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Thematische Bibliographien / Evolutionary development of neural network / Zeitschriftenartikel

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Zeitschriftenartikel zum Thema „Evolutionary development of neural network“

Autor: Grafiati

Veröffentlicht am 28. Juni 2021

Zuletzt aktualisiert am 29. Juli 2025

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1

Leoshchenko, S. D., A. O. Oliinyk, S. A. Subbotin, Ye O. Gofman, and M. B. Ilyashenko. "EVOLUTIONARY METHOD FOR SYNTHESIS SPIKING NEURAL NETWORKS USING THE NEUROPATTHERN MECHANISM." Radio Electronics, Computer Science, Control, no. 3 (October 20, 2022): 77. http://dx.doi.org/10.15588/1607-3274-2022-3-8.

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Context. The problem of synthesizing pulsed neural networks based on an evolutionary approach to the synthesis of artificial neural networks using a neuropathic mechanism for constructing diagnostic models with a high level of accuracy is considered. The object of research is the process of synthesis of pulsed neural networks using an evolutionary approach and a neuropathic mechanism. Objective of the work is to develop a method for synthesizing pulsed neural networks based on an evolutionary approach using a neuropathic mechanism to build diagnostic models with a high level of accuracy

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Al-Khowarizmi, Al-Khowarizmi. "Model Classification Of Nominal Value And The Original Of IDR Money By Applying Evolutionary Neural Network." JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING 3, no. 2 (2020): 258–65. http://dx.doi.org/10.31289/jite.v3i2.3284.

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Indonesian Rupiah (IDR) banknotes have unique characteristics that distinguish them from one another, both in the form of numbers, zeros and background images. This pattern of each type of banknote will be modeled in order to test the nominal value and authenticity of IDR, so as to be able to distinguish not only IDR banknotes but also other denominations. Evolutionary Neural Network is the development of the concept of evolution to get a neural network (NN) using genetic algorithms (GA). In this paper the application of evolutionary neural networks with less input is able to have a better suc

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YASHIN, Sergei N., Egor V. KOSHELEV, and Dar'ya A. BYKOVA. "Evolutionary neural network modeling of the impact of digital technologies on the economic development of regions." Finance and Credit 30, no. 5 (2024): 1036–60. http://dx.doi.org/10.24891/fc.30.5.1036.

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Subject. This article focuses on the issues of evolutionary neural network modeling of the impact of digital technologies on economic development. Objectives. The article aims to study evolutionary neural network modeling of the impact of digital technologies on the economic development of the regions of Russia. The article also aims to identify regions that are leaders where the impact is positive and significant, as well as regions with prospects for such positive influence. Results. The article presents the author-developed methodology for evolutionary neural network modeling of the impact

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Kan, Xinglong, and Lin Li. "Comprehensive Evaluation of Tourism Resources Based on Multispecies Evolutionary Genetic Algorithm-Enabled Neural Networks." Computational Intelligence and Neuroscience 2021 (December 14, 2021): 1–11. http://dx.doi.org/10.1155/2021/1081814.

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With the development of neural network technology and the rapid growth of China’s tourism economic income at this stage, the research on the comprehensive evaluation of tourism resources has gradually emerged. Based on this, this paper studies the neural network comprehensive evaluation model based on multispecies evolutionary genetic algorithm and designs the neural network analysis system of influencing factors of tourism resources based on multispecies evolutionary genetic algorithm. The collection and acquisition of data information are realized from the aspects of resource income status,

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Retaj, Matroud Jasim, and Salman Atia Tayseer. "An evolutionary-convolutional neural network for fake image detection." An evolutionary-convolutional neural network for fake image detection 29, no. 3 (2023): 1657–67. https://doi.org/10.11591/ijeecs.v29.i3.pp1657-1667.

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The fast development in deep learning techniques, besides the wide spread of social networks, facilitated fabricating and distributing images and videos without prior knowledge. This paper developed an evolutionary learning algorithm to automatically design a convolutional neural network (CNN) architecture for deepfake detection. Genetic algorithm (GA) based on residual network (ResNet) and densely connected convolutional network (DenseNet) as building block units for feature extraction versus multilayer perceptron (MLP), random forest (RF) and support vector machine (SVM) as classifiers gener

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QIAO, Zenglin, Xinchao ZHAO, and Lingyu WU. "A Review of Evolutionary Deep Neural Architecture Search with Performance Predictor." Bulletin of Chinese Applied Mathematics 1, no. 1 (2023): 1–9. http://dx.doi.org/10.48014/bcam.20230822002.

