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322 results on '"Dhanalakshmi, Samiappan"'

4. A Cost-Effective Recursive Adaptive Filter Setup for Reducing Vehicle Interior Sound

Author

Pauline, S. Hannah, Latha, S., Dhanalakshmi, Samiappan, 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, Sharma, Harish, editor, Shrivastava, Vivek, editor, Tripathi, Ashish Kumar, editor, and Wang, Lipo, editor

Published
2024
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5. Enhancing Brain MRI Tumor Detection: Exploring Vision Transformers and Fine-Tuned Convolutional Neural Network Architecture for Improved Performance

Author

Goswami, Somak, Srivastava, Utkarsh, Chinni, Prem Mudit, Dhanalakshmi, Samiappan, 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, Shaw, Rabindra Nath, editor, Das, Sanjoy, editor, Paprzycki, Marcin, editor, Ghosh, Ankush, editor, and Bianchini, Monica, editor

Published
2024
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6. Detection of Echinoderms Underwater Using Deep Learning Network

Author

Srivastava, Utkarsh, Balaji, Adithya, Yogesh, S., Kalyaan, C. K., Narayanamoorthi, R., Dhanalakshmi, Samiappan, Celebi, Emre, Series Editor, Chen, Jingdong, Series Editor, Gopi, E. S., Series Editor, Neustein, Amy, Series Editor, Liotta, Antonio, Series Editor, Di Mauro, Mario, Series Editor, and Maheswaran, P, editor

Published
2024
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7. Advanced Real-Time Monitoring System for Marine Net Pens: Integrating Sensors, GPRS, GPS, and IoT with Embedded Systems

Author

Panda, Sayantan, Narayanamoorthi, R., Dhanalakshmi, Samiappan, 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, Sharma, Devendra Kumar, editor, Peng, Sheng-Lung, editor, Sharma, Rohit, editor, and Jeon, Gwanggil, editor

Published
2024
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9. Drone Charging System Using Wireless Power Transfer by Inductive Coupling

Author

Shravan, P., Sharvesh, S., Sumanth Rakshith, A., Narayanamoorthi, R., Dhanalakshmi, Samiappan, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, 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, 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, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Oneto, Luca, Series Editor, Panigrahi, Bijaya Ketan, 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, Tan, Kay Chen, Series Editor, Gabbouj, Moncef, editor, Pandey, Shyam Sudhir, editor, Garg, Hari Krishna, editor, and Hazra, Ranjay, editor

Published
2024
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10. Optimization of Compact Zoom Lens Design for Optical Imaging Systems

Author

Rudiran, Sureshkumar, Maik, Vivek, Dhanalakshmi, Samiappan, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, 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, 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, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Oneto, Luca, Series Editor, Panigrahi, Bijaya Ketan, 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, Tan, Kay Chen, Series Editor, Gabbouj, Moncef, editor, Pandey, Shyam Sudhir, editor, Garg, Hari Krishna, editor, and Hazra, Ranjay, editor

Published
2024
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11. An Effective Deep Learning Pipeline for Improved Question Classification into Bloom's Taxonomy's Domains

Author

Sharma, Harsh, Mathur, Rohan, Chintala, Tejas, Dhanalakshmi, Samiappan, and Senthil, Ramalingam

Abstract

Examination assessments undertaken by educational institutions are pivotal since it is one of the fundamental steps to determining students' understanding and achievements for a distinct subject or course. Questions must be framed on the topics to meet the learning objectives and assess the student's capability in a particular subject. The generation of examination questions from extensive text material is challenging and complicated. For example, massive volumes of textbooks make it time-consuming for faculties to annotate good-quality questions, keeping them manually well balanced. Thus, teachers rely on the Bloom's taxonomy's cognitive domain, a popular framework to assess students' intellectual abilities. This study's motivation is to propose a pipeline that could provide new questions from a given text corpus that could be retrieved from a particular input. These generated questions could be incorporated into a question recommender while being automatically classified under the specific cognitive domain under the Bloom's taxonomy. Literature reviews showed that the work done over the Bloom's taxonomy domain had obtained results by implementing classical machine learning methods and few with deep neural networks. The proposed network architectures have shown remarkable results and state-of-the-art architectures compared to the literature. This research study concluded that the pipeline is effective and significant in generating questions, like manually drafting questions, categorizing them into the Bloom's taxonomy's domains, and providing explicit content-based question recommendations.

