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Journal articles on the topic 'Models of neural elements'

Author: Grafiati

Published: 30 May 2022

Last updated: 31 July 2025

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1

NAMAZOV, S. A., R. M. KHUSAINOV, and N. G. TALIPOV. "MODELS FOR RECOGNITION OF ROAD INFRASTRUCTURE ELEMENTS." Herald of Technological University 27, no. 3 (2024): 90–95. http://dx.doi.org/10.55421/1998-7072_2024_27_3_90.

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The paper considers the problem of recognising road infrastructure elements (temporary signs, guardrail tapes, traffic cones). To solve it, we propose to use the following types of models: Deep Learning model, Random Forest model, Support Vector Machines model, Directional Gradient Histogram model. The principles of operation of the models presented in the article are considered. Recognizing road infrastructure elements in three of the four models studied requires a significant amount of time. The factors influencing the implementation of road works and object recognition are analyzed. The nee

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Teslyuk, V. M., and S. S. Ivasiv. "Models and means of clothing elements patterns classification using machine learning." Ukrainian Journal of Information Technology 6, no. 1 (2024): 37–47. http://dx.doi.org/10.23939/ujit2024.01.037.

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The task of pattern classification remains relevant in the fields of trends, style, fashion, personalization, manufacturing, and design. Research aimed at the design and development of models and means of classification of patterns of clothing elements using machine learning is highlighted. The study addresses a pertinent issue in computer vision, namely: increasing the efficiency of classification of patterns of clothing elements. The research was conducted with a proprietary dataset comprising 600 images. The following patterns are defined for classification: “checkered”, “dotted”, “vegetati

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І.Г., Цмоць, Лукащук Ю.А., Хавалко В.М. та Рабик В.Г. "Моделі нейроподібного елемента паралельно-паралельного типу". Моделювання та інформаційні технології, № 86 (15 листопада 2019): 119–26. https://doi.org/10.5281/zenodo.3610679.

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Models of neural elements of parallel-parallel type with direct and tabular formation of macroparticle products have been developed, the main components of such neural elements have been identified, and it has been proposed to adapt the neural elements to the requirements by splitting into conveyor steps of appropriate complexity.

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Bainbridge, William Sims. "Neural Network Models of Religious Belief." Sociological Perspectives 38, no. 4 (1995): 483–95. http://dx.doi.org/10.2307/1389269.

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This paper applies neural network technology, a standard approach in computer science that has been unaccountably ignored by sociologists, to the problem of developing rigorous sociological theories. A simulation program employing a “varimax” model of human learning and decision-making models central elements of the Stark-Bainbridge theory of religion. Individuals in a micro-society of 24 simulated people learn which categories of potential exchange partners to seek for each of four material rewards which in fact can be provided by other actors in the society. However, when they seek eternal l

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Yakov, Vladimir, and Petar Hristov. "Predicting the parameters of reinforced concrete elements by neural network." IOP Conference Series: Materials Science and Engineering 1323, no. 1 (2024): 012019. https://doi.org/10.1088/1757-899x/1323/1/012019.

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Abstract Obtaining predictive results at planning and conceptual design phase often represents a serious problem on which the choice of design options depends to a significant extent This paper seeks solutions to the problem using neural networks and artificial intelligence and illustrates the idea by simple examples of pre-trained neural network models.

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Yaish, Ofir, and Yaron Orenstein. "Computational modeling of mRNA degradation dynamics using deep neural networks." Bioinformatics 38, no. 4 (2021): 1087–101. http://dx.doi.org/10.1093/bioinformatics/btab800.

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Abstract Motivation messenger RNA (mRNA) degradation plays critical roles in post-transcriptional gene regulation. A major component of mRNA degradation is determined by 3′-UTR elements. Hence, researchers are interested in studying mRNA dynamics as a function of 3′-UTR elements. A recent study measured the mRNA degradation dynamics of tens of thousands of 3′-UTR sequences using a massively parallel reporter assay. However, the computational approach used to model mRNA degradation was based on a simplifying assumption of a linear degradation rate. Consequently, the underlying mechanism of 3′-U

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Govindwar, Vaibhav, Aman Akbani, Aachal Nandeshwar, Aishwarya Wanjari, Prachi Agashe, and Charan Pote. "Facial Expression Recognition Using Convolutional Neural Network." International Journal of Scientific Research in Computer Science, Engineering and Information Technology 10, no. 2 (2024): 786–94. http://dx.doi.org/10.32628/cseit24102122.