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Automated design of deep neural networks using performance predictor has become a hot topic in current research. Neural architecture search (NAS) methods can be used to enable automatic design of neural network structures by defining different search spaces, search strategies, or optimization strategies. Evolutionary computation by many researchers as the search strategy for NAS, which is called evolutionary NAS (ENAS) . However, ENAS is time-consuming in evaluating the performance of network structures, which hinders the development of ENAS. Therefore, predicting network architecture performa

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Jasim, Retaj Matroud, and Tayseer Salman Atia. "An evolutionary- convolutional neural network for fake image detection." Indonesian Journal of Electrical Engineering and Computer Science 29, no. 3 (2023): 1657. http://dx.doi.org/10.11591/ijeecs.v29.i3.pp1657-1667.

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<p><span lang="EN-US">The fast development in deep learning techniques, besides the wide spread of social networks, facilitated fabricating and distributing images and videos without prior knowledge. This paper developed an evolutionary learning algorithm to automatically design a convolutional neural network (CNN) architecture for deepfake detection. Genetic algorithm (GA) based on residual network (ResNet) and densely connected convolutional network (DenseNet) as building block units for feature extraction versus multilayer perceptron (MLP), random forest (RF) and support vector

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Li, Xiao Guang. "Research on the Development and Applications of Artificial Neural Networks." Applied Mechanics and Materials 556-562 (May 2014): 6011–14. http://dx.doi.org/10.4028/www.scientific.net/amm.556-562.6011.

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Intelligent control is a class of control techniques that use various AI computing approaches like neural networks, Bayesian probability, fuzzy logic, machine learning, evolutionary computation and genetic algorithms. In computer science and related fields, artificial neural networks are computational models inspired by animals’ central nervous systems (in particular the brain) that are capable of machine learning and pattern recognition. They are usually presented as systems of interconnected “neurons” that can compute values from inputs by feeding information through the network. Like other

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Xue, Yu, Pengcheng Jiang, Ferrante Neri, and Jiayu Liang. "A Multi-Objective Evolutionary Approach Based on Graph-in-Graph for Neural Architecture Search of Convolutional Neural Networks." International Journal of Neural Systems 31, no. 09 (2021): 2150035. http://dx.doi.org/10.1142/s0129065721500350.

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With the development of deep learning, the design of an appropriate network structure becomes fundamental. In recent years, the successful practice of Neural Architecture Search (NAS) has indicated that an automated design of the network structure can efficiently replace the design performed by human experts. Most NAS algorithms make the assumption that the overall structure of the network is linear and focus solely on accuracy to assess the performance of candidate networks. This paper introduces a novel NAS algorithm based on a multi-objective modeling of the network design problem to design

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Zhao, Hongfei, Zhiguo Shi, Zhefeng Gong, and Shibo He. "Modeling the Evolution of Biological Neural Networks Based on Caenorhabditis elegans Connectomes across Development." Entropy 25, no. 1 (2022): 51. http://dx.doi.org/10.3390/e25010051.

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Knowledge of the structural properties of biological neural networks can help in understanding how particular responses and actions are generated. Recently, Witvliet et al. published the connectomes of eight isogenic Caenorhabditis elegans hermaphrodites at different postembryonic ages, from birth to adulthood. We analyzed the basic structural properties of these biological neural networks. From birth to adulthood, the asymmetry between in-degrees and out-degrees over the C. elegans neuronal network increased with age, in addition to an increase in the number of nodes and edges. The degree dis

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LI, KANG, and JIAN-XUN PENG. "SYSTEM ORIENTED NEURAL NETWORKS — PROBLEM FORMULATION, METHODOLOGY AND APPLICATION." International Journal of Pattern Recognition and Artificial Intelligence 20, no. 02 (2006): 143–58. http://dx.doi.org/10.1142/s0218001406004570.

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A novel methodology is proposed for the development of neural network models for complex engineering systems exhibiting nonlinearity. This method performs neural network modeling by first establishing some fundamental nonlinear functions from a priori engineering knowledge, which are then constructed and coded into appropriate chromosome representations. Given a suitable fitness function, using evolutionary approaches such as genetic algorithms, a population of chromosomes evolves for a certain number of generations to finally produce a neural network model best fitting the system data. The ob

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Boyko, N. I., and T. O. Salanchii. "DEVELOPMENT OF INNOVATIVE APPROACHES FOR NETWORK OPTIMIZATION USING GEOSPATIAL MULTI-COMPONENT SYSTEMS." Radio Electronics, Computer Science, Control, no. 2 (June 29, 2025): 182–95. https://doi.org/10.15588/1607-3274-2025-2-16.

