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Journal articles on the topic 'Neural data processing'

Author: Grafiati

Published: 3 June 2025

Last updated: 31 July 2025

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1

Blank, T. B., and S. D. Brown. "Data processing using neural networks." Analytica Chimica Acta 277, no. 2 (1993): 273–87. http://dx.doi.org/10.1016/0003-2670(93)80440-v.

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Majoros, Tamás, Balázs Ujvári, and Stefan Oniga. "EEG data processing with neural network." Carpathian Journal of Electronic and Computer Engineering 12, no. 2 (2019): 33–36. http://dx.doi.org/10.2478/cjece-2019-0014.

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Abstract Machine-learning techniques allow to extract information from electroencephalographic (EEG) recordings of brain activity. By processing the measurement results of a publicly available EEG dataset, we were able to obtain information that could be used to train a feedforward neural network to classify two types of volunteer activities with high efficiency.

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Worswick, Steven G., James A. Spencer, Gunnar Jeschke, and Ilya Kuprov. "Deep neural network processing of DEER data." Science Advances 4, no. 8 (2018): eaat5218. http://dx.doi.org/10.1126/sciadv.aat5218.

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Prodan, Roman, Denys Shutka, and Vasyl Tataryn. "PREVENTING POTENTIAL ROBBERY CRIMES USING DEEP LEARNING ALGORITHM OF DATA PROCESSING." Measuring Equipment and Metrology 84, no. 3 (2023): 16–22. http://dx.doi.org/10.23939/istcmtm2023.03.016.

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Recently, deep learning technologies, namely Neural Networks [1], are attracting more and more attention from businesses and the scientific community, as they help optimize processes and find real solutions to problems much more efficiently and economically than many other approaches. In particular, Neural Networks are well suited for situations when you need to detect objects or look for similar patterns in videos and images, making them relevant in the field of information and measurement technologies in mechatronics and robotics. With the increasing number of robbed apartments and houses ev

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Xu, Yingying, Zhi Liu, Yujun Li, et al. "Feature data processing: Making medical data fit deep neural networks." Future Generation Computer Systems 109 (August 2020): 149–57. http://dx.doi.org/10.1016/j.future.2020.02.034.

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Hrusha, Volodymyr. "Intelligent Processing of Data From Chlorophyll Fluorometric Sensors." Cybernetics and Computer Technologies, no. 1 (June 30, 2022): 42–48. http://dx.doi.org/10.34229/2707-451x.22.1.5.

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Introduction. Chlorophyll fluorescence induction (CFI) is a monitoring method of plant objects. CFI is a radiation of chlorophyll in red spectrum during a chlorophyll lighting of alive plant in blue spectrum. Chlorophyll fluorometers – the special devices that are used for measurement of CFI. Series of such devices were developed in V.M. Glushkov Institute of Cybernetics of the NAS of Ukraine. In particular, fluorometer «Floratest» and a network of wireless sensors were developed for CFI measurement. An accumulation of massive amount of measurements resulted into possibility to use intellectua

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Apiecionek, Łukasz. "Fully Scalable Fuzzy Neural Network for Data Processing." Sensors 24, no. 16 (2024): 5169. http://dx.doi.org/10.3390/s24165169.

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The primary objective of the research presented in this article is to introduce an artificial neural network that demands less computational power than a conventional deep neural network. The development of this ANN was achieved through the application of Ordered Fuzzy Numbers (OFNs). In the context of Industry 4.0, there are numerous applications where this solution could be utilized for data processing. It allows the deployment of Artificial Intelligence at the network edge on small devices, eliminating the need to transfer large amounts of data to a cloud server for analysis. Such networks

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Vladislav, Goncharenko, Durdymyradov Kerven, and Parrondo Isaac. "EEG DATA PROCESSING FOR BRAIN COMPUTER INTERFACE." International Journal of Multidisciplinary Research Transactions 5, no. 4 (2023): 79–80. https://doi.org/10.5281/zenodo.7779282.

