Qualification: 
M.E, B-Tech
Email: 
t_deepika@cb.amrita.edu

Deepika T. currently serves as Faculty Associate at the Department of Computer Science and Engineering, School of Engineering, Amrita Vishwa Vidyapeetham, Coimbatore. She received her B. Tech (Information Technology) from PSNA College of Engineering and Technology, Dindigul, and M.E degree (Computer Science and Engineering) from RVS College of Engineering and Technology, Dindigul. She is currently pursuing a doctoral study in Computer Science and Engineering, Amrita School of Engineering, Coimbatore. Her research area includes Cloud Computing and Machine Learning.

Seminar and Workshop Attended

  • Attended an International Workshop on Clouds2018 organized by Amrita Vishwa Vidyapeetham on 19 December 2018 at Amrita Vishwa Vidyapeetham, Amritapuri, India.
  • Attended a Faculty Development Programme on Digital Image Processing with MATLAB Applications on 14-15 October 2011 at RVS College of Engineering and Technology, Dindigul, Tamil Nadu, India.
  • Attended a Seminar on Soft Computing and Simulators on 24-25 March 2011 at PSNA College of Engineering and Technology, Dindigul, Tamil Nadu, India.

Publications

Publication Type: Journal Article

Year of Publication Title

2020

Deepika T., “Analysis and Comparison of Different Wavelet Transform Methods Using Benchmarks for Image Fusion”, arXiv preprint arXiv:2007.11488, 2020.

2020

Deepika T. and G. Kannan, K., “A Novel adaptive optimization of Dual-Tree Complex Wavelet Transform for Medical Image Fusion”, arXiv preprint arXiv:2007.13538, 2020.[Abstract]


In recent years, many research achievements are made in the medical image fusion field. Fusion is basically extraction of best of inputs and conveying it to the output. Medical Image fusion means that several of various modality image information is comprehended together to form one image to express its information. The aim of image fusion is to integrate complementary and redundant information. In this paper, a multimodal image fusion algorithm based on the dual-tree complex wavelet transform (DT-CWT) and adaptive particle swarm optimization (APSO) is proposed. Fusion is achieved through the formation of a fused pyramid using the DTCWT coefficients from the decomposed pyramids of the source images. The coefficients are fused by the weighted average method based on pixels, and the weights are estimated by the APSO to gain optimal fused images. The fused image is obtained through conventional inverse dual-tree complex wavelet transform reconstruction process. Experiment results show that the proposed method based on adaptive particle swarm optimization algorithm is remarkably better than the method based on particle swarm optimization. The resulting fused images are compared visually and through benchmarks such as Entropy (E), Peak Signal to Noise Ratio, (PSNR), Root Mean Square Error (RMSE), Standard deviation (SD) and Structure Similarity Index Metric (SSIM) computations.

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2020

Deepika T., Prakash, P., and M, D. N., “Efficient Resource Prediction Model for Small and Medium Scale Cloud Data Centers”, Journal of Intelligent & Fuzzy Systems, vol. 39, pp. 4731-4747, 2020.[Abstract]


By leveraging the performance of small and medium-scale data centers (SMSDCs), which are involved in high-performance computing, data centers are central to the current modern industrial business world. Extensive enhancements in the SMSDC infrastructure comprise a diverse set of connected devices that disseminate resources to the end users. The high certainty workloads of end users and over resource provisioning result in high power consumption in SMSDCs, which are pivotal factors contributing to high carbon footprints from SMSDCs. The excessive emission of CO2 is higher in SMSDCs compared with that of hyperscale data centers (HSDCs). An exorbitant amount of electricity is utilized by 8.6 million data centers worldwide, and is expected to increase by up to 13% in 2030. The power requirement of an SMSDC domain is expected to be 5% of the global power production. However, the power consumption of SMSDCs changes annually. To aid SMSDCs, machine learning prediction is deployed. Literature review indicates that many studies have focused on the recurring issues of HSDCs rather than those of SMSDC. Herein, a regressive predictive analysis, i.e., multi-output random forest regressor, is proposed to forecast the resource usage and power utilization of virtual machines. These prediction results in diminishes the power utilization of SMSDC whilst reduces the CO2 emission from SMSDC. The obtained result shows that the proposed approach yields better predictions than other single-output prediction methods for future resource demand from end users.

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2020

Deepika T. and Prakash, P., “Power Consumption Prediction in Cloud Data Center using Machine Learning”, International Journal of Electrical and Computer Engineering, vol. 10, no. 2, pp. 1524-1532, 2020.[Abstract]


<p>The flourishing development of the cloud computing paradigm provides several services in the industrial business world. Power consumption by cloud data centers is one of the crucial issues for service providers in the domain of cloud computing. Pursuant to the rapid technology enhancements in cloud environments and data centers augmentations, power utilization in data centers is expected to grow unabated. A diverse set of numerous connected devices, engaged with the ubiquitous cloud, results in unprecedented power utilization by the data centers, accompanied by increased carbon footprints. Nearly a million physical machines (PM) are running all over the data centers, along with (5 – 6) million virtual machines (VM). In the next five years, the power needs of this domain are expected to spiral up to 5% of global power production. The virtual machine power consumption reduction impacts the diminishing of the PM’s power, however further changing in power consumption of data center year by year, to aid the cloud vendors using prediction methods. The sudden fluctuation in power utilization will cause power outage in the cloud data centers. This paper aims to forecast the VM power consumption with the help of regressive predictive analysis, one of the Machine Learning (ML) techniques. The potency of this approach to make better predictions of future value, using Multi-layer Perceptron (MLP) regressor which provides 91% of accuracy during the prediction process.</p>

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Publication Type: Conference Proceedings

Year of Publication Title

2010

Deepika T. and Jaiganesh, M., “Comparison of Cloud Computing Benchmarks towards an Optimized Approach”, proceedings of National Conference on Advanced Computing and its Applications. 2010.

2008

Deepika T. and Jaiganesh, M., “Global Positioning System-A Satellite-based Navigation System”, SYMPOMKCE proceedings on National Conference. 2008.

Faculty Research Interest: