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Journal articles on the topic 'Multilayer self organizing neural network'

Author: Grafiati

Published: 3 June 2025

Last updated: 20 July 2025

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1

Yu, Xiujin, Shengfu Liu, and Hui Zhang. "Chinese Language Feature Analysis Based on Multilayer Self-Organizing Neural Network and Data Mining Techniques." Computational Intelligence and Neuroscience 2021 (October 14, 2021): 1–9. http://dx.doi.org/10.1155/2021/4105784.

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As one of the oldest languages in the world, Chinese has a long cultural history and unique language charm. The multilayer self-organizing neural network and data mining techniques have been widely used and can achieve high-precision prediction in different fields. However, they are hardly applied to Chinese language feature analysis. In order to accurately analyze the characteristics of Chinese language, this paper uses the multilayer self-organizing neural network and the corresponding data mining technology for feature recognition and then compared it with other different types of neural ne

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Pakhomova, V., and A. Vydish. "Study of the combined variant of determination of attacks using neural network technologies." System technologies 3, no. 140 (2022): 79–86. http://dx.doi.org/10.34185/1562-9945-3-140-2022-08.

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The modern world is impossible to imagine without computer networks: both local and global; therefore, the issue of network security is becoming increasingly topical. Currently, methods of detecting attacks can be strengthened by using neural networks, which confirms the relevance of the topic. The aim of the study is a comparative analysis of the quality parameters of network attacks using a combined variant consisting of different neural networks. As research methods used: neural network; multilayer perceptron; Kohonen's self-organizing map. The software implementation of the Kohonen self-or

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Al-Khasawneh, Ahmad. "Diagnosis of Breast Cancer Using Intelligent Information Systems Techniques." International Journal of E-Health and Medical Communications 7, no. 1 (2016): 65–75. http://dx.doi.org/10.4018/ijehmc.2016010104.

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Breast cancer is the second leading cause of cancer deaths in women worldwide. Early diagnosis of this illness can increase the chances of long-term survival of cancerous patients. To help in this aid, computerized breast cancer diagnosis systems are being developed. Machine learning algorithms and data mining techniques play a central role in the diagnosis. This paper describes neural network based approaches to breast cancer diagnosis. The aim of this research is to investigate and compare the performance of supervised and unsupervised neural networks in diagnosing breast cancer. A multilaye

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Han, Hong-Gui, Li-Dan Wang, and Jun-Fei Qiao. "Efficient self-organizing multilayer neural network for nonlinear system modeling." Neural Networks 43 (July 2013): 22–32. http://dx.doi.org/10.1016/j.neunet.2013.01.015.

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Bhattacharyya, Siddhartha, Pankaj Pal, and Sandip Bhowmick. "Binary image denoising using a quantum multilayer self organizing neural network." Applied Soft Computing 24 (November 2014): 717–29. http://dx.doi.org/10.1016/j.asoc.2014.08.027.

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Sarukkai, Ramesh R. "Supervised Networks That Self-Organize Class Outputs." Neural Computation 9, no. 3 (1997): 637–48. http://dx.doi.org/10.1162/neco.1997.9.3.637.

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Supervised, neural network, learning algorithms have proved very successful at solving a variety of learning problems; however, they suffer from a common problem of requiring explicit output labels. In this article, it is shown that pattern classification can be achieved, in a multilayered, feedforward, neural network, without requiring explicit output labels, by a process of supervised self-organization. The class projection is achieved by optimizing appropriate within-class uniformity and between-class discernibility criteria. The mapping function and the class labels are developed together

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Sultana, Zakia, Md Ashikur Rahman Khan, and Nusrat Jahan. "Early Breast Cancer Detection Utilizing Artificial Neural Network." WSEAS TRANSACTIONS ON BIOLOGY AND BIOMEDICINE 18 (March 18, 2021): 32–42. http://dx.doi.org/10.37394/23208.2021.18.4.

