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Editorial: Securing Internet of Things Through Big Data Analytics

Muhammad Alam, Ting Wu, Fazl Ullah, Yuanfang Chen
2019 Journal on spesial topics in mobile networks and applications  
A new algorithm for processing feature that makes two optimizations into a random vector functional link (RVFL) network is proposed in this paper.  ...  The first paper tittled "Image Steganalysis via Random Subspace Fisher Linear Discriminant Vector Functional Link Network and Feature Mapping" presents a comprehensive steganography (the hiding of data  ...  A new algorithm for processing feature that makes two optimizations into a random vector functional link (RVFL) network is proposed in this paper.  ... 
doi:10.1007/s11036-019-01279-7 fatcat:uy72lpnvvze7jo6fs5mrzqinqy

Baseline Model Training in Sensor-Based Human Activity Recognition: An Incremental Learning Approach

Jianyu Xiao, Linlin Chen, Haipeng Chen, Xuemin Hong
2021 IEEE Access  
BASIC BLS MODEL As illustrated in Fig.4 , the BLS network is constructed based on the random vector functional link neural network (RVFLNN) [44] , which is a random vector single-layer neural network  ...  learning can be used for continuous online learning when the incoming training data is biased.  ... 
doi:10.1109/access.2021.3077764 fatcat:mar2s7ur35geda4wcnbzoxhya4

ANRL: Attributed Network Representation Learning via Deep Neural Networks

Zhen Zhang, Hongxia Yang, Jiajun Bu, Sheng Zhou, Pinggang Yu, Jianwei Zhang, Martin Ester, Can Wang
2018 Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence  
Network representation learning (RL) aims to transform the nodes in a network into low-dimensional vector spaces while preserving the inherent properties of the network.  ...  The results empirically show that ANRL can achieve relatively significant gains in node classification and link prediction tasks.  ...  Mining knowledge in networks has drawn continuous attention in both academia and industry, e.g., online advertisement targeting and recommendation.  ... 
doi:10.24963/ijcai.2018/438 dblp:conf/ijcai/ZhangYBZYZE018 fatcat:7vqslkhb6jglhac6w7qmkcsykm

Android malware classification based on random vector functional link and artificial Jellyfish Search optimizer

Emad T. Elkabbash, Reham R. Mostafa, Sherif I. Barakat, Seyedali Mirjalili
2021 PLoS ONE  
Here, we introduce a novel detection system based on optimizing the random vector functional link (RVFL) using the artificial Jellyfish Search (JS) optimizer following dimensional reduction of Android  ...  Notably, most Android malware detection tools depend on conventional machine-learning algorithms; hence, they lose the benefits of metaheuristic optimization.  ...  Random vector functional link (RVFL) networks are randomized functional-link neural networks.  ... 
doi:10.1371/journal.pone.0260232 pmid:34797851 pmcid:PMC8604294 fatcat:t4xizji3efeotmak3mqza57tti

Online Learned Siamese Network with Auto-Encoding Constraints for Robust Multi-Object Tracking

Peixin Liu, Xiaofeng Li, Han Liu, Zhizhong Fu
2019 Electronics  
With the new features, an online incremental learned tracking framework is established.  ...  Different from recent deep learning methods, the simple two layers stacked auto-encoder structure enables the Siamese network to operate efficiently only with small-scale online sample data.  ...  [12] proposed a deep continuous conditional random field (DCCRF) model for solving online MOT problems.  ... 
doi:10.3390/electronics8060595 fatcat:tdqfuxudnna3fe3q4pfmpop7hi

Network representation learning systematic review: Ancestors and current development state

Amina Amara, Mohamed Ali Hadj Taieb, Mohamed Ben Aouicha
2021 Machine Learning with Applications  
Real-world information networks are increasingly occurring across various disciplines including online social networks and citation networks.  ...  As part of machine learning techniques, graph embedding approaches are originally conceived for graphs constructed from feature represented datasets, like image dataset, in which links between nodes are  ...  ., 2016a) used precision@k as the evaluation metric to predict hidden links in sparse networks using learned vectors.  ... 
doi:10.1016/j.mlwa.2021.100130 fatcat:axhg2gxkzfds3icebro6hlman4

Financial Fraud Detection in Healthcare Using Machine Learning and Deep Learning Techniques

Abolfazl Mehbodniya, Izhar Alam, Sagar Pande, Rahul Neware, Kantilal Pitambar Rane, Mohammad Shabaz, Mangena Venu Madhavan, Chinmay Chakraborty
2021 Security and Communication Networks  
Hence, continuous enhancement is necessary for the system for detecting frauds. Various fraud scenarios happen continuously, which has a massive impact on financial losses.  ...  In this paper, various machine learning and deep learning approaches are used for detecting frauds in credit cards and different algorithms such as Naive Bayes, Logistic Regression, K-Nearest Neighbor  ...  Manhattan or Euclidean function mainly deals with continuous variable, while the Minkowski deals with categorical data. e Euclidean function is used for mea- suring the distance in the KNN classifier  ... 
doi:10.1155/2021/9293877 fatcat:lattsosu2je2zk37v2czq32drq

Multi-Objective Congestion Control [article]

Yiqing Ma, Han Tian, Xudong Liao, Junxue Zhang, Weiyan Wang, Kai Chen, Xin Jin
2021 arXiv   pre-print
The core of MOCC is a novel multi-objective reinforcement learning framework for CC that can automatically learn the correlations between different application requirements and the corresponding optimal  ...  Under this framework, MOCC further applies transfer learning to transfer the knowledge from past experience to new applications, quickly adapting itself to a new objective even if it is unforeseen.  ...  We use measured maximum throughput and minmum delay to estimate the Link Capacity and Base Link Latency in the online phase.  ... 
arXiv:2107.01427v1 fatcat:wjacttsajzfu5khwwkmu52mqmm

