Neural networks: the panacea in fraud detection?

Purpose: The purpose of the paper is to test the use of artificial neural networks (ANNs) as a tool in fraud detection. Design/methodology/approach: Following a review of the relevant literature on fraud detection by auditors, the authors developed a questionnaire which they distributed to auditors attending a fraud detection seminar. The questionnaire was then used to develop seven ANNs to test the usage of these models in fraud detection. Findings: Utilizing exogenous and endogenous factors as input variables to ANNs and in developing seven different models, an average of 90 per cent accuracy was found in the fraud detection prediction model. It has, therefore, been demonstrated that ANNs can be used by auditors to identify fraud-prone companies. Originality/value: Whilst previous researchers have looked at empirical predictors of fraud, fraud risk assessment methods and mechanically fraud risk assessment methods, no other research has combined both exogenous and endogenous factors in developing ANNs to be used in fraud detection. Thus, auditors can use ANNs as complementary to other techniques at the planning stage of their audit to predict if a particular audit client is likely to have been victimized by a fraudster

Neural networks: the panacea in fraud detection?

Purpose – The purpose of the paper is to test the use of artificial neural networks (ANNs) as a tool in fraud detection. Design/methodology/approach – Following a review of the relevant literature on fraud detection by auditors, the authors developed a questionnaire which they distributed to auditors attending a fraud detection seminar. The questionnaire was then used to develop seven ANNs to test the usage of these models in fraud detection. Findings – Utilizing exogenous and endogenous factors as input variables to ANNs and in developing seven different models, an average of 90 per cent accuracy was found in the fraud detection prediction model. It has, therefore, been demonstrated that ANNs can be used by auditors to identify fraud-prone companies. Originality/value – Whilst previous researchers have looked at empirical predictors of fraud, fraud risk assessment methods and mechanically fraud risk assessment methods, no other research has combined both exogenous and endogenous factors in developing ANNs to be used in fraud detection. Thus, auditors can use ANNs as complementary to other techniques at the planning stage of their audit to predict if a particular audit client is likely to have been victimized by a fraudster.Auditors, Financial reporting, Fraud, Neural nets

FlockAI: A Testing Suite for ML-Driven Drone Applications

Machine Learning (ML) is now becoming a key driver empowering the next generation of drone technology and extending its reach to applications never envisioned before. Examples include precision agriculture, crowd detection, and even aerial supply transportation. Testing drone projects before actual deployment is usually performed via robotic simulators. However, extending testing to include the assessment of on-board ML algorithms is a daunting task. ML practitioners are now required to dedicate vast amounts of time for the development and configuration of the benchmarking infrastructure through a mixture of use-cases coded over the simulator to evaluate various key performance indicators. These indicators extend well beyond the accuracy of the ML algorithm and must capture drone-relevant data including flight performance, resource utilization, communication overhead and energy consumption. As most ML practitioners are not accustomed with all these demanding requirements, the evaluation of ML-driven drone applications can lead to sub-optimal, costly, and error-prone deployments. In this article we introduce FlockAI, an open and modular by design framework supporting ML practitioners with the rapid deployment and repeatable testing of ML-driven drone applications over the Webots simulator. To show the wide applicability of rapid testing with FlockAI, we introduce a proof-of-concept use-case encompassing different scenarios, ML algorithms and KPIs for pinpointing crowded areas in an urban environment

FlockAI: A Testing Suite for ML-Driven Drone Applications

Machine Learning (ML) is now becoming a key driver empowering the next generation of drone technology and extending its reach to applications never envisioned before. Examples include precision agriculture, crowd detection, and even aerial supply transportation. Testing drone projects before actual deployment is usually performed via robotic simulators. However, extending testing to include the assessment of on-board ML algorithms is a daunting task. ML practitioners are now required to dedicate vast amounts of time for the development and configuration of the benchmarking infrastructure through a mixture of use-cases coded over the simulator to evaluate various key performance indicators. These indicators extend well beyond the accuracy of the ML algorithm and must capture drone-relevant data including flight performance, resource utilization, communication overhead and energy consumption. As most ML practitioners are not accustomed with all these demanding requirements, the evaluation of ML-driven drone applications can lead to sub-optimal, costly, and error-prone deployments. In this article we introduce FlockAI, an open and modular by design framework supporting ML practitioners with the rapid deployment and repeatable testing of ML-driven drone applications over the Webots simulator. To show the wide applicability of rapid testing with FlockAI, we introduce a proof-of-concept use-case encompassing different scenarios, ML algorithms and KPIs for pinpointing crowded areas in an urban environment

