With the growing demand and commercial availability of cloud services, the need for comparison of their functionality against different prices and performance has arisen. A relevant and fair ...comparison is still challenging due to diverse deployment options and dissimilar features of different services. This paper addresses a hybrid multi-criteria decision-making model involving the selection of cloud services among the available alternatives. The proposed methodology assigns various ranks to cloud services based on the quantified quality-of-service parameters using a novel extended Grey Technique for Order of Preference by Similarity to Ideal Solution integrated with analytical hierarchical process. Further, we analyse the proposed cloud service selection method in terms of sensitivity analysis, adequacy under change in alternatives, adequacy to support group decision-making, and handling of uncertainty. This analysis helps both researchers and practitioners for analysing more fruitful approaches for cloud service selection.
The study presents an innovative diagnostic framework that synergises Convolutional Neural Networks (CNNs) with a Multi-feature Kernel Supervised within-class-similar Discriminative Dictionary ...Learning (MKSCDDL). This integrative methodology is designed to facilitate the precise classification of individuals into categories of Alzheimer's Disease, Mild Cognitive Impairment (MCI), and Cognitively Normal (CN) statuses while also discerning the nuanced phases within the MCI spectrum. Our approach is distinguished by its robustness and interpretability, offering clinicians an exceptionally transparent tool for diagnosis and therapeutic strategy formulation. We use scandent decision trees to deal with the unpredictability and complexity of neuroimaging data. Considering that different people's brain scans are different, this enables the model to make more detailed individualised assessments and explains how the algorithm illuminates the specific neuroanatomical regions that are indicative of cognitive impairment. This explanation is beneficial for clinicians because it gives them concrete ideas for early intervention and targeted care. The empirical review of our model shows that it makes diagnoses with a level of accuracy that is unmatched, with a classification efficacy of 98.27%. This shows that the model is good at finding important parts of the brain that may be damaged by cognitive diseases.
Fraud in healthcare services dissipates funds that are important for improving the quality of life of people, thus enhancing the interest in predictive fraud analysis. The predictive analysis of ...fraudulent activity can be done by looking for unusual patterns in healthcare claims. However, unusual patterns may also occur due to sudden changes, isolated events, or concept drifts that frequently happen in healthcare which should not be considered fraud. Furthermore, analyzing drifts also supports predicting future trends and behaviors. In this study, we propose a novel approach, Drift Analysis on Decomposed Healthcare Claims (DADHC), to analyze the hidden patterns that hinder the performance of fraud prediction and detection. Our proposed model decomposes the series of healthcare claims into regular and irregular patterns using Psuedo Additive Decomposition (PAD) integrated with Simple Moving Average (SMA) smoothing technique. Then ART (Adaptive Resonance Theory) based Topological Clustering (TC) is used to analyze unusual patterns and identify the actual fraudulent activities. Our proposed model also incorporates correntropy based vigilance testing in ART to enhance adaptivity. Empirical evaluation on CMS Part B claims shows that our proposed approach has significantly improved detection accuracy compared to existing models due to the drift analysis.
The availability of many open source systems offers affordable opportunities for organizations to build and adopt various types of cloud computing environments.
Several cloud services with comparable functionality are now available to customers at different prices and performance levels. Often, there may be trade-offs among different functional and ...non-functional requirements fulfilled by different cloud providers. Hence, it is difficult to evaluate the relative performances of the cloud services and their ranking based on various quality of service attributes. In this paper, we propose a modified data envelopment analysis and a modified super-efficiency data envelopment analysis for evaluating the cloud services and their efficiencies considering user preferences. We compare these methods of cloud service selection based on sensitivity analysis, adequacy to changes in DMUs, adequacy to support decision making and modeling of uncertainty. The comparison helps customers to choose a cloud service that is most suitable to their requirements and also creates a healthy competition among the cloud service providers.
