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Journal articles on the topic 'Fully convoluted neural network'

Author: Grafiati

Published: 5 June 2025

Last updated: 1 August 2025

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1

Kotlyar, D. I., and A. N. Lomanov. "SEGMENTATION OF PICTURES CONTAINING BLADE EDGE OF A GAS TURBINE ENGINE." Vestnik komp'iuternykh i informatsionnykh tekhnologii, no. 227 (May 2023): 3–10. http://dx.doi.org/10.14489/vkit.2023.05.pp.003-010.

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The article describes common techniques for semantic segmentation pictures containing edges of gas turbine engines blades for detecting left and right borders for further using in forming trajectory algorithms with direct metal deposition. For analysis such metrics, as pixel accuracy, mean pixel accuracy, intersection over union, frequency weighed intersection over union are used. Classic method of computer vision with threshold filters, border segmentation neural network method, fully convoluted neural network for semantic segmentation are focused on. The classic method of computer vision pro

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Mruthyunjaya and Suresh Kumar Mandala. "A brain tumor identification using convolution neural network and fully convolution neural network." MATEC Web of Conferences 392 (2024): 01130. http://dx.doi.org/10.1051/matecconf/202439201130.

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Brain tumor identification, along with an investigation, is harmful to the patient. Segmentation, therefore, of paying attention to near-neighborhood growth remains accurate, effective, and healthy. Fully Convolution Neural Network (FCNN) is a reliable picture model to capitulate the hide quality. The form of the multifaceted with the incessant pixels taught with the crest state and the symbolic picture taught. In this research, the making of a totally convoluted method to obtain the participation of a random element and the production of correspondingly large-scale output with a resourceful a

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S., Muthamil Selvan, Pudhota Maneesh, Gunnam Sridhar, and Kumar GP Kaushik. "Movie Recommendation Based on Posters and Still Frames using Machine Learning." International Journal of Engineering and Advanced Technology (IJEAT) 9, no. 4 (2020): 1747–50. https://doi.org/10.35940/ijeat.D7255.049420.

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Movie recommendation system has become a key part in online movie services to gain and maintain the huge market. While within the preceding studies works Convolution neural network (CNN) concept is employed to spot the various movies with similar posters or stills to recommend the users. Using CNN, similar posters and stills are classified into group and any hard cash within the poster may place it out of the group. But the CNN method isn't fully connected and uses back propagation technique which could be a touch slow within the poster identification and more over just with posters the fi

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Saida, D., KLSDT Keerthi Vardhan, and P. Premchand. "Effective Brain Tumor Classification Using Deep Residual Network-Based Transfer Learning." International journal of electrical and computer engineering systems 14, no. 6 (2023): 625–34. http://dx.doi.org/10.32985/ijeces.14.6.2.

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Brain tumor classification is an essential task in medical image processing that provides assistance to doctors for accurate diagnoses and treatment plans. A Deep Residual Network based Transfer Learning to a fully convoluted Convolutional Neural Network (CNN) is proposed to perform brain tumor classification of Magnetic Resonance Images (MRI) from the BRATS 2020 dataset. The dataset consists of a variety of pre-operative MRI scans to segment integrally varied brain tumors in appearance, shape, and histology, namely gliomas. A Deep Residual Network (ResNet-50) to a fully convoluted CNN is prop

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Hou, Yuanyuan, Shiyu Wang, Bing Bai, H. C. Stephen Chan, and Shuguang Yuan. "Accurate Physical Property Predictions via Deep Learning." Molecules 27, no. 5 (2022): 1668. http://dx.doi.org/10.3390/molecules27051668.

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Neural networks and deep learning have been successfully applied to tackle problems in drug discovery with increasing accuracy over time. There are still many challenges and opportunities to improve molecular property predictions with satisfactory accuracy even further. Here, we proposed a deep-learning architecture model, namely Bidirectional long short-term memory with Channel and Spatial Attention network (BCSA), of which the training process is fully data-driven and end to end. It is based on data augmentation and SMILES tokenization technology without relying on auxiliary knowledge, such

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Shi, Cuiping, Xinlei Zhang, Tianyi Wang, and Liguo Wang. "A Lightweight Convolutional Neural Network Based on Hierarchical-Wise Convolution Fusion for Remote-Sensing Scene Image Classification." Remote Sensing 14, no. 13 (2022): 3184. http://dx.doi.org/10.3390/rs14133184.

