A TAXONOMY OF MACHINE LEARNING-BASED FRAUD DETECTION SYSTEMS

As fundamental changes in information systems drive digitalization, the heavy reliance on computers today significantly increases the risk of fraud. Existing literature promotes machine learning as a potential solution approach for the problem of fraud detection as it is able able to detect patterns in large datasets efficiently. However, there is a lack of clarity and awareness on which components and functionalities of machine learning-based fraud detection systems exist and how these systems can be classified consistently. We draw on 54 identified relevant machine learning-based fraud detection systems to address this research gap and develop a taxonomic scheme. By deriving three archetypes of machine learning-based fraud detection systems, the taxonomy paves the way for research and practice to understand and advance fraud detection knowledge to combat fraud and abuse

Motion symmetry evaluation using accelerometers and energy distribution

Analysis of motion symmetry constitutes an important area with many applications in engineering, robotics, neurology and biomedicine. This paper presents the use ofmicroelectromechanical sensors (MEMS), including accelerometers and gyrometers, to acquire data via mobile devices so as to monitor physical activities and their irregularities. Special attention is devoted to the analysis of the symmetry of the motion of the body when the same exercises are performed by the right and the left limb. The analyzed data include the motion of the legs on a home exercise bike under different levels of load. The method is based on signal analysis using the discrete wavelet transform and the evaluation of signal segment features such as the relative energy at selected decomposition levels. The subsequent classification of the evaluated features is performed by k-nearest neighbours, a Bayesian approach, a support vector machine, and neural networks. The highest average classification accuracy attained is 91.0% and the lowest mean cross-validation error is 0.091, resulting from the use of a neural network. This paper presents the advantages of the use of simple sensors, their combination and intelligent data processing for the numerical evaluation of motion features in the rehabilitation and monitoring of physical activities. © 2019 by the authors.Ministry of Health of the Czech Republic [FN HK 00179906]; Charles University in Prague, Czech Republic [PROGRES Q40]; project PERSONMED - European Regional Development Fund (ERDF) [CZ.02.1.010.00.017_0480007441]; governmental budget of the Czech Republi

Dr. A. LeGrand Richards, et al., Plaintiffs/Appellees, vs. Spencer Cox, as Lieutenant Governor of the state of Utah, Defendant/Appellant, : Brief of Appellee

BRIEF OF APPELLEES On appeal from the Third Judicial District CourtThe Honorable Andrew H. StoneNo. 17090407

The BG News February 28, 1986

The BGSU campus student newspaper February 28, 1986. Volume 68 - Issue 89https://scholarworks.bgsu.edu/bg-news/5494/thumbnail.jp

Secure internet financial transactions: a framework integrating multi-factor authentication and machine learning

Securing online financial transactions has become a critical concern in an era where financial services are becoming more and more digital. The transition to digital platforms for conducting daily transactions exposed customers to possible risks from cybercriminals. This study proposed a framework that combines multi-factor authentication and machine learning to increase the safety of online financial transactions. Our methodology is based on using two layers of security. The first layer incorporates two factors to authenticate users. The second layer utilizes a machine learning component, which is triggered when the system detects a potential fraud. This machine learning layer employs facial recognition as a decisive authentication factor for further protection. To build the machine learning model, four supervised classifiers were tested: logistic regression, decision trees, random forest, and naive Bayes. The results showed that the accuracy of each classifier was 97.938%, 97.881%, 96.717%, and 92.354%, respectively. This study’s superiority is due to its methodology, which integrates machine learning as an embedded layer in a multi-factor authentication framework to address usability, efficacy, and the dynamic nature of various e-commerce platform features. With the evolving financial landscape, a continuous exploration of authentication factors and datasets to enhance and adapt security measures will be considered in future work

Mycologia Obscura: Hidden and Layered Realms of Fungal Diversity

Collectively, this dissertation explores taxonomy, biodiversity studies, natural history collections, ecology, and theory. The first chapter is focused on the genus Prolixandromyces, in which 4 new species are described, representing the first records of this genus in South America. The genus is emended and a key to the genus is provided. The second chapter is focused on the genus Laboulbenia, in which four new species are described on a new host family, Gerridae (Heteroptera) or water striders. The third chapter also includes the description of 4 new species of Laboulbenia on Heteroptera, and targets significant gaps in the literature on Heteroptera associated Laboulbeniales. By utilizing the entomological collection at the American Museum of Natural History, this chapter explores host utilization patterns in the group and tracks insect infection rate at the family level. These three chapters all emphasize the scientific value of maintaining our natural heritage in accessible, research oriented biological collections. The fourth chapter is a field-based ecological pilot study focused on exploring how Laboulbeniales and their insect hosts are impacted by urbanization at two lakes in central Florida. Using the historical records of Laboulbeniales diversity from 1897 of mycologist Roland Thaxter, a comparison is drawn to modern (2018) diversity. A rapid biodiversity assessment was conducted on insects and fungi at a protected area and a developed area in 2018 and the results are compared. This study highlights the potential relevance of Laboulbeniales as environmental health indicators and a proposal for future directions is included in the appendix. Lastly, the fifth chapter approaches the field of mycology through a theoretical framework rooted in queer and feminist theories, as well as philosophy of science and Traditional Ecological Knowledge. This chapter is relevant as it challenges, pushes, and explores central tenets of institutional science and functions to socially and historically situate current research dilemmas in mycology. By excavating and laying bare ingrained, systemic biases in scientific institutions, this chapter seeks to disarm fallacious assertions of ―purity‖ in science. Additionally, this work reiterates themes introduced in the preceding chapters, such as the value of taxonomy and biodiversity studies, the importance of biological collections, and the urgent need for expanded and imaginative conservation practices in the age of climate change

Winona Daily News

https://openriver.winona.edu/winonadailynews/2180/thumbnail.jp