Neural

Comparative Predictive Modeling on CNX Nifty with Artificial Neural Network By Bikramaditya Ghosh, First and Corresponding Author Asst. Professor, ISME, Bangalore Address 301, Raghav Harmony, S R layout Off Wind Tunnel Road Bangalore-560017 INDIA E Mail- bikram77777@gmail.com Phone- +919535015777 Dr. Padma Srinivasan Assoc. Professor , Christ University, Bangalore Abstract CNX Nifty being an important barometer to indicate country’s growth has always been followed with lots

Neural Networks in Finance 2600 Words By Maria L. Vicente University of Hawaiʻi at Hilo QBA 362 Fall 2016 Introduction Predictions are an important component of determining the financial progress of a business. Business decisions rely on forecasting techniques to predict things such as price movements or overall success in markets. In the attempt to forecast market predictions, it must be assumed that future occurrences may be partly based on present and past data (Abu-Mostafa

pruning such as Genetic Algorithm (GA). A fitness function is genetically optimized to get a subset of classifiers which minimizes the error. Various variants of genetic based ensemble pruning have been proposed such as Genetic Algorithm based Selective Neural Network Ensemble (GASEN) [15], GAB: EPA [16]. Objective of GASEN is to select the best PE and maximize the accuracy of the PE by assigning the best weight to the classifiers of the PE. It uses fitness function, which is function of the generalization

Between day twenty-five and twenty-seven of pregnancy, normally before a woman even knows she is pregnant, the neural tube that ultimately becomes the brain and spinal cord begins to develop and eventually close. When the neural tube fails to close, the outcome is a neural tube defect called anencephaly. Anencephaly is a congenital birth defect and is characterized by the absence of the prosencephalon (fore-brain), cerebrum, cerebellum, and cranium. Part of the brain-stem is usually present. The

Introduction Face Images convey a significant amount of knowledge including information about identity, emotional state, ethnic origin, gender, age, and head orientation of a person shown in face image. This type of information plays a significant role during face-to-face communication between humans [1]. Above prospects of facial images can be used in emerging branch of Human Computer Interaction (HCI). Human age has following characteristics: Aging is uncontrollable process: Aging cannot be delayed

SPEAKER IDENTIFICATION AND VERIFICATION OVER SHORT DISTANCE TELEPHONE LINES USING ARTIFICIAL NEURAL NETWORKS Ganesh K Venayagamoorthy, Narend Sunderpersadh, and Theophilus N Andrew gkumar@ieee.org sundern@telkom.co.za theo@wpo.mlsultan.ac.za Electronic Engineering Department, M L Sultan Technikon, P O Box 1334, Durban, South Africa. ABSTRACT Crime and corruption have become rampant today in our society and countless money is lost each year due to white collar crime, fraud, and embezzlement. This

project budgeting and controlling if reliable. This paper presents a comparison between two cost estimate predictive modeling, an artificial intelligence approach using neural networks as non-parametric modeling; and multiplely regression modeling that represents advanced engineering statics - a parametric modeling. Results show that neural networks have advantages when dealing with data that for which there is little a priori knowledge of the appropriate cost estimate relationship to select

In this paper we present an analysis of face recognition system with a combination of Neural networks withSub-space method of feature extraction. Here we are considering both single layer such as Generalized Regression neural network (GRNN) and Multi layer such as Learning Vector quantization (LVQ). The analysis of these neural networks are done between feature vectors with respect to the recognition performance of subspace methods, namely Principal Component Analysis (PCA) and Fisher Linear Discriminant

Neural Recording and Processing The most critical element of a Brain Machine Interface (BMI) is the recording and processing of the neural signal. We use an invasive neural signal recording to achieve higher performance of the BMI and to obtain better resolution. We will be recording the neural signal on central sulcus located in the cortex region of the brain. This recording is referred to as Electrocorticography (ECoG). We will be using subdural grid electrodes (surface electrodes) with 48 contact

Researchers in the field of neuroscience have long disputed the type of neural modelling that allows for the processing of visual stimuli in the brain. I believe that a hierarchical framework exists in which both distributed and localised modelling can occur at different stages. Distributed modelling occurs at lower hierarchal levels and localised modelling, characterised by grandmother cells, occurs at higher levels. Neurons in higher levels pool information passed on from lower levels to so that