A policy-based web service redundancy detection in wireless sensor networks

Wireless sensor networks (WSNs) are attractive for monitoring and gathering physical information (e.g. temperature) via lots of deployed sensors. For the applications in WSNs, Web service is one of the recommended frameworks to publish, invoke, and manage services. However, the standard Web service description language (WSDL), defines only the service input and output while ignoring the corresponding input-to-output mapping relationships. This presents a serious challenge in distinguishing services with similar input and output interface. In this paper, we address this challenge by embedding the service policy into the traditional WSDL2.0 schema to describe the input-to-output mapping relationships. The service policy is then transformed into a policy binary tree so that the similarity between different Web services can be quantitatively evaluated. Furthermore, a new service redundancy detection approach is proposed based on this similarity. Finally, the case study and experimental analysis illustrate the applicability and capability of the proposed service redundancy detection approach.

A similarity measure on tree structured business data

In many business situations, products or user profile data are so complex that they need to be described by use of tree structures. Evaluating the similarity between tree-structured data is essential in many applications, such as recommender systems. To evaluate the similarity between two trees, concept corresponding nodes should be identified by constructing an edit distance mapping between them. Sometimes, the intension of one concept includes the intensions of several other concepts. In that situation, a one-to-many mapping should be constructed from the point of view of structures. This paper proposes a tree similarity measure model that can construct this kind of mapping. The similarity measure model takes into account all the information on nodes’ concepts, weights, and values. The conceptual similarity and the value similarity between two trees are evaluated based on the constructed mapping, and the final similarity measure is assessed as a weighted sum of their conceptual and value similarities. The effectiveness of the proposed similarity measure model is shown by an illustrative example and is also demonstrated by applying it into a recommender system.

Empirical investigation of decision tree ensembles for monitoring cardiac complications of diabetes

Cardiac complications of diabetes require continuous monitoring since they may lead to increased morbidity or sudden death of patients. In order to monitor clinical complications of diabetes using wearable sensors, a small set of features have to be identified and effective algorithms for their processing need to be investigated. This article focuses on detecting and monitoring cardiac autonomic neuropathy (CAN) in diabetes patients. The authors investigate and compare the effectiveness of classifiers based on the following decision trees: ADTree, J48, NBTree, RandomTree, REPTree, and SimpleCart. The authors perform a thorough study comparing these decision trees as well as several decision tree ensembles created by applying the following ensemble methods: AdaBoost, Bagging, Dagging, Decorate, Grading, MultiBoost, Stacking, and two multi-level combinations of AdaBoost and MultiBoost with Bagging for the processing of data from diabetes patients for pervasive health monitoring of CAN. This paper concentrates on the particular task of applying decision tree ensembles for the detection and monitoring of cardiac autonomic neuropathy using these features. Experimental outcomes presented here show that the authors' application of the decision tree ensembles for the detection and monitoring of CAN in diabetes patients achieved better performance parameters compared with the results obtained previously in the literature.

Stable feature selection for clinical prediction: Exploiting ICD tree structure using Tree-Lasso

Modern healthcare is getting reshaped by growing Electronic Medical Records (EMR). Recently, these records have been shown of great value towards building clinical prediction models. In EMR data, patients' diseases and hospital interventions are captured through a set of diagnoses and procedures codes. These codes are usually represented in a tree form (e.g. ICD-10 tree) and the codes within a tree branch may be highly correlated. These codes can be used as features to build a prediction model and an appropriate feature selection can inform a clinician about important risk factors for a disease. Traditional feature selection methods (e.g. Information Gain, T-test, etc.) consider each variable independently and usually end up having a long feature list. Recently, Lasso and related l1-penalty based feature selection methods have become popular due to their joint feature selection property. However, Lasso is known to have problems of selecting one feature of many correlated features randomly. This hinders the clinicians to arrive at a stable feature set, which is crucial for clinical decision making process. In this paper, we solve this problem by using a recently proposed Tree-Lasso model. Since, the stability behavior of Tree-Lasso is not well understood, we study the stability behavior of Tree-Lasso and compare it with other feature selection methods. Using a synthetic and two real-world datasets (Cancer and Acute Myocardial Infarction), we show that Tree-Lasso based feature selection is significantly more stable than Lasso and comparable to other methods e.g. Information Gain, ReliefF and T-test. We further show that, using different types of classifiers such as logistic regression, naive Bayes, support vector machines, decision trees and Random Forest, the classification performance of Tree-Lasso is comparable to Lasso and better than other methods. Our result has implications in identifying stable risk factors for many healthcare problems and therefore can potentially assist clinical decision making for accurate medical prognosis.

