Journals

Article Types

Countries / Regions

Search Results (66)

Search Parameters:
Keywords = Kohonen neural networks

Order results
Result details
Results per page
Select all
Export citation of selected articles as:
20 pages, 1226 KiB  
Article
Diagnostic Signal Acquisition Time Reduction Technique in the Induction Motor Fault Detection and Localization Based on SOM-CNN
by Jeremi Jan Jarosz, Maciej Skowron, Oliwia Frankiewicz, Marcin Wolkiewicz, Sebastien Weisse, Jerome Valire and Krzysztof Szabat
Electronics 2025, 14(12), 2373; https://doi.org/10.3390/electronics14122373 - 10 Jun 2025
Viewed by 427
Abstract
Diagnostic systems for drive with AC motors of key importance for machine safety require the use of limitations related to the processing of measurement information. These limitations result in significant difficulties in assessing the technical condition of the object’s components. The article proposes [...] Read more.
Diagnostic systems for drive with AC motors of key importance for machine safety require the use of limitations related to the processing of measurement information. These limitations result in significant difficulties in assessing the technical condition of the object’s components. The article proposes the use of a combination of artificial intelligence techniques in the form of shallow and convolutional structures in the diagnostics of stator winding damage from an induction motor. The proposed approach ensures a high level of defect detection efficiency while using information preserved in samples from three periods of current signals. The research presents the possibility of combining the data classification capabilities of self-organizing maps (SOMs) with the automatic feature extraction of a convolutional neural network (CNN). The system was verified in steady and transient operating states on a test stand with a 1.5 kW motor. Remarkably, this approach achieves a high detection precision of 97.92% using only 600 samples, demonstrating that this reduced data acquisition does not compromise performance. On the contrary, this efficiency facilitates effective fault detection even in transient operating states, a challenge for traditional methods, and surpasses the 97.22% effectiveness of a reference system utilizing a full 6 s signal. Full article
Show Figures

Figure 1

23 pages, 7777 KiB  
Article
Research on GIS Circuit Breaker Fault Diagnosis Based on Closing Transient Vibration Signals
by Yue Yu and Hongyan Zhao
Machines 2025, 13(4), 335; https://doi.org/10.3390/machines13040335 - 18 Apr 2025
Viewed by 550
Abstract
GIS circuit breakers play a critical role in maintaining the reliability of modern power systems. However, mechanical failures, such as spring fatigue, transmission rod jamming, and loosening of structural components, can significantly impact their performance. Traditional diagnostic methods struggle to identify these issues [...] Read more.
GIS circuit breakers play a critical role in maintaining the reliability of modern power systems. However, mechanical failures, such as spring fatigue, transmission rod jamming, and loosening of structural components, can significantly impact their performance. Traditional diagnostic methods struggle to identify these issues effectively due to the enclosed nature of GIS equipment. This study explores the use of vibration signal analysis, specifically during the closing transient phase of the GIS circuit breaker. The proposed method combines wavelet packet decomposition, rough set theory for feature extraction and dimensionality reduction, and the S_Kohonen neural network for fault type identification. Experimental results demonstrate the robustness and accuracy of the method, achieving a diagnostic accuracy of 96.7% in identifying mechanical faults. Compared with traditional methods, this approach offers improved efficiency and accuracy in diagnosing GIS circuit breaker faults. The proposed method is highly applicable for predictive maintenance and fault diagnosis in power grid systems, contributing to enhanced operational safety and reliability. Full article
(This article belongs to the Section Machines Testing and Maintenance)
Show Figures

