{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,6]],"date-time":"2025-06-06T04:04:36Z","timestamp":1749182676619,"version":"3.41.0"},"publisher-location":"Cham","reference-count":19,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031789397","type":"print"},{"value":"9783031789403","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025]]},"DOI":"10.1007\/978-3-031-78940-3_30","type":"book-chapter","created":{"date-parts":[[2025,6,5]],"date-time":"2025-06-05T06:07:51Z","timestamp":1749103671000},"page":"292-299","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Enhancing Image Quality: Geometric Correction with CNN\u2009+\u2009STN Model"],"prefix":"10.1007","author":[{"given":"J.","family":"Aswini","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"A.","family":"Revathi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ramayanam Jhansi","family":"Rani","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kuntrapakam","family":"Divya","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"T. Ravi","family":"Kumar","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"A. Basi","family":"Reddy","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,6,6]]},"reference":[{"key":"30_CR1","doi-asserted-by":"crossref","unstructured":"Shan, S., et al.: Distortion\u2010corrected image reconstruction with deep learning on an MRI\u2010Linac. Magnetic Resonance in Medicine (2023)","DOI":"10.1002\/mrm.29684"},{"key":"30_CR2","doi-asserted-by":"crossref","unstructured":"Li, X., Zhang, B., Sander, P.V., Liao, J.: Blind geometric distortion correction on images through deep learning. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 4855\u20134864 (2019)","DOI":"10.1109\/CVPR.2019.00499"},{"issue":"2","key":"30_CR3","doi-asserted-by":"publisher","first-page":"161","DOI":"10.1097\/RCT.0000000000000928","volume":"44","author":"Y Nakamura","year":"2020","unstructured":"Nakamura, Y., et al.: Possibility of deep learning in medical imaging focusing improvement of computed tomography image quality. J. Comput. Assist. Tomogr. 44(2), 161\u2013167 (2020)","journal-title":"J. Comput. Assist. Tomogr."},{"key":"30_CR4","doi-asserted-by":"crossref","unstructured":"Charan, N.S., et al.: Solid Waste Management Using Deep Learning. In: International Conference on Soft Computing and Pattern Recognition, pp. 44\u201351. Springer Nature Switzerland, Cham (2022)","DOI":"10.1007\/978-3-031-27524-1_5"},{"key":"30_CR5","doi-asserted-by":"crossref","unstructured":"Sowmya, T.S., Narasimhulu, T., Sunitha, G., Manikanta, T., Venkatesh, T.: Vision Transformer based ResNet Model for Pneumonia Prediction. In: 2023 4th International Conference on Electronics and Sustainable Communication Systems (ICESC), pp. 316\u2013321. IEEE (2023)","DOI":"10.1109\/ICESC57686.2023.10193644"},{"key":"30_CR6","doi-asserted-by":"crossref","unstructured":"Shereesha, M., et al.: Precision Mango Farming: Using Compact Convolutional Transformer for Disease Detection. In: International Conference on Innovations in Bio-Inspired Computing and Applications, pp. 458\u2013465. Springer Nature Switzerland, Cham (2022)","DOI":"10.1007\/978-3-031-27499-2_43"},{"issue":"13","key":"30_CR7","doi-asserted-by":"publisher","DOI":"10.1002\/cpe.6239","volume":"33","author":"E Mohan","year":"2021","unstructured":"Mohan, E., Rajesh, A., Sunitha, G., Konduru, R.M., Avanija, J., Ganesh Babu, L.: A deep neural network learning-based speckle noise removal technique for enhancing the quality of synthetic-aperture radar images. Concurrency and Computation: Practice and Experience 33(13), e6239 (2021)","journal-title":"Concurrency and Computation: Practice and Experience"},{"key":"30_CR8","doi-asserted-by":"crossref","unstructured":"Finnveden, L., Jansson, Y., Lindeberg, T.: Understanding when spatial transformer networks do not support invariance, and what to do about it. In: 2020 25th International Conference on Pattern Recognition (ICPR), pp. 3427\u20133434. IEEE (2021)","DOI":"10.1109\/ICPR48806.2021.9412997"},{"key":"30_CR9","doi-asserted-by":"crossref","unstructured":"Chianucci, D., Savakis, A.: Unsupervised change detection using spatial transformer networks. In: 2016 IEEE Western New York Image and Signal Processing Workshop (WNYISPW), pp. 1\u20135. IEEE (2016)","DOI":"10.1109\/WNYIPW.2016.7904833"},{"key":"30_CR10","doi-asserted-by":"publisher","unstructured":"Apat, S.K., Mishra, J., Srujan Raju, K., Padhy, N.: State of the art of ensemble learning approach for crop prediction. In: Kumar, R., Pattnaik, P.K.R.S., Tavares, J.M. (eds.) Next Generation of Internet of Things. Lecture Notes in Networks and Systems, vol 445. Springer, Singapore (2023). https:\/\/doi.org\/10.1007\/978-981-19-1412-6_58","DOI":"10.1007\/978-981-19-1412-6_58"},{"issue":"8","key":"30_CR11","doi-asserted-by":"publisher","first-page":"4945","DOI":"10.1080\/03772063.2021.1962742","volume":"69","author":"VR Raju","year":"2023","unstructured":"Raju, V.R., Reddy, D.A., Narsimha, D., Srinivas, K., Rani, B.K.: Adaptive closed-loop deep brain stimulator coding techniques for target detections in Parkinson\u2019s. IETE J. Res. 69(8), 4945\u20134960 (2023). https:\/\/doi.org\/10.1080\/03772063.2021.1962742","journal-title":"IETE J. Res."},{"key":"30_CR12","unstructured":"Fayaz, R., et al.: An Intelligent Harris Hawks Optimization (IHHO) based Pivotal Decision Tree (PDT) Machine Learning Model for Diabetes Prediction. Int. J. Intel. Sys. Appl. Eng. 10(4), 415\u2013423 (2022)"},{"key":"30_CR13","doi-asserted-by":"publisher","unstructured":"Priya Remamany, K., et al.: A Localized Bloom Filter-Based CP-ABE in Smart Healthcare. Applied Sciences 12(24), 12720 (2022). https:\/\/doi.org\/10.3390\/app122412720","DOI":"10.3390\/app122412720"},{"key":"30_CR14","doi-asserted-by":"publisher","first-page":"42163","DOI":"10.1007\/s11042-021-11508-5","volume":"81","author":"BK Rani","year":"2022","unstructured":"Rani, B.K., Rao, M.V., Patra, R.K., et al.: Vehicle type classification using graph ant colony optimizer based stack autoencoder model. Multimed Tools Appl 81, 42163\u201342182 (2022). https:\/\/doi.org\/10.1007\/s11042-021-11508-5","journal-title":"Multimed Tools Appl"},{"key":"30_CR15","unstructured":"Munawar, S., Geetha, A., Srinivas, K.: Squirrel Search-based Optimal Feature Extraction with Bi-LSTM for the Arrhythmia Classification using ECG. J. Theoret. Appl. Info. Technol. 100 (2022)"},{"key":"30_CR16","doi-asserted-by":"crossref","unstructured":"Pothalaiah, S., Lakshmaiah, D., Doss, B., Sairam, N., Srikanth, K.: Design of CMOS Base Band Analog. Cognitive Computing Models in Communication Systems, 123 (2022)","DOI":"10.1002\/9781119865605.ch7"},{"key":"30_CR17","doi-asserted-by":"crossref","unstructured":"Lakshmaiah, D., et al.: A Novel Low-Power Frequency-Modulated Continuous Wave Radar Based on Low-Noise Mixer. Cognitive Computing Models in Communication Systems, 165 (2022)","DOI":"10.1002\/9781119865605.ch11"},{"key":"30_CR18","unstructured":"Hemanand, D., et al.: An intelligent intrusion detection and classification system using CSGO-LSVM model for wireless sensor networks (WSNs). Int. J. Intel. Sys. Appl. Eng. 10(3), 285\u2013293 (2022)"},{"key":"30_CR19","doi-asserted-by":"publisher","unstructured":"Natesan, G., Konda, S., de Prado, R.,P., Wozniak, M.: A hybrid mayfly-aquila optimization algorithm based energy-efficient clustering routing protocol for wireless sensor networks. Sensors 22(17), 6405 (2022). https:\/\/doi.org\/10.3390\/s22176405","DOI":"10.3390\/s22176405"}],"container-title":["Lecture Notes in Networks and Systems","Bio-Inspired Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-78940-3_30","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,5]],"date-time":"2025-06-05T06:08:00Z","timestamp":1749103680000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-78940-3_30"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"ISBN":["9783031789397","9783031789403"],"references-count":19,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-78940-3_30","relation":{},"ISSN":["2367-3370","2367-3389"],"issn-type":[{"value":"2367-3370","type":"print"},{"value":"2367-3389","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025]]},"assertion":[{"value":"6 June 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"IBICA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Innovations in Bio-Inspired Computing and Applications","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Kochi","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"India","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"14 December 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"15 December 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ibica2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.mirlabs.net\/ibica23\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}