The Efficacy of Deep Learning-Based Mixed Model for Speech Emotion Recognition

Human speech indirectly represents the mental state or emotion of others.The use of Artificial Intelligence(AI)-based techniques may bring revolution in this modern era by recognizing emotion from speech.In this study,we introduced a robust method for emotion recognition from human speech using a well-performed preprocessing technique together with the deep learning-based mixed model consisting of Long Short-Term Memory(LSTM)and Convolutional Neural Network(CNN).About 2800 audio files were extracted from the Toronto emotional speech set(TESS)database for this study.A high pass and Savitzky Golay Filter have been used to obtain noise-free as well as smooth audio data.A total of seven types of emotions;Angry,Disgust,Fear,Happy,Neutral,Pleasant-surprise,and Sad were used in this study.Energy,Fundamental frequency,and Mel Frequency Cepstral Coefficient(MFCC)have been used to extract the emotion features,and these features resulted in 97.5%accuracy in the mixed LSTM+CNN model.This mixed model is found to be performed better than the usual state-of-the-art models in emotion recognition from speech.It also indicates that this mixed model could be effectively utilized in advanced research dealing with sound processing.

The efficacy of deep learning based LSTM model in forecasting the outbreak of contagious diseases

The coronavirus disease that outbreak in 2019 has caused various health issues.According to the WHO,the first positive case was detected in Bangladesh on 7th March 2020,but while writing this paper in June 2021,the total confirmed,recovered,and death cases were 826922,766266 and 13118,respectively.Due to the emergence of COVID-19 in Bangladesh,the country is facing a major public health crisis.Unfortunately,the country does not have a comprehensive health policy to address this issue.This makes it hard to predict how the pandemic will affect the population.Machine learning techniques can help us detect the disease's spread.To predict the trend,parameters,risks,and to take preventive measure in Bangladesh;this work utilized the Recurrent Neural Networks based Deep Learning methodologies like LongShort-Term Memory.Here,we aim to predict the epidemic's progression for a period of more than a year under various scenarios in Bangladesh.We extracted the data for daily confirmed,recovered,and death cases from March 2020 to August 2021.The obtained Root Mean Square Error(RMSE)values of confirmed,recovered,and death cases indicates that our result is more accurate than other contemporary techniques.This study indicates that the LSTM model could be used effectively in predicting contagious diseases.The obtained results could help in explaining the seriousness of the situation,also mayhelp the authorities to take precautionary steps to control the situation.

Next generation sequencing under de novo genome assembly

The next generation sequencing （NGS） is an important process which assures inexpen- sive organization of vast size of raw sequence dataset over any traditional sequencing systems or methods. Various aspects of NGS such as template preparation, sequencing imaging and genome alignment and assembly outline the genome sequencing and align- ment. Consequently, de Bruijn graph （dBG） is an important mathematical tool that graphically analyzes how the orientations are constructed in groups of nucleotides. Basi- cally, dBG describes the formation of the genome segments in circular iterative fashions. Some pivotal dBG-based de novo algorithms and software packages such as T-IDBA, Oases, IDBA-tran, Euler, Velvet, ABYSS, AllPaths, SOAPde novo and SOAPde novo2 are illustrated in this paper. Consequently, overlap layout consensus （OLC） graph-based algorithms also play vital role in NGS assembly. Some important OLC-based algorithms such as MIRA3, CABOG, Newbler, Edena, Mosaik and SHORTY are portrayed in this paper. It has been experimented that greedy graph-based algorithms and software pack- ages are also vital for proper genome dataset assembly. A few algorithms named SSAKE, SHARCGS and VCAKE help to perform proper genome sequencing.