Hedgehog signaling and the retina: insights into the mechanisms controlling the proliferative properties of neural precursors

Hedgehog signaling has been linked to cell proliferation in a variety of systems; however, its effects on the cell cycle have not been closely studied. In the vertebrate retina, Hedgehog's effects are controversial, with some reports emphasizing increased proliferation and others pointing to a role in cell cycle exit. Here we demonstrate a novel role for Hedgehog signaling in speeding up the cell cycle in the developing retina by reducing the length of G1 and G2 phases. These fast cycling cells tend to exit the cell cycle early. Conversely, retinal progenitors with blocked Hedgehog signaling cycle more slowly, with longer G1 and G2 phases, and remain in the cell cycle longer. Hedgehog may modulate cell cycle kinetics through activation of the key cell cycle activators cyclin D1, cyclin A2, cyclin B1, and cdc25C. These findings support a role for Hedgehog in regulating the conversion from slow cycling stem cells to fast cycling transient amplifying progenitors that are closer to cell cycle exit

Neutrophil metabolomics in severe COVID-19 reveal GAPDH as a suppressor of neutrophil extracellular trap formation

Abstract Severe COVID-19 is characterized by an increase in the number and changes in the function of innate immune cells including neutrophils. However, it is not known how the metabolome of immune cells changes in patients with COVID-19. To address these questions, we analyzed the metabolome of neutrophils from patients with severe or mild COVID-19 and healthy controls. We identified widespread dysregulation of neutrophil metabolism with disease progression including in amino acid, redox, and central carbon metabolism. Metabolic changes in neutrophils from patients with severe COVID-19 were consistent with reduced activity of the glycolytic enzyme GAPDH. Inhibition of GAPDH blocked glycolysis and promoted pentose phosphate pathway activity but blunted the neutrophil respiratory burst. Inhibition of GAPDH was sufficient to cause neutrophil extracellular trap (NET) formation which required neutrophil elastase activity. GAPDH inhibition increased neutrophil pH, and blocking this increase prevented cell death and NET formation. These findings indicate that neutrophils in severe COVID-19 have an aberrant metabolism which can contribute to their dysfunction. Our work also shows that NET formation, a pathogenic feature of many inflammatory diseases, is actively suppressed in neutrophils by a cell-intrinsic mechanism controlled by GAPDH

A directional Wnt/β-catenin-Sox2-proneural pathway regulates the transition from proliferation to differentiation in the Xenopus retina

Progenitor cells in the central nervous system must leave the cell cycle to become neurons and glia, but the signals that coordinate this transition remain largely unknown. We previously found that Wnt signaling, acting through Sox2, promotes neural competence in the Xenopus retina by activating proneural gene expression. We now report that Wnt and Sox2 inhibit neural differentiation through Notch activation. Independently of Sox2, Wnt stimulates retinal progenitor proliferation and this, when combined with the block on differentiation, maintains retinal progenitor fates. Feedback inhibition by Sox2 on Wnt signaling and by the proneural transcription factors on Sox2 mean that each element of the core pathway activates the next element and inhibits the previous one, providing a directional network that ensures retinal cells make the transition from progenitors to neurons and glia

Maternal vitamin C regulates reprogramming of DNA methylation and germline development

Development is often assumed to be hardwired in the genome, but several lines of evidence indicate that it is susceptible to environmental modulation with potential long-term consequences, including in mammals1,2. The embryonic germline is of particular interest because of the potential for intergenerational epigenetic effects. The mammalian germline undergoes extensive DNA demethylation3–7 that occurs in large part by passive dilution of methylation over successive cell divisions, accompanied by active DNA demethylation by TET enzymes3,8–10. TET activity has been shown to be modulated by nutrients and metabolites, such as vitamin C11–15. Here we show that maternal vitamin C is required for proper DNA demethylation and the development of female fetal germ cells in a mouse model. Maternal vitamin C deficiency does not affect overall embryonic development but leads to reduced numbers of germ cells, delayed meiosis and reduced fecundity in adult offspring. The transcriptome of germ cells from vitamin-C-deficient embryos is remarkably similar to that of embryos carrying a null mutation in Tet1. Vitamin C deficiency leads to an aberrant DNA methylation profile that includes incomplete demethylation of key regulators of meiosis and transposable elements. These findings reveal that deficiency in vitamin C during gestation partially recapitulates loss of TET1, and provide a potential intergenerational mechanism for adjusting fecundity to environmental conditions.SCOPUS: ar.jinfo:eu-repo/semantics/publishe