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Stauder, Ralf, Daniel Ostler, Thomas Vogel, et al. "Surgical data processing for smart intraoperative assistance systems." Innovative Surgical Sciences 2, no. 3 (2017): 145–52. http://dx.doi.org/10.1515/iss-2017-0035.

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AbstractDifferent components of the newly defined field of surgical data science have been under research at our groups for more than a decade now. In this paper, we describe our sensor-driven approaches to workflow recognition without the need for explicit models, and our current aim is to apply this knowledge to enable context-aware surgical assistance systems, such as a unified surgical display and robotic assistance systems. The methods we evaluated over time include dynamic time warping, hidden Markov models, random forests, and recently deep neural networks, specifically convolutional ne

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Ashida, Yuzuru. "Data processing of reflection seismic data by use of neural network." Journal of Applied Geophysics 35, no. 2-3 (1996): 89–98. http://dx.doi.org/10.1016/0926-9851(96)00010-9.

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Zaytar, Mohamed Akram, and Amrani Chaker El. "MetOp Satellites Data Processing for Air Pollution Monitoring in Morocco." International Journal of Electrical and Computer Engineering (IJECE) 8, no. 6 (2018): 4584–92. https://doi.org/10.11591/ijece.v8i6.pp4584-4592.

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This paper presents a data processing system based on an architecture comprised of multiple stacked layers of computational processes that transforms Raw Binary Pollution Data coming directly from Two EUMETSAT MetOp satellites to our servers, into ready to interpret and visualise continuous data stream in near real time using techniques varying from task automation, data preprocessing and data analysis to machine learning using feedforward artificial neural networks. The proposed system handles the acquisition, cleaning, processing, normalizing, and predicting of Pollution Data in our area of

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Gridin, V. N., I. A. Evdokimov, B. R. Salem, and V. I. Solodovnikov. "OPTIMIZATION PROCESS ANALYSIS FOR HYPERPARAMETERS OF NEURAL NETWORK DATA PROCESSING STRUCTURES." Vestnik komp'iuternykh i informatsionnykh tekhnologii, no. 196 (October 2020): 3–10. http://dx.doi.org/10.14489/vkit.2020.10.pp.003-010.

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The analysis of key stages, implementation features and functioning principles of the neural networks, including deep neural networks, has been carried out. The problems of choosing the number of hidden elements, methods for the internal topology selection and setting parameters are considered. It is shown that in the training and validation process it is possible to control the capacity of a neural network and evaluate the qualitative characteristics of the constructed model. The issues of construction processes automation and hyperparameters optimization of the neural network structures are

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Gridin, V. N., I. A. Evdokimov, B. R. Salem, and V. I. Solodovnikov. "OPTIMIZATION PROCESS ANALYSIS FOR HYPERPARAMETERS OF NEURAL NETWORK DATA PROCESSING STRUCTURES." Vestnik komp'iuternykh i informatsionnykh tekhnologii, no. 196 (October 2020): 3–10. http://dx.doi.org/10.14489/vkit.2020.10.pp.003-010.

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The analysis of key stages, implementation features and functioning principles of the neural networks, including deep neural networks, has been carried out. The problems of choosing the number of hidden elements, methods for the internal topology selection and setting parameters are considered. It is shown that in the training and validation process it is possible to control the capacity of a neural network and evaluate the qualitative characteristics of the constructed model. The issues of construction processes automation and hyperparameters optimization of the neural network structures are

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Obukhov, A. D., and M. N. Krasnyansky. "Neural network method of data processing and transmission in adaptive information systems." Vestnik Udmurtskogo Universiteta. Matematika. Mekhanika. Komp'yuternye Nauki 31, no. 1 (2021): 149–64. http://dx.doi.org/10.35634/vm210111.

