{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,5,13]],"date-time":"2025-05-13T16:24:27Z","timestamp":1747153467009,"version":"3.40.5"},"publisher-location":"Cham","reference-count":22,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031702587"},{"type":"electronic","value":"9783031702594"}],"license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"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":[[2024]]},"DOI":"10.1007\/978-3-031-70259-4_12","type":"book-chapter","created":{"date-parts":[[2024,9,8]],"date-time":"2024-09-08T09:02:15Z","timestamp":1725786135000},"page":"155-167","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["New Methodology for\u00a0Attack Patterns Classification in\u00a0Deep Brain Stimulation"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5499-5248","authenticated-orcid":false,"given":"Jihen","family":"Fourati","sequence":"first","affiliation":[]},{"given":"Mohamed","family":"Othmani","sequence":"additional","affiliation":[]},{"given":"Hela","family":"Ltifi","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,9,9]]},"reference":[{"key":"12_CR1","series-title":"Communications in Computer and Information Science","doi-asserted-by":"publisher","first-page":"123","DOI":"10.1007\/978-3-031-41774-0_10","volume-title":"Advances in Computational Collective Intelligence - ICCCI 2023","author":"J Fourati","year":"2023","unstructured":"Fourati, J., Othmani, M., Ltifi, H.: An improved approach for Parkinson\u2019s disease classification based on convolutional neural network. In: Nguyen, N.T., et al. (eds.) ICCCI 2023. CCIS, vol. 1864, pp. 123\u2013135. Springer, Cham (2023). https:\/\/doi.org\/10.1007\/978-3-031-41774-0_10"},{"key":"12_CR2","doi-asserted-by":"crossref","unstructured":"Fourati, J., Othmani, M., Ltifi, H.: A hybrid model based on convolutional neural networks and long short-term memory for rest tremor classification. In: ICAART, vol. 3, pp. 75\u201382 (2022)","DOI":"10.5220\/0010773600003116"},{"key":"12_CR3","doi-asserted-by":"crossref","unstructured":"Ben Salah, K., Othmani, M., Fourati, J., Kherallah, M.: Advancing spatial mapping for satellite image road segmentation with multi-head attention. Vis. Comput. 1\u201311 (2024)","DOI":"10.1007\/s00371-024-03431-1"},{"key":"12_CR4","doi-asserted-by":"crossref","unstructured":"Salah, K.B., Othmani, M., Saida, S., Kherallah, M.: Improved approach for semantic segmentation of MBRSC aerial imagery based on transfer learning and modified UNet. In: 2023 International Conference on Cyberworlds (CW), pp. 46\u201353. IEEE (2023)","DOI":"10.1109\/CW58918.2023.00017"},{"key":"12_CR5","doi-asserted-by":"crossref","unstructured":"Othmani, M., Issaoui, B., El Khediri, S., Khan, R.U.: Hybrid active shape model and deep neural network approach for lung cancer detection. Int. J. Info. Technol. 1\u201312 (2024)","DOI":"10.1007\/s41870-024-01853-7"},{"issue":"20","key":"12_CR6","doi-asserted-by":"publisher","first-page":"28347","DOI":"10.1007\/s11042-022-12715-4","volume":"81","author":"M Othmani","year":"2022","unstructured":"Othmani, M.: A vehicle detection and tracking method for traffic video based on faster R-CNN. Multimedia Tools Appl. 81(20), 28347\u201328365 (2022)","journal-title":"Multimedia Tools Appl."},{"key":"12_CR7","doi-asserted-by":"crossref","unstructured":"Pedrosa, T.\u00cd., et al.: Machine learning application to quantify the tremor level for Parkinson\u2019s disease patients. Procedia Comput. Sci. 138, 215\u2013220 (2018)","DOI":"10.1016\/j.procs.2018.10.031"},{"issue":"4","key":"12_CR8","doi-asserted-by":"publisher","first-page":"168","DOI":"10.1016\/j.icte.2016.10.005","volume":"2","author":"SV Perumal","year":"2016","unstructured":"Perumal, S.V., Sankar, R.: Gait and tremor assessment for patients with Parkinson\u2019s disease using wearable sensors. ICT Express 2(4), 168\u2013174 (2016)","journal-title":"ICT Express"},{"key":"12_CR9","doi-asserted-by":"publisher","first-page":"24154","DOI":"10.1109\/ACCESS.2019.2899558","volume":"7","author":"H Rathore","year":"2019","unstructured":"Rathore, H., et al.: A novel deep learning strategy for classifying different attack patterns for deep brain implants. IEEE Access 7, 24154\u201324164 (2019)","journal-title":"IEEE Access"},{"issue":"3","key":"12_CR10","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/1525856.1525863","volume":"5","author":"K Ni","year":"2009","unstructured":"Ni, K., et al.: Sensor network data fault types. ACM Trans. Sensor Netw. (TOSN) 5(3), 1\u201329 (2009)","journal-title":"ACM Trans. Sensor Netw. (TOSN)"},{"issue":"8","key":"12_CR11","doi-asserted-by":"publisher","first-page":"883","DOI":"10.1007\/s11869-018-0585-1","volume":"11","author":"U Pak","year":"2018","unstructured":"Pak, U., Kim, C., Ryu, U., Sok, K., Pak, S.: A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction. Air Qual. Atmos. Health 11(8), 883\u2013895 (2018)","journal-title":"Air Qual. Atmos. Health"},{"issue":"10","key":"12_CR12","first-page":"7974","volume":"34","author":"A Al Hamoud","year":"2022","unstructured":"Al Hamoud, A., Hoenig, A., Roy, K.: Sentence subjectivity analysis of a political and ideological debate dataset using LSTM and BiLSTM with attention and GRU models. J. King Saud Univ.