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Understanding others' intentions through nonverbal cues like facial emotions is crucial in human communication. To design and train Deep Learning Models, this paper describes in detail how Convolutional Neural Network Models are developed using tf. Keras. The aim is to Sort facial photos into one of the seven face detection classifiers, our model is developed in such a manner that it learns hidden nonlinearity from the entered facial images, which is vital for discriminating the form of emotion someone is expressing. The model proposed on the Lenet-5 architecture by Yann LeCun uses the subsamp

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Wang, Zhaojun, Jiangning Wang, Congtian Lin, Yan Han, Zhaosheng Wang, and Liqiang Ji. "Identifying Habitat Elements from Bird Images Using Deep Convolutional Neural Networks." Animals 11, no. 5 (2021): 1263. http://dx.doi.org/10.3390/ani11051263.

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With the rapid development of digital technology, bird images have become an important part of ornithology research data. However, due to the rapid growth of bird image data, it has become a major challenge to effectively process such a large amount of data. In recent years, deep convolutional neural networks (DCNNs) have shown great potential and effectiveness in a variety of tasks regarding the automatic processing of bird images. However, no research has been conducted on the recognition of habitat elements in bird images, which is of great help when extracting habitat information from bird

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Seeliger, K., L. Ambrogioni, Y. Güçlütürk, L. M. van den Bulk, U. Güçlü, and M. A. J. van Gerven. "End-to-end neural system identification with neural information flow." PLOS Computational Biology 17, no. 2 (2021): e1008558. http://dx.doi.org/10.1371/journal.pcbi.1008558.

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Neural information flow (NIF) provides a novel approach for system identification in neuroscience. It models the neural computations in multiple brain regions and can be trained end-to-end via stochastic gradient descent from noninvasive data. NIF models represent neural information processing via a network of coupled tensors, each encoding the representation of the sensory input contained in a brain region. The elements of these tensors can be interpreted as cortical columns whose activity encodes the presence of a specific feature in a spatiotemporal location. Each tensor is coupled to the m

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Pinjarkar, Latika, Aditya Nittala, Mahantesh P. Mattada, Vedant Pinjarkar, Bhumika Neole, and Manisha Kogundi Math. "Optimizing neural radiance field: a comprehensive review of the impact of different optimizers on neural radiance fields." Bulletin of Electrical Engineering and Informatics 14, no. 1 (2025): 476–84. http://dx.doi.org/10.11591/eei.v14i1.8315.

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Neural radiance field (NeRF) is a form of deep learning model that may be used to depict 3D scenes from a collection of photos. It has been demonstrated that NeRF can produce photorealistic photographs of fresh perspectives on a scene even from a small number of input images. However, the optimizer that is employed can have a significant impact on the quality of the final reconstruction. Finding an effective optimizer is one of the biggest challenges while learning NeRF models. The optimizer is responsible for making changes to the model's parameters to minimize the discrepancy between the mod

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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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Kyrylov, S., L. Kyrylova, and S. Suvorova. "NEURONET MATHEMATICAL MODELS OF HUMAN-MACHINE COMMUNICATION AND RECOGNITION." Mechanics And Mathematical Methods 6, no. 2 (2024): 147–59. http://dx.doi.org/10.31650/2618-0650-2024-6-2-147-159.

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The study is devoted to the neural network interpretation of the task of human-machine communication and recognition by multiple criteria, considered as a task of assignment. The main goal is to reduce this problem to a standard form, where the number of criteria groups is equal to the number of ranked documents. The study defined the architecture of a neural network and proposed the use of a network of binary neurons, which is a matrix of a certain dimension. The proposed ranking model is based on a neural network that contains arbitrary feedback. This allows the excitation to be transmitted

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Savytskyi, M. V., V. V. Danishevskyy, and A. M. Gaidar. "IDENTIFICATION OF CRACKS IN VERTICAL ELEMENTS OF BUILDING STRUCTURES USING CONVOLUTIONAL NEURAL NETWORKS." Ukrainian Journal of Civil Engineering and Architecture, no. 2 (014) (January 22, 2025): 65–78. https://doi.org/10.30838/j.bpsacea.2312.250423.65.933.

