Please note that our instructions for authors have been modified. There are currently no calls for papers open. 1, will include a special section on Image Analysis, Classification and Protection, to be published in late March 2023. Many thanks to the Editors and Authors for their cooperation with us on this issue! Preface. 4, contains a special section on Big Data and Artificial Intelligence for Cooperative Vehicle-Infrastructure Systems (Baozhen Yao, Shuaian (Hans) Wang and Sobhan (Sean) Asian, Eds.) and covers altogether 12 papers. Source Normalized Impact per Paper (SNIP): 0.981 (2021).JCR Journal Impact Factor (JIF): 2.157 (2021).Her research has been published in some of the most renowned journals like Neuroinformatics, Computers in Biology and Medicine, and the Journal of Neural Engineering. She is particularly interested in developing a neural data science research program and in leveraging her research expertise to develop neural data science and neurotechnology-oriented courses for computer science students. She is a teacher at heart, with more than 10 years of experience in teaching computer science courses. Her research interests are in advanced neural signal analysis for BCI performance improvement, multimodal neuroimaging data fusion techniques, computational neuroscience, and the application of machine learning in neurotechnology and healthcare. in biomedical engineering from the University of Rhode Island in 2021 after earning the URI Graduate Student Research and Scholarship Excellence Award in the Life Science, Physical Sciences, and Engineering in 2021. She earned a master's degree in computer sciences from Ain Shams University, Cairo, Egypt in 2010, and a Ph.D. She is a postdoctoral researcher affiliated with the University of Rhode Island (URI), NeuralPC lab working with Yalda Shahriari. She will further discuss what the future holds for the interdisciplinary field of BCI research in light of recent advances in neuroimaging techniques along with my future plans on establishing a neural data science and neurotechnology-oriented course for computer science students to introduce them to the emerging Neurotech-AI space.īio: Sarah is an interdisciplinary researcher, a neural data scientist, a neural engineer, and a computer science lecturer in Old Dominion University (ODU), VA. In this talk, Sarah will demonstrate how she overcame these challenges by leveraging advanced neural data science approaches including graph-based modeling to unlock complex nonlinear dynamics of brain responses and by decoding neural information from various sources both on unimodal (electrical/vascular) and multimodal (electrical-vascular) levels. These features might be buried in physiological noise, pathologically altered, or not effectively captured using a single neuroimaging modality. To date, non-invasive brain-computer interface (BCI) systems fall short of their users’ expectations due to their modest performance improvement attributed to many challenges including the lack of computational frameworks that effectively exploit the neural discriminative features. Improving Brain-Computer Interface (BCI) performance: From Neural Data Science to Neurotech-AIĭescription: The spectral, spatial and temporal information underlying the complex brain responses with its billions of neurons and unknown dynamics calls for innovative neural data analytics to model its embedded patterns for various neurotechnology applications. Presenter: Sarah Hosni, Lecturer, Computer Science, Old Dominion Universityĭescription: In this sample 20-25 minutes lecture, Sarah Hosni will introduce the Quick Sort algorithm through an example, its implementation in C++ and will touch on its average and worst-case complexity analysis.
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