Essay On Extreme Learning Machine

The objective of the neural network is to transform the input to meaningful output. Neural networks are often used for statistical analysis and data modeling. Neural network has many uses in data processing, robotics, and medical diagnosis [2]. From the starting of the neural network there are various types found, but each and every types has some advantages and disadvantages. Deep learning and -neural network software are the categories of artificial neural network. The parallel process also allows ANNs to process the large amount of data very efficiently. The artificial neural network is built with a systematic

Abstract: Incidence rate of skin cancer are increasing day by day. Skin cancer is one of the deadliest forms of cancer but detected earlier can save the life time of the human being. An automated screening system is introduced to identify the presence of skin cancer in advance. In this paper, texture distinctiveness lesion segmentation algorithm is used. Experience and training-based characteristics of back propagation neural network is used with texture distinctiveness lesion segmentation algorithm, for identifying the normal and abnormal portions of skin .The most commonly occurring skin cancers are Melanoma, Basal and squamous cell carcinoma and actinic keratosis. The proposed system is to diagnose the presence of these skin cancers with high segmentation accuracy.

Extreme learning Machine (ELM) [1] is a single hidden layer feed forward network (SLFN) introduced by G. B. Huang in 2006. In ELM, the weights between input and hidden neurons and the bias for each hidden neuron are assigned randomly. The weight between output neurons and hidden neurons are generated using the Moore Penrose Generalized Inverse [18]. This makes ELM a fast learning classifier. It surmounts various traditional gradient based learning algorithms [1] such as Back Propagation (BP) and well known classifier Support Vector Machine (SVM) .

Partitioning methods: This algorithm groups dataset into q clusters, where q is a predefined parameter.

In the second research paper, researchers focus on a varia¬tion of the standard back propagation algorithm. Since nodes of each layer are fully connected in the standard back propagation neural networks, huge computing resour¬ces are cost to adjust weights. Therefore researchers pro¬pose an exclusive connecting network.

Compared with other data mining application fields, medical data mining plays an important role and it has some unique characteristics. The medical data processing has the high potential in medical domain for extracting the hidden patterns within the dataset [15]. These patterns are used for clinical diagnosis and prognosis. The medical data are generally distributed, heterogeneous and voluminous in nature. The data ought to be integrated and collected to provide a user oriented approach to hidden patterns of data. An important problem in medical analysis is to achieve the correct diagnosis of certain important information. This paper describes classification algorithms and it is used to analyze the performance of these algorithms. The performance factor is used for analysis are accuracy and error measures. The accuracy measures are True Positive (TP) rate, F Measure, Receiver Operating Characteristics (ROC) area and Kappa Statistics. The error measures are Mean Absolute Error (M.A.E), Root Mean Squared Error (R.M.S.E), Relative Absolute Error (R.A.E) and Relative Root Squared Error (R.R.S.E) [5]. Section 2 describes the classification algorithms. Experimental results are analyzed in section 3 and section 4 describes the conclusion of this paper.

In order to accomplish this, we are making use of Torch framework [2]. It is a scientific computing framework and supports wide range of machine learning algorithms. Torch has built-in support for large ecosystem of community-driven packages in machine learning. For more efficient and faster results, we are making use of CUDA, which stands for Compute Unified Device Architecture [3]. It is a parallel computing platform built by NVIDIA. As the torch framework has support for CUDA libraries, it is possible to deploy the neural network directly on the GPU instead of the CPU. This greatly helps in optimizing the performance of neural network and provides much faster results than standard CPUs.

Real valued classification is a popular decision making problem, having wide practical application in various fields. Extreme Learning Machine (ELM) pro-

With the development of artificial intelligence in recent years, there has been a growing interest in algorithms inspired by the observation of natural phenomena. We can see that all the algorithms are good replacements as method to solve complex computational problems.

Profound learning is a subset of machine learning in Artificial Intelligence (AI) that has systems which are fit for taking in unsupervised from information that is unstructured or unlabelled. I have prepared 9 articles related to deep learning which is basically of my understanding in depth about the artificial intelligence and its different functionalities.

Since this task of recognizing a visual concept is relatively trivial for a human to perform,there are several challenges,as follows, to overcome in order to create a perfect classifier.\\

In the last years, several studies have focused on improving feature selection and dimensionality reduction techniques and substantial progress has been obtained in selecting, extracting and constructing useful feature sets. However, due to the strong inuence of different feature subset selection methods on the classification accuracy, there are still several open questions in this research field. Moreover, due to the often increased number of candidate features for various application areas new questions arise.

On other hand of the proposed recognition system, another feature extraction is modified and optimized to be used known as

In Paper [5] they actualized K-implies alongside Genetic algorithm for dimensionality diminishment and bolster vector machine to group the informational collection. K-implies calculation is utilized to expel exceptions and the loud information. The ideal highlights are chosen by utilizing the hereditary calculation and afterward Support vector machine orders the lessened information space utilizing 10 crease cross approval strategy. Genetic algorithm chooses diverse highlights from unique arrangement of highlight amid each run. To get steady outcomes, the analysis was evaluated fifty times. The consequence shows that the expected replica gains the correctness of 98.82%.

The one of the famous applications of Article Intelligence is expert systems (ESs). The expert systems offer