The interaction of BDNF with estrogen in the development of hypertension and obesity, particularly during menopause

Autor: Zhongming Zhang, Ziyi He, Jing Pan, Minghui Yuan, Yini Lang, Xiaomeng Wei, Chaoyun Zhang

Publikováno v: Frontiers in Endocrinology, Vol 15 (2024)

The expression of BDNF in both neuronal and non-neuronal cells is influenced by various stimuli, including prenatal developmental factors and postnatal conditions such as estrogens, dietary habits, and lifestyle factors like obesity, blood pressure,

Externí odkaz: https://doaj.org/article/f81c1cec0ea04d5d9c4ba844c60bb25e

A 3D-CNN and LSTM Based Multi-Task Learning Architecture for Action Recognition

Autor: Xi Ouyang, Shuangjie Xu, Chaoyun Zhang, Pan Zhou, Yang Yang, Guanghui Liu, Xuelong Li

Publikováno v: IEEE Access, Vol 7, Pp 40757-40770 (2019)

Multi-task learning (MTL) is a machine learning method to share knowledge for multiple related machine learning tasks via learning those tasks jointly. It has been shown to be capable of effectively improving the generalization capability of each sin

Externí odkaz: https://doaj.org/article/9a37a48b3c6c416e8f6421096c8ffff7

AdnFM: An Attentive DenseNet based Factorization Machine for CTR Prediction

Autor: Wang, Kai, Shen, Chunxu, Ma, Chaoyun Zhang Wenye

In this paper, we consider the Click-Through-Rate (CTR) prediction problem. Factorization Machines and their variants consider pair-wise feature interactions, but normally we won't do high-order feature interactions using FM due to high time complexi

Externí odkaz: http://arxiv.org/abs/2012.10820

Improving Rating Prediction in Multi-Criteria Recommender Systems Via a Collective Factor Model

Autor: Ge Fan, Chaoyun Zhang, Junyang Chen, Paul Li, Yingjie Li, Victor C. M. Leung

Publikováno v: IEEE Transactions on Network Science and Engineering. :1-11

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::62d60f042ac147afeb53c5c844d35662
https://doi.org/10.1109/tnse.2023.3270910

Adversarial Attacks Against Deep Learning-Based Network Intrusion Detection Systems and Defense Mechanisms

Autor: Chaoyun Zhang, Xavier Costa-Perez, Paul Patras

Publikováno v: Zhang, C, Costa-Pérez, X & Patras, P 2022, ' Adversarial Attacks Against Deep Learning-based Network Intrusion Detection Systems and Defense Mechanisms ', IEEE/ACM Transactions on Networking, vol. 30, no. 3, pp. 1294-1311 . https://doi.org/10.1109/TNET.2021.3137084

Neural networks (NNs) are increasingly popular in developing NIDS, yet can prove vulnerable to adversarial examples. Through these, attackers that may be oblivious to the precise mechanics of the targeted NIDS add subtle perturbations to malicious tr

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::fa317aae3bc9ca45fa2db8391e867046
https://doi.org/10.1109/tnet.2021.3137084