AIMC Topic: Fraud

Clear Filters Showing 21 to 30 of 44 articles

New Software Interface for Registering Rapid Antigen Test Results to Prevent Fraud.

Disaster medicine and public health preparedness
Donald O. Besong has already documented that the online registration of unsupervised lateral flow test results poses concerns in the case of a serious pandemic where there are not enough medics to read scans or watch videos of candidates' results (Be...

Monitoring and Analysis of Venture Capital and Corporate Fraud Based on Deep Learning.

Computational intelligence and neuroscience
With the continuous expansion of global investment institutions, the development of the investment industry is gradually accelerating, but the risks behind the investment are also constantly increasing. Using the data of A-share companies in China's ...

E-Commerce Fraud Detection Model by Computer Artificial Intelligence Data Mining.

Computational intelligence and neuroscience
This study aims to identify e-commerce fraud, solve the financial risks of e-commerce enterprises through big data mining (BDM), further explore more effective solutions through Information fusion technology (IFT), and create an e-commerce fraud dete...

Feature engineering solution with structured query language analytic functions in detecting electricity frauds using machine learning.

Scientific reports
Detecting fraud related to electricity consumption is usually a difficult challenge as the input datasets are sometimes unreliable due to missing and inconsistent records, faults, misinterpretation of meter reading remarks, status, etc. In this paper...

Credit card fraud detection using a hierarchical behavior-knowledge space model.

PloS one
With the advancement in machine learning, researchers continue to devise and implement effective intelligent methods for fraud detection in the financial sector. Indeed, credit card fraud leads to billions of dollars in losses for merchants every yea...

A Novel Feature-Engineered-NGBoost Machine-Learning Framework for Fraud Detection in Electric Power Consumption Data.

Sensors (Basel, Switzerland)
This study presents a novel feature-engineered-natural gradient descent ensemble-boosting (NGBoost) machine-learning framework for detecting fraud in power consumption data. The proposed framework was sequentially executed in three stages: data pre-p...

Detecting fabrication in large-scale molecular omics data.

PloS one
Fraud is a pervasive problem and can occur as fabrication, falsification, plagiarism, or theft. The scientific community is not exempt from this universal problem and several studies have recently been caught manipulating or fabricating data. Current...

Detection of fraud in ginger powder using an automatic sorting system based on image processing technique and deep learning.

Computers in biology and medicine
Ginger is a well-known product in the food and pharmaceutical industries. Ginger is one of the spices which are adulterated for economic gain. The lack of marketability of grade 3 chickpeas (small and broken chickpeas) and their very low price have m...

Machine learning based approach to exam cheating detection.

PloS one
The COVID-19 pandemic has impelled the majority of schools and universities around the world to switch to remote teaching. One of the greatest challenges in online education is preserving the academic integrity of student assessments. The lack of dir...

A novel method based on machine vision system and deep learning to detect fraud in turmeric powder.

Computers in biology and medicine
Assessing the quality of food and spices is particularly important in ensuring proper human nutrition. The use of computer vision method as a non-destructive technique in measuring the quality of food and spices has always been taken into considerati...