-Comput. Inf. Sci. 34(10), 7974\u20137987 (2022)","journal-title":"J. King Saud Univ.-Comput. Inf. Sci."},{"key":"12_CR13","doi-asserted-by":"crossref","unstructured":"Goldberger, A.l., Amaral, L.an., Glass, L., et al.: PhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals. Circulation 101(23), e215\u2013e220 (2000)","DOI":"10.1161\/01.CIR.101.23.e215"},{"issue":"23","key":"12_CR14","doi-asserted-by":"publisher","first-page":"17351","DOI":"10.1007\/s00521-020-04867-x","volume":"32","author":"IE Livieris","year":"2020","unstructured":"Livieris, I.E., Pintelas, E., Pintelas, P.: A CNN-LSTM model for gold price time-series forecasting. Neural Comput. Appl. 32(23), 17351\u201317360 (2020)","journal-title":"Neural Comput. Appl."},{"key":"12_CR15","doi-asserted-by":"crossref","unstructured":"Esteban, S., et al.: Deep bidirectional recurrent neural networks as end-To-end models for smoking status extraction from clinical notes in Spanish. bioRxiv: 320846 (2018)","DOI":"10.1101\/320846"},{"key":"12_CR16","doi-asserted-by":"crossref","unstructured":"Yuan, H., et al.: Detection and quantification of resting tremor in Parkinson\u2019s disease using long-term acceleration data. Math. Probl. Eng. (2021)","DOI":"10.1155\/2021\/5669932"},{"key":"12_CR17","doi-asserted-by":"publisher","first-page":"77","DOI":"10.1016\/j.parkreldis.2022.01.011","volume":"95","author":"SB Lee","year":"2022","unstructured":"Lee, S.B., Kim, Y.J., Hwang, S., Son, H., Lee, S.K., Park, K.I., Kim, Y.G.: Predicting Parkinson\u2019s disease using gradient boosting decision tree models with electroencephalography signals. Parkinsonism Rel. Disord. 95, 77\u201385 (2022)","journal-title":"Parkinsonism Rel. Disord."},{"key":"12_CR18","doi-asserted-by":"crossref","unstructured":"Abdaoui, A., Al-Ali, A., Riahi, A., Mohamed, A., Du, X., Guizani, M.: Secure medical treatment with deep learning on embedded board. In: Energy Efficiency of Medical Devices and Healthcare Applications, pp. 131\u2013151. Academic Press (2020)","DOI":"10.1016\/B978-0-12-819045-6.00007-8"},{"key":"12_CR19","unstructured":"Chollet, V.: Keras: theano-based deep learning library (2015). Code https:\/\/github.com\/fchollet, Documentation http:\/\/keras.io"},{"key":"12_CR20","unstructured":"The Theano Development: A Python framework for fast computation of mathematical expressions. arXiv:1605.02688 (2016)"},{"key":"12_CR21","doi-asserted-by":"crossref","unstructured":"Hameed, S.S., et al.: A systematic review of security and privacy issues in the internet of medical things; the role of machine learning approaches. PeerJ Comput. Sci. 7, e414 (2021)","DOI":"10.7717\/peerj-cs.414"},{"key":"12_CR22","series-title":"EAI\/Springer Innovations in Communication and Computing","doi-asserted-by":"publisher","first-page":"275","DOI":"10.1007\/978-3-031-23602-0_16","volume-title":"Artificial Intelligence for Smart Healthcare","author":"AS Joseph","year":"2023","unstructured":"Joseph, A.S., Lazar, A.J.P., Sharma, D.K., Maria, A.B., Ganesan, N., Sengan, S.: ConvNet-based deep brain stimulation for attack patterns. In: Agarwal, P., Khanna, K., Elngar, A.A., Obaid, A.J., Polkowski, Z. (eds.) Artificial Intelligence for Smart Healthcare. EAI\/Springer Innovations in Communication and Computing, pp. 275\u2013292. Springer, Cham (2023). https:\/\/doi.org\/10.1007\/978-3-031-23602-0_16"}],"container-title":["Communications in Computer and Information Science","Advances in Computational Collective Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-70259-4_12","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,9,8]],"date-time":"2024-09-08T09:17:03Z","timestamp":1725787023000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-70259-4_12"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9783031702587","9783031702594"],"references-count":22,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-70259-4_12","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"type":"print","value":"1865-0929"},{"type":"electronic","value":"1865-0937"}],"subject":[],"published":{"date-parts":[[2024]]},"assertion":[{"value":"9 September 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICCCI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Computational Collective Intelligence","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Leipzig","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Germany","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"9 September 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"11 September 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"16","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"iccci2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/iccci.pwr.edu.pl\/2024\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}