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Nowadays, the speech emotion recognition (SER) model has enhanced as the main research topic in various fields including human–computer interaction as well as speech…
Nowadays, the speech emotion recognition (SER) model has enhanced as the main research topic in various fields including human–computer interaction as well as speech processing. Generally, it focuses on utilizing the models of machine learning for predicting the exact emotional status from speech. The advanced SER applications go successful in affective computing and human–computer interaction, which is making as the main component of computer system's next generation. This is because the natural human machine interface could grant the automatic service provisions, which need a better appreciation of user's emotional states.
This paper implements a new SER model that incorporates both gender and emotion recognition. Certain features are extracted and subjected for classification of emotions. For this, this paper uses deep belief network DBN model.
Through the performance analysis, it is observed that the developed method attains high accuracy rate (for best case) when compared to other methods, and it is 1.02% superior to whale optimization algorithm (WOA), 0.32% better from firefly (FF), 23.45% superior to particle swarm optimization (PSO) and 23.41% superior to genetic algorithm (GA). In case of worst scenario, the mean update of particle swarm and whale optimization (MUPW) in terms of accuracy is 15.63, 15.98, 16.06% and 16.03% superior to WOA, FF, PSO and GA, respectively. Under the mean case, the performance of MUPW is high, and it is 16.67, 10.38, 22.30 and 22.47% better from existing methods like WOA, FF, PSO, as well as GA, respectively.
This paper presents a new model for SER that aids both gender and emotion recognition. For the classification purpose, DBN is used and the weight of DBN is used and this is the first work uses MUPW algorithm for finding the optimal weight of DBN model.
Quantum-dot cellular automata (QCA) has attracted computer scientists as new emerging nanotechnology for replacement the current CMOS technology because it has unique…
Quantum-dot cellular automata (QCA) has attracted computer scientists as new emerging nanotechnology for replacement the current CMOS technology because it has unique characteristics such as high frequency, extremely small feature size and low power consumption. The main building blocks in QCA are the majority gate and inverter so any Boolean function can be represented using these gates. Many important circuits were the target for implemented in this technology in an optimal form, such as random-access memory (RAM) cell. QCA-RAM cells were introduced in literature with different forms but most of them are not optimized enough. This paper aims to demonstrate QCA inherent capabilities that can facilitate the design of many important gates such as the XOR gate and multiplexer (MUX) without following any Boolean function to get an optimum design in terms of complexity and delay.
In this paper, a novel structure of QCA-MUX in an optimal form will be used to design two unique structures of a RAM cell. The proposed RAM cells are the lowest cost required compared with different counterparts. The presented RAM cells used a new approach that follows the new suggested block diagram. The presented circuits are simulated and tested with QCADesigner and QCAPro tools.
The comparison of the proposed circuits with the previously reported in the literature show noticeable improvements in speed, area, and the number of cells. The cost function analysis results for the proposed RAM cells show significant improvement compared to older circuits.
A novel structure of QCA-MUX in an optimal form will be used to design two unique structures of a RAM cell.