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641 results on '"Suganthi, S."'

15. Stain Normalized Breast Histopathology Image Recognition using Convolutional Neural Networks for Cancer Detection

Author

Krishna, Sruthi, S, Suganthi S., Krishnamoorthy, Shivsubramani, and Bhavsar, Arnav

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition
Abstract

Computer assisted diagnosis in digital pathology is becoming ubiquitous as it can provide more efficient and objective healthcare diagnostics. Recent advances have shown that the convolutional Neural Network (CNN) architectures, a well-established deep learning paradigm, can be used to design a Computer Aided Diagnostic (CAD) System for breast cancer detection. However, the challenges due to stain variability and the effect of stain normalization with such deep learning frameworks are yet to be well explored. Moreover, performance analysis with arguably more efficient network models, which may be important for high throughput screening, is also not well explored.To address this challenge, we consider some contemporary CNN models for binary classification of breast histopathology images that involves (1) the data preprocessing with stain normalized images using an adaptive colour deconvolution (ACD) based color normalization algorithm to handle the stain variabilities; and (2) applying transfer learning based training of some arguably more efficient CNN models, namely Visual Geometry Group Network (VGG16), MobileNet and EfficientNet. We have validated the trained CNN networks on a publicly available BreaKHis dataset, for 200x and 400x magnified histopathology images. The experimental analysis shows that pretrained networks in most cases yield better quality results on data augmented breast histopathology images with stain normalization, than the case without stain normalization. Further, we evaluated the performance and efficiency of popular lightweight networks using stain normalized images and found that EfficientNet outperforms VGG16 and MobileNet in terms of test accuracy and F1 Score. We observed that efficiency in terms of test time is better in EfficientNet than other networks; VGG Net, MobileNet, without much drop in the classification accuracy., Comment: 26 pages, 11 figures

Published
2022

17. An IoT-Enabled Smart Network Traffic Signal Assistant System for Emergency Vehicles Using Computer Vision

Author

Senthil, G. A., Prabha, R., Suganthi, S., Sridevi, S., Shanthi, N., 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, Raj, Jennifer S., editor, Perikos, Isidoros, editor, and Balas, Valentina Emilia, editor

Published
2023
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18. Hardening Containers with Static and Dynamic Analysis

Author

Patil, Sachet Rajat Kumar, John, Neil, Kunja, Poorvi Sameep, Dwivedi, Anushka, Suganthi, S., Honnnavali, Prasad B., Onwubiko, Cyril, editor, Rosati, Pierangelo, editor, Rege, Aunshul, editor, Erola, Arnau, editor, Bellekens, Xavier, editor, Hindy, Hanan, editor, and Jaatun, Martin Gilje, editor

Published
2023
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20. LabVIEW Based Anomaly Detection for Screening Diabetic Retinopathy

Author

Pravin, Sheena Christabel, Priya, K. Sindhu, Suganthi, S., Saranya, J., Kumar, V. S. Selva, 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, Joby, P. P., editor, Balas, Valentina E., editor, and Palanisamy, Ram, editor

Published
2023
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21. Online Exam Monitoring Application as Microservices

Author

Ganugapati, Sree Pranavi, Natarajan, Rakshashri, Kashyap, Abhijnana, Suganthi, S., Honnavalli, Prasad B., 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, Gupta, Deepak, editor, Khanna, Ashish, editor, Bhattacharyya, Siddhartha, editor, Hassanien, Aboul Ella, editor, Anand, Sameer, editor, and Jaiswal, Ajay, editor

Published
2023
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26. Self-Reported Sleepiness, Fatigue and Risk of Obstructive Sleep Apnea Using the STOP BANG Components: Women’s Perspective

Author

Jasmine M, Suganthi S, Manoj P, Divya Ilanchoorian, and Vijayalakshmi

Subjects
Obstructive sleep apnea, hypoxia, screening, STOP BANG questionnaire, Public aspects of medicine, RA1-1270
Abstract

Introduction: About 2-4% of adults have obstructive sleep apnoea (OSA), a frequent chronic condition. The disorder is characterised by recurrent bouts of whole or partial collapse of the upper airway, primarily the oropharyngeal tract, while sleeping, which results in a decrease or stoppage of airflow. The aim of the study is to assess the relationship between the self-reported sleepiness, fatigue and the risk of Obstructive Sleep Apnea among the midlife women aged 40 to 65 years Methodology: This was a cross sectional study conducted in the field practice area of rural and urban health and training centre of a tertiary care teaching hospital, Chengalpattu district, Tamilnadu among 380 women aged 40 to 65 years. Results: Mean age of the study population was 49.69 (± 11.2). On risk categorization by STOP-BANG Questionnaire, it was found that 72.9% (277), 26.1% (99) and 1.1% (4) had low, intermediate and high risk for sleep apnea. 16.9% of the study participants had excessive daytime sleepiness. Fatigue severity scale of more than or equal to 36 was seen among 34.5% of the study participants. Conclusion: The STOP-Bang questionnaire has been shown in studies to be a quick, efficient, and accurate OSA screening tool. It can make it easier to allocate resources effectively for detecting and treating OSA that had not yet been recognised.