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Problem statement. The relevant issue of modern Civil Engineering is the development of new methods for identifying defects in building structures, which would save human resources and reduce the dependence of survey results on subjective human factors. The purpose of the research is to develop artificial neural networks for the identification and classification of cracks in vertical elements of building structures (e. g., concrete and reinforced concrete walls). Methodology. The cloud tool Teachable Machine is used to develop a neural network with a pre-defined internal architecture. The libr

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De Wolf, E. D., and L. J. Franel. "Neural Networks That Distinguish Infection Periods of Wheat Tan Spot in an Outdoor Environment." Phytopathology® 87, no. 1 (1997): 83–87. http://dx.doi.org/10.1094/phyto.1997.87.1.83.

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Tan spot of wheat, caused by Pyrenophora tritici-repentis, provided a model system for testing disease forecasts based on an artificial neural network. Infection periods for P. tritici-repentis on susceptible wheat cultivars were identified from a bioassay system that correlated tan spot incidence with crop growth stage and 24-h summaries of environmental data, including temperature, relative humidity, wind speed, wind direction, solar radiation, precipitation, and flat-plate resistance-type wetness sensors. The resulting data set consisted of 97 discrete periods, of which 32 were reserved for

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Mrs. R. Auxilia anitha mary and Dr.T.Ramaprabha. "Predicting Heart Disease Algorithm Using DNN and MNN in Deep Learning." International Research Journal on Advanced Engineering Hub (IRJAEH) 2, no. 04 (2024): 783–92. http://dx.doi.org/10.47392/irjaeh.2024.0110.

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Heart diseases remain a significant global health concern, necessitating advanced predictive models for early detection and intervention. Based on an extensive collection of patient data, this study suggests a method for predicting cardiac disease using deep learning algorithms. The architecture employs a multi-layered neural network and deep neural network to capture intricate patterns within the data, optimizing predictive accuracy. The data set downloaded from Kaggle with 14 attributes is used in this study. The deep learning model multilayer neural network and deep neural network are chose

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Boriskov, Petr, and Andrei Velichko. "Switch Elements with S-Shaped Current-Voltage Characteristic in Models of Neural Oscillators." Electronics 8, no. 9 (2019): 922. http://dx.doi.org/10.3390/electronics8090922.

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In this paper, we present circuit solutions based on a switch element with the S-type I–V characteristic implemented using the classic FitzHugh–Nagumo and FitzHugh–Rinzel models. Using the proposed simplified electrical circuits allows the modeling of the integrate-and-fire neuron and burst oscillation modes with the emulation of the mammalian cold receptor patterns. The circuits were studied using the experimental I–V characteristic of an NbO2 switch with a stable section of negative differential resistance (NDR) and a VO2 switch with an unstable NDR, considering the temperature dependences o

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Marchesin, Stefano, Alberto Purpura, and Gianmaria Silvello. "Focal elements of neural information retrieval models. An outlook through a reproducibility study." Information Processing & Management 57, no. 6 (2020): 102109. http://dx.doi.org/10.1016/j.ipm.2019.102109.

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18

B., Zahran. "Using Neural Networks to Predict the Hardness of Aluminum Alloys." Engineering, Technology & Applied Science Research 5, no. 2 (2015): 757–59. https://doi.org/10.5281/zenodo.14903.

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Aluminum alloys have gained significant industrial importance being involved in many of the light and heavy industries and especially in aerospace engineering. The mechanical properties of aluminum alloys are defined by a number of principal microstructural features. Conventional mathematical models of these properties are sometimes very complex to be analytically calculated. In this paper, a neural network model is used to predict the correlations between the hardness of aluminum alloys in relation to certain alloying elements. A backpropagation neural network is trained using a thorough data

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19

Rimshin, V., P. Amelin, and L. Suleymanova. "NEURAL NETWORK FORECASTING OF THE LOAD-BEARING CAPACITY OF REINFORCED CONCRETE ELEMENTS." Bulletin of Belgorod State Technological University named after. V. G. Shukhov 9, no. 11 (2024): 42–55. https://doi.org/10.34031/2071-7318-2024-9-11-42-55.

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The article is devoted to the study of the force resistance of reinforced concrete bendable elements at various stages of their life cycle, taking into account corrosion damage to the reinforcement and subsequent composite reinforcement. The paper considers changes in the characteristics of reinforced concrete structures under the influence of operational and aggressive environmental factors, which leads to a decrease in their bearing capacity. The main attention is paid to the corrosion of reinforcing steel, which causes cracking of the concrete coating and a decrease in the cross section of

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20

Chen, Chieh-Huang, Jung-Pin Lai, Yu-Ming Chang, Chi-Ju Lai, and Ping-Feng Pai. "A Study of Optimization in Deep Neural Networks for Regression." Electronics 12, no. 14 (2023): 3071. http://dx.doi.org/10.3390/electronics12143071.