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Breast cancer is one of the most dangerous cancer diseases for women in worldwide. A Computeraided diagnosis system is very helpful for radiologist for diagnosing micro calcification patterns earlier and faster than typical screening techniques. Maximum breast cancer cells are eventually form a lump or mass called a tumor. Moreover, some tumors are cancerous and some are not cancerous. The cancerous tumors are called malignant and non-cancerous tumors are called benign. The benign tumors are not dangerous to health. But the unchecked malignant tumors have the ability to spread in other organs

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Stepanyan, Ivan, Sergey Grokhovsky, and Mikhail Savkin. "Identification of pathobiomechanical markers of statokinesiograms on the example of neural network identification of a post-stroke state." Russian journal of biomechanics. 27, no. 1 (2023): 84–93. http://dx.doi.org/10.15593/rjbiomech/2023.1.09.

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The purpose of this study is neural network modeling and determination of the parameters of statokinesiograms, which are carriers of useful information about the features of postural regulation, which determined the obtained trajectory of movements of the human center of mass. A technique for obtaining informative markers by identifying clustering centroids based on self-organizing Kohonen neural networks with the Euclidean metric has been developed. Kohonen networks trained without a teacher (that is, without the use of a priori diagnostic information about the state of the subjects) are a po

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Minnix, J. I., E. S. McVey, and R. M. Inigo. "A multilayered self-organizing artificial neural network for invariant pattern recognition." IEEE Transactions on Knowledge and Data Engineering 4, no. 2 (1992): 162–67. http://dx.doi.org/10.1109/69.134253.

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BHATTACHARYA, UJJWAL, TANMOY KANTI DAS, AMITAVA DATTA, SWAPAN KUMAR PARUI, and BIDYUT BARAN CHAUDHURI. "A HYBRID SCHEME FOR HANDPRINTED NUMERAL RECOGNITION BASED ON A SELF-ORGANIZING NETWORK AND MLP ClASSIFIERS." International Journal of Pattern Recognition and Artificial Intelligence 16, no. 07 (2002): 845–64. http://dx.doi.org/10.1142/s0218001402002027.

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This paper proposes a novel approach to automatic recognition of handprinted Bangla (an Indian script) numerals. A modified Topology Adaptive Self-Organizing Neural Network is proposed to extract a vector skeleton from a binary numeral image. Simple heuristics are considered to prune artifacts, if any, in such a skeletal shape. Certain topological and structural features like loops, junctions, positions of terminal nodes, etc. are used along with a hierarchical tree classifier to classify handwritten numerals into smaller subgroups. Multilayer perceptron (MLP) networks are then employed to uni

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Intelligence and Neuroscience, Computational. "Retracted: Chinese Language Feature Analysis Based on Multilayer Self-Organizing Neural Network and Data Mining Techniques." Computational Intelligence and Neuroscience 2023 (June 28, 2023): 1. http://dx.doi.org/10.1155/2023/9809864.

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Sokhal, A., Z. Benaissa, S. A. Ouadfeul, and A. Boudella. "Dynamic Rock Type Characterization Using Artificial Neural Networks in Hamra Quartzites Reservoir: A Multidisciplinary Approach." Engineering, Technology & Applied Science Research 9, no. 4 (2019): 4397–404. http://dx.doi.org/10.48084/etasr.2861.

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A new multidisciplinary workflow is suggested to re-characterize the Hamra Quartzite (QH) formation using artificial neural networks. This approach involves core description, routine core analysis, special core analysis and raw logs of fourteen wells. An efficient electrofacies clustering neural network technology based on a self-organizing map is performed. The inputs in the model computation are: neutron porosity, gamma ray and bulk density logs. According to the self-organizing map results, the reservoir is composed of five electrofacies (EF1 to EF5): EF1, EF2 and EF3 with good reservoir qu

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Л. А., Гамидуллаева. "Разработка методики комплексной оценки и прогнозирования инновационного развития региона с использованием самоорганизующейся нейросети". ИННОВАЦИИ, № 7(261) (6 липня 2020): 57–64. http://dx.doi.org/10.26310/2071-3010.2020.261.7.009.

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Цель данной статьи состоит в исследовании перспективных направлений использования технологий обработки экономической информации на основе нейросетевого моделирования для комплексной оценки и прогнозирования инновационного развития регионов. Нейросетевой подход предполагает использование нейронных сетей, способных обучаться и обобщать накопленные знания, для решения задач классификации, идентификации и прогнозирования, что в конечном итоге позволяет объединить механизмы регулирования и самоорганизации в управлении региональными инновационными системами. Автором предложено использовать самоорган

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De Blasi, R., A. Blonda, G. Pasquariello, et al. "An Approach with Neural Network to Detection of MRI Anatomy." Rivista di Neuroradiologia 7, no. 1 (1994): 47–52. http://dx.doi.org/10.1177/197140099400700106.

