92,840 research outputs found
Decision support methods in diabetic patient management by insulin administration neural network vs. induction methods for knowledge classification
Diabetes mellitus is now recognised as a major worldwide
public health problem. At present, about 100
million people are registered as diabetic patients. Many
clinical, social and economic problems occur as a
consequence of insulin-dependent diabetes. Treatment
attempts to prevent or delay complications by applying
‘optimal’ glycaemic control. Therefore, there is a
continuous need for effective monitoring of the patient.
Given the popularity of decision tree learning
algorithms as well as neural networks for knowledge
classification which is further used for decision
support, this paper examines their relative merits by
applying one algorithm from each family on a medical
problem; that of recommending a particular diabetes
regime. For the purposes of this study, OC1 a
descendant of Quinlan’s ID3 algorithm was chosen as
decision tree learning algorithm and a generating
shrinking algorithm for learning arbitrary
classifications as a neural network algorithm. These
systems were trained on 646 cases derived from two
countries in Europe and were tested on 100 cases
which were different from the original 646 cases
Learning-based Analysis on the Exploitability of Security Vulnerabilities
The purpose of this thesis is to develop a tool that uses machine learning techniques to make predictions about whether or not a given vulnerability will be exploited. Such a tool could help organizations such as electric utilities to prioritize their security patching operations. Three different models, based on a deep neural network, a random forest, and a support vector machine respectively, are designed and implemented. Training data for these models is compiled from a variety of sources, including the National Vulnerability Database published by NIST and the Exploit Database published by Offensive Security. Extensive experiments are conducted, including testing the accuracy of each model, dynamically training the models on a rolling window of training data, and filtering the training data by various features. Of the chosen models, the deep neural network and the support vector machine show the highest accuracy (approximately 94% and 93%, respectively), and could be developed by future researchers into an effective tool for vulnerability analysis
Code Prediction by Feeding Trees to Transformers
We advance the state-of-the-art in the accuracy of code prediction (next
token prediction) used in autocomplete systems. First, we report that using the
recently proposed Transformer architecture even out-of-the-box outperforms
previous neural and non-neural systems for code prediction. We then show that
by making the Transformer architecture aware of the syntactic structure of
code, we further increase the margin by which a Transformer-based system
outperforms previous systems. With this, it outperforms the accuracy of an
RNN-based system (similar to Hellendoorn et al. 2018) by 18.3\%, the Deep3
system (Raychev et al 2016) by 14.1\%, and an adaptation of Code2Seq (Alon et
al., 2018) for code prediction by 14.4\%.
We present in the paper several ways of communicating the code structure to
the Transformer, which is fundamentally built for processing sequence data. We
provide a comprehensive experimental evaluation of our proposal, along with
alternative design choices, on a standard Python dataset, as well as on a
Facebook internal Python corpus. Our code and data preparation pipeline will be
available in open source
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