Figure 1

23 pages, 6926 KiB  
Article
Characterising the Thematic Content of Image Pixels with Topologically Structured Clustering
by Giles M. Foody
Remote Sens. 2025, 17(1), 130; https://doi.org/10.3390/rs17010130 - 2 Jan 2025
Viewed by 1817
Abstract
The location of a pixel in feature space is a function of its thematic composition. The latter is central to an image classification analysis, notably as an input (e.g., training data for a supervised classifier) and/or an output (e.g., predicted class label). Whether [...] Read more.
The location of a pixel in feature space is a function of its thematic composition. The latter is central to an image classification analysis, notably as an input (e.g., training data for a supervised classifier) and/or an output (e.g., predicted class label). Whether as an input to or output from a classification, little if any information beyond a class label is typically available for a pixel. The Kohonen self-organising feature map (SOFM) neural network however offers a means to both cluster together spectrally similar pixels that can be allocated suitable class labels and indicate relative thematic similarity of the clusters generated. Here, the thematic composition of pixels allocated to clusters represented by individual SOFM output units was explored with two remotely sensed data sets. It is shown that much of the spectral information of the input image data is maintained in the production of the SOFM output. This output provides a topologically structured representation of the image data, allowing spectrally similar pixels to be grouped together and the similarity of different clusters to be assessed. In particular, it is shown that the thematic composition of both pure and mixed pixels can be characterised by a SOFM. The location of the output unit in the output layer of the SOFM associated with a pixel conveys information on its thematic composition. Pixels in spatially close output units are more similar spectrally and thematically than those in more distant units. This situation also enables specific sub-areas of interest in the SOFM output space and/or feature space to be identified. This may, for example, provide a means to target efforts in training data acquisition for supervised classification as the most useful training cases may have a tendency to lie within specific sub-areas of feature space. Full article
(This article belongs to the Section Environmental Remote Sensing)
Show Figures

Figure 1

23 pages, 7096 KiB  
Article
Kohonen Mapping of the Space of Vibration Parameters of an Intact and Damaged Wheel Rim Structure
by Arkadiusz Rychlik, Oleksandr Vrublevskyi and Daria Skonieczna
Appl. Sci. 2024, 14(23), 10937; https://doi.org/10.3390/app142310937 - 25 Nov 2024
Cited by 1 | Viewed by 740
Abstract
The research presented in this paper takes another step towards developing methods for automatic condition verification to detect structural damage to vehicle wheel rims. This study presents the utilisation of vibration spectra via Fast Fourier Transform (FFT) and a neural network’s learning capabilities [...] Read more.
The research presented in this paper takes another step towards developing methods for automatic condition verification to detect structural damage to vehicle wheel rims. This study presents the utilisation of vibration spectra via Fast Fourier Transform (FFT) and a neural network’s learning capabilities for evaluating structural damage. Amplitude and time cycles of acceleration were analyzed as the structural response. These cycles underwent FFT analysis, leading to the identification of four diagnostic symptoms described by 20 features of the diagnostic signal, which in turn defined a condition vector. In the subsequent stage, the amplitude and frequency cycles served as input data for the neural network, and based on them, self-organizing maps (SOM) were generated. From these maps, a condition vector was defined for each of the four positions of the rim. Therefore, the technical condition of the wheel rim was determined based on the variance in condition parameter features, using reference frequencies of vibration spectra and SOM visualisations. The outcome of this work is a unique synergetic diagnostic system with innovative features, identifying the condition of a wheel rim through vibration and acoustic analysis along with neural network techniques in the form of Kohonen maps. Full article
(This article belongs to the Section Acoustics and Vibrations)
Show Figures

Figure 1

26 pages, 2617 KiB  
Article
Fixed-Wing UAV Pose Estimation Using a Self-Organizing Map and Deep Learning
by Nuno Pessanha Santos
Robotics 2024, 13(8), 114; https://doi.org/10.3390/robotics13080114 - 27 Jul 2024
Cited by 5 | Viewed by 2511
Abstract
In many Unmanned Aerial Vehicle (UAV) operations, accurately estimating the UAV’s position and orientation over time is crucial for controlling its trajectory. This is especially important when considering the landing maneuver, where a ground-based camera system can estimate the UAV’s 3D position and [...] Read more.
In many Unmanned Aerial Vehicle (UAV) operations, accurately estimating the UAV’s position and orientation over time is crucial for controlling its trajectory. This is especially important when considering the landing maneuver, where a ground-based camera system can estimate the UAV’s 3D position and orientation. A Red, Green, and Blue (RGB) ground-based monocular approach can be used for this purpose, allowing for more complex algorithms and higher processing power. The proposed method uses a hybrid Artificial Neural Network (ANN) model, incorporating a Kohonen Neural Network (KNN) or Self-Organizing Map (SOM) to identify feature points representing a cluster obtained from a binary image containing the UAV. A Deep Neural Network (DNN) architecture is then used to estimate the actual UAV pose based on a single frame, including translation and orientation. Utilizing the UAV Computer-Aided Design (CAD) model, the network structure can be easily trained using a synthetic dataset, and then fine-tuning can be done to perform transfer learning to deal with real data. The experimental results demonstrate that the system achieves high accuracy, characterized by low errors in UAV pose estimation. This implementation paves the way for automating operational tasks like autonomous landing, which is especially hazardous and prone to failure. Full article
(This article belongs to the Special Issue UAV Systems and Swarm Robotics)
Show Figures