Published
2023
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27. Occupational stress among IT professionals in Chennai, Tamilnadu

Author

Jasmine Maruthupandian, Buvnesh Kumar M, Suganthi S, Rajeshkannan S, Divya Ilanchoorian, Gowthamkarthic R, and Manoj P

Subjects
Stress, Software, Workplace, Hobbies, Public aspects of medicine, RA1-1270
Abstract

Background: Stress in the workplace can have impact on physical and mental health. It can also be result in absenteeism, reduced productivity and monetary problems among the working population. The objective of this study is to assess the stress levels among the Software Professionals working in a private firm, Chennai Methods: A cross-sectional study was conducted over six months in a private software firm in Chennai. A sample size of 364 participants was determined based on previous research, with data collected through interviews using a pre-tested questionnaire. This questionnaire covered demographic information, perceived stress scale, and various lifestyle factors, including exercise and hobbies. Data were analyzed using statistical tests, including Chi-square, and significance was set at p < 0.05. Results: The study included predominantly young male participants (mean age 27 ± 3.5), with 81.9% experiencing moderate stress levels. High stress was more prevalent among males and those under 28 years old. Software professionals with over three years of experience and those working on computers for more than six hours a day were more likely to report moderate to high stress levels. However, individuals who engaged in exercise and hobbies exhibited lower stress levels. Conclusion: Coping strategies and periodic health education should be recommended to reduce the development of stress and its consequences.

Published
2023
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33. Four-Layer Authentication with Honeypot and Cloud Data Encryption

Author

Chari, Sanjay, Umesh, Harshith, Sandosh, Athul, Suganthi, S., Honnavalli, Prasad, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Hirche, Sandra, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Li, Yong, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Möller, Sebastian, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zamboni, Walter, Series Editor, Zhang, Junjie James, Series Editor, Shetty, N. R., editor, Patnaik, L. M., editor, Nagaraj, H. C., editor, Hamsavath, Prasad N., editor, and Nalini, N., editor

Published
2022
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41. Artificial Intelligence-Based Electric Vehicle Smart Charging System in Malaysia.

Author

Shern, Siow Jat, Sarker, Md Tanjil, Ramasamy, Gobbi, Thiagarajah, Siva Priya, Al Farid, Fahmid, and Suganthi, S. T.

Subjects
INFRASTRUCTURE (Economics), ARTIFICIAL intelligence, BATTERY management systems, SUSTAINABLE transportation, MACHINE learning
Abstract

The worldwide transition to electric vehicles (EVs) is gaining momentum, propelled by the imperative to reduce carbon emissions and foster sustainable transportation. In Malaysia, the government is facilitating this transformation through targeted initiatives aimed at promoting the use of electric vehicles (EVs) and developing the required infrastructure. This paper investigates the crucial role of artificial intelligence (AI) in developing intelligent electric vehicle (EV) charging infrastructure, specifically focusing on the context of Malaysia. The paper examines the current electric vehicle (EV) charging infrastructure in Malaysia, highlights advancements led by artificial intelligence (AI), and references both local and international case studies. Fluctuations in the Total Industry Volume (TIV) and Total Industry Production (TIP) reflect changes in market demand and production capabilities, with notable peaks in March 2023 and March 2024. The research reveals that AI technologies, such as machine learning and predictive analytics, can enhance charging efficiency, improve user experience, and support grid stability. A mathematical model for an AI-based smart charging system was developed, and the implemented system achieved 30% energy savings and a 20.38% reduction in costs compared to traditional methods. These findings underscore the system's energy and cost efficiency. In addition, we outline the potential advantages and challenges associated with incorporating artificial intelligence (AI) into Malaysia's electric vehicle (EV) charging infrastructure. Furthermore, we offer recommendations for researchers, industry stakeholders, and regulators. Malaysia can enhance the uptake of electric vehicles and make a positive impact on the environment by leveraging artificial intelligence (AI) to enhance its electric vehicle charging system (EVCS). [ABSTRACT FROM AUTHOR]

Published
2024
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43. Analysis of Topologies for Cooperative Tracking Control of Multi-agent Systems

Author

Suganthi, S., Jeevamma, Jacob, 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, Suma, V., editor, Chen, Joy Iong-Zong, editor, Baig, Zubair, editor, and Wang, Haoxiang, editor

Published
2021
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48. Gain and Bandwidth Enhancement by Optimizing Four Elements Corporate-Fed Microstrip Array for 2.4 GHz Applications

Author

Shashi Kumar, D., Suganthi, S., Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Hirche, Sandra, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Liang, Qilian, Series Editor, Martin, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Möller, Sebastian, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zhang, Junjie James, Series Editor, Janyani, Vijay, editor, Singh, Ghanshyam, editor, Tiwari, Manish, editor, and d’Alessandro, Antonio, editor

Published
2020
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