Neural networks : the official journal of the International Neural Network Society
Aug 17, 2016
This study is mainly concerned with the problem on synchronization criteria for Markovian jumping time delayed bidirectional associative memory neural networks and their applications in secure image communications. Based on the variable transformatio...
As one of the most popular statistical and machine learning models, logistic regression with regularization has found wide adoption in biomedicine, social sciences, information technology, and so on. These domains often involve data of human subjects...
Neural networks : the official journal of the International Neural Network Society
May 30, 2016
This paper investigates the problem of global exponential anti-synchronization of a class of switched neural networks with time-varying delays and lag signals. Considering the packed circuits, the controller is dependent on the output of the system a...
Botnet phenomenon in smartphones is evolving with the proliferation in mobile phone technologies after leaving imperative impact on personal computers. It refers to the network of computers, laptops, mobile devices or tablets which is remotely contro...
Intelligent agents and healthcare have been intimately linked in the last years. The intrinsic complexity and diversity of care can be tackled with the flexibility, dynamics and reliability of multi-agent systems. The purpose of this review is to sho...
This study aims to secure medical data by combining them into one file format using steganographic methods. The electroencephalogram (EEG) is selected as hidden data, and magnetic resonance (MR) images are also used as the cover image. In addition to...
The 2014 i2b2/UTHealth Natural Language Processing (NLP) shared task featured a new longitudinal corpus of 1304 records representing 296 diabetic patients. The corpus contains three cohorts: patients who have a diagnosis of coronary artery disease (C...
Electronic medical records (EMRs) for diabetic patients contain information about heart disease risk factors such as high blood pressure, cholesterol levels, and smoking status. Discovering the described risk factors and tracking their progression ov...
For the 2014 i2b2/UTHealth de-identification challenge, we introduced a new non-parametric Bayesian hidden Markov model using a Dirichlet process (HMM-DP). The model intends to reduce task-specific feature engineering and to generalize well to new da...
The second track of the 2014 i2b2 challenge asked participants to automatically identify risk factors for heart disease among diabetic patients using natural language processing techniques for clinical notes. This paper describes a rule-based system ...