Figure 1

15 pages, 5025 KiB  
Article
High-Throughput Phenotyping for the Evaluation of Agronomic Potential and Root Quality in Tropical Carrot Using RGB Sensors
by Fernanda Gabriela Teixeira Coelho, Gabriel Mascarenhas Maciel, Ana Carolina Silva Siquieroli, Rodrigo Bezerra de Araújo Gallis, Camila Soares de Oliveira, Ana Luisa Alves Ribeiro and Lucas Medeiros Pereira
Agriculture 2024, 14(5), 710; https://doi.org/10.3390/agriculture14050710 - 30 Apr 2024
Cited by 2 | Viewed by 1386
Abstract
The objective of this study was to verify the genetic dissimilarity and validate image phenotyping using RGB (red, green, and blue) sensors in tropical carrot germplasms. The experiment was conducted in the city of Carandaí-MG, Brazil, using 57 tropical carrot entries from Seminis [...] Read more.
The objective of this study was to verify the genetic dissimilarity and validate image phenotyping using RGB (red, green, and blue) sensors in tropical carrot germplasms. The experiment was conducted in the city of Carandaí-MG, Brazil, using 57 tropical carrot entries from Seminis and three commercial entries. The entries were evaluated agronomically and two flights with Remotely Piloted Aircraft (RPA) were conducted. Clustering was performed to validate the existence of genetic variability among the entries using an artificial neural network to produce a Kohonen’s self-organizing map. The genotype–ideotype distance index was used to verify the best entries. Genetic variability among the tropical carrot entries was evidenced by the formation of six groups. The Brightness Index (BI), Primary Colors Hue Index (HI), Overall Hue Index (HUE), Normalized Green Red Difference Index (NGRDI), Soil Color Index (SCI), and Visible Atmospherically Resistant Index (VARI), as well as the calculated areas of marketable, unmarketable, and total roots, were correlated with agronomic characters, including leaf blight severity and root yield. This indicates that tropical carrot materials can be indirectly evaluated via remote sensing. Ten entries were selected using the genotype–ideotype distance (2, 15, 16, 22, 34, 37, 39, 51, 52, and 53), confirming the superiority of the entries. Full article
(This article belongs to the Section Crop Genetics, Genomics and Breeding)
Show Figures

Figure 1

13 pages, 4913 KiB  
Article
THz Data Analysis and Self-Organizing Map (SOM) for the Quality Assessment of Hazelnuts
by Manuel Greco, Sabino Giarnetti, Emilio Giovenale, Andrea Taschin, Fabio Leccese, Andrea Doria and Luca Senni
Appl. Sci. 2024, 14(4), 1555; https://doi.org/10.3390/app14041555 - 15 Feb 2024
Cited by 1 | Viewed by 1749
Abstract
In recent years, the use of techniques based on electromagnetic radiation as an investigative tool in the agri-food industry has grown considerably, and between them, the application of imaging and THz spectroscopy has gained significance in the field of food quality control. This [...] Read more.
In recent years, the use of techniques based on electromagnetic radiation as an investigative tool in the agri-food industry has grown considerably, and between them, the application of imaging and THz spectroscopy has gained significance in the field of food quality control. This study presents the development of an experimental setup operating in transmission mode within the frequency range of 18 to 40 GHz, which was specifically designed for assessing various quality parameters of hazelnuts. The THz measurements were conducted to distinguish between healthy and rotten hazelnut samples. Two different data analysis techniques were employed and compared: a traditional approach based on data matrix manipulation and curve fitting for parameter extrapolation, and the utilization of a Self-Organizing Map (SOM), for which we use a neural network commonly known as the Kohonen neural network, which is recognized for its efficacy in analyzing THz measurement data. The classification of hazelnuts based on their quality was performed using these techniques. The results obtained from the comparative analysis of coding efforts, analysis times, and outcomes shed light on the potential applications of each method. The findings demonstrate that THz spectroscopy is an effective technique for quality assessment in hazelnuts, and this research serves to clarify the suitability of each analysis technique. Full article
Show Figures