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The problem of automation of the processes of information transmission and processing in adaptive information systems is considered. An analysis of existing approaches to solving this problem showed the prospects of using neural network technologies. A neural network method for processing and transmitting information in adaptive information systems is formulated. The method includes a formalized description of a neural network data channel - a software tool for analysis, data processing and selection of data transfer protocol. The main stages of the proposed method are outlined: classification

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Szymczyk, Piotr, Sylwia Tomecka-Suchoń, and Magdalena Szymczyk. "Neural Networks as a Tool for Georadar Data Processing." International Journal of Applied Mathematics and Computer Science 25, no. 4 (2015): 955–60. http://dx.doi.org/10.1515/amcs-2015-0068.

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Abstract In this article a new neural network based method for automatic classification of ground penetrating radar (GPR) traces is proposed. The presented approach is based on a new representation of GPR signals by polynomials approximation. The coefficients of the polynomial (the feature vector) are neural network inputs for automatic classification of a special kind of geologic structure—a sinkhole. The analysis and results show that the classifier can effectively distinguish sinkholes from other geologic structures.

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N.A., Zavalko. "Neural Network System for Processing Large-Volume Diagnostic Data." International Journal of Advanced Trends in Computer Science and Engineering 9, no. 3 (2020): 3211–15. http://dx.doi.org/10.30534/ijatcse/2020/113932020.

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Halgamuge, S. K. "Self-evolving neural networks for rule-based data processing." IEEE Transactions on Signal Processing 45, no. 11 (1997): 2766–73. http://dx.doi.org/10.1109/78.650103.

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Stringer, Carsen, and Marius Pachitariu. "Computational processing of neural recordings from calcium imaging data." Current Opinion in Neurobiology 55 (April 2019): 22–31. http://dx.doi.org/10.1016/j.conb.2018.11.005.

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Kuang, Hongyu, Jian Wang, Ruilin Li, Chao Feng, and Xing Zhang. "Automated Data-Processing Function Identification Using Deep Neural Network." IEEE Access 8 (2020): 55411–23. http://dx.doi.org/10.1109/access.2020.2981537.

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Grytsyk, V. V., I. G. Tsmots, and O. V. Skorokhoda. "Methods of parallel vertical data processing in neural networks." Reports of the National Academy of Sciences of Ukraine, no. 10 (October 25, 2014): 40–44. http://dx.doi.org/10.15407/dopovidi2014.10.040.

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Golagani, Lavanya Devi, Naresh Nelaturi, and Srinivasa Rao Kurapati. "Deep neural network-based approach for processing sequential data." CSI Transactions on ICT 8, no. 2 (2020): 263–70. http://dx.doi.org/10.1007/s40012-020-00309-0.

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Tyutyunnik, A. A., and A. I. Lazarev. "CRYPTOGRAPHIC DATA CONTAINERIZATION IN PROCESSING DEEP LEARNING NEURAL NETWORKS." H&ES Research 13, no. 3 (2021): 68–75. http://dx.doi.org/10.36724/2409-5419-2021-13-3-68-75.

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Introduction: the existing development of methods for ensuring information security in the field of information technology allows you to control the confidentiality of personal data at variable levels. Hardware solutions can be portable cryptographic keys, as well as narrowly targeted biometric identification devices. Software solutions are methods of static verification using sequences and side-tools for deploying data virtualization systems. The main problem that stands out among the methods described above is the need for additional hardware that acts as tokens, infrared cameras, and biomet

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Rejfek, Lubos, Tan N. Nguyen, Pavel Chmelar, Ladislav Beran, and Phuong T. Tran. "Neural Networks Application for Processing of the Data from the FMICW Radars." Symmetry 11, no. 10 (2019): 1308. http://dx.doi.org/10.3390/sym11101308.

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In this paper the results of the Neural Networks and machine learning applications for radar signal processing are presented. The radar output from the primary radar signal processing is represented as a 2D image composed from echoes of the targets and noise background. The Frequency Modulated Interrupted Continuous Wave (FMICW) radar PCDR35 (Portable Cloud Doppler Radar at the frequency 35.4 GHz) was used. Presently, the processing is realized via a National Instruments industrial computer. The neural network of the proposed system is using four or five (optional for the user) signal processi

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Danilovskii, K. N., and Loginov G. N. "Lateral scanning logging while drilling data processing using convolutional neural networks." Russian Journal of Geophysical Technologies, no. 2 (January 13, 2022): 24–35. http://dx.doi.org/10.18303/2619-1563-2021-2-24.