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Due to rapid development in information technology in both hardware and software, deep neural networks for regression have become widely used in many fields. The optimization of deep neural networks for regression (DNNR), including selections of data preprocessing, network architectures, optimizers, and hyperparameters, greatly influence the performance of regression tasks. Thus, this study aimed to collect and analyze the recent literature surrounding DNNR from the aspect of optimization. In addition, various platforms used for conducting DNNR models were investigated. This study has a number

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Fischer, M. M. "Computational Neural Networks: A New Paradigm for Spatial Analysis." Environment and Planning A: Economy and Space 30, no. 10 (1998): 1873–91. http://dx.doi.org/10.1068/a301873.

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In this paper a systematic introduction to computational neural network models is given in order to help spatial analysts learn about this exciting new field. The power of computational neural networks viz-à-viz conventional modelling is illustrated for an application field with noisy data of limited record length: spatial interaction modelling of telecommunication data in Austria. The computational appeal of neural networks for solving some fundamental spatial analysis problems is summarized and a definition of computational neural network models in mathematical terms is given. Three definiti

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Bellier, Ludovic, Anaïs Llorens, Déborah Marciano, et al. "Music can be reconstructed from human auditory cortex activity using nonlinear decoding models." PLOS Biology 21, no. 8 (2023): e3002176. http://dx.doi.org/10.1371/journal.pbio.3002176.

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Music is core to human experience, yet the precise neural dynamics underlying music perception remain unknown. We analyzed a unique intracranial electroencephalography (iEEG) dataset of 29 patients who listened to a Pink Floyd song and applied a stimulus reconstruction approach previously used in the speech domain. We successfully reconstructed a recognizable song from direct neural recordings and quantified the impact of different factors on decoding accuracy. Combining encoding and decoding analyses, we found a right-hemisphere dominance for music perception with a primary role of the superi

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KATERMINA, TATYANA, KADIR TAGIROV, and TAGIR TAGIROV. "ELEMENTS OF ARTIFICIAL INTELLIGENCE IN SOLVING PROBLEMS OF TEXT ANALYSIS." Computational Nanotechnology 9, no. 2 (2022): 35–44. http://dx.doi.org/10.33693/2313-223x-2022-9-2-35-44.

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Due to the ever-increasing volume of textual information on the Internet and the need to navigate it, the automation of the text analysis process has become urgent. The analysis of the subject area has shown a great demand for the identification of textual information coloring and the application of works on this problem in practice. This paper deals with the development of a neural network model for analyzing commentary tone. Recurrent neural network models with long short-term memory modules (LSTM) are used for the purpose. We have developed an information system that determines the tone of

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Zapata Usandivaras, José Felix, Annafederica Urbano, Michael Bauerheim, and Bénédicte Cuenot. "Data Driven Models for the Design of Rocket Injector Elements." Aerospace 9, no. 10 (2022): 594. http://dx.doi.org/10.3390/aerospace9100594.

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Improving the predictive capabilities of reduced-order models for the design of injector and chamber elements of rocket engines could greatly improve the quality of early rocket chamber designs. In the present work, we propose an innovative methodology that uses high-fidelity numerical simulations of turbulent reactive flows and artificial intelligence for the generation of surrogate models. The surrogate models that were generated and analyzed are deep learning networks trained on a dataset of 100 large eddy simulations of a single-shear coaxial injector chamber. The design of experiments was

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Yuille, Alan L. "Generalized Deformable Models, Statistical Physics, and Matching Problems." Neural Computation 2, no. 1 (1990): 1–24. http://dx.doi.org/10.1162/neco.1990.2.1.1.

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We describe how to formulate matching and combinatorial problems of vision and neural network theory by generalizing elastic and deformable templates models to include binary matching elements. Techniques from statistical physics, which can be interpreted as computing marginal probability distributions, are then used to analyze these models and are shown to (1) relate them to existing theories and (2) give insight into the relations between, and relative effectivenesses of, existing theories. In particular we exploit the power of statistical techniques to put global constraints on the set of a

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Fujiwara, Yusuke, Yoichi Miyawaki, and Yukiyasu Kamitani. "Modular Encoding and Decoding Models Derived from Bayesian Canonical Correlation Analysis." Neural Computation 25, no. 4 (2013): 979–1005. http://dx.doi.org/10.1162/neco_a_00423.

