Browsing by Author "Gopakumar, G."

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Improved Transfer Learning through Shallow Network Embedding for Classification of Leukemia Cells
(2018) Kalmady, K.S.; Kamath, A.S.; Gopakumar, G.; Subrahmanyam, G.R.K.S.; Gorthi, S.S.
One of the most crucial parts in the diagnosis of a wide variety of ailments is cytopathological testing. This process is often laborious, time consuming and requires skill. These constraints have led to interests in automating the process. Several deep learning based methods have been proposed in this domain to enable machines to gain human expertise. In this paper, we investigate the effectiveness of transfer learning using fine-tuned features from modified deep neural architectures and certain ensemble learning methods for classifying the leukemia cell lines HL60, MOLT, and K562. Microfluidics-based imaging flow cytometry (mIFC) is used for obtaining the images instead of image cytometry. This is because mIFC guarantees significantly higher throughput and is easy to set up with minimal expenses. We find that the use of fine-tuned features from a modified deep neural network for transfer learning provides a substantial improvement in performance compared to earlier works. We also identify that without any fine tuning, feature selection using ensemble methods on the deep features also provide comparable performance on the considered Leukemia cell classification problem. These results show that automated methods can in fact be a valuable guide in cytopathological testing especially in resource limited settings. � 2017 IEEE.
Improved Transfer Learning through Shallow Network Embedding for Classification of Leukemia Cells
(Institute of Electrical and Electronics Engineers Inc., 2018) Kalmady, K.S.; Kamath, A.S.; Gopakumar, G.; Subrahmanyam, G.R.K.S.; Gorthi, S.S.
One of the most crucial parts in the diagnosis of a wide variety of ailments is cytopathological testing. This process is often laborious, time consuming and requires skill. These constraints have led to interests in automating the process. Several deep learning based methods have been proposed in this domain to enable machines to gain human expertise. In this paper, we investigate the effectiveness of transfer learning using fine-tuned features from modified deep neural architectures and certain ensemble learning methods for classifying the leukemia cell lines HL60, MOLT, and K562. Microfluidics-based imaging flow cytometry (mIFC) is used for obtaining the images instead of image cytometry. This is because mIFC guarantees significantly higher throughput and is easy to set up with minimal expenses. We find that the use of fine-tuned features from a modified deep neural network for transfer learning provides a substantial improvement in performance compared to earlier works. We also identify that without any fine tuning, feature selection using ensemble methods on the deep features also provide comparable performance on the considered Leukemia cell classification problem. These results show that automated methods can in fact be a valuable guide in cytopathological testing especially in resource limited settings. Â© 2017 IEEE.
Taxonomy of Fault Analysis Methods for Protection of Transmission Lines
(Institute of Electrical and Electronics Engineers Inc., 2024) Balimidi, M.; Gopakumar, G.; Kishan, K.
Transmission lines are bridging elements between the generation and load centre. Thus, any disturbance to it results in an unreliable electric supply to the consumers and also degrades equipment's life. It will be more severe if this prevails in complex power grids. Therefore, this inevitable condition should be sensed early to take necessary protection and control actions. This paper presents a taxonomy of fault analysis methodologies of transmission lines. This paper will be a good reference on the state of art fault analysis methods for the researchers, the academicians, and the power system engineers. Â© 2024 IEEE.