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This article discusses a new approach to processing lateral scanning logging while drilling data based on a combination of three-dimensional numerical modeling and convolutional neural networks. We prepared dataset for training neural networks. Dataset contains realistic synthetic resistivity images and geoelectric layer boundary layouts, obtained based on true values of their spatial orientation parameters. Using convolutional neural networks two algorithms have been developed and programmatically implemented: suppression of random noise and detection of layer boundaries on the resistivity im

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MARTYNIUK, Tatiana, Andrii KOZHEMIAKO, Bohdan KRUKIVSKYI, and Antonina BUDA. "ASSOCIATIVE OPERATIONS BASED ON DIFFERENCE-SLICE DATA PROCESSING." Herald of Khmelnytskyi National University. Technical sciences 311, no. 4 (2022): 159–63. http://dx.doi.org/10.31891/2307-5732-2022-311-4-159-163.

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Associative operations are effectively used to solve such application problems as sorting, searching for certain features, and identifying extreme (maximum/minimum) elements in data sets. Thus, determining the maximum number as a result of sorting a numerical array is an acceptable operation in implementing the competition mechanism in neural networks. In addition, determining the average number in a numerical series by sorting significantly speeds up the process of median filtering of images and signals. In this case, the implementation of median filtering requires the use of sorting with the

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Guo, Qiong, and Jing Niu. "The Research of Data Mining Based on Neural Networks." Advanced Materials Research 989-994 (July 2014): 2080–83. http://dx.doi.org/10.4028/www.scientific.net/amr.989-994.2080.

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Artificial neural network is a kind of network system that simulating human brain information processing mechanism developed on the basis of modern neurobiology research. It not only has the ability to deal with general calculation of numerical data, but also has the thinking for processing knowledge and memory ability of learning. Data mining process based on neural network consists of data preparation, rules extraction and evaluation. In this paper, the research status of data mining, neural network, development trend and application field are reviewed and this paper expounds the basic conce

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Seyidova, Irada, and Elgun Gamzaev. "Big Data Processing Methods in GIS." Land and Architecture 4 (May 13, 2025): 183. https://doi.org/10.56294/la2025183.

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The article discusses methods for processing big data in geographic information systems (GIS) with an emphasis on the use of recurrent neural networks (RNN) for forecasting geospatial processes. Modern approaches are described, including distributed computing on clusters (Hadoop, Spark) and cloud platforms (Google Earth Engine), providing efficient processing of spatial data. Particular attention is paid to RNN architectures, such as LSTM, their application in temporal forecasting problems (weather, transport, land use) and comparison with traditional methods. The article provides a numerical

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Jung, Se-Hoon, Jong-Chan Kim, and Chun-Bo Sim. "Prediction Data Processing Scheme using an Artificial Neural Network and Data Clustering for Big Data." International Journal of Electrical and Computer Engineering (IJECE) 6, no. 1 (2016): 330. http://dx.doi.org/10.11591/ijece.v6i1.9334.

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Various types of derivative information have been increasing exponentially, based on mobile devices and social networking sites (SNSs), and the information technologies utilizing them have also been developing rapidly. Technologies to classify and analyze such information are as important as data generation. This study concentrates on data clustering through principal component analysis and K-means algorithms to analyze and classify user data efficiently. We propose a technique of changing the cluster choice before cluster processing in the existing K-means practice into a variable cluster cho

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Jung, Se-Hoon, Jong-Chan Kim, and Chun-Bo Sim. "Prediction Data Processing Scheme using an Artificial Neural Network and Data Clustering for Big Data." International Journal of Electrical and Computer Engineering (IJECE) 6, no. 1 (2016): 330. http://dx.doi.org/10.11591/ijece.v6i1.pp330-336.

