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Articles by T. Bhuvaneswari
Total Records ( 4 ) for T. Bhuvaneswari
  T. Bhuvaneswari , K. Prathiba and S.K. Srivatsa
  This study pertains to an application of electronic commerce in the field of healthcare administration and is based on Distributed Knowledge Management (DKM). The DKM is a concept that originated as an abstraction of a business model prepared for the mechanical and agricultural industry. This study suggests a new business model based on DKM for more general use, in the context of healthcare administration.
  T. Bhuvaneswari , Nabajit Dutta and S.K. Srivatsa
  Distributed virtual patient record system is proposed for diagnosis and analysis. It permits physicians located at various places to consult on the status of the patient. In spite of patient data located at various sites, the data can be assembled and the patient record can be constructed dynamically by using the patient’s Social Security Number (SSN). Later, a graphical model could be constructed through which consulting physicians can derive useful information about the current status of the patient. It uses modern distributed objects and emerging telecollaboration tools. In this study, we describe the meaning of distributed virtual patient record, the barriers for implementing the system, requirements of the system and services. In addition, an example named “TeleMed” which implements this concept has been described.
  E. Seshatheriand and T. Bhuvaneswari
  In current times, huge volume of data at a very high velocity gets produced through social media and various sensors in embedded systems that are associated to the internet which causes a very big data problem. These challenging big data’s need to beprocessed and stored by traditional Relational Database Management Systems (RDBMS). Due to this motive, the need for new software solutions has occurred for managing the big data in an efficient, scalable and cool way. In this study, an approach to combine the concept of batch processing and stream processing to an end where it can query the data set which also supports adhoc querying with less latency that can be run on any large scale machine learning algorithms for recognizing any interest pattern in the streaming data set was employed. The functionalities of Hadoop ecosystem ’s tool HIVE can also be used to produce the results to ad hoc queries, User Defined Functions (UDF) similar to writing a SQL stored procedures in the spark system. An interface with serdes which is serialization and de-serialization that helps us to talk to the standard stream where it can exactly query the dataset are employed. By proposing a new software solution AllJoyn Lambda in which AllJoyn is integrated in the lambda architecture and the prototype implementation of the architecture is done using Apache Hadoop Yarn over Apache Spark Streaming are presented. This study light up the high velocity streaming data set on a database without losing any data from the streaming domain, to support adhoc querying from the data set and to provide a mechanism for fast data processing and analytics using large scale machine learning. This research study highlights the analysis of large scale dataset processing, handling challenges and its comprehensive systematic review. From this study, here it conclude that building a smart environment by using the big data setup platform improves and enhances the results for the smart environment.
  J. Hossen , S. Sayeed , T. Bhuvaneswari , C. Venkataseshaiah , J. Emerson and Chung Ren Fatt
  In recent years, there has been increasing interest in developing new designs of low cost floor cleaning mobile robots. One of the challenges has been to reduce the number of sensors as it contributes considerably to the cost of the robot. In this study, the design and development of an automated floor cleaning robot which can navigate and clean a floor at the same time is discussed. It involves hardware construction and software implementation. The 2 wheels are centre mounted are used to move the robot in all direction. Electric brush is mounted in front of the cleaning robot to perform the sweep cleaning process and a mini-water pump is installed within the water tank mounted at the back of the cleaning robot to perform the mopping function. A PIC18F46K22 microcontroller and a L293D motor driver are incorporated into the cleaning robot for processing sensors signals and controlling DC motors. The 3 ultrasonic sensors are mounted on the cleaning robot for obstacle avoidance during its navigation. A RF module is installed on the main board to enable wireless control of the robot. Fuzzy logic techniques have been implemented in the control process. A prototype robot was fabricated and tested in a real environment. It has been found that the robot is able to collect hair, dust, small materials and mop the room floor avoiding obstacles during the cleaning process. The proposed method results in a self-navigating obstacle avoiding cleaning robot which is capable of dry and wet cleaning at a lower cost.
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