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36 results on '"Koppu, Srinivas"'

1. Impact of Value-Added Courses and Personalized Method of Delivery in Achieving Industry Readiness: An Industry 4.0 Perspective

Author

Vyas, Pratik, Bhattacharya, Sweta, Supriya, Y., Bhulakshmi, Dasari, Gadekallu, Thippa Reddy, Kaluri, Rajesh, Sumathy, S., Koppu, Srinivas, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Fortino, Giancarlo, editor, Kumar, Akshi, editor, Swaroop, Abhishek, editor, and Shukla, Pancham, editor

Published
2025
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14. Fault Control Using Triple Modular Redundancy (TMR)

Author

Hudson, Sharon, Shyama Sundar, R. S., Koppu, Srinivas, Kacprzyk, Janusz, Series Editor, Pal, Nikhil R., Advisory Editor, Bello Perez, Rafael, Advisory Editor, Corchado, Emilio S., Advisory Editor, Hagras, Hani, Advisory Editor, Kóczy, László T., Advisory Editor, Kreinovich, Vladik, Advisory Editor, Lin, Chin-Teng, Advisory Editor, Lu, Jie, Advisory Editor, Melin, Patricia, Advisory Editor, Nedjah, Nadia, Advisory Editor, Nguyen, Ngoc Thanh, Advisory Editor, Wang, Jun, Advisory Editor, Pattnaik, Prasant Kumar, editor, Rautaray, Siddharth Swarup, editor, Das, Himansu, editor, and Nayak, Janmenjoy, editor

Published
2018
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21. Remora Namib Beetle Optimization Enabled Deep Learning for Severity of COVID-19 Lung Infection Identification and Classification Using CT Images.

Author

Shanthi, Amgothu and Koppu, Srinivas

Subjects
*DEEP learning, *COMPUTED tomography, *LUNG infections, *COVID-19, *OPTIMIZATION algorithms, *FEATURE extraction
Abstract

Coronavirus disease 2019 (COVID-19) has seen a crucial outburst for both females and males worldwide. Automatic lung infection detection from medical imaging modalities provides high potential for increasing the treatment for patients to tackle COVID-19 disease. COVID-19 detection from lung CT images is a rapid way of diagnosing patients. However, identifying the occurrence of infectious tissues and segmenting this from CT images implies several challenges. Therefore, efficient techniques termed as Remora Namib Beetle Optimization_ Deep Quantum Neural Network (RNBO_DQNN) and RNBO_Deep Neuro Fuzzy Network (RNBO_DNFN) are introduced for the identification as well as classification of COVID-19 lung infection. Here, the pre-processing of lung CT images is performed utilizing an adaptive Wiener filter, whereas lung lobe segmentation is performed employing the Pyramid Scene Parsing Network (PSP-Net). Afterwards, feature extraction is carried out wherein features are extracted for the classification phase. In the first level of classification, DQNN is utilized, tuned by RNBO. Furthermore, RNBO is designed by merging the Remora Optimization Algorithm (ROA) and Namib Beetle Optimization (NBO). If a classified output is COVID-19, then the second-level classification is executed using DNFN for further classification. Additionally, DNFN is also trained by employing the newly proposed RNBO. Furthermore, the devised RNBO_DNFN achieved maximum testing accuracy, with TNR and TPR obtaining values of 89.4%, 89.5% and 87.5%. [ABSTRACT FROM AUTHOR]

Published
2023
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22. Digital Twins for Healthcare 4.0—Recent Advances, Architecture, and Open Challenges

Author

Alazab, Mamoun, Khan, Latif U., Koppu, Srinivas, Ramu, Swarna Priya, M, Iyapparaja, Boobalan, Parimala, Baker, Thar, Maddikunta, Praveen Kumar Reddy, Gadekallu, Thippa Reddy, and Aljuhani, Ahamed

Abstract

Recent trends have shown a widespread increase in the landscape of digital healthcare (i.e., Healthcare 4.0) services, such as personalized healthcare, intelligent rehabilitation, telemedicine, and smart diet management, among others. These healthcare services are based on a variety of diverse requirements. Fulfilling these requirements require proactive intelligent analytics and self-sustainability of networks. Self-sustainability enables the operation of a network with minimum possible interaction from the end-users/network operators, whereas proactive intelligent analytics enables efficient management of resources in response to users’ requests. To enable healthcare 4.0 with proactive online analytics and self-sustainability, one can leverage digital twins. In this article, we present an overview and recent advances of digital twins for healthcare 4.0. An architecture of digital twins for healthcare is also proposed. Furthermore, we present several use cases of digital twins. Finally, we present open research challenges with possible solutions.

Published
2023
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35. 2D Chaotic Map Based on 2D Adaptive Grey Wolf Algorithm for Ultra Sound Medical Image Security.

Author

Koppu, Srinivas and Viswanatham, V. Madhu

Subjects
ULTRASONIC imaging, IMAGE encryption, GENETIC algorithms, PIXELS, AUTOCORRELATION (Statistics)
Abstract

In this paper, we have proposed a chaos-based visual encryption method that can be applied to Ultra Sound Medical Images. We used adaptive Grey Wolf Optimization (GWO) to archive Ultra Sound Medical Image encryption. The robustness of the proposed image encryption are measured by various security attacks such as key sensitivity, histogram analysis, adjacent pixel autocorrelation, chi-square test, etc. Moreover, analytical outcomes are compared with the conventional algorithms like Genetic Algorithm (GA) and GWO. The experimental result shows that the proposed method is faster with low complexity. [ABSTRACT FROM AUTHOR]

Published
2017
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