Titlebook: Intelligent Human Computer Interaction; 14th International C Hakimjon Zaynidinov,Madhusudan Singh,Dhananjay Sin Conference proceedings 2023

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A Multi-layered Deep Learning Approach for Human Stress Detection, mental stress at an earlier stage can prevent many associated health problems. There are significant changes in the multiple bio-signals, such as electrical, thermal, optical, etc., when an individual is under stress. Such bio-signals can be utilized to identify stress. In this paper, we propose a

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Digital Processing Algorithms of Biomedical Signals Using Cubic Base Splines,cation of 21 sensors in the EEG apparatus along the brain, the naming of the sensors, their connection types, the use of bipolar coupling in the detection of disease symptoms, interpolation of received signals, disease symptoms. The processes of separating parts into scales have been studied. During

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Methods for Creating a Morphological Analyzer,e translation systems, and electronic dictionaries. This article describes the stages of a text analyzer, methods for creating a morphological analyzer and a morphological generator. Ways to use the NLTK package tools in Python when creating a morphological analyzer, examples of software codes are g

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Uzbek Speech Synthesis Using Deep Learning Algorithms,thods for the Uzbek language was relevant, due to the lack of research in this direction. The paper presents a method consisting of the acoustic model Tacotron and the neural vocoder parallel waveGAN. The formed speech corpus with the volume of 31?h of Uzbek speech is described. The quality of the s

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,Emotion Recognition in?VAD Space During Emotional Events Using CNN-GRU Hybrid Model on?EEG Signals,n different combinations of valence(V), arousal(A) and dominance(D) dimensions and compared their results. DENS data is used for this purpose which is primarily recorded on the Indian population. STFT is used for feature extraction and used in the classification model consisting of CNN-GRU hybrid la

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A Higher Performing DARTS Model for CIFAR-10,. Differentiable Architecture Search (DARTS) is an algorithm that solves a Neural Architecture Search problem using a gradient-based approach. We found an architecture that shows higher test accuracy than the existing DARTS architecture with the DARTS algorithm on the CIFAR-10 dataset. The architect