Zobrazeno 1 - 10
of 1 242
pro vyhledávání: '"Local Outlier Factor"'
Autor:
Gregorius Airlangga
Publikováno v:
Jurnal Lebesgue, Vol 5, Iss 1, Pp 37-48 (2024)
This study presents an integrated analysis of machine learning algorithms for the detection of seismic anomalies in Indonesia, a region within the volatile Pacific Ring of Fire. Employing Local Outlier Factor, Isolation Forest, and Elliptic Envelope
Externí odkaz:
https://doaj.org/article/a66da82262e3424dbcdbfda7fdfcc21e
Autor:
Gregorius Airlangga
Publikováno v:
Jurnal Lebesgue, Vol 5, Iss 1, Pp 49-61 (2024)
This study presents a comprehensive comparison of three machine learning algorithms for anomaly detection within seismic data, focusing on the unique geographical and geological context of Indonesia, a region prone to frequent seismic events. Local O
Externí odkaz:
https://doaj.org/article/cfdd438cd4b3481eb473465265fbfcf6
Autor:
Edmund Fosu Agyemang
Publikováno v:
Scientific African, Vol 26, Iss , Pp e02386- (2024)
This study presents a comprehensive evaluation of five prominent unsupervised machine learning anomaly detection algorithms: One-Class Support Vector Machine (One-Class SVM), One-Class SVM with Stochastic Gradient Descent (SGD), Isolation Forest (iFo
Externí odkaz:
https://doaj.org/article/aa6a3d4aa3194f3aac81cba8ab595940
Autor:
Nan Liu
Publikováno v:
Systems Science & Control Engineering, Vol 12, Iss 1 (2024)
The internal simulation market system can stimulate employee initiative, reduce costs and improve information processing efficiency. However, the complexity of the internal simulation market poses a challenge to computing resources. Efficient data pr
Externí odkaz:
https://doaj.org/article/0486f5503b9a4e53805e3618dad5d851
Autor:
Taher Ali Al-Shehari, Domenico Rosaci, Muna Al-Razgan, Taha Alfakih, Mohammed Kadrie, Hammad Afzal, Raheel Nawaz
Publikováno v:
IEEE Access, Vol 12, Pp 34820-34834 (2024)
In today’s interconnected world, cybersecurity has emerged as a critical domain for ensuring the integrity, confidentiality, and availability of digital assets. Within this sphere, insider threats represent a unique and particularly insidious class
Externí odkaz:
https://doaj.org/article/bb6e661b84fb4509977c185e7c9c513f
Autor:
Gregorius Airlangga
Publikováno v:
Jurnal Lebesgue, Vol 4, Iss 3, Pp 1892-1901 (2023)
Indonesia's location on the "Ring of Fire" poses a high risk for seismic events. Addressing this, our study applied the Local Outlier Factor (LOF) algorithm for advanced seismic anomaly detection, crucial for geotectonic upheaval prediction. The LOF,
Externí odkaz:
https://doaj.org/article/e7ac36834ab64d97a274d3f3006d21e9
Publikováno v:
Journal of Petroleum Exploration and Production Technology, Vol 14, Iss 1, Pp 343-363 (2023)
Abstract Anomalies in oil-producing wells can have detrimental financial implications, leading to production disruptions and increased maintenance costs. Machine learning techniques offer a promising solution for detecting and preventing such anomali
Externí odkaz:
https://doaj.org/article/7d44d819597d4de7ad5d731a7a1a9157
Publikováno v:
iEnergy, Vol 2, Iss 3, Pp 165-171 (2023)
To improve the safety of electric vehicles and battery energy storage systems, early prediction of thermal runaway (TR) is of great significance. This work proposes a novel method for early warning and short-term prediction of the TR. To give warning
Externí odkaz:
https://doaj.org/article/da3589ceff974b57b650f16457a9313d
Publikováno v:
Sensors, Vol 24, Iss 17, p 5628 (2024)
Detection of abnormal situations in mobile systems not only provides predictions about risky situations but also has the potential to increase energy efficiency. In this study, two real-world drives of a battery electric vehicle and unsupervised hybr
Externí odkaz:
https://doaj.org/article/8394b73463624b65a0710165fcdd92d8
Publikováno v:
Energies, Vol 17, Iss 7, p 1568 (2024)
Accurately identifying a specific faulty monomer in a battery pack in the early stages of battery failure is essential to preventing safety accidents and minimizing property damage. While there are existing lithium-ion power battery fault diagnosis m
Externí odkaz:
https://doaj.org/article/17deff568cd4415d9cd250f8b0917796