I am second year PhD student at School of Engineering and Applied Science of University of Virginia , Departement of Systems and Information Engineering. I am fortunate to be advised by professor Laura E. Barnes and co-advised by professor Donald E. Brown. I earned my Master of Science from The George Washington University in 2014, my master advisor was Professor Simon Berkovich. I started an important research project in my MS. with using Golay Code technique for clustering into big data. My experience includes numerous projects and academic projects. my research interests area is medical informatics, mobile health, machine learning, mathematical modeling, algorithms and data structure, and data manning, biomedical computing and visualization.
The continually increasing number of complex datasets each year necessitates ever improving machine learning methods for robust and accurate categorization of these data. This paper introduces Random Multimodel Deep Learning (RMDL): a new ensemble, deep learning approach for classification. Deep learning models have achieved state-of-the-art results across many domains. RMDL solves the problem of finding the best deep learning structure and architecture while simultaneously improving robustness and accuracy through ensembles of deep learning architectures. RDML can accept as input a variety data to include text, video, images, and symbolic. This paper describes RMDL and shows test results for image and text data including MNIST, CIFAR-10, WOS, Reuters, IMDB, and 20newsgroup. These test results show that RDML produces consistently better performance than standard methods over a broad range of data types and classification problems.
Kamran Kowsari, Mojtaba Heidarysafa, Donald E. Brown, Kiana Jafari Meimandi, and Laura E. Barnes. 2018. RMDL: Random Multimodel Deep Learning for Classification. In ICISDM ’18: 2018 2nd International Conference on Information System and Data Mining ICISDM ’18, April 9–11, 2018,
Lakeland, FL, USA. ACM, New York, NY, USA