An uncountable number of computational resources are shared for various applications using a transformative technology called Cloud computing. It is an emerging technology that can offer scalable and on-demand resourc...
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A system that attempts to predict our preferred movies based on our own past selections and preferences is referred to as a 'movie recommendation system.' Machine Learning algorithms like content-based filteri...
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Different web management strategies are used in the cloud computing deployment to address various issues. Here, information security and protection has developed into a significant issue that restricts a lot of cloud ...
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Mobile edge computing (MEC) migrates computing, storage, and network resources to the network edge, providing users with low-latency data access. As a crucial part of MEC, edge storage systems (ESS) offer storage capa...
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Many kinds of information entropy are employed for feature selection, but they lack corresponding probabilities to interpret;Despite many statistical indicators utilized in feature selection, neither probability nor m...
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Many kinds of information entropy are employed for feature selection, but they lack corresponding probabilities to interpret;Despite many statistical indicators utilized in feature selection, neither probability nor mathematical expectation was applied to perform feature selection directly. To address such two problems, this article redefines three kinds of probabilities and their corresponding mathematical expectations from the perspective of granular computing and investigates their properties. These novel probabilities and mathematical expectations extend the meanings of classical probability and mathematical expectation and provide statistical interpretation for their corresponding information entropy, and then, attribute reducts based on probabilities and mathematical expectations are defined, which are proved to be equivalent to those based on their corresponding information entropy. A framework of feature selection algorithms based on probabilities and mathematical expectations (ARME) is designed after the presentation of their properties. Moreover, a novel definition form for feature selection is proposed, and another feature selection algorithm based on the mathematical expectation of conditional probability (ARMEC) is designed to reduce negative features on classification. Theoretical analysis and experimental results show that probabilities and mathematical expectations have super efficiency than their corresponding information entropy when they are considered as criteria of feature selection. Therefore, the novel method has the advantage over many state-of-the-art algorithms.
Artificial Intelligence (AI) has seen widespread application across various sectors, with recent decades witnessing a surge in available data. The primary objective of this research study is to facilitate better, fast...
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The technology known as Wireless Power Transmission (WPT) is gaining popularity and finding use in a variety of industries. Without the need for interconnections, power is moved from a input device to an electrical lo...
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Visually impaired students face many obstacles in higher education, including access to adapted course material. While accessibility is a legal requirement in many countries, its implementation is linked to practical ...
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Leveraging the principles of quantum mechanics, quantum computing become a rapidly evolving research field that solves certain types of problems exponentially faster than classical computers. In quantum computing quan...
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In the current scenario, the social media demands the growing popularity of any services is entirely based on the huge number of user communications in form of customer comments, reviews and opinions. Therefore, it is...
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