Figure 1

19 pages, 3865 KiB  
Article
Exploring the Dynamics of Profitability–Liquidity Relations in Crisis, Pre-Crisis and Post-Crisis
by Piotr Ratajczak, Dawid Szutowski and Jarosław Nowicki
Int. J. Financial Stud. 2024, 12(1), 16; https://doi.org/10.3390/ijfs12010016 - 10 Feb 2024
Cited by 5 | Viewed by 4414
Abstract
The aim of this study is to verify the stability of the profitability–liquidity relationship over time, as well as to determine this relationship in terms of its level and structure. In this context, three main research questions were formulated. First, is the profitability–liquidity [...] Read more.
The aim of this study is to verify the stability of the profitability–liquidity relationship over time, as well as to determine this relationship in terms of its level and structure. In this context, three main research questions were formulated. First, is the profitability–liquidity relationship stable in times of crisis? Second, what is the profitability of companies with high and low liquidity? Third, what is the liquidity of companies with high and low profitability? This study uses a self-organizing map (SOM), a data visualization technique that is a type of artificial neural network trained in an unsupervised manner. A dataset covering the period from 2019 to 2021, consisting of 300 Polish companies from the wholesale and retail sectors, was used. The main results of this study indicate that: (1) companies with a balanced profitability–liquidity relationship in the pre-crisis period (2019) maintained this relationship in the crisis (2020) and post-crisis periods (2021); (2) companies in the clusters with the relatively highest and lowest profitability have the relatively lowest and moderate liquidity both before and after the crisis period; (3) the majority of companies during non-crisis periods demonstrate that profitability is not reliant on liquidity, suggesting an absence of a clear relationship; (4) in the post-crisis period, companies with the relatively lowest operating cash flow margin (OCFM) exhibited the relatively highest net profit margin (NPM) and other profitability ratios, as opposed to the pre-crisis and crisis periods. This study fills the gap resulting from the incomplete—most of all static—understanding of the relationship between profitability and liquidity. Moreover, this study employs a self-organizing map (SOM) which has not been used in the literature regarding the research area undertaken. Full article
Show Figures

Figure 1

41 pages, 1965 KiB  
Article
The ESG Patterns of Emerging-Market Companies: Are There Differences in Their Sustainable Behavior after COVID-19?
by Barbara Rocha Gonzaga, Marcelo Cabus Klotzle, Talles Vianna Brugni, Ileana-Sorina Rakos, Ionela Cornelia Cioca, Cristian-Marian Barbu and Teodora Cucerzan
Sustainability 2024, 16(2), 676; https://doi.org/10.3390/su16020676 - 12 Jan 2024
Cited by 4 | Viewed by 4297
Abstract
We aim to map the ESG patterns of emerging-market companies from 2018 to 2021 in order to determine whether the COVID-19 pandemic exerted any influence on sustainable corporate behavior. Thus, the ESG performances were assessed by employing the Kohonen Self-Organizing Map (also known [...] Read more.
We aim to map the ESG patterns of emerging-market companies from 2018 to 2021 in order to determine whether the COVID-19 pandemic exerted any influence on sustainable corporate behavior. Thus, the ESG performances were assessed by employing the Kohonen Self-Organizing Map (also known as the Kohonen neural network) for clustering purposes at three levels: (i) ESG overall, including country and sectoral perspectives; (ii) ESG thematic; and (iii) ESG four-folded (stakeholder, perspective, management, and focus strategic views). Our results show that emerging-market companies focus their ESG efforts on social and governance issues rather than on environmental. However, environmental and social behavior differ more acutely than governance behavior across clusters. The analyses of country-level ESG performance and that of eleven market-based economic sectors corroborate the geographic and sector dependence of ESG performance. The thematic-level analysis indicates that operational activities and community issues received more attention, which suggests that emerging-market companies address distinct ESG topics according to their particularities and competitiveness. Furthermore, our empirical findings provide evidence that the ESG behavior of companies has changed over the course of the COVID-19 pandemic. Thus, our findings are relevant to policy makers involved in regulating ESG disclosure practices, investors focused on enhancing their sustainable investment strategies, and firms engaged in improving their ESG involvement. Full article
Show Figures

