Search results for: Shuxiang Xu

Items from 1 to 17 out of 17 results

article

Retinoic acid receptor gamma is required for proliferation of pancreatic cancer cells

Kaiyuan Ji, Wenlong Dou, Ningfang Zhang, Bolun Wen, more

Cell Biology International > 47 > 1 > 144 - 155

Despite the expectation that retinoic acid receptor could be the potential therapeutic targets for pancreatic cancers, there has been the lack of information about the role and the impact of retinoic acid receptor gamma (RARγ, RARG) on pancreatic cancer, unlike other two RARs. Herein, we applied TCGA and GEO database to show that the expression and prognosis of RARG is closely related to pancreatic...

article

Core pluripotency factors promote glycolysis of human embryonic stem cells by activating GLUT1 enhancer

Lili Yu, Kai-yuan Ji, Jian Zhang, Yanxia Xu, more

Protein & Cell > 2019 > 10 > 9 > 668-680

Human embryonic stem cells (hESCs) depend on glycolysis for energy and substrates for biosynthesis. To understand the mechanisms governing the metabolism of hESCs, we investigated the transcriptional regulation of glucose transporter 1 (GLUT1, SLC2A1), a key glycolytic gene to maintain pluripotency. By combining the genome-wide data of binding sites of the core pluripotency factors (SOX2, OCT4, NANOG,...

article

Signal fusion-based deep fast random forest method for machine health assessment

Nanqi Yuan, Wenli Yang, Byeong Kang, Shuxiang Xu, more

Journal of Manufacturing Systems > 2018 > 48 > PA > 1-8

The health assessment of machine is of great significance in modern industry. Vibration and acoustic signals collected from machine have been extensively and effectively applied in these fields. In view of the existing deficiencies, this paper reports a deep fast random forest (DFRF) fusion technique for machine running condition analysis by replacing the shallow learning theories with the deep learning...

article

A new multiple seeds based genetic algorithm for discovering a set of interesting Boolean association rules

Mir Md. Jahangir Kabir, Shuxiang Xu, Byeong Ho Kang, Zongyuan Zhao

Expert Systems with Applications > 2017 > 74 > C > 55-69

Association rule mining algorithms mostly use a randomly generated single seed to initialize a population without paying attention to the effectiveness of that population in evolutionary learning. Recently, research has shown significant impact of the initial population on the production of good solutions over several generations of a genetic algorithm. Single seed based genetic algorithms suffer...

chapter

An Adaptive Activation Function for Higher Order Neural Networks

Shuxiang Xu, Ming Zhang

Lecture Notes in Computer Science > AI 2002: Advances in Artificial Intelligence > Neural Networks I > 356-362

This paper deals with higher order feed-forward neural networks with a new activation function - neuron-adaptive activation function. Experiments with function approximation and stock market movement simulation have been conducted to justify the new activation function. Experimental results have revealed that higher order feed-forward neural networks with the new neuron-adaptive activation function...

chapter

Application of Genetic Algorithms to the Optimisation of Neural Network Configuration for Stock Market Forecasting

Daniel Hulme, Shuxiang Xu

Lecture Notes in Computer Science > AI 2001: Advances in Artificial Intelligence > Papers > 285-296

Neural networks are recognised as an effective tool for predicting stock prices (Shin & Han, 2000), but little is known about which configurations are best and for which indices. The present study uses genetic algorithms to find a near optimal learning rate, momentum, tolerance and network architecture for 47 indices listed on the Australian Stock Exchange (ASX). Some relationships were determined...

chapter

A New Adaptive Neural Network Model for Financial Data Mining

Shuxiang Xu, Ming Zhang

Lecture Notes in Computer Science > Advances in Neural Networks – ISNN 2007 > Learning and Approximation > 1265-1273

Data Mining is an analytic process designed to explore data (usually large amounts of data - typically business or market related) in search of consistent patterns and/or systematic relationships between variables, and then to validate the findings by applying the detected patterns to new subsets of data. One of the most commonly used techniques in data mining, Artificial Neural Networks provide non-linear...

chapter

Data Mining Using an Adaptive HONN Model with Hyperbolic Tangent Neurons

Shuxiang Xu

Lecture Notes in Computer Science > Knowledge Management and Acquisition for Smart Systems and Services > Data Mining > 73-81

An Artificial Neural Network (ANN) works by creating connections between different processing elements (artificial neurons). ANNs have been extensively used for Data Mining, which extracts hidden patterns and valuable information from large databases. This paper introduces a new adaptive Higher Order Neural Network (HONN) model and applies it in data mining tasks such as determining breast cancer...

chapter

Adaptive Higher Order Neural Networks for Effective Data Mining

Shuxiang Xu, Ling Chen

Advances in Intelligent and Soft Computing > The Sixth International Symposium on Neural Networks (ISNN 2009) > Session 2: Machine Learning > 165-173

