{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,10]],"date-time":"2026-04-10T15:32:10Z","timestamp":1775835130323,"version":"3.50.1"},"reference-count":35,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2026,2,5]],"date-time":"2026-02-05T00:00:00Z","timestamp":1770249600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,2,5]],"date-time":"2026-02-05T00:00:00Z","timestamp":1770249600000},"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":["SN COMPUT. SCI."],"DOI":"10.1007\/s42979-026-04720-3","type":"journal-article","created":{"date-parts":[[2026,2,5]],"date-time":"2026-02-05T11:30:54Z","timestamp":1770291054000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["A Multi-model Approach Using XAI and Anomaly Detection to Predict Asteroid Hazards"],"prefix":"10.1007","volume":"7","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-0353-9915","authenticated-orcid":false,"given":"Amit Kumar","family":"Mondal","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Nafisha","family":"Aslam","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8057-6963","authenticated-orcid":false,"given":"Prasenjit","family":"Maji","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9403-4724","authenticated-orcid":false,"given":"Hemanta Kumar","family":"Mondal","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2026,2,5]]},"reference":[{"key":"4720_CR1","doi-asserted-by":"publisher","first-page":"302","DOI":"10.1016\/j.neucom.2020.07.053","volume":"417","author":"M Alam","year":"2020","unstructured":"Alam M, Samad M, Vidyaratne L, Glandon A, Iftekharuddin K. Survey on deep neural networks in speech and vision systems. Neurocomputing. 2020;417:302\u201321.","journal-title":"Neurocomputing"},{"key":"4720_CR2","doi-asserted-by":"publisher","unstructured":"Babulal KS, Das AK (2022) Deep learning-based object detection: an investigation. In: Futuristic Trends in Networks and Computing Technologies. Springer . https:\/\/doi.org\/10.1007\/978-981-19-5037-7_50","DOI":"10.1007\/978-981-19-5037-7_50"},{"key":"4720_CR3","doi-asserted-by":"publisher","DOI":"10.4018\/IJEHMC.309930","author":"KS Babulal","year":"2022","unstructured":"Babulal KS, Das AK, Kumar P, Rajput DS, Alam A, Obaid AJ. Real-time surveillance system for detection of social distancing. Int J E-Health Med Commun. 2022. https:\/\/doi.org\/10.4018\/IJEHMC.309930.","journal-title":"Int J E-Health Med Commun"},{"key":"4720_CR4","doi-asserted-by":"publisher","first-page":"752","DOI":"10.3390\/aerospace10090752","volume":"10","author":"V Bacu","year":"2023","unstructured":"Bacu V, Nandra C, Sabou A, Stefanut T, Gorgan D. Assessment of asteroid classification using deep convolutional neural networks. Aerospace. 2023;10:752. https:\/\/doi.org\/10.3390\/aerospace10090752.","journal-title":"Aerospace"},{"key":"4720_CR5","doi-asserted-by":"publisher","first-page":"1999","DOI":"10.3390\/app10061999","volume":"10","author":"MM Bad\u017da","year":"2020","unstructured":"Bad\u017da MM, Barjaktarovi\u0107 MC. Classification of brain tumors from MRI images using a convolutional neural network. Appl Sci. 2020;10:1999.","journal-title":"Appl Sci"},{"key":"4720_CR6","doi-asserted-by":"crossref","unstructured":"Bahel V, Bhongade P, Sharma J, Shukla S, Gaikwad M (2021) Supervised classification for analysis and detection of potentially hazardous asteroids. In: 2021 International Conference on Computational Intelligence and Computing Applications (ICCICA). pp.\u00a01\u20134. IEEE","DOI":"10.1109\/ICCICA52458.2021.9697222"},{"key":"4720_CR7","unstructured":"Basu V. Prediction of asteroid diameter with the help of multi-layer perceptron regressor. Int J Adv Electron Computer Sci. 2019;6(4):36\u201340"},{"issue":"1","key":"4720_CR8","doi-asserted-by":"publisher","first-page":"1377","DOI":"10.1093\/mnras\/stz1795","volume":"488","author":"V Carruba","year":"2019","unstructured":"Carruba V, Aljbaae S, Lucchini A. Machine-learning identification of asteroid groups. Mon Not R Astron Soc. 2019;488(1):1377\u201386.","journal-title":"Mon Not R Astron Soc"},{"key":"4720_CR9","doi-asserted-by":"publisher","unstructured":"Carruba V, Aljbaae S, Domingos RDC, Barletta W (2022) Machine learning applied to asteroid dynamics. Celestial Mech Dyn Astronomy. https:\/\/doi.org\/10.1007\/s10569-022-10088-2","DOI":"10.1007\/s10569-022-10088-2"},{"key":"4720_CR10","doi-asserted-by":"crossref","unstructured":"Chhibber M, Bhatia M, Chaudhary A, Stewart C (2022) Comparing the efficacy of machine learning models on potentially hazardous objects. In: Proceedings of the 2nd International Conference on Technology Advancements in Computer Science (ICTACS). pp. 725\u2013730","DOI":"10.1109\/ICTACS56270.2022.9988738"},{"key":"4720_CR11","doi-asserted-by":"publisher","DOI":"10.1016\/j.ascom.2023.100693","volume":"42","author":"P Cowan","year":"2023","unstructured":"Cowan P, Bond IA, Reyes NH. Towards asteroid detection in microlensing surveys with deep learning. Astronomy and Computing. 