Heart Attack Anxiety Disorder using Machine Learning and Artificial Neural Networks (ANN) Approaches

Sattaru, Naveen Chakravarthy; Baker, Mohammed Rashad; Umrao, Dhananjay; Pandey, Umesh Kumar; Tiwari, Mohit; Chakravarthi, M. Kalyan

doi:10.1109/icacite53722.2022.9823697

Cited by 10 publications

(2 citation statements)

References 12 publications

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“…The traditional routing protocols differ significantly in operation: the AODV is a reactive protocol, which tries to create a route when it is necessary, while DSR uses source routing and adds the entire route to the header of the packet [15], [16] . While these protocols behave differently, they all seem to have similar disadvantages, such as a high level of overhead, the impact of network partitions, and low enough efficiency in a highly mobile network [17]- [19].…”

Section: Introductionmentioning

confidence: 99%

Improving Routing Performance in Mobile Ad Hoc Networks Using Artificial Neural Networks for Mobility Prediction using deep learning

Mohammad Arif

2024

jes

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In this study, a research was done to examine the effectiveness of deep learning models, such as the Artificial Neural Networks, Convolutional Neural Networks, Gated Recurrent Units and Generative Adversarial Networks, to boost routing’s efficiency and performance in the Mobile Ad Hoc Networks through mobility prediction. In total, 10 experiments were conducted, and ANNs demonstrated an average prediction accuracy of 0.86, MAE of 0.10, RMSE of 0.16, and correlation coefficient of 0.93. CNNs showed the most impressive performance, featuring the following indicators: the average prediction accuracy of 0.87, MAE of 0.09, RMSE of 0.15, and the correlation coefficient of 0.94. GRUs, in their turn, displayed quite decent performance, with an average prediction accuracy of 0.87, MAE of 0.10, RMSE of 0.16, and the correlation coefficient of 0.94; in the meanwhile, GANs can be also seen as offering a decent performance, with the average prediction accuracy of 0.87, MAE of 0.10, RMSE of 0.16, and the correlation coefficient of 0.94. In the end, the identified findings imply that deep learning models can be used to enhance the routing efficiency in MANETs, since they can predict the node movement with the high level of accuracy. The identified changes are possible to lead to the creation of effective wireless communication networks.

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Section: Introductionmentioning

confidence: 99%

Improving Routing Performance in Mobile Ad Hoc Networks Using Artificial Neural Networks for Mobility Prediction using deep learning

Mohammad Arif

2024

jes

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“…In this research, an ANN model was developed to predict the responses of Al 6065 alloy reinforced with nanoparticles of Silicon Carbide and Graphene. The ANN model was trained and tested on the experimental data obtained from the wear test [19]- [21].…”

Section: Introductionmentioning

confidence: 99%

Development of a Neural Network Model and Taguchi-Based Optimization for Dry Sliding Wear Performance of Al 6065 Alloy Reinforced with Nano SiC and Graphene Nanoplatelets

Pandiaraj,

Thiyagarajan,

Sivasakthi

et al. 2023

MSF

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The Al6065 alloy is widely used in various applications, including tubing for furniture, railway and bus structures, pylons, platforms, and pipelines. However, due to the wear experienced in these applications, it is essential to enhance the wear confrontation of this alloy. To address this issue, this research focuses on reinforcing Al6065 with nanoparticles of silicon carbide and graphene by means of the stir casting method. The wear behaviour of the alloy is studied by varying the rotational speed, load, and composition of reinforcement in the stir casting machine using Taguchi design of experiment. The rate of wear and friction coefficient are measured as responses. The obtained results are then analysed and optimized for the minimum of the output responses using S/N ratio analysis. Further, ANOVA is carried out to determine the influence of each parameter, and a model of the neural network is developed to predict the response. The findings indicate that cumulative the percentage of reinforcement enhances the wear resistance of the alloy. The optimized values of the rotational speed, load, and composition of reinforcement lead to improved wear resistance, with a corresponding decrease in the coefficient of friction. The ANOVA results reveal that the rotational speed and load significantly affect the responses, while the reinforcement composition has a moderate effect. The developed neural network model accurately predicts the response with a high degree of accuracy. The model can be used to optimize the wear behaviour of Al6065 alloy reinforced with nanoparticles of silicon carbide and graphene, as well as for prediction of the alloy's wear behaviour in various application environments. In conclusion, this research delivers a complete thoughtful of the wear behaviour of Al6065 alloy reinforced with nanoparticles of silicon carbide and graphene. The findings can be used to optimize the wear resistance and improve the routine of this alloy in various applications.

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Enhancing Heart Disease Prediction with Ensemble Deep Learning and Feature Fusion in a Smart Healthcare Monitoring System

Verma,

Singh,

Kumari

2024

Lecture Notes in Networks and Systems

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Heart Attack Anxiety Disorder using Machine Learning and Artificial Neural Networks (ANN) Approaches

Cited by 10 publications

References 12 publications

Improving Routing Performance in Mobile Ad Hoc Networks Using Artificial Neural Networks for Mobility Prediction using deep learning

Improving Routing Performance in Mobile Ad Hoc Networks Using Artificial Neural Networks for Mobility Prediction using deep learning

Development of a Neural Network Model and Taguchi-Based Optimization for Dry Sliding Wear Performance of Al 6065 Alloy Reinforced with Nano SiC and Graphene Nanoplatelets

Enhancing Heart Disease Prediction with Ensemble Deep Learning and Feature Fusion in a Smart Healthcare Monitoring System

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