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Context. Developing a geospatial multi-agent system for optimizing transportation networks is crucial for enhancing efficiency and reducing travel time. This involves employing optimization algorithms and simulating agent behavior within the network.Objective. The aim of this study is to develop a geospatial multi-agent system for optimizing transportation networks, focusing on improving network efficiency and minimizing travel time through the application of advanced optimization algorithms and agentbased modeling.Method. The proposed method for optimizing transportation networks combines fou

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Tinoco, Joaquim, António Gomes Correia, Paulo Cortez, and David Toll. "An Evolutionary Neural Network Approach for Slopes Stability Assessment." Applied Sciences 13, no. 14 (2023): 8084. http://dx.doi.org/10.3390/app13148084.

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A current big challenge for developed or developing countries is how to keep large-scale transportation infrastructure networks operational under all conditions. Network extensions and budgetary constraints for maintenance purposes are among the main factors that make transportation network management a non-trivial task. On the other hand, the high number of parameters affecting the stability condition of engineered slopes makes their assessment even more complex and difficult to accomplish. Aiming to help achieve the more efficient management of such an important element of modern society, a

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Vladov, Serhii, Ruslan Yakovliev, Victoria Vysotska, Dmytro Uhryn, and Artem Karachevtsev. "Polymorphic Radial Basis Functions Neural Network." International Journal of Intelligent Systems and Applications 16, no. 4 (2024): 1–21. http://dx.doi.org/10.5815/ijisa.2024.04.01.

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The work is devoted to the development of the radial basis functions (RBF networks) neural network new architecture – a polymorphic RBF network in which the one-dimensional radial basis functions (RBFs) in the hidden layer instead, multidimensional RBFs are used, which makes it possible to better approximate complex functions that depend on several independent variables. Moreover, in its second layer, the summing the RBF outputs one by one from each group instead, multiplication is used, which allows the polymorphic RBF network to better identify relations between independent variables. Based

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Hirianskyi, Bohdan, and Bogdan Bulakh. "A review of practice of using evolutionary algorithms for neural network synthesis and training." Technology audit and production reserves 4, no. 2(72) (2023): 22–26. http://dx.doi.org/10.15587/2706-5448.2023.286278.

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The object of this research is the application of evolutionary algorithms for the synthesis and training of neural networks. The paper aims to select and review the existing experience on using evolutionary algorithms as competitive methods to conventional approaches in neural network training and creation, and to evaluate such existing solutions for further development of this field. The essence of the obtained results lies in the successful application of genetic algorithms in conjunction with neural networks to optimize parameters, architecture, and weight coefficients of the networks. The

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Verenich, Vadim. "Neural Networks in Legal Theory." Studia Humana 13, no. 3 (2024): 41–51. http://dx.doi.org/10.2478/sh-2024-0018.

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Abstract This article explores the domain of legal analysis and its methodologies, emphasising the significance of generalisation in legal systems. It discusses the process of generalisation in relation to legal concepts and the development of ideal concepts that form the foundation of law. The article examines the role of logical induction and its similarities with semantic generalisation, highlighting their importance in legal decision-making. It also critiques the formal-deductive approach in legal practice and advocates for more adaptable models, incorporating fuzzy logic, non-monotonic de

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Wu, Tao, Jiao Shi, Deyun Zhou, Xiaolong Zheng, and Na Li. "Evolutionary Multi-Objective One-Shot Filter Pruning for Designing Lightweight Convolutional Neural Network." Sensors 21, no. 17 (2021): 5901. http://dx.doi.org/10.3390/s21175901.

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Deep neural networks have achieved significant development and wide applications for their amazing performance. However, their complex structure, high computation and storage resource limit their applications in mobile or embedding devices such as sensor platforms. Neural network pruning is an efficient way to design a lightweight model from a well-trained complex deep neural network. In this paper, we propose an evolutionary multi-objective one-shot filter pruning method for designing a lightweight convolutional neural network. Firstly, unlike some famous iterative pruning methods, a one-shot

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Ziyadullaev, Davron, Dildora Muhamediyeva, Sholpan Ziyaeva, Umirzoq Xoliyorov, Khasanturdi Kayumov, and Otabek Ismailov. "Development of a traditional transport system based on the bee colony algorithm." E3S Web of Conferences 365 (2023): 01017. http://dx.doi.org/10.1051/e3sconf/202336501017.

