An intelligent optimization framework to predict the vulnerable range of tumor cells using Internet of things

Gorantla, Venkata Ashok K; Sriramulugari, Shiva Kumar; Mewada, Amit Hasmukhbhai; Logeshwaran, J.

doi:10.1109/icidea59866.2023.10295269

Cited by 47 publications

(1 citation statement)

References 19 publications

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“…Examples include the use of ML in predicting chemotherapy responses, as explored by (Kourou et al, in their study on machine learning applications for cancer prognosis. Furthermore, (Gorantla et al, 2023)propose an intelligent optimization framework that leverages the Internet of Things (IoT) to predict the vulnerability range of tumor cells, integrating cloud computing and data analytics for enhanced predictive accuracy.…”

Section: Prognosis and Treatment Predictionsmentioning

confidence: 99%

Advanced AI and Machine Learning Techniques and Algorithms in Cancer Research

Sekhar

2024

Preprint

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The integration of Artificial Intelligence (AI) and Machine Learning (ML) technologies into cancer research is reshaping the paradigm of diagnosis, treatment, and prevention of this complex and diverse group of diseases. AI and ML offer sophisticated analytical capabilities that significantly enhance the precision of cancer diagnostics, optimize treatment regimens through predictive analytics, and accelerate the discovery of novel therapeutic agents. This paper explores the application of advanced Artificial Intelligence (AI) and Machine Learning (ML) techniques in oncology. It emphasizes their mathematical foundations and practical implementations across various facets of cancer research, including detection, diagnosis, prognosis, and personalized treatment strategies. We provide a comprehensive review of influential algorithms, including Support Vector Machines (SVM), Convolutional Neural Networks (CNNs), and clustering techniques, discussing their significant roles in improving diagnostic accuracy and treatment efficacy.

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Section: Prognosis and Treatment Predictionsmentioning

confidence: 99%