Healthcare fraud is a significant problem greatly affecting the quality of healthcare services. Manual auditing of insurance claims extends to the delay in finding fraudulent behaviors causing huge ...financial loss and also putting the patients' health conditions at risk. Since the past decade, the automation of fraud detection using machine learning techniques has become a prominent research topic. Several automated fraud detection systems using machine learning techniques have been proposed so far. However, developing a healthcare fraud detection system that is adaptive to the systematic changes is still missing. Therefore, in this article, we develop primitive sub peer group analysis (PSPGA) for identifying the suspicious behaviors in health insurance claims. PSPGA is inspired by peer group analysis, a popular unsupervised learning technique, which identifies suspicious behaviors based on local pattern analysis. PSPGA distinguishes between the concept drifts and the sudden drifts and flags the sudden drifts as fraudulent. Moreover, PSPGA makes the fraud detection system adaptive to the concept drifts by considering the updates for peer groups over time.
Multiple benefits (submitting healthcare bills to one or more organizations) and data breaches (tampering of healthcare data by healthcare organization authorities) are typical frauds happening due ...to the centralization of authority in healthcare systems. Preventing multiple benefits and data breaches can greatly lessen the huge losses to healthcare systems. In this paper, we propose an extended lightweight blockchain (ELB) based collaborative healthcare system for decentralizing the authority in healthcare organizations. The proposed architecture is rigorously tested in avoiding multiple benefits and data breaches and compared with the state-of-the-art methods. The experimental results evidenced that the proposed ELB architecture can effectively avoid multiple benefits and data breaches with less computation and communication overheads.
Fraud is an aggravating problem in the health insurance system, causing a substantial increase in the cost of medical services. Many models have been developed using data mining or machine learning ...techniques to lessen the impact of fraud on healthcare system. However, achieving good accuracy is still challenging as the claims data is multivariate with multiple class overlappings. In this paper, we propose a novel approach of unsupervised multivariate analysis for healthcare claims submitted by the providers. Our proposed model analyzes multivariate categorical data and continuous data in two stages to observe providers’ behaviors. The first stage constructs Weighted MultiTree (WMT) for categorical data to analyze similarity among provider profiles, the relation among profiles, and rendered services to identify false services. The second stage detects false claims by developing a univariate fraud detection model using different Density Based Clustering (DBC) techniques on continuous data of claims such as service counts and service charges. The performance of our proposed WMTDBC is measured by conducting experiments on the claims within various medical specialties or provider types of CMS part B program. Our empirical results evidence that the detection performance is enhanced with our WMTDBC approach when compared with the state-of-the-art models.
•WMTDBC model does not require any labeled data of healthcare claims.•WMTDBC can analyze both categorical and continuous types of data.•WMT has no class overlaps and intermediate results while grouping similar claims.•WMT can be constructed with reduced number of nodes and reduce complexity.
Fog computing has become a state-of-art technology for cloud applications in collaboration with physical IOT devices on the edge of the network.We propose a Fog of Things (FOT) framework for ...optimized resource allocation to manage issues of over demanding and load balancing. The work discussed here is based on the residential demand side model for the areas that generate large number of requests per hour and resource allocation to these requests that require large amount of time and resources. In our FOT Framework, we have two main layers: fog layer and consumer layer. The fog layer performs the resource allocation in optimal time using Jaya optimization algorithm. The consumer layer makes decision for selection of particular appliances in residential buildings using a Multiple Knapsack algorithm. Both layers act as players following the extensive form of the game theory approach to share their moves to each other.
Data centers evolve constantly in size, complexity, and power consumption. Energy management in cloud data centers is a critical and challenging research issue. It becomes necessary to minimize the ...operational costs as well as environmental impact and to guarantee the service-level agreements for the services provided by the data centers. We propose a modified discrete particle swarm optimization based on the characteristic particle swarm optimization for the initial placement of virtual machines and a novel virtual machine selection algorithm for optimizing the current allocation based on memory utilization, bandwidth utilization, and size of the virtual machine. By means of simulations, we observe that the proposed method not only saves the energy significantly than the other approaches, but also minimizes the violations of service-level agreements.
