A Review of fine grained access control techniques

Nowadays, Dynamic industries like tourism is a enhance to boost many countries economy in recent years. The hotel sector leads to a significant role among all aspects of the tourism industry. Online travel platforms, in association with hotel management, are a part of hotel E-tourism that helps users to make travel plans online, suggest precise recommendations in consideration with the earlier feedbacks upon hotel stay. From the past few months, the user roaming ratio falls rapidly due to the COVID-19 Pandemic across the globe. In a business concern, this Pandemic affects all tourism sectors, specifically in the hotels’ occupancy. To retain business positions and attract the users in these problematic scenarios, hotel and travel platform management concerns about past preferences of users, which have a significant impact on gratification for a comfortable stay. The current market trends show that user reviews are playing a vital role to select a hotel based on a safe location. Our work tries to find and recommend the dimensions that contribute more towards higher satisfaction levels of users in Asian continental top tourism city hotels listed by the Master and VISA Inc survey with item collaborative filtering and regression techniques based on Tripadvisor reviews from social media in recent times. The outcome of the work is helpful for Asian continental hotel management to concentrate more on the dimensions for obtaining better reviews. From an online travel platform perspective, this work suggests the contexts for the travel recommender system to gain things effectively from users.

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RETRACTED: Detecting Faults within a Cloud Using Machine Learning Techniques

Sagar

Kavitha²,

Kadaru³

et al. 2020

IOP Conf. Ser.: Mater. Sci. Eng.

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In distributed computing, clients can get to cloud administrations using the web. In present days, in superior registering and cloud frameworks, disappointment is an inexorably significant issue. Alleviating the impact of misfortune and making stable conjectures with excellent lead time stays an overwhelming exploration issue as huge scope frameworks keep on creating in scale and multifaceted nature. Because of the advancing unpredictability of elite figuring frameworks, specific current adaptation to internal failure procedures, for Example, successive registration and replication are not adequate. It includes the significance of having a productive and useful way to deal with disappointment the executives set up to relieve the impacts of disappointment inside the framework. With the approach of AI methods, the capacity to gain from past data to anticipate future personal conduct standards makes it conceivable to foresee potential framework disappointment all the more precisely. Along these lines, in this paper, by applying a few calculations to improve the exactness of disappointment forecast, we investigate the prescient capacities of AI. We have set up an expectation of disappointment. The fundamental analysis that we have Random backwoods (RF), SVM, Classification and Regression Trees (CART) considered). Exploratory discoveries show that comparative with different calculations, the typical expectation precision of our paradigm utilising SVM while foreseeing breakdown is 92% exact and productive. This discovering implies that all likely future device and application disappointments can be viably imagined by our cycle inside.

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A Review of fine grained access control techniques

Cited by 3 publications

References 9 publications

Smart Agricultural Solutions Through Machine Learning

Smart Agricultural Solutions Through Machine Learning

Collaborative Filtering and Regression Techniques based location Travel Recommender System based on social media reviews data due to the COVID-19 Pandemic

RETRACTED: Detecting Faults within a Cloud Using Machine Learning Techniques

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