Qualification: 
Ph.D, M.Tech, B-Tech
nm_dhanya@cb.amrita.edu

Dr. Dhanya N. M. currently serves as Assistant Professor at Department of Computer Science, School of Engineering, Coimbatore. Her research includes energy efficiency in smartphones using cloud technology. She is also interested in android application development for various applications like IoT.

Education

  • 2017 : PhD in Information and Communication Engineering
    Anna University
    Chennai, India.
  • 2006-2008 : M.E. in Computer Science and Engineering
    Sri Krishna College of Engineering and Technology
    Combatore, Tamilnadu India
  • 2000-2004 : BTech in Computer Science and Engineering
    College of Engineering
    Kidangoor, Kerala, India

Experience

Year Affiliation
July 2014 - Present Assistant Professor, Department of Computer Science and Engineering at Amrita Vishwa Vidyapeetham, Coimbatore
January 2008 to July 2014 Assistant Professor, Department of Computer Science and Engineering at Sri Krishna College of Engineering & Technology, Coimbatore

Teaching

  • Formal Language and Automata Theory
  • Computer Language Engineering
  • Android Application Development
  • Service Oriented Architecture
  • CTPS & Computer Programming

Invited Talks

  • Delivered a technical talk on “Web services and Mobile Cloud Computing” at Karpagam University, Coimbatore.
  • Delivered a technical talk on “Mobile Cloud Offloading” at Info Institute of Technology, Coimbatore.

Publications

Publication Type: Journal Article

Year of Publication Publication Type Title

2018

Journal Article

N.M.a Dhanya and Harish, U. C., “Sentiment analysis of twitter data on demonetization using machine learning techniques”, Lecture Notes in Computational Vision and Biomechanics, vol. 28, pp. 227-237, 2018.[Abstract]


Social media like twitter and Facebook is seen as a space where public opinions are formed in today’s world. The data from these tweets and posts can provide valuable insights for policy makers and other agencies to propose and implement policies better. An attempt is made in this paper to understand the public opinion on the recently implemented demonetization policy in India. A sentiment analysis is carried out on twitter data set using machine learning approaches. Twitter data from November 9th to December 3rd is considered for analysis. The data set is pre-processed for cleaning the data and making it possible for analysis. A final set of 5000 tweets are analysed using machine learning techniques like SVM, Naïve Bayes classifier and Decision tree and the results are compared. © 2018, Springer International Publishing AG.

More »»

2017

Journal Article

N.M.a Dhanya, Kousalya, G., and Balakrishnan P., “Dynamic mobile cloud offloading prediction based on statistical regression”, Journal of Intelligent and Fuzzy Systems, vol. 32, pp. 3081-3089, 2017.[Abstract]


Due to the advancement of mobile technology, a large number of computationally intensive applications are created for smart phones. But the limitations of battery and processing power of smart phones are making it inferior to laptops and desktop computers. Mobile Cloud Offloading (MCO) allows the smart phones to offload computationally intensive tasks to the cloud, making it more effective in terms of energy, speed or both. Increased networking capacity due to the availability of high speed Wi-Fi and cellular connections like 3G/4G makes offloading more efficient. Even then, the choice of offloading is not always advisable, because of the highly dynamic context information of mobile devices and clouds. In this paper, we propose a dynamic decision making system, which will decide whether to offload or do the tasks locally, depending on the current context information and the optimization choice of the user. Metrics are developed for time, energy and combination of time and energy to assess the proposed system. A test bed is implemented and the results are showing improvements from the currently existing methods. © 2017-IOS Press and the authors. All rights reserved.

More »»

2016

Journal Article

N.M.a Dhanya and Kousalya, Gb, “Context aware offloading decision and partitioning in mobile cloud computing”, Asian Journal of Information Technology, vol. 15, pp. 2177-2185, 2016.[Abstract]


The development of cloud and mobile technology leads to Mobile Cloud Computing (MCC). MCC has become a major service structure now a days. The limitations in the battery power of mobile can be overcome with the help of cloud technology which is having infinite amount of resources. Offloading is a method for improving the capabilities of resource limited smartphones by augmenting with cloud resources. The mobile applications can be partitioned into two in such a way that heavier parts are executed at the cloud and the rest is executed in the mobile itself. This study designs a system for offloading and partitioning architecture which will take into consideration of all the contextual information related to a mobile. The decision of offloading and partitioning is taken considering the current connectivity, memory status, battery charge, etc. The evaluation results reveal that this algorithm gives performance improvement, less overhead to the mobile side and the prediction accuracy of context aware decision engine. A light weight partition algorithm is used for splitting the application. The results shows significant improvement in time and energy consumed. © Medwell Journals, 2016.