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At present, a significant part of optimization problems, particularly questions of combinatorial optimization, are considered NP-complete problems. When solving optimization problems, the neural network approach increases the probability of obtaining an optimal solution. The traveling salesman problem is considered a test optimization problem. This problem was solved using the Hopfield neural network. In solving optimization problems, numerous computation processes and computation time are required. To improve performance and increase the program's speed, there are cases of inappropriate purch

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Zhu, Lei. "Integrating neural network and multimedia technologies to enhance college students’ career development." Molecular & Cellular Biomechanics 22, no. 4 (2025): 857. https://doi.org/10.62617/mcb857.

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Combining neural network technologies and computational techniques, this research establishes a career development promotion system based on a multi-modal neural network. It reveals that computer simulation technology and multimedia have positive intervention effects on college students’ career decision-making behaviors, similar to how biomolecular interactions regulate biological processes. This technology ensures scientific rigor, objectivity, and authenticity. A knowledge fusion algorithm, built on attributes and rules within the Hadoop platform and MapReduce parallel computing framework, f

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YASHIN, Sergei N., Egor V. KOSHELEV, and Dmitrii A. SUKHANOV. "Evolutionary neural network modeling of import substitution in the electronics industry of regions." Finance and Credit 30, no. 4 (2024): 765–87. http://dx.doi.org/10.24891/fc.30.4.765.

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Subject. This article focuses on the issues of evolutionary neural network modeling of import substitution capabilities and opportunities. Objectives. The article aims to study evolutionary neural network modeling in terms of identifying opportunities for import substitution in the electronics industry in the regions of Russia. The article also aims to identify the regions that are leaders in terms of the possibility of import substitution, and the regions that have prospects for the future development of the electronics industry within their territory. Results. The article presents the author

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Odri, Stevan V., Dusan P. Petrovacki, and Gordana A. Krstonosic. "Evolutional development of a multilevel neural network." Neural Networks 6, no. 4 (1993): 583–95. http://dx.doi.org/10.1016/s0893-6080(05)80061-9.

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Verezubova, Natalia, Natalia Sakovich, Olga Yukovleva, Artur Chekulaev, and Irina Verezubova. "Eco-assessment of meat raw materials: A convolutional neural network approach to sustainable quality control." E3S Web of Conferences 614 (2025): 03014. https://doi.org/10.1051/e3sconf/202561403014.

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This paper explores an approach to analyzing the quality of meat raw materials using convolutional neural networks. The study focuses on the development and application of a comprehensive system that integrates deep learning capabilities with evolutionary algorithms to enhance the accuracy and efficiency of estimating parameters such as the hydrogen index of raw meat. Genetic algorithms are employed to optimize hyperparameters, which significantly improve model performance. The paper presents the results of comparisons between genetically optimized networks and non-optimized ones. Special atte

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Bury, Y. A., та D. I. Samal. "APPLICATION OF THE EVOLUTIONARY PARADIGM TO DESIGNING ARCHITEСTURE OF A NEURAL NETWORK FOR RECOGNIZING THE DISTORTED TEXT". «System analysis and applied information science», № 4 (8 лютого 2018): 45–50. http://dx.doi.org/10.21122/2309-4923-2017-4-45-50.

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The paper presents an attempt to apply of evolutionary methods to the design and training of a system for recognizing distorted text.Over the past decades, artificial neural networks are widely used in many areas of artificial intelligence, such as forecasting, optimization, data analysis, pattern recognition and decision making. Nevertheless, the traditional heuristic approaches to design of multi-layer neural networks are based on the recombination of already existing neural network architectures.This approach allows us to solve a wide range of problems, but implies compliance with specific

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Jung, Sung Young. "A Topographical Method for the Development of Neural Networks for Artificial Brain Evolution." Artificial Life 11, no. 3 (2005): 293–316. http://dx.doi.org/10.1162/1064546054407185.

