Multiple Classifier System for Offline Malayalam Character Recognition

This talk reviews the recent progress in the extraction of bound-state characteristics from the operator-product expansion (OPE) for field-theory correlators, which constitutes the basis of the method of QCD sum rules. This progress is mainly related to a deeper understanding of one of the key ingredients of the method of sum rules -the effective continuum threshold. The latter determines to a great extent the numerical values of the bound-state parameters obtained by sum rules. The understanding of properties of the effective continuum threshold allows one to formulate a new algorithm for fixng this quantity for various correlators and in this way gain control over the systematic uncertainties of the bound-state parameters extracted from sum rules. We start with examples from quantum mechanics, where bound-state properties may be calculated independently in two ways: exactly, by solving the Schrdinger equation, and approximately, by the method of dispersive sum rules. Knowing the exact solution is crucial as it allows us to control each step of the sum-rule extraction procedure. On the basis of this analysis, we formulate several improvements of the standard procedures adopted in the method of sum rules in QCD. We then show the impact of these modifications on the extraction of the decay constants of heavy mesons.

Multiple Classifier System (MCS) has attracted increasing interest in the field of pattern recognition and machine learning where this technique has also been introduced in remote sensing. The importance of classifier diversity in MCS has been raised recently; nevertheless, only a few of the researches have been studied in land cover classification problem. In this paper, a SPOT IV satellite image covering the Hong Kong Island and Kowloon Peninsula with six land cover classes were classified with four base classifiers: Minimum Distance Classifier, Maximum Likelihood Classifier, Mahalanobis Classifier and K-Nearest Neighbor Classifier. Same training and testing data sets were applied throughout the experiments and five Bayesian decision rules, including product rule, sum rule, max rule, min rule, and median rule, were utilized to construct different ensemble of classifiers. Performance of MCS was measured using the overall accuracy and kappa statistics, and three statistical tests including McNemarpsilas test, Cochranpsilas Q test and F-test were introduced to examine the dependence of the classification results. The experimental comparison reveals that (i) increasing the number of base classifiers may not improve the overall accuracy in MCS, (ii) significant diversity in base classifiers cannot enhance the overall performance and vice versa. These findings are noted with the condition in using the same data set and the same training set.

This paper focus to analyze several fusion rule at matching score level to combine important features extracted from gait sequence images for human identification system. Gait sequence image is a non-stationary data and can be modelled using a statistical learning technique. The propose technique consists of three different stages. The pre-processing stage computes the average silhouette images to capture the important information and get a better representation for gait silhouette data. Then a principle component analysis (PCA) technique is applied on the average silhouette to extract the important gait features and reduce a dimension of gait data. Three different features are fused at matching score level by using sum, product and max rule. The proposed algorithm has been tested using a benchmark CASIA datasets. The experimental results show that the best recognition rate is 90% when the fusion is performed using sum rule.

Single-hidden-layer feed-forward quantum neural network based on Grover learning

We investigate a hidden-variable theory introduced by Kochen and Specker. The “hidden” results of measurements are either 1 or − 1. We suppose the validity of Sum rule and Product rule. Kochen and Specker suppose the two operations Sum rule and Product rule commute with each other. It is shown that the two operations Sum rule and Product rule do not commute with each other when we want to avoid the Kochen and Specker paradox. Otherwise we encounter the Kochen and Specker paradox. We mention the supposition for Greenberger, Horne, and Zeilinger paradox. It is discussed that only Product rule is necessary for the paradox. We give up the two paradoxes if (1) Sum rule and Product rule do not commute with each other and (2) Product rule is not valid.

Artificial intelligent provides diverse solutions for the complex problems in agriculture research. The study aimed to use three models of artificial neural networks (Feed Forward Neural Network (FFNN), Generalized Regression Neural Network (GRNN) and Radial-Basis Neural Network (RBNN)) in the field of wheat yield prediction. 27-year data for the period (1986-2012) were utilized to improve the models and four-year data (2013 and 2016) were used to estimate the models, to compare their outputs with the measured data. Prediction data was not entered in the process of building neural network models. The results showed that the optimal configuration of the FFNN model consists of 40 neurons in the hidden layer (8-40-1). The Tan Sigmoid activation function was used in both the hidden layer and the output layer using all of these models (anterior neural feeding network and the regression neural network and radial base neural network) in the 4-year wheat yield forecast field for production (2013-2016) by applying 8 input parameters that were result of NMMS (8.6%, 7.6% and 15.7% resp.), To find that FFNN and GRNN provide the best result from BRNN because while the information set was large or in a wide range, then the range data ranges from -1 to +1 (normalization data) , GRNN gives better outcomes after the information or sample data were in large range.

