Ensemble Learning News Today

Domain Name System (DNS) is a fundamental protocol and backbone of the internet that translates domain names to Internet Protocol (IP) addresses. Initially, it was only meant for mapping domain names, however, currently it is used to transfer data over the internet in the form of plain text. This attracts attackers to perform cyberattacks such as DNS spoofing, DNS amplification, and cache poisoning etc. Various solutions were proposed to protect DNS protocol from such attacks such as using DNS l...

Distal radius fractures (DRFs) are one of the most common types of wrist fracture and can be subdivided into intra- and extra-articular fractures. Compared with extra-articular DRFs which spare the joint surface, intra-articular DRFs extend to the articular surface and can be more difficult to treat. Identification of articular involvement can provide valuable information about the characteristics of fracture patterns. In this study, a two-stage ensemble deep learning framework was proposed to d...

Over the past few years, the awful COVID-19 pandemic effect has become a lethal sickness. The processing of the gathered samples requires extra time due to the use of medical diagnostic equipment, methodologies, and clinical testing procedures for the early diagnosis of infected individuals. An innovative multimodal paradigm for the early diagnosis and precise categorization of COVID-19 is put up as a solution to this issue. To extract distinguishing features from the prepared chest X-ray pictur...

Network traffic, which records users’ behaviors, is valuable data resources for diagnosing the health of the network. Mining anomaly in network is essential for network defense. Although traditional machine learning approaches have good performance, their dependence on huge training data set with expensive labels make them impractical. Furthermore, after complex hyperparameters tuning, the detection model may not work. Facing these challenges, in this paper, we propose Autoperman through super...

An effective approach is proposed to predict the remaining fatigue life (RFL) of structures with stochastic parameters. The extended finite element method (XFEM) was firstly used to produce a large amount of datasets associated with structural responses and RFL. Then, a RFL prediction model was developed using the ensemble learning algorithm, which employed multiple machine-learning algorithms to learn useful degradation patterns of the structures from the XFEM datasets. Several numerical exampl...

Abstract Run-to-failure experiment is efficient and effective to investigate bearing deterioration process. Periodic transient waveform carries rich information of health conditions of bearings but the transient waveform matching is a challenging problem for evaluating bearing fatigue life because the shapes and parameters of the waveform vary with the evolution of the bearing degradation. A wavelet function such as a Morlet wavelet is able to extract essential features from the transient wavef...