{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,19]],"date-time":"2026-02-19T16:39:33Z","timestamp":1771519173014,"version":"3.50.1"},"publisher-location":"Singapore","reference-count":25,"publisher":"Springer Singapore","isbn-type":[{"value":"9789811575297","type":"print"},{"value":"9789811575303","type":"electronic"}],"license":[{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020]]},"DOI":"10.1007\/978-981-15-7530-3_42","type":"book-chapter","created":{"date-parts":[[2020,8,13]],"date-time":"2020-08-13T15:50:27Z","timestamp":1597333827000},"page":"553-567","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Optimizing the Efficiency of Machine Learning Techniques"],"prefix":"10.1007","author":[{"given":"Anwar","family":"Ullah","sequence":"first","affiliation":[]},{"given":"Muhammad Zubair","family":"Asghar","sequence":"additional","affiliation":[]},{"given":"Anam","family":"Habib","sequence":"additional","affiliation":[]},{"given":"Saiqa","family":"Aleem","sequence":"additional","affiliation":[]},{"given":"Fazal Masud","family":"Kundi","sequence":"additional","affiliation":[]},{"given":"Asad Masood","family":"Khattak","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2020,8,14]]},"reference":[{"key":"42_CR1","unstructured":"Singh, A., Thakur, N., Sharma, A.: A review of supervised machine learning algorithms. In: Hoda, M.N. (ed.) 3rd International Conference on Computing for Sustainable Global Development (INDIACom) 2016, pp. 1310\u20131315. IEEE (2016)"},{"key":"42_CR2","doi-asserted-by":"publisher","first-page":"8","DOI":"10.1016\/j.csbj.2014.11.005","volume":"13","author":"K Kourou","year":"2015","unstructured":"Kourou, K., Exarchos, T.P., Exarchos, K.P., Karamouzis, M.V., Fotiadis, D.I.: Machine learning applications in cancer prognosis and prediction. Comput. Struct. Biotechnol. J. 13, 8\u201317 (2015)","journal-title":"Comput. Struct. Biotechnol. J."},{"issue":"3","key":"42_CR3","doi-asserted-by":"publisher","first-page":"271","DOI":"10.1007\/s10588-019-09292-7","volume":"25","author":"MZ Asghar","year":"2019","unstructured":"Asghar, M.Z., Rahman, F., Kundi, F.M., Ahmad, S.: Development of stock market trend prediction system using multiple regression. Comput. Math. Organ. Theory 25(3), 271\u2013301 (2019). \nhttps:\/\/doi.org\/10.1007\/s10588-019-09292-7","journal-title":"Comput. Math. Organ. Theory"},{"issue":"5","key":"42_CR4","doi-asserted-by":"publisher","first-page":"504","DOI":"10.1177\/0165551517722741","volume":"44","author":"YH Liu","year":"2018","unstructured":"Liu, Y.H., Chen, Y.L.: A two-phase sentiment analysis approach for judgement prediction. J. Inf. Sci. 44(5), 504\u2013607 (2018)","journal-title":"J. Inf. Sci."},{"key":"42_CR5","doi-asserted-by":"crossref","unstructured":"Habib, A., Akbar, S., Asghar, M.Z., Khattak, A.M., Ali, R., Batool, U.: Rumor detection in business reviews using supervised machine learning. In: 2018 5th International Conference on Behavioral, Economic, and Socio-Cultural Computing (BESC), pp. 233\u2013237. IEEE, Taiwan (2018)","DOI":"10.1109\/BESC.2018.8697323"},{"key":"42_CR6","doi-asserted-by":"crossref","unstructured":"Katz, D.M., Bommarito, I.I., Michael, J., Blackman, J.: Predicting the behavior of the supreme court of the united states: A general approach. arXiv preprint \narXiv:1407.6333\n\n (2014)","DOI":"10.2139\/ssrn.2463244"},{"key":"42_CR7","unstructured":"Medvedeva, M., Vols, M., Wieling, M.: Judicial decisions of the European court of human rights: looking into the crystal ball. In: Proceedings of the Conference on Empirical Legal Studies. Michigan (2018)"},{"key":"42_CR8","doi-asserted-by":"publisher","first-page":"e93","DOI":"10.7717\/peerj-cs.93","volume":"2","author":"N Aletras","year":"2016","unstructured":"Aletras, N., Tsarapatsanis, D., Preo\u0163iuc-Pietro, D., Lampos, V.: Predicting judicial decisions of the European court of human rights: a natural language processing perspective. PeerJ Comput. Sci. 2, e93 (2016)","journal-title":"PeerJ Comput. Sci."},{"issue":"4","key":"42_CR9","doi-asserted-by":"publisher","first-page":"e0174698","DOI":"10.1371\/journal.pone.0174698","volume":"12","author":"DM Katz","year":"2017","unstructured":"Katz, D.M., Bommarito II, M.J., Blackman, J.: A general approach for predicting the behavior of the Supreme Court of the United States. PLoS ONE 12(4), e0174698 (2017)","journal-title":"PLoS ONE"},{"key":"42_CR10","unstructured":"The Supreme Court Database. \nhttp:\/\/scdb.wustl.edu\/documentation.php?var=caseDisposition,last\n\n. Accessed 24 Nov 2019"},{"issue":"4","key":"42_CR11","doi-asserted-by":"publisher","first-page":"462","DOI":"10.1016\/j.jksus.2017.05.013","volume":"29","author":"IS Thaseen","year":"2017","unstructured":"Thaseen, I.S., Kumar, C.A.: Intrusion detection model using fusion of chi-square feature selection and multi class SVM. J. King Saud Univ.-Comput. Inf. Sci. 29(4), 462\u2013472 (2017)","journal-title":"J. King Saud Univ.-Comput. Inf. Sci."