Dima Bykhovsky - IEEE Xplore Author Profile

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Author details

Dima Bykhovsky

Also published under: D. Bykhovsky

Publications

27

Citations

422

Publications by Year

20112025

Co-Authors:

S. ArnonMerav ArogetiIsaac AugustMishel Shagas AzimovAlmog Azulay

Show All Co-Authors (31)

Dima Bykhovsky

Also published under: D. Bykhovsky

Affiliation

Department of Electrical

Electronics Engineering

Shamoon College of Engineering, Ashdod, Israel

Publication Topics

Additive Noise,
Arrival Time,
Convolutional Neural Network,
Deep Learning,
Energy Spectrum,
Gamma Spectrometry,
Homogeneous Poisson Process,
Machine Learning,
Pile-up Events,
Poisson Process,
Pulse Shape,
Simulated Signals

Biography

Author's biographies not available at the time of publication.(Based on document published on 15 April 2015).

Publications

27

Citations

422

Publications by Year

20112025

Co-Authors:

S. Arnon
Merav Arogeti
Isaac August
Mishel Shagas Azimov
Almog Azulay

Show All Co-Authors (31)

Author's Published Works

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Showing 1-4 of 4 results

Conferences (4)

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Bingxing Wang(4)
Hongye Zheng(3)
Honglin Qin(3)
Zhizhong Wu(2)
Xu Yan(1)
Illinois Institute of Technology, Chicago, USA(4)
Stevens Institute of Technology, Hoboken, USA(3)
Chinese University of Hong Kong, Hong Kong, China(3)
University of California, Berkeley, Berkeley, USA(2)
Trine University, Phoenix, USA(2)
2024 International Conference on Electronics and Devices, Computational Science (ICEDCS)(2)
2024 4th International Conference on Computer Science and Blockchain (CCSB)(1)
2024 IEEE 6th International Conference on Power, Intelligent Computing and Systems (ICPICS)(1)
IEEE(4)
Marseille, France(2)
Shenyang, China(1)
Shenzhen, China(1)
Deep Learning(3)
Neural Network(3)
Adaptive Optimization(2)
Artificial Neural Network(2)
Attention Mechanism(2)

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Stock Type Prediction Model Based on Hierarchical Graph Neural Network

Jianhua Yao;Yuxin Dong;Jiajing Wang;Bingxing Wang;Hongye Zheng;Honglin Qin

2024 International Conference on Electronics and Devices, Computational Science (ICEDCS)

Year: 2024 | Conference Paper |

Cited by: Papers (5)

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This paper introduces a novel approach to stock data analysis by employing a Hierarchical Graph Neural Network (HGNN) model that captures multi-level information and relational structures in the stock market. The HGNN model integrates stock relationship data and hierarchical attributes to predict stock types effectively. The paper discusses the construction of a stock industry relationship graph a...Show More

Stock Type Prediction Model Based on Hierarchical Graph Neural Network

Jianhua Yao;Yuxin Dong;Jiajing Wang;Bingxing Wang;Hongye Zheng;Honglin Qin

2024 International Conference on Electronics and Devices, Computational Science (ICEDCS)

Year: 2024 | Conference Paper |

The Synergistic Role of Deep Learning and Neural Architecture Search in Advancing Artificial Intelligence

Xu Yan;Junliang Du;Lun Wang;Yingbin Liang;Jiacheng Hu;Bingxing Wang

2024 International Conference on Electronics and Devices, Computational Science (ICEDCS)

Year: 2024 | Conference Paper |

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This paper delves into the significance and interaction between deep learning (DL) and neural architecture search (NAS) within the realm of artificial intelligence. As DL has become integral in addressing complex problems through its robust learning capabilities, NAS offers the potential to autonomously discover efficient neural network structures. The fusion of these technologies not only enhance...Show More

The Synergistic Role of Deep Learning and Neural Architecture Search in Advancing Artificial Intelligence

Xu Yan;Junliang Du;Lun Wang;Yingbin Liang;Jiacheng Hu;Bingxing Wang

2024 International Conference on Electronics and Devices, Computational Science (ICEDCS)

Year: 2024 | Conference Paper |

Adaptive Friction in Deep Learning: Enhancing Optimizers with Sigmoid and Tanh Function

Hongye Zheng;Bingxing Wang;Minheng Xiao;Honglin Qin;Zhizhong Wu;Lianghao Tan

2024 IEEE 6th International Conference on Power, Intelligent Computing and Systems (ICPICS)

Year: 2024 | Conference Paper |

Cited by: Papers (8)

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Adaptive optimizers are pivotal in guiding the weight updates of deep neural networks, yet they often face challenges such as poor generalization and oscillation issues. To counter these, we introduce sigSignGrad and tanhSignGrad, two novel optimizers that integrate adaptive friction coefficients based on the Sigmoid and Tanh functions, respectively. These algorithms leverage short-term gradient i...Show More

Adaptive Friction in Deep Learning: Enhancing Optimizers with Sigmoid and Tanh Function

Hongye Zheng;Bingxing Wang;Minheng Xiao;Honglin Qin;Zhizhong Wu;Lianghao Tan

2024 IEEE 6th International Conference on Power, Intelligent Computing and Systems (ICPICS)

Year: 2024 | Conference Paper |

Reducing Bias in Deep Learning Optimization: The RSGDM Approach

Honglin Qin;Hongye Zheng;Bingxing Wang;Zhizhong Wu;Bingyao Liu;Yuanfang Yang

2024 4th International Conference on Computer Science and Blockchain (CCSB)

Year: 2024 | Conference Paper |

Cited by: Papers (6)

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Currently, widely used first-order deep learning optimizers include non-adaptive learning rate optimizers and adaptive learning rate optimizers. The former is represented by SGDM (Stochastic Gradient Descent with Momentum), while the latter is represented by Adam. Both of these methods use exponential moving averages to estimate the overall gradient. However, estimating the overall gradient using ...Show More

Reducing Bias in Deep Learning Optimization: The RSGDM Approach

Honglin Qin;Hongye Zheng;Bingxing Wang;Zhizhong Wu;Bingyao Liu;Yuanfang Yang

2024 4th International Conference on Computer Science and Blockchain (CCSB)

Year: 2024 | Conference Paper |

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