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The large intra-class difference and inter-class similarity of scene images bring great challenges to the research of remote-sensing scene image classification. In recent years, many remote-sensing scene classification methods based on convolutional neural networks have been proposed. In order to improve the classification performance, many studies increase the width and depth of convolutional neural network to extract richer features, which increases the complexity of the model and reduces the running speed of the model. In order to solve this problem, a lightweight convolutional neural netwo

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Luo, Honglin, Lin Bo, Chang Peng, and Dongming Hou. "An Improved Convolutional-Neural-Network-Based Fault Diagnosis Method for the Rotor–Journal Bearings System." Machines 10, no. 7 (2022): 503. http://dx.doi.org/10.3390/machines10070503.

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More layers in a convolution neural network (CNN) means more computational burden and longer training time, resulting in poor performance of pattern recognition. In this work, a simplified global information fusion convolution neural network (SGIF-CNN) is proposed to improve computational efficiency and diagnostic accuracy. In the improved CNN architecture, the feature maps of all the convolutional and pooling layers are globally convoluted into a corresponding one-dimensional feature sequence, and then all the feature sequences are concatenated into the fully connected layer. On this basis, t

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Herbert, Christoph, Joan Francesc Munoz-Martin, David Llaveria, Miriam Pablos, and Adriano Camps. "Sea Ice Thickness Estimation Based on Regression Neural Networks Using L-Band Microwave Radiometry Data from the FSSCat Mission." Remote Sensing 13, no. 7 (2021): 1366. http://dx.doi.org/10.3390/rs13071366.

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Several methods have been developed to provide polar maps of sea ice thickness (SIT) from L-band brightness temperature (TB) and altimetry data. Current process-based inversion methods to yield SIT fail to address the complex surface characteristics because sea ice is subject to strong seasonal dynamics and ice-physical properties are often non-linearly related. Neural networks can be trained to find hidden links among large datasets and often perform better on convoluted problems for which traditional approaches miss out important relationships between the observations. The FSSCat mission lau

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Коваленко, Ю. Ф., Е. А. Шулаева, И. А. Табаков та М. Д. Мамлеев. "Моделирование безопасного проведения процесса сжигания абгазов с использованием нейронных сетей". Южно-Сибирский научный вестник, № 6(58) (31 грудня 2024): 207–12. https://doi.org/10.25699/sssb.2024.58.6.036.

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Использование технологий обнаружения возгораний с помощью нейросетей становится всё более востребованным в промышленности благодаря их эффективности и точности. Однако оптимизация работы таких систем остаётся сложной задачей из-за сложного характера взаимодействия факторов, таких как изменения освещения, задымление и другие визуальные шумы. В данном тексте представлен обзор методов, используемых для анализа и разработки систем на основе нейросетей для обнаружения огня. Для реализации такой системы был проведён обзор существующих подходов в области компьютерного зрения и машинного обучения, а т

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Kishimoto, Masashi, Yodai Matsui, and Hiroshi Iwai. "Conditional GAN for Generation of 3D SOFC Electrode Microstructure Dataset." ECS Meeting Abstracts MA2023-01, no. 54 (2023): 82. http://dx.doi.org/10.1149/ma2023-015482mtgabs.

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Synthetic three-dimensional porous structures of solid oxide fuel cell anode are generated using the deep convolutional conditional generative adversarial neural network (DCCGAN). The developed network consists of a generator that produces an artificial structure dataset from random numbers, so-called latent variables, and a discriminator that judges whether the input structure dataset is real or fake. The generator and discriminator are alternately trained to improve their performance in an adversarial manner so that the generator can eventually create realistic structures indistinguishable f

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Abbas, Sabah Khudhair, and Rusul Sabah Obied. "Novel Computer Aided Diagnostic System Using Synergic Deep Learning Technique for Early Detection of Pancreatic Cancer." Webology 18, Special Issue 02 (2021): 367–79. http://dx.doi.org/10.14704/web/v18si02/web18105.

