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Stochastic Gradient Descent (SGD) is the method of choice for large scale problems, most notably in deep learning. Recent studies target improving convergence and speed of the SGD algorithm. In this paper, we equip the SGD algorithm and its advanced versions with an intriguing feature, namely handling constrained problems. Constraints such as orthogonality are pervasive in learning theory. Nevertheless...
Emotions are the state of feelings resulting from physical and psychological change which in turn influence our behaviour. Emotions play an important role in corporate activities like organizing, planning, motivating etc. The negative emotions may lead to chronic emotional disorders like depression, anxiety, stress. Therefore, there is a need to analyse and classify these emotional changes through...
The paper presents a system to accurately differentiate between unique individuals by utilizing the various eye-movement biometric features. Eye Movements are highly resistant to forgery as the generation of eye movements occur due to the involvement of complex neurological interactions and extra ocular muscle properties. We have employed Linear Multiclass SVM model to classify the numerous eye movement...
Clustering algorithms are used for systematic retrieval of data by organizing them into several clusters. K-Means is one such algorithm which partitions data into groups based on distance metric in an unsupervised way. Clustering is used to organize data for efficient retrieval. In this paper, we study Denoising of images corrupted with variable Gaussian noise spread across the images (dataset). The...
Fuzzy Logic is a system that nearly represents human activities of thinking and linguistics. Fuzzy logic is very useful in handling uncertainty and is used in various other applications. In this paper, fuzzy logic is used to handle wavelet transformed image statistics which is embedded with Gaussian noise spread variably across the image. Adaptive Multilevel Soft Thresholding of the noisy image was...
Successful implementation of type-1 Fuzzy Systems (FS) in diverse application areas have been accomplished till date. Nonetheless type-1 FS are not able to handle significant amount of uncertainties present in dynamic real world applications. An improved performance against these uncertainties is achieved by type-2 FS. In this paper, a type-2 FS is proposed to remove variable gaussian noise from a...
This paper presents the use of Error Back Propagation Algorithm for Adaptive Filter Applications. The Artificial Neural Network (ANN) exploits correlation between the pure speech signal and echo corrupted signal, to generate an estimate of the echo, which in turn subtracts the noise from the echo corrupted signal. Training of ANN is performed using the offline recordings of male and female voice data...
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