Results 41 to 50 of about 274,113 (349)

Determination of Ethanol Content in Alcoholic Products by LF-NMR

Analytica
Nuclear magnetic resonance (NMR) is a technique used for many years by chemists for elucidation of molecular structure. Technological progress over the years has enabled this technique, making it easier to use.
Gianluca Farine, Maria Scilinguo, Chiara Calvagna, Silvia Romano, Marco Polito, Salvatore Sgrò   +5 more
doaj   +1 more source

The entities enabling scientific fraud at scale are large, resilient, and growing rapidly

Proceedings of the National Academy of Sciences of the United States of America
Significance Numerous recent scientific and journalistic investigations demonstrate that systematic scientific fraud is a growing threat to the scientific enterprise.
Reese A K Richardson, Spencer S. Hong, J. Byrne, T. Stoeger, L. A. Nunes Amaral   +4 more
semanticscholar   +1 more source

A Neural Network Ensemble With Feature Engineering for Improved Credit Card Fraud Detection

IEEE Access, 2022
Recent advancements in electronic commerce and communication systems have significantly increased the use of credit cards for both online and regular transactions.
Ebenezer Esenogho, Ibomoiye Domor Mienye, T. Swart, Kehinde D. Aruleba, G. Obaido   +4 more
semanticscholar   +1 more source

FRAUD RISK MANAGEMENT AND FRAUD REDUCTION

Malaysian Management Journal, 2022
The study examined the effect of fraud risk management techniques on fraud reduction in the oil and gas sector in Nigeria. The study used primary data which were obtained through a structured questionnaire.
Aderemi Olalere Adebayo, Adebayo Olagunju, Oluwaseun Emmanuel Bankole   +2 more
doaj   +1 more source

A systematic review of AI-enhanced techniques in credit card fraud detection

Journal of Big Data
The rapid increase of fraud attacks on banking systems, financial institutions, and even credit card holders demonstrate the high demand for enhanced fraud detection (FD) systems for these attacks.
Ibrahim Y. Hafez, Ahmed Y. Hafez, Ahmed Saleh, A. A. Abd El-Mageed, A. Abohany   +4 more
semanticscholar   +1 more source

Online payment fraud: from anomaly detection to risk management

Financial Innovation, 2023
Online banking fraud occurs whenever a criminal can seize accounts and transfer funds from an individual’s online bank account. Successfully preventing this requires the detection of as many fraudsters as possible, without producing too many false alarms.
P. Vanini, Sebastiano Rossi, Ermin Zvizdic, T. Domenig   +3 more
semanticscholar   +1 more source

Fraud Risk Analysis Fraud Prevention Detection with Fraud Triangle and Financial Ratios at PT. Garuda Indonesia (Persero) Tbk. [PDF]

, 2022
This study aims to detect the risk of fraud in the financial statements and ongoing fraud prevention measures if there are irregularities in the financial statements.
Sari, Eko Gumaya
core   +1 more source

Secure and Transparent Banking: Explainable AI-Driven Federated Learning Model for Financial Fraud Detection

Journal of Risk and Financial Management
The increasing sophistication of fraud has rendered rule-based fraud detection obsolete, exposing banks to greater financial risk, reputational damage, and regulatory penalties.
Saif Khalifa Aljunaid, Saif Almheiri, Hussain Dawood, Muhammad Adnan Khan   +3 more
semanticscholar   +1 more source

Application of handheld NIR spectrometer for simultaneous identification and quantification of quality parameters in intact mango fruits

Smart Agricultural Technology, 2023
: There are several varieties of mango fruits, and the most important quality indicators for determining mango maturity are pH and total soluble solids (TSS).
Francis Padi Lamptey, Ernest Teye, Ernest Ekow Abano, Charles L.Y. Amuah   +3 more
doaj   +1 more source

Enhancing Fraud Detection in Banking With Deep Learning: Graph Neural Networks and Autoencoders for Real-Time Credit Card Fraud Prevention

IEEE Access
Under the umbrella of artificial intelligence (AI), deep learning enables systems to cluster data and provide incredibly accurate results. This study explores deep learning for fraud detection, utilizing Graph Neural Networks (GNNs) and Autoencoders to ...
F. Alarfaj, Shabnam Shahzadi
semanticscholar   +1 more source