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Pancreatic cancer (PC) in the more extensive sense alludes to in excess of 277 distinct kinds of cancer sickness. Researchers have recognized distinctive phase of pancreatic cancers, showing that few quality transformations are engaged with cancer pathogenesis. These quality transformations lead to unusual cell multiplication. Therefore, in this study we propose a Computer Aided Diagnosis (CAD) system using Synergic Inception ResNet-V2, Deep convoluted neural network architecture for the identification of PC cases from publically Usable CT images that could extract PC graphical functionality t

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Ran, Xiangdong, Zhiguang Shan, Yong Shi, and Chuang Lin. "Short-Term Travel Time Prediction: A Spatiotemporal Deep Learning Approach." International Journal of Information Technology & Decision Making 18, no. 04 (2019): 1087–111. http://dx.doi.org/10.1142/s0219622019500202.

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Traffic prediction is a complex, nonlinear spatiotemporal relationship modeling task with the randomness of traffic demand, the spatial and temporal dependency between traffic flows, and other recurrent and nonrecurrent factors. Based on the ability to learn generic features from history information, deep learning approaches have been recently applied to traffic prediction. Convolutional neural network (CNN) methods that learn traffic as images can improve the predictive accuracy by leveraging the implicit correlations among nearby links. Traffic prediction based on CNN is still in its initial

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Obaid, Mustafa Amer, and Wesam M. Jasim. "Pre-convoluted neural networks for fashion classification." Bulletin of Electrical Engineering and Informatics 10, no. 2 (2021): 750–58. http://dx.doi.org/10.11591/eei.v10i2.2750.

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In this work, concept of the fashion-MNIST images classification constructed on convolutional neural networks is discussed. Whereas, 28×28 grayscale images of 70,000 fashion products from 10 classes, with 7,000 images per category, are in the fashion-MNIST dataset. There are 60,000 images in the training set and 10,000 images in the evaluation set. The data has been initially pre-processed for resizing and reducing the noise. Then, this data is normalized for ensuring that all the data are on the same scale and this usually improves the performance. After normalizing the data, it is augmented

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Mustafa, Amer Obaid, and M. Jasim Wesam. "Pre-convoluted neural networks for fashion classification." Bulletin of Electrical Engineering and Informatics 10, no. 2 (2021): 750~758. https://doi.org/10.11591/eei.v10i2.2750.

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In this work, concept of the fashion-MNIST images classification constructed on convolutional neural networks is discussed. Whereas, 28×28 grayscale images of 70,000 fashion products from 10 classes, with 7,000 images per category, are in the fashion-MNIST dataset. There are 60,000 images in the training set and 10,000 images in the evaluation set. The data has been initially pre-processed for resizing and reducing the noise. Then, this data is normalized for ensuring that all the data are on the same scale and this usually improves the performance. After normalizing the data, it is augm

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Om Raju Bejankiwar, V. Madhava Reddy, K. Harshith, and Dheeraj Sundaragiri. "Bird Species Recognition System Using Deep Convolutional Neural Network." Cuestiones de Fisioterapia 54, no. 3 (2025): 2062–74. https://doi.org/10.48047/z2xf8m11.

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The "identification of bird species" through mechanized strategies is a fundamental part of ecological studies and biodiversity monitoring. By the by, this attempt is convoluted by impediments, for example, intra-species variety and between species similitude

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Dharmali, Michael Joses, Teddy Lioner, and Venezia Valen Susilo. "SISTEM KLASIFIKASI KERAPIHAN KAMAR HOTEL MENGGUNAKAN CONVOLUTED NEURAL NETWORK (CNN)." Computatio : Journal of Computer Science and Information Systems 5, no. 2 (2021): 61. http://dx.doi.org/10.24912/computatio.v5i2.15175.