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Neural encoding and decoding provide perspectives for understanding neural representations of sensory inputs. Recent functional magnetic resonance imaging (fMRI) studies have succeeded in building prediction models for encoding and decoding numerous stimuli by representing a complex stimulus as a combination of simple elements. While arbitrary visual images were reconstructed using a modular model that combined the outputs of decoder modules for multiscale local image bases (elements), the shapes of the image bases were heuristically determined. In this work, we propose a method to establish m

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Tunakova, Yulia, Svetlana Novikova, Aligejdar Ragimov, Rashat Faizullin, and Vsevolod Valiev. "A Method for Assessing the Retention of Trace Elements in Human Body Using Neural Network Technology." Journal of Healthcare Engineering 2017 (2017): 1–7. http://dx.doi.org/10.1155/2017/3471616.

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Models that describe the trace element status formation in the human organism are essential for a correction of micromineral (trace elements) deficiency. A direct trace element retention assessment in the body is difficult due to the many internal mechanisms. The trace element retention is determined by the amount and the ratio of incoming and excreted substance. So, the concentration of trace elements in drinking water characterizes the intake, whereas the element concentration in urine characterizes the excretion. This system can be interpreted as three interrelated elements that are in equi

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Cid, Juan M., Jesús García, Javier Monge, and Juan Zapata. "Design of microwave devices by segmentation, finite elements, reduced-order models, and neural networks." Microwave and Optical Technology Letters 49, no. 3 (2007): 655–59. http://dx.doi.org/10.1002/mop.22248.

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Guryanova, L., O. Yevsyeyev, and M. Simakova. "Application of generative AI in managing projects for 3D game environment development." Bulletin of V. N. Karazin Kharkiv National University Economic Series, no. 107 (December 30, 2024): 5–18. https://doi.org/10.26565/2311-2379-2024-107-01.

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The study focuses on the development of modern technology for creating 3D models of environmental elements for video games using generative neural networks. This approach significantly accelerates the development process of the final product while enhancing the quality and uniqueness of 3D content and ensuring its compliance with the overall design requirements of video games. The article substantiates the feasibility of using artificial intelligence tools for creating prototypes of 3D models of environmental elements and proposes an optimized development technology. The study examines the spe

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Ćirić, Tomislav, Rohan Dhuri, Zlatica Marinković, Olivera Pronić-Rančić, Vera Marković, and Larissa Vietzorreck. "Neural Based Lumped Element Model of Capacitive RF MEMS Switches." Frequenz 72, no. 11-12 (2018): 539–46. http://dx.doi.org/10.1515/freq-2018-0023.

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Abstract In this paper a lumped element model of RF MEMS capacitive switches which is scalable with the lateral dimensions of the bridge is proposed. The dependence of the elements of the model on the bridge dimensions is introduced by using one or more artificial neural networks to model the relationship between the bridge dimensions and the inductive and resistive elements of the lumped element model. The achieved results show that the developed models have a good accuracy over the whole considered range of the bridge dimension values.

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Austern, Guy, Tanya Bloch, and Yael Abulafia. "Incorporating Context into BIM-Derived Data—Leveraging Graph Neural Networks for Building Element Classification." Buildings 14, no. 2 (2024): 527. http://dx.doi.org/10.3390/buildings14020527.

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The application of machine learning (ML) for the automatic classification of building elements is a powerful technique for ensuring information integrity in building information models (BIMs). Previous work has demonstrated the favorable performance of such models on classification tasks using geometric information. This research explores the hypothesis that incorporating contextual information into the ML models can improve classification accuracy. To test this, we created a graph data structure where each building element is represented as a node assigned with basic geometric information. Th

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Wang, Hao, Ruibin Feng, Chi-Sing Leung, Hau Ping Chan та Anthony G. Constantinides. "A Lagrange Programming Neural Network Approach with an ℓ0-Norm Sparsity Measurement for Sparse Recovery and Its Circuit Realization". Mathematics 10, № 24 (2022): 4801. http://dx.doi.org/10.3390/math10244801.

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Many analog neural network approaches for sparse recovery were based on using ℓ1-norm as the surrogate of ℓ0-norm. This paper proposes an analog neural network model, namely the Lagrange programming neural network with ℓp objective and quadratic constraint (LPNN-LPQC), with an ℓ0-norm sparsity measurement for solving the constrained basis pursuit denoise (CBPDN) problem. As the ℓ0-norm is non-differentiable, we first use a differentiable ℓp-norm-like function to approximate the ℓ0-norm. However, this ℓp-norm-like function does not have an explicit expression and, thus, we use the locally compe

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Zahran, B. "Using Neural Networks to Predict the Hardness of Aluminum Alloys." Engineering, Technology & Applied Science Research 5, no. 1 (2015): 757–59. http://dx.doi.org/10.48084/etasr.529.

