Classification of Short Time Series in Early Parkinson’s Disease With Deep Learning of Fuzzy Recurrence Plots

There are many techniques using sensors and wearable devices for detecting and monitoring patients with Parkinson’s disease(PD).A recent development is the utilization of human interaction with computer keyboards for analyzing and identifying motor signs in the early stages of the disease.Current designs for classification of time series of computer-key hold durations recorded from healthy control and PD subjects require the time series of length to be considerably long.With an attempt to avoid discomfort to participants in performing long physical tasks for data recording,this paper introduces the use of fuzzy recurrence plots of very short time series as input data for the machine training and classification with long short-term memory(LSTM)neural networks.Being an original approach that is able to both significantly increase the feature dimensions and provides the property of deterministic dynamical systems of very short time series for information processing carried out by an LSTM layer architecture,fuzzy recurrence plots provide promising results and outperform the direct input of the time series for the classification of healthy control and early PD subjects.

BIC-based node order learning for improving Bayesian network structure learning

Node order is one of the most important factors in learning the structure of a Bayesian network(BN)for probabilistic reasoning.To improve the BN structure learning,we propose a node order learning algorithmbased on the frequently used Bayesian information criterion(BIC)score function.The algorithm dramatically reduces the space of node order and makes the results of BN learning more stable and effective.Specifically,we first find the most dependent node for each individual node,prove analytically that the dependencies are undirected,and then construct undirected subgraphs UG.Secondly,the UG-is examined and connected into a single undirected graph UGC.The relation between the subgraph number and the node number is analyzed.Thirdly,we provide the rules of orienting directions for all edges in UGC,which converts it into a directed acyclic graph(DAG).Further,we rank the DAG’s topology order and describe the BIC-based node order learning algorithm.Its complexity analysis shows that the algorithm can be conducted in linear time with respect to the number of samples,and in polynomial time with respect to the number of variables.Finally,experimental results demonstrate significant performance improvement by comparing with other methods.

Evaluating and predicting social behavior of arsenic affected communities:Towards developing arsenic resilient society

This study uses six machine learning(ML)algorithms to evaluate and predict individuals'social resilience towards arsenicosis-affected people in an arsenic-risk society of rural India.Over 50%of the surveyed communities were found to be resilient towards arsenicosis patients.Logistic regression with inbuilt cross-validation(LRCV)model scored the highest accuracy(76%),followed by Gaussian distributionbased naïve Bayes(GNB)model(74%),C-Support Vector(SVC)(74%),K-neighbors(Kn)(73%),Random Forest(RF)(72%),and Decision Tree(DT)(67%).The LRCV also scored the highest kappa value of 0.52,followed by GNB(0.48),SVC(0.48),Kn(0.46),RF(0.42),and DT(0.31).Caste,education,occupation,housing status,sanitation behaviors,trust in others,non-profit and private organizations,social capital,and awareness played a key role in shaping social resilience towards arsenicosis patients.The authors opine that LRCV and GNB could be promising methods to develop models on similar data generated from a risk society.