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Various types of derivative information have been increasing exponentially, based on mobile devices and social networking sites (SNSs), and the information technologies utilizing them have also been developing rapidly. Technologies to classify and analyze such information are as important as data generation. This study concentrates on data clustering through principal component analysis and K-means algorithms to analyze and classify user data efficiently. We propose a technique of changing the cluster choice before cluster processing in the existing K-means practice into a variable cluster cho

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Tsmots, Ivan, Vasyl Teslyuk, Yurii Opotyak, Taras Mamchur, and Oleksandr Oliinyk. "Synthesis of recursive-type neural elements with parallel vertical-group data processing." Eastern-European Journal of Enterprise Technologies 3, no. 2 (135) (2025): 6–16. https://doi.org/10.15587/1729-4061.2025.329139.

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The object of this study is the processes of parallel vertical-group data processing and minimization of equipment costs, which enable the synthesis of real-time recursive neural elements with high efficiency of equipment use. A model of a recursive-type neural element has been built, which, through the use of a parallel vertical-group method for calculating the scalar product and the ability to choose the number of bits in the group for the formation of partial products, coordinates the time of receipt of weights and input data with the time of calculating the result at the output of the neur

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Butts, Daniel A. "Data-Driven Approaches to Understanding Visual Neuron Activity." Annual Review of Vision Science 5, no. 1 (2019): 451–77. http://dx.doi.org/10.1146/annurev-vision-091718-014731.

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With modern neurophysiological methods able to record neural activity throughout the visual pathway in the context of arbitrarily complex visual stimulation, our understanding of visual system function is becoming limited by the available models of visual neurons that can be directly related to such data. Different forms of statistical models are now being used to probe the cellular and circuit mechanisms shaping neural activity, understand how neural selectivity to complex visual features is computed, and derive the ways in which neurons contribute to systems-level visual processing. However,

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Manoharan, J. Samuel. "Audio Tagging Using CNN Based Audio Neural Networks for Massive Data Processing." December 2021 3, no. 4 (2021): 365–74. http://dx.doi.org/10.36548/jaicn.2021.4.008.

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Sound event detection, speech emotion classification, music classification, acoustic scene classification, audio tagging and several other audio pattern recognition applications are largely dependent on the growing machine learning technology. The audio pattern recognition issues are also addressed by neural networks in recent days. The existing systems operate within limited durations on specific datasets. Pretrained systems with large datasets in natural language processing and computer vision applications over the recent years perform well in several tasks. However, audio pattern recognitio

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Gao, Cai Yun, Xi Min Cui, and Xue Qian Hong. "Study on the Applications of Neural Networks for Processing Deformation Monitoring Data." Applied Mechanics and Materials 501-504 (January 2014): 2149–53. http://dx.doi.org/10.4028/www.scientific.net/amm.501-504.2149.

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Accurately estimating the deformation of high-rise building is a very important work for surveyors, however it is very difficult to get an accurate and reliable predictor. In this paper, artificial neural network has been applied here because of its good ability of nonlinear fitting. On the basis of the high-rise building monitoring data, three prediction models including the BP, RBF and GRNN neural network prediction models were established, the comparative analysis for the prediction accuracy of the three models was obtained. The results show that neural network is capable for prediction, an

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Nikiforov, Aleksandr, Aleksei Kuchumov, Sergei Terentev, Inessa Karamulina, Iraida Romanova, and Sergei Glushakov. "Neural network method as means of processing experimental data on grain crop yields." E3S Web of Conferences 161 (2020): 01031. http://dx.doi.org/10.1051/e3sconf/202016101031.