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Developmental neural networks, which are constructed according to developmental rules (i.e., genes), have the potential to be differentiated into heteromorphic neural structures capable of performing various kinds of activities. The fact that the biological neural architectures are found to be highly repetitive, layered, and topographically organized has important consequences for neural development methods. The purpose of this article is to propose a neural development method that can construct topographical neural connections, that is, a topographical development method, to facilitate fast a

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Ge, Meng. "Recognition and Detection Methods of Artificial Intelligence in Computer Network Faults under the Background of Big Data." Wireless Communications and Mobile Computing 2022 (May 12, 2022): 1–13. http://dx.doi.org/10.1155/2022/5332876.

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With the widespread use of computers and the rapid development of Internet technology, computer application technology has become more and more important in people’s work and life. The article mainly studies particle swarm optimization (PSO) and radial basis neural network function (RBF). Particle swarm optimization is an evolutionary swarm intelligence algorithm, such as nonderivative node transfer function or gradient information loss. Because its principle is simple and easy to implement, it can deal with some problems that cannot be solved by traditional methods. It is widely used in neura

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Debeljak, Željko, Viktor Marohnić, Goran Srečnik, and Marica Medić-Šarić. "Novel approach to evolutionary neural network based descriptor selection and QSAR model development." Journal of Computer-Aided Molecular Design 19, no. 12 (2006): 835–55. http://dx.doi.org/10.1007/s10822-005-9022-2.

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V, Antony Asir Daniel, Basarikodi K, Suresh S, Nallasivan G, Bhuvanesh A, and Milner Paul V. "Development of Evolutionary Gravity Neocognitron Neural Network Model for Behavioral Studies in Rodents." Measurement: Sensors 33 (June 2024): 101194. http://dx.doi.org/10.1016/j.measen.2024.101194.

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Khan, Gul Muhammad, Julian F. Miller, and David M. Halliday. "Evolution of Cartesian Genetic Programs for Development of Learning Neural Architecture." Evolutionary Computation 19, no. 3 (2011): 469–523. http://dx.doi.org/10.1162/evco_a_00043.

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Although artificial neural networks have taken their inspiration from natural neurological systems, they have largely ignored the genetic basis of neural functions. Indeed, evolutionary approaches have mainly assumed that neural learning is associated with the adjustment of synaptic weights. The goal of this paper is to use evolutionary approaches to find suitable computational functions that are analogous to natural sub-components of biological neurons and demonstrate that intelligent behavior can be produced as a result of this additional biological plausibility. Our model allows neurons, de

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Azib, Lamia, Redouane Tlemsani, Khadidja Belbachir, and Asmaa Ouradighi. "Enhancing neural network applications in phonetic classification through population-based incremental learning." Brazilian Journal of Technology 7, no. 4 (2024): e75601. https://doi.org/10.38152/bjtv7n4-027.

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Significant progress has been achieved in the field of Automatic Speech Recognition (ASR) thanks to the development of powerful algorithms. Research based on simplified biological models has led to the emergence of a new class called Estimation of Distribution Algorithms (EDA), which preserves significant partial solutions. Population-Based Incremental Learning (PBIL) is a technique that combines stochastic search and optimization. It is a statistical approach with evolutionary computation similar to EDAs, aimed at adapting recognition systems based on artificial Neural Networks (NN). Our main

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Azarias, Felipe Rici, Gustavo Henrique Doná Rodrigues Almeida, Luana Félix de Melo, Rose Eli Grassi Rici, and Durvanei Augusto Maria. "The Journey of the Default Mode Network: Development, Function, and Impact on Mental Health." Biology 14, no. 4 (2025): 395. https://doi.org/10.3390/biology14040395.

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The Default Mode Network has been extensively studied in recent decades due to its central role in higher cognitive processes and its relevance for understanding mental disorders. This neural network, characterized by synchronized and coherent activity at rest, is intrinsically linked to self-reflection, mental exploration, social interaction, and emotional processing. Our understanding of the DMN extends beyond humans to non-human animals, where it has been observed in various species, highlighting its evolutionary basis and adaptive significance throughout phylogenetic history. Additionally,

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Ardesch, Dirk Jan, Lianne H. Scholtens, Longchuan Li, Todd M. Preuss, James K. Rilling, and Martijn P. van den Heuvel. "Evolutionary expansion of connectivity between multimodal association areas in the human brain compared with chimpanzees." Proceedings of the National Academy of Sciences 116, no. 14 (2019): 7101–6. http://dx.doi.org/10.1073/pnas.1818512116.

