Stress has an impact, not only on a person’s physical health, but also on the ability to perform at the workplace in daily life. The well-established relation between psychological stress and its pathogeneses highlights the need for detecting psychological stress early, in order to prevent disease advancement and to save human lives. Electroencephalography (EEG) signal recording tools are widely used to collect these psychological signals/brain rhythms in the form of electric waves. The aim of the current research was to apply automatic feature extraction to decomposed multichannel EEG recordings, in order to efficiently detect psychological stress. The traditional deep learning techniques, namely the convolution neural network (CNN), long short-term memory (LSTM), bidirectional long short-term memory (BiLSTM), gated recurrent unit (GRU) and recurrent neural network (RNN) models, have been frequently used for stress detection. A hybrid combination of these techniques may provide improved performance, and can handle long-term dependencies in non-linear brain signals. Therefore, this study proposed an integration of deep learning models, called DWT-based CNN, BiLSTM, and two layers of a GRU network, to extract features and classify stress levels. Discrete wavelet transform (DWT) analysis was used to remove the non-linearity and non-stationarity from multi-channel (14 channel) EEG recordings, and to decompose them into different frequency bands. The decomposed signals were utilized for automatic feature extraction using the CNN, and the stress levels were classified using BiLSTM and two layers of GRU. This study compared five combinations of the CNN, LSTM, BiLSTM, GRU and RNN models with the proposed model. The proposed hybrid model performed better in classification accuracy compared to the other models. Therefore, hybrid combinations are appropriate for the clinical intervention and prevention of mental and physical problems.

Hand written character recognition is widely used in modern research. Recognize the characters from an image has always been active research area in the computer vision community. Apart from English, there are several regional languages available worldwide. In India there are 22 official languages, and Bengali is one of them. Bengali script has its own writing pattern. Like any other language number system, Bengali numeral system has ten different digits to indicate 0-9. It has its own alphabets and numerals. In this paper, Histogram of oriented gradient (HOG) and color histogram for selection of features algorithm is proposed. HOG are used as the feature set to represent each digit sample at the feature space and Support Vector Machine (SVM) used to produce the output from input image. Proposed algorithm is efficient and gives an accuracy of 98.05 on CMATERDB3.1.1 dataset. This type of numeric image recognition can be use in the post offices to acknowledge the pin code written in Bengali.

A steganographic method of embedding textual information in an audio file is presented in this paper. In the proposed technique, first the audio file is sampled and then an appropriate bit of each alternate sample is altered to embed the textual information. As a steganographic approach the perceptual quality of the host audio signal was not to be degraded.

Automatic Speech Attribute Transcription framework is a recently proposed paradigm for detection based bottom-up speech recognition. Speech signal contains a large set of related information, known as speech attributes that include a set of fundamental speech sound with their linguistic identification that is phonological features. In this study, a bank of Deep Neural Network based attribute detectors has been applied to Bengali continuous speech corpus to detect phonological features of Bengali language. 89.17% of average attribute detection accuracy was achieved for Bengali continuous speech. This experiment was repeated using TIMIT speech corpus to ensure the system robustness, and the average detection accuracy was 89.40%.