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In the work based on agroecological and technological testing of varieties of grain crops of domestic and foreign breeding, winter triticale in particular, conducted on the experimental field of the Smolensk State Agricultural Academy between 2015 and 2019, we present the methodology and results of processing the experimental data used for constructing the neural network model. Neural networks are applicable for solving tasks that are difficult for computers of traditional design and humans alike. Those are processing large volumes of experimental data, automation of image recognition, approxi

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Oscar CLAVERIA, Oscar CLAVERIA, Enric MONTE, and Salvador TORRA. "DATA PRE-PROCESSING FOR NEURAL NETWORK-BASED FORECASTING: DOES IT REALLY MATTER?" Technological and Economic Development of Economy 23, no. 5 (2015): 709–25. http://dx.doi.org/10.3846/20294913.2015.1070772.

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This study aims to analyze the effects of data pre-processing on the forecasting performance of neural network models. We use three different Artificial Neural Networks techniques to predict tourist demand: multi-layer perceptron, radial basis function and the Elman neural networks. The structure of the networks is based on a multiple-input multiple-output (MIMO) approach. We use official statistical data of inbound international tourism demand to Catalonia (Spain) and compare the forecasting accuracy of four processing methods for the input vector of the networks: levels, growth rates, season

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Barfod, Adrian S., Léa Lévy, and Jakob Juul Larsen. "Automatic processing of time domain induced polarization data using supervised artificial neural networks." Geophysical Journal International 224, no. 1 (2020): 312–25. http://dx.doi.org/10.1093/gji/ggaa460.

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SUMMARY Processing of geophysical data is a time consuming task involving many different steps. One approach for accelerating and automating processing of geophysical data is to look towards machine learning (ML). ML encompasses a wide range of tools, which can be used to automate complicated and/or tedious tasks. We present strategies for automating the processing of time-domain induced polarization (IP) data using ML. An IP data set from Grindsted in Denmark is used to investigate the applicability of neural networks for processing such data. The Grindsted data set consists of eight profiles

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37

Leonov, M. G., and B. S. Zhirnov. "Processing of experimental data of coking process using neural network." Oil and Gas Business, no. 2 (April 2014): 151–65. http://dx.doi.org/10.17122/ogbus-2014-2-151-165.

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ASHIDA, Yuzuru. "Application of Neural Network to Processing of Seismic Reflection Data." Journal of the Society of Materials Science, Japan 44, no. 502 (1995): 880–84. http://dx.doi.org/10.2472/jsms.44.880.

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Hassanpour, Mehdi, Marc Riera, and Antonio González. "A Survey of Near-Data Processing Architectures for Neural Networks." Machine Learning and Knowledge Extraction 4, no. 1 (2022): 66–102. http://dx.doi.org/10.3390/make4010004.

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Data-intensive workloads and applications, such as machine learning (ML), are fundamentally limited by traditional computing systems based on the von-Neumann architecture. As data movement operations and energy consumption become key bottlenecks in the design of computing systems, the interest in unconventional approaches such as Near-Data Processing (NDP), machine learning, and especially neural network (NN)-based accelerators has grown significantly. Emerging memory technologies, such as ReRAM and 3D-stacked, are promising for efficiently architecting NDP-based accelerators for NN due to the

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Urazbakhtin, I. G., and N. I. Rykov. "Application of Data Filtering and Neural Networks in Signal Processing." Telecommunications and Radio Engineering 62, no. 12 (2004): 1037–45. http://dx.doi.org/10.1615/telecomradeng.v62.i12.20.

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Chen, Bing H., and John M. Woodley. "Wavelet shrinkage data processing for neural networks in bioprocess modeling." Computers & Chemical Engineering 26, no. 11 (2002): 1611–20. http://dx.doi.org/10.1016/s0098-1354(02)00146-1.

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42

Watson, J. N., C. A. Fairfield, and C. L. Wan. "NDT of Piled Foundations: Data Processing with Artificial Neural Networks." Journal of Low Frequency Noise, Vibration and Active Control 20, no. 3 (2001): 157–75. http://dx.doi.org/10.1260/0263092011493118.

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43

D’Addona, D. M., and R. Teti. "Image Data Processing via Neural Networks for Tool Wear Prediction." Procedia CIRP 12 (2013): 252–57. http://dx.doi.org/10.1016/j.procir.2013.09.044.