A new adaptive Higher Order Neural Network (HONN) is introduced and applied in data mining tasks such as determining automobile yearly losses and edible mushrooms. Experiments demonstrate that the new adaptive HONN model offers advantages over conventional Artificial Neural Network (ANN) models such as higher generalization capability and the ability in handling missing values in a dataset. A new...

chapter

Comparative analysis of genetic based approach and Apriori algorithm for mining maximal frequent item sets

Mir Md. Jahangir Kabir, Shuxiang Xu, Byeong Ho Kang, Zongyuan Zhao

2015 IEEE Congress on Evolutionary Computation (CEC) > 39 - 45

2015 IEEE Congress on Evolutionary Computation (CEC)

In the data mining research area, discovering frequent item sets is an important issue and key factor for mining association rules. For large datasets, a huge amount of frequent patterns are generated for a low support value, which is a major challenge in frequent pattern mining tasks. A Maximal frequent pattern mining task helps to resolve this problem since a maximal frequent pattern contains information...

article

Investigation and improvement of multi-layer perceptron neural networks for credit scoring

Zongyuan Zhao, Shuxiang Xu, Byeong Ho Kang, Mir Md Jahangir Kabir, more

Expert Systems With Applications > 2015 > 42 > 7 > 3508-3516

Multi-Layer Perceptron (MLP) neural networks are widely used in automatic credit scoring systems with high accuracy and efficiency. This paper presents a higher accuracy credit scoring model based on MLP neural networks that have been trained with the back propagation algorithm. Our work focuses on enhancing credit scoring models in three aspects: (i) to optimise the data distribution in datasets...

article

Letter to the Editor

Silky Chotai, Songtao Qi, Shuxiang Xu

Clinical Neurology and Neurosurgery > 2014 > 120 > Complete > 144-145

article

Prediction of outcomes for brainstem cavernous malformation

Silky Chotai, Songtao Qi, Shuxiang Xu

Clinical Neurology and Neurosurgery > 2013 > 115 > 10 > 2117-2123

Brainstem cavernous malformation (CM) poses a challenge to neurosurgeons in terms of operability, postoperative complications and unpredictable outcomes. The present study was conducted to analyze the clinical parameters that might predict the outcomes and to summarize our center experience in treatment of brainstem CM.A total 59 patients with radiological and histologically confirmed brainstem CM...

chapter

HONNs with ELM algorithm for medical applications

Shuxiang Xu, Yunling Liu

2012 12th International Conference on Control Automation Robotics & Vision (ICARCV) > 1215 - 1219

2012 12th International Conference on Control Automation Robotics & Vision (ICARCV 2012)

Higher Order Neural Networks (HONNs) are Artificial Neural Networks (ANNs) in which the net input to a computational neuron is a weighted sum of products of its inputs (rather than just a weighted sum of its inputs as in traditional ANNs). It was known that HONNs can implement invariant pattern recognition as well as handling high frequency and high order nonlinear business data. Extreme Learning...

chapter

Features of Higher Order Neural Network with adaptive neurons

Shuxiang Xu

The 2nd International Conference on Software Engineering and Data Mining > 484 - 488

2nd International Conference on Software Engineering and Data Mining (SEDM 2010)

One of the most popular machine learning algorithms, ANN (Artificial Neural Network) has been extensively used for Data Mining, which extracts hidden patterns and valuable information from large databases. Data mining has extensive and significant applications in a large variety of areas. This paper introduces a new adaptive Higher Order Neural Network (HONN) model and applies it in data mining tasks...

chapter

Adaptive Higher Order Neural Networks

Shuxiang Xu, Ling Chen

2009 WRI Global Congress on Intelligent Systems > 4 > 26 - 30

2009 WRI Global Congress on Intelligent Systems (GCIS)

This paper introduces an adaptive higher order neural network (HONN) and applies it in data mining such as Australian credit card assessment. The proposed adaptive HONN model offers significant advantages over conventional artificial neural network (ANN) models such as more accurate predictions and the ability in handling incomplete/missing values in a dataset. A new approach for determining the best...

chapter

Application of New Adaptive Higher Order Neural Networks in Data Mining

Shuxiang Xu, Ling Chen

2008 International Conference on Computer Science and Software Engineering > 1 > 115 - 118

2008 International Conference on Computer Science and Software Engineering (CSSE 2008)

This paper introduces an adaptive Higher Order Neural Network (HONN) model and applies it in data mining such as simulating and forecasting government taxation revenues. The proposed adaptive HONN model offers significant advantages over conventional Artificial Neural Network (ANN) models such as much reduced network size, faster training, as well as much improved simulation and forecasting errors...

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