Neural Network Based Mental Depression Identification and Sentiments Classification Technique From Speech Signals: A COVID-19 Focused Pandemic Study

Ahmed, Syed Thouheed; Singh, Dollar Konjengbam; Basha, Syed Muzamil; Nasr, Emad Abouel; Kamrani, Ali Κ.; Aboudaif, Mohamed K.

doi:10.3389/fpubh.2021.781827

Cited by 13 publications

(6 citation statements)

References 19 publications

(26 reference statements)

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“…This article provides a challenges and technological lags for enhancing the user experience while using remote healthcare services under telemedicine. Typically, the numerical patterns and evaluation is customized and progressed in [11] with numerical clustering of datasets for distributed computing under Controlled Learning based Neural Networking model based on mental health corrections and computation using speech signals.…”

Section: Literature Reviewmentioning

confidence: 99%

Real-Time HealthCare Recommendation System for Social Media Platforms

Maruthavani,

Shantharajah

2024

IEEE Access

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Biomedical services play a vital role in calibration and validation of services with the influence of social media platform and big-data processing toolkits such as "Spark", the processing and recommendation system can achieve a higher order of reliability and validation. In this research article, a novel framework is proposed to provide a recommendation of biomedical services from various social media platforms datasets. The framework is supported by a distributed processing technique under spark framework principle to segregate and process datasets based on SQL injection queries. The proposed technique's distributed datasets are trained and validated under a distributed storage. The Spark-bits are processed with a recommendation system for healthcare services. The technique deploys a fuzzy neural principle in crossreference identification of recommended framework with supportive decision making. The proposed technique has provided a higher value of reliability in real-time recommendation environment.

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Section: Literature Reviewmentioning

confidence: 99%

Real-Time HealthCare Recommendation System for Social Media Platforms

Maruthavani,

Shantharajah

2024

IEEE Access

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“…The CT-based [18] classification under trivial approaches and the historiographic representations are included for reliable decision-making and support ecosystem development. This support system can be derived from contempory studies related to the classification process, as [19] with the extraction of patterns from the wave file and annotating them into depression and [20] with CT images classification-based on a fuzzy system.…”

Section: Advanced Modelsmentioning

confidence: 99%

Ensemble Federated Learning Approach for Diagnostics of Multi-Order Lung Cancer

Subashchandrabose,

John,

Anbazhagu

et al. 2023

Diagnostics

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The early detection and classification of lung cancer is crucial for improving a patient’s outcome. However, the traditional classification methods are based on single machine learning models. Hence, this is limited by the availability and quality of data at the centralized computing server. In this paper, we propose an ensemble Federated Learning-based approach for multi-order lung cancer classification. This approach combines multiple machine learning models trained on different datasets allowing for improvising accuracy and generalization. Moreover, the Federated Learning approach enables the use of distributed data while ensuring data privacy and security. We evaluate the approach on a Kaggle cancer dataset and compare the results with traditional machine learning models. The results demonstrate an accuracy of 89.63% with lung cancer classification.

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“…Hence, detecting heart disease in the early stage helps the patient to prevent danger but also helps the practitioner learn the significant cause of the heart attack and help avoid the danger before the actual occurrence inpatient. [12] [13] stated that diagnosing heart disease using an end-to-end deep learning method using the ECG signal channel has been presented in the study. Due to the popularity of deep learning algorithms for time classification, series based on the one-dimensional CNN, unlike the traditional CNN model-based classification, have been proposed.…”

Section: Literature Surveymentioning

confidence: 99%

Multi-Layered Architecture Convolution Neural Networks for Diagnosing and Predicting Heart Diseases on Multi-Modal

Maran

2023

MJCS

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Recently, heart diseases are increasing daily with the current lifestyle. Different types of cardiac diseases need to be diagnosed accurately for faster treatment to avoid death rates. Heart diseases are susceptible to attack a person speedily and cause sudden deaths. Medical experts are confused while diagnosing a person who comes in a medical emergency. In-time diagnosis, accurate prediction, and the proper treatment can save patients from heart diseases. Heart-related diseases cannot be predicted only by ECG data related to other medical information of a patient. This paper aims to develop an advanced artificial intelligence algorithm for analyzing and detecting heart diseases on multi-modal data. One of the advanced machine learning algorithms, Convolution Neural Network, is implemented with different architectural configurations for analyzing Body Mass Index (BMI), Electrocardiogram (ECG), and Physikalisch-Technische Bundesanstalt (PTB). Multiple CNN models are used for analyzing the multi-modal patient data to predict the presence of heart diseases. The CNN models are created by 80% of the training data and validated by 20% of the testing data. Different hyperparameters of the CNN models are tuned well to extract more data features to improve the prediction accuracy. The CNN models are developed in Python for processing and categorizing the feature variables as to whether heart disease is present or not. Medical data analytics require this model to get 98% of prediction accuracy.

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Neural Network Based Mental Depression Identification and Sentiments Classification Technique From Speech Signals: A COVID-19 Focused Pandemic Study

Cited by 13 publications

References 19 publications

Real-Time HealthCare Recommendation System for Social Media Platforms

Real-Time HealthCare Recommendation System for Social Media Platforms

Ensemble Federated Learning Approach for Diagnostics of Multi-Order Lung Cancer

Multi-Layered Architecture Convolution Neural Networks for Diagnosing and Predicting Heart Diseases on Multi-Modal

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