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Journal articles on the topic 'Wide neural network'

Author: Grafiati

Published: 2 June 2025

Last updated: 31 July 2025

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1

Casper, Stephen, Xavier Boix, Vanessa D'Amario, et al. "Frivolous Units: Wider Networks Are Not Really That Wide." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 8 (2021): 6921–29. http://dx.doi.org/10.1609/aaai.v35i8.16853.

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A remarkable characteristic of overparameterized deep neural networks (DNNs) is that their accuracy does not degrade when the network width is increased. Recent evidence suggests that developing compressible representations allows the complexity of large networks to be adjusted for the learning task at hand. However, these representations are poorly understood. A promising strand of research inspired from biology involves studying representations at the unit level as it offers a more granular interpretation of the neural mechanisms. In order to better understand what facilitates increases in w

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Jais, Imran Khan Mohd, Amelia Ritahani Ismail, and Syed Qamrun Nisa. "Adam Optimization Algorithm for Wide and Deep Neural Network." Knowledge Engineering and Data Science 2, no. 1 (2019): 41. http://dx.doi.org/10.17977/um018v2i12019p41-46.

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The objective of this research is to evaluate the effects of Adam when used together with a wide and deep neural network. The dataset used was a diagnostic breast cancer dataset taken from UCI Machine Learning. Then, the dataset was fed into a conventional neural network for a benchmark test. Afterwards, the dataset was fed into the wide and deep neural network with and without Adam. It was found that there were improvements in the result of the wide and deep network with Adam. In conclusion, Adam is able to improve the performance of a wide and deep neural network.

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Yakovlev, A. S., E. V. Shayakberov, and V. M. Giniyatullin. "Variability of the Wide Learning Neural Network Learning Algorithm." Programmnaya Ingeneria 16, no. 3 (2025): 134–42. https://doi.org/10.17587/prin.16.134-142.

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There are a large number of datasets on the publicly available kaggle resource, from which five datasets and their corresponding structures of artificial neural networks were selected. After training neural networks, the work of the neurons of the first hidden layers was reproduced in spreadsheets. A significant number of useless neurons were found (20—60 %). A neuron is called useless if the scalar products of all instances of the training sample are less than zero. The relu activation function converts negative values to zero, therefore, such a neuron does not contribute to the work of the n

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Liu, Hengyan, Limen Zhang, Wenjun Yan, and Yajie Li. "Last Subcode Neural Network Assisted Polar Decoding." Journal of Physics: Conference Series 2356, no. 1 (2022): 012040. http://dx.doi.org/10.1088/1742-6596/2356/1/012040.

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A fast successive-cancellation decoding scheme based on neural network (NNFSCD) called last subcode neural network assisted decoding (LSNNACD) scheme for polar codes is proposed to improve decoding performance. First, a neural network node (NNN) is proposed and a neutral network model is trained to decode it. Then, the node recognizer is proposed to recognize a polar code with a NNN. Simulations with polar codes and binary phase shift keying (BPSK) shows good performance and low latency in the additive white Gaussian noise (AWGN) channel. Moreover, the proposed scheme has a wide application ra

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Eetemadi, Ameen, and Ilias Tagkopoulos. "Genetic Neural Networks: an artificial neural network architecture for capturing gene expression relationships." Bioinformatics 35, no. 13 (2018): 2226–34. http://dx.doi.org/10.1093/bioinformatics/bty945.

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Abstract Motivation Gene expression prediction is one of the grand challenges in computational biology. The availability of transcriptomics data combined with recent advances in artificial neural networks provide an unprecedented opportunity to create predictive models of gene expression with far reaching applications. Results We present the Genetic Neural Network (GNN), an artificial neural network for predicting genome-wide gene expression given gene knockouts and master regulator perturbations. In its core, the GNN maps existing gene regulatory information in its architecture and it uses ce

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Yang, Jia Xuan. "Ship Transportation Forecasting Based on Extension Neural Network." Applied Mechanics and Materials 241-244 (December 2012): 2055–58. http://dx.doi.org/10.4028/www.scientific.net/amm.241-244.2055.

