Results 251 to 260 of about 696,171 (284)

Degree of Accuracy in Credit Card Fraud Detection Using Local Outlier Factor and Isolation Forest Algorithm

Confluence, 2022
In this era of digitalization where everyone prefers online-based transactional activities, this increases the demand for a credit card, the fraudulent cases are increasing day by day which causes tremendous loss to an individual.
Kanishka Negi, Gaddam Prathik Kumar, G. Raj, S. Sahana, Vishal Jain   +4 more
semanticscholar   +1 more source

LoRaLOFT-A Local Outlier Factor-based Malicious Nodes detection Method on MAC Layer for LoRaWAN

Global Communications Conference, 2022
LoRaWAN is one of the network technologies that provide a long-range wireless network at low energy consumption. However, the pure Aloha MAC protocol and the duty-cycle limitation at both end devices and gateway make LoRaWAN very sensitive to malicious ...
Mi Chen, L. Mokdad, J. Ben-othman, J. Fourneau   +3 more
semanticscholar   +1 more source

Predictive Maintenance for Industrial Equipment: Using XGBoost and Local Outlier Factor with Explainable AI for analysis

Confluence
In industrial operations, the need to minimize downtime and enhance productivity has produced the need for predictive maintenance techniques. Using artificial intelligence (AI) in this domain has revolutionized maintenance practices, but the lack of ...
P. Ghadekar, Aman Manakshe, Sarthak Madhikar, Sushrut Patil, Mehvish Mukadam, Tejas Gambhir   +5 more
semanticscholar   +1 more source

Mean-Shift and Local Outlier Factor-Based Ensemble Machine Learning Approach for Anomaly Detection in IoT Devices

International Congress on Information and Communication Technology, 2022
IoT devices are rapidly being used in everyday life. However, many of these devices are susceptible as a result of insecure design, implementation, and setup.
Amit Kumar Gulhare, Abhishek Badholia, Anurag Sharma   +2 more
semanticscholar   +1 more source

A Comparative Study of Local Outlier Factor Algorithms for Outliers Detection in Data Streams

2018
Outlier detection analyzes data, finds out anomalies, and helps to discover unforeseen activities in safety crucial systems. Outlier detection helps in early prediction of various fraudulent activities like credit card theft, fake insurance claim, tax stealing, real-time monitoring, medical systems, online transactions, and many more.
Supriya Mishra, Meenu Chawla
openaire   +1 more source

Performance Enhancement of Unsupervised Hardware Trojan Detection Algorithm using Clustering-based Local Outlier Factor Technique for Design Security

2022 IEEE International Test Conference India (ITC India), 2022
Internet of Things (IoT) has become extremely prominent for industrial applications and stealthy modification deliberately done by insertion of Hardware Trojans has increased widely due to globalization of Integrated Circuit (IC) production.
S. Meenakshi, N. M
semanticscholar   +1 more source

GMBLOF: A Machine Learning Algorithm of Novelty Detection Based on Local Outlier Factor

2022 5th International Conference on Pattern Recognition and Artificial Intelligence (PRAI), 2022
Local Outlier Factor (LOF) algorithm is a typical machine learning algorithm and has good accuracy in novelty detection for detecting global and local outlier.
Xing Yang, Kexin Peng, Le An, Pan Huang, Peng Feng   +4 more
semanticscholar   +1 more source

Distributed Local Outlier Factor with Locality-Sensitive Hashing

2019
Outlier detection remains a heated area due to its essential role in a wide range of applications, including intrusion detection, fraud detection in finance, medical diagnosis, etc. Local Outlier Factor (LOF) has been one of the most influential outlier detection techniques over the past decades.
openaire   +2 more sources

Self-Adaptive Negative Selection Using Local Outlier Factor

2012
Negative selection algorithm (NSA) classifies a given data either as normal (self) or anomalous (non-self). To make this classification, it is trained using normal (self) samples. NSA generates detectors to cover the complementary space of self in training phase. The classification of NSAs is mainly specified by two issues, self space determination and
Zafer Ataser, Ferda Nur Alpaslan
openaire   +1 more source

Outlier Detection for Transformer's Oil Chromatographic Data Based on Metric Learning and the Weighted Local Outlier Factor

2019 6th International Conference on Systems and Informatics (ICSAI), 2019
Detecting the outliers for transformer's oil chromatographic data is very important for analyzing and monitoring the status of a transformer. However, the existing methods are usually developed for some specific situations and may not perform well in many cases.
Jiafeng Qin, Yi Yang, Chao Gu, Zijing Hong, Hongyi Du   +4 more
openaire   +1 more source