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The development of complex cognitive functions during human evolution coincides with pronounced encephalization and expansion of white matter, the brain’s infrastructure for region-to-region communication. We investigated adaptations of the human macroscale brain network by comparing human brain wiring with that of the chimpanzee, one of our closest living primate relatives. White matter connectivity networks were reconstructed using diffusion-weighted MRI in humans (n= 57) and chimpanzees (n= 20) and then analyzed using network neuroscience tools. We demonstrate higher network centrality of c

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Bian, Qing. "Social Media Marketing Optimization Method Based on Deep Neural Network and Evolutionary Algorithm." Scientific Programming 2021 (December 2, 2021): 1–11. http://dx.doi.org/10.1155/2021/5626351.

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Under the background of the vigorous development of China’s market economy, the marketing mix is constantly updated, which promotes the all-round development of various industries. Social media marketing has formed a relatively solid theoretical and practical foundation, especially with the continuous updating and iteration of Internet technology and the improvement of people’s requirements for experience, and we must find ways to optimize the methods of social media marketing. This study mainly introduces several optimization methods of social media marketing based on deep neural networks and

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Kitano, Hiroaki. "A Simple Model of Neurogenesis and Cell Differentiation Based on Evolutionary Large-Scale Chaos." Artificial Life 2, no. 1 (1994): 79–99. http://dx.doi.org/10.1162/artl.1994.2.1.79.

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This article reports on a simple neurogenesis model that is combined with evolutionary computation. Because the integration of an evolutionary process with neural networks is such an exciting field of study, with the promise of discovering new computational models and, possibly, providing novel biological insights, much research has been conducted in this area. However, only a few studies have incorporated a development stage, and none have modeled metabolism and other chemical reactions in a consistent manner. In this article, we present a simple model of neurogenesis and cell differentiation

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Zhang, Hongni, and Xiangyi Xu. "Innovative Technology Method Based on Evolutionary Game Model of Enterprise Sustainable Development and CNN–GRU." Sustainability 15, no. 5 (2023): 4058. http://dx.doi.org/10.3390/su15054058.

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Realizing the sustainable innovation growth of enterprises is one of the important research directions of management science. Traditional enterprise growth innovation methods cannot effectively estimate the emotional tendency of online public opinion (PO), and they cannot guide the effective growth of enterprises. For this reason, This paper proposes an enterprise growth innovation technology based on the evolutionary game (EG) model of sustainable development and deep learning (DL). Firstly, by obtaining the game payment matrix between network users and enterprises, combined with the deep neu

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Bronner-Fraser, Marianne. "Ancient evolutionary origin of the neural crest gene regulatory network." Developmental Biology 319, no. 2 (2008): 470. http://dx.doi.org/10.1016/j.ydbio.2008.05.474.

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Du, Depeng, and Zhendong Cui. "An Evolutionary Model for Earthquake Prediction Considering Time-Series Evolution and Feature Extraction and Its Application." Journal of Physics: Conference Series 2333, no. 1 (2022): 012013. http://dx.doi.org/10.1088/1742-6596/2333/1/012013.

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Abstract Seismic activity presents characteristics such as relatively concentrated distribution, high destructiveness, huge secondary hazards, complex change patterns, and unpredictable future activity conditions. It is of great theoretical significance and application and promotion value to research magnitude prediction for earthquake-prone areas. This study attempts to develop an earthquake prediction model based on seismic data from 1970 to 2021 and to predict earthquakes of magnitude (4.5-6). Firstly, the data are statistically analyzed using data statistics to analyze trends, identify pat

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Nidhi Mishra. "Employing Evolutionary Computing and Hybrid Artificial Neural Networks to Improve Drug Discovery." Communications on Applied Nonlinear Analysis 32, no. 2s (2024): 64–75. http://dx.doi.org/10.52783/cana.v32.2251.

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An ultimate relevant translational scientific endeavour that contributes to human vulnerability and happiness might be the creation and advancement of medications. Fast drug discovery procedures necessitate using contemporary computational approaches to tackle pharma data's complexities and high complexity. By combining Evolutionary Computing with Hybrid Artificial Neural Networks (EC-HANNs), this research introduces a novel strategy for optimizing and speeding up the drug development process. To handle various drug-target relations, predict the efficacy of compounds, and discover more accurat

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Igodan, E. C., K. C. Ukaoha, and S. O. P. Oliomogbe. "TOWARDS GLOBAL OPTIMIZATION OF NEURAL NETWORK: A COMPARATIVE ANALYSIS USING GENETIC AND WHALE OPTIMIZATION ALGORITHMS." Journal of Biomedical Engineering and Medical Imaging 8, no. 6 (2021): 89–101. http://dx.doi.org/10.14738/jbemi.86.11004.