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Over the last decade, neural networks have found application for solving a wide range of areas from business, commerce, data mining and service systems. Hence, this paper constructs a new model based extension theory and neural network to forecast the ship transportation. The new neural network is a combination of extension theory and neural network. It uses an extension distance to measure the similarity between data and cluster center, and seek out the useless data, then to use neural network to forecast. When presenting a test example of prediction of ship transportation, the results verifi

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Xi, Jiangbo, Ming Cong, Okan K. Ersoy, et al. "Dynamic Wide and Deep Neural Network for Hyperspectral Image Classification." Remote Sensing 13, no. 13 (2021): 2575. http://dx.doi.org/10.3390/rs13132575.

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Recently, deep learning has been successfully and widely used in hyperspectral image (HSI) classification. Considering the difficulty of acquiring HSIs, there are usually a small number of pixels used as the training instances. Therefore, it is hard to fully use the advantages of deep learning networks; for example, the very deep layers with a large number of parameters lead to overfitting. This paper proposed a dynamic wide and deep neural network (DWDNN) for HSI classification, which includes multiple efficient wide sliding window and subsampling (EWSWS) networks and can grow dynamically acc

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Pan, Yumin. "Different Types of Neural Networks and Applications: Evidence from Feedforward, Convolutional and Recurrent Neural Networks." Highlights in Science, Engineering and Technology 85 (March 13, 2024): 247–55. http://dx.doi.org/10.54097/6rn1wd81.

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Neural networks have achieved great process in the 90 years since they were officially introduced in 1943. Because of its wide application and huge research and development potential, this technology attracts more and more scientific and technological workers to the research of neural networks. Neural network technology is an essential component of AI development, and it is a significant indicator of a country's overall strength. In this paper, this study will demonstrate Feedforward Neural Network, Convolution Neural Network and Recurrent Neural networks and evaluate them through datasets fro

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Liang, Yunyi, Zhiyong Cui, Yu Tian, Huimiao Chen, and Yinhai Wang. "A Deep Generative Adversarial Architecture for Network-Wide Spatial-Temporal Traffic-State Estimation." Transportation Research Record: Journal of the Transportation Research Board 2672, no. 45 (2018): 87–105. http://dx.doi.org/10.1177/0361198118798737.

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This study proposes a deep generative adversarial architecture (GAA) for network-wide spatial-temporal traffic-state estimation. The GAA is able to combine traffic-flow theory with neural networks and thus improve the accuracy of traffic-state estimation. It consists of two Long Short-Term Memory Neural Networks (LSTM NNs) which capture correlation in time and space among traffic flow and traffic density. One of the LSTM NNs, called a discriminative network, aims to maximize the probability of assigning correct labels to both true traffic-state matrices (i.e., traffic flow and traffic density

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Warstadt, Alex, Amanpreet Singh, and Samuel R. Bowman. "Neural Network Acceptability Judgments." Transactions of the Association for Computational Linguistics 7 (November 2019): 625–41. http://dx.doi.org/10.1162/tacl_a_00290.

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This paper investigates the ability of artificial neural networks to judge the grammatical acceptability of a sentence, with the goal of testing their linguistic competence. We introduce the Corpus of Linguistic Acceptability (CoLA), a set of 10,657 English sentences labeled as grammatical or ungrammatical from published linguistics literature. As baselines, we train several recurrent neural network models on acceptability classification, and find that our models outperform unsupervised models by Lau et al. (2016) on CoLA. Error-analysis on specific grammatical phenomena reveals that both Lau

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Hujakulov, Hamidullo. "ANALYSIS OF NEURAL NETWORK TRAINING TECHNOLOGIES." Eurasian Journal of Academic Research 2, no. 2 (2022): 197–200. https://doi.org/10.5281/zenodo.6139277.

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Gong, Shuicheng, Fuhao Zhang, Gang Xun, and Xuesong Li. "An Improved Convolutional Neural Network for Particle Image Velocimetry." Journal of Physics: Conference Series 2645, no. 1 (2023): 012013. http://dx.doi.org/10.1088/1742-6596/2645/1/012013.

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Abstract With the wide application of Particle Image Velocimetry (PIV) technology in various engineering and research fields, the requirements for the accuracy, computational efficiency, and robustness of PIV algorithms are increasing. Although traditional algorithms have wide applicability, they suffer from low accuracy, large computational cost, and poor robustness. Recently, deep learning algorithms have provided new solutions, especially, convolutional neural networks with different structures, which have achieved good performance on synthetic PIV datasets. This paper proposes a structural

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Jouni, Hassan. "Wide Range Analog CMOS Multiplier for Neural Network Application." International Journal of Digital Information and Wireless Communications 8, no. 2 (2018): 106–9. http://dx.doi.org/10.17781/p002415.

