Futuristic Classification with Dynamic Reference Frame Strategy
Classification is one of the widely used analytical techniques in data science domain across different business to associate a pattern which contribute to the occurrence of certain event which is predicted with some likelihood. This Paper address a lacuna of creating some time window before the prediction actually happen to enable organizations some space to act on the prediction. There are some really good state of the art machine learning techniques to optimally identify the possible churners in either customer base or employee base, similarly for fault prediction too if the prediction does not come with some buffer time to act on the fault it is very difficult to provide a seamless experience to the user. New concept of reference frame creation is introduced to solve this problem in this paper
Code (0)
등록된 구현이 없습니다.
Tasks
ClassificationGeneral ClassificationPredictionSimilar Papers 제목 키워드 기반
Learning Based Task Offloading in Digital Twin Empowered Internet of Vehicles
Mobile edge computing has become an effective and fundamental paradigm for futuristic autonomous vehicles to offload computing tasks. However, due to the high mobility of vehicles, the dynamics of the wireless conditions…
Autonomous VehiclesSchedulingcvpaper.challenge in 2016: Futuristic Computer Vision through 1,600 Papers Survey
The paper gives futuristic challenges disscussed in the cvpaper.challenge. In 2015 and 2016, we thoroughly study 1,600+ papers in several conferences/journals such as CVPR/ICCV/ECCV/NIPS/PAMI/IJCV.
Futuristic methods in virus genome evolution using the Third-Generation DNA sequencing and artificial neural networks
The Third-Generation in DNA sequencing has emerged in the last few years using new technologies that allow the production of long-read sequences. Applications of the Third-Generation sequencing enable real-time and on-si…
BIG-bench Machine LearningVirologyNonlinear Model Predictive Control of A Gasoline HCCI Engine Using Extreme Learning Machines
Homogeneous charge compression ignition (HCCI) is a futuristic combustion technology that operates with a high fuel efficiency and reduced emissions. HCCI combustion is characterized by complex nonlinear dynamics which n…
Model Predictive ControlMetric Elicitation; Moving from Theory to Practice
Metric Elicitation (ME) is a framework for eliciting classification metrics that better align with implicit user preferences based on the task and context. The existing ME strategy so far is based on the assumption that …
Binary ClassificationClassification