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Menjaga kebersihan kamar hotel merupakan salah satu aspek terpenting dalam menentukan keberhasilan pelayanan hotel. Tujuan yang ingin dicapai dari penelitian ini adalah untuk membuat program yang dapat membuat klasifikasi kerapihan kamar secara otomatis dengan menggunakan algoritma convoluted neural network (CNN). Metode penelitian bersifat kuantitatif dengan menggunakan data berupa citra / gambar kamar dengan tingkat kerapihan kamar yang berbeda-beda. Model yang telah dibuat pada penelitian ini mencapai tingkat akurasi sebesar 94,92% dan 100% untuk 20 data validasi. Adapun hasil dan kesimpula

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Jien, Julius Yong Wu, Aslina Baharum, Shaliza Hayati A. Wahab, Nordin Saad, Muhammad Omar, and Noorsidi Aizuddin Mat Noor. "Age-based facial recognition using convoluted neural network deep learning algorithm." IAES International Journal of Artificial Intelligence (IJ-AI) 9, no. 3 (2020): 424. http://dx.doi.org/10.11591/ijai.v9.i3.pp424-428.

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Face recognition is the use of biometric innovations that can see or validate a person by seeing and investigating designs depending on the shape of the individual. Face recognition is used largely for the purpose of well-being, despite the fact that passion for different areas of use is growing. Overall, face recognition innovations are worth considering because they have the potential for broad legal jurisdiction and different business applications. It is widely used in many spaces. How it works is a product of facial recognition processing facial geometry. The hole between the ear and the g

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Go, Kathryn J. "Beauty (quality) is in the eye of the convoluted neural network." Fertility and Sterility 113, no. 4 (2020): 756–57. http://dx.doi.org/10.1016/j.fertnstert.2020.01.005.

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Julius, Yong Wu Jien, Baharum Aslina, Hayati A. Wahab Shaliza, Saad Nordin, Omar Muhammad, and Aizuddin Mat Noor Noorsidi. "Age-based facial recognition using convoluted neural network deep learning algorithm." International Journal of Artificial Intelligence (IJ-AI) 9, no. 3 (2020): 424–28. https://doi.org/10.11591/ijai.v9.i3.pp424-428.

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Face recognition is the use of biometric innovations that can see or validate a person by seeing and investigating designs depending on the shape of the individual. Face recognition is used largely for the purpose of well-being, despite the fact that passion for different areas of use is growing. Overall, face recognition innovations are worth considering because they have the potential for broad legal jurisdiction and different business applications. It is widely used in many spaces. How it works is a product of facial recognition processing facial geometry. The hole between the ear and the g

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Zhang, Wei, Zhi Han, Xiai Chen, Baichen Liu, Huidi Jia, and Yandong Tang. "Fully Kernected Neural Networks." Journal of Mathematics 2023 (June 28, 2023): 1–9. http://dx.doi.org/10.1155/2023/1539436.

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In this paper, we apply kernel methods to deep convolutional neural network (DCNN) to improve its nonlinear ability. DCNNs have achieved significant improvement in many computer vision tasks. For an image classification task, the accuracy comes to saturation when the depth and width of network are enough and appropriate. The saturation accuracy will not rise even by increasing the depth and width. We find that improving nonlinear ability of DCNNs can break through the saturation accuracy. In a DCNN, the former layer is more inclined to extract features and the latter layer is more inclined to

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Смородинов, А. Д., Т. В. Гавриленко, and А. А. Рассадин. "Applicability of Convoluted Neural Networks to the Dataset Fitting Problem." Успехи кибернетики / Russian Journal of Cybernetics 4, no. 3(15) (2023): 47–54. http://dx.doi.org/10.51790/2712-9942-2023-4-3-05.

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в работе рассматривается возможность применения сверточных искусственных нейронных сетей для решения задачи идентификации типа зависимости в наборах данных. Для обучения сверточной нейронной сети использовались обучающие выборки, состоящие из графиков функций. В качестве базового набора функций для обучения искусственной нейронной сети были выбраны 6 функций, ключевым свойством которых является линеаризуемость. Сверточная нейронная сеть применялась для определения типа зависимостей в наборах данных, полученных из международной базы данных MNIST, предназначенной для тестирования статистического

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de Sales Carvalho, Nonato Rodrigues, Maria da Conceição Leal Carvalho Rodrigues, Antonio Oseas de Carvalho Filho, and Mano Joseph Mathew. "Automatic method for glaucoma diagnosis using a three-dimensional convoluted neural network." Neurocomputing 438 (May 2021): 72–83. http://dx.doi.org/10.1016/j.neucom.2020.07.146.

