Automated Detection of Brain Abnormalities Using Multi-Directional Features and Randomized Learning

2020 ◽  
pp. 20-41
Author(s):  
Deepak Ranjan Nayak ◽  
Dibyasundar Das ◽  
Ratnakar Dash ◽  
Banshidhar Majhi
Keyword(s):  
Learning Algorithm ◽  
Single Layer ◽  
Curvelet Transform ◽  
Automated Detection ◽  
Directional Selectivity ◽  
Brain Abnormalities ◽  
Feed Forward Neural Network ◽  
Discriminant Ability ◽  
Magnetic Resonance Imaging Mri ◽  
Learning Machine

Automated detection of brain abnormalities through magnetic resonance imaging (MRI) has made a significant stride in the past decade. The feature extractors exploited in the literature suffer from issues like limited directional selectivity and high dimensionality, and the classifiers used have critical drawbacks like slow learning speed, poor computational scalability, and trivial human intervention. The fast curvelet transform (FCT) and ripplet-II transform (R2T) provides improved discriminant ability and high directional selectivity. Extreme learning machine (ELM), a randomized learning algorithm for single layer feed-forward neural network, has received significant attention as it provides good generalization performance at much faster speed. In this chapter, the authors compare the effectiveness of two feature extractors based on FCT and R2T along with different ELM algorithms. These schemes have been evaluated on three brain MR datasets and comparative analyses have been made on several combinations of methods. Finally, the potential of the best scheme is compared to the state of the art.

scholarly journals Convergence Analysis of An Improved Extreme Learning Machine Based on Gradient Descent Method

2016 ◽  
Vol 8 (1) ◽  
pp. 5-15
Author(s):  
Liu Yusong ◽  
Su Zhixun ◽  
Zhang Bingjie ◽  
Gong Xiaoling ◽  
Sang Zhaoyang
Keyword(s):  
Extreme Learning Machine ◽  
Learning Algorithm ◽  
Error Function ◽  
Gradient Descent Method ◽  
Testing Time ◽  
Training Procedure ◽  
Feed Forward Neural Network ◽  
Learning Machine ◽  
Theoretical Results ◽  
Hidden Nodes

Abstract Extreme learning machine (ELM) is an efficient algorithm, but it requires more hidden nodes than the BP algorithms to reach the matched performance. Recently, an efficient learning algorithm, the upper-layer-solution-unaware algorithm (USUA), is proposed for the single-hidden layer feed-forward neural network. It needs less number of hidden nodes and testing time than ELM. In this paper, we mainly give the theoretical analysis for USUA. Theoretical results show that the error function monotonously decreases in the training procedure, the gradient of the error function with respect to weights tends to zero (the weak convergence), and the weight sequence goes to a fixed point (the strong convergence) when the iterations approach positive infinity. An illustrated simulation has been implemented on the MNIST database of handwritten digits which effectively verifies the theoretical results..

Classifying Spam Emails Using Artificial Intelligent Techniques

2016 ◽  
Vol 22 ◽  
pp. 152-161 ◽  
Author(s):  
Sanjiban Sekhar Roy ◽  
V. Madhu Viswanatham
Keyword(s):  
Support Vector Machine ◽  
Single Layer ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Feed Forward Neural Network ◽  
Learning Techniques ◽  
Class Level ◽  
Learning Machine ◽  
The Comparative Study

Spam emails have become an increasing difficulty for the entire web-users.These unsolicited messages waste the resources of network unnecessarily. Customarily, machine learning techniques are adopted for filtering email spam. This article examines the capabilities of the extreme learning machine (ELM) and support vector machine (SVM) for the classification of spam emails with the class level (d). The ELM method is an efficient model based on single layer feed-forward neural network, which can choose weights from hidden layers,randomly. Support vector machine is a strong statistical learning theory used frequently for classification. The performance of ELM has been compared with SVM. The comparative study examines accuracy, precision, recall, false positive, true positive.Moreover, a sensitivity analysis has been performed by ELM and SVM for spam email classification.

Comparison of the Extreme Learning Machine with the BP Neural Network for Short-Term Prediction of Wind Power

2012 ◽  
Vol 608-609 ◽  
pp. 564-568 ◽  
Author(s):  
Yi Hui Zhang ◽  
He Wang ◽  
Zhi Jian Hu ◽  
Meng Lin Zhang ◽  
Xiao Lu Gong ◽  
...  
Keyword(s):  
Neural Network ◽  
Wind Power ◽  
Extreme Learning Machine ◽  
Bp Neural Network ◽  
Learning Algorithm ◽  
Power Prediction ◽  
Feed Forward Neural Network ◽  
Wind Power Prediction ◽  
Learning Machine ◽  
Hidden Layer

Extreme learning machine (ELM) is a new and effective single-hidden layer feed forward neural network learning algorithm. Extreme learning machine only needs to set the number of hidden layer nodes of the network, and there is no need to adjust the neural network input weights and the hidden units bias, and it generates the only optimum solution, so it has the advantage of fast learning and good generalization ability. And the back propagation (BP) neural network is the most maturely applied. This paper has introduced the extreme learning machine into the wind power prediction. By comparing the wind power prediction method using the BP neural network. Study shows that the extreme learning machine has better prediction accuracy and shorter model training time.