Economic evaluation of a group-based exercise program for falls prevention among the older community-dwelling population

BACKGROUND: Falls among older people are of growing concern globally. Implementing cost-effective strategies for their prevention is of utmost importance given the ageing population and associated potential for increased costs of fall-related injury over the next decades. The purpose of this study was to undertake a cost-utility analysis and secondary cost-effectiveness analysis from a healthcare system perspective, of a group-based exercise program compared to routine care for falls prevention in an older community-dwelling population.

METHODS: A decision analysis using a decision tree model was based on the results of a previously published randomised controlled trial with a community-dwelling population aged over 70. Measures of falls, fall-related injuries and resource use were directly obtained from trial data and supplemented by literature-based utility measures. A sub-group analysis was performed of women only. Cost estimates are reported in 2010 British Pound Sterling (GBP).

RESULTS: The ICER of GBP£51,483 per QALY for the base case analysis was well above the accepted cost-effectiveness threshold of GBP£20,000 to £30,000 per QALY, but in a sensitivity analysis with minimised program implementation the incremental cost reached GBP£25,678 per QALY. The ICER value at 95% confidence in the base case analysis was GBP£99,664 per QALY and GBP£50,549 per QALY in the lower cost analysis. Males had a 44% lower injury rate if they fell, compared to females resulting in a more favourable ICER for the women only analysis. For women only the ICER was GBP£22,986 per QALY in the base case and was below the cost-effectiveness threshold for all other variations of program implementation. The ICER value at 95% confidence was GBP£48,212 in the women only base case analysis and GBP£23,645 in the lower cost analysis. The base case incremental cost per fall averted was GBP£652 (GBP£616 for women only). A threshold analysis indicates that this exercise program cannot realistically break even.

CONCLUSIONS: The results suggest that this exercise program is cost-effective for women only. There is no evidence to support its cost-effectiveness in a group of mixed gender unless the costs of program implementation are minimal. Conservative assumptions may have underestimated the true cost-effectiveness of the program.

Prosthetic motor imaginary task classification based on EEG quality assessment features

Brain Computer Interface (BCI) plays an important role in the communication between human and machines. This communication is based on the human brain signals. In these systems, users use their brain instead of the limbs or body movements to do tasks. The brain signals are analyzed and translated into commands to control any communication devices, robots or computers. In this paper, the aim was to enhance the performance of a brain computer interface (BCI) systems through better prosthetic motor imaginary tasks classification. The challenging part is to use only a single channel of electroencephalography (EEG). Arm movement imagination is the task of the user, where (s)he was asked to imagine moving his arm up or down. Our system detected the imagination based on the input brain signal. Some EEG quality features were extracted from the brain signal, and the Decision Tree was used to classify the participant's imagination based on the extracted features. Our system is online which means that it can give the decision as soon as the signal is given to the system (takes only 20 ms). Also, only one EEG channel is used for classification which reduces the complexity of the system which leads to fast performance. Hundred signals were used for testing, on average 97.4% of the up-down prosthetic motor imaginary tasks were detected correctly. This method can be used in many different applications such as: moving artificial limbs and wheelchairs due to it's high speed and accuracy.

LC–MS and GC–MS metabolite profiling of nickel(II) complexes in the latex of the nickel-hyperaccumulating tree Sebertia acuminata and identification of methylated aldaric acid as a new nickel(II) ligand

Targeted liquid chromatography–mass spectrometry (LC–MS) technology using size exclusion chromatography and metabolite profiling based on gas chromatography–mass spectrometry (GC–MS) were used to study the nickel-rich latex of the hyperaccumulating tree Sebertia acuminata. More than 120 compounds were detected, 57 of these were subsequently identified. A methylated aldaric acid (2,4,5-trihydroxy-3-methoxy-1,6-hexan-dioic acid) was identified for the first time in biological extracts and its structure was confirmed by 1D and 2D nuclear magnetic resonance (NMR) spectroscopy. After citric acid, it appears to be one of the most abundant small organic molecules present in the latex studied. Nickel(II) complexes of stoichiometry NiII:acid = 1:2 were detected for these two acids as well as for malic, itaconic, erythronic, galacturonic, tartaric, aconitic and saccharic acids. These results provide further evidence that organic acids may play an important role in the transport and possibly in the storage of metal ions in hyperaccumulating plants.

