K. R. Manjula ¹

Affiliations
1 Computer Science and Engineering, SASTRA University, Thanjavur - 613401, Tamil Nadu, IN

Abstract

Objectives: Many of the mortality cases we see today especially in rural and remote areas are due to lack of proper medical facilities in emergency cases and lack of genuine information about hospital infrastructure and facilities i.e. whether the treatment for particular disease is available in the hospital they choose to go. In order to aid the decision making in choosing the correct hospital, a methodology based on Decision Tree Induction is adopted. Methods: It is based on the recent statistics about staff and infrastructure of hospitals. The core idea of “Decision Tree Induction Methodology” is to provide ranking for the hospitals based on the facilities available in the hospital. The methodology is applied on a set of hospitals in a location. Findings: The medical statistics and ranking plays a key role in the functioning of the methodology adopted. The data about facilities is gathered and fed to a trained paradigm which predicts the rank of a specific hospital. The representatives grasp the information from the GUI built for this methodology. Application: The result can be fed to all the possible medical centers so that public can get correct guidance from the representatives present there.

Keywords

Classification, Data Mining, Decision Support System, Decision Tree Induction, Health Centre Facility Modeling, Statistical Analysis.

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K. R. Manjula ¹, Amrita Kumari Keshari ², Atul Pahlazani ²

Affiliations
1 School of Computing, SAP, CSE, Sastra University, Thanjavur - 613401, Tamil Nadu, IN
2 B. Tech, CSE, Sastra University, Thanjavur - 613401, Tamil Nadu, IN

Abstract

Background: In past years, many methods have been implemented for maintaining and supervising uncertain data that may occur due to collection of data in new ways which results in missing values, erroneous data. The main aim of this work is to help the end user to get correct information about spatial data. Method: The behaviour of data as an outlier is the result of uncertainty. The challenge in spatial data sets is to cluster uncertain objects. Hence, unsupervised clustering can be used to deal with this type of data. In this paper, the difficulty of outlier detection with uncertain data is examined. Finding: To improve the performance and quality, Voronoi Diagram is used which partition the objects into each cell and helps to see the exact location of an object. The integral part is the pre-processing step of removing uncertainty to avoid wrong interpretation. Furthermore, CLARA (Clustering LARge Applications) algorithm is applied to produce the high quality clusters. It has an in-built function of outlier detection too and it is suitable for large data set. This algorithm uses Mahalanobis Distance to calculate the distance between cluster and its members, to remove outliers and reduce uncertainty for feasible and supporting inputs. This procedure can be a valid provision to be use in geo-database creation. Improvement: The methodology can be enhanced by designing the procedure to develop a Decision Support System (DSS) for spatial database creation.

Keywords

CLARA Algorithm, Clustering, Mahalanobis Distance, Spatial Uncertainty, Varonooi Polygon

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Venkateswari Pichaimani ¹, K. R. Manjula ²

Affiliations
1 SRC, CSE, Sastra University, Kumbakonam – 612001, Tamil Nadu, IN
2 School of Computing, SAP, CSE, Sastra University, Thanjavur - 613401, Tamil Nadu, IN

Abstract

Objective: Geospatial Information System (GIS) is predominantly used in urban planning, and improves quality of people living in an area in many ways. Even though so many hardware and software systems are employed in GIS, the database gains the greatest significance. The intension is to progress a spatial database that should be used as a representation or model of the world, particularly to design for a very specific application. Analysis: The GIS data framework is the methodology proposed to promote an application specific spatial database. It comprises of integrating heterogeneous type of data, followed by constructing a semi-structured multi dimensional data model which directs to design a spatial database. Findings: The novelty in this GIS data framework is Building Information Model (BIM) integrated with the traditional data in support of answering indoor spatial queries. Moreover, this framework worked with BigData to support heterogeneous type of data and to automate decision support system. Enhancements: The views focused on the case studies in this paper help to travel in a new direction of GIS specification, utilization and research from the routine methodologies. This paper widens the scope of research directions in order to establish new techniques in each diverse field.

Keywords

Big Data, BIM, GIS, Spatial Database.

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S. Anand Kumar Varma ¹, K. R. Manjula ²

Affiliations
1 Department of Civil Engineering, V. S. B Engineering College, Karur - 639111, Tamil Nadu, IN
2 CSE Department, School of Computing, Sastra University, Thanjavur - 613401, Tamil Nadu, IN

Abstract

Background: Present study deals with the prediction of nitrogen oxides in the vicinity of the Rayalaseema Thermal Power Project, Andhra Pradesh, India. It provides information to the public and the control agencies to efficiently implement the control strategies of the air quality management program. Methods: In this prediction, the industrial source complex short-term version 3 (ISCST3) model was used. ISCST3 is the preferred model of USEPA. ISCST3 is a refined dispersion modeling technique using site specific input data. Findings: Predictions were made around the power plant with the radius of 10,000 meters; for the period of one year from June 2012 to May 2013. ISCST3 and Golden software surfer version 8 was used to produce Isopleths and 3-Dimensional view of NOX in the vicinity of the Rayalaseema Thermal Power Project. Similarity beside measurements of Ambient Air Quality Monitoring Stations (AAQMS) was made. Genuine results were found well with measured data. Coefficients of determination (R2) were found in the range between 0.76 to 0.96. The ISCST3 model can be used for NOX prediction with good accuracy. Application and Improvement: Further forecasting can be carried out in coal fired power stations for using wide ranging parameters, which will have significant impact on human health and environment using ISCST3 modeling technique.

Keywords

Dispersion Model, Ground Level Concentrations, ISCST3, Nitrogen Oxides, Thermal Power Project.

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