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- 저자 : Marzouk Radwa (검색결과 2 건)
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Quantum-Enhanced Machine Learning Algorithms for Heart Disease Prediction
Alotaibi Saud S.,Mengash Hanan Abdullah,Dhahbi Sami,Alazwari Sana,Marzouk Radwa,Alkhonaini Mimouna Abdullah,Mohamed Abdullah,Hilal Anwer Mustafa 한국컴퓨터산업협회 2023 Human-centric Computing and Information Sciences Vol.13 No.-
Heart disease has grown more prominent among various age groups. Early prediction of heart failure and treating them with the most care can the human life. Today healthcare system depends on a computer-aided diagnosis system. Quantum improved machine learning approaches are a critical factor, play a significant role in healthcare systems due to their robust nature, and build novel medical traits, patient data, and management of patients’ record and chronic disease detection, etc. Traditional machine learning approaches effectively predict heart disease but still lack efficiency due to noise and appropriate feature size. This informs the researchers to use quantum improved ML that will provide the accurate prediction of chronic diseases in a granular way. Applying these merits of quantum computing, healthcare systems are implementing quantum-based machine learning (QML) approaches for predicting heart disease. This paper proposes a quantum ML with quantum particle swarm optimization (QPSO) to predict heart disease and compare it with the traditional ML approach called multilayer perceptron (MLP) using the evaluation metrics. It uses exploratory preprocessing to normalize the input heart disease data. The number of qubits is the number of features in the dataset. The efficiency of the quantum-ML approaches is evaluated using publicly available heart disease dataset. The proposed QML with QPSO secured an improved accuracy of 96.7%, a false detection rate of 0.09, and a computation time is 135ms. However, the comparison results prove that QML with QPSO confirmed satisfactory results in predicting heart disease with improved accuracy.
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Intelligent Autonomous Vehicle Computation Using Deep Learning with Grasshopper Optimization
Hamza Manar Ahmed,Alotaibi Saud S.,Alabdulkreem Eatedal,Mahgoub Hany,Yaseen Ishfaq,Motwakel Abdelwahed,Rizwanullah Mohammed,Marzouk Radwa 한국컴퓨터산업협회 2023 Human-centric Computing and Information Sciences Vol.13 No.-
Autonomous vehicles (AV) are in-demand future technologies for intelligent driving applications. The fast-decision-making techniques like artificial intelligence (AI) are required to train AV. Recently deep learning achieves tremendous results in fast computation and training models. Due to the sensing devices' data generation, AI has been used to process big data for making the appropriate decision in motion. Full automation system of AV can be achieved using AI techniques, and it is the recent research focus. In this paper, AI and deep learning-based approaches are used for AV in the applications such as vehicle detection, localization & mapping, and decision-making. With the large structural and variations of vehicle appearances, vehicle detection is a challenging task. In this paper, we proposed an efficient vehicle object detection method using a multi-task 2D deep convolution neural network (CNN) and a Cartesian product. Then the network loss is optimized with an evolutionary algorithm called grasshopper optimization. In this paper, localization and mapping are performed using the Kalman filter with fast correlation-based elephant herding optimization. The Kalman filter is used here to address the limitation on the sensory unit. Decision-making on vehicle parking of AV is done using deep bagging CNN. This proposed system works better than the traditional AV approach.
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