Evaluation of neural network pattern classifiers for a remote sensing application

This paper evaluates the classification accuracy of three neural network classifiers on a satellite image-based pattern classification problem. The neural network classifiers used include two types of the Multi-Layer-Perceptron (MLP) and the Radial Basis Function Network. A normal (conventional) classifier is used as a benchmark to evaluate the performance of neural network classifiers. The satellite image consists of 2,460 pixels selected from a section (270 x 360) of a Landsat-5 TM scene from the city of Vienna and its northern surroundings. In addition to evaluation of classification accuracy, the neural classifiers are analysed for generalization capability and stability of results. Best overall results (in terms of accuracy and convergence time) are provided by the MLP-1 classifier with weight elimination. It has a small number of parameters and requires no problem-specific system of initial weight values. Its in-sample classification error is 7.87% and its out-of-sample classification error is 10.24% for the problem at hand. Four classes of simulations serve to illustrate the properties of the classifier in general and the stability of the result with respect to control parameters, and on the training time, the gradient descent control term, initial parameter conditions, and different training and testing setshttps://ssrn.com/abstract=1523788%20or%20http://dx.doi.org/10.2139/ssrn.1523788Published versio

Evaluation of Neural Pattern Classifiers for a Remote Sensing Application

This paper evaluates the classification accuracy of three neural network classifiers on a satellite image-based pattern classification problem. The neural network classifiers used include two types of the Multi-Layer-Perceptron (MLP) and the Radial Basis Function Network. A normal (conventional) classifier is used as a benchmark to evaluate the performance of neural network classifiers. The satellite image consists of 2,460 pixels selected from a section (270 x 360) of a Landsat-5 TM scene from the city of Vienna and its northern surroundings. In addition to evaluation of classification accuracy, the neural classifiers are analysed for generalization capability and stability of results. Best overall results (in terms of accuracy and convergence time) are provided by the MLP-1 classifier with weight elimination. It has a small number of parameters and requires no problem-specific system of initial weight values. Its in-sample classification error is 7.87% and its out-of-sample classification error is 10.24% for the problem at hand. Four classes of simulations serve to illustrate the properties of the classifier in general and the stability of the result with respect to control parameters, and on the training time, the gradient descent control term, initial parameter conditions, and different training and testing sets. (authors' abstract)Series: Discussion Papers of the Institute for Economic Geography and GIScienc

An Enhanced CNN-based ELM Classification for Disease Prediction in the Rice Crop

To meet the demands of a constantly expanding population, intensive farming is becoming more popular in the modern day. This strategy, meanwhile, increases the possibility of a wider range of plant illnesses. By reducing crop productivity in terms of both quantity and quality, these infections represent a threat to food production and ultimately result in a fall in the economy. Fortunately, new opportunities for early diagnosis of such epidemics have emerged because of technological improvements, which are advantageous for society as a whole. The difficulties created by technology and bio-mutations create a potential for additional breakthroughs, notwithstanding the significant contributions made by researchers in the field of agricultural disease diagnosis. The suggested framework comprises three key phases: preprocessing, feature extraction, and the classification of leaf diseases. To optimize computational resources and memory utilization, the input image undergoes pre-processing as a preliminary step. Afterward, a Convolutional Neural Network (CNN) is utilized on an extensive dataset of labeled images to capture pertinent features for the diagnosis of rice leaf diseases. The suggested model utilizes an Efficient Selective Pruning of Hidden Nodes (ELM) classifier based on the RBF kernel to classify the input data

Use of Machine Learning for Automated Convergence of Numerical Iterative Schemes

Convergence of a numerical solution scheme occurs when a sequence of increasingly refined iterative solutions approaches a value consistent with the modeled phenomenon. Approximations using iterative schemes need to satisfy convergence criteria, such as reaching a specific error tolerance or number of iterations. The schemes often bypass the criteria or prematurely converge because of oscillations that may be inherent to the solution. Using a Support Vector Machines (SVM) machine learning approach, an algorithm is designed to use the source data to train a model to predict convergence in the solution process and stop unnecessary iterations. The discretization of the Navier Stokes (NS) equations for a transient local hemodynamics case requires determining a pressure correction term from a Poisson-like equation at every time-step. The pressure correction solution must fully converge to avoid introducing a mass imbalance. Considering time, frequency, and time-frequency domain features of its residual’s behavior, the algorithm trains an SVM model to predict the convergence of the Poisson equation iterative solver so that the time-marching process can move forward efficiently and effectively. The fluid flow model integrates peripheral circulation using a lumped-parameter model (LPM) to capture the field pressures and flows across various circulatory compartments. Machine learning opens the doors to an intelligent approach for iterative solutions by replacing prescribed criteria with an algorithm that uses the data set itself to predict convergence