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Кonarev, D., and А. Gulamov. "ACCURACY IMPROVING OF PRE-TRAINED NEURAL NETWORKS BY FINE TUNING." EurasianUnionScientists 5, no. 1(82) (2021): 26–28. http://dx.doi.org/10.31618/esu.2413-9335.2021.5.82.1231.

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Methods of accuracy improving of pre-trained networks are discussed. Images of ships are input data for the networks. Networks are built and trained using Keras and TensorFlow machine learning libraries. Fine tuning of previously trained convoluted artificial neural networks for pattern recognition tasks is described. Fine tuning of VGG16 and VGG19 networks are done by using Keras Applications. The accuracy of VGG16 network with finetuning of the last convolution unit increased from 94.38% to 95.21%. An increase is only 0.83%. The accuracy of VGG19 network with fine-tuning of the last convolut

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Huang, Jiande, Shuangyin Liu, Shahbaz Gul Hassan, and Longqin Xu. "Pollution index of waterfowl farm assessment and prediction based on temporal convoluted network." PLOS ONE 16, no. 7 (2021): e0254179. http://dx.doi.org/10.1371/journal.pone.0254179.

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Environmental quality is a major factor that directly impacts waterfowl productivity. Accurate prediction of pollution index (PI) is the key to improving environmental management and pollution control. This study applied a new neural network model called temporal convolutional network and a denoising algorithm called wavelet transform (WT) for predicting future 12-, 24-, and 48-hour PI values at a waterfowl farm in Shanwei, China. The temporal convoluted network (TCN) model performance was compared with that of recurrent architectures with the same capacity, long-short time memory neural netwo

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Priya G. G., Lakshmi, and L. B. Krithika. "CLBPGNN Convoluted Local Binary Pattern based Grouping Neural Network for Face Emotion Recognition." Journal of Engineering Science and Technology Review 10, no. 6 (2017): 79–86. http://dx.doi.org/10.25103/jestr.106.11.

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Lin, Gaoming. "2D image edge detection enhancement using convolutional neural network." Applied and Computational Engineering 2, no. 1 (2023): 779–86. http://dx.doi.org/10.54254/2755-2721/2/20220519.

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Traditional edge detection operators are usually applied on edge detection in 2D image processing. However, the edge detection system equipped with simple operators has many disadvantages, such as high sensitivity to noise and neglect of significant edge details. This work proposed a method to enhance edge detection with convolutional neural net-work. To overcome the shortcomings of the system using simple edge detection operators in 2D image processing, an edge detection system using convolutional neural network was developed with Python language. In the convolutional neural network, two conv

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Gusynina, Yu S., and T. A. Shornikova. "Using the neural network in clinical systems." Journal of Physics: Conference Series 2131, no. 4 (2021): 042008. http://dx.doi.org/10.1088/1742-6596/2131/4/042008.

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Abstract The article examines the identification of human bone fractures using convoluted neural networks. The method of recognition of photographs of patients is intended for automated systems of identification and video recording of images. Convolutional neural networks have a number of advantages, such as invariability when reducing or increasing image size, immunity to photo movements and deviations, changes in image perspective, and many other image errors. In addition, convolutional neural networks allow you to combine neurons at a local level in two dimensions, connect photographic elem

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Yao, Peng, Huaqiang Wu, Bin Gao, et al. "Fully hardware-implemented memristor convolutional neural network." Nature 577, no. 7792 (2020): 641–46. http://dx.doi.org/10.1038/s41586-020-1942-4.

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Jang, Junmyoung, Donghyun Van, Hyojin Jang, et al. "Residual neural network-based fully convolutional network for microstructure segmentation." Science and Technology of Welding and Joining 25, no. 4 (2019): 282–89. http://dx.doi.org/10.1080/13621718.2019.1687635.

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Raina, Diya, Aarna Dawange, Thamaru Bandha, et al. "Convoluted neural network and transfer learning algorithm for improved brain tumor classifications in MRI." Journal of Knowledge Learning and Science Technology ISSN: 2959-6386 (online) 3, no. 4 (2024): 200–212. http://dx.doi.org/10.60087/jklst.v3.n4.p200.

