FINDING PROBLEM SPECIFIC SHANNON INFORMATION IN HIGH DIMENSIONAL INPUT SPACE FOR ARTIFICIAL NEURAL NETWORKS

2013 ◽  
Vol 07 (02) ◽  
pp. 147-155
Author(s):  
JOSEPH R. BARR ◽  
W. KURT DOBSON
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Data Mining ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Credit Card ◽  
Sparse Data ◽  
Shannon Information ◽  
Input Space ◽  
Artificial Neural

Artificial neural networks, due to their ability to find the underlying model even in complex highly nonlinear and highly coupled problems, have found significant use as prediction engines in many domains. However, in problems where the input space is of high dimensionality, there is the unsolved problem of reducing dimensionality in some optimal way such that Shannon information important to the prediction is preserved. The important Shannon information may be a subset of total information with an unknown partition, unknown coupling and linear or nonlinear in nature. Solving this problem is an important step in classes of machine learning problems and many data mining applications. This paper describes a semi-automatic algorithm that was developed over a 5-year period while solving problems with increasing dimensionality and difficulty in (a) flow prediction for a magnetically levitated artificial heart (13 dimensions), (b) simultaneous chemical identification/concentration in gas chromatography (22 detection dimensions with wavelet compressed time series of 180,000 points), and finally in (c) financial analytics portfolio prediction in credit card and sub-prime debt problems (80 to 300 dimensions of sparse data with a portfolio value of approximately US$300,000,000.00). The algorithm develops a map of input space combinations and their importance to the prediction. This information is used directly to construct the optimal neural network topology for a given error performance. Importantly, the algorithm also produces information that shows whether the space between input nodes is linear or nonlinear; an important parameter in determining the number of training points required in the reduced dimensionality of the training set. Software was developed in the MatLAB environment using the Artificial Neural Network Toolbox, Parallel and Distributed Computing toolboxes, and runs on Windows or Linux based supercomputers. Trained neural networks can be compiled and linked to server applications and run on normal servers or clusters for transaction or web based processing. In this paper, application of the algorithm to two separate financial analytics prediction problems with large dimensionality and sparse data sets are shown. The algorithm is an important development in machine learning for an important class of problems in prediction, clustering, image analysis, and data mining. In the first example application for subprime debt portfolio analysis, performance of the neural network provided a 98.4% prediction rate, compared to 33% rate using traditional linear methods. In the second example application regarding credit card debt, performance of the algorithm provided a 95% accurate prediction (in terms of match rate), and is 10% better than other methods we have compared against, primarily logistic regression.

Fundamental Categories of Artificial Neural Networks

2021 ◽  
pp. 30-64
Author(s):  
Arunaben Prahladbhai Gurjar ◽  
Shitalben Bhagubhai Patel
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Artificial Neural Networks ◽  
Mathematical Operation ◽  
Artificial Neural ◽  
Classification Prediction ◽  
Artificial Neural Network Ann ◽  
Fundamental Categories

The new era of the world uses artificial intelligence (AI) and machine learning. The combination of AI and machine learning is called artificial neural network (ANN). Artificial neural network can be used as hardware or software-based components. Different topology and learning algorithms are used in artificial neural networks. Artificial neural network works similarly to the functionality of the human nervous system. ANN is working as a nonlinear computing model based on activities performed by human brain such as classification, prediction, decision making, visualization just by considering previous experience. ANN is used to solve complex, hard-to-manage problems by accruing knowledge about the environment. There are different types of artificial neural networks available in machine learning. All types of artificial neural networks work based of mathematical operation and require a set of parameters to get results. This chapter gives overview on the various types of neural networks like feed forward, recurrent, feedback, classification-predication.

Fundamental Categories of Artificial Neural Networks

2022 ◽  
pp. 1-30
Author(s):  
Arunaben Prahladbhai Gurjar ◽  
Shitalben Bhagubhai Patel
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Artificial Neural Networks ◽  
Mathematical Operation ◽  
Artificial Neural ◽  
Classification Prediction ◽  
Artificial Neural Network Ann ◽  
Fundamental Categories

The new era of the world uses artificial intelligence (AI) and machine learning. The combination of AI and machine learning is called artificial neural network (ANN). Artificial neural network can be used as hardware or software-based components. Different topology and learning algorithms are used in artificial neural networks. Artificial neural network works similarly to the functionality of the human nervous system. ANN is working as a nonlinear computing model based on activities performed by human brain such as classification, prediction, decision making, visualization just by considering previous experience. ANN is used to solve complex, hard-to-manage problems by accruing knowledge about the environment. There are different types of artificial neural networks available in machine learning. All types of artificial neural networks work based of mathematical operation and require a set of parameters to get results. This chapter gives overview on the various types of neural networks like feed forward, recurrent, feedback, classification-predication.