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Tsoulos, Ioannis G., Alexandros Tzallas, and Evangelos Karvounis. "A Two-Phase Evolutionary Method to Train RBF Networks." Applied Sciences 12, no. 5 (2022): 2439. http://dx.doi.org/10.3390/app12052439.

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This article proposes a two-phase hybrid method to train RBF neural networks for classification and regression problems. During the first phase, a range for the critical parameters of the RBF network is estimated and in the second phase a genetic algorithm is incorporated to locate the best RBF neural network for the underlying problem. The method is compared against other training methods of RBF neural networks on a wide series of classification and regression problems from the relevant literature and the results are reported.

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Ilina, Olga, Vadim Ziyadinov, Nikolay Klenov, and Maxim Tereshonok. "A Survey on Symmetrical Neural Network Architectures and Applications." Symmetry 14, no. 7 (2022): 1391. http://dx.doi.org/10.3390/sym14071391.

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A number of modern techniques for neural network training and recognition enhancement are based on their structures’ symmetry. Such approaches demonstrate impressive results, both for recognition practice, and for understanding of data transformation processes in various feature spaces. This survey examines symmetrical neural network architectures—Siamese and triplet. Among a wide range of tasks having various mathematical formulation areas, especially effective applications of symmetrical neural network architectures are revealed. We systematize and compare different architectures of symmetri

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Fang, Yu, Zheng Wei Chang, Hao Wu, and Xian Feng Tang. "Identification the Faulty Components in Power Networks Based on Wide Area Information and RBF Neural Network." Applied Mechanics and Materials 568-570 (June 2014): 842–47. http://dx.doi.org/10.4028/www.scientific.net/amm.568-570.842.

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Using the wide area information of the IED, the identification faulty components network is constructed based on RBF neural network. Using the state information collected by line IED as the input vector, training samples matrix of identification faulty components network is established to train RBF neural network of faulty components identification, and to test the recognition network using the sample matrix under random failure, and then the faulty line IED can be identified, the faulty components can be determined. Experiments show that the new algorithm based on RBF has higher accuracy rate

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Ramachandra, H. V., Pundalik Chavan, S. Supreeth, et al. "Secured Wireless Network Based on a Novel Dual Integrated Neural Network Architecture." Journal of Electrical and Computer Engineering 2023 (September 28, 2023): 1–11. http://dx.doi.org/10.1155/2023/9390660.

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The development of the fifth generation (5G) and sixth generation (6G) wireless networks has gained wide spread importance in all aspects of life through the network due to their significantly higher speeds, extraordinarily low latency, and ubiquitous availability. Owing to the importance of their users, components, and services to our everyday lives, the network must secure all of these. With such a wide range of devices and service types being present in the 5G ecosystem, security issues are now much more prevalent. Security solutions, are not implemented, must already be envisioned in order

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Burakov, M. V., V. F. Shishlakov, and A. S. Konovalov. "ADAPTIVE NEURAL NETWORK PID CONTROLLER." Issues of radio electronics, no. 10 (October 20, 2018): 86–92. http://dx.doi.org/10.21778/2218-5453-2018-10-86-92.

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The problem of constructing an adaptive PID controller based on the Hopfield neural network for a linear dynamic plant of the second order is considered. A description of the plant in the form of a discrete transfer function is used, the coefficients of which are determined with the help of a neural network that minimizes the discrepancy between the outputs of the plant and the model. The neural network processes the current and delayed input and output signals of the plant, forming an output for estimating the coefficients of the model. Another neural network determines the PID regulator coef

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

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

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Gaura, E. I., R. J. Rider, and N. Steele. "Closed-loop neural network controlled accelerometer." Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering 214, no. 2 (2000): 129–38. http://dx.doi.org/10.1243/0959651001540852.

