Design of high-speed, low-power non-volatile master slave flip flop (NVMSFF) for memory registers designs

Muthappa, K.A.; Nisha, A. Sahaya Anselin; Shastri, Rajveer; Avasthi, Vinay; Reddy, Pundru Chandra Shaker

doi:10.1007/s13204-023-02814-5

Cited by 42 publications

(2 citation statements)

References 24 publications

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“…In the current era of data proliferation, it is increasingly imperative to efficiently manage, retrieve, and disseminate the extensive data generated from diverse sources, despite the challenges associated with comprehending and analyzing such data. Against this backdrop, researchers are actively seeking methods to enhance model precision and achieve more reliable disease prediction, particularly given the exponential growth in data volume and the escalating costs of diagnostic procedures [10].…”

Section: Experimental Designmentioning

confidence: 99%

Convolutional neural network-based cardiovascular disease prediction with adaptability

Wu,

Liu,

Yang

et al. 2024

Third International Symposium on Computer Applications and Information Systems (ISCAIS 2024)

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Cardiovascular diseases (CVD) are a leading cause of global mortality, as documented by the World Health Organization (WHO), resulting in a significant annual death toll. Accurate forecasting of CVD has the potential to significantly improve patient outcomes and potentially save lives. The advent of the COVID-19 pandemic has necessitated the adaptation of traditional disease prediction methodologies, underscoring the importance of a unified and precise predictive system. The integration of Artificial Intelligence (AI) in disease diagnosis and identification has been transformative, largely due to the rapid progress and reliability of deep learning technologies. This study proposes the application of Convolutional Neural Networks (CNNs) to the heart disease dataset from the University of California, Irvine (UCI) repository, employing machine learning (ML) techniques for the early detection of CVD. The analysis encompasses 14 critical attributes from the dataset. The performance of the proposed model is assessed using accuracy and confusion matrix evaluation metrics, which are employed to validate multiple prospective findings. To refine the dataset, the Isolation Forest algorithm is utilized to eliminate irrelevant features, and data standardization techniques are implemented to enhance the model's predictive accuracy. The application of deep learning methodologies has resulted in an impressive accuracy rate of 98%.

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Section: Experimental Designmentioning

confidence: 99%

Convolutional neural network-based cardiovascular disease prediction with adaptability

Wu,

Liu,

Yang

et al. 2024

Third International Symposium on Computer Applications and Information Systems (ISCAIS 2024)

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“…During most recent years it has additionally seen a blast of sensor distribution, along with the development of adaptive systems; internet‐of‐things current advancements of sensor-based systems have emerged. Currently, the advantages of social communication and internet‐of‐things distributions for smart urban areas have likewise been exhibited 7 .…”

Section: Introductionmentioning

confidence: 99%

A vehicular network based intelligent transport system for smart cities using machine learning algorithms

Prakash,

Murali,

Manikandan

et al. 2024

Sci Rep

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Smart cities and the Internet of Things have enabled the integration of communicating devices for efficient decision-making. Notably, traffic congestion is one major problem faced by daily commuters in urban cities. In developed countries, specialized sensors are deployed to gather traffic information to predict traffic patterns. Any traffic updates are shared with the commuters via the Internet. Such solutions become impracticable when physical infrastructure and Internet connectivity are either non-existent or very limited. In case of developing countries, no roadside units are available and Internet connectivity is still an issue in remote areas. Internet traffic analysis is a thriving field of study due to the myriad ways in which it may be put to practical use. In the intelligent Internet-of-Vehicles (IOVs), traffic congestion can be predicted and identified using cutting-edge technologies. Using tree-based decision-tree, random-forest, extra-tree, and XGBoost machine learning (ML) strategies, this research proposes an intelligent-transport-system for the IOVs-based vehicular network traffic in a smart city set-up. The suggested system uses ensemble learning and averages the selection of crucial features to give high detection accuracy at minimal computational costs, as demonstrated by the simulation results. For IOV-based vehicular network traffic, the tree-based ML approaches with feature-selection (FS) outperformed those without FS. When contrasted to the lowest KNN accuracy of 96.6% and the highest SVM accuracy of 98.01%, the Stacking approach demonstrates superior accuracy as 99.05%.

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Web application based Diabetes prediction using Machine Learning

Kumar,

Reddy,

et al. 2023

2023 International Conference on Advances in Computing, Communication and Applied Informatics (ACCAI)

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Design of high-speed, low-power non-volatile master slave flip flop (NVMSFF) for memory registers designs

Cited by 42 publications

References 24 publications

Convolutional neural network-based cardiovascular disease prediction with adaptability

Convolutional neural network-based cardiovascular disease prediction with adaptability

A vehicular network based intelligent transport system for smart cities using machine learning algorithms

Web application based Diabetes prediction using Machine Learning

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