Figure 1

15 pages, 2585 KiB  
Article
Feeding Patterns of Fish in Relation to the Trophic Status of Reservoirs: A Case Study of Rutilus rutilus (Linnaeus, 1758) in Five Fishing Waters in Serbia
by Milena Radenković, Aleksandra Milošković, Milica Stojković Piperac, Tijana Veličković, Angela Curtean-Bănăduc, Doru Bănăduc and Vladica Simić
Fishes 2024, 9(1), 21; https://doi.org/10.3390/fishes9010021 - 31 Dec 2023
Cited by 2 | Viewed by 2815
Abstract
The roach, Rutilus rutilus (Linnaeus, 1758), is one of the most common fish species in mesotrophic and eutrophic lakes throughout Europe. In the Serbian reservoirs selected for this study, this species accounts for the majority of juvenile fish biomass. The aim of this [...] Read more.
The roach, Rutilus rutilus (Linnaeus, 1758), is one of the most common fish species in mesotrophic and eutrophic lakes throughout Europe. In the Serbian reservoirs selected for this study, this species accounts for the majority of juvenile fish biomass. The aim of this study was to investigate the diet composition of juvenile roach to assess their niche based on resource availability in five Serbian reservoirs with different trophic statuses. A modified Costello graph and Kohonen artificial neural network (i.e., a self-organizing map, SOM) were employed to examine the feeding habits of 142 specimens of roach caught in five reservoirs. Our results show that juvenile roach use zooplankton, benthic macroinvertebrates, algae and detritus in their diet. In addition, five neuron clusters (A, B, C, D and E) were isolated in the SOM output network. The SOM identifies specimens that share similar feeding patterns and categorizes them onto the same or adjacent neurons, determined by dominant prey. In terms of the number of specimens, cluster B was the most numerous, and the predominant prey of these specimens were Daphnia sp., Bosmina sp. and calanoid and cyclopoid copepods. The cluster with the lowest number of specimens is cluster C, and the specimens in it benefited from Chironomidae and Insecta. Due to the different trophic statuses of the reservoirs selected for this study, knowledge of fish feeding habits is essential for the formulation of effective conservation and management strategies for both the species and the reservoirs. Full article
Show Figures

Figure 1

13 pages, 7946 KiB  
Article
Disease Severity Index in Parkinson’s Disease Based on Self-Organizing Maps
by Suellen M. Araújo, Sabrina B. M. Nery, Bianca G. Magalhães, Kelson James Almeida and Pedro D. Gaspar
Appl. Sci. 2023, 13(18), 10019; https://doi.org/10.3390/app131810019 - 5 Sep 2023
Cited by 2 | Viewed by 1788
Abstract
Parkinson’s disease is a progressive neurodegenerative condition whose prevalence has significantly increased. This work proposes the development of a severity index to classify patients from symptoms, mainly motor ones, using an Artificial Neuronal Network (ANN) trained by the Self-Organizing Maps (SOMs) algorithm. The [...] Read more.
Parkinson’s disease is a progressive neurodegenerative condition whose prevalence has significantly increased. This work proposes the development of a severity index to classify patients from symptoms, mainly motor ones, using an Artificial Neuronal Network (ANN) trained by the Self-Organizing Maps (SOMs) algorithm. The FOX Insight database was used, which offers data in the form of questionnaires answered by patients or caregivers from all over the world, with information regarding this pathology. After pre-processing the data, a set of 597 questionnaires containing 28 defined questions was selected. The symptoms were individually analyzed after mapping and divided into four classes. In class 1, most symptoms were not present. In class 2, the presence of certain symptoms demonstrated early milestones of the disease. In class 3, symptoms related to the patient’s mobility, in particular pain, stand out among the most reported. In class 4, the intense presence of all symptoms is observed. To test the tool, data were used from some of these patients, who answered the same questionnaire at different times (simulating medical appointments). The presented severity index to classify patients allowed identifying the current stage of the disease allowing the follow-up. This AI-based decision-support tool can help medical professionals to predict the evolution of Parkinson’s disease, which can result in longer life quality of patients, in terms of symptoms and medication requirements. Full article
Show Figures