In present days, real time navigation depends on Kalman filter to fuse data from Global Positioning System (GPS) and Inertial Navigation System (INS). But there exist drawbacks like long design time, requirement of prior knowledge in fusing data from GPS and INS using Kalman Filter. In order to overcome the drawbacks of Kalman filter, GPS and INS integration are done using Artificial Neural Networks (ANN) like FFNNs. But still there exists certain drawbacks like less accuracy in FFNNs for GPS and INS data integration. Therefore, this paper introduces Higher Order Neural Networks (HONNs) approach for INS and GPS data integration to overcome the drawback. A higher order feed forward neural network architecture with optimum number of nodes is used. In the given architectures, the replacement of summation at each node by multiplication results in more powerful mapping, because of its capability of processing higher-order information from training data. Performance comparison of HONNs with FFNNs shows that the proposed architecture provides satisfactory results in terms of error rate and number of epochs. The HONNs like Multiplicative Neural Network (MNN) module and Sigma-Pi-ANN module and Traditional Feed Forward Neural Networks(FFNNs) like Radial Basis Function Neural Network (RBF) module and Back propagation Neural Network (BPN) module are trained to predict the INS position error and provide accurate position of the moving aircrafts. Sigma-Pi ANN is found to be the best in terms of accuracy, number of epochs and execution time among the two HONNs.

Ultrasound (US) imaging is the initial phase in the preliminary diagnosis for the treatment of kidney diseases, particularly to estimate kidney size, shape and position, to give information about kidney function, and to help in diagnosis of abnormalities like cysts, stones, junctional parenchyma and tumors which is shown in Figs. 7–9. This study proposes Grey Level Co-occurrence Matrix (GLCM)-based Probabilistic Principal Component Analysis (PPCA) and Artificial Neural Network (ANN) method for the classification of kidney images. Grey Wolf Optimization (GWO) is used to update the current positions of abnormal kidney images in the discrete searching space, thus getting the optimal feature subset for better classification purposes based on Feed Forward Neural Network (FFNN). The scanned image is pre-processed and the required features are extracted by GLCM, among those, some features are selected by PPCA. Feed Forward Back propagation Neural Network (FFBN) is used to classify the normalities and abnormalities in the part of kidney images. The proposed methodology is implemented in MATLAB platform and the analyzed result produces 98% accuracy using GWO-FFBN technique.

The adoption of wind energy has grown significantly in recent years. New, cost-effective technologies have been developed, led by customer awareness of green technologies and a legal framework proposed at the European Union level. The stochastic nature of wind speed is transferred to wind turbine output, making wind energy difficult to predict. The main scope of predicting wind energy production is to be proactive in balancing and reserving energy to meet demand. When the prediction identifies a potential gap between supply and demand, additional energy from other sources must be generated and supplied. Creating a synergy of physical devices through advanced sensing capabilities, software, storage and analytics capabilities, the Industrial Internet of Things is enabling the effective transition to wind energy through automation by removing many of the disadvantages in a way that has recently become accessible. This research focuses on the data analytics, proposing a fast univariate network-based approach for wind energy prediction, using Feed Forward Neural Networks, Recurrent Neural Networks, Long-Short Term Memory, Gated Recurrent Unit, and Convolutional Neural Networks. Moreover, by introducing the theoretical fundamentals, the implementation method and the hyperparameters of the final models, this article becomes unique in the context of wind energy. At the time of this study, no prior research studies have presented a direct comparison between feedforward, recurrent, and convolutional neural networks ? these being the most important in the field of supervised learning.