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The intelligence and adaptability features of the neural network has made it a technique that is widely used to solve problems in diverse areas such as; detection, monitoring, prediction, diagnostics, data mining, classification, recognition, robotics, biomedicine, etc. However, determination of the optimal number of hidden layers of neural network and other parameters are still a difficult task. Usually, these parameters are decided by trial-and-error which increases the computational complexity and it is human dependent in obtaining the optimal model and parameters alike for any particular t

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Zhu, Yan. "Study on evolutionary neural networks and software development with Java." Chinese Journal of Mechanical Engineering (English Edition) 13, supp (2000): 52. http://dx.doi.org/10.3901/cjme.2000.supp.052.

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Mrówczyńska, Maria. "Elements of an algorithm for optimizing a parameter-structural neural network." Reports on Geodesy and Geoinformatics 101, no. 1 (2016): 27–35. http://dx.doi.org/10.1515/rgg-2016-0019.

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Abstract The field of processing information provided by measurement results is one of the most important components of geodetic technologies. The dynamic development of this field improves classic algorithms for numerical calculations in the aspect of analytical solutions that are difficult to achieve. Algorithms based on artificial intelligence in the form of artificial neural networks, including the topology of connections between neurons have become an important instrument connected to the problem of processing and modelling processes. This concept results from the integration of neural ne

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Nazirah Wan Md Adna, Wan n., Nofri Yenita Dahlan, and Ismail Musirin. "Development of Hybrid Artificial Neural Network for Quantifying Energy Saving using Measurement and Verification." Indonesian Journal of Electrical Engineering and Computer Science 8, no. 1 (2017): 137. http://dx.doi.org/10.11591/ijeecs.v8.i1.pp137-145.

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This paper presents a Hybrid Artificial Neural Network (HANN) for chiller system Measurement and Verification (M&amp;V) model development. In this work, hybridization of Evolutionary Programming (EP) and Artificial Neural Network (ANN) are considered in modeling the baseline electrical energy consumption for a chiller system hence quantifying saving. EP with coefficient of correlation (R) objective function is used in optimizing the neural network training process and selecting the optimal values of ANN initial weights and biases. Three inputs that are affecting energy use of the chiller s

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Dobrovska, Lyudmila, and Olena Nosovets. "Development of the classifier based on a multilayer perceptron using genetic algorithm and cart decision tree." Eastern-European Journal of Enterprise Technologies 5, no. 9 (113) (2021): 82–90. http://dx.doi.org/10.15587/1729-4061.2021.242795.

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The problem of developing universal classifiers of biomedical data, in particular those that characterize the presence of a large number of parameters, inaccuracies and uncertainty, is urgent. Many studies are aimed at developing methods for analyzing these data, among them there are methods based on a neural network (NN) in the form of a multilayer perceptron (MP) using GA. The question of the application of evolutionary algorithms (EA) for setting up and learning the neural network is considered. Theories of neural networks, genetic algorithms (GA) and decision trees intersect and penetrate

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Lyudmila, Dobrovska, and Nosovets Olena. "Development of the classifier based on a multilayer perceptron using genetic algorithm and cart decision tree." Eastern-European Journal of Enterprise Technologies 5, no. 9 (113) (2021): 82–90. https://doi.org/10.15587/1729-4061.2021.242795.

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The problem of developing universal classifiers of biomedical data, in particular those that characterize the presence of a large number of parameters, inaccuracies and uncertainty, is urgent. Many studies are aimed at developing methods for analyzing these data, among them there are methods based on a neural network (NN) in the form of a multilayer perceptron (MP) using GA. The question of the application of evolutionary algorithms (EA) for setting up and learning the neural network is considered. Theories of neural networks, genetic algorithms (GA) and decision trees intersect and penetrate

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Xiao, Maohua, Weichen Wang, Kaixin Wang, Wei Zhang, and Hengtong Zhang. "Fault Diagnosis of High-Power Tractor Engine Based on Competitive Multiswarm Cooperative Particle Swarm Optimizer Algorithm." Shock and Vibration 2020 (August 3, 2020): 1–13. http://dx.doi.org/10.1155/2020/8829257.