Figure 1

20 pages, 3511 KiB  
Article
Safety Assessment of the Main Beams of Historical Buildings Based on Multisource Data Fusion
by Ying Chen, Ran Zhang, Yanfeng Li, Jiyuan Xie, Dong Guo and Laiqiang Song
Buildings 2023, 13(8), 2022; https://doi.org/10.3390/buildings13082022 - 8 Aug 2023
Viewed by 1318
Abstract
Taking the main beams of historical buildings as the engineering background, existing theoretical research results related to influencing structural factors were used along with numerical simulation and data fusion methods to examine their integrity. Thus, the application of multifactor data fusion in the [...] Read more.
Taking the main beams of historical buildings as the engineering background, existing theoretical research results related to influencing structural factors were used along with numerical simulation and data fusion methods to examine their integrity. Thus, the application of multifactor data fusion in the safety assessment of the main beams of historical buildings was performed. On the basis of existing structural safety assessment methods, neural networks and rough set theory were combined and applied to the safety assessment of the main beams of historical buildings. The bearing capacity of the main beams was divided into five levels according to the degree to which they met current requirements. The safety assessment database established by a Kohonen neural network was clustered. Thus, the specific evaluation indices corresponding to the five types of safety levels were presented. The rough neural network algorithm, integrating the rough set and neural network, was applied for data fusion with this database. The attribute reduction function of the rough set was used to reduce the input dimension of the neural network, which was trained, underwent a learning process, and then used for predictions. The trained neural network was applied for the safety assessment of the main beams of historical buildings, and six specific attribute index values corresponding to the main beams were directly input to obtain the current safety statuses of the buildings. Corresponding management suggestions were also provided. Full article
(This article belongs to the Special Issue Sustainable Preservation of Buildings and Infrastructure)
Show Figures

Figure 1

19 pages, 4742 KiB  
Article
Application of Dimensionality Reduction and Machine Learning Methods for the Interpretation of Gas Sensor Array Readouts from Mold-Threatened Buildings
by Grzegorz Łagód, Magdalena Piłat-Rożek, Dariusz Majerek, Ewa Łazuka, Zbigniew Suchorab, Łukasz Guz, Václav Kočí and Robert Černý
Appl. Sci. 2023, 13(15), 8588; https://doi.org/10.3390/app13158588 - 26 Jul 2023
Cited by 3 | Viewed by 1936
Abstract
Paper is in the scope of moisture-related problems which are connected with mold threat in buildings, sick building syndrome (SBS) as well as application of electronic nose for evaluation of different building envelopes and building materials. The machine learning methods used to analyze [...] Read more.
Paper is in the scope of moisture-related problems which are connected with mold threat in buildings, sick building syndrome (SBS) as well as application of electronic nose for evaluation of different building envelopes and building materials. The machine learning methods used to analyze multidimensional signals are important components of the e-nose system. These multidimensional signals are derived from a gas sensor array, which, together with instrumentation, constitute the hardware of this system. The accuracy of the classification and the correctness of the classification of mold threat in buildings largely depend on the appropriate selection of the data analysis methods used. This paper proposes a method of data analysis using Principal Component Analysis, metric multidimensional scaling and Kohonen self-organizing map, which are unsupervised machine learning methods, to visualize and reduce the dimensionality of the data. For the final classification of observations and the identification of datasets from gas sensor arrays analyzing air from buildings threatened by mold, as well as from other reference materials, supervised learning methods such as hierarchical cluster analysis, MLP neural network and the random forest method were used. Full article
Show Figures