A randomized neural network for data streams

Mahardhika Pratama, Plamen P. Angelov, Jie Lu, Edwin Lughofer, Manjeevan Seera, C. P. Lim
2017 2017 International Joint Conference on Neural Networks (IJCNN)  
The how-to-learn process combines an open structure of evolving concept and a randomized learning algorithm of random vector functional link network (RVFLN).  ...  This paper proposes a novel RNN, namely recurrent type-2 random vector functional link network (RT2McRVFLN), which provides a highly scalable solution for data streams in a strictly online and integrated  ...  CONCLUSION A novel randomized neural network, namely Recurrent Type-2 Metacognitive Random Vector Functional Link Network (RT2McRVFLN) is proposed in this paper.  ... 
doi:10.1109/ijcnn.2017.7966286 dblp:conf/ijcnn/PratamaALLSL17 fatcat:eehn4jdqvrhibdbmzv6y7zdc4e

A Survey on Embedding Dynamic Graphs [article]

Claudio D. T. Barros, Matheus R. F. Mendonça, Alex B. Vieira, Artur Ziviani
2021 arXiv   pre-print
Embedding static graphs in low-dimensional vector spaces plays a key role in network analytics and inference, supporting applications like node classification, link prediction, and graph visualization.  ...  Afterward, we describe existing techniques and propose a taxonomy for dynamic graph embedding techniques based on algorithmic approaches, from matrix and tensor factorization to deep learning, random walks  ...  It uses a deep autoencoder neural network to learn vector representations through a stream of random walks while minimizes the pairwise distance among all nodes in each walk.  ... 
arXiv:2101.01229v2 fatcat:lqjkkksn45g7beizhcstakf6ry

Parsimonious random vector functional link network for data streams

Mahardhika Pratama, Plamen P. Angelov, Edwin Lughofer, Meng Joo Er
2018 Information Sciences  
The theory of random vector functional link network (RVFLN) has provided a breakthrough in the design of neural networks (NNs) since it conveys solid theoretical justification of randomized learning.  ...  A novel class of RVLFN, namely parsimonious random vector functional link network (pRVFLN), is proposed in this paper. pRVFLN features an open structure paradigm where its network structure can be built  ...  Conclusions This paper presents a novel random vector functional link network, namely Parsimonious Random Vector Functional Link Network (pRVFLN), inspired by the three issues of metacognition of human  ... 
doi:10.1016/j.ins.2017.11.050 fatcat:na2bkrp6gzamfgunqihekqeghu

A Robust Sliding Mode Control With RBFNN Compensation For Uncertain Networked Control System

Liman Yang, Yunhua Li, Li Zuo
2008 2008 IEEE Conference on Robotics, Automation and Mechatronics  
In view of the coupling influence of time-variable delay and plant model error as well as exterior disturbance, RBFNN is used to approach the equivalent disturbance online and output assistant control  ...  For the uncertain NCS with stochastic network delay less than one period, a sort of RBFNN-DSMC algorithm combining discrete sliding mode control and RBF neural network is presented.  ...  The weight vector of link from latent layer to output layer is 1 2 , , , 1 2 3 , , , , T n X x x x x = is input vector. 1 2 T j m Φ , , , , , φ φ φ φ   =   is RBF vector. j φ is Gauss base function  ... 
doi:10.1109/ramech.2008.4681512 dblp:conf/ram/YangLZ08 fatcat:3lco5zivjrbyvbj7zogzivhppm

Actor-critic neural network reinforcement learning for walking control of a 5-link bipedal robot

Yasaman Vaghei, Ahmad Ghanbari, Sayyed Mohammad Reza Sayyed Noorani
2014 2014 Second RSI/ISM International Conference on Robotics and Mechatronics (ICRoM)  
In this paper, our main goal was to combine the advantages of the artificial neural networks and the RL to reduce the learning time length and enhance the control accuracy.  ...  functions.  ...  The actor-critic algorithms have been shown to be more effective than value function approximation (VFA), and policy search in online learning control tasks with continuous spaces [16] .  ... 
doi:10.1109/icrom.2014.6990997 fatcat:i35kwwhqlrfvbd6pgv2cpm2lbu

Application and Implementation of Deep Learning for Evaluation of Martial Arts Trainings

Ma Jin, Mian Ahmad Jan
2022 Mobile Information Systems  
The online learning behavior is obtained by training the detection model of target, model for detection of face, and face segmentation model and then merging them with the online system.  ...  Wushu training evaluation is a hot research area, and deep learning has long been an essential tool for ensuring and promoting continuous development in the quality of Wushu training assessment.  ...  , online random supervision and evaluation, as well as the implementation of the above steps are evaluated using supervision experience [20] .  ... 
doi:10.1155/2022/3979817 fatcat:x2th3fldljbrzhnftdamzyuyqe

Link Load Prediction using Support Vector Regression and Optimization

Debashree Priyadarshin, Milu Acharya, Ambika Prasad Mishra
2011 International Journal of Computer Applications  
They are also becoming a popular subject in networking domain. This topic explores link load prediction of a network using Support Vector Regression and Optimization techniques.  ...  Support Vector Regression(SVR) is robust to outliers and can be used to online and adaptive learning.SVR has been used in other problems of networking like TCP throughput prediction, latency prediction  ...  Some of these are: SVM models are robust to parameter variation, they can generalize to unseen data, and they are well accepted in continuous and adaptive online learning.  ... 
doi:10.5120/2966-3964 fatcat:hkx55twwy5cdnjnojc2hpqns5y
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