High-utility Itemset Mining (HUIM) finds patterns from a transaction database with their utility no less than a user-defined threshold. The utility of an itemset is defined as the sum of the utilities of its items. The utility notion enables a data analyst to associate a profit score with each item and thereof to a pattern. We extend the notion of high-utility with diversity to define a new pattern type called High-utility and Diverse pattern (HUD). The notion of diversity of a pattern captures the extent of the different categories covered by the selected items in the pattern. An application of diverse-pattern lies in the recommendation task where a system can recommend to a customer a set of items from a new class based on her previously bought items. Our notion of diversity is easy to compute and also captures the basic essence of a previously proposed diversity notion. The existing algorithm to compute frequent-diverse patterns is 2-phase, i.e., in the first phase, frequent patterns are computed, out of which diverse patterns are filtered out in the second phase. We, in this paper, give an integrated algorithm that efficiently computes high-utility and diverse patterns in a single phase. Our experimental study shows that our proposed algorithm is very efficient as compared to a 2-phase algorithm that extracts high-utility itemsets in the first phase and filters out the diverse itemsets in the second phase.
The extensively utilized tool to detect novel coronavirus (COVID-19) is a real-time polymerase chain reaction (RT-PCR). However, RT-PCR kits are costly and consume critical time, around 6 to 9 hours to classify the subjects as COVID-19(+) or COVID-19(-). Due to the less sensitivity of RT-PCR, it suffers from high false-negative results. To overcome these issues, many deep learning models have been implemented in the literature for the early-stage classification of suspected subjects. To handle the sensitivity issue associated with RT-PCR, chest CT scans are utilized to classify the suspected subjects as COVID-19 (+), tuberculosis, pneumonia, or healthy subjects. The extensive study on chest CT scans of COVID-19 (+) subjects reveals that there are some bilateral changes and unique patterns. But the manual analysis from chest CT scans is a tedious task. Therefore, an automated COVID-19 screening model is implemented by ensembling the deep transfer learning models such as Densely connected convolutional networks (DCCNs), ResNet152V2, and VGG16. Experimental results reveal that the proposed ensemble model outperforms the competitive models in terms of accuracy, f-measure, area under curve, sensitivity, and specificity.
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The availability of cheap network based video cameras and the prevalence of wireless networks has lead to a major thrust towards
deployment of large scale Distributed Video Surveillance (DVS) systems. This has opened up an important area of research to
deal with the issues involved in DVS system for efficient collection and transmission of large scale video streams from the
cameras at the guarded sites, to the end users in possibly constrained network conditions. In this paper, we propose a framework
based on content-based video classification and scalable compression scheme to provide a robust bandwidth efficient video
transmission for DVS. The scheme builds on a Discrete Wavelet Transform (DWT) based Color-Set Partitioning for Hierarchical
Trees (CSPIHT) coding to obtain a scalable bitstream. Wavelet domain segmentation and compression assists in development of
a DVS architecture. The architecture includes a novel module for dynamic allocation of Network bandwidth based on the current
available resources and constraints. Different frame constituents are optimally coded based on their relative significance,
perceptual quality, and available estimate of network bandwidth. Experimental result over different video sequences and simulations
for Network conditions demonstrate the efficient performance of the approach. 相似文献
In this paper we have proposed a dynamic pricing scheme for the contributing peers in the Video on Demand (VoD) system. The
scheme provides an effective mechanism to maximize the profit through the residual resources of the contributing peers. A
utilization function is executed for each contributing peer to estimate the utility factor based on the parameters such as
initial setup cost, holding cost, chaining cost and salvage cost. In this paper, we urge an effective dynamic pricing algorithm
that efficiently utilizes a range of parameters with a varying degree of complexity. The key findings of the algorithm are
(i) each contributing peers are benefitted by the monetary based on its resource contributions to the VoD system and (ii)
a high degree of social optimum is established by proficiently aggregating the contributing peer’s resources with the overall
resources of the VoD system. We validate our claim by simulating the proposed dynamic pricing scheme with other standard pricing
schemes such as altruism, cost model and game theory perspective. The result of our dynamic pricing scheme shows the best
utility factor than other standard pricing schemes. 相似文献