Efficient Speech Enhancement Using Recurrent Convolution Encoder and Decoder

Karthik, A.; MazherIqbal, J. L.

doi:10.1007/s11277-021-08313-6

Cited by 25 publications

(11 citation statements)

References 22 publications

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“…We test the efficacy of our models on the remaining 20% of the COVID-19 dataset after training them on the remaining 80%. The improved prediction accuracy [12] is a direct result of the invented device.…”

Section: Literature Reviewmentioning

confidence: 99%

Covid-19 Forecasting Using CNN Approach With A Halbinomial Distribution And A Linear Decreasing Inertia Weight-Based Cat Swarm Optimization

Murugesan¹,

Madhu²,

Sambandam³

et al. 2023

INT J COMPUT COMMUN, Int. J. Comput. Commun. Control

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In recent times, the COVID-19 epidemic has spread to over 170 nations. Authorities all around the world are feeling the strain of COVID-19 since the total of infected people is rising as well as they does not familiar to handle the problem. The majority of current research effort is thus being directed on the analysis of COVID-19 data within the framework of the machines learning method. Researchers looked the COVID 19 data to make predictions about who would be treated, who would die, and who would get infected in the future. This might prompt governments worldwide to develop strategies for protecting the health of the public. Previous systems rely on Long Short- Term Memory (LSTM) networks for predicting new instances of COVID-19. The LSTM network findings suggest that the pandemic might be over by June of 2020. However, LSTM may have an over-fitting issue, and it may fall short of expectations in terms of true positive. For this issue in COVID-19 forecasting, we suggest using two methods such as Cat Swarm Optimization (CSO) for reducing the inertia weight linearly and then artificial intelligence based binomial distribution is used. In this proposed study, we take the COVID-19 predicting database as an contribution and normalise it using the min-max approach. The accuracy of classification is improved with the use of the first method to choose the optimal features. In this method, inertia weight is added to the CSO optimization algorithm convergence. Death and confirmed cases are predicted for a certain time period throughout India using Convolutional Neural Network with Partial Binomial Distribution based on carefully chosen characteristics. The experimental findings validate that the suggested scheme performs better than the baseline system in terms of f-measure, recall, precision, and accuracy.

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Section: Literature Reviewmentioning

confidence: 99%

Covid-19 Forecasting Using CNN Approach With A Halbinomial Distribution And A Linear Decreasing Inertia Weight-Based Cat Swarm Optimization

Murugesan¹,

Madhu²,

Sambandam³

et al. 2023

INT J COMPUT COMMUN, Int. J. Comput. Commun. Control

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“…In order to shorten the amount of time it takes for an ambulance or other sort of medical help to reach a patient throughout a smart city, researchers are examining how an IoMT system could be used to track patients in the city. Uslu et al [18] describe these factors in detail. The improper use of patient information, cybercrime, data aggregation, and so on are among the most significant problems that can arise during the development of an IoT-based health monitoring system.…”

Section: Related Workmentioning

confidence: 99%

Healthcare Monitoring using Machine Learning Based Data Analytics

Janani¹,

Subramanian²,

Karthik³

et al. 2023

INT J COMPUT COMMUN, Int. J. Comput. Commun. Control

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In this paper, we develop a machine learning based healthcare monitoring and analytics from various Internet of Medical Things (IoMT) devices for possible prediction of cardiovascular risk in patients. The study uses random forest for feature selection and then the fuzzy logic classifier is used for prediction of Cardio Vascular Disease (CVD). The simulation is conducted to test the efficacy of the proposed machine learning based data analytics model over various other methods. The results show than the proposed method has higher rate of classification accuracy in classifying the CVD with higher recall and F1-score than other methods.

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“…Through projected the E-mail content onto the graph using the created graph model, we change the mail classification issue into a graph node classification problem. Furthermore, it was shown that Graph Neural Networks performed convincingly on such a challenge [20][21]. It is suggested to use Graph Neural Network to cooperatively aggregate data from graph structure.…”

Section: Sgnn-cnn Mechanismmentioning

confidence: 99%

Semantic Graph Based Convolutional Neural Network for Spam e-mail Classification in Cybercrime Applications

Muthurajkumar¹,

Nisha²

2023

INT J COMPUT COMMUN, Int. J. Comput. Commun. Control

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Spam is characterized as unnecessary and garbage E-mails. Due to the increasing of unsolicited E-mails, it is becoming more and more crucial for mail users to utilize a trustworthy spam E-mail filter. The shortcomings of spam classifier are defined by their increasing inability to manage large amounts of relevant messages and to effectively detect and effectively detect spam messages. Numerous characteristics in spam classifications are problematic. Given that selecting features is one of the most often used and successful techniques for feature reduction, it is a crucial duty in the identification of keyword content. As a result, features that are unnecessary and pointless yet potentially harm effciency would be removed. In this study, we present SGNNCNN (Semantic Graph Neural Network With CNN) as a solution to tackle the diffcult task of mail identification. By projections E-mails onto a graph and by using the SGNN-CNN model for classifications, this technique transforms the E-mail classification issue into a graph classification challenge. There is no need to integrate the word into a representation since the E-mail characteristics are produced from the semantic network. On several open databases, the technique's effectiveness is evaluated. Some few public databases were used in experiments to demonstrate the high accuracy of the proposed approach for classifying E-mails. In term of spam classification, the performance is superior to state-of-the-art deep learning-based methods.

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Efficient Speech Enhancement Using Recurrent Convolution Encoder and Decoder

Cited by 25 publications

References 22 publications

Covid-19 Forecasting Using CNN Approach With A Halbinomial Distribution And A Linear Decreasing Inertia Weight-Based Cat Swarm Optimization

Covid-19 Forecasting Using CNN Approach With A Halbinomial Distribution And A Linear Decreasing Inertia Weight-Based Cat Swarm Optimization

Healthcare Monitoring using Machine Learning Based Data Analytics

Semantic Graph Based Convolutional Neural Network for Spam e-mail Classification in Cybercrime Applications

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