Local environmental covariates are important for predicting fire history from tree stem diameters

In fire-prone landscapes, knowing when vegetation was last burnt is important for understanding how species respond to fire and to develop effective fire management strategies. However, fire history is often incomplete or non-existent. We developed a fire-age prediction model for two mallee woodland tree species in southern Australia. The models were based on stem diameters from ∼1172 individuals surveyed along 87 transects. Time since fire accounted for the greatest proportion of the explained variation in stem diameter for our two mallee tree species but variation in mean stem diameters was also influenced by local environmental factors. We illustrate a simple tool that enables time since fire to be predicted based on stem diameter and local covariates. We tested our model against new data but it performed poorly with respect to the mapped fire history. A combination of different covariate effects, variation in among-tree competition, including above- and below-ground competition, and unreliable fire history may have contributed to poor model performance. Understanding how the influence of covariates on stem diameter growth varies spatially is critical for determining the generality of models that predict time since fire. Models that were developed in one region may need to be independently verified before they can be reliably applied in new regions.

Classification tree analysis of postal questionnaire data to identify risk of excessive gestational weight gain

OBJECTIVE: overweight/obese weight status during pregnancy increases risk of a range of adverse health outcomes for mother and child. Whereas identification of those who are overweight/obese pre-pregnancy and in early pregnancy is straightforward, prediction of who will experience excessive gestational weight gain (EGWG), and thus be at greater risk of becoming overweight or obese during pregnancy is more challenging. The present study sought to better identify those at risk of EGWG by exploring pre-pregnancy BMI as well as a range of psychosocial risk factors identified as risk factors in prior research. METHODS: 225 pregnant women completed self-reported via postal survey measures of height, weight, and psychosocial variables at 16-18 weeks gestation, and reported their weight again at 32-34 weeks to calculate GWG. Classification and regression tree analysis (CART) was used to find subgroups in the data with increased risk of EGWG based on their pre-pregnancy BMI and psychosocial risk factor scores at Time 1. FINDINGS: CART confirmed that self-reported BMI status was a strong predictor of EGWG risk for women who were overweight/obese pre-pregnancy. Normal weight women with low motivation to maintain a healthy diet and who reported lower levels of partner support were also at considerable risk of EGWG. IMPLICATIONS FOR PRACTICE: present findings offer support for inclusion of psychosocial measures (in addition to BMI) in early antenatal visits to detect risk of EGWG. However, these findings also underscore the need for further consideration of effect modifiers that place women at increased or decreased risk of EGWG. Proposed additional constructs are discussed to direct further theory-driven research.

Data mining via minimal spanning tree clustering for prolonging lifetime of wireless sensor networks

Clustering is applied in wireless sensor networks for increasing energy efficiency. Clustering methods in wireless sensor networks are different from those in traditional data mining systems. This paper proposes a novel clustering algorithm based on Minimal Spanning Tree (MST) and Maximum Energy resource on sensors named MSTME. Also, specified constrains of clustering in wireless sensor networks and several evaluation metrics are given. MSTME performs better than already known clustering methods of Low Energy Adaptive Clustering Hierarchy (LEACH) and Base Station Controlled Dynamic Clustering Protocol (BCDCP) in wireless sensor networks when they are evaluated by these evaluation metrics. Simulation results show MSTME increases energy efficiency and network lifetime compared with LEACH and BCDCP in two-hop and multi-hop networks, respectively. © World Scientific Publishing Company.

Dynamic minimal spanning tree routing protocol for large wireless sensor networks

Hundreds or thousands of wireless sensor nodes with limited energy resource are randomly scattered in the observation fields to extract the data messages for users. Because their energy resource cannot be recharged, energy efficiency becomes one of the most important problems. LEACH is an energy efficient protocol by grouping nodes into clusters and using cluster heads (CH) to fuse data before transmitting to the base station (BS). BCDCP improves LEACH by introducing a minimal spanning tree (MST) to connect CHs and adopting iterative cluster splitting algorithm to choose CHs or form clusters. This paper proposes another innovative cluster-based routing protocol named dynamic minimal spanning tree routing protocol (DMSTRP), which improves BCDCP by introducing MSTs instead of clubs to connect nodes in clusters. Simulation results show that DMSTRP excels LEACH and BCDCP in terms of both network lifetime and delay when the network size becomes large.