Forest Type Classification: A Hybrid NN-GA Model Based Approach
01 Jan 2016-pp 227-236
TL;DR: The authors have proposed a GA trained Neural Network classifier to tackle the task of classify tree species and one mixed forest class using geographically weighted variables calculated for Cryptomeria japonica and Chamaecyparis obtusa.
Abstract: Recent researches have used geographically weighted variables calculated for two tree species, Cryptomeria japonica (Sugi, or Japanese Cedar) and Chamaecyparis obtusa (Hinoki, or Japanese Cypress) to classify the two species and one mixed forest class. In machine learning context it has been found to be difficult to predict that a pixel belongs to a specific class in a heterogeneous landscape image, especially in forest images, as ground features of nearly located pixel of different classes have very similar spectral characteristics. In the present work the authors have proposed a GA trained Neural Network classifier to tackle the task. The local search based traditional weight optimization algorithms may get trapped in local optima and may be poor in training the network. NN trained with GA (NN-GA) overcomes the problem by gradually optimizing the input weight vector of the NN. The performance of NN-GA has been compared with NN, SVM and Random Forest classifiers in terms of performance measures like accuracy, precision, recall, F-Measure and Kappa Statistic. The results have been found to be satisfactory and a reasonable improvement has been made over the existing performances in the literature by using NN-GA.
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01 Jan 2019
TL;DR: Experimental results have suggested that the proposed deep learning-based model to predict benzene quantity in order to determine the quality of air is superior to the other models under current study.
Abstract: Recent studies have revealed the adverse effect of benzene as an air pollutant. Benzene has been proved to be causing several health hazards in unbar areas. Researchers have employed machine learning methods to predict the available benzene concentration in a particular area. Motivated by the recent advancements in the field of machine learning, the authors have proposed a deep learning-based model to predict benzene quantity in order to determine the quality of air as well. Benzene quantity prediction in the atmosphere has been accomplished with respect to certain specified elements (like carbon monoxide, PT08.S1, PT08.S2) that coexist along with benzene (C6H6). A feature selection stage has been employed using correlation analysis to find the most suitable set of features. Six features have been selected for the experimental purpose. Further, the proposed model has been compared with well-known machine learning models such as linear regression, polynomial regression, K-nearest neighbor, multilayer perceptron feedforward network (MLP-FFN) in terms of RMSE. Experimental results have suggested that the proposed deep learning-based model is superior to the other models under current study.
2 citations
21 Aug 2020
TL;DR: In this article, the authors developed a model for the forest cover type determination based on environmental characteristics and machine learning as the currently developing project part “Monitoring the trees condition using drones”.
Abstract: Today, one of lot global problems is forest deforestation and monitoring. The article describes the system creation for forests control and monitoring. This work aims is to develop a model for the forest cover type determination based on environmental characteristics and machine learning as the currently developing project part “Monitoring the trees condition using drones”. The project aims is to simplify and partially automate the control and monitoring of trees using drones and machine learning to improve the forest situation. The task is to create a model for predict what trees types grow in the area based on environmental characteristics. Therefore, the main system will able to compare the existing values/characteristics with the predicted ones (which tree should normally there) to find discrepancies. Eventually, the main system will able to use this information to report and inform relevant staff and authorities. This work is based on a data set for learning system that includes observations of trees from four areas of Roosevelt National Forest in Colorado. All observations are cartographic variables (without remote sensing) from \(30\times 30\)-m forest areas. In total, there are more than half a million measurements. The work aim is the development of a forest cover types classification model depending on the environment and its characteristics.
2 citations
Journal Article•
TL;DR: This forecast will prove accurate, it will predict rainfall based on previous records and will predict the rainfall state of the future using a Random forest rainfall prediction algorithm with factors including temperature, humidity, and wind.
Abstract: Machine learning seems to be an artificially intelligent application that demonstrates systems with both the ability to analyze and enhance inherently via experience whilst being specifically programmed. Algorithms rely on software programs that are developed that could also access information and using that to learn for itself. The prediction of rainfall is regarded as very significant in everyday life, from cultivation to event. Previous prediction of rainfall was using the complex combination of mathematical abstractions and it was inadequate to get such a high classification rate Prediction of rainfall is rendered via acquiring quantitative data about the present atmospheric state. Algorithms models could learn complicated mappings, based solely on samples, from inputs to outputs, and require minimal mapping. Due to the dynamic nature of the atmosphere, a precise prediction of weather conditions is a difficult task. To forecast the rainfall state of the future, the variability in situations in earlier years need to be used. The likelihood it will fit throughout the past year's neighboring fortnight is a very high Random forest rainfall prediction algorithm with factors including temperature, humidity, and wind. Therefore this forecast will prove accurate, it will predict rainfall based on previous records.The platform used is anaconda and the language is python which is portable and interactive. The libraries used for implementation are numpy, matlib, seaborn and pandas.
2 citations
01 Jan 2019
TL;DR: This paper represents the detection of human in a room on the basis of some identical features which has been done by using the artificial neural network with three data sets of training and testing with the help of a suitable algorithm from which 97% accuracy for detecting occupancy is being calculated.
Abstract: Accurate occupancy information in a room helps to provide different valuable applications like security, dynamic seat allocation, energy management etc. This paper represents the detection of human in a room on the basis of some identical features which has been done by using the artificial neural network with three data sets of training and testing with the help of a suitable algorithm from which 97% accuracy for detecting occupancy is being calculated.