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Paschalis, P., C. Sarlanis, and H. Mavromichalaki. "Artificial Neural Network Approach of Cosmic Ray Primary Data Processing." Solar Physics 282, no. 1 (2012): 303–18. http://dx.doi.org/10.1007/s11207-012-0125-3.

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Ormos, L. "NEURAL PRE-PROCESSING OF DATA ORIGINATED FROM THE NATURAL ENVIRONMENT." Acta Horticulturae, no. 562 (November 2001): 269–83. http://dx.doi.org/10.17660/actahortic.2001.562.31.

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Bass, Leonid Petrovich, Margarita Georgievna Kuzmina, and Olga Vasilievna Nikolaeva. "Deep convolutional neural networks in hyperspectral remote sensing data processing." Keldysh Institute Preprints, no. 282 (2018): 1–32. http://dx.doi.org/10.20948/prepr-2018-282.

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Журавлёв, Д. В., and А. А. Резниченко. "USING A NEURAL NETWORK FOR PROCESSING AND ANALYZING EEG DATA." СИСТЕМНЫЙ АНАЛИЗ И УПРАВЛЕНИЕ В БИОМЕДИЦИНСКИХ СИСТЕМАХ 23, no. 1 (2024): 99–105. http://dx.doi.org/10.36622/1682-6523.2024.23.1.015.

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Был проведён анализ данных электроэнцефалограммы (ЭЭГ), взятых из библиотеки MNE-python, по материалам открытого источника. Эти данные были получены с помощью системы Neuromag Vectorview в центре биомедицинской визуализации MGH/HMS/MIT Athinoula A. Martinos. Данные ЭЭГ с 60-канальной электродной шапочки регистрировались одновременно с данными магнитоэнцефалограммы (МЭГ). Исходный набор исследуемых данных был получен с помощью сканера Siemens 1,5 T Sonata, с использованием последовательности MPRAGE. В ходе эксперимента испытуемому предъявлялись шахматные узоры в левом и правом поле зрения, пере

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Pustovit, Yu V., and Ye P. Lytveniuk. "Neural-Network-Based Methods for ARPES Data Processing (Review Article)." Ukrainian Journal of Physics 69, no. 1 (2024): 53. http://dx.doi.org/10.15407/ujpe69.1.53.

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In recent years, many developed upgrades of angle-resolved photoemission spectroscopy (ARPES) have significantly increased the amount of the obtained data. In this article, we briefly review the methods of processing of ARPES spectra with the use of convolutional neural networks (CNNs). In addition, we have made a short checkup of the potential application of CNNs that outperforms the existing methods or gives the possibility to achieve previously unachievable results.

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Tarigan, Heskyel Pranata. "Design And Implementation Of Adversarial Neural Network For Voice Data Processing." Jurnal Komputer Indonesia 2, no. 1 (2023): 1–8. http://dx.doi.org/10.37676/jki.v2i1.562.

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In today's digital age, voice data processing has become an important area in information and communication technology. Adversarial Neural Networks (GANs) are one of the recent methods that show great potential in improving the quality and efficiency of voice data processing. This article discusses the design and implementation of GANs for speech data processing, focusing on model architecture, optimization techniques, and performance evaluation. The results show that GANs can produce better speech representations and improve processing quality compared to traditional methods. It also explores

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Onishchenko, P. S., K. Y. Klyshnikov, and E. A. Ovcharenko. "Artificial Neural Networks in Cardiology: Analysis of Numerical and Text Data." Mathematical Biology and Bioinformatics 15, no. 1 (2020): 40–56. http://dx.doi.org/10.17537/2020.15.40.

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This review discusses works on the use of artificial neural networks for processing numerical and textual data. Application of a number of widely used approaches is considered, such as decision support systems; prediction systems, providing forecasts of outcomes of various methods of treatment of cardiovascular diseases, and risk assessment systems. The possibility of using artificial neural networks as an alternative approach to standard methods for processing patient clinical data has been shown. The use of neural network technologies in the creation of automated assistants to the attending

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