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Leveraging Semi-Supervised Learning to Enhance Data Mining for Image Classification under Limited Labeled Data
Aoran Shen;Minghao Dai;Jiacheng Hu;Yingbin Liang;Shiru Wang;Junliang Du
2024 4th International Conference on Electronic Information Engineering and Computer Communication (EIECC)
Year: 2024 | Conference Paper |
In the 21st-century information age, with the development of big data technology, effectively extracting valuable information from massive data has become a key issue. Traditional data mining methods are inadequate when faced with large-scale, high-dimensional and complex data. Especially when labeled data is scarce, their performance is greatly limited. This study optimizes data mining algorithms...Show More
Integrative Analysis of Financial Market Sentiment Using CNN and GRU for Risk Prediction and Alert Systems
You Wu;Mengfang Sun;Hongye Zheng;Jinxin Hu;Yingbin Liang;Zhenghao Lin
2024 International Conference on Electronics and Devices, Computational Science (ICEDCS)
Year: 2024 | Conference Paper |
Cited by: Papers (3)
This document presents an in-depth examination of stock market sentiment through the integration of Convolutional Neural Networks (CNN) and Gated Recurrent Units (GRU), enabling precise risk alerts. The robust feature extraction capability of CNN is utilized to preprocess and analyze extensive network text data, identifying local features and patterns. The extracted feature sequences are then inpu...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 |
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
Convolutional Neural Networks for Predictive Modeling of Lung Disease
Yingbin Liang;Xiqing Liu;Haohao Xia;Yiru Cang;Zitao Zheng;Yuanfang Yang
2024 IEEE 6th International Conference on Power, Intelligent Computing and Systems (ICPICS)
Year: 2024 | Conference Paper |
Cited by: Papers (2)
In this paper, Pro-HRnet-CNN, an innovative model combining HRN et and void-convolution techniques, is proposed for disease prediction under lung imaging. Through the experimental comparison on the authoritative LIDC-IDRI dataset, we found that compared with the traditional ResNet-50, Pro-HRnet-CNN showed better performance in the feature extraction and recognition of small-size nodules, significa...Show More
A Comparative Study of Generative Adversarial Networks for Image Recognition Algorithms Based on Deep Learning and Traditional Methods
Yihao Zhong;Yijing Wei;Yingbin Liang;Xiqing Liu;Rongwei Ji;Yiru Cang
2024 IEEE 6th International Conference on Power, Intelligent Computing and Systems (ICPICS)
Year: 2024 | Conference Paper |
Cited by: Papers (9)
This paper delves into an innovative image recognition algorithm that merges deep learning techniques with Generative Adversarial Networks (GANs) and offers a comparative analysis against traditional image recognition methods. The primary objective of this study is to evaluate the benefits and future prospects of deep learning, with a particular focus on GANs, within the realm of image recognition...Show More
Leveraging Deep Learning Techniques for Enhanced Analysis of Medical Textual Data
Yiru Cang;Yihao Zhong;Rongwei Ji;Yingbin Liang;Yiming Lei;Jinyin Wang
2024 IEEE 2nd International Conference on Sensors, Electronics and Computer Engineering (ICSECE)
Year: 2024 | Conference Paper |
Cited by: Papers (2)
This study examines the implementation of deep learning technologies for data mining within medical texts. Initially, medical textual data is transformed into vectorial representations through the application of the word2vec algorithm, thereby facilitating the effective encoding of complex textual information. Subsequently, this research introduces the use of a Hierarchical Attention Network (HAN)...Show More
Dynamic Fraud Detection: Integrating Reinforcement Learning into Graph Neural Networks
Yuxin Dong;Jianhua Yao;Jiajing Wang;Yingbin Liang;Shuhan Liao;Minheng Xiao
2024 6th International Conference on Data-driven Optimization of Complex Systems (DOCS)
Year: 2024 | Conference Paper |
Cited by: Papers (9)
Financial fraud refers to the act of obtaining financial benefits through dishonest means. Such behavior not only disrupts the order of the financial market but also harms economic and social development and breeds other illegal and criminal activities. With the popularization of the internet and online payment methods, many fraudulent activities and money laundering behaviors in life have shifted...Show More
Towards General Function Approximation in Nonstationary Reinforcement Learning
Songtao Feng;Ming Yin;Ruiquan Huang;Yu-Xiang Wang;Jing Yang;Yingbin Liang
2024 IEEE International Symposium on Information Theory (ISIT)
Year: 2024 | Conference Paper |
Function approximation has experienced significant success in the field of reinforcement learning (RL). Despite a handful of progress on developing theory for Nonstationary RL with function approximation under structural assumptions, existing work for nonstationary RL with general function approximation is still limited. In this work, we propose a UCB-type of algorithm LSVI-Nonstationary following...Show More
Advanced Multimodal Deep Learning Architecture for Image-Text Matching
Jinyin Wang;Haijing Zhang;Yihao Zhong;Yingbin Liang;Rongwei Ji;Yiru Cang
2024 IEEE 4th International Conference on Electronic Technology, Communication and Information (ICETCI)
Year: 2024 | Conference Paper |
Cited by: Papers (11)
