Zobrazeno 1 - 8
of 8
pro vyhledávání: '"Aarushi Agarwal"'
Publikováno v:
2022 International Mobile and Embedded Technology Conference (MECON).
Publikováno v:
Neuropsychological Trends. :47-63
Publikováno v:
Humanities & Social Sciences Reviews. 7:516-521
Purpose of the study: To answer the two existing controversies regarding attention and consciousness as brain processes. 1) Can one be aware of objects or events without attending to it? 2) Can one attend to objects or events without being aware of i
Publikováno v:
IC3I
Adolescence is an age of opportunities for children. It can also be described as the threshold from childhood to adulthood. It is a critical time where elders are required to help the children in their adolescence and guide them correctly. According
The advancement of information and communication technology has led to a multi-dimensional impact in the areas of law, regulation, and governance. Many countries have declared data protection a fundamental right and established reforms of data protec
Autor:
Aarushi Agarwal, Brittany A. Gonzalez, Marcos Gonzalez Perez, Jesus Lopez, Pablo Morales, Krishna Rivas Wagner, Frank G. Scholl, Sharan Ramaswamy, Steven Bibevski, Elena Ladich, Lazaro E. Hernandez, Jennifer Bibevski
Publikováno v:
Structural Heart. 5:65
Objective: Critical congenital heart valve disease at birth has limited treatment options and can benefit from valves with regenerative capacities. Following a pilot study of porcine small intestin...
Publikováno v:
2017 Third International Conference on Biosignals, Images and Instrumentation (ICBSII).
Parkinson's disease (PD) is a widespread chronic neurological disease prevalent in old age. Speech is found to be an effective marker for the identification of Parkinson's disease. In the following paper, we have proposed using factor analysis to sel
Publikováno v:
2016 International Conference on Electrical, Electronics, and Optimization Techniques (ICEEOT).
Speech impairments analysis has been used as an efficient tool for early detection of Parkinson's disease (PD). In this paper, we have proposed an efficient approach using Extreme Learning Machine to predict Parkinson's disease accurately utilising s