Handwritten mathematical expressions recognition is yet challenging task due to its intricate spatial structure, tangled semantics and 2-dimensional layout of the characters. There is a still room for enhancement in recognition rate. Artificial neural network is superior to disentangle classification problems. In this paper, feedforward back propagation neural network is implemented to achieve both character recognition and mathematical structure recognition with upgrade in effective performance in addition to accuracy of the experimental results including lessen efforts. System proves its potency by recognizing expressions in analysis of math documents.
Currently researchers have shown immeasurable awareness in Brain Computer Interface (BCI) systems, which enable any user to exchange intelligence and knowledge with surrounding and control instruments by using brain signals; concept is identified as Affective Computing. In this work we are using the SEED database, which is publically available to classify three emotions Positive, Negative and Neutral. Five electrode pairs from various brain regions like Prefrontal, Frontal, Temporal, Parietal and Occipital are selected for this work based on previous research. Diverse time domain and time frequency domain features are extracted from EEG signals. Wavelet Transform (WT) is used to extract a variety of time frequency domain features. Daubechies wavelet function (db6) with 6 levels of decomposition is used to split EEG signals into various frequency bands (δ, θ, α, β and γ). SVM and k-NN algorithms are used as classifiers to estimate classification performance. Hypothetical results illustrate an average classification accuracy of 62.4% for classifying three emotions. Gamma and Beta, the higher frequency bands perform well in emotion recognition.
Compiler designer needs years or sometimes months to construct programs using heuristic optimization rules for a specified compiler. For every novel processor, the modelers require readjusting the heuristics to get the probable performances of processor. The most important purpose of the developed approach is to build a prediction approach with optimization constraints for transforming programs with a lesser training overhead. The problem has occurred in the optimization and it is needed to address it with novel prediction model with derived features & neural network. Here, a novel Compiler Optimization Prediction Model is developed. The features like static and dynamic features as well as improved Relief based features are derived, which are provided as input to Neural Network (NN) scheme, in which the weights are tuned via Honey Badger Adopted BES (HBA-BEO) model. Finally, the NN offers the final predicted output. The analysis outcomes prove the superiority of HBA-BEO model.
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