Convolutional Neural Network

2022 ◽  
pp. 1559-1575
Author(s):  
Mário Pereira Véstias
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Machine Learning Algorithms ◽  
Training Data ◽  
Machine Learning Model ◽  
Artificial Neural

Machine learning is the study of algorithms and models for computing systems to do tasks based on pattern identification and inference. When it is difficult or infeasible to develop an algorithm to do a particular task, machine learning algorithms can provide an output based on previous training data. A well-known machine learning model is deep learning. The most recent deep learning models are based on artificial neural networks (ANN). There exist several types of artificial neural networks including the feedforward neural network, the Kohonen self-organizing neural network, the recurrent neural network, the convolutional neural network, the modular neural network, among others. This article focuses on convolutional neural networks with a description of the model, the training and inference processes and its applicability. It will also give an overview of the most used CNN models and what to expect from the next generation of CNN models.

scholarly journals Exploiting Artificial Neural Networks Machine Learning Errors for Attacks on AI Systems

2021 ◽  
pp. 23-32
Author(s):  
Т. В. Гавриленко ◽  
А. В. Гавриленко
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Machine Learning ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Artificial Neural ◽  
Current Paradigm ◽  
Artificial Intelligence Systems

В статье приведен обзор различных методов атак и подходов к атакам на системы искусственного интеллекта, построенных на основе искусственных нейронных сетей. Показано, что начиная с 2015 года исследователи в различных странах активно развивают методы атак и подходы к атакам на искусственные нейронные сети, при этом разработанные методы и подходы могут иметь критические последствия при эксплуатации систем искусственного интеллекта. Делается вывод о необходимости развития методологической и теоретической базы искусственных нейронных сетей и невозможности создания доверительных систем искусственного интеллекта в текущей парадигме. The paper provides an overview of methods and approaches to attacks on neural network-based artificial intelligence systems. It is shown that since 2015, global researchers have been intensively developing methods and approaches for attacks on artificial neural networks, while the existing ones may have critical consequences for artificial intelligence systems operations. We come to the conclusion that theory and methodology for artificial neural networks is to be elaborated, since trusted artificial intelligence systems cannot be created in the framework of the current paradigm.

scholarly journals Neural networks in recognition of handwriting

2020 ◽  
Vol 15 ◽  
pp. 109-113
Author(s):  
Bernadetta Michalik ◽  
Marek Miłosz
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Human Brain ◽  
Handwriting Recognition ◽  
Processing Data ◽  
Artificial Neural

Artificial neural networks consist of many simple elements capable of processing data. These are tools inspired by the construction of the human brain, used in machine learning. The aim of the research was to analyze the occuracy of the created neural network in the process of handwriting recognition. The article presents the results obtained during the learning and testing of a convolution network with a different number of hidden layers. Each time learning and testing the network was carried out using the same set of images (taken from the publicly available IAM database) depicting handwritten words in English.

scholarly journals Convolutional Neural Network for Image Recognition

2021 ◽  
Vol 9 (11) ◽  
pp. 1619-1624
Author(s):  
Ankith I
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Neural Networks ◽  
Pattern Recognition ◽  
Artificial Neural Networks ◽  
Convolutional Neural Network ◽  
Image Recognition ◽  
Computational Models ◽  
Simplified Approach ◽  
Artificial Neural

Abstract: Recent developments in the field of machine learning have changed the way it operates for ever, especially with the rise of Artificial Neural Networks (ANN). There is no doubt that these biologically inspired computational models are capable of performing far better than previous forms of artificial intelligence in common machine learning tasks as compared to their previous versions. There are several different forms of artificial neural networks (ANNs), but one of the most impressive is the convolutional neural network (CNN). CNN's have been extensively used for solving difficult pattern recognition tasks using images. With their simple yet precise architecture, they offer a simplified approach to getting started with ANNs. The goal of this paper is to provide a brief introduction to CNN. It discusses the latest papers and newly formed techniques in order to develop these absolutely brilliant models of image recognition. This introduction assumes that you already have a basic understanding of ANNs and machine learning. Keywords: Pattern recognition, artificial neural networks, machine learning, image analysing.

scholarly journals Credit Card Fraud Analysis using Robust Space Invariant Artificial Neural Networks (RSIANN)

2019 ◽  
Vol 8 (2) ◽  
pp. 6413-6417
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Credit Card ◽  
Convolution Neural Network ◽  
Support Vector ◽  
Credit Card Fraud ◽  
Tuning Parameters ◽  
Deep Convolution Neural Network ◽  
Artificial Neural

One of the impact factor for any organizations or banks revenue and service quality is credit card fraud activities. Hence, need of efficient approach for detect early potential fraud and/or prevent them. In this paper, we considered pre-processing and used deep convolution neural network called as Space Invariant Artificial Neural Networks for classifying fraudsters. Available Credit card fraud dataset may not have sufficient information hence need pre-processing. The proposed approach has pre-processing phrase to make as robust. This approach used leverage layers and suitable tuning parameters for getting good classification accuracy. In neural network applications, choosing of tuning parameters and model selection has great role in solving the problems. We have done careful analysis and selected leverage layers and corresponding parameter values. The proposed architecture tested with all possible tuning parameters to evaluate the performance on pre-processed credit card fraud records. We found the proposed robust SIANN (RSIANN) is outperformed other state-of-art machine learning (ML) algorithms (Support vector machine (SVM), random forest (RF), Navie bayes and deep convolution neural network (DCNN) in terms of accuracy (85%). Thus, this model analyses the transaction and decide it fraud or not.