Image-text matching is a key multimodal task that aims to model the semantic association between images and text as a matching relationship. With the advent of multimedia information age, image, and text data show explosive growth, and how to accurately realize the efficient and accurate semantic correspondence between them has become the core issue of common concern in academia and industry. In t...Show More
On fading poisson channels with varying noise levels
2014 48th Annual Conference on Information Sciences and Systems (CISS)
Year: 2014 | Conference Paper |
Cited by: Papers (1)
In this paper, we study Poisson fading channels with varying noise levels. This model is motivated by applications of visible light communication systems, in which the noise level is affected by the strength of the background light. We consider scenarios with and without delay constraints. For the case without a delay constraint, we characterize the optimal power allocation scheme that maximizes t...Show More
Session MA3a: Relaying and cooperation I
2008 42nd Asilomar Conference on Signals, Systems and Computers
Year: 2008 | Conference Paper |
First Page of the Article
Show MoreWireless broadcast networks: Reliability, security, and stability
2008 Information Theory and Applications Workshop
Year: 2008 | Conference Paper |
Cited by: Papers (10)
A secure wireless broadcast network model is investigated, in which a source node broadcasts K confidential message flows to N user nodes, with each message intended to be decoded accurately by one user and to be kept secret from all of other users (who are thus considered to be eavesdroppers with regard to all other messages but their own). The source maintains a queue for each message flow if it...Show More
Optimality of beamforming in MIMO multi-access channels via virtual representation
2008 IEEE International Symposium on Information Theory
Year: 2008 | Conference Paper |
In this paper, we consider the optimality of the beamforming scheme for both the multiple-input multiple-output (MIMO) point-to-point channel and the MIMO multiple access channel (MAC), where all communication terminals are assumed to be equipped with multiple antennas. For both channels, the channel matrices have correlated elements and are modelled by virtual representation. For the point-to-poi...Show More
Cooperative relay broadcast channels
2005 International Conference on Wireless Networks, Communications and Mobile Computing
Year: 2005 | Conference Paper |
The capacity regions are investigated for cooperative relay broadcast channels (RBCs), where relay links are incorporated into standard two-user broadcast channels to support user cooperation. The partially cooperative relay broadcast channel is first studied, where only one user is allowed to transmit to the other user through a relay link. An upper bound on the capacity region is derived and is ...Show More
Gaussian orthogonal relay channels: optimal resource allocation and capacity
A Gaussian orthogonal relay model is investigated, where the source transmits to the relay and destination in channel 1, and the relay transmits to the destination in channel 2, with channels 1 and 2 being orthogonalized in the time-frequency plane in order to satisfy practical constraints. The total available channel resource (time and bandwidth) is split into the two orthogonal channels, and the...Show More
Correlated MIMO wireless channels: capacity, optimal signaling, and asymptotics
The capacity of the multiple-input multiple-output (MIMO) wireless channel with uniform linear arrays (ULAs) of antennas at the transmitter and receiver is investigated. It is assumed that the receiver knows the channel perfectly but that the transmitter knows only the channel statistics. The analysis is carried out using an equivalent virtual representation of the channel that is obtained via a s...Show More
Resource allocation for wireless relay channels
Conference Record of the Thirty-Eighth Asilomar Conference on Signals, Systems and Computers, 2004.
Year: 2004 | Conference Paper |
Cited by: Papers (27)
Resource allocation for a fading orthogonal relay channel model is investigated, where the source transmits to the relay and destination in one channel, and the relay transmits to the destination in an orthogonal channel. Separate power constraints at the source and relay are assumed. The fading state information is assumed to be known at both the transmitter and receiver. The source and relay can...Show More
Correlated MIMO Rayleigh fading channels: capacity and optimal signaling
The Thrity-Seventh Asilomar Conference on Signals, Systems & Computers, 2003
Year: 2003 | Conference Paper |
Cited by: Papers (7)
The capacity of the MIMO channel is investigated under the assumption that the elements of the channel matrix are zero mean proper complex Gaussian random variables with a general correlation structure. It is assumed that the receiver knows the channel perfectly but that the transmitter knows only the channel statistics. The analysis is carried out using an equivalent virtual representation of the...Show More
Capacity of noncoherent time-selective block Rayleigh flat-fading channel
Proceedings IEEE International Symposium on Information Theory,
Year: 2002 | Conference Paper |
Cited by: Papers (13)
We study the capacity of a noncoherent time-selective block fading channel, under the assumption that the channel changes slowly over the block period N rather than remaining constant. The predictability of the channel is characterized through the rank Q (1 /spl les/ Q /spl les/ N) of the correlation matrix of the vector of channel gains over the block. The model includes the standard block fading...Show More
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