1 citations
01 Dec 2019
TL;DR: A new neuro-genetic system named Apical Dominance based Genetic Algorithm based Neural Network (ADGA-NN) is proposed in this research work, which surpasses S GA-NN concerning convergence rate and generalization capability.
Abstract: Local minimum incorporated with premature saturation and slower convergence limits the performance of the Simple Genetic Algorithm based Neural Network (SGA-NN) algorithm. When the network reaches in local minima, the weights of the neural network become idle. To overcome this premature saturation and slow convergence a new neuro-genetic system named Apical Dominance based Genetic Algorithm based Neural Network (ADGA-NN) is proposed in this research work. As ‘Apical Dominance’ is a natural genetic event in plants, this algorithm may accelerate the training by updating the stationary weights of the neural network. ADGA-NN is experimented on five actual world's classification problems which are breast cancer, glass, Australian credit card, heart disease and thyroid problem. ADGA-NN surpasses SGA-NN concerning convergence rate and generalization capability.
1 citations
Cites methods from "Forest Type Classification: A Hybri..."
...Then a comparison is shown between NSGA-II and single objective functioned genetic algorithm-based neural network, which is proposed in [23] for forest type classification....
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References
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TL;DR: Internal estimates monitor error, strength, and correlation and these are used to show the response to increasing the number of features used in the forest, and are also applicable to regression.
Abstract: Random forests are a combination of tree predictors such that each tree depends on the values of a random vector sampled independently and with the same distribution for all trees in the forest. The generalization error for forests converges a.s. to a limit as the number of trees in the forest becomes large. The generalization error of a forest of tree classifiers depends on the strength of the individual trees in the forest and the correlation between them. Using a random selection of features to split each node yields error rates that compare favorably to Adaboost (Y. Freund & R. Schapire, Machine Learning: Proceedings of the Thirteenth International conference, aaa, 148–156), but are more robust with respect to noise. Internal estimates monitor error, strength, and correlation and these are used to show the response to increasing the number of features used in the splitting. Internal estimates are also used to measure variable importance. These ideas are also applicable to regression.
79,257 citations
TL;DR: Issues such as solving SVM optimization problems theoretical convergence multiclass classification probability estimates and parameter selection are discussed in detail.
Abstract: LIBSVM is a library for Support Vector Machines (SVMs). We have been actively developing this package since the year 2000. The goal is to help users to easily apply SVM to their applications. LIBSVM has gained wide popularity in machine learning and many other areas. In this article, we present all implementation details of LIBSVM. Issues such as solving SVM optimization problems theoretical convergence multiclass classification probability estimates and parameter selection are discussed in detail.
40,826 citations
TL;DR: High generalization ability of support-vector networks utilizing polynomial input transformations is demonstrated and the performance of the support- vector network is compared to various classical learning algorithms that all took part in a benchmark study of Optical Character Recognition.
Abstract: The support-vector network is a new learning machine for two-group classification problems. The machine conceptually implements the following idea: input vectors are non-linearly mapped to a very high-dimension feature space. In this feature space a linear decision surface is constructed. Special properties of the decision surface ensures high generalization ability of the learning machine. The idea behind the support-vector network was previously implemented for the restricted case where the training data can be separated without errors. We here extend this result to non-separable training data.
High generalization ability of support-vector networks utilizing polynomial input transformations is demonstrated. We also compare the performance of the support-vector network to various classical learning algorithms that all took part in a benchmark study of Optical Character Recognition.
37,861 citations
Book•
08 Sep 2000TL;DR: This book presents dozens of algorithms and implementation examples, all in pseudo-code and suitable for use in real-world, large-scale data mining projects, and provides a comprehensive, practical look at the concepts and techniques you need to get the most out of real business data.
Abstract: The increasing volume of data in modern business and science calls for more complex and sophisticated tools. Although advances in data mining technology have made extensive data collection much easier, it's still always evolving and there is a constant need for new techniques and tools that can help us transform this data into useful information and knowledge. Since the previous edition's publication, great advances have been made in the field of data mining. Not only does the third of edition of Data Mining: Concepts and Techniques continue the tradition of equipping you with an understanding and application of the theory and practice of discovering patterns hidden in large data sets, it also focuses on new, important topics in the field: data warehouses and data cube technology, mining stream, mining social networks, and mining spatial, multimedia and other complex data. Each chapter is a stand-alone guide to a critical topic, presenting proven algorithms and sound implementations ready to be used directly or with strategic modification against live data. This is the resource you need if you want to apply today's most powerful data mining techniques to meet real business challenges. * Presents dozens of algorithms and implementation examples, all in pseudo-code and suitable for use in real-world, large-scale data mining projects. * Addresses advanced topics such as mining object-relational databases, spatial databases, multimedia databases, time-series databases, text databases, the World Wide Web, and applications in several fields. *Provides a comprehensive, practical look at the concepts and techniques you need to get the most out of real business data
23,600 citations
01 Mar 1996
TL;DR: The article discusses the motivations behind the development of ANNs and describes the basic biological neuron and the artificial computational model, and outlines network architectures and learning processes, and presents some of the most commonly used ANN models.
Abstract: Artificial neural nets (ANNs) are massively parallel systems with large numbers of interconnected simple processors. The article discusses the motivations behind the development of ANNs and describes the basic biological neuron and the artificial computational model. It outlines network architectures and learning processes, and presents some of the most commonly used ANN models. It concludes with character recognition, a successful ANN application.
4,281 citations