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Aluminum alloys have gained significant industrial importance being involved in many of the light and heavy industries and especially in aerospace engineering. The mechanical properties of aluminum alloys are defined by a number of principal microstructural features. Conventional mathematical models of these properties are sometimes very complex to be analytically calculated. In this paper, a neural network model is used to predict the correlations between the hardness of aluminum alloys in relation to certain alloying elements. A backpropagation neural network is trained using a thorough data

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Mouratidou, Aliki D., and Georgios E. Stavroulakis. "A Neural Network Constitutive Model and Automatic Stiffness Evaluation for Multiscale Finite Elements." Applied Sciences 15, no. 7 (2025): 3697. https://doi.org/10.3390/app15073697.

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A neural network model for a constitutive law in nonlinear structures is proposed in this paper. The artificial neural network (ANN) model is constructed based on a data set of responses from representative volume elements, which was calculated by finite elements and using an open scientific software machine learning platform. The tangential stiffness matrix, which can be used within a multiscale finite element analysis, is calculated via the method of automatic differentiation. Two types of constitutive neural networks are proposed. The first approach involves training a residual model with t

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Chapeau-Blondeau, François, and Nicolas Chambet. "Synapse Models for Neural Networks: From Ion Channel Kinetics to Multiplicative Coefficient wij." Neural Computation 7, no. 4 (1995): 713–34. http://dx.doi.org/10.1162/neco.1995.7.4.713.

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This paper relates different levels at which the modeling of synaptic transmission can be grounded in neural networks: the level of ion channel kinetics, the level of synaptic conductance dynamics, and the level of a scalar synaptic coefficient. The important assumptions to reduce a synapse model from one level to the next are explicitly exhibited. This coherent progression provides control on what is discarded and what is retained in the modeling process, and is useful to appreciate the significance and limitations of the resulting neural networks. This methodic simplification terminates with

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Ahmad, Afaq, Vagelis Plevris, and Qaiser-uz-Zaman Khan. "Prediction of Properties of FRP-Confined Concrete Cylinders Based on Artificial Neural Networks." Crystals 10, no. 9 (2020): 811. http://dx.doi.org/10.3390/cryst10090811.

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Recently, the use of fiber-reinforced polymers (FRP)-confinement has increased due to its various favorable effects on concrete structures, such as an increase in strength and ductility. Therefore, researchers have been attracted to exploring the behavior and efficiency of FRP-confinement for concrete structural elements further. The current study investigates improved strength and strain models for FRP confined concrete cylindrical elements. Two new physical methods are proposed for use on a large preliminary evaluated database of 708 specimens for strength and 572 specimens for strain from p

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Hajdin, Rade, Rico Richter, Lazar Rakic, et al. "Digitalization of bridge inventory via automated analysis of point clouds for generation of BIM models." ce/papers 6, no. 5 (2023): 1189–97. http://dx.doi.org/10.1002/cepa.1995.

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AbstractBuilding Information Modelling (BIM) is becoming increasingly prevalent in infrastructure asset management, as it facilitates current management practices. This includes the construction of BIM models for roads, rails, bridges, tunnels etc. Bridges are particularly challenging to digitalize due to their complex geometry. The manual construction of bridge BIM models based on 2D plans is hardly feasible due to the related workload. Given the recent advancements in the field of 3D surveying and artificial intelligence, new possibilities emerge for an automated generation of as‐is bridge B

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Kusdarwati, Heni, and Samingun Handoyo. "System for Prediction of Non Stationary Time Series based on the Wavelet Radial Bases Function Neural Network Model." International Journal of Electrical and Computer Engineering (IJECE) 8, no. 4 (2018): 2327. http://dx.doi.org/10.11591/ijece.v8i4.pp2327-2337.

