The article, entitled "quantum machine learning algorithms for Big Data Analytics in Cyber Security," offers a pioneering investigation into the convergence of quantum computing, machinelearning, and cyber ...
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ISBN:
(数字)9783031746826
ISBN:
(纸本)9783031746819;9783031746826
The article, entitled "quantum machine learning algorithms for Big Data Analytics in Cyber Security," offers a pioneering investigation into the convergence of quantum computing, machinelearning, and cyber security. With the increasing volume and complexity of data in the field of cyber security, traditional computing methods are struggling to efficiently and effectively handle and analyze large datasets. This study explores the revolutionary capacity of quantum machine learning algorithms to change big data analytics in the field of cyber security. quantum machine learning algorithms utilize the distinct characteristics of quantum computing, such as superposition, entanglement, and quantum parallelism, to provide exceptional skills in identifying patterns, detecting anomalies, and making predictions in the field of cyber security. This paper provides a thorough examination of quantummachinelearning methods, such as quantum neural networks, quantum support vector machines, and quantum clustering algorithms. It aims to clarify the theoretical principles and real-world applications of quantum-enhanced algorithms for big data analytics. Through the utilization of quantum computers' computing capabilities, researchers and practitioners may get access to novel insights, detect emerging threats, and effectively reduce cyber risks with unmatched speed and accuracy. The abstract closes by emphasizing the revolutionary capacity of quantum machine learning algorithms to tackle the changing obstacles of cyber security in a progressively networked and data-driven society.
Sarcopenia is a condition where older individuals experience gradual loss of muscle mass and function. It can be caused by reduced physical activity, hormonal changes, and changes in nutrient intake. Studies have foun...
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Sarcopenia is a condition where older individuals experience gradual loss of muscle mass and function. It can be caused by reduced physical activity, hormonal changes, and changes in nutrient intake. Studies have found that sarcopenia increases the risk of falls, fractures, physical disability, hospitalization, and mortality because strong muscles are important for balance, mobility, and overall health. To predict the risk of sarcopenia, this paper explores the use of two quantum models: quantum K-nearest neighbor (QKNN) and amplitude encoding variational quantum classifier (AE-VQC). The models were tested using three sets of experiments with 8, 16, and 32 features, and a feature selection mechanism was used to determine the most important set of features. Both models used an amplitude encoding technique which converts the input data into quantum state amplitude. The QKNN uses the quantum K-minimum-finding algorithm to identify the K closest neighbors of the test state, where the distance among the two quantum states is measured by using fidelity. A swap test is used to produce the statistical assessment of fidelity among the two arbitrary qubits. The VQC model contains a feature map, a variational quantum circuit, a measurement circuit, and a COBYLA optimizer. The models have been analyzed for the desired set of features, and the results are compared with both their classical version and the previously published quantum models. Both the proposed models perform well interms of better accuracy and complexity reduction. The QKNN model was found to be 3%, 3%, and 0.5% more accurate than the classical model for all sets of features in the optimal scenario. Similarly, the AE-VQC model was also 0.3% and 0.5% more accurate than their corresponding classical models for the desired sets of features in the classification of sarcopenia disease.
quantum computing (QC) stands apart from traditional computing systems by employing revolutionary techniques for processing information. It leverages the power of quantum bits (qubits) and harnesses the unique propert...
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quantum computing (QC) stands apart from traditional computing systems by employing revolutionary techniques for processing information. It leverages the power of quantum bits (qubits) and harnesses the unique properties exhibited by subatomic particles, such as superposition, entanglement, and interference. These quantum phenomena enable quantum computers to operate on an entirely different level, exponentially surpassing the computational capabilities of classical computers. By manipulating qubits and capitalising on their quantum states, QC holds the promise of solving complex problems that are currently intractable in the case of traditional computers. The potential impact of QC extends beyond its computational power and reaches into various critical sectors, including healthcare. Scientists and engineers are working diligently to overcome various challenges and limitations associated with QC technology. These include issues related to qubit stability, error correction, scalability, and noise reduction. In such a scenario, our proposed work provides a concise summary of the most recent state of the art based on articles published between 2018 and 2023 in the healthcare domain. Additionally, the approach follows the necessary guidelines for conducting a systematic literature review. This includes utilising research questions and evaluating the quality of the articles using specific metrics. Initially, a total of 2,038 records were acquired from multiple databases, with 468 duplicate records and 1,053 records unrelated to healthcare subsequently excluded. A further 258, 68, and 39 records were eliminated based on title, abstract, and full-text criteria, respectively. Ultimately, the remaining 49 articles were subject to evaluation, thus providing a brief overview of the recent literature and contributing to existing knowledge and comprehension of quantummachinelearning (QML) algorithms and their applications in the healthcare sector. This analysis establishes a
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