Artificial Neural Networks ( Anns ) - 1749 Words

Artificial neural networks (ANNs) are computational algorithms loosely based on the human biological nervous system which work to model statistical data. An ANN â€Å"consists of processing elements known as neurons that are interconnected to each other and work in unison to answer a particular problem [, and] can be used in places where detecting trends and extracting patterns are too complex to be detected by either humans or other computer techniques.† Although recent in their explosion in popularity, the underlying logic behind ANNs have existed for â€Å"nearly a half-century†, however due to the pervasive and ubiquitous adoption of powerful computational tools in our contemporary society, ANNs have had a sort of renaissance, much to the avail of scientists, engineers, and consumers. The current cutting-edge in deep-learning and ANNs focuses highly on both their ability to model and interpret complex data, and their ability to be put to scale due through optimization and parallelization. The current framework for designing ANNs is one that is widely available, with a myriad of tools facilitating their development. Python, C++, Google’s Tensorflow, Theano, Matlab, and Spark all contain a robust set of mathematical operations which necessitate ANNs. Due to the algorithm behind ANNs, the models are inherently apt in â€Å"extract[ing] meaning from imprecise or intricate problems†. Speaking reductively, ANNs are data modeling tools which are trained on a given dataset, and canShow MoreRelatedArtificial Neural Networks ( Ann )2161 Words   |  9 PagesCHAPTER 5 Artificial Neural Networks (ANN) 5.1 Machine Learning In machine learning, systems are trained to infer patterns from observational data. A particularly simple type of pattern, a mapping between input and output, can be learnt through a process called supervised learning. A supervised-learning system is given training data consisting of example inputs and the corresponding outputs, and comes up with a model to explain those data (a process called function approximation). It does thisRead MoreArtificial Neural Networks : An Example Of Machine Learning920 Words   |  4 PagesIn 1958 Psychologist Frank Rosenblatt invented the first artificial neural network. He called it Perceptron and hoped it would model the human brain and process visual data and learn to recognize objects. Artificial Neural Networks (ANNs) are expected to grow within the next few years. An artificial neural network is composed of interconnected artificial neurons that mimic some properties of biological neurons. ANNs work like simulated brains. They are given softwar e and be set up in a way that mimicsRead MoreThe Importance Of Breast Cancer810 Words   |  4 Pagesmalignant mammographic findings, however, is difficult. Only 15%-30% of biopsies performed on nonpalpable but mammographically suspicious lesions prove malignant. Automated classification of mammographic findings using discriminant analysis and artificial neural networks has indicated the potential usefulness of computer-aided diagnosis .(Shmit 2003) If breast cancer is left untreated, it will usually spread over into other areas of the body such as under the arm and to the lymph nodes which causes moreRead MoreStock Market Prediction Using Artificial Neural Networks And Regression Analysis871 Words   |  4 Pages Stock Market Prediction Using Artificial Neural Networks and Regression Analysis Tyler T. Procko Embry-Riddle Aeronautical University TO: Professor Michael Perez, M.A., M.F.A. FROM: Tyler T. Procko DATE: 10/03/2016 SUBJECT: Analytical Report Proposal I. Purpose / Background / Audience: Relatively accurate prediction of multi-tiered, non-linear events has long been a difficult and time-consuming task to perform; forecasting the movement ofRead MoreAnalyzing The Field Of Big Data954 Words   |  4 PagesResearch Paper: Artificial Neural Networks (ANN) Authors: Khushboo Arora, Shrutika Suri , Divya Arora and Vaishali Pandey Published online: 30 April 2014 Purpose: As mentioned in the abstract, It is difficult to find patterns in massive amount of unstructured data. Thereby, this paper addresses an important technique called Artificial Neural Networks which is being used a lot these days in field of machine learning, Artificial intelligence etc. The goal of Artificial neural networks is to find patternsRead MoreOptical Character Recognition Is Becoming Popular Areas Of Research Under Pattern Recognition And Smart Device Applications1212 Words   |  5 PagesProject Title:OPTICAL CHARACTER RECOGNITION USING ARTIFICIAL NEURAL NETWORKS Abstract: The Optical Character Recognition (OCR) is becoming popular areas of research under pattern recognition and smart device applications. It requires the intelligence like human brain to recognize the various handwritten characters. Artificial Neural Network (ANN) is used together the information required to recognize the characters adaptively. This paper presents a performance analysis of character recognitionRead MoreAn Effective Machine Learning Model1164 Words   |  5 Pagesestimating the class and location of objects contained within the images. With the improvements in object representations and machine learning models, it is possible to achieve much advancement in Object Recognition. For the last few years, Deep Neural Network has proven to be an effective machine learning model. DNNs have a varied approach to classification problems. They consist of deep architectures which makes it possible to understand more complex models than shallow ones. With this ability andRead MoreThe Deregulation Of The Electrical Power Industry1682 Words   |  7 Pagesperformance analysis of various neural networks (NN) for short term price forecasting. Several NN models are trained and tested on the half-hourly data from Australian Energy Market and their performances have been compared. Overall findings suggest that the value of mean absolute percentage Error (MAPE) in the case of 3-Layered cascaded neural network (CNN) is better than other proposed models. Keywords— Short term price forecasting, Cascaded Neural Network, Recurrent neural network, Australian energy marketRead MoreWireless Technology : A Growing Expansion Of Wireless Communication Technology1809 Words   |  8 Pagesan efficient usage of EM simulation for RF and microwave is the use of artificial neural network (ANN) models trained by full-wave EM simulators [2]. In order to design the patch antenna, CST-MWS simulators have been used. It is a special tool for the simulation of high frequency components. Training ANN configurations by using the data obtained from EM simulations develops an ANN model for each one of the components. Such ANN models have shown to retain the accuracy obtainable for EM simulators andRead MoreArtificial Neural Network Essay937 Words   |  4 PagesIn these project functional models of Artificial Neural Networks (ANNs) is proposed to aid existing diagnosis methods. ANNs are currently a â€Å"hot† research area in medicine, particu larly in the fields of radiology, cardiology, and oncology. In this an attempt is made to make use of ANNs in the medical field One of the important goals of Artificial Neural Networks is the processing of information similar to human interaction actually neural network is used when there is a need for brain capabilities