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With the rapid development of high-power tractor, the fault diagnosis of high-power tractor has become more and more important for ensuring the operating safety and efficiency. PSO is an iterative optimization evolutionary algorithm, which can iterate through different particles to find the optimal solution. However, there is only one population in the standard PSO algorithm, and the information exchange between the populations is relatively single, which can easily lead to the stagnation of the development of the population. In this paper, due to high-power tractor diesel engine fault complex

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Pásztor, Ádám, and Richárd Ürmös. "PROBLEM PREDICTION DURING TRIP IN AND TRIP OUT PROCEDURES WITH ARTIFICIAL NEURAL NETWORKS." Acta Tecnología 7, no. 3 (2021): 71–77. http://dx.doi.org/10.22306/atec.v7i3.111.

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In recent times, the adaptation of artificial intelligence (AI) technologies has been spread in the petroleum industry. Such methods as Artificial Neural Networks (ANN), Fuzzy Logic, or Evolutionary Computing have the potential to improve the currently applied methods in every sector of the industry. They provide an advanced encroachment of the complex physics of downhole parameters, which directly add to their modeling ability compared to the traditional empirical and analytical methods. In this study, the development of a feed-forward neural network is presented. The purpose of the developme

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Zhu, Yourun, Senlin Ren, and Xiaolong Li. "Novel High-Efficiency Nanocomposite Gate Design of Quantum-Dot Cellular Automata Based on Deep Learning." Computational Intelligence and Neuroscience 2022 (June 8, 2022): 1–11. http://dx.doi.org/10.1155/2022/9596165.

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With the development of science and technology, the feature size of CMOS devices will always shrink to the limit. Therefore, some new nanodevices will eventually become substitutes for microelectronic devices. A new electronic revolution will break out. Nanoscience and technology is the high-tech frontier technology of the century and one of the main contents of scientific development in the new era. Its development will have a profound impact on other disciplines, industries, and society. Nanoelectronics is an important part of the discipline of nanoscience and technology, which represents th

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Woodford, Grant W., Christiaan J. Pretorius, and Mathys C. du Plessis. "Concurrent controller and Simulator Neural Network development for a differentially-steered robot in Evolutionary Robotics." Robotics and Autonomous Systems 76 (February 2016): 80–92. http://dx.doi.org/10.1016/j.robot.2015.10.011.

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Woodford, Grant W., Mathys C. du Plessis, and Christiaan J. Pretorius. "Concurrent controller and Simulator Neural Network development for a snake-like robot in Evolutionary Robotics." Robotics and Autonomous Systems 88 (February 2017): 37–50. http://dx.doi.org/10.1016/j.robot.2016.11.018.

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Naji, Dr Loma Shafiq MOHD. "The Impact of Artificial Intelligence Applications on the Digital Marketing Development on the Telecommunications Companies in Jordan." Webology 19, no. 1 (2022): 854–66. http://dx.doi.org/10.14704/web/v19i1/web19059.

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This research mainly aims to analyze the main effect of the Artificial Intelligence applications; neural networks, expert systems and evolutionary computation, on digital marketing development. Specifically, the aim is to evaluate the new artificial intelligence applications in Jordan, especially those which can affect digital marketing development. Besides, the researcher designed questionnaires which were given on the basis of a technique of simple sampling. Moreover, they were applied on the Jordanian telecommunications companies. A number of 375 questionnaires were distributed. Furthermore

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Bukhtoyarov, Vladimir V., Vadim S. Tynchenko, Vladimir A. Nelyub, Igor S. Masich, Aleksey S. Borodulin, and Andrei P. Gantimurov. "A Study on a Probabilistic Method for Designing Artificial Neural Networks for the Formation of Intelligent Technology Assemblies with High Variability." Electronics 12, no. 1 (2023): 215. http://dx.doi.org/10.3390/electronics12010215.

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Currently, ensemble approaches based, among other things, on the use of non-network models are powerful tools for solving data analysis problems in various practical applications. An important problem in the formation of ensembles of models is ensuring the synergy of solutions by using the properties of a variety of basic individual solutions; therefore, the problem of developing an approach that ensures the maintenance of diversity in a preliminary pool of models for an ensemble is relevant for development and research. This article is devoted to the study of the possibility of using a method

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