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2,667 results on '"Manikandan M."'

101. Convolutional Neural Network Based Sound Recognition Methods for Detecting Presence of Amateur Drones in Unauthorized Zones

Author

Ganapathi, Ungati, Sabarimalai Manikandan, M., Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Kotenko, Igor, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Bhattacharjee, Arup, editor, Borgohain, Samir Kr., editor, Soni, Badal, editor, Verma, Gyanendra, editor, and Gao, Xiao-Zhi, editor

Published
2020
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105. Conceptual Design Optimization of High-Altitude Airship Having a Tri-Lobed Envelope

Author

Manikandan, M., Pant, Rajkumar S., Cavas-Martínez, Francisco, Series Editor, Chaari, Fakher, Series Editor, Gherardini, Francesco, Series Editor, Haddar, Mohamed, Series Editor, Ivanov, Vitalii, Series Editor, Kwon, Young W., Series Editor, Trojanowska, Justyna, Series Editor, Salagame, Raviprakash R., editor, Ramu, Palaniappan, editor, Narayanaswamy, Indira, editor, and Saxena, Dhish Kumar, editor

Published
2020
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106. Intravascular Ultrasound Image Classification Using Wavelet Energy Features and Random Forest Classifier

Author

Swarnalatha, A., Manikandan, M., 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, Solanki, Vijender Kumar, editor, Hoang, Manh Kha, editor, Lu, Zhonghyu (Joan), editor, and Pattnaik, Prasant Kumar, editor

Published
2020
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107. Textile Sensor-Based Exoskeleton Suits for the Disabled

Author

Sriram, P. R., Muthu Manikandan, M., Ayyappaa, Nithin, Murali, Rahul, 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, Yang, Xin-She, editor, Sherratt, Simon, editor, Dey, Nilanjan, editor, and Joshi, Amit, editor

Published
2020
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125. Analyzing microstructural, residual stress, and mechanical characteristics in dissimilar welds of Inconel 59 and AISI 904L through double-pulsed gas metal arc welding.

Author

Muthukumaran, N, Arulmurugan, B, and Manikandan, M

Abstract

In this research, dissimilar welding of the nickel-based superalloy Inconel 59 and austenitic stainless steel AISI 904L steel was successfully done with double-pulsed gas metal arc welding (DP-GMAW) using the filler ERNiCrMo-13. Macroscopic analysis showed a defect-free weld with an aspect ratio of 2.4, attributed to the efficient heat delivery (0.642 kJ/mm) in DP-GMAW. The microstructure of the weld joint was examined through both optical microscopy and scanning electron microscopy. Examination of the microstructure of the weld interface exposed grain coarsening near the heat-affected zone on the AISI 904L side. In contrast, the weld fusion zone contained fine equiaxed dendrites. Microsegregation and alloying element migration across the entire weld area were assessed through energy dispersive X-ray spectroscopy point analysis and line mapping, revealing controlled microsegregation across the region. X-ray diffraction (XRD) analysis was used to ascertain the average grain size (38.41 nm) and secondary phase composition. The XRD-cosα method was used to assess the residual stress of the weld joint, revealing tensile residual stress in the weld zone and compressive residual stress in other regions. Tensile, Charpy impact, and Vickers hardness tests were conducted in the study to evaluate the strength, toughness, and hardness of a dissimilar weld. The impact of two cryogenic treatments, namely shallow and deep, on tensile and toughness characteristics was investigated in this study. The cryogenic treatments significantly enhanced the mechanical properties of weld samples compared to their as-welded condition, leading to an 11.5% increase in tensile strength and a 10% improvement in toughness. [ABSTRACT FROM AUTHOR]

Published
2024
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129. A Genetic-Based Bayesian Framework for Stateless Group Key Management in Mobile Ad Hoc Networks

Author

Janani, V. S., Manikandan, M. S. K., 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, Bansal, Jagdish Chand, editor, Das, Kedar Nath, editor, Nagar, Atulya, editor, Deep, Kusum, editor, and Ojha, Akshay Kumar, editor

Published
2019
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146. A review of survival and machine learning models for real time data.

Author

Ramakrishnan, M., Manikandan, M., and Jagathesan, T.

Subjects
*SUPERVISED learning, *RANDOM forest algorithms, *DECISION trees, *SURVIVAL analysis (Biometry), *LOGISTIC regression analysis
Abstract

Survival analysis is a branch of statistics deals to analyze and model the time to event data. To tackle this censoring problem, statistical strategies have been widely explored in the literature. To deal with survival data, traditionally many methods including Nonparametric, Semiparametric, Parametric and Bayesian Models were incorporated. But these methods require some assumptions, but many machine learning techniques (Survival Decision Tree, Survival Random Forest, Naive Bayes and Logistic Regression) are designed to handle survival data and other challenges that may emerge in real-time data. In this paper, the author elaborates significance of these methods for survival data and identified important variables for the specified event for three real time data. Important variables were prioritized using random forest's variable selection methods, and the results were compared to survival approaches. Supervised Machine Learning Methods were applied, and survival analysis was used to validate them. [ABSTRACT FROM AUTHOR]

Published
2024
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