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In this paper an artificial modular system applied to object classification in brain MR images is presented. It consists of two modules based on neural architectures joined in sequence to perform first an image segmentation and then an object classification. For these two steps a Self Organizing Map and a Multilayer Perceptron trained with the Back-Propagation learning rule have been used. The objective of the system is the automatic recognition of the anatomic structures in MR images of the cerebral section passing through the orbits and the visual pathways. To reach this goal we have submitt

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Ewert, Pawel, Teresa Orlowska-Kowalska, and Kamila Jankowska. "Effectiveness Analysis of PMSM Motor Rolling Bearing Fault Detectors Based on Vibration Analysis and Shallow Neural Networks." Energies 14, no. 3 (2021): 712. http://dx.doi.org/10.3390/en14030712.

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Permanent magnet synchronous motors (PMSMs) are becoming more popular, both in industrial applications and in electric and hybrid vehicle drives. Unfortunately, like the others, these are not reliable drives. As in the drive systems with induction motors, the rolling bearings can often fail. This paper focuses on the possibility of detecting this type of mechanical damage by analysing mechanical vibrations supported by shallow neural networks (NNs). For the extraction of diagnostic symptoms, the Fast Fourier Transform (FFT) and the Hilbert transform (HT) were used to obtain the envelope signal

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Cherepanova, V. О., and I. V. Sylka. "Optimizing the Intellectual Property Management in Accordance with a Process-Functional Approach." Business Inform 9, no. 524 (2021): 41–51. http://dx.doi.org/10.32983/2222-4459-2021-9-41-51.

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The article is aimed at developing a way to optimize the management of intellectual property (IP) objects by a process-functional approach based on the use of neural networks in combination with planning networks in conditions of uncertainty. When analyzing the works of various scholars, conceptual approaches to the formation of IP management according to both the process and the functional approaches to management were considered. The use of artificial neural networks in intellectual property management at industrial enterprises in combination with network planning in conditions of uncertaint

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Bondarenko, Andrey, and Arkady Borisov. "Research of Artificial Neural Networks Abilities in Printed Words Recognition." Scientific Journal of Riga Technical University. Computer Sciences 42, no. 1 (2010): 124–29. http://dx.doi.org/10.2478/v10143-010-0053-3.

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Research of Artificial Neural Networks Abilities in Printed Words Recognition This paper provides a brief overview on document analysis and recognition area, highlighting main steps and modules that are used to build recognition systems of the mentioned type. We underline basic workflow of such system down to the problem of single character recognition problem and highlighting possibilities and ways for artificial neural networks usage. Further we are conducting a formal comparison of abilities of printed characters recognition between two well known types of second generation neural networks,

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Shepelev, I. E. "Application of self-organizing maps and multilayer neural networks in problems of person identification." Pattern Recognition and Image Analysis 19, no. 1 (2009): 190–92. http://dx.doi.org/10.1134/s1054661809010313.

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Ludueña, Guillermo A., and Claudius Gros. "A Self-Organized Neural Comparator." Neural Computation 25, no. 4 (2013): 1006–28. http://dx.doi.org/10.1162/neco_a_00424.

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Learning algorithms need generally the ability to compare several streams of information. Neural learning architectures hence need a unit, a comparator, able to compare several inputs encoding either internal or external information, for instance, predictions and sensory readings. Without the possibility of comparing the values of predictions to actual sensory inputs, reward evaluation and supervised learning would not be possible. Comparators are usually not implemented explicitly. Necessary comparisons are commonly performed by directly comparing the respective activities one-to-one. This im

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Simões, Priscyla W., Narjara B. Izumi, Ramon S. Casagrande, et al. "Classification of Images Acquired with Colposcopy Using Artificial Neural Networks." Cancer Informatics 13 (January 2014): CIN.S17948. http://dx.doi.org/10.4137/cin.s17948.