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This paper proposes and examines the performance of a hybrid model called the wavelet radial bases function neural networks (WRBFNN). The model will be compared its performance with the wavelet feed forward neural networks (WFFN model by developing a prediction or forecasting system that considers two types of input formats: input9 and input17, and also considers 4 types of non-stationary time series data. The MODWT transform is used to generate wavelet and smooth coefficients, in which several elements of both coefficients are chosen in a particular way to serve as inputs to the NN model in b

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Heni, Kusdarwati, and Handoyo Samingun. "System for Prediction of Non Stationary Time Series based on the Wavelet Radial Bases Function Neural Network Model." International Journal of Electrical and Computer Engineering (IJECE) 8, no. 4 (2018): 2327–37. https://doi.org/10.11591/ijece.v8i4.pp2327-2337.

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This paper proposes and examines the performance of a hybrid model called the wavelet radial bases function neural networks (WRBFNN). The model will be compared its performance with the wavelet feed forward neural networks (WFFN model by developing a prediction or forecasting system that considers two types of input formats: input9 and input17, and also considers 4 types of non-stationary time series data. The MODWT transform is used to generate wavelet and smooth coefficients, in which several elements of both coefficients are chosen in a particular way to serve as inputs to the NN model in b

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Lahoz Navarro, Mar, Jonas Siegfried Jehle, Patricia A. Apellániz, Juan Parras, Santiago Zazo, and Matthias Gerdts. "Deep Learning as a New Framework for Passive Vehicle Safety Design Using Finite Elements Models Data." Applied Sciences 14, no. 20 (2024): 9296. http://dx.doi.org/10.3390/app14209296.

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In recent years, passive vehicle safety has become one of the major concerns for the automotive industry due to the considerable increase in the use of cars as a means of daily transport. Since real crash testing has a high financial cost, finite element simulations are generally used, which entail high computational cost and long simulation times. In this paper, we make use of the recent advances in the deep learning field to propose an affordable method to provide reliable approximations of the finite element simulator model that significantly reduce the computational load and time required.

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KRASILENKO, VLADIMIR, NATALIYA YURCHUK, and Diana NIKITOVICH. "DESIGN AND SIMULATION OF NEURON-EQUIVALENTORS ARRAY FOR CREATION OF SELF-LEARNING EQUIVALENT-CONVOLUTIONAL NEURAL STRUCTURES (SLECNS)." HERALD OF KHMELNYTSKYI NATIONAL UNIVERSITY 297, no. 3 (2021): 58–69. http://dx.doi.org/10.31891/2307-5732-2021-297-3-58-69.

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In the paper, we consider the urgent need to create highly efficient hardware accelerators for machine learning algorithms, including convolutional and deep neural networks (CNN and DNNS), for associative memory models, clustering, and pattern recognition. We show a brief overview of our related works the advantages of the equivalent models (EM) for describing and designing bio-inspired systems. The capacity of NN on the basis of EM and of its modifications is in several times quantity of neurons. Such neural paradigms are very perspective for processing, clustering, recognition, storing large

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Kumar, Vinod, Parveen Sihag, Ali Keshavarzi, Shevita Pandita, and Andrés Rodríguez-Seijo. "Soft Computing Techniques for Appraisal of Potentially Toxic Elements from Jalandhar (Punjab), India." Applied Sciences 11, no. 18 (2021): 8362. http://dx.doi.org/10.3390/app11188362.

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The contamination of potentially toxic elements (PTEs) in agricultural soils is a serious concern around the globe, and modelling approaches is imperative in order to determine the possible hazards linked with PTEs. These techniques accurately assess the PTEs in soil, which play a pivotal role in eliminating the weaknesses in determining PTEs in soils. This paper aims to predict the concentration of Cu, Co and Pb using neural networks (NNs) based on multilayer perceptron (MLP) and boosted regression trees (BT). Statistical performance estimation factors were rummage-sale to measure the perform

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Jin, Long. "Serialized Recommendation Technology Based on Deep Neural Network." Wireless Communications and Mobile Computing 2022 (July 14, 2022): 1–10. http://dx.doi.org/10.1155/2022/3216043.

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Since the construction of brain network is like organic brain organization, profound brain network has high effectiveness and high accuracy in separating data from profound elements, fit for multifacet learning, conceptual component portrayal, cross-space learning capacity, multisource, heterogeneous data content. This paper presents brain network models, multifacet perceptrons, RBM models, and convolutional brain organizations. In this paper, the experimental design is carried out according to the principle of the model. Compared with the traditional commonly used CMF, PMF, SVD, and item-base

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MARWALA, T., and H. E. M. HUNT. "FAULT IDENTIFICATION USING FINITE ELEMENT MODELS AND NEURAL NETWORKS." Mechanical Systems and Signal Processing 13, no. 3 (1999): 475–90. http://dx.doi.org/10.1006/mssp.1998.1218.