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Artificial intelligence (AI) has made significant use cases to improve patient care, particularly in medical image analysis. This study aims to develop a deep-learning model for disease classification in medical images and compare its performance in four-class MRI and two-class X-ray classification tasks. We utilize Convolutional Neural Networks (CNNs) for diagnosing pneumonia from chest X-rays and various tumors from brain MRIs, leveraging transfer learning to improve performance. Transfer learning, which reuses pre-trained models like VGG-16, is more efficient than building models from scrat

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Om, Raju Bejankiwar, DHEERAJ SUNDARAGIRI, Madhava Reddy V, and Harshith K. "Bird Species Recognition System Using Deep Convolutional Neural Network." Bird Species Recognition System Using Deep Convolutional Neural Network 54, no. 3 (2025): 2062–74. https://doi.org/10.48047/z2xf8m11.

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The "identification of bird species" through mechanized strategies is a fundamental part of ecological studies andbiodiversity monitoring. By the by, this attempt is convoluted by impediments, for example, intra-species variety andbetween species similitude. A methodology that depends on "deep learning" and transfer learning how to furtherdevelop order accuracy is proposed in this review. The Whelp “200-2011” dataset is the stage whereupon westreamline pre-prepared "convolutional neural networks (CNNs)", like “ResNet and EfficientNet.” Rotation, turning,and cropping are

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Voronov, R., and O. Donets. "AUTOMATIC CONTROL OF THE TECHNOLOGICAL PROCESS USING NEURAL NETWORKS TO DETERMINE THE PARAMETERS OF THE PRODUCTION PROCESS." Municipal economy of cities 3, no. 170 (2022): 7–11. http://dx.doi.org/10.33042/2522-1809-2022-3-170-7-11.

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In multifactorial systems using textual and graphical information in matrix factorization to facilitate the problem of separate data processing. Recently, in some studies, the study of neural networks to understand the content of text and graphic elements more deeply and to achieve efficacy by creating more accurate patterns of recognition of elements. However, the open question remains about how to effectively use graphic data from the thermal imager in matrix factorization. In this paper, we proposed a double-regularized matrix factorization with deep neural networks (DRMF) to solve this pro

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Meng, Liang Kim, Azira Khalil, Muhamad Hanif Ahmad Nizar, et al. "Carpal Bone Segmentation Using Fully Convolutional Neural Network." Current Medical Imaging Formerly Current Medical Imaging Reviews 15, no. 10 (2019): 983–89. http://dx.doi.org/10.2174/1573405615666190724101600.

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Background: Bone Age Assessment (BAA) refers to a clinical procedure that aims to identify a discrepancy between biological and chronological age of an individual by assessing the bone age growth. Currently, there are two main methods of executing BAA which are known as Greulich-Pyle and Tanner-Whitehouse techniques. Both techniques involve a manual and qualitative assessment of hand and wrist radiographs, resulting in intra and inter-operator variability accuracy and time-consuming. An automatic segmentation can be applied to the radiographs, providing the physician with more accurate delinea

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Erichsen, R., W. K. Theumann, and D. R. C. Dominguez. "Categorization in fully connected multistate neural network models." Physical Review E 60, no. 6 (1999): 7321–31. http://dx.doi.org/10.1103/physreve.60.7321.

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Hsu, K. Y., H. Y. Li, and D. Psaltis. "Holographic implementation of a fully connected neural network." Proceedings of the IEEE 78, no. 10 (1990): 1637–45. http://dx.doi.org/10.1109/5.58357.

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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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Cai, Nian, Zhenghang Su, Zhineng Lin, Han Wang, Zhijing Yang, and Bingo Wing-Kuen Ling. "Blind inpainting using the fully convolutional neural network." Visual Computer 33, no. 2 (2015): 249–61. http://dx.doi.org/10.1007/s00371-015-1190-z.

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Kosiński, R. A., and A. Zagórski. "Memory Properties of Non-Fully Interconnected Neural Network." Acta Physica Polonica A 86, no. 3 (1994): 427–33. http://dx.doi.org/10.12693/aphyspola.86.427.

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Peleshchak, R. М., V. V. Lytvyn, О. І. Cherniak, І. R. Peleshchak, and М. V. Doroshenko. "STOCHASTIC PSEUDOSPIN NEURAL NETWORK WITH TRIDIAGONAL SYNAPTIC CONNECTIONS." Radio Electronics, Computer Science, Control, no. 2 (July 7, 2021): 114–22. http://dx.doi.org/10.15588/1607-3274-2021-2-12.