Recognition of transport means in GPS data using machine-learning methods

Bicycle transport is today one of the most important measures in urban traffic with a view to moving towards more sustainable mobility. Nowadays, smartphones are equipped with Global Positioning System (GPS), which allows cyclists, through smartphone applications, to record their own routes on a daily basis, which is very useful information for traffic and transport planners.The problem appears when there is invalid data due to errors in the measurement or in the GPS signal. The solution is transport mode recognition, which consists of classifying the different existing transport modes on the basis of a set of data. The emerging techniques of machine learning allow the development of very powerful models capable of recognizing means of transport with great effectiveness, based on other studies.Accordingly, this study aims to separate GPS bicycle tracks from the other modes studied (inner-city train (S-Bahn), walk, bike, tram, bus), also classifying the tracks of each means of transport separately. The key contribution of this study is the design and implementation of a machine learning model capable of classifying existing modes of transport in urban traffic in the city of Dresden in Germany.For this purpose, a cascading classifiers model was designed so that in each phase tracks belonging to a different mode are separated, studying in each phase which of the machine learning algorithms used (Decision Tree, Support Vector Machine and Neural Network) has the best performance. The GPS data was collected with the application for smartphone Cyface and from there it was carried out the structuring of data and calculation and selection of features that serve as inputs of the model.To separate inner-city train (S-Bahn), bike and walk tracks (first three phases) accuracy values above 98 % are obtained for any of the mentioned algorithms. For the fourth phase, where the classification between bus and tram tracks is carried out, the performance of the model is not so outstanding, due to its similar characteristics, but nevertheless reaches an accuracy value of 83 % using a Neural Network Multi-layer Perceptron model. The great performance of the model after the training phase allowed its implementation using unlabeled tracks, achieving very good results with an accuracy of 92.6 % in the prediction of the tracks used, making only mistakes in distinguishing between tram and bus tracks.<br /

Classifiers consensus system approach for credit scoring

Banks take great care when dealing with customer loans to avoid any improper decisions that can lead to loss of opportunity or financial losses. Regarding this, researchers have developed complex credit scoring models using statistical and artificial intelligence (AI) techniques to help banks and financial institutions to support their financial decisions. Various models, from easy to advanced approaches, have been developed in this domain. However, during the last few years there has been marked attention towards development of ensemble or multiple classifier systems, which have proved their ability to be more accurate than single classifier models. However, among the multiple classifier systems models developed in the literature, there has been little consideration given to: 1) combining classifiers of different algorithms (as most have focused on building classifiers of the same algorithm); or 2) exploring different classifier output combination techniques other than the traditional ones, such as majority voting and weighted average. In this paper, the aim is to present a new combination approach based on classifier consensus to combine multiple classifier systems (MCS) of different classification algorithms. Specifically, six of the main well-known base classifiers in this domain are used, namely, logistic regression (LR), neural networks (NN), support vector machines (SVM), random forests (RF), decision trees (DT) and naïve Bayes (NB). Two benchmark classifiers are considered as a reference point for comparison with the proposed method and the other classifiers. These are used in combination with LR, which is still considered the industry-standard model for credit scoring models, and multivariate adaptive regression splines (MARS), a widely adopted technique in credit scoring studies. The experimental results, analysis and statistical tests demonstrate the ability of the proposed combination method to improve prediction performance against all base classifiers, namely, LR, MARS and seven traditional combination methods, in terms of average accuracy, area under the curve (AUC), the H-measure and Brier score (BS). The model was validated over five real-world credit scoring datasets

An Ensemble Classification and Hybrid Feature Selection Approach for Fake News Stance Detection

The developments in Internet and notions of social media have revolutionised representations and disseminations of news. News spreads quickly while costing less in social media. Amidst these quick distributions, dangerous or seductive information like user generated false news also spread equally. on social media. Distinguishing true incidents from false news strips create key challenges. Prior to sending the feature vectors to the classifier, it was suggested in this study effort to use dimensionality reduction approaches to do so. These methods would not significantly affect the result, though. Furthermore, utilising dimensionality reduction techniques significantly reduces the time needed to complete a forecast. This paper presents a hybrid feature selection method to overcome the above mentioned issues. The classifications of fake news are based on ensembles which identify connections between stories and headlines of news items. Initially, data is pre-processed to transform unstructured data into structures for ease of processing. In the second step, unidentified qualities of false news from diverse connections amongst news articles are extracted utilising PCA (Principal Component Analysis). For the feature reduction procedure, the third step uses FPSO (Fuzzy Particle Swarm Optimization) to select features. To efficiently understand how news items are represented and spot bogus news, this study creates ELMs (Ensemble Learning Models). This study obtained a dataset from Kaggle to create the reasoning. In this study, four assessment metrics have been used to evaluate performances of classifying models