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Objective To explore the advantages of using artificial neural networks (ANNs) to recognize patterns in colposcopy to classify images in colposcopy. PURPOSE: Transversal, descriptive, and analytical study of a quantitative approach with an emphasis on diagnosis. The training test e validation set was composed of images collected from patients who underwent colposcopy. These images were provided by a gynecology clinic located in the city of Criciúma (Brazil). The image database ( n = 170) was divided; 48 images were used for the training process, 58 images were used for the tests, and 64 images

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Siwek, Krzysztof, Stanisław Osowski, and Ryszard Szupiluk. "Ensemble Neural Network Approach for Accurate Load Forecasting in a Power System." International Journal of Applied Mathematics and Computer Science 19, no. 2 (2009): 303–15. http://dx.doi.org/10.2478/v10006-009-0026-2.

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Ensemble Neural Network Approach for Accurate Load Forecasting in a Power SystemThe paper presents an improved method for 1-24 hours load forecasting in the power system, integrating and combining different neural forecasting results by an ensemble system. We will integrate the results of partial predictions made by three solutions, out of which one relies on a multilayer perceptron and two others on self-organizing networks of the competitive type. As the expert system we will apply different integration methods: simple averaging, SVD based weighted averaging, principal component analysis and

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Alizadeh, Mohsen, Hasan Zabihi, Fatemeh Rezaie, et al. "Earthquake Vulnerability Assessment for Urban Areas Using an ANN and Hybrid SWOT-QSPM Model." Remote Sensing 13, no. 22 (2021): 4519. http://dx.doi.org/10.3390/rs13224519.

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Tabriz city in NW Iran is a seismic-prone province with recurring devastating earthquakes that have resulted in heavy casualties and damages. This research developed a new computational framework to investigate four main dimensions of vulnerability (environmental, social, economic and physical). An Artificial Neural Network (ANN) Model and a SWOT-Quantitative Strategic Planning Matrix (QSPM) were applied. Firstly, a literature review was performed to explore indicators with significant impact on aforementioned dimensions of vulnerability to earthquakes. Next, the twenty identified indicators w

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de Albuquerque, Victor Hugo C., Auzuir Ripardo de Alexandria, Paulo César Cortez, and João Manuel R. S. Tavares. "Evaluation of multilayer perceptron and self-organizing map neural network topologies applied on microstructure segmentation from metallographic images." NDT & E International 42, no. 7 (2009): 644–51. http://dx.doi.org/10.1016/j.ndteint.2009.05.002.

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Malik, Anurag, Anil Kumar, Mohammad Ali Ghorbani, Mahsa H. Kashani, Ozgur Kisi, and Sungwon Kim. "The viability of co-active fuzzy inference system model for monthly reference evapotranspiration estimation: case study of Uttarakhand State." Hydrology Research 50, no. 6 (2019): 1623–44. http://dx.doi.org/10.2166/nh.2019.059.

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Abstract Reference evapotranspiration (ETo) is a major component of the hydrological cycle linking the irrigation water requirement and planning and management of water resources. In this research, the potential of co-active neuro-fuzzy inference system (CANFIS) was investigated against the multilayer perceptron neural network (MLPNN), radial basis neural network (RBNN), self-organizing map neural network (SOMNN) and multiple linear regression (MLR) to estimate the monthly ETo at Pantnagar and Ranichauri stations, located in the foothills of Indian central Himalayas of Uttarakhand State, India

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RAMPONE, SALVATORE, VINCENZO PIERRO, LUIGI TROIANO, and INNOCENZO M. PINTO. "NEURAL NETWORK AIDED GLITCH-BURST DISCRIMINATION AND GLITCH CLASSIFICATION." International Journal of Modern Physics C 24, no. 11 (2013): 1350084. http://dx.doi.org/10.1142/s0129183113500848.

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We investigate the potential of neural-network based classifiers for discriminating gravitational wave bursts (GWBs) of a given canonical family (e.g. core-collapse supernova waveforms) from typical transient instrumental artifacts (glitches), in the data of a single detector. The further classification of glitches into typical sets is explored. In order to provide a proof of concept, we use the core-collapse supernova waveform catalog produced by H. Dimmelmeier and co-Workers, and the data base of glitches observed in laser interferometer gravitational wave observatory (LIGO) data maintained

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Sokhal, Abdallah, Zahia Benaissa, Sid-Ali Ouadfeul, and Amar Boudella. "Dynamic Rock Type Characterization Using Artificial Neural Networks in Hamra Quartzites Reservoir: A Multidisciplinary Approach." Engineering, Technology & Applied Science Research 9, no. 4 (2019): 4397–404. https://doi.org/10.5281/zenodo.3370604.