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Context. To reduce the computational resource time in the problems of diagnosing and recognizing distorted images based on a fully connected stochastic pseudospin neural network, it becomes necessary to thin out synaptic connections between neurons, which is solved using the method of diagonalizing the matrix of synaptic connections without losing interaction between all neurons in the network. Objective. To create an architecture of a stochastic pseudo-spin neural network with diagonal synaptic connections without loosing the interaction between all the neurons in the layer to reduce its lear

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Zhang, Zhikui, and Lina Wu. "Research on Continuous Pipeline Life Prediction Method Based on Fully Connected Neural Network." Academic Journal of Science and Technology 8, no. 3 (2023): 69–73. http://dx.doi.org/10.54097/fcqfsz74.

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Aiming at the low accuracy of traditional empirical formulas in predicting the fatigue life of continuous oil pipelines, a fully connected neural network is utilized to predict the low-week fatigue life of continuous oil pipelines. Considering the influence of internal pressure on the fatigue life of continuous oil pipeline during operation, a prediction method combining the fully connected neural network and gated recirculation unit is proposed, and the experiment proves that the FCNN-GRU neural network performs better in terms of prediction accuracy and stability compared with the BP neural

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Asadullaev, R. G., and M. A. Sitnikova. "INTELLIGENT MODEL FOR CLASSIFYING HEMODYNAMIC PATTERNS OF BRAIN ACTIVATION TO IDENTIFY NEUROCOGNITIVE MECHANISMS OF SPATIAL-NUMERICAL ASSOCIATIONS." Vestnik komp'iuternykh i informatsionnykh tekhnologii, no. 235 (January 2024): 38–45. http://dx.doi.org/10.14489/vkit.2024.01.pp.038-045.

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The study presents the results of the development and testing of deep learning neural network architectures, which demonstrate high accuracy rates in classifying neurophysiological data, in particular hemodynamic brain activation patterns obtained by functional near-infrared spectroscopy, during solving mathematical problems on spatial-numerical associations. The analyzed signal represents a multidimensional time series of oxyhemoglobin and deoxyhemoglobin dynamics. Taking the specificity of the fNIRS signal into account, a comparative analysis of 2 types of neural network architectures was ca

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Balipa, Mamatha, and Dr Balasubramani R. "Retrieval of Disease-Treatment Information from Online Forums Using NLP and Convoluted Neural Network Pipeline." International Journal of Research in Advent Technology 7, no. 1 (2019): 45–50. http://dx.doi.org/10.32622/ijrat.71201913.

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Li, Gang, Xing San Qian, Chun Ming Ye, and Lin Zhao. "A Clustering Method for Pruning Fully Connected Neural Network." Advanced Materials Research 204-210 (February 2011): 600–603. http://dx.doi.org/10.4028/www.scientific.net/amr.204-210.600.

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This paper focuses mainly on a clustering method for pruning Fully Connected Backpropagation Neural Network (FCBP). The initial neural network is fully connected, after training with sample data, a clustering method is employed to cluster weights between input to hidden layer and from hidden to output layer, and connections that are relatively unnecessary are deleted, thus the initial network becomes a PCBP (Partially Connected Backpropagation) Neural Network. PCBP can be used in prediction or data mining more efficiently than FCBP. At the end of this paper, An experiment is conducted to illus

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Jindam, Sowjanya, Jaimini Keerthan Mannem, Meena Nenavath, and Vineela Munigala. "Heritage Identification of Monuments using Deep Learning Techniques." Indian Journal of Image Processing and Recognition 3, no. 4 (2023): 1–7. http://dx.doi.org/10.54105/ijipr.d1022.063423.

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India is a nation with a plethora of cultural landmarks, including notable architectural masterpieces, 37 of which are UNESCO World Heritage master pieces. We must protect cultural heritages because they bind successive generations together over time. Architectural Designers, researchers, and travellers, etc. visit numerous historical locations, where it is frequently challenging for them to recognise and learn more about the historical significance of the monument in which they are interested. Due to the size and dependability of the information, the work of archiving, recording, and sharing

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Sowjanya, Jindam, Keerthan Mannem Jaimini, Nenavath Meena, and Munigala Vineela. "Heritage Identification of Monuments using Deep Learning Techniques." Indian Journal of Image Processing and Recognition (IJIPR) 3, no. 4 (2023): 1–7. https://doi.org/10.54105/ijipr.D1022.063423.

