Characterized by recurrent and rapid seizures, epilepsy is a great threat to the livelihood of the human beings. Abnormal transient behaviour of neurons in the cortical regions of the brain leads to a seizure which characterizes epilepsy. The physical and mental activities of the patient are totally dampened with this epileptic seizure. A significant clinical tool for the study, analysis and di...

Stock market prediction is forever important issue for investor. Computer science plays vital role to solve this problem. From the evolution of machine learning, people from this area are busy to solve this problem effectively. Many different techniques are used to build predicting system. This research describes different state of the art techniques used for stock forecasting and compare them ...

Lip reading provides useful information in speech perception and language understanding, especially when the auditory speech is degraded. However, many current automatic lip reading systems impose some restrictions on users. In this paper, we present our research e orts, in the Interactive System Laboratory, towards unrestricted lip reading. We rst introduce a top-down approach to automatically...

This paper describes a new baseline system for automatic speech recognition (ASR) in the CHiME-4 challenge to promote the development of noisy ASR in speech processing communities by providing 1) state-of-the-art system with a simplified single system comparable to the complicated top systems in the challenge, 2) publicly available and reproducible recipe through the main repository in the Kald...

A new class of neural networks is proposed for the dynamic classification of spatio-temporal signals. These networks are designed to classify signals of different durations, taking into account correlations among different signal segments. Such networks are applicable to SONAR and speech signal classification problems, among others. Network parameters are adapted based on the biologically obser...

Opto-electronic reconfigurable interconnection networks are limited by significant control latency when used in large multiprocessor systems. This latency is the time required to analyze the current traffic and reconfigure the network to establish the required paths. The goal of latency hiding is to minimize the effect of this control overhead. In this paper, we introduce a technique that perfo...

Recently the deep learning techniques have achieved success in multi-label classification due to its automatic representation learning ability and the end-to-end learning framework. Existing deep neural networks in multi-label classification can be divided into two kinds: binary relevance neural network (BRNN) and threshold dependent neural network (TDNN). However, the former needs to train a s...

Hand activity and speech comprise the most important modalities of human-to-agent interaction. So a multimodal interface can achieve more natural and effective human-agent interaction. In this paper, we suggest a novel technique for improving the performance of accelerometer-based hand activity recognition system using fusion of speech. The speech data is used in our experiment as the complemen...

A methodology based on adaptive ANN models is proposed for flood routing in river systems. The proposed methodology is capable of modeling both converging and diverging river networks. A Multilayer Perceptron Network (MLP), a Recurrent Neural Network (RNN), a Time Delay Neural Network (TDNN) and a Time Delay Recurrent Neural Network (TDRNN) are applied in this study. An Adaptive training proced...