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A new multidisciplinary workflow is suggested to recharacterize the Hamra Quartzite (QH) formation using artificial neural networks. This approach involves core description, routine core analysis, special core analysis and raw logs of fourteen wells. An efficient electrofacies clustering neural network technology based on a self-organizing map is performed. The inputs in the model computation are: neutron porosity, gamma ray and bulk density logs. According to the selforganizing map results, the reservoir is composed of five electrofacies (EF1 to EF5): EF1, EF2 and EF3 with good reservoir qual

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Harata, Miho, and Masataka Tokumaru. "Emotion Generation Model with Growth Functions for Robots." Journal of Advanced Computational Intelligence and Intelligent Informatics 17, no. 2 (2013): 335–42. http://dx.doi.org/10.20965/jaciii.2013.p0335.

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In this paper, we propose an emotion model with growth functions for robots. Many emotion models for robots have been developed using Neural Networks (NN), which focus on the functions of emotion recognition, control, and expression. One problem that affects these emotion models for robots is the development of a “simplified” emotion generation algorithm. Users readily lose interest in “simple” systems. Most models have attempted to generate complex emotional expressions, whereas no previous studies have considered the “growth of a robot.” Therefore, we propose a growth model for emotions base

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Al-Mehdhara, Mohammed, and Na Ruan. "MSOM: Efficient Mechanism for Defense against DDoS Attacks in VANET." Wireless Communications and Mobile Computing 2021 (April 9, 2021): 1–17. http://dx.doi.org/10.1155/2021/8891758.

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The wireless nature of the Vehicular Ad Hoc Network (VANET), a technology that offers facilities such as traffic management and safety services, makes it vulnerable to distributed denial-of-service (DDoS) attacks that exploit network communications and reduce network reliability and performance. This paper proposes a design of a secure VANET architecture using a Software-Defined Networking (SDN) controller and Neural Network Self-Organizing Maps (SOMs). In the proposed design, we adopt the SDN architecture by using its separation of the control plane from the data plane and adding intelligent

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Fejfar, Jiří, Jiří Šťastný, and Miroslav Cepl. "Time series classification using k-Nearest neighbours, Multilayer Perceptron and Learning Vector Quantization algorithms." Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis 60, no. 2 (2012): 69–72. http://dx.doi.org/10.11118/actaun201260020069.

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We are presenting results comparison of three artificial intelligence algorithms in a classification of time series derived from musical excerpts in this paper. Algorithms were chosen to represent different principles of classification – statistic approach, neural networks and competitive learning. The first algorithm is a classical k-Nearest neighbours algorithm, the second algorithm is Multilayer Perceptron (MPL), an example of artificial neural network and the third one is a Learning Vector Quantization (LVQ) algorithm representing supervised counterpart to unsupervised Self Organizing Map

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López-Gonzales, Javier Linkolk, Ana María Gómez Lamus, Romina Torres, Paulo Canas Rodrigues, and Rodrigo Salas. "Self-Organizing Topological Multilayer Perceptron: A Hybrid Method to Improve the Forecasting of Extreme Pollution Values." Stats 6, no. 4 (2023): 1241–59. http://dx.doi.org/10.3390/stats6040077.

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Forecasting air pollutant levels is essential in regulatory plans focused on controlling and mitigating air pollutants, such as particulate matter. Focusing the forecast on air pollution peaks is challenging and complex since the pollutant time series behavior is not regular and is affected by several environmental and urban factors. In this study, we propose a new hybrid method based on artificial neural networks to forecast daily extreme events of PM2.5 pollution concentration. The hybrid method combines self-organizing maps to identify temporal patterns of excessive daily pollution found at

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Kussul, N., S. Skakun, A. Shelestov, M. Lavreniuk, B. Yailymov, and O. Kussul. "Regional scale crop mapping using multi-temporal satellite imagery." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-7/W3 (April 28, 2015): 45–52. http://dx.doi.org/10.5194/isprsarchives-xl-7-w3-45-2015.

