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Omer Dror, Author at MedCity News

Omer Dror, Author at MedCity News
medcitynews.com - get the latest breaking news, showbiz & celebrity photos, sport news & rumours, viral videos and top stories from medcitynews.com Daily Mail and Mail on Sunday newspapers.

Tomer Dror , Weizmann Institute Of Science , Tel Aviv University , Chief Technology Officer , General Manager , Weizmann Institute , Ensemble Learning , Weight Loss , Medical Devices , Healthcare Innovation , Medcity News , Medcity News , San Francisco , Life Sciences , Rock Health , Startup Advice , Startup Funding , Patient Monitoring System , Otion Sensors , Healthcare It , Foodborne Pathogens , Iagnostics Company , Iagnostic Platforms , Minimally Invasive Surgery , Aser Surgery , Ealth And Wellness ,

"Ensemble multimodal deep learning for early diagnosis and accurate cla" by Santosh Kumar, Sachin Kumar Gupta et al.

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 picture and cough (audio) database, chest X-ray-based and cough-based model are used here. Other public chest X-ray image datasets, and the Coswara cough (audio) dataset containing 92 COVID-19 positive, and 1079 healthy subjects (people) using the deep Uniform-Net, and Convolutional Neural Network (CNN). The weighted sum-rule fusion method and ensemble deep learning algorithms are utilized to further combine the extracted features. For the early diagnosis of patients, the framework offers an accuracy ....

Convolutional Neural Network , Covid 19 , Deep Learning , Ensemble Learning , Machine Learning ,

"Autoperman: Automatic Network Traffic Anomaly Detection with Ensemble " by Shangbin Han, Qianhong Wu et al.

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 supervised learning. In Autoperman, machine learning algorithms with fixed hyperparameters as feature extractors are integrated, which utilize a small amount of training data to be initialized. Then Random Forest is selected as the anomaly classifier and achieves automatic parameters tuning via well studied online optimization theory. We compare the performance of Autoperman against traditional anomaly detection algorithms using public traffic datasets. The results demonstrate that Autoperman can per ....

Random Forest , Anomaly Detection , Ensemble Learning ,

"Ensemble learning for remaining fatigue life prediction of structures " by S. Z. Feng, X. Han et al.

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 examples were investigated to evaluate the performance of proposed RFL prediction approach. The analysis results demonstrate that the ensemble learning is able to effectively predict the RFL of the structures with stochastic parameters. ....

Ensemble Learning , Genetic Algorithm , Emaining Fatigue Life , Tochastic Parameters ,

"Transient waveform matching based on ascending multi-wavelets for diag" by Kuosheng Jiang, Zhixiong Li et al.

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 waveform but limited to a single transient component. The multi-wavelet may provide a solution to fit a variety of primary components in the transient waveform, so as to track the degradation trend of the bearing; however, very limited work has been done to address this issue. To bridge the research gap in the transient waveform matching, a novel ascension multi-wavelet method is proposed in this paper for diagnosing the undergoing degradation state and predicting the remaining useful life (R ....

Bearing Deterioration , Ensemble Learning , Ulti Wavelet , Ransient Waveform ,