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The purpose of this paper is to present aspects of an integrated micromachined sensor-neural network transducer development. Micromachined sensors exhibit particular problems such as non-linear characteristics, manufacturing tolerances and the need for complex electronic circuitry. The novel transducer design described here, based on a mathematical model of the micromachined sensor, is aimed at improving in-service performance and facilitating design and manufacture over conventional transducers. The proposed closed-loop transducer structure incorporates two modular artificial neural networks:

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Li, Matthew, Laurent Demanet, and Leonardo Zepeda-Núñez. "Wide-Band Butterfly Network: Stable and Efficient Inversion Via Multi-Frequency Neural Networks." Multiscale Modeling & Simulation 20, no. 4 (2022): 1191–227. http://dx.doi.org/10.1137/20m1383276.

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Denby, Bruce. "The Use of Neural Networks in High-Energy Physics." Neural Computation 5, no. 4 (1993): 505–49. http://dx.doi.org/10.1162/neco.1993.5.4.505.

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In the past few years a wide variety of applications of neural networks to pattern recognition in experimental high-energy physics has appeared. The neural network solutions are in general of high quality, and, in a number of cases, are superior to those obtained using "traditional'' methods. But neural networks are of particular interest in high-energy physics for another reason as well: much of the pattern recognition must be performed online, that is, in a few microseconds or less. The inherent parallelism of neural network algorithms, and the ability to implement them as very fast hardware

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Awwad, Ameen, Ghaleb A. Husseini, and Lutfi Albasha. "AI-Aided Robotic Wide-Range Water Quality Monitoring System." Sustainability 16, no. 21 (2024): 9499. http://dx.doi.org/10.3390/su16219499.

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Waterborne illnesses lead to millions of fatalities worldwide each year, particularly in developing nations. In this paper, we introduce a comprehensive system designed for the autonomous early detection of viral outbreaks transmitted through water to ensure sustainable access to healthy water resources, especially in remote areas. The system utilizes an autonomous water quality monitoring setup consisting of an airborne water sample collector, an autonomous sample processor, and an artificial intelligence-aided microscopic detector for risk assessment. The proposed system replaces the time-co

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Moon, Sunghwan. "ReLU Network with Bounded Width Is a Universal Approximator in View of an Approximate Identity." Applied Sciences 11, no. 1 (2021): 427. http://dx.doi.org/10.3390/app11010427.

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Deep neural networks have shown very successful performance in a wide range of tasks, but a theory of why they work so well is in the early stage. Recently, the expressive power of neural networks, important for understanding deep learning, has received considerable attention. Classic results, provided by Cybenko, Barron, etc., state that a network with a single hidden layer and suitable activation functions is a universal approximator. A few years ago, one started to study how width affects the expressiveness of neural networks, i.e., a universal approximation theorem for a deep neural networ

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Chandwani, Vinay, Vinay Agrawal, and Ravindra Nagar. "Modeling Slump of Ready Mix Concrete Using Genetically Evolved Artificial Neural Networks." Advances in Artificial Neural Systems 2014 (November 11, 2014): 1–9. http://dx.doi.org/10.1155/2014/629137.

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Artificial neural networks (ANNs) have been the preferred choice for modeling the complex and nonlinear material behavior where conventional mathematical approaches do not yield the desired accuracy and predictability. Despite their popularity as a universal function approximator and wide range of applications, no specific rules for deciding the architecture of neural networks catering to a specific modeling task have been formulated. The research paper presents a methodology for automated design of neural network architecture, replacing the conventional trial and error technique of finding th

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Jiang, Bing. "Rate of approximaton by some neural network operators." AIMS Mathematics 9, no. 11 (2024): 31679–95. http://dx.doi.org/10.3934/math.20241523.

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<p>First, we construct a new type of feedforward neural network operators on finite intervals, and give the pointwise and global estimates of approximation by the new operators. The new operator can approximate the continuous functions with a very good rate, which can not be obtained by polynomial approximation. Second, we construct a new type of feedforward neural network operator on infinite intervals and estimate the rate of approximation by the new operators. Finally, we investigate the weighted approximation properties of the new operators on infinite intervals and show that our new

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Jiang, Yiyue, Andrius Vaicaitis, John Dooley, and Miriam Leeser. "Efficient Neural Networks on the Edge with FPGAs by Optimizing an Adaptive Activation Function." Sensors 24, no. 6 (2024): 1829. http://dx.doi.org/10.3390/s24061829.