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One of the problems in dealing with optical images for large territories (more than 10,000 sq. km) is the presence of clouds and shadows that result in having missing values in data sets. In this paper, a new approach to classification of multi-temporal optical satellite imagery with missing data due to clouds and shadows is proposed. First, self-organizing Kohonen maps (SOMs) are used to restore missing pixel values in a time series of satellite imagery. SOMs are trained for each spectral band separately using nonmissing values. Missing values are restored through a special procedure that sub

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Bilokon, Oleksandr. "Review and Analysis of the Development of Artificial Neural Networks." Cybernetics and Computer Technologies, no. 3 (September 29, 2023): 68–80. http://dx.doi.org/10.34229/2707-451x.23.3.6.

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Introduction. The creation of intelligent cyber-physical systems is impossible without knowledge of the analysis and process of development of scientific thought regarding artificial neural networks. The main task of this article is research and analysis of the concept of intelligent technologies based on artificial neural networks. Knowledge of the peculiarities of the creation, formation, and development of knowledge about artificial neural networks is of particular importance for scientists, developers, and design engineers. The article consists of the following parts: first, different appr

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Osowski, Stanislaw, Robert Szmurlo, Krzysztof Siwek, and Tomasz Ciechulski. "Neural Approaches to Short-Time Load Forecasting in Power Systems—A Comparative Study." Energies 15, no. 9 (2022): 3265. http://dx.doi.org/10.3390/en15093265.

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Background: The purpose of the paper is to propose different arrangements of neural networks for short-time 24-h load forecasting in Power Systems. Methods: The study discusses and compares different techniques of data processing, applying the feedforward and recurrent neural structures. They include such networks as multilayer perceptron, radial basis function, support vector machine, self-organizing Kohonen networks, deep autoencoder, and recurrent deep LSTM structures. The important point in getting high-quality results is the composition of many solutions in the common ensemble and their f

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Лямкин, Игорь Владимирович, and Анна Александровна Костяшина. "Modern approaches to simulation of control intelligent systems. Part 1. From analogue processes to neural synthesis." SCIENCE & TECHNOLOGIES OIL AND OIL PRODUCTS PIPELINE TRANSPORTATION, no. 6 (December 31, 2021): 660–95. http://dx.doi.org/10.28999/2541-9595-2021-11-6-660-695.

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Статья посвящена исследованию принципов и подходов к моделированию производственно-технических систем, имеющих сложную многомерную структуру, с применением математического аппарата. Рассматриваются принципы формирования математических моделей управления производственными процессами и ресурсами, применение которых перспективно и актуально в системе трубопроводного транспорта нефти и нефтепродуктов. Анализируются методические подходы к структурно-функциональному синтезу моделируемых объектов (систем) различного уровня сложности и назначения для возможности их внедрения в отрасли нефтепроводного

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Mancini, F., F. S. Sousa, A. D. Hummel, et al. "Classification of Postural Profiles among Mouth-breathing Children by Learning Vector Quantization." Methods of Information in Medicine 50, no. 04 (2011): 349–57. http://dx.doi.org/10.3414/me09-01-0039.

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SummaryBackground: Mouth breathing is a chronic syndrome that may bring about postural changes. Finding characteristic patterns of changes occurring in the complex musculoskeletal system of mouth-breathing children has been a challenge. Learning vector quantization (LVQ) is an artificial neural network model that can be applied for this purpose.Objectives: The aim of the present study was to apply LVQ to determine the characteristic postural profiles shown by mouth-breathing children, in order to further understand abnormal posture among mouth breathers.Methods: Postural training data on 52 ch

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Sun, Zhibin, Sandhya Samarasinghe, and Jenny Jago. "Detection of mastitis and its stage of progression by automatic milking systems using artificial neural networks." Journal of Dairy Research 77, no. 2 (2009): 168–75. http://dx.doi.org/10.1017/s0022029909990550.

