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1.
Technol Cancer Res Treat ; 23: 15330338241250324, 2024.
Article in English | MEDLINE | ID: mdl-38775067

ABSTRACT

Advancements in AI have notably changed cancer research, improving patient care by enhancing detection, survival prediction, and treatment efficacy. This review covers the role of Machine Learning, Soft Computing, and Deep Learning in oncology, explaining key concepts and algorithms (like SVM, Naïve Bayes, and CNN) in a clear, accessible manner. It aims to make AI advancements understandable to a broad audience, focusing on their application in diagnosing, classifying, and predicting various cancer types, thereby underlining AI's potential to better patient outcomes. Moreover, we present a tabular summary of the most significant advances from the literature, offering a time-saving resource for readers to grasp each study's main contributions. The remarkable benefits of AI-powered algorithms in cancer care underscore their potential for advancing cancer research and clinical practice. This review is a valuable resource for researchers and clinicians interested in the transformative implications of AI in cancer care.


Subject(s)
Algorithms , Artificial Intelligence , Neoplasms , Humans , Neoplasms/diagnosis , Neoplasms/therapy , Biomedical Research , Machine Learning
2.
Neural Netw ; 71: 172-81, 2015 Nov.
Article in English | MEDLINE | ID: mdl-26363960

ABSTRACT

In this study, we introduce an indirect adaptive fuzzy wavelet neural controller (IAFWNC) as a power system stabilizer to damp inter-area modes of oscillations in a multi-machine power system. Quantum computing is an efficient method for improving the computational efficiency of neural networks, so we developed an identifier based on a quantum neural network (QNN) to train the IAFWNC in the proposed scheme. All of the controller parameters are tuned online based on the Lyapunov stability theory to guarantee the closed-loop stability. A two-machine, two-area power system equipped with a static synchronous series compensator as a series flexible ac transmission system was used to demonstrate the effectiveness of the proposed controller. The simulation and experimental results demonstrated that the proposed IAFWNC scheme can achieve favorable control performance.


Subject(s)
Fuzzy Logic , Neural Networks, Computer , Wavelet Analysis , Algorithms , Computer Simulation , Industry , Machine Learning , Nonlinear Dynamics , Power Plants
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