Design and Simulation of Ring Network-on-Chip for Different Configured Nodes

Jain, Arpit; Dwivedi, R.; Alshazly, Hammam; Kumar, Adesh; Bourouis, Sami; Kaur, Manjit

doi:10.32604/cmc.2022.023017

Cited by 40 publications

(6 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To increase model accuracy, deep residual nets use residual blocks. Te strength of this form of neural network is the notion of "skip connections," which lies at the heart of the residual blocks [24]. As a reference architecture for learning food-domain properties, we used a residual network with 50 layers.…”

Section: Resnet50mentioning

confidence: 99%

An Efficient Approach of Face Detection and Prediction of Drowsiness Using SVM

Shukla

Tiwari²,

Jha

2023

Mathematical Problems in Engineering

View full text Add to dashboard Cite

This article investigates an issue of road safety and a method for detecting drowsiness in images. More fatal accidents may be averted if fatigued drivers are using this technology accurately and the proposed models provide quick response by recognising the driver’s state of falling asleep. There are the following drowsiness models for depicting the possible eye state classifications as VGG16, VGG19, RESNET50, RESNET101 and MobileNetV2. The absence of a readily available and trustworthy eye dataset is perceived acutely in the realm of eye closure detection. On extracting the deep features of faces with VGG16, 98.68% accuracy has been achieved, VGG19 provides an accuracy of 98.74%, ResNet50 works with 65.69% accuracy, ResNet101 has achieved 95.77%, and MobileNetV2 is achieving 96.00% accuracy with the proposed dataset. The put forth model using the support vector machine (SVM) has been used to evaluate several models, and the present results in terms of loss function and accuracy have been obtained. In the proposed dataset, 99.85% accuracy in detecting facial expressions has been achieved. These experimental results show that the eye closure estimation has a higher accuracy and cheap processing cost, as well as the ability of the proposed framework for drowsiness.

show abstract

Section: Resnet50mentioning

confidence: 99%

An Efficient Approach of Face Detection and Prediction of Drowsiness Using SVM

Shukla

Tiwari²,

Jha

2023

Mathematical Problems in Engineering

View full text Add to dashboard Cite

show abstract

“…The program will gather the data for the input and then make an accurate prediction of what the output will be. When new data is input into these algorithms, they can learn and improve their performance by optimizing themselves [17][18][19].…”

Section: Algorithmic Detectionmentioning

confidence: 99%

Effective Detection of Heart Disease Symptoms using Machine Learning

JaiSadhashiva,

Mohaboob Shareef,

Aditya

et al. 2023

CJACSIT

View full text Add to dashboard Cite

Cardiac disease is leading around the globe for deaths, although early detection and prevention can improve survival rates. Using machine learning, various Activation Functions create new models and make predictions using the data they collect. In previous studies, the detection of disease signs was accomplished through the application of machine learning algorithms. Several pieces of paper were examined. Dr. Mohan was able to predict heart disease by analyzing blood pressure. It was a supervised machine-learning technique that he called random forest. In this study, principal component analysis as well as five different techniques were used. Using the methods described above, we projected that the Random Forest technique would provide the highest accuracy.

show abstract

“…A voiced-unvoiced-silence classification technique based on a time-frequency description of the measured signal, regarded as a data matrix, is proposed. e study [21] is based on a hierarchical dual-geometry data matrix analysis that makes use of the tight connection between time frames and frequency bins. e method allows for the separation of spoken and quiet frames, and then voiced and unvoiced frames, by progressively learning the associated geometry in two phases.…”

Section: Related Workmentioning

confidence: 99%

Improved Frame-Wise Segmentation of Audio Signals for Smart Hearing Aid Using Particle Swarm Optimization-Based Clustering

Mehrotra

Shukla

Chaudhary

et al. 2022

Mathematical Problems in Engineering

View full text Add to dashboard Cite

Labeling speech signals is a critical activity that cannot be overlooked in any of the early phases of designing a system based on speech technology. For this, an efficient particle swarm optimization (PSO)-based clustering algorithm is proposed to classify the speech classes, i.e., voiced, unvoiced, and silence. A sample of 10 signal waves is selected, and their audio features are extracted. The audio signals are then partitioned into frames, and each frame is classified by using the proposed PSO-based clustering algorithm. The performance of the proposed algorithm is evaluated using various performance metrics such as accuracy, sensitivity, and specificity that are examined. Extensive experiments reveal that the proposed algorithm outperforms the competitive algorithms. The average accuracy of the proposed algorithm is 97%, sensitivity is 98%, and specificity is 96%, which depicts that the proposed approach is efficient in detecting and classifying the speech classes.

show abstract

Design and Simulation of Ring Network-on-Chip for Different Configured Nodes

Cited by 40 publications

References 31 publications

An Efficient Approach of Face Detection and Prediction of Drowsiness Using SVM

An Efficient Approach of Face Detection and Prediction of Drowsiness Using SVM

Effective Detection of Heart Disease Symptoms using Machine Learning

Improved Frame-Wise Segmentation of Audio Signals for Smart Hearing Aid Using Particle Swarm Optimization-Based Clustering

Contact Info

Product

Resources

About