Identification and Recognition of Speaker Voice Using a Neural Network-Based Algorithm

Koppula, Neeraja; Sarada, K.; Patel, Ibrahim; Aamani, R.; Saikumar, K.

doi:10.4018/978-1-7998-6870-5.ch019

Cited by 13 publications

(3 citation statements)

References 19 publications

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“…Face expressions throughout civilizations are examined in this article. Three methodologies are used to analyse emotional facial reactions: sociological, physiological and computerized Presentations from world-renowned experts in behavioural, physiological, and mathematical disciplines are included [26].…”

Section: Literature Surveymentioning

confidence: 99%

Deep Learning-Based Micro Facial Expression Recognition Using an Adaptive Tiefes FCNN Model

Durga,

Rajesh,

Jagannadham

et al. 2023

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The scientific community and media have increasingly recognized the significance of microexpressions as indicators for detecting deception, as they reveal genuine emotions that individuals attempt to conceal. To capitalize on these subtle cues of deceit, researchers have developed applications capable of automatically detecting and recognizing microexpressions, which are typically imperceptible to the human eye. Facial expressions serve as fundamental ground truth determinants in multimedia applications. Earlier models, such as GA, RFO, X-Boosting, and Gradient Boosting, demonstrate greater efficiency in terms of time and accuracy. However, not all applications are capable of detecting micro facial expressions. In this study, a deep learning-based Tiefes FCNN model is designed specifically for micro facial expression recognition. Implemented using Python software, the proposed model consists of two stages: first, pre-processing is performed using image segmentation, followed by the application of a deep learning model employing Tiefes FCNN technology in the second stage. The experimental results exhibit significant performance improvements, including an accuracy of 99.02%, precision of 98.82%, F1-score of 97.8%, PSNR of 56.31, and CC of 96.31.

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Section: Literature Surveymentioning

confidence: 99%

Deep Learning-Based Micro Facial Expression Recognition Using an Adaptive Tiefes FCNN Model

Durga,

Rajesh,

Jagannadham

et al. 2023

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“…The supplied image's grayscale power remains between 80 and 250 dots per inch. Essential characteristics, including image contrast, Aspect Ratio-AR, resolution, & brightness, were supplied in stealth mode [23]. Adaptive histogram equalization is a process to extract edge-level features from an input image.…”

Section: Brain Mri Image Enhancementmentioning

confidence: 99%

Cloud-Based LeNet-5 CNN for MRI Brain Tumor Diagnosis and Recognition

Srinivasarao,

Rajesh,

Saikumar

et al. 2023

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Early and accurate diagnosis of brain tumors is crucial in the medical field, as undetected or misdiagnosed tumors can lead to sudden death. Traditional models for diagnosing brain tumors suffer from low Time of Conversion (ToC) and low accuracy, contributing to a high mortality rate among the 5 million people affected by brain disease annually, as reported by the World Health Organization (WHO). Previous methods, such as Elastic Net Regression (ENR), Logistic Regression (LR), and other machine learning models, struggle to accurately locate and identify brain lesions. Moreover, these models are not suited for cloud-based platforms. To address this issue, we developed a sophisticated, cloud-based brain abnormality detection application using the LeNet-5 Convolutional Neural Network (CNN) on the DriveHQ platform. The pre-trained LeNet-5 model extracts features from ADNI-1, ADNI-2, and MIRIAD datasets. Real-time MRI brain images were collected from Manipal Hospital in Vijayawada, Andhra Pradesh, India. The LeNet-5 model employs hidden layers, flattened layers, max-pooling layers, dense layers, and ReLu layers for optimal performance. Our 2D-LeNet-5 CNN approach preprocesses images using split and merge techniques of binary mask segmentation. The Python 3.7 software tool was used to train and test datasets to identify abnormalities in MRI brain images. The proposed application achieved remarkable performance metrics, including 99.65% accuracy, 99.59% sensitivity, 99.72% F1-score, 99.25% recall, 59.32 PSNR, and 0.9929 MCC. These results demonstrate the superiority of our methodology in comparison to existing models, making it a promising solution for cloud-based brain tumor diagnosis and recognition.

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“…The following work concluded that regression classification is used to predict HD more accurately than other techniques by Saikumar and Rajesh, 2020a,b. R-C4.5 is proposed, and its features are extractí from the given technique by Koppula et al (2021). The study used their equipment and found it a very beneficial machine in the healthcare industry for predicting MLbased approaches Garigipati et al (2022).…”

Section: Related Workmentioning

confidence: 99%

Heart disease detection based on internet of things data using linear quadratic discriminant analysis and a deep graph convolutional neural network

Saikumar

Rajesh²,

Srivastava³

et al. 2022

Front. Comput. Neurosci.

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Heart disease is an emerging health issue in the medical field, according to WHO every year around 10 billion people are affected with heart abnormalities. Arteries in the heart generate oxygenated blood to all body parts, however sometimes blood vessels become clogged or restrained due to cardiac issues. Past heart diagnosis applications are outdated and suffer from poor performance. Therefore, an intelligent heart disease diagnosis application design is required. In this research work, internet of things (IoT) sensor data with a deep learning-based heart diagnosis application is designed. The heart disease IoT sensor data is collected from the University of California Irvine machine learning repository free open-source dataset which is useful for training the deep graph convolutional network (DG_ConvoNet) deep learning network. The testing data has been collected from the Cleveland Clinic Foundation; it is a collection of 350 real-time clinical instances from heart patients through IoT sensors. The K-means technique is employed to remove noise in sensor data and clustered the unstructured data. The features are extracted to employ Linear Quadratic Discriminant Analysis. DG_ConvoNet is a deep learning process to classify and predict heart diseases. The diagnostic application achieves an accuracy of 96%, sensitivity of 80%, specificity of 73%, precision of 90%, F-Score of 79%, and area under the ROC curve of 75% implementing the proposed model.

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Identification and Recognition of Speaker Voice Using a Neural Network-Based Algorithm

Cited by 13 publications

References 19 publications

Deep Learning-Based Micro Facial Expression Recognition Using an Adaptive Tiefes FCNN Model

Deep Learning-Based Micro Facial Expression Recognition Using an Adaptive Tiefes FCNN Model

Cloud-Based LeNet-5 CNN for MRI Brain Tumor Diagnosis and Recognition

Heart disease detection based on internet of things data using linear quadratic discriminant analysis and a deep graph convolutional neural network

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