Zobrazeno 1 - 10
of 44
pro vyhledávání: '"Joffrey L. Leevy"'
Publikováno v:
Journal of Big Data, Vol 11, Iss 1, Pp 1-20 (2024)
Abstract OCR2SEQ represents an innovative advancement in Optical Character Recognition (OCR) technology, leveraging a multi-modal generative augmentation strategy to overcome traditional limitations in OCR systems. This paper introduces OCR2SEQ’s u
Externí odkaz:
https://doaj.org/article/bcfa728aeb4e41c6a4c2023e9c0de0a9
Publikováno v:
Journal of Big Data, Vol 10, Iss 1, Pp 1-16 (2023)
Abstract Research into machine learning methods for fraud detection is of paramount importance, largely due to the substantial financial implications associated with fraudulent activities. Our investigation is centered around the Credit Card Fraud Da
Externí odkaz:
https://doaj.org/article/5bc138e6b6b14476a2aeb338f4e2b686
Publikováno v:
Journal of Big Data, Vol 10, Iss 1, Pp 1-13 (2023)
Abstract The yearly increase in incidents of credit card fraud can be attributed to the rapid growth of e-commerce. To address this issue, effective fraud detection methods are essential. Our research focuses on the Credit Card Fraud Detection Datase
Externí odkaz:
https://doaj.org/article/04017e2602d24b4ea60a59b568f0a51f
Publikováno v:
Journal of Big Data, Vol 10, Iss 1, Pp 1-22 (2023)
Abstract Output thresholding is well-suited for addressing class imbalance, since the technique does not increase dataset size, run the risk of discarding important instances, or modify an existing learner. Through the use of the Credit Card Fraud De
Externí odkaz:
https://doaj.org/article/f8470606c8074b5ab96cacc7fd4e2643
Publikováno v:
Journal of Big Data, Vol 10, Iss 1, Pp 1-30 (2023)
Abstract With the massive resources and strategies accessible to attackers, countering Denial of Service (DoS) attacks is getting increasingly difficult. One of these techniques is application-layer DoS. Due to these challenges, network security has
Externí odkaz:
https://doaj.org/article/4e1c0cf815714079a7dded9b55d86b82
Publikováno v:
Journal of Big Data, Vol 10, Iss 1, Pp 1-17 (2023)
Abstract Training a machine learning algorithm on a class-imbalanced dataset can be a difficult task, a process that could prove even more challenging under conditions of high dimensionality. Feature extraction and data sampling are among the most po
Externí odkaz:
https://doaj.org/article/24f22fd03963412d8fc845f6e60b2fd3
Publikováno v:
Journal of Big Data, Vol 9, Iss 1, Pp 1-48 (2022)
Abstract The recent years have seen a proliferation of Internet of Things (IoT) devices and an associated security risk from an increasing volume of malicious traffic worldwide. For this reason, datasets such as Bot-IoT were created to train machine
Externí odkaz:
https://doaj.org/article/d4605ff8d8fd4b9c8aa16603020017b8
Publikováno v:
Journal of Big Data, Vol 8, Iss 1, Pp 1-16 (2021)
Abstract Label noise is an important data quality issue that negatively impacts machine learning algorithms. For example, label noise has been shown to increase the number of instances required to train effective predictive models. It has also been s
Externí odkaz:
https://doaj.org/article/978a0676e9214d40b2e53b347947b1fd
Publikováno v:
Journal of Big Data, Vol 8, Iss 1, Pp 1-29 (2021)
Abstract Machine learning algorithms efficiently trained on intrusion detection datasets can detect network traffic capable of jeopardizing an information system. In this study, we use the CSE-CIC-IDS2018 dataset to investigate ensemble feature selec
Externí odkaz:
https://doaj.org/article/eeb5f5de972346ae8403d45f4b468783
Publikováno v:
Journal of Big Data, Vol 7, Iss 1, Pp 1-22 (2020)
Abstract The increasing reliance on electronic health record (EHR) in areas such as medical research should be addressed by using ample safeguards for patient privacy. These records often tend to be big data, and given that a significant portion is s
Externí odkaz:
https://doaj.org/article/a09ca30997834f4aa4b24c4abb792ff6