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The implementation of neural networks (NNs) on edge devices enables local processing of wireless data, but faces challenges such as high computational complexity and memory requirements when deep neural networks (DNNs) are used. Shallow neural networks customized for specific problems are more efficient, requiring fewer resources and resulting in a lower latency solution. An additional benefit of the smaller network size is that it is suitable for real-time processing on edge devices. The main concern with shallow neural networks is their accuracy performance compared to DNNs. In this paper, w

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Andreev, Vyacheslav V., Leonid A. Slavutskii, and Elena V. Slavutskaya. "FEED FORWARD NEURAL NET SIGNAL PROCESSING: APPROXIMATION AND DECISION MAKING." Vestnik Chuvashskogo universiteta, no. 1 (March 30, 2022): 14–22. http://dx.doi.org/10.47026/1810-1909-2022-1-14-22.

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Feed forward artificial neural networks (multilayer perceptron) allow solving a wide range of regression (approximation) and classification (data splitting into subsets) problems. The corresponding algorithms are applied in electrical and power engineering. The peculiarity of such an artificial neural network is that the training sample can be submitted to the input in an arbitrary sequence. Therefore, the signals themselves during artificial neural network training should be formed taking into account their time form. The paper proposes the use of artificial neural network in a sliding time w

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Sun, Yike. "Research on Application of artificial Neural Network in econometrics." Advances in Engineering Technology Research 3, no. 1 (2022): 309. http://dx.doi.org/10.56028/aetr.3.1.309.

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As a fringe subject of artificial intelligence and systems engineering, artificial neuron network can not only discover more valuable data information, but also accelerate the calculation speed of data information in econometrics. With the rapid development of econometrics, the artificial neural network method has received wide attention of scholars research at home and abroad, compared with the traditional econometric method, artificial neural network algorithm with adjustable parameters and highly nonlinear simulation operation ability, self-learning and self-organization can function in dea

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Smith, Lauren C., and Adam Kimbrough. "Leveraging Neural Networks in Preclinical Alcohol Research." Brain Sciences 10, no. 9 (2020): 578. http://dx.doi.org/10.3390/brainsci10090578.

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Alcohol use disorder is a pervasive healthcare issue with significant socioeconomic consequences. There is a plethora of neural imaging techniques available at the clinical and preclinical level, including magnetic resonance imaging and three-dimensional (3D) tissue imaging techniques. Network-based approaches can be applied to imaging data to create neural networks that model the functional and structural connectivity of the brain. These networks can be used to changes to brain-wide neural signaling caused by brain states associated with alcohol use. Neural networks can be further used to ide

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Gafarov, Fail, Andrey Berdnikov, and Pavel Ustin. "Online social network user performance prediction by graph neural networks." International Journal of Advances in Intelligent Informatics 8, no. 3 (2022): 285. http://dx.doi.org/10.26555/ijain.v8i3.859.

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Online social networks provide rich information that characterizes the user’s personality, his interests, hobbies, and reflects his current state. Users of social networks publish photos, posts, videos, audio, etc. every day. Online social networks (OSN) open up a wide range of research opportunities for scientists. Much research conducted in recent years using graph neural networks (GNN) has shown their advantages over conventional deep learning. In particular, the use of graph neural networks for online social network analysis seems to be the most suitable. In this article we studied the use

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Zolotareva, T. A. "Artificial intelligence in a rugged design based on multi-bit rules." Journal of Physics: Conference Series 2094, no. 3 (2021): 032009. http://dx.doi.org/10.1088/1742-6596/2094/3/032009.

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Abstract In this paper, the technologies for training large artificial neural networks are considered: the first technology is based on the use of multilayer “deep” neural networks; the second technology involves the use of a “wide” single-layer network of neurons giving 256 private binary solutions. A list of attacks aimed at the simplest one-bit neural network decision rule is given: knowledge extraction attacks and software data modification attacks; their content is considered. All single-bit decision rules are unsafe for applying. It is necessary to use other decision rules. The security

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Sánchez-Hevia, Héctor A., Roberto Gil-Pita, Manuel Utrilla-Manso, and Manuel Rosa-Zurera. "Age group classification and gender recognition from speech with temporal convolutional neural networks." Multimedia Tools and Applications 81, no. 3 (2022): 3535–52. http://dx.doi.org/10.1007/s11042-021-11614-4.