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Two types of artificial neural networks, multilayer perceptron (MLP) and self-organizing feature map (SOM) were used to detect mastitis by automatic milking systems (AMS) using a new mastitis indicator that combined two previously reported indicators based on higher electrical conductivity (EC) and lower quarter yield (QY). Four MLPs with four combinations of inputs were developed to detect infected quarters. One input combination involved principal components (PC) adopted for addressing multi-collinearity in the data. The PC-based MLP model was superior to other non-PC-based models in terms o

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Новикова, Екатерина Ивановна, Екатерина Александровна Андрианова, Елена Евгеньевна Удодова, Анастасия Юрьевна Корниенко, and Александр Станиславович Панов. "DEVELOPMENT OF AN EXPERT SYSTEM FOR DIAGNOSTICS OF LUNG DISEASES BASED ON NEURAL NETWORK MODELING." СИСТЕМНЫЙ АНАЛИЗ И УПРАВЛЕНИЕ В БИОМЕДИЦИНСКИХ СИСТЕМАХ, no. 1 (April 19, 2021): 155–59. http://dx.doi.org/10.36622/vstu.2021.20.1.021.

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В статье рассматриваются вопросы диагностики заболеваний легких, таких как кавернозный, инфильтративный, очаговой, диссеминированный туберкулез, онкология и пневмония. Медико-социальное значение болезней органов дыхания в современных условиях велико и определяется, прежде всего, их крайне высокой частотой среди различных контингентов населения. Учитывая значимость дыхания для организма, необходимо вовремя выявлять различные патологии и применять незамедлительные меры лечения. Одним из средств повышения эффективности диагностики данных патологий является автоматизация обработки диагностических

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Новикова, Екатерина Ивановна, Ольга Викторовна Великая, Олег Игоревич Дворников, and Светлана Александровна Недомолкина. "CREATING A SYSTEM OF DIFFERENTIAL DIAGNOSIS OF SOCIALLY SIGNIFICANT LUNG DISEASES." СИСТЕМНЫЙ АНАЛИЗ И УПРАВЛЕНИЕ В БИОМЕДИЦИНСКИХ СИСТЕМАХ, no. 4 (December 14, 2022): 123–28. http://dx.doi.org/10.36622/vstu.2022.21.4.017.

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Медико-социальное значение болезней органов дыхания в современных условиях велико и определяется, прежде всего, их крайне высокой частотой среди различных контингентов населения. Болезни органов дыхания по уровню заболеваемости и по распространенности занимают первое место. Согласно статистике, сегодня на долю органов дыхания приходится около 40 % всех случаев заболеваемости, которая превосходит уровни заболеваемости другими классами болезней. В структуре причин обращаемости за медицинской помощью их удельный вес на различных территориях составляет от 29,2 до 43,5 % среди взрослых и от 65,4 до

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Banirostam, Hamid, Touraj BaniRostm, Mir Mohsen Pedram, and Amir Masoud Rahmani. "Providing and evaluating a comprehensive model for detecting fraudulent electronic payment card transactions with a two-level filter based on flow processing in big data." International Journal of Information Technology 15, no. 8 (2025): 4161–66. https://doi.org/10.1007/s41870-023-01501-6.

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Previous research on fraud detection modeling is often based on a single algorithm, optimizing categories and clusters to find fraudulent patterns that they have provided unsupervised or supervised methods alone and within the framework of Hadoop. The proposed model, a model based on big data analysis extracts important features of user behavior patterns such as time, device type, values, and type of transaction, and their behavioral modeling. By creating different profiles for users, threshold values will be set for each of them. The proposed model for real-time fraud detection of electronic

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Saleh, Arafat T., and Mohammed S. Al-Jawad. "Pilot Area Formation Evaluation: Upper Shale Member/ Rumaila Oil Field." Iraqi Journal of Chemical and Petroleum Engineering 25, no. 2 (2024): 61–71. http://dx.doi.org/10.31699/ijcpe.2024.2.6.

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This study aims to conduct a comprehensive formation evaluation of a pilot area within the Upper Shale Member of the Rumaila Oil Field. This evaluation is an essential step in the full development of the field. The application of well-log data and core analyses can help in obtaining the desired information about the geological characteristics of the formation. The process begins with measuring the formation temperature and water resistance utilizing Schlumberger’s charts and equations. The volume of shale was determined by two different methods, which were then used together to obtain the fina

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Chen, Qili, Junfei Qiao, and Yi Ming Zou. "A Self Organizing Recurrent Neural Network." International Journal of Artificial Intelligence & Applications 8, no. 4 (2017): 11–23. http://dx.doi.org/10.5121/ijaia.2017.8402.