More »»

2015

Journal Article

N.M.a Dhanya and Kousalya, Gb, “The effect of cloudlets in offloading application to cloud from mobile devices for energy efficiency”, International Journal of Applied Engineering Research, vol. 10, pp. 10041-10054, 2015.[Abstract]


Mobile is a device which is increasing in popularity day by day with powerful cameras and other facilities. We can utilize the power of mobiles in lot of applications. But battery capacity is a scarce resource in mobiles till now. So utilizing the high resolution cameras and using minimum battery power applications are advisable for mobile devices. Similarly we are considering two more application one a chess game and another is a sorting applications with different constraints. In this paper we propose a three tier architecture. Instead of depending on the distant cloud resources alone we are utilizing the nearby resource called cloudlet which can be accessed through wifi connectivity which is available everywhere nowadays. The cloudlet is a powerful resource which is in close proximity and by using this, the overall performance such as response time, efficiency can be increased. This paper analyses the effect of cloudlet in each application. © Research India Publications.

More »»

Publication Type: Conference Paper

Year of Publication Publication Type Title

2017

Conference Paper

N.M.a Dhanya and Athira, C. K., “Three dimensional tagcloud visualization for intelligent tourism”, in 2017 4th International Conference on Advanced Computing and Communication Systems, ICACCS 2017, 2017.[Abstract]


Metadata creation along with growth of social bookmarking emerged an approach named tagging. Often people look for location along with its route and detailed information about its surrounding. Mobile users may opt for current event that is taking place in the current location along with historical background of their surroundings and events that happen over time. They are provided with information on spatial context of location which harvests context information from freely available source and tag cloud visualization is created for this data. Firstly, Geo-referenced data which is close to selected point is gathered. Then gathered information is filtered based on the frequency of words from harvested data. Filtered data is then visualized as tag cloud in android mobile. Based on client's interest, visualization detail can interactively be adjusted by changing radius of selected region. Summary of tag cloud is visualized to help user gain overview of current situation. © 2017 IEEE.

More »»

2017

Conference Paper

C. R. Athira and N.M.a Dhanya, “Three dimensional geo-tweet visualization system for spatio-temporal events”, in Proceedings of the 2017 2nd IEEE International Conference on Electrical, Computer and Communication Technologies, ICECCT 2017, 2017.[Abstract]


Geo-tweet visualization help users know the events that is happening over the space and time from the tweets or wikipedia while they click on the specified location for a 3D based tag visualization. Normal events are detected by system which happens anywhere or anytime using machine learning algorithm and special events are also extracted by comparing current situation to normal regularities. Generally, people look for location along with its route and information about surrounding tourist locations for which system provides spatial context of location which is being harvested from freely available source like wikipedia, Twitter, tumblr etc... Initially, Geo-referenced information are gathered which are closed to specified points and then gathered data is filtered based on frequency of words from data harvested, which is then visualized as a tag cloud in android mobile. Visualization details could be adjusted interactively based on interest of the client by changing radius of specified region. Visualization of tag cloud is then summarized to aid user to gain an overview of current situation that is happening over the time.

More »»

2016

Conference Paper

N.M.a Dhanya, Dr. Senthil Kumar T., Sujithra, C., Prasanth, S., and Shruthi, U. K., “Pedagogue: A Model for Improving Core Competency Level in Placement Interviews Through Interactive Android Application”, in Proceedings of the International Conference on Soft Computing Systems, 2016.[Abstract]


This paper discusses about developing a mobile application running on the cloud server. The Cloud acclaims a new era of computing, where application services are provided through the Internet. Though mobile systems are resource-constrained devices with limited computation power, memory, storage, and energy, the use of cloud computing enhances the capability of mobile systems by offering virtually unlimited dynamic resources for computation and storage. The challenge faced here is that traditional smartphones do not support cloud, these applications require specialized mobile cloud application model. The core innovativeness of the application lies in its delivery structure as an interactive android application centered on emerging technologies like mobile cloud computing–that improves the core competencies of the students by taking up online tests posted by the faculty in the campus. The performance of this application has been presented using scalability, accessibility, portability, security, data consistency, user session migration, and redirection delay

More »»

2015

Conference Paper

N.M.a Dhanya and Kousalya, G., “Adaptive and Secure Application Partitioning for Offloading in Mobile Cloud Computing”, in Security in Computing and Communications, Cham, 2015.[Abstract]


Smart phones are capable of providing smart services to the users very similar to laptops and desktop computers. Despite of all these capabilities battery life and computational capabilities are still lacking. By combining mobiles with cloud will reduce all these disadvantages because cloud is having infinite resources for processing. But in cloud security is a major concern. Since mobile devices contain private data a secure offloading of application is necessary. In this paper we are proposing a secure partitioning of application so that the most sensitive or vulnerable part of the application can be kept in the mobile and rest of the application can be offloaded to the cloud.

More »»
  • Scopus: Author ID: 56595089600
  • ORCID
207
PROGRAMS
OFFERED
6
AMRITA
CAMPUSES
15
CONSTITUENT
SCHOOLS
A
GRADE BY
NAAC, MHRD
8th
RANK(INDIA):
NIRF 2018
150+
INTERNATIONAL
PARTNERS