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AbstractThis paper analyses the performance of different types of Deep Neural Networks to jointly estimate age and identify gender from speech, to be applied in Interactive Voice Response systems available in call centres. Deep Neural Networks are used, because they have recently demonstrated discriminative and representation capabilities in a wide range of applications, including speech processing problems based on feature extraction and selection. Networks with different sizes are analysed to obtain information on how performance depends on the network architecture and the number of free par

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Caswell, Joseph M. "Combination of Wavelet Analysis and Artificial Neural Networks Applied to Forecast of Daily Cosmic Ray Impulses." International Letters of Chemistry, Physics and Astronomy 34 (May 2014): 55–63. http://dx.doi.org/10.18052/www.scipress.com/ilcpa.34.55.

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Artificial neural network modelling has proven incredibly effective in an impressively wide range of scientific disciplines. The combination of these various methods with wavelet decomposition signal processing has similarly proven to be a powerful development for statistical forecasting of a number of environmental processes. Space weather modelling and prediction has often been applied to forecasting of solar activity and that of the planetary magnetic field. However, prediction of cosmic ray impulses has seen little development in the context of neural network modelling. In the present stud

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Caswell, Joseph M. "Combination of Wavelet Analysis and Artificial Neural Networks Applied to Forecast of Daily Cosmic Ray Impulses." International Letters of Chemistry, Physics and Astronomy 34 (May 30, 2014): 55–63. http://dx.doi.org/10.56431/p-6mpq01.

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Artificial neural network modelling has proven incredibly effective in an impressively wide range of scientific disciplines. The combination of these various methods with wavelet decomposition signal processing has similarly proven to be a powerful development for statistical forecasting of a number of environmental processes. Space weather modelling and prediction has often been applied to forecasting of solar activity and that of the planetary magnetic field. However, prediction of cosmic ray impulses has seen little development in the context of neural network modelling. In the present stud

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Cantareira, Gabriel D., Elham Etemad, and Fernando V. Paulovich. "Exploring Neural Network Hidden Layer Activity Using Vector Fields." Information 11, no. 9 (2020): 426. http://dx.doi.org/10.3390/info11090426.

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Deep Neural Networks are known for impressive results in a wide range of applications, being responsible for many advances in technology over the past few years. However, debugging and understanding neural networks models’ inner workings is a complex task, as there are several parameters and variables involved in every decision. Multidimensional projection techniques have been successfully adopted to display neural network hidden layer outputs in an explainable manner, but comparing different outputs often means overlapping projections or observing them side-by-side, presenting hurdles for use

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Yu, Lu, Yuliang Lu, Yi Shen, Jun Zhao, and Jiazhen Zhao. "PBDiff: Neural network based program-wide diffing method for binaries." Mathematical Biosciences and Engineering 19, no. 3 (2022): 2774–99. http://dx.doi.org/10.3934/mbe.2022127.

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<abstract><p>Program-wide binary code diffing is widely used in the binary analysis field, such as vulnerability detection. Mature tools, including BinDiff and TurboDiff, make program-wide diffing using rigorous comparison basis that varies across versions, optimization levels and architectures, leading to a relatively inaccurate comparison result. In this paper, we propose a program-wide binary diffing method based on neural network model that can make diffing across versions, optimization levels and architectures. We analyze the target comparison files in four different granulari

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Pan, Borui. "Predicting Heart Disease Based on Wide and Deep Neural Network." Applied and Computational Engineering 2, no. 1 (2023): 174–79. http://dx.doi.org/10.54254/2755-2721/2/20220665.

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Nowadays, more and more people suffer from heart disease because of stressful life, irregular diet, lack of exercise and other reasons. The population affected by heart disease is also younger than ever. If heart disease can be diagnosed as early as possible, it will be of great help in the treatment of heart disease. Thus, this paper proposes models to predict heart disease based on wide and deep neural network, and the result shows that the current work has maintain good performance. Analysis is also provided in this paper to state factors that can affect performance.

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Zavatone-Veth, Jacob A., Abdulkadir Canatar, Benjamin S. Ruben, and Cengiz Pehlevan. "Asymptotics of representation learning in finite Bayesian neural networks*." Journal of Statistical Mechanics: Theory and Experiment 2022, no. 11 (2022): 114008. http://dx.doi.org/10.1088/1742-5468/ac98a6.