Published
2023
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14. Calorie Measurement for Raw Vegan Diet Using Deep Learning Networks

Author

Saraf, Shivani, Bagaria, Ram Kumar, Kuresan, Harisudha, Dhanalakshmi, Samiappan, 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, Senjyu, Tomonobu, editor, So-In, Chakchai, editor, and Joshi, Amit, editor

Published
2023
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15. Design, Optimization and Characterization of Optical Imaging Systems for Complex Temperature Environment

Author

Srinivasan, T. S., Maik, Vivek, Dhanalakshmi, Samiappan, 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, Sharma, Devendra Kumar, editor, Peng, Sheng-Lung, editor, Sharma, Rohit, editor, and Jeon, Gwanggil, editor

Published
2023
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25. Analysis of Carotid Artery Ultrasound Images Using Gabor Filter, Maximally Stable Extremal Regions and Convolutional Neural Network

Author

Dutta, Soumyajyoti, Khandelwal, Viththal, Sood, Rohan, Dhanalakshmi, Samiappan, Latha, S., Magjarevic, Ratko, Series Editor, Ładyżyński, Piotr, Associate Editor, Ibrahim, Fatimah, Associate Editor, Lackovic, Igor, Associate Editor, Rock, Emilio Sacristan, Associate Editor, Usman, Juliana, editor, Liew, Yih Miin, editor, and Ahmad, Mohd Yazed, editor

Published
2022
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26. Perceptual Energy Weight Matrix Based Adaptive Block Compressed Sensing for Marine Image Compression

Author

Monika, R., Dhanalakshmi, Samiappan, Kumar, R., 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, Mahapatra, Rajendra Prasad, editor, Peddoju, Sateesh Kumar, editor, Roy, Sudip, editor, Parwekar, Pritee, editor, and Goel, Lavika, editor

Published
2022
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27. Pulmonary Image Analysis with Computer Vision Using Binary Classification Method for COVID-19 Detection

Author

Vaibhav Ram, S. V. N. S., Dhanalakshmi, Samiappan, 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, Mahapatra, Rajendra Prasad, editor, Peddoju, Sateesh Kumar, editor, Roy, Sudip, editor, Parwekar, Pritee, editor, and Goel, Lavika, editor

Published
2022
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28. Structural Optimization of LMS Adaptive Filter Using Multi-stage Cascaded Configuration

Author

Hannah Pauline, S., Dhanalakshmi, Samiappan, Kumar, R., 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, Subramani, C., editor, Vijayakumar, K., editor, Dakyo, Brayima, editor, and Dash, Subhransu Sekhar, editor

Published
2022
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36. Coefficient-Shuffled Variable Block Compressed Sensing for Medical Image Compression in Telemedicine Systems.

Author

Monika, R, Dhanalakshmi, Samiappan, Rajamanickam, Narayanamoorthi, Yousef, Amr, and Alroobaea, Roobaea

Subjects
*DIAGNOSTIC imaging, *IMAGE compression, *IMAGE reconstruction, *COMPRESSED sensing, *FEATURE extraction
Abstract

Medical professionals primarily utilize medical images to detect anomalies within the interior structures and essential organs concealed by the skeletal and dermal layers. The primary purpose of medical imaging is to extract image features for the diagnosis of medical conditions. The processing of these images is indispensable for evaluating a patient's health. However, when monitoring patients over extended periods using specific medical imaging technologies, a substantial volume of data accumulates daily. Consequently, there arises a necessity to compress these data in order to remove duplicates and speed up the process of acquiring data, making it appropriate for effective analysis and transmission. Compressed Sensing (CS) has recently gained widespread acceptance for rapidly compressing images with a reduced number of samples. Ensuring high-quality image reconstruction using conventional CS and block-based CS (BCS) poses a significant challenge since they rely on randomly selected samples. This challenge can be surmounted by adopting a variable BCS approach that selectively samples from diverse regions within an image. In this context, this paper introduces a novel CS method that uses an energy matrix, namely coefficient shuffling variable BCS (CSEM-VBCS), tailored for compressing a variety of medical images with balanced sparsity, thereby achieving a substantial compression ratio and good reconstruction quality. The results of experimental evaluations underscore a remarkable enhancement in the performance metrics of the proposed method when compared to contemporary state-of-the-art techniques. Unlike other approaches, CSEM-VBCS uses coefficient shuffling to prioritize regions of interest, allowing for more effective compression without compromising image quality. This strategy is especially useful in telemedicine, where bandwidth constraints often limit the transmission of high-resolution medical images. By ensuring faster data acquisition and reduced redundancy, CSEM-VBCS significantly enhances the efficiency of remote patient monitoring and diagnosis. [ABSTRACT FROM AUTHOR]

Published
2024
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