2023;42:100693. https:\/\/doi.org\/10.1016\/j.ascom.2023.100693.","journal-title":"Astronomy Comput"},{"key":"4720_CR12","doi-asserted-by":"publisher","unstructured":"Gorgan D, Vaduvescu O, Stefanut T, Bacu V, Sabou A, Balazs DC, Nandra CI, Boldea C, Boldea AL, Predatu M et al. (2019) Nearby platform for automatic asteroids detection and euronear surveys. https:\/\/doi.org\/10.48550\/arXiv.1903.03479","DOI":"10.48550\/arXiv.1903.03479"},{"key":"4720_CR13","doi-asserted-by":"publisher","unstructured":"Gupta MK, Romaszewski M, Gawron P. (2023) Potential of quantum machine learning for processing multispectral earth observation data. https:\/\/doi.org\/10.36227\/techrxiv.21898902","DOI":"10.36227\/techrxiv.21898902"},{"key":"4720_CR14","doi-asserted-by":"publisher","first-page":"A45","DOI":"10.1051\/0004-6361\/201935983","volume":"634","author":"JD Hefele","year":"2020","unstructured":"Hefele JD, Bortolussi F, Zwart SP. Identifying earth-impacting asteroids using an artificial neural network. Astronomy Astrophys. 2020;634:A45.","journal-title":"Astronomy & Astrophysics"},{"key":"4720_CR15","doi-asserted-by":"crossref","unstructured":"Kumar A, Malik A (2021) A comparative analysis of various models for assessment of trust in digital age accelerated by covid-19. In: 2021 International Conference on Computing Sciences (ICCS). pp. 156\u2013160","DOI":"10.1109\/ICCS54944.2021.00039"},{"key":"4720_CR16","unstructured":"Linares R, Furfaro R (2016) Space object classification using deep convolutional neural networks. In: 2016 19th International Conference on Information Fusion (FUSION). IEEE"},{"key":"4720_CR17","doi-asserted-by":"crossref","unstructured":"M BR, G A, J A, K NJ (2023) Hazardous asteroid prediction using machine learning. In: 2023 2nd International Conference on Vision Towards Emerging Trends in Communication and Networking Technologies (ViTECoN). pp.\u00a01\u20136","DOI":"10.1109\/ViTECoN58111.2023.10157937"},{"key":"4720_CR18","unstructured":"McIntyre KJ (2019) Applying machine learning to asteroid classification utilizing spectroscopically derived spectrophotometry. Tech. rep., Department of Space Studies, University of North Dakota, Grand Forks."},{"key":"4720_CR19","unstructured":"NASA: 115 years ago: The tunguska asteroid impact event (2023), Accessed: Feb. 2024"},{"issue":"8","key":"4720_CR20","doi-asserted-by":"publisher","first-page":"1496","DOI":"10.2514\/1.G006487","volume":"45","author":"N Ozaki","year":"2022","unstructured":"Ozaki N, Yanagida K, Chikazawa T, Pushparaj N, Takeishi N, Hyodo R. Asteroid flyby cycler trajectory design using deep neural networks. J Guid Control Dyn. 2022;45(8):1496\u2013511.","journal-title":"J Guid Control Dyn"},{"key":"4720_CR21","doi-asserted-by":"publisher","first-page":"400","DOI":"10.1016\/j.procs.2018.08.190","volume":"135","author":"B Pardamean","year":"2018","unstructured":"Pardamean B, Cenggoro TW, Rahutomo R, Budiarto A, Karuppiah EK. Transfer learning from chest x-ray pre-trained convolutional neural network for learning mammogram data. Procedia Computer Science. 2018;135:400\u20137.","journal-title":"Procedia Computer Science"},{"key":"4720_CR22","doi-asserted-by":"crossref","unstructured":"Pasko V (2018) Prediction of orbital parameters for undiscovered potentially hazardous asteroids using machine learning. In: Stardust Final Conference. Springer, Cham","DOI":"10.1007\/978-3-319-69956-1_3"},{"key":"4720_CR23","doi-asserted-by":"crossref","unstructured":"Ranaweera RN, Fernando T (2022) Prediction of potentially hazardous asteroids using deep learning. In: 2022 2nd International Conference on Advanced Research in Computing (ICARC). pp. 31\u201336","DOI":"10.1109\/ICARC54489.2022.9753945"},{"key":"4720_CR24","doi-asserted-by":"publisher","first-page":"386","DOI":"10.1037\/h0042519","volume":"65","author":"F Rosenblatt","year":"1958","unstructured":"Rosenblatt F. The perceptron: a probabilistic model for information storage and organization in the brain. Psychol Rev. 1958;65:386\u2013408.","journal-title":"Psychol Rev"},{"key":"4720_CR25","doi-asserted-by":"crossref","unstructured":"Shamneesh S, Manoj M, Keshav K (2020) Node-level self-adaptive network path restructuring technique for internet of things (iot). In: Lecture Notes in Electrical Engineering, vol.