Convolutional Neural Network

2021 ◽  
pp. 12-26
Author(s):  
Mário Pereira Véstias
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Machine Learning Algorithms ◽  
Training Data ◽  
Machine Learning Model ◽  
Artificial Neural

Machine learning is the study of algorithms and models for computing systems to do tasks based on pattern identification and inference. When it is difficult or infeasible to develop an algorithm to do a particular task, machine learning algorithms can provide an output based on previous training data. A well-known machine learning model is deep learning. The most recent deep learning models are based on artificial neural networks (ANN). There exist several types of artificial neural networks including the feedforward neural network, the Kohonen self-organizing neural network, the recurrent neural network, the convolutional neural network, the modular neural network, among others. This article focuses on convolutional neural networks with a description of the model, the training and inference processes and its applicability. It will also give an overview of the most used CNN models and what to expect from the next generation of CNN models.

scholarly journals The Budapest Amyloid Predictor and Its Applications

Biomolecules ◽  
2021 ◽  
Vol 11 (4) ◽  
pp. 500
Author(s):  
László Keresztes ◽  
Evelin Szögi ◽  
Bálint Varga ◽  
Viktor Farkas ◽  
András Perczel ◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Support Vector ◽  
User Needs ◽  
Computational Technique ◽  
Linear Support Vector Machine ◽  
Β Sheets ◽  
Artificial Neural ◽  
Amorphous Aggregation

The amyloid state of proteins is widely studied with relevance to neurology, biochemistry, and biotechnology. In contrast with nearly amorphous aggregation, the amyloid state has a well-defined structure, consisting of parallel and antiparallel β-sheets in a periodically repeated formation. The understanding of the amyloid state is growing with the development of novel molecular imaging tools, like cryogenic electron microscopy. Sequence-based amyloid predictors were developed, mainly using artificial neural networks (ANNs) as the underlying computational technique. From a good neural-network-based predictor, it is a very difficult task to identify the attributes of the input amino acid sequence, which imply the decision of the network. Here, we present a linear Support Vector Machine (SVM)-based predictor for hexapeptides with correctness higher than 84%, i.e., it is at least as good as the best published ANN-based tools. Unlike artificial neural networks, the decisions of the linear SVMs are much easier to analyze and, from a good predictor, we can infer rich biochemical knowledge. In the Budapest Amyloid Predictor webserver the user needs to input a hexapeptide, and the server outputs a prediction for the input plus the 6 × 19 = 114 distance-1 neighbors of the input hexapeptide.

scholarly journals A Machine Learning Approach as a Surrogate for a Finite Element Analysis: Status of Research and Application to One Dimensional Systems

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1654
Author(s):  
Poojitha Vurtur Badarinath ◽  
Maria Chierichetti ◽  
Fatemeh Davoudi Kakhki
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Finite Element Analysis ◽  
Finite Element ◽  
Artificial Neural Networks ◽  
Stress Distribution ◽  
Maintenance Scheduling ◽  
Element Analysis ◽  
One Dimensional ◽  
Artificial Neural

Current maintenance intervals of mechanical systems are scheduled a priori based on the life of the system, resulting in expensive maintenance scheduling, and often undermining the safety of passengers. Going forward, the actual usage of a vehicle will be used to predict stresses in its structure, and therefore, to define a specific maintenance scheduling. Machine learning (ML) algorithms can be used to map a reduced set of data coming from real-time measurements of a structure into a detailed/high-fidelity finite element analysis (FEA) model of the same system. As a result, the FEA-based ML approach will directly estimate the stress distribution over the entire system during operations, thus improving the ability to define ad-hoc, safe, and efficient maintenance procedures. The paper initially presents a review of the current state-of-the-art of ML methods applied to finite elements. A surrogate finite element approach based on ML algorithms is also proposed to estimate the time-varying response of a one-dimensional beam. Several ML regression models, such as decision trees and artificial neural networks, have been developed, and their performance is compared for direct estimation of the stress distribution over a beam structure. The surrogate finite element models based on ML algorithms are able to estimate the response of the beam accurately, with artificial neural networks providing more accurate results.

Sign in / Sign up

Export Citation Format

Share Document