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Abstract Recent works have suggested that finite Bayesian neural networks may sometimes outperform their infinite cousins because finite networks can flexibly adapt their internal representations. However, our theoretical understanding of how the learned hidden layer representations of finite networks differ from the fixed representations of infinite networks remains incomplete. Perturbative finite-width corrections to the network prior and posterior have been studied, but the asymptotics of learned features have not been fully characterized. Here, we argue that the leading finite-width correc

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Zhaxybayev, D. O. "The Application of Neural Networks in the Construction of a Program for the Markup of Text." Programmnaya Ingeneria 13, no. 3 (2022): 142–47. http://dx.doi.org/10.17587/prin.13.142-147.

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This paper explores the use and applications of neural networks in the construction of a text markup program. This research paper describes various tasks that require textual data analysis and discusses the problems, issues, and solutions that accompany them. Interest in neural networks has increased in recent years, and they are finding applications in a wide variety of fields, such as business, medicine, engineering, geology, and physics. Neural networks have made great strides in forecasting, planning, and management. There are several reasons for this situation. Neural networks are very p

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Naidu, D. J. Samatha, and T. Mahammad Rafi. "HANDWRITTEN CHARACTER RECOGNITION USING CONVOLUTIONAL NEURAL NETWORKS." International Journal of Computer Science and Mobile Computing 10, no. 8 (2021): 41–45. http://dx.doi.org/10.47760/ijcsmc.2021.v10i08.007.

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Handwritten character Recognition is one of the active area of research where deep neural networks are been utilized. Handwritten character Recognition is a challenging task because of many reasons. The Primary reason is different people have different styles of handwriting. The secondary reason is there are lot of characters like capital letters, small letters & special symbols. In existing were immense research going on the field of handwritten character recognition system has been design using fuzzy logic and created on VLSI(very large scale integrated)structure. To Recognize the tamil

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Zhang, Liang, Jingqun Li, Bin Zhou, and Yan Jia. "Rumor Detection Based on SAGNN: Simplified Aggregation Graph Neural Networks." Machine Learning and Knowledge Extraction 3, no. 1 (2021): 84–94. http://dx.doi.org/10.3390/make3010005.

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Identifying fake news on media has been an important issue. This is especially true considering the wide spread of rumors on popular social networks such as Twitter. Various kinds of techniques have been proposed for automatic rumor detection. In this work, we study the application of graph neural networks for rumor classification at a lower level, instead of applying existing neural network architectures to detect rumors. The responses to true rumors and false rumors display distinct characteristics. This suggests that it is essential to capture such interactions in an effective manner for a

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Cheng, Zhi, Yanxi Li, Minjing Dong, Xiu Su, Shan You, and Chang Xu. "Neural Architecture Search for Wide Spectrum Adversarial Robustness." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 1 (2023): 442–51. http://dx.doi.org/10.1609/aaai.v37i1.25118.

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One major limitation of CNNs is that they are vulnerable to adversarial attacks. Currently, adversarial robustness in neural networks is commonly optimized with respect to a small pre-selected adversarial noise strength, causing them to have potentially limited performance when under attack by larger adversarial noises in real-world scenarios. In this research, we aim to find Neural Architectures that have improved robustness on a wide range of adversarial noise strengths through Neural Architecture Search. In detail, we propose a lightweight Adversarial Noise Estimator to reduce the high cost

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Dominguez, D., K. Koroutchev, E. Serrano, and F. B. Rodríguez. "Information and Topology in Attractor Neural Networks." Neural Computation 19, no. 4 (2007): 956–73. http://dx.doi.org/10.1162/neco.2007.19.4.956.

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A wide range of networks, including those with small-world topology, can be modeled by the connectivity ratio and randomness of the links. Both learning and attractor abilities of a neural network can be measured by the mutual information (MI) as a function of the load and the overlap between patterns and retrieval states. In this letter, we use MI to search for the optimal topology with regard to the storage and attractor properties of the network in an Amari-Hopfield model. We find that while an optimal storage implies an extremely diluted topology, a large basin of attraction leads to moder

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Al-Tabtabai, H., N. Kartam, I. Flood, and A. P. Alex. "Construction project control using artificial neural networks." Artificial Intelligence for Engineering Design, Analysis and Manufacturing 11, no. 1 (1997): 45–57. http://dx.doi.org/10.1017/s0890060400001839.