A multilayer feedforward neural network with a modification to the standard backpropagation training of neural nets is considered. Information on the gradients of target outputs with respect to network inputs is used in this modification. This modified neural network is then applied for approximating design requirements of aerospace composite components, such as an aircraft engine guide vane and a satellite reflector assembly. This neural network functional approximator is then used with an optimization algorithm for determining the optimal parameters for component design requirements. The modified neural network provides for faster convergence of learning process and requires a smaller number of hidden layer nodes for convergence to similar training error levels. HE application of neural networks as universal approximators has been the focus of tremendous amount of activity in the past few years.16 Neural networks have been used for approximating and modeling both memoryless systems and dynamical systems with memory. This paper does not go into the various neural net architectures and training schemes, and the interested reader could refer to standard texts on this subject matter.1'2 This paper considers static neural networks (systems without memory) formed by interconnections without loops, called feedforward networks.2 In addition, this work considers a backpropagation learning scheme with adaptation of learning rate. There have been many additions to, or adaptations of, the basic backpropagation scheme, such as adaptive learning rate, learning with momentum, and the like,1'2 for improving convergence of the learning scheme, and these are not addressed in this paper. This paper explores a modification of the backpropagation learning scheme using information on the gradient of the target outputs with respect to the inputs of the network. The performance of the modified learning scheme is compared with that of the standard backpropagation learning scheme.2 A feedforward network with a single hidden layer trained with this modified learning scheme is applied in structural optimization problems. Figure 1 shows a feedforward net with a single hidden layer. The restriction to a single hidden layer is not a limitation of the modified learning scheme, and the new training scheme can be used on networks with multiple hidden layers also. The use of neural networks for structural response approximation has been reported by several authors.46 The primary reason for investigating such properties is to assess the applicability of trained neural networks for quick and accurate approximation of response functions during structural design optimization. Structural optimization, utilizing either gradient-based numerical search or genetic search algorithms, requires repeated function (response) evaluations during the search process. A single finite element analysis of large-scale structures requires a significant amount of computational time and resource and hence the need for robust approxima

On the Circuit Complexity of Sigmoid Feedforward Neural Networks

This paper aims to represent a proposition of an innovative and novel methodology applicable in detecting and classifying the power quality disturbances present in the supply to the induction motor. In all practicality, considering circumstantial real world applications, induction motors are usually operated on load. If the supply voltage is varied in any way, it would adversely affect the normal operation of the motor. In the present work, a healthy induction motor is subjected to power quality disturbances like balanced voltage sag, balanced voltage swell, unbalanced voltage sag and unbalanced voltage swell. For the purpose of detecting these power quality disturbances, discrete wavelet transform is applied to the stator current of the induction motor. The stator current wavelet coefficients are fed as input to the neural network for the classification purpose. Radial basis neural network and feed forward neural network have been independently trained and tested. The observation about the feedforward network having higher performance efficiency as compared to the radial basis network, has been seen.

The application of artificial neural networks to the estimation of bioprocess variables will be discussed. In fermentation processes, direct on-line measurements of primary process variables usually are unavailable. The state of the cultivation, therefore, has to be inferred from measurements of secondary variables and any previous knowledge of process dynamics. This research investigates the learning, recall and generalization characteristics of neural networks trained to model the nonlinear behavior of a fermentation process. Two different neural network methodologies are discussed, namely, feed-forward and recurrent neural networks, which differ in their treatment of time dependence. The neural networks are trained by backpropagation using a conjugate gradient technique, which provides a dramatic improvement in the convergence speed. The objective is to use environmental and physiological information available from on-line sensors to estimate concentrations of species in a bioreactor. Results of the neural network estimators are presented, based on experimental data available from the ethanol production by Zymomonas mobilis fermentation. The feed-forward and recurrent neural network methodologies are demonstrated to perform suitably as unmeasurable state estimators. Both networks offer comparable abilities of recall, but recurrent networks perform better than feed-forward networks in generalization.

On-line Estimation of Bioreactors using Recurrent Neural Networks

The Borda Count method, a widely used ranked voting system, is known for its fairness and simplicity. However, when applied to large-scale voting systems, it faces challenges related to computational complexity, scalability, and system reliability. This paper proposes an optimization model for the Borda Count method by integrating blockchain consensus mechanisms, including Proof of Work (PoW), Proof of Stake (PoS), and Byzantine Fault Tolerance (BFT), aiming to enhance the voting process's efficiency, accuracy, and fault tolerance.We explore how blockchain technology can address the computational challenges of Borda Count, ensuring secure, transparent, and decentralized voting while maintaining high system reliability. By leveraging blockchain's immutability and consensus mechanisms, the proposed model significantly reduces computational overhead, increases the robustness of the system against node failures, and improves the accuracy of the voting results.This paper presents an in-depth analysis of the Borda Count method and blockchain consensus mechanisms, outlines a novel optimization algorithm, and provides a theoretical evaluation of the model's performance. We conclude by discussing the advantages of integrating blockchain with Borda Count for distributed voting systems and suggest potential directions for future research.