预测器重要性是经典和量子机学习（QML）数据预处理管道的关键部分。这项工作介绍了此类研究的第一个研究，其中探索了对QML模型的重要性与其经典的机器学习（CML）等效物进行了对比。我们开发了一种混合量子式体系结构，其中训练了QML模型，并根据现实世界数据集上的经典算法计算特征重要性值。该体系结构已在ESPN幻想足球数据上使用Qiskit StateSvector模拟器和IBM量子硬件（例如IBMQ Mumbai和IBMQ Montreal Systems）实现。即使我们处于嘈杂的中间量子量子（NISQ）时代，物理量子计算结果还是有希望的。为了促进当前量子标尺，我们创建了一个数据分层，模型聚合和新颖的验证方法。值得注意的是，与经典模型相比，量子模型的特征重要性具有更高的变化。我们可以证明等效QML和CML模型通过多样性测量是互补的。 QML和CML之间的多样性表明，两种方法都可以以不同的方式促进解决方案。在本文中，我们关注量子支持向量分类器（QSVC），变分量子电路（VQC）及其经典对应物。 ESPN和IBM幻想足球贸易助理将高级统计分析与沃森发现的自然语言处理相结合，以提供公平的个性化贸易建议。在这里，已经考虑了每个播放器的播放器评估数据，并且可以扩展此工作以计算其他QML模型（例如Quantum Boltzmann机器）的特征重要性。

Migraine is a high-prevalence and disabling neurological disorder. However, information migraine management in real-world settings could be limited to traditional health information sources. In this paper, we (i) verify that there is substantial migraine-related chatter available on social media (Twitter and Reddit), self-reported by migraine sufferers; (ii) develop a platform-independent text classification system for automatically detecting self-reported migraine-related posts, and (iii) conduct analyses of the self-reported posts to assess the utility of social media for studying this problem. We manually annotated 5750 Twitter posts and 302 Reddit posts. Our system achieved an F1 score of 0.90 on Twitter and 0.93 on Reddit. Analysis of information posted by our 'migraine cohort' revealed the presence of a plethora of relevant information about migraine therapies and patient sentiments associated with them. Our study forms the foundation for conducting an in-depth analysis of migraine-related information using social media data.

To train deep learning models, which often outperform traditional approaches, large datasets of a specified medium, e.g., images, are used in numerous areas. However, for light field-specific machine learning tasks, there is a lack of such available datasets. Therefore, we create our own light field datasets, which have great potential for a variety of applications due to the abundance of information in light fields compared to singular images. Using the Unity and C# frameworks, we develop a novel approach for generating large, scalable, and reproducible light field datasets based on customizable hardware configurations to accelerate light field deep learning research.

The core of the computer business now offers subscription-based on-demand services with the help of cloud computing. We may now share resources among multiple users by using virtualization, which creates a virtual instance of a computer system running in an abstracted hardware layer. It provides infinite computing capabilities through its massive cloud datacenters, in contrast to early distributed computing models, and has been incredibly popular in recent years because to its continually growing infrastructure, user base, and hosted data volume. This article suggests a conceptual framework for a workload management paradigm in cloud settings that is both safe and performance-efficient. A resource management unit is used in this paradigm for energy and performing virtual machine allocation with efficiency, assuring the safe execution of users' applications, and protecting against data breaches brought on by unauthorised virtual machine access real-time. A secure virtual machine management unit controls the resource management unit and is created to produce data on unlawful access or intercommunication. Additionally, a workload analyzer unit works simultaneously to estimate resource consumption data to help the resource management unit be more effective during virtual machine allocation. The suggested model functions differently to effectively serve the same objective, including data encryption and decryption prior to transfer, usage of trust access mechanism to prevent unauthorised access to virtual machines, which creates extra computational cost overhead.