9,622 research outputs found
An Intelligent Data Mining System to Detect Health Care Fraud
The chapter begins with an overview of the types of healthcare fraud. Next, there is a brief discussion of issues with the current fraud detection approaches. The chapter then develops information technology based approaches and illustrates how these technologies can improve current practice. Finally, there is a summary of the major findings and the implications for healthcare practice
Role of Artificial Intelligence (AI) art in care of ageing society: focus on dementia
open access articleBackground: Art enhances both physical and mental health wellbeing. The health
benefits include reduction in blood pressure, heart rate, pain perception and briefer
inpatient stays, as well as improvement of communication skills and self-esteem. In
addition to these, people living with dementia benefit from reduction of their noncognitive,
behavioural changes, enhancement of their cognitive capacities and being
socially active.
Methods: The current study represents a narrative general literature review on
available studies and knowledge about contribution of Artificial Intelligence (AI) in
creative arts.
Results: We review AI visual arts technologies, and their potential for use among
people with dementia and care, drawing on similar experiences to date from
traditional art in dementia care.
Conclusion: The virtual reality, installations and the psychedelic properties of the AI
created art provide a new venue for more detailed research about its therapeutic use in
dementia
Correlating Medi-Claim Service by Deep Learning Neural Networks
Medical insurance claims are of organized crimes related to patients,
physicians, diagnostic centers, and insurance providers, forming a chain
reaction that must be monitored constantly. These kinds of frauds affect the
financial growth of both insured people and health insurance companies. The
Convolution Neural Network architecture is used to detect fraudulent claims
through a correlation study of regression models, which helps to detect money
laundering on different claims given by different providers. Supervised and
unsupervised classifiers are used to detect fraud and non-fraud claims
Privacy Violation and Detection Using Pattern Mining Techniques
Privacy, its violations and techniques to bypass privacy violation have grabbed the centre-stage of both academia and industry in recent months. Corporations worldwide have become conscious of the implications of privacy violation and its impact on them and to other stakeholders. Moreover, nations across the world are coming out with privacy protecting legislations to prevent data privacy violations. Such legislations however expose organizations to the issues of intentional or unintentional violation of privacy data. A violation by either malicious external hackers or by internal employees can expose the organizations to costly litigations. In this paper, we propose PRIVDAM; a data mining based intelligent architecture of a Privacy Violation Detection and Monitoring system whose purpose is to detect possible privacy violations and to prevent them in the future. Experimental evaluations show that our approach is scalable and robust and that it can detect privacy violations or chances of violations quite accurately. Please contact the author for full text at [email protected]
Super-resolution community detection for layer-aggregated multilayer networks
Applied network science often involves preprocessing network data before
applying a network-analysis method, and there is typically a theoretical
disconnect between these steps. For example, it is common to aggregate
time-varying network data into windows prior to analysis, and the tradeoffs of
this preprocessing are not well understood. Focusing on the problem of
detecting small communities in multilayer networks, we study the effects of
layer aggregation by developing random-matrix theory for modularity matrices
associated with layer-aggregated networks with nodes and layers, which
are drawn from an ensemble of Erd\H{o}s-R\'enyi networks. We study phase
transitions in which eigenvectors localize onto communities (allowing their
detection) and which occur for a given community provided its size surpasses a
detectability limit . When layers are aggregated via a summation, we
obtain , where is the number of
layers across which the community persists. Interestingly, if is allowed to
vary with then summation-based layer aggregation enhances small-community
detection even if the community persists across a vanishing fraction of layers,
provided that decays more slowly than . Moreover,
we find that thresholding the summation can in some cases cause to decay
exponentially, decreasing by orders of magnitude in a phenomenon we call
super-resolution community detection. That is, layer aggregation with
thresholding is a nonlinear data filter enabling detection of communities that
are otherwise too small to detect. Importantly, different thresholds generally
enhance the detectability of communities having different properties,
illustrating that community detection can be obscured if one analyzes network
data using a single threshold.Comment: 11 pages, 8 figure
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