},{"key":"42_CR12","unstructured":"Sivakumar, S.: Predicting US Supreme Court Decision Making (2015). \nhttp:\/\/srisai85.github.io\/courts\/courts.html\n\n. Accessed 21 Oct 2019"},{"key":"42_CR13","doi-asserted-by":"crossref","unstructured":"Luo, B., Feng, Y., Xu, J., Zhang, X., Zhao, D.: Learning to predict charges for criminal cases with a legal basis. arXiv preprint \narXiv:1707.09168\n\n (2017)","DOI":"10.18653\/v1\/D17-1289"},{"issue":"4","key":"42_CR14","doi-asserted-by":"publisher","first-page":"761","DOI":"10.1017\/S1537592704040502","volume":"2","author":"AD Martin","year":"2004","unstructured":"Martin, A.D., Quinn, K.M., Ruger, T.W., Kim, P.T.: Competing approaches to predicting supreme court decision making. Perspect. Polit. 2(4), 761\u2013767 (2004)","journal-title":"Perspect. Polit."},{"key":"42_CR15","doi-asserted-by":"crossref","unstructured":"Sulea, O.M., Zampieri, M., Vela, M., van Genabith, J.: Predicting the law area and decisions of French supreme court cases. arXiv preprint \narXiv:1708.01681\n\n (2017)","DOI":"10.26615\/978-954-452-049-6_092"},{"key":"42_CR16","unstructured":"Landthaler, J., Waltl, B., Holl, P., Matthes, F.: Extending full text search for legal document collections using word embeddings. In: JURIX, pp. 73\u201382 (2016)"},{"key":"42_CR17","doi-asserted-by":"crossref","unstructured":"Ye, H., Jiang, X., Luo, Z., Chao, W.: Interpretable charge predictions for criminal cases: Learning to generate court views from fact descriptions. arXiv preprint \narXiv:1802.08504\n\n (2018)","DOI":"10.18653\/v1\/N18-1168"},{"key":"42_CR18","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"558","DOI":"10.1007\/978-3-030-32381-3_45","volume-title":"Chinese Computational Linguistics","author":"S Long","year":"2019","unstructured":"Long, S., Tu, C., Liu, Z., Sun, M.: Automatic judgment prediction via legal reading comprehension. In: Sun, M., Huang, X., Ji, H., Liu, Z., Liu, Y. (eds.) CCL 2019. LNCS (LNAI), vol. 11856, pp. 558\u2013572. Springer, Cham (2019). \nhttps:\/\/doi.org\/10.1007\/978-3-030-32381-3_45"},{"key":"42_CR19","doi-asserted-by":"crossref","unstructured":"Das, A.K., Ashrafi, A., Ahmmad, M.: Joint Cognition of Both Human and Machine for Predicting Criminal Punishment in Judicial System, pp. 36\u201340. IEEE (2019)","DOI":"10.1109\/CCOMS.2019.8821655"},{"key":"42_CR20","unstructured":"Spaeth, H.: The Supreme Court Database (2018). \nhttp:\/\/scdb.wustl.edu\/index.php\n\n. Accessed 1 Nov 2019"},{"key":"42_CR21","unstructured":"Spaeth, H.: The Supreme Court Database. \nhttp:\/\/supremecourtdatabase.org\/\n\n. Accessed 5 Nov 2019"},{"key":"42_CR22","doi-asserted-by":"publisher","first-page":"1000","DOI":"10.1016\/j.asoc.2017.07.027","volume":"70","author":"Y Li","year":"2018","unstructured":"Li, Y., Yan, C., Liu, W., Li, M.: A principle component analysis-based random forest with the potential nearest neighbor method for automobile insurance fraud identification. Appl. Soft Comput. 70, 1000\u20131009 (2018)","journal-title":"Appl. Soft Comput."},{"issue":"7","key":"42_CR23","doi-asserted-by":"publisher","first-page":"1024","DOI":"10.1016\/j.knosys.2011.04.014","volume":"24","author":"H U\u011fuz","year":"2011","unstructured":"U\u011fuz, H.: A two-stage feature selection method for text categorization by using information gain, principal component analysis and genetic algorithm. Knowl.-Based Syst. 24(7), 1024\u20131032 (2011)","journal-title":"Knowl.-Based Syst."},{"key":"42_CR24","unstructured":"Brownlee, J.: \nhttps:\/\/machinelearningmastery.com\/feature-selection-machine-learning-python\/\n\n (2016). Accessed 15 Sept 2019"},{"key":"42_CR25","unstructured":"Lahoti, S.: Packt. \nhttps:\/\/hub.packtpub.com\/4-ways-implement-feature-selection-python-machine-learning\n\n. Accessed 19 Sept 2019"}],"container-title":["Communications in Computer and Information Science","Big Data and Security"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-15-7530-3_42","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2020,8,13]],"date-time":"2020-08-13T16:05:53Z","timestamp":1597334753000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-981-15-7530-3_42"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"ISBN":["9789811575297","9789811575303"],"references-count":25,"URL":"https:\/\/doi.org\/10.1007\/978-981-15-7530-3_42","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"value":"1865-0929","type":"print"},{"value":"1865-0937","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020]]},"assertion":[{"value":"14 August 2020","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICBDS","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Big Data and Security","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Nanjing","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2019","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"20 December 2019","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22 December 2019","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"1","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icbds2019","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/2019.icbds.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"OJS\/PKP","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"251","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"37","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"12","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"15% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}