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Filter bubbles in recommender systems: Fact or fallacy—A systematic review
A filter bubble refers to the phenomenon where Internet customization effectively isolates
individuals from diverse opinions or materials, resulting in their exposure to only a select set …
individuals from diverse opinions or materials, resulting in their exposure to only a select set …
COVID-19 and beyond: leveraging artificial intelligence for enhanced outbreak control
COVID-19 has brought significant changes to our political, social, and technological
landscape. This paper explores the emergence and global spread of the disease and …
landscape. This paper explores the emergence and global spread of the disease and …
Analysis of techniques for detection and removal of zero-day attacks (zda)
Zero-day attacks (ZDAs) are previously unknown flaws and errors in operating systems,
networks, and general-purpose software. ZDAs are the cause to open security breach holes …
networks, and general-purpose software. ZDAs are the cause to open security breach holes …
A Deep Dive into Fairness, Bias, Threats, and Privacy in Recommender Systems: Insights and Future Research
Recommender systems are essential for personalizing digital experiences on e-commerce
sites, streaming services, and social media platforms. While these systems are necessary for …
sites, streaming services, and social media platforms. While these systems are necessary for …
[HTML][HTML] Privacy protection against user profiling through optimal data generalization
Personalized information systems are information-filtering systems that endeavor to tailor
information-exchange functionality to the specific interests of their users. The ability of these …
information-exchange functionality to the specific interests of their users. The ability of these …
Detecting unknown vulnerabilities in smart contracts with multi-label classification model using CNN-BiLSTM
W Gu, G Wang, P Li, X Li, G Zhai, X Li… - … Conference on Ubiquitous …, 2022 - Springer
Smart contracts are frequently targeted by hackers because they hold large amounts of
money and cannot be modified once they are published. Existing detection methods mainly …
money and cannot be modified once they are published. Existing detection methods mainly …
FedTA: Locally-Differential Federated Learning with Top-k Mechanism and Adam Optimization
Y Li, G Wang, T Peng, G Feng - International Conference on Ubiquitous …, 2022 - Springer
With the explosive development of fields including big data and cloud computing, it has
become a global trend for the public to place a premium on data security and privacy. A …
become a global trend for the public to place a premium on data security and privacy. A …
Detecting Brain Cancer Using Explainable AI
A Rahman, SS Sohail, MS Alam… - 2024 7th …, 2024 - ieeexplore.ieee.org
The accurate and early diagnosis of brain tumors that is one of the deadly cancers may save
many humans. Proposed research focused on primary types of tumors detection (ie gliomas …
many humans. Proposed research focused on primary types of tumors detection (ie gliomas …
Decentralized Collaborative Filtering Algorithm with Privacy Preserving for Recommendation in Mobile Edge Computing
X Liu, P Yin, P Liu, S Chen - International Conference on Ubiquitous …, 2022 - Springer
Mobile edge computing (MEC) deploys network services closer to the user's wireless access
network side and provides IT service environment and cloud computing capabilities at the …
network side and provides IT service environment and cloud computing capabilities at the …
[PDF][PDF] Unravelling Filter Bubbles in Recommender Systems: A Comprehensive Review
Abstracts: The prevalence of filter bubbles in recommender systems has raised concerns
about the potential impact on user experiences and information exposure. This systematic …
about the potential impact on user experiences and information exposure. This systematic …