PRIORITY R-TREE WITH K-MODE FOR EFFICIENT CLUSTERING OF UNCERTAIN DATA

Uncertain data contains the specific uncertainty. Uncertain data is usually found in the area of sensor networks. To find the uncertain data is very expensive. Many of the algorithms have been proposed for handling the uncertain data such as k-means, uk means, global kernel k-means, u-rule and Fuzzy c-means. However, most of previous approaches try to cluster the dataset, whereas the overlap data is not well treated. In this paper, we propose two novel active learning algorithms: 1) k-mode for classifying the certain and uncertain dataset in a whole dataset, 2) Priority R-Tree clustering the certain and uncertain data for each domain. They handle both supervised and unsupervised dataset. These techniques improve the robustness and accuracy of the clustering outcome to a great extent. By minimizing the expected error with respect to the optimal classifier,  experimental results display the cluster using the Gas sensor array drift Dataset

A review of symptomatic leg length inequality following total hip arthroplasty

Leg length inequality (LLI) following total hip replacement is a complication which features increasingly in the recent literature. The definition of LLI is complicated by lack of consensus regarding radiological measurement, clinical measurement and the incomplete relationship between LLI and associated symptoms. This paper reviews 79 reports relating to LLI post hip replacement, detailing definitions and classification and highlighting patient populations prone to symptomatic LLI. While there is no universal definition of LLI, there is a broad consensus that less than 10 mm of difference on AP view plain radiographs is clinically acceptable. There are few techniques described that consistently produce a postoperative LLI of less than this magnitude. Where postoperative LLI exists, lengthening appears to cause more problems than shortening. In cases of mild LLI, non-surgical management produces adequate outcomes in the majority of cases, with functional LLI cases doing better than those with true LLI. Operative correction is effective in half of cases, even where nerve palsy is present, and remains an important option of last resort. Poor outcomes in patients with LLI may be minimised if individuals at risk are identified and counselled appropriately

UNCERTAINTY DATA CLUSTERING USING ENHANCED K-MODE ALGORITHM

Clustering problem is partitioning the dataset into different groups where one cluster consists of similar points whereas the other cluster consists of different points. In the real world, data mining applications where affected by data’s uncertainty. Due to uncertainty into account during the computations, designing of data mining technique has become critical. Uncertainty caused by error measuring, sensor effects and other external factors such as Humidity, temperature etc., Clustering the certain data is a normal process but clustering the uncertainty data is not an easy task. In traditional clustering algorithms, while the clustering process occurs, it takes lot of calculations. Because of its complexity, the clustering takes high execution time resulting in high computational cost. In this we propose a Enhance K-Mode algorithm which is also called as EK-Mode to cluster the uncertainty data. It supports both the categorical and numerical data. The PDF function is used to find out the similarity parameters and the execution time is very less as well as cost wise cheaper than other algorithms. The K-mode concept classifies the dataset and separates the uncertainty from the whole dataset. Again enhanced K-Mode is used to cluster the uncertainty data. The Gas sensor’s values are taken in to the account for experiments. The experiment shows that the proposed algorithm is very efficient and fast execution time with low complexity

A NOVEL APPLICATION OF SPATIAL DATA MINING IN AIR POLLUTION

Spatial Data Mining is an exploratory process aimed at discovering hidden patterns from spatial data. The extracted knowledge can be used to perform efficient spatial prediction. It allows taking advantage of the growing availability of geographically referenced data and their potential richness. Spatial data mining techniques can be applied to various fields’ namely health care, metrological data, traffic analysis, customer intelligence, transport management, urban planning and utilities industry. This paper proposes to support spatial data mining techniques of air pollution. A web based system is proposed to investigate the effect of meteorological and air pollutant elements on air pollution. It majorly consists of the collection, transformation and query and mining elements. The collecting element helps providing access to collection of data. The transformers convert the data into the required format and the query and mining element provide an interface to the user, for querying and mining requests and provide the results

Comparing the Analgesia Effects of Single-injection and Continuous Femoral Nerve Blocks with Patient Controlled Analgesia after Total Knee Arthroplasty

We compared the analgesic effects of single-injection or continuous femoral nerve block (FNB) with intravenous patient controlled analgesia (PCA) opioids. Two hundred patients undergoing knee arthroplasty were randomized to one of the three regimens. Significant knee pain on movement at postoperative 24h was reduced with single-injection (OR 0.30; 95% CI 0.12 to 0.74; P=0.009) or continuous (OR 0.21; 95% CI 0.08 to 0.51; P=0.001) FNB, compared with PCA. Allocation to FNBs also resulted in significantly less opioid consumption, fewer episodes of nausea and vomiting, and achieved knee flexion 90 degrees earlier than allocation to PCA. Compared to single-injection FNB, patients with continuous FNB had lower pain scores on movement at 24h (mean difference -0.57; 95% CI -1.14 to -0.01; P=0.045), consumed less opioid, and had fewer incidences of nausea and vomiting. The analgesic efficacy of single-injection and continuous FNBs was superior to PCA in the immediate postoperative period; with continuous FNB providing better analgesia than single-injection FNB

Resonance Risk and Mode Shape Management in the Frequency Domain to Prevent Squeak and Rattle

Avoiding quality problems in passenger cars, such as squeak and rattle (S&R), has been a remarkable cost-saving consideration. The introduction of electric engines and autonomous driving is expected to further stress the need for quieter cabins. However, the complexity of S&R events has obstructed the practical treatment of these quality issues in the pre-design-freeze phases of product development. In this study, new quantified frequency-domain metrics are proposed to measure the risk of S&R generation in car subsystems. The proposed metrics measure the resonance risk and the mode shape similarity in the critical interfaces for S&R. The calculations are done based on the system response in the frequency domain. Compared with the time-domain evaluation methods, the knowledge about the system excitation levels is not essential and the calculations are more time-efficient. The proposed metrics can be used in design optimization processes to involve S&R attributes in the pre-design-freeze attribute trade-off activities besides other attributes. In this work, these metrics were used in a previously developed two-stage optimization approach to determine the connection configuration in two industrial cases. As compared with the baseline design, the risk for S&R was reduced by improving the system behavior in terms of resonance risk and mode shape similarity. This was achieved by applying adjustments to the location of the fasteners while maintaining the same general connection configuration concept

Brain natriuretic peptide and N-terminal brain natriuretic peptide for the diagnosis of hemodynamically significant patent ductus arteriosus in preterm neonates

This is a protocol for a Cochrane Review (Diagnostic test accuracy). The objectives are as follows: Primary objective To determine the diagnostic accuracy of the cardiac biomarkers BNP and NT-proBNP for diagnosis of hemodynamically significant patent ductus arteriosus (hsPDA) in preterm neonate