Generalization ability of Extreme Learning Machine using different Sample Selection Methods

2021 ◽  
Vol 3 (1) ◽  
pp. 57-72
Author(s):  
Saher Fatima ◽  
Rana Aamir Raza ◽  
Maruf Pasha ◽  
Asghar Ali
Keyword(s):  
Neural Network ◽  
Extreme Learning Machine ◽  
Data Reduction ◽  
Sample Selection ◽  
Single Layer ◽  
Knowledge Discovery In Databases ◽  
Instance Selection ◽  
Feed Forward Neural Network ◽  
Generalization Ability ◽  
Learning Machine

The recent explosion of data has triggered the need of data reduction for completing the effective data mining task in the process of knowledge discovery in databases (KDD). The process of instance selection (IS) plays a significant role for data reduction by eliminating the redundant, noisy, unreliable and irrelevant instances, which, in-turn reduces the computational resources, and helps to increase the capabilities and generalization abilities of the learning models. . This manuscript expounds the concept and functionalities of seven different instance selection techniques (i.e., ENN, AllKNN, MENN, ENNTh, Mul- tiEdit, NCNEdit, and RNG), and also evaluates their effectiveness by using single layer feed-forward neural network (SLFN), which is trained with extreme learning machine (ELM). Unlike traditional neural network, ELM randomly chooses the weights and biases of hidden layer nodes and analytically determines the weights of output layer node. The generalization ability of ELM is analyzed by using both original and reduced datasets. Experiment results depict that ELM provides better generalization with these IS methods.

scholarly journals Rank based Pseudoinverse Computation in Extreme Learning Machine for large Datasets

2019 ◽  
Vol 8 (10) ◽  
pp. 1341-1346
Keyword(s):  
Extreme Learning Machine ◽  
Learning Algorithm ◽  
Matrix Decomposition ◽  
Least Square ◽  
Feed Forward Neural Network ◽  
Worst Case ◽  
Training Time ◽  
Learning Machine ◽  
Hidden Layer ◽  
Hidden Nodes

Extreme Learning Machine (ELM) is an efficient and effective least-square-based learning algorithm for classification, regression problems based on single hidden layer feed-forward neural network (SLFN). It has been shown in the literature that it has faster convergence and good generalization ability for moderate datasets. But, there is great deal of challenge involved in computing the pseudoinverse when there are large numbers of hidden nodes or for large number of instances to train complex pattern recognition problems. To address this problem, a few approaches such as EM-ELM, DF-ELM have been proposed in the literature. In this paper, a new rank-based matrix decomposition of the hidden layer matrix is introduced to have the optimal training time and reduce the computational complexity for a large number of hidden nodes in the hidden layer. The results show that it has constant training time which is closer towards the minimal training time and very far from worst-case training time of the DF-ELM algorithm that has been shown efficient in the recent literature.

scholarly journals Road Traffic Prediction Model Using Extreme Learning Machine: The Case Study of Tangier, Morocco

Information ◽  
2020 ◽  
Vol 11 (12) ◽  
pp. 542
Author(s):  
Mouna Jiber ◽  
Abdelilah Mbarek ◽  
Ali Yahyaouy ◽  
My Abdelouahed Sabri ◽  
Jaouad Boumhidi
Keyword(s):  
Extreme Learning Machine ◽  
Intelligent Transportation Systems ◽  
High Speed ◽  
Road Traffic ◽  
Learning Algorithm ◽  
Route Planning ◽  
Transportation Systems ◽  
Feed Forward Neural Network ◽  
Data Set ◽  
Learning Machine

An efficient and credible approach to road traffic management and prediction is a crucial aspect in the Intelligent Transportation Systems (ITS). It can strongly influence the development of road structures and projects. It is also essential for route planning and traffic regulations. In this paper, we propose a hybrid model that combines extreme learning machine (ELM) and ensemble-based techniques to predict the future hourly traffic of a road section in Tangier, a city in the north of Morocco. The model was applied to a real-world historical data set extracted from fixed sensors over a 5-years period. Our approach is based on a type of Single hidden Layer Feed-forward Neural Network (SLFN) known for being a high-speed machine learning algorithm. The model was, then, compared to other well-known algorithms in the prediction literature. Experimental results demonstrated that, according to the most commonly used criteria of error measurements (RMSE, MAE, and MAPE), our model is performing better in terms of prediction accuracy. The use of Akaike’s Information Criterion technique (AIC) has also shown that the proposed model has a higher performance.