Figure 1

15 pages, 5523 KiB  
Article
The Use of Neural Network Modeling Methods to Determine Regional Threshold Values of Hydrochemical Indicators in the Environmental Monitoring System of Waterbodies
by Yulia Tunakova, Svetlana Novikova, Vsevolod Valiev, Evgenia Baibakova and Ksenia Novikova
Sensors 2023, 23(13), 6160; https://doi.org/10.3390/s23136160 - 5 Jul 2023
Cited by 3 | Viewed by 1422
Abstract
The regulation of the anthropogenic load on waterbodies is carried out based on water quality standards that are determined using the threshold values of hydrochemical indicators. These applied standards should be defined both geographically and differentially, taking into account the regional specifics of [...] Read more.
The regulation of the anthropogenic load on waterbodies is carried out based on water quality standards that are determined using the threshold values of hydrochemical indicators. These applied standards should be defined both geographically and differentially, taking into account the regional specifics of the formation of surface water compositions. However, there is currently no unified approach to defining these regional standards. It is, therefore. appropriate to develop regional water quality standards utilizing modern technologies for the mathematical purpose of methods analysis using both experimental data sources and information system technologies. As suggested by the use of sets of chemical analysis and neural network cluster analysis, both methods of analysis and an expert assessment could identify surface water types as well as define the official regional threshold values of hydrochemical system indicators, to improve the adequacy of assessments and ensure the mathematical justification of developed standards. The process for testing the proposed approach was carried out, using the surface water resource objects in the territory of the Republic of Tatarstan as our example, in addition to using the results of long-term systematic measurements of informative hydrochemical indicators. In the first stage, typing was performed on surface waters using the neural network clustering method. Clustering was performed based on sets of determined hydrochemical parameters in Kohonen’s self-organizing neural network. To assess the uniformity of data, groups in each of the selected clusters were represented by specialists in this subject area’s region. To determine the regional threshold values of hydrochemical indicators, statistical data for the corresponding clusters were calculated, and the ranges of these values were used. The results of testing this proposed approach allowed us to recommend it for identifying surface water types, as well as to define the threshold values of hydrochemical indicators in the territory of any region with different surface water compositions. Full article
(This article belongs to the Special Issue Probing for Environmental Monitoring)
Show Figures

Figure 1

19 pages, 3455 KiB  
Article
Unveiling the Comorbidities of Chronic Diseases in Serbia Using ML Algorithms and Kohonen Self-Organizing Maps for Personalized Healthcare Frameworks
by Nevena Rankovic, Dragica Rankovic, Igor Lukic, Nikola Savic and Verica Jovanovic
J. Pers. Med. 2023, 13(7), 1032; https://doi.org/10.3390/jpm13071032 - 22 Jun 2023
Cited by 8 | Viewed by 2518
Abstract
In previous years, significant attempts have been made to enhance computer-aided diagnosis and prediction applications. This paper presents the results obtained using different machine learning (ML) algorithms and a special type of a neural network map to uncover previously unknown comorbidities associated with [...] Read more.
In previous years, significant attempts have been made to enhance computer-aided diagnosis and prediction applications. This paper presents the results obtained using different machine learning (ML) algorithms and a special type of a neural network map to uncover previously unknown comorbidities associated with chronic diseases, allowing for fast, accurate, and precise predictions. Furthermore, we are presenting a comparative study on different artificial intelligence (AI) tools like the Kohonen self-organizing map (SOM) neural network, random forest, and decision tree for predicting 17 different chronic non-communicable diseases such as asthma, chronic lung diseases, myocardial infarction, coronary heart disease, hypertension, stroke, arthrosis, lower back diseases, cervical spine diseases, diabetes mellitus, allergies, liver cirrhosis, urinary tract diseases, kidney diseases, depression, high cholesterol, and cancer. The research was developed as an observational cross-sectional study through the support of the European Union project, with the data collected from the largest Institute of Public Health “Dr. Milan Jovanovic Batut” in Serbia. The study found that hypertension is the most prevalent disease in Sumadija and western Serbia region, affecting 9.8% of the population, and it is particularly prominent in the age group of 65 to 74 years, with a prevalence rate of 33.2%. The use of Random Forest algorithms can also aid in identifying comorbidities associated with hypertension, with the highest number of comorbidities established as 11. These findings highlight the potential for ML algorithms to provide accurate and personalized diagnoses, identify risk factors and interventions, and ultimately improve patient outcomes while reducing healthcare costs. Moreover, they will be utilized to develop targeted public health interventions and policies for future healthcare frameworks to reduce the burden of chronic diseases in Serbia. Full article
Show Figures

Figure 1

Back to TopTop