128,539 research outputs found
Modeling Big Medical Survival Data Using Decision Tree Analysis with Apache Spark
In many medical studies, an outcome of interest is not only whether an event occurred, but when an event occurred; and an example of this is Alzheimer’s disease (AD). Identifying patients with Mild Cognitive Impairment (MCI) who are likely to develop Alzheimer’s disease (AD) is highly important for AD treatment. Previous studies suggest that not all MCI patients will convert to AD. Massive amounts of data from longitudinal and extensive studies on thousands of Alzheimer’s patients have been generated. Building a computational model that can predict conversion form MCI to AD can be highly beneficial for early intervention and treatment planning for AD. This work presents a big data model that contains machine-learning techniques to determine the level of AD in a participant and predict the time of conversion to AD. The proposed framework considers one of the widely used screening assessment for detecting cognitive impairment called Montreal Cognitive Assessment (MoCA). MoCA data set was collected from different centers and integrated into our large data framework storage using a Hadoop Data File System (HDFS); the data was then analyzed using an Apache Spark framework. The accuracy of the proposed framework was compared with a semi-parametric Cox survival analysis model
Mapping Big Data into Knowledge Space with Cognitive Cyber-Infrastructure
Big data research has attracted great attention in science, technology,
industry and society. It is developing with the evolving scientific paradigm,
the fourth industrial revolution, and the transformational innovation of
technologies. However, its nature and fundamental challenge have not been
recognized, and its own methodology has not been formed. This paper explores
and answers the following questions: What is big data? What are the basic
methods for representing, managing and analyzing big data? What is the
relationship between big data and knowledge? Can we find a mapping from big
data into knowledge space? What kind of infrastructure is required to support
not only big data management and analysis but also knowledge discovery, sharing
and management? What is the relationship between big data and science paradigm?
What is the nature and fundamental challenge of big data computing? A
multi-dimensional perspective is presented toward a methodology of big data
computing.Comment: 59 page
Big Data analytics and Cognitive Computing: future opportunities for Astronomical research
The days of the lone astronomer with his optical telescope and photographic
plates are long gone: Astronomy in 2025 will not only be multi-wavelength, but
multi-messenger, and dominated by huge data sets and matching data rates.
Catalogues listing detailed properties of billions of objects will in
themselves require a new industrial-scale approach to scientific discovery,
requiring the latest techniques of advanced data analytics and an early
engagement with the first generation of cognitive computing systems.
Astronomers have the opportunity to be early adopters of these new technologies
and methodologies: the impact can be profound and highly beneficial to
effecting rapid progress in the field. Areas such as SETI research might
favourably benefit from cognitive intelligence that does not rely on human bias
and preconceptions.Comment: 4 pages, 1 figure. Paper presented at 2nd Radio and Antenna Days of
the Indian Ocean (RADIO 2014
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Language acquisition and machine learning
In this paper, we review recent progress in the field of machine learning and examine its implications for computational models of language acquisition. As a framework for understanding this research, we propose four component tasks involved in learning from experience - aggregation, clustering, characterization, and storage. We then consider four common problems studied by machine learning researchers - learning from examples, heuristics learning, conceptual clustering, and learning macro-operators - describing each in terms of our framework. After this, we turn to the problem of grammar acquisition, relating this problem to other learning tasks and reviewing four AI systems that have addressed the problem. Finally, we note some limitations of the earlier work and propose an alternative approach to modeling the mechanisms underlying language acquisition
Channel Fragmentation in Dynamic Spectrum Access Systems - a Theoretical Study
Dynamic Spectrum Access systems exploit temporarily available spectrum
(`white spaces') and can spread transmissions over a number of non-contiguous
sub-channels. Such methods are highly beneficial in terms of spectrum
utilization. However, excessive fragmentation degrades performance and hence
off-sets the benefits. Thus, there is a need to study these processes so as to
determine how to ensure acceptable levels of fragmentation. Hence, we present
experimental and analytical results derived from a mathematical model. We model
a system operating at capacity serving requests for bandwidth by assigning a
collection of gaps (sub-channels) with no limitations on the fragment size. Our
main theoretical result shows that even if fragments can be arbitrarily small,
the system does not degrade with time. Namely, the average total number of
fragments remains bounded. Within the very difficult class of dynamic
fragmentation models (including models of storage fragmentation), this result
appears to be the first of its kind. Extensive experimental results describe
behavior, at times unexpected, of fragmentation under different algorithms. Our
model also applies to dynamic linked-list storage allocation, and provides a
novel analysis in that domain. We prove that, interestingly, the 50% rule of
the classical (non-fragmented) allocation model carries over to our model.
Overall, the paper provides insights into the potential behavior of practical
fragmentation algorithms
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