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Egamberdiev, N.A. Xolmuminov O.T. Ochilov X.R. "ANALYSIS OF CLASSICAL MODELS OF CLASSIFICATION OF SLOWLY FORMED PROCESSES." SOLUTION OF SOCIAL PROBLEMS IN MANAGEMENT AND ECONOMY 1, no. 4 (2022): 12–16. https://doi.org/10.5281/zenodo.7156184.

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In this work, classical models of classification of weakly formed processes were analyzed. The theory of fuzzy sets, the use of fuzzy relations in the modeling of objects and the information about the object in the linguistic form are presented again. Combining the elements of fuzzy set theory with evolutionary algorithms leads to the formation of hybrid algorithms. In this regard, the researches of a number of scientists on fuzzy sets, neural networks, fuzzy logical conclusions and evolutionary algorithms were analyzed.

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Fan, Angela, Jack Urbanek, Pratik Ringshia, et al. "Generating Interactive Worlds with Text." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 02 (2020): 1693–700. http://dx.doi.org/10.1609/aaai.v34i02.5532.

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Procedurally generating cohesive and interesting game environments is challenging and time-consuming. In order for the relationships between the game elements to be natural, common-sense has to be encoded into arrangement of the elements. In this work, we investigate a machine learning approach for world creation using content from the multi-player text adventure game environment LIGHT (Urbanek et al. 2019). We introduce neural network based models to compositionally arrange locations, characters, and objects into a coherent whole. In addition to creating worlds based on existing elements, our

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Kim, Mansu, Sungwon Jung, and Joo-won Kang. "Artificial Neural Network-Based Residential Energy Consumption Prediction Models Considering Residential Building Information and User Features in South Korea." Sustainability 12, no. 1 (2019): 109. http://dx.doi.org/10.3390/su12010109.

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When researching the energy consumption of residential buildings, it is becoming increasingly important to consider how residents use energy. With the advancement of computing power and data analysis techniques, it is now possible to analyze user information using big data techniques. Here, we endeavored to integrate user information with the physical characteristics of residential buildings to analyze how these elements impact energy consumption. Regression analysis was conducted to accurately identify the impact of each element on energy consumption. It was found that six elements were influ

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Litavrin, Andrey V., and Tatyana V. Moiseenkova. "About One Groupoid Associated with the Composition of Multilayer Feedforward Neural Networks." Zhurnal Srednevolzhskogo Matematicheskogo Obshchestva 26, no. 2 (2024): 111–22. http://dx.doi.org/10.15507/2079-6900.26.202402.111-122.

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Abstract. The authors construct a groupoid whose elements are associated with multilayer feedforward neural networks. This groupoid is called the complete groupoid of the composition of neural networks. Multilayer feedforward neural networks (hereinafter referred to as neural networks) are modelled by defining a special type of tuple. Its components define layers of neurons and structural mappings that specify weights of synaptic connections, activation functions and threshold values. Using the artificial neuron model (that of McCulloch-Pitts) for each such tuple it is possible to define a map

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Xue, Lanqing, Xiaopeng Li, and Nevin L. Zhang. "Not All Attention Is Needed: Gated Attention Network for Sequence Data." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 04 (2020): 6550–57. http://dx.doi.org/10.1609/aaai.v34i04.6129.

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Although deep neural networks generally have fixed network structures, the concept of dynamic mechanism has drawn more and more attention in recent years. Attention mechanisms compute input-dependent dynamic attention weights for aggregating a sequence of hidden states. Dynamic network configuration in convolutional neural networks (CNNs) selectively activates only part of the network at a time for different inputs. In this paper, we combine the two dynamic mechanisms for text classification tasks. Traditional attention mechanisms attend to the whole sequence of hidden states for an input sent

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Avanthi Nagelli and Dr. Srinivas Babu. "In-depth Investigation into Supervised and Unsupervised Models within Artificial Neural Networks." Journal of Advances in Science and Technology 20, no. 2 (2024): 120–23. http://dx.doi.org/10.29070/ddtgna06.

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An Artificial Neural Network (ANN) is an information-handling worldview that is jazzed up by exactly how natural nerves, like the mind, procedure details. The essential element of this particular worldview is the initial Building of the information dealing with the unit. A neural network is a difficult development that comprises a gathering of interconnected nerve cells, which offers a remarkably electrifying possibility unlike complicated essential reasoning and also various other treatments which may think a notable component in today's software engineering industry, thus professionals from

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