\u00a0989. Springer, Singapore","DOI":"10.1007\/978-981-13-8618-3_48"},{"key":"4720_CR26","doi-asserted-by":"crossref","unstructured":"Sharma R, M V, Moharir M (2016) Revolutionizing machine learning algorithms using gpus. In: 2016 International Conference on Computation System and Information Technology for Sustainable Solutions (CSITSS). pp. 318\u2013323","DOI":"10.1109\/CSITSS.2016.7779378"},{"key":"4720_CR27","doi-asserted-by":"crossref","unstructured":"Sharma T, Kaur U, Sharma S (2022) Inclusive and relative analysis of nature inspired optimization algorithms in reference to sworn intelligence. In: 2022 8th International Conference on Signal Processing and Communication (ICSC). pp. 397\u2013403","DOI":"10.1109\/ICSC56524.2022.10009248"},{"key":"4720_CR28","unstructured":"Si A. Hazardous asteroid classification through various machine learning techniques. Int Res J Eng Technol (IRJET) 2020;7(3):5388\u20135390."},{"key":"4720_CR29","doi-asserted-by":"publisher","unstructured":"Singh P, Kumar P, Patel H, Babulal KS, Ananthakrishnan G. Premier dynamic bandwidth management and tensile wavelength selection ensuring qos for ng-epons. IEEE Access. 2025. https:\/\/doi.org\/10.1109\/ACCESS.2025.3532496.","DOI":"10.1109\/ACCESS.2025.3532496"},{"issue":"3","key":"4720_CR30","first-page":"1","volume":"7","author":"P Singh","year":"2021","unstructured":"Singh P, Verma S, Khan I, Sharma S. Machine learning: a comprehensive survey on existing algorithms. J Comput Sci Eng Softw Test. 2021;7(3):1\u20139.","journal-title":"J Comput Sci Eng Softw Test"},{"key":"4720_CR31","doi-asserted-by":"crossref","unstructured":"Singh S, Sharma C, Sharma S, Verma NK (2021) Re-learning emotional intelligence through artificial intelligence. In: 2021 9th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO). pp.\u00a01\u20135","DOI":"10.1109\/ICRITO51393.2021.9596091"},{"issue":"2","key":"4720_CR32","doi-asserted-by":"publisher","first-page":"2024","DOI":"10.1093\/mnras\/stx974","volume":"469","author":"EA Smirnov","year":"2017","unstructured":"Smirnov EA, Shevchenko II. Identification of asteroids trapped inside three-body mean motion resonances: a machine-learning approach. Mon Not R Astron Soc. 2017;469(2):2024\u201331. https:\/\/doi.org\/10.1093\/mnras\/stx974.","journal-title":"Mon Not R Astron Soc"},{"key":"4720_CR33","doi-asserted-by":"publisher","first-page":"1280","DOI":"10.2514\/1.G007043","volume":"46","author":"S Takahashi","year":"2023","unstructured":"Takahashi S, Scheeres DJ. Autonomous reconnaissance trajectory guidance at small near-earth asteroids via reinforcement learning. J Guid Control Dyn. 2023;46:1280\u201397.","journal-title":"J Guid Control Dyn"},{"key":"4720_CR34","doi-asserted-by":"publisher","first-page":"101","DOI":"10.3390\/app12010101","volume":"12","author":"D Varga","year":"2022","unstructured":"Varga D. No-reference image quality assessment with convolutional neural networks and decision fusion. Appl Sci. 2022;12:101.","journal-title":"Appl Sci"},{"key":"4720_CR35","doi-asserted-by":"crossref","unstructured":"Yidirim T, Cigizoglu HK (2002) Comparison of generalized regression neural network and mlp performances on hydrologic data forecasting. In: Proceedings of the 9th International Conference on Neural Information Processing, ICONIP\u201902 IEEE. vol.\u00a05.","DOI":"10.1109\/ICONIP.2002.1201942"}],"container-title":["SN Computer Science"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s42979-026-04720-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s42979-026-04720-3","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s42979-026-04720-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,2,5]],"date-time":"2026-02-05T11:30:58Z","timestamp":1770291058000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s42979-026-04720-3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,2,5]]},"references-count":35,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2026,2]]}},"alternative-id":["4720"],"URL":"https:\/\/doi.org\/10.1007\/s42979-026-04720-3","relation":{},"ISSN":["2661-8907"],"issn-type":[{"value":"2661-8907","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,2,5]]},"assertion":[{"value":"16 April 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"3 January 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"5 February 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no Conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"This study did not involve any human participants or animal subjects.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical Approval"}},{"value":"Not applicable.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Informed Consent"}}],"article-number":"176"}}