Informatics Publishing Limited

Ajaya K. Akaspau ¹, P. Srinivasa Rao ², Lokesh K. Sharma ³

Affiliations
1 Rungta College of Engineering and Technology, Bhilai (CG), IN
2 Department of Computer Science and Systems Engineering, Andhra University, Visakhapatnam, IN
3 Department of Computer Science and Engineering, Rungta College of Engineering and Technology, Bhilai (CG), IN

Abstract

Trajectory data mining is an emerging area of research, having a large variety of applications. This paper proposes an efficient nearest neighbour based trajectory data classification. The nearest neighbour classification is simplest method. The main issue of a Nearest Neighbour classifier is measuring the distance between two items, and this becomes more complicated for Trajectory Data. The closeness between objects is determined using a distance measure. Despite its simplicity, Nearest Neighbour also has some drawbacks: 1) it suffers from expensive computational cost in training when the training set contains millions of objects; 2) its classification time is linear to the size of the training set. The larger the training set, the longer it takes to search for the nearest neighbors. To improve the efficiency of algorithm an R-tree data structure is used. Extensive experiments were conducted using real datasets of moving vehicles in Milan (Italy) and London (UK). Our experimental investigation yields output as classified test trajectories, significant in terms of correctly classified success rate being 98.2%, the results are discussed with the summaries of confusion matrix. To measure the agreement between predicted and observed categorization of the dataset is carried out using Kappa statistics.

Keywords

Trajectory Data Mining, Trajectory Classification, Mobility Data, Nearest Neighbour.

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K. Srinivasa Rao ¹, K. Kiran Kumar ¹, P. Srinivasa Rao ¹

Affiliations
1 Department of Information Technology, Bapatla Engineering College, Bapatla, Andhra Pradesh, IN

Abstract

Cluster analysis is one of the major data analysis methods for clustering the large data sets. The cluster analysis deals with the problems of organization of a collection of data objects into clusters based on some similarity. K-means is one of the most popular data partitioning algorithms that solve the well known clustering problem. Performance of the k-means clustering greatly depends upon the correctness of the initial centroids. Typically the initial centroids for the original k-means clustering are determined randomly. So, the clustering result may reach the local optimal solutions, not the global optimum. Several improvements have been proposed to improve the performance of k-means algorithm. This paper proposes an Efficient k-means algorithm for finding the better initial centroids and an efficient way for assigning data points to appropriate clusters. The proposed algorithm is tested with six bench mark datasets, which are taken from UCI machine learning data repository and found that the proposed algorithm gives better result than the existing.

Keywords

Clustering, Data Partitioning, Data Mining, Heuristic K-Means, K-Means Algorithm.

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M. Vykunta Rao ¹, P. Srinivasa Rao ², B. Surendra Babu ³

Affiliations
1 Department of Mechanical Engineering, GMR Institute of Technology, GMR Nagar, Rajam - 532127, Andhra Pradesh, IN
2 Department of Mechanical Engineering, Centurion University, Parlakhemundi - 761211, Odisha, IN
3 Department of Industrial Engineering, GITAM Institute of Technology, GITAM University, Visakhapatnam - 530045, Andhra Pradesh, IN

Abstract

Objectives: This paper presents the influence of vibratory Tungsten inert gas welding on the hardness of aluminum 5052- H32 alloy weldments are studied. Methods/Statistical Analysis: Hardness of Al5052-H32 is analyzed for different voltage inputs keeping other parameters like welding current, welding speed and gas flow rate constant. Findings: Hardness values are compared for the specimens prepared with vibrations and without vibrations. Hardness of Al5052-H32 specimens prepared at 160 V input voltage is more compared to the specimens prepared at 70 V, 230 V and without vibration. There is a decrease of hardness value is observed for specimens prepared at 230 V compared to the specimens prepared at without any vibrations.

Keywords

Aluminum 5052-H32, Grain Refinement, Hardness, Tungsten Inert Gas Welding, Vibratory Welding.

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Kunjam Nageswara Rao ¹, P. Srinivasa Rao ¹, Allam Appa Rao ², G. R. Sridhar ³

Affiliations
1 Dept. of CS&SE, Andhra University, Visakhapatnam, IN
2 Dr. CR Rao AIMSCS, University of Hydeabad, Hyderabad, IN
3 Endocrine and Diabetes Centre, Visakhapatnam, IN

Abstract

The purpose of this study is to identify and quantify the neurovascular risk factors for foot problems in patients with diabetes mellitus, which is a major public health concern these days. The most feared factor among the diabetic patients is lower extremity amputation. The sequence of events leading to amputation is initiated by ulceration combined with sensation loss. To prevent complications and amputations it is necessary to detect the foot at risk of plantar ulceration at an early stage of sensation loss. Doctors use imaging test to determine whether the plantar ulceration has spread to upper areas in the leg, and to evaluate the size and location of the ulcer. To locate the ulceration area and assess the severity, doctor's uses an imaging technology, such as ultrasound, fluoroscopy, a computed tomography (CT or CAT) scan, x-ray, or a magnetic resonance imaging (MRI) test, this procedure is called as an image-guided biopsy. To access the severity of foot ulcer, a new method is introduced, which is a popular edge detection method. This method is known as Sobel method, which uses edge detection function for derivative approximation to find edges. The Sobel edge method returns edges at those points where the gradient of the considered image is maximum, so the recognition of risk factors will be analyzed in efficient manner. Based on the severity of foot ulcer, preventive foot maintenance and regular foot examinations will take place in diabetes patients.

Keywords

Diabetes, Foot Ulcer, Amputation, Plantar, Imaging.

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