Optical-based near-real time deforestation alert systems in the Brazilian Amazon are ineffective rainy season. This study identify clear-cut deforested areas through Neural Network (NN) algorithm based on C-band, VV- and VH-polarized, Sentinel-1 images. Statistical parameters of backscatter coefficients (mean, standard deviation, difference between maximum minimum values – MMD) were computed fr...

Herein, novel neural network (NN) methods that improve prediction accuracy and reduce output variance of the optimized input in gradient method for cross-sectional data are proposed, variability evaluation approach inputs semiconductor process is suggested. Specifically, electrical parameter measurements (EPMs) power-delay product industrial high-k metal gate DRAM peripheral 29-stage ring oscil...

: This paper presents the application of non-destructive evaluation by eddy currents for the determination of the geometrical and physical parameters of metal sheet, obedient to a sensor of a double coil (method of Adding-Opposing (A O)). The forward problem is solved by using an analytical model. The electrical impedance for coil is measured for two frequencies ranging from 1 kHz and 1 MHz. Th...

Model-based testing for real-life software systems often require a large number of tests, all of which cannot exhaustively be run due to time and cost constraints. Thus, it is necessary to prioritize the test cases in accordance with their importance the tester perceives. In this paper, this problem is solved by improving our given previous study, namely, applying classification approach to the...

In pattern recognition tasks, we usually do not pay much attention to the arbitrarily chosen training set of a pattern classiier beforehand. This paper proposes several methods for pruning data sets based on graph theory in order to alleviate the redundancy in the original data set whilst retaining the original data structure as far as possible. The proposed methods are applied to the training ...

We introduce Learn++, an algorithm for incremental training of neural network (NN) pattern classifiers. The proposed algorithm enables supervised NN paradigms, such as the multilayer perceptron (MLP), to accommodate new data, including examples that correspond to previously unseen classes. Furthermore, the algorithm does not require access to previously used data during subsequent incremental l...

Recognition of human activities aims a wide diversity of applications. However, identifying complicated activities continues a challenging and active research area. In this work, we assess a new approach of feature selection for human activity recognition. For the task, we also compare state-of-the-art classifiers, e.g., Bayes classifier, kNN, MLP, SVM, MLM and MLM-NN. Based on the experiments,...

System administrators have to analyze a number of system parameters to identify performance bottlenecks in a system. The major contribution of this paper is a utility – EvoPerf – which has the ability to autonomously monitor different system-wide parameters, requiring no user intervention, to accurately identify performance based anomalies (or bottlenecks). EvoPerf uses Windows Perfmon utility ...

In this paper, a new Multi-Layer Perceptron Neural Network (MLP NN) classifier is proposed for classifying sonar targets and non-targets from the acoustic backscattered signals. Besides capabilities of MLP NNs, it uses Back Propagation (BP) Gradient Descent (GD) training; therefore, NNs face with not only impertinent classification accuracy but also getting stuck in local minima as well low-con...

This paper proposes two hybrid connectionist structural acoustical models for robust context independent phone like and word like units for speaker-independent recognition system. Such structure combines strength of Hidden Markov Models (HMM) in modeling stochastic sequences and the non-linear classification capability of Artificial Neural Networks (ANN). Two kinds of Neural Networks (NN) are i...