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<strong>Abstract: </strong>India is a nation with a plethora of cultural landmarks, including notable architectural masterpieces, 37 of which are UNESCO World Heritage master pieces. We must protect cultural heritages because they bind successive generations together over time. Architectural Designers, researchers, and travellers, etc. visit numerous historical locations, where it is frequently challenging for them to recognise and learn more about the historical significance of the monument in which they are interested. Due to the size and dependability of the information, the work of archivi

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Wu, Jiajie. "Diabetes classification and prediction using artificial neural networks." Applied and Computational Engineering 4, no. 1 (2023): 804–9. http://dx.doi.org/10.54254/2755-2721/4/2023434.

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Diabetes is a chronic disease which threatens the global human health. Over time it could lead to other serious medical problems and does not have a permanent cure. Hence if we could diagnose or predict it early, then it might be possible to prevent it. Several researches have shown that computer technology can effectively assist in the diagnosis of diseases. And neural network could be used for classification and prediction. In order to identify the most significant element that has the greatest impact on the classification and prediction, this paper applies artificial neural networks to the

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Lytvyn, Vasyl, Roman Peleshchak, Ivan Peleshchak, Oksana Cherniak, and Lyubomyr Demkiv. "Building a mathematical model and an algorithm for training a neural network with sparse dipole synaptic connections for image recognition." Eastern-European Journal of Enterprise Technologies 6, no. 4 (114) (2021): 21–27. http://dx.doi.org/10.15587/1729-4061.2021.245010.

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Large enough structured neural networks are used for solving the tasks to recognize distorted images involving computer systems. One such neural network that can completely restore a distorted image is a fully connected pseudospin (dipole) neural network that possesses associative memory. When submitting some image to its input, it automatically selects and outputs the image that is closest to the input one. This image is stored in the neural network memory within the Hopfield paradigm. Within this paradigm, it is possible to memorize and reproduce arrays of information that have their own int

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Vasyl, Lytvyn, Peleshchak Roman, Peleshchak Ivan, Cherniak Oksana, and Demkiv Lyubomyr. "Building a mathematical model and an algorithm for training a neural network with sparse dipole synaptic connections for image recognition." Eastern-European Journal of Enterprise Technologies 6, no. 4 (114) (2021): 21–27. https://doi.org/10.15587/1729-4061.2021.245010.

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Large enough structured neural networks are used for solving the tasks to recognize distorted images involving computer systems. One such neural network that can completely restore a distorted image is a fully connected pseudospin (dipole) neural network that possesses associative memory. When submitting some image to its input, it automatically selects and outputs the image that is closest to the input one. This image is stored in the neural network memory within the Hopfield paradigm. Within this paradigm, it is possible to memorize and reproduce arrays of information that have their own int

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Zhang, Jiayuan. "Application and Performance Comparison of Compound Neural Network Model based on CNN Feature Extraction in House Price Forecast." Applied and Computational Engineering 96, no. 1 (2024): 210–17. http://dx.doi.org/10.54254/2755-2721/96/20241281.

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Abstract. This study used a total of eight machine learning algorithms to forecast property prices, it not only provides a robust comparison of the predictive power of different algorithms but also significantly advances our understanding of the factors that influence property prices. In this paper, four traditional machine learning algorithms and four neural network models are selected for comparative study and analysis, of which the neural network models include fully connected neural networks (FCNN), convolutional fully connected neural networks (FCNN+CNN), generative adversarial fully conn

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Badoni, Davide, Roberto Riccardi, and Gaetano Salina. "LEARNING ATTRACTOR NEURAL NETWORK: THE ELECTRONIC IMPLEMENTATION." International Journal of Neural Systems 03, supp01 (1992): 13–24. http://dx.doi.org/10.1142/s0129065792000334.

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In this article we describe the electronic implementation of an attractor neural network with plastic analog synapses. The project for a 27 neurons fully connected network will be shown together with the most important electronic tests we have carried out on a smaller network.

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