Conference Papers
Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/28506
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Item Energy efficient quality of service aware virtual machine migration in cloud computing(Institute of Electrical and Electronics Engineers Inc., 2018) Sharma, N.; Sharma, P.; Guddeti, R.M.This paper deals with mulit-objective (network aware, energy efficient, and Service Level Agreement (SLA) aware) Virtual Machines (VMs) migration at the cloud data center. The proposed VMs migration technique migrate the VMs from the underutilized PMs to the energy efficient Physical Machines (PMs) at the cloud data center. Further, the multi-objective VMs migration technique not only reduces the power consumption of PMs and switches but also guarantees the quality of service by maintaining the SLA at the cloud data center. Our proposed VMs migration approach can find the good balance between three conflict objectives as compared to other algorithms. Further, the cloudsim based experimental results demonstrate the superiority of our proposed multi-objective VMs migration technique in terms of energy efficiency and also reduces the SLA violation over state-of-the-art VMs migration techniques such as Interquartile Range (IQR), and Random VMs migration techniques at the cloud data center. © 2018 IEEE.Item GWOTS: Grey Wolf Optimization Based Task Scheduling at the Green Cloud Data Center(Institute of Electrical and Electronics Engineers Inc., 2018) Natesha, B.V.; Sharma, N.; Domanal, S.; Guddeti, R.M.Task Scheduling is a key challenging issue of Infrastructure as a Service (IaaS) based cloud data center and it is well-known NP-complete problem. As the number of users' requests increases then the load on the cloud data center will also increase gradually. To manage the heavy load on the cloud data center, in this paper, we propose multiobjective Grey Wolf Optimization (GWO) technique for task scheduling. The main objective of our proposed GWO based scheduling algorithm is to achieve optimum utilization of cloud resources for reducing both the energy consumption of the data center and total makespan of the scheduler for the given list of tasks while providing the services as requested by the users. Our proposed scheduling algorithm is compared with non meta-heuristic algorithms (First-Come-First-Serve (FCFS) and Modified Throttle (MT)), and meta-heuristic algorithms (Genetic Algorithm (GA), Particle Swarm Optimization (PSO) and Cat Swarm Optimization (CSO)). Experimental results demonstrate that the proposed GWO based scheduler outperforms all algorithms considered for performance evaluation in terms of makespan for the list of tasks, resource utilization and energy consumption. © 2018 IEEE.Item COVID-19 Prediction Using Chest X-rays Images(Institute of Electrical and Electronics Engineers Inc., 2021) Kumar, A.; Sharma, N.; Naik, D.Understanding covid-19 became very important since large scale vaccination of this was not possible. Chest X-ray is the first imaging technique that plays an important role in the diagnosis of COVID-19 disease. Till now in various fields, great success has been achieved using convolutional neural networks(CNNs) for image recognition and classification. However, due to the limited availability of annotated medical images, the classification of medical images remains the biggest challenge in medical diagnosis. The proposed research work has performed transfer learning using deep learning models like Resnet50 and VGG16 and compare their performance with a newly developed CNN based model. Resnet50 and VGG16 are state of the art models and have been used extensively. A comparative analysis with them will give us an idea of how good our model is. Also, this research work develops a CNN model as it is expected to perform really good on image classification related problems. The proposed research work has used kaggle radiography dataset for training, validating and testing. Moreover, this research work has used another x-ray images dataset which have been created from two different sources. The result shows that the CNN model developed by us outperforms VGG16 and Resnet50 model. © 2021 IEEE.Item Stock Market Prediction Using Historical Stock Prices And Dependence On Other Companies In Automotive Sector(Institute of Electrical and Electronics Engineers Inc., 2022) Sharma, N.; Mohan, B.R.Stock market investment, due to its volatile nature and dependence on many factors like own company policies, dependence on other companies' stock value, people's outlook on the company, etc., is a big gamble. However, if understood, it can heap in big rewards to investors. This is one of the reasons why stock market analysis has been such a hot topic and a highly researched field. Fundamental and Technical analysis are two ways to study and predict future company stocks. A lot of work has been done previously to predict stock prices using either sentiment analysis or historical stock data, but a very little emphasis has been put on combining multiple factors to predict stock prices. In this study, we will work on companies registered in the automotive sector in NSE. We have focused on historical companies' stock details and the dependence of stock price of one company on other companies in the same sector to predict future stocks. Both of these factors were studied and analyzed, and then a comparative analysis was done to see which model better predicts the closing stock price of Tata Motors, our target company. We have used Autoregressive integrated moving average, Artificial Neural Network, Long Short-Term Memory (LSTM), a type of Recurrent Neural Network models in our research and a comparative analysis among them will be done. © 2022 IEEE.Item Sequential Memory Modelling for Video Captioning(Institute of Electrical and Electronics Engineers Inc., 2022) Puttaraja, P.; Nayaka, C.; Manikesh, M.; Sharma, N.; Anand Kumar, A.M.In recent years, the automatic generation of natural language descriptions of video has focused on deep learning research and natural voice processing. Video understanding has multiple applications such as video search and indexing, but video subtitles are a correct sophisticated topic for complex and diverse types of video content. However, the understanding between video and natural language sets remains an open issue to better understand the video and create multiple methods to create a set automatically. The deep learning method has a major focus on the direction of video processing with performance and high-speed computing capabilities. This polling discusses an encoder-decoder network end-in-frame based on a deep learning approach to generate caption. In this paper we will describe the model, dataset and parameters used to evaluate the model. © 2022 IEEE.