A Machine Learning-Based Algorithm for Early Detection of Sepsis in Hospitalized Patients: Development and Evaluation

Burugadda, Venkata Raghuveer; Mane, Priyanka Makrand; Kumar, Abhishek; Bhati, Neha

doi:10.1109/ccpis59145.2023.10291447

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Cited by 4 publications

References 15 publications

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Advancing Precision Agriculture: Leveraging YOLOv8 for Robust Deep Learning Enabled Crop Diseases Detection

Vyas,

Dutt

2023

2023 International Conference on Integration of Computational Intelligent System (ICICIS)

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Advancing Precision Agriculture: Leveraging YOLOv8 for Robust Deep Learning Enabled Crop Diseases Detection

Vyas,

Dutt

2023

2023 International Conference on Integration of Computational Intelligent System (ICICIS)

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Advanced Deep Learning Approaches for Alzheimer's Disease

Kumar,

Sharma,

Rokade

et al. 2024

Advances in Medical Technologies and Clinical Practice

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In the field of computer vision, in particular, deep learning, a cutting-edge method for machine learning, has surpassed conventional machine learning in its ability to detect intricate structures in complicated, high-dimensional data. With the advent of large-scale multi-modal neuroimaging data made possible by the fast development of neuroimaging techniques, there has been a surge of interest in using deep learning for the automated classification and early detection of Alzheimer's disease (AD). This chapter presents the results of a literature review on the topic of Alzheimer's disease diagnostic classification using deep learning methods and neuroimaging data. The authors also summarize the data sources, processing steps, training protocols, and evaluation methods to help future deep-learning studies on Alzheimer's disease researchers. There are limitations to deep learning, despite its promising performance across studies and tasks.

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AI-Powered Paradigm Shift

Sharma,

Kumar,

Baig

et al. 2024

Advances in Medical Technologies and Clinical Practice

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Recent alarming data and projections have elevated Alzheimer's disease (AD) to the status of a silent pandemic. Researchers are now focusing on less invasive treatments due to the drawbacks of invasive methods and the ineffectiveness of clinical trials. This highlights the increasing importance of developing non-invasive methods for the early diagnosis of AD. However, computerized analysis of the massive amounts of data produced presents obstacles that might impede progress toward an early AD diagnosis. Therefore, to overcome these obstacles, combining AI with deep learning is essential. To usher in a new era of non-invasive medicine, this work seeks to investigate the present state of these methods for Alzheimer's disease diagnosis, with the hope of harnessing the power of these tools and using the mountain of non-invasive data that exists. The purpose of this study is to compile all published reports on AI-based Alzheimer's disease detection over the past few years.

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A Machine Learning-Based Algorithm for Early Detection of Sepsis in Hospitalized Patients: Development and Evaluation

Cited by 4 publications

References 15 publications

Advancing Precision Agriculture: Leveraging YOLOv8 for Robust Deep Learning Enabled Crop Diseases Detection

Advancing Precision Agriculture: Leveraging YOLOv8 for Robust Deep Learning Enabled Crop Diseases Detection

Advanced Deep Learning Approaches for Alzheimer's Disease

AI-Powered Paradigm Shift

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