A Heterogeneous AdaBoost Ensemble Based Extreme Learning Machines for Imbalanced Data

2019 ◽  
Vol 13 (3) ◽  
pp. 19-35 ◽  
Author(s):  
Adnan Omer Abuassba ◽  
Dezheng Zhang ◽  
Xiong Luo
Keyword(s):  
Real World ◽  
Image Quality Assessment ◽  
Learning Algorithm ◽  
Imbalanced Data ◽  
Learning Performance ◽  
Feed Forward Neural Network ◽  
Unseen Data ◽  
Benchmark Datasets ◽  
Learning Machine ◽  
Hidden Layer

Extreme learning machine (ELM) is an effective learning algorithm for the single hidden layer feed-forward neural network (SLFN). It is diversified in the form of kernels or feature mapping functions, while achieving a good learning performance. It is agile in learning and often has good performance, including kernel ELM and Regularized ELM. Dealing with imbalanced data has been a long-term focus for the learning algorithms to achieve satisfactory analytical results. It is obvious that the unbalanced class distribution imposes very challenging obstacles to implement learning tasks in real-world applications, including online visual tracking and image quality assessment. This article addresses this issue through advanced diverse AdaBoost based ELM ensemble (AELME) for imbalanced binary and multiclass data classification. This article aims to improve classification accuracy of the imbalanced data. In the proposed method, the ensemble is developed while splitting the trained data into corresponding subsets. And different algorithms of enhanced ELM, including regularized ELM and kernel ELM, are used as base learners, so that an active learner is constructed from a group of relatively weak base learners. Furthermore, AELME is implemented by training a randomly selected ELM classifier on a subset, chosen by random re-sampling. Then, the labels of unseen data could be predicted using the weighting approach. AELME is validated through classification on real-world benchmark datasets.

SMART IDENTIFICATION OF POWER QUALITY EVENTS USING NEW STOCKWELL TRANSFORM AND MACHINE LEARNING ALGORITHM

2019 ◽  
Vol XVI (4) ◽  
pp. 95-113
Author(s):  
Muhammad Tariq ◽  
Tahir Mehmood
Keyword(s):  
Machine Learning ◽  
Power Quality ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Noisy Environments ◽  
Satisfactory Performance ◽  
Feature Values ◽  
Learning Machine ◽  
Stockwell Transform ◽  
Electrical Utilities

Accurate detection, classification and mitigation of power quality (PQ) distortive events are of utmost importance for electrical utilities and corporations. An integrated mechanism is proposed in this paper for the identification of PQ distortive events. The proposed features are extracted from the waveforms of the distortive events using modified form of Stockwell’s transform. The categories of the distortive events were determined based on these feature values by applying extreme learning machine as an intelligent classifier. The proposed methodology was tested under the influence of both the noisy and noiseless environments on a database of seven thousand five hundred simulated waveforms of distortive events which classify fifteen types of PQ events such as impulses, interruptions, sags and swells, notches, oscillatory transients, harmonics, and flickering as single stage events with their possible integrations. The results of the analysis indicated satisfactory performance of the proposed method in terms of accuracy in classifying the events in addition to its reduced sensitivity under various noisy environments.

Study on Deep Structure of Extreme Learning Machine (DS-ELM) for Datasets with Noise

2014 ◽  
Vol 989-994 ◽  
pp. 3679-3682 ◽  
Author(s):  
Meng Meng Ma ◽  
Bo He
Keyword(s):  
Extreme Learning Machine ◽  
Simple Structure ◽  
Learning Algorithm ◽  
Deep Structure ◽  
Noisy Data ◽  
Machine Learning Algorithm ◽  
Feed Forward Neural Networks ◽  
Learning Machine ◽  
Hidden Layer ◽  
Auto Associative Neural Network

Extreme learning machine (ELM), a relatively novel machine learning algorithm for single hidden layer feed-forward neural networks (SLFNs), has been shown competitive performance in simple structure and superior training speed. To improve the effectiveness of ELM for dealing with noisy datasets, a deep structure of ELM, short for DS-ELM, is proposed in this paper. DS-ELM contains three level networks (actually contains three nets ): the first level network is trained by auto-associative neural network (AANN) aim to filter out noise as well as reduce dimension when necessary; the second level network is another AANN net aim to fix the input weights and bias of ELM; and the last level network is ELM. Experiments on four noisy datasets are carried out to examine the new proposed DS-ELM algorithm. And the results show that DS-ELM has higher performance than ELM when dealing with noisy data.

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