RESEARCH OUTPUT
Publications
38 publications and patents in optimization, machine learning, cloud computing, IoT and intelligent systems.
12 results · Clear filters
2024
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Real-Time Application Layer Protocols to support lightweight mechanism in Internet of Things for e-Healthcare systems
5th International Conference on Recent Trends in Computer Science and Technology, ICRTCST 2024 - Proceedings, pp. 676–694, 2024
2023
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Hybrid Methods for Increasing Security of IoT and Cloud Data
Lecture Notes in Electrical Engineering, pp. 571–586, 2023
2021
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Magnetic Resonance Imaging Classification Methods: A Review
Nanoelectronics, Circuits and Communication Systems, pp. 417–427, 2021
BibTeX Cited by 2Abstract
Magnetic resonance imaging (MRI) is one of the most important medical diagnosis methods in the field of computer-aided detection of medical images. The MRI images help to find the presence of abnormal cells or tissues, referred as tumors. Prior to classification, preprocessing of MRI images is performed. These preprocessing operations help to reduce undesired distortions and select only relevant features for further analysis, making the classification technique more accurate and efficient. In this paper, we present various image classification techniques used over MRI images and their performance.
2020
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A Review on Medical Image Data Compression Techniques
2nd International Conference on Data, Engineering and Applications, IDEA 2020, pp. 1–6, 2020
DOI Link BibTeX Cited by 20Abstract
In most of the telemedicine applications, the role of image compression techniques is important to deal with the medical images. This will be used for storage and transfer of data over a low bandwidth channel like the Internet by pathologist to a doctor for diagnosis problems of patient.If a medical image is compressed using methods of lossy compression, the doctor will not be able to perceive any deterioration in quality with respect to the original input image. One of the main disadvantage of the lossy compression algorithms that are commonly used for multimedia applications not for medical image, while the overall quality of the image can be controlled to some extent, in these cases, it is necessary to use lossless compression algorithms, because the compression and decompression of an image is identical to the original image since the information is preserved during the decompression process after image compression the reconstructed image is exact replica of the original image means no information is lost in the coding process. The aim of this paper is to provide a review on various image compression techniques, which is used in medical image compression, performance analysis and compared existing research on medical image compression.
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Bell pepper leaf disease classification using CNN
2nd International Conference on Data, Engineering and Applications, IDEA 2020, pp. 1–5, 2020
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Comparative Analysis and Performance Evaluation of Medical Image Compression Method for Telemedicine
2nd International Conference on Data, Engineering and Applications, IDEA 2020, vol. 22, pp. 1–5, 2020
DOI Link BibTeX Cited by 3Abstract
In most of the telemedicine applications, the role of image compression techniques is important to deal with the medical images. This will be used for storage and transfer of data over a low bandwidth channel. One of the main drawbacks of the lossy compression algorithms is that it is not applicable for multimedia application while the overall quality of the image can be controlled to some extent. Due to the multiple properties of the Wavelet Transform over DCT, the wavelet based techniques (JPEG 2000) have produced better results compared to DCT based techniques (JPEG). Results of the above-noted techniques were compared and different parameters for medical images such as compression, MSE, SNR, PSNR analysis were carried out.
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Plant leaf disease classification using grid search based SVM
2nd International Conference on Data, Engineering and Applications, IDEA 2020, pp. 1–6, 2020
2019
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Gameplay using Reinforcement Learning
Devices for Integrated Circuit (DevIC), vol. 529, pp. 177–180, 2019
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Role of Internet of Things (IoT) in Smart Farming: A Brief Survey
Proceedings of 3rd International Conference on 2019 Devices for Integrated Circuit, DevIC 2019, pp. 141–145, 2019
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Ubiquitous and Emerging Concepts of Sensors
Proceedings of 3rd International Conference on 2019 Devices for Integrated Circuit, DevIC 2019, pp. 341–347, 2019
2016
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Greedy approaches for deadline based task consolidation in cloud computing
International Conference on Computing, Communication and Automation, vol. 3, pp. 1271–1276, 2016
DOI Link BibTeX Cited by 2Abstract
In cloud infrastructure, an active users can demand for various of services to the cloud infrastructure simultaneously. So it must be a provision that all resources are made available to the user in efficient manner to satisfy the need. Effective utilization of a resource is analogous to lesser cost of utilization of that resource. Under Visualization, VMs are resources used to map incoming users request/tasks before the task is executed on the physical machine. This article shows an analysis of Greedy approach algorithms to efficiently map tasks to VMs and reduce the cost of utilization of VMs.
2015
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Performance analysis of greedy load balancing algorithms in heterogeneous distributed computing system
2014 International Conference on High Performance Computing and Applications, ICHPCA 2014, pp. 1–7, 2015