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AbstractArtificial neural networks are finding wide application to a variety of problems in civil engineering. This paper describes how artificial neural networks can be applied in the area of construction project control. A project control system capable of predicting and monitoring project performance (e.g., cost variance and schedule variance) based on observations made from the project environment is described. This project control system has five neural network modules that allow a project manager to automatically generate revised project plans at regular intervals during the progress of

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Kolar, Davor, Dragutin Lisjak, Michał Pająk, and Danijel Pavković. "Fault Diagnosis of Rotary Machines Using Deep Convolutional Neural Network with Wide Three Axis Vibration Signal Input." Sensors 20, no. 14 (2020): 4017. http://dx.doi.org/10.3390/s20144017.

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Fault diagnosis is considered as an essential task in rotary machinery as possibility of an early detection and diagnosis of the faulty condition can save both time and money. This work presents developed and novel technique for deep-learning-based data-driven fault diagnosis for rotary machinery. The proposed technique input raw three axes accelerometer signal as high definition 1D image into deep learning layers which automatically extract signal features, enabling high classification accuracy. Unlike the researches carried out by other researchers, accelerometer data matrix with dimensions

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Alfonso, Gerardo, and Daniel R. Ramirez. "Neural Networks in Narrow Stock Markets." Symmetry 12, no. 8 (2020): 1272. https://doi.org/10.3390/sym12081272.

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Narrow markets are typically considered those that due to limited liquidity or peculiarities in its investor base, such as a particularly high concentration of retail investors, make the stock market less e cient and arguably less predictable. We show in this article that neural networks, applied to narrow markets, can provide relatively accurate forecasts in narrow markets. However, practical considerations such as potentially suboptimal trading infrastructure and stale prices should be taken into considerations. There is ample existing literature describing the use of neural network as a for

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Zhang, Huan, Pengchuan Zhang, and Cho-Jui Hsieh. "RecurJac: An Efficient Recursive Algorithm for Bounding Jacobian Matrix of Neural Networks and Its Applications." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 5757–64. http://dx.doi.org/10.1609/aaai.v33i01.33015757.

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The Jacobian matrix (or the gradient for single-output networks) is directly related to many important properties of neural networks, such as the function landscape, stationary points, (local) Lipschitz constants and robustness to adversarial attacks. In this paper, we propose a recursive algorithm, RecurJac, to compute both upper and lower bounds for each element in the Jacobian matrix of a neural network with respect to network’s input, and the network can contain a wide range of activation functions. As a byproduct, we can efficiently obtain a (local) Lipschitz constant, which plays a cruci

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Xu, Kun, Shunming Li, Jinrui Wang, Zenghui An, and Yu Xin. "A novel adaptive and fast deep convolutional neural network for bearing fault diagnosis under different working conditions." Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering 234, no. 4 (2019): 1167–82. http://dx.doi.org/10.1177/0954407019861028.

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Deep learning method is gradually applied in the field of mechanical equipment fault diagnosis because it can learn complex and useful features automatically from the vibration signals. Among the many intelligent diagnostic models, convolutional neural network has been gradually applied to intelligent fault diagnosis of bearings due to its advantages of local connection and weight sharing. However, there are still some drawbacks. (1) The training process of convolutional neural network is slow and unstable. It has more training parameters. (2) It cannot perform well under different working con

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Hwang, Wonjun, Yoora Kim, and Kyunghan Lee. "Augmenting Epidemic Models with Graph Neural Networks." ACM SIGMETRICS Performance Evaluation Review 50, no. 4 (2023): 11–13. http://dx.doi.org/10.1145/3595244.3595249.

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Conventional epidemic models are limited in their ability to capture the dynamics of real world epidemics in a sense that they either place restrictions on the models such as their topology and contact process for mathematical tractability or focus only on the average global behavior, which lacks details for further analysis. We propose a novel modeling approach that augments the conventional epidemic models using Graph Neural Networks to improve their expressive power while preserving useful mathematical structures. Simulation results show that our proposed model can predict spread times in b

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