Hybrid Deep Shallow Network for Assessment of Depression Using Electroencephalogram Signals
Depression is a mental health disorder characterised by persistently depressed mood or loss of interest in activities resulting impairment in daily life significantly. Electroencephalography (EEG) can assist with the accurate diagnosis of depression. In this paper, we present two different hybrid deep learning models for classification and assessment of patient suffering with depression. We have combined convolutional neural network with Gated recurrent units (RGUs), thus the proposed network is shallow and much smaller in size in comparison to its counter LSTM network. In addition to this, proposed approach is less sensitive to parameter settings. Extensive experiments on EEG dataset shows…
Deep Residual Neural Network for Child’s Spontaneous Facial Expressions Recognition
Early identification of deficits in emotion recognition and expression skills may prevent low social functioning in adulthood. Deficits in young children’s ability to recognize facial expressions can lead to impairments in social functioning. Kids may need extra help learning to read facial expressions. Most of the earlier efforts consider the problem of emotion recognition in adults; however, ignore the child’s emotions, especially in an unconstrained environment. In this paper, we present progressive light residual learning to classify spontaneous emotion recognition in children. Unlike earlier residual neural network, we reduce the skip connection at the earlier part of the network and i…
Fusion of CNN and sparse representation for threat estimation near power lines and poles infrastructure using aerial stereo imagery
Abstract Fires or electrical hazards and accidents can occur if vegetation is not controlled or cleared around overhead power lines, resulting in serious risks to people and property and significant costs to the community. There are numerous blackouts due to interfering the trees with the power transmission lines in hilly and urban areas. Power distribution companies are facing a challenge to monitor the vegetation to avoid blackouts and flash-over threats. Recently, several methods have been developed for vegetation monitoring; however, existing methods are either not accurate or could not provide better disparity map in the textureless region. Moreover, are not able to handle depth discon…