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SASAKAWA, Takafumi, Jinglu HU, and Kotaro HIRASAWA. "Self-organizing Function Localization Neural Network." Transactions of the Society of Instrument and Control Engineers 41, no. 1 (2005): 67–74. http://dx.doi.org/10.9746/sicetr1965.41.67.

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Kim, Dongwon, and Gwi-Tae Park. "Advanced self-organizing polynomial neural network." Neural Computing and Applications 16, no. 4-5 (2006): 443–52. http://dx.doi.org/10.1007/s00521-006-0070-x.

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Ling, Junyao. "Score Prediction of Sports Events Based on Parallel Self-Organizing Nonlinear Neural Network." Computational Intelligence and Neuroscience 2022 (January 15, 2022): 1–10. http://dx.doi.org/10.1155/2022/4882309.

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This paper introduces the basic concepts and main characteristics of parallel self-organizing networks and analyzes and predicts parallel self-organizing networks through neural networks and their hybrid models. First, we train and describe the law and development trend of the parallel self-organizing network through historical data of the parallel self-organizing network and then use the discovered law to predict the performance of the new data and compare it with its true value. Second, this paper takes the prediction and application of chaotic parallel self-organizing networks as the main r

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Hahn-Ming Lee, Chih-Ming Chen, and Yung-Feng Lu. "A self-organizing HCMAC neural-network classifier." IEEE Transactions on Neural Networks 14, no. 1 (2003): 15–27. http://dx.doi.org/10.1109/tnn.2002.806607.

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Maundy, B. J., and E. I. El-Masry. "A self-organizing switched-capacitor neural network." IEEE Transactions on Circuits and Systems 38, no. 12 (1991): 1556–63. http://dx.doi.org/10.1109/31.108511.

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Jurišica, L., and M. Sedláček. "Self-Organizing Fuzzy Controller with Neural Network." IFAC Proceedings Volumes 25, no. 25 (1992): 239–44. http://dx.doi.org/10.1016/s1474-6670(17)49611-x.

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Wang, Jung-Hua, Chun-Shun Tseng, Sih-Yin Shen, and Ya-Yun Jheng. "Self-Organizing Fusion Neural Networks." Journal of Advanced Computational Intelligence and Intelligent Informatics 11, no. 6 (2007): 610–19. http://dx.doi.org/10.20965/jaciii.2007.p0610.

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This paper presents a self-organizing fusion neural network (SOFNN) effective in performing fast clustering and segmentation. Based on a counteracting learning scheme, SOFNN employs two parameters that together control the training in a counteracting manner to obviate problems of over-segmentation and under-segmentation. In particular, a simultaneous region-based updating strategy is adopted to facilitate an interesting fusion effect useful for identifying regions comprising an object in a self-organizing way. To achieve reliable merging, a dynamic merging criterion based on both intra-regiona

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Tung, W. L., and C. Quek. "GenSoFNN: a generic self-organizing fuzzy neural network." IEEE Transactions on Neural Networks 13, no. 5 (2002): 1075–86. http://dx.doi.org/10.1109/tnn.2002.1031940.

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Hariri, Fajar Rohman, and Danar Putra Pamungkas. "Self Organizing Map-Neural Network untuk Pengelompokan Abstrak." Creative Information Technology Journal 3, no. 2 (2016): 160. http://dx.doi.org/10.24076/citec.2016v3i2.74.

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Data berukuran besar yang sudah disimpan jarang digunakan secara optimal karena kemampuan manusia yang terbatas untuk mengelolanya. Salah satu data berskala besar adalah data teks. Data teks memiliki fitur yang besar sehingga untuk mengolahnya memerlukan waktu komputasi yang besar pula. Proses clustering menggunakan metode Self Organizing Map dengan menerapkan reduksi dimensi pada tahap preprosesing. Metode ini diterapkan untuk mengelompokkan data tugas akhir mahasiswa Teknik Informatika Universitas Trunojoyo Madura. Dalam metode yang diusulkan, analisis morfologi dilakukan pada teks abstrak t

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