{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,15]],"date-time":"2026-02-15T15:02:29Z","timestamp":1771167749250,"version":"3.50.1"},"reference-count":41,"publisher":"Association for Computing Machinery (ACM)","issue":"3","license":[{"start":{"date-parts":[[2021,6,9]],"date-time":"2021-06-09T00:00:00Z","timestamp":1623196800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Internet Technol."],"published-print":{"date-parts":[[2021,8,31]]},"abstract":"<jats:p>Edge Artificial Intelligence (AI) is the latest trend for next-generation computing for data analytics, particularly in predictive edge analytics for high-risk diseases like Parkinson\u2019s Disease (PD). Deep learning learning techniques facilitate edge AI applications for enhanced, real-time handling of data. Dopamine is the cause of Parkinson\u2019s that happens due to the interference of brain cells that produce the substance to regulate the communication of brain cells. The brain cells responsible for generating the dopamine perform adaptation, control, and movement with fluency. Parkinson\u2019s motor symptoms appear on the loss of 60% to 80% of cells, due to the non-production of appropriate dopamine. Recent research found a close connection between the speech impairment and PD. Many researchers have developed a classification algorithm to identify the PD from speech signals. In this article, Adaptive Crow Search Algorithm\u00a0(ACSA) and Deep Learning (DL)\u2013based optimal feature selection method are introduced. The proposed model is the combination of CROW Search and Deep learning (CROWD) stack sparse autoencoder neural network. Parkinson\u2019s dataset is taken for the experiment from the Irvine dataset repository at the University of California (UCI). In the first phase, dataset cleaning is performed to handle the missing values in the dataset. After that, the proposed ACSA algorithm is employed to find the scrunched feature vector. Furthermore, stack spare autoencoder with seven hidden layers is employed to generate the compressed feature vector. The performance of the proposed CROWD autoencoder model is compared with three feature selection approaches for six supervised classification techniques. The experiment result demonstrates that the performance of the proposed CROWD autoencoder feature selection model has outperformed the benchmarked feature selection techniques: (i) Maximum Relevance (mRMR) (ii) Recursive Feature Elimination (RFE), and (iii) Correlation-based Feature Selection (CFS), to classify Parkinson\u2019s disease. This research has significance in the healthcare sector for the enhancement of classification accuracy up to 0.96%.<\/jats:p>","DOI":"10.1145\/3418500","type":"journal-article","created":{"date-parts":[[2021,6,9]],"date-time":"2021-06-09T14:41:16Z","timestamp":1623249676000},"page":"1-18","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":33,"title":["CROWD: Crow Search and Deep Learning based Feature Extractor for Classification of Parkinson\u2019s Disease"],"prefix":"10.1145","volume":"21","author":[{"given":"Mehedi","family":"Masud","sequence":"first","affiliation":[{"name":"Taif University, Taif-AlHeiwah, KSA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Parminder","family":"Singh","sequence":"additional","affiliation":[{"name":"Lovely Professional University, Phagwara, Punjab, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Gurjot Singh","family":"Gaba","sequence":"additional","affiliation":[{"name":"Lovely Professional University, Phagwara, Punjab, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Avinash","family":"Kaur","sequence":"additional","affiliation":[{"name":"Lovely Professional University, Phagwara, Punjab, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Roobaea","family":"Alroobaea","sequence":"additional","affiliation":[{"name":"Taif University, Taif-AlHeiwah, KSA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mubarak","family":"Alrashoud","sequence":"additional","affiliation":[{"name":"King Saud University, Riyadh, KSA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Salman Ali","family":"Alqahtani","sequence":"additional","affiliation":[{"name":"King Saud University, Riyadh, KSA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2021,6,9]]},"reference":[{"key":"e_1_2_2_1_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.future.2018.02.009"},{"key":"e_1_2_2_2_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.patrec.2019.04.005"},{"key":"e_1_2_2_3_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.compstruc.2016.03.001"},{"key":"e_1_2_2_4_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2011.04.028"},{"key":"e_1_2_2_5_1","doi-asserted-by":"publisher","DOI":"10.5555\/3163587.3163748"},{"key":"e_1_2_2_6_1","volume-title":"Carlo Maremmani, and Erika Rovini","author":"Cavallo Filippo","year":"2019","unstructured":"Filippo Cavallo , Alessandra Moschetti , Dario Esposito , Carlo Maremmani, and Erika Rovini . 2019 . Upper limb motor pre-clinical assessment in Parkinson\u2019s disease using machine learning. Parkinson. Relat. Disord . 63 (June 2019), 111\u2013116. DOI:https:\/\/doi.org\/10.1016\/j.parkreldis.2019.02.028 10.1016\/j.parkreldis.2019.02.028 Filippo Cavallo, Alessandra Moschetti, Dario Esposito, Carlo Maremmani, and Erika Rovini. 2019. Upper limb motor pre-clinical assessment in Parkinson\u2019s disease using machine learning. Parkinson. Relat. Disord. 63 (June 2019), 111\u2013116. DOI:https:\/\/doi.org\/10.1016\/j.parkreldis.2019.02.028"},{"key":"e_1_2_2_7_1","doi-asserted-by":"publisher","DOI":"10.1002\/ana.21995"},{"key":"e_1_2_2_8_1","doi-asserted-by":"publisher","DOI":"10.1142\/S0219720005001004"},{"key":"e_1_2_2_9_1","volume-title":"Progress in Parkinson Research","author":"Forno Lysia S.","unstructured":"Lysia S. Forno . 1988. The neuropathology of Parkinson\u2019s disease . In Progress in Parkinson Research . Springer , Boston, MA , 11\u201321. DOI:https:\/\/doi.org\/10.1007\/978-1-4613-0759-4_2 10.1007\/978-1-4613-0759-4_2 Lysia S. Forno. 1988. The neuropathology of Parkinson\u2019s disease. In Progress in Parkinson Research. Springer, Boston, MA, 11\u201321. DOI:https:\/\/doi.org\/10.1007\/978-1-4613-0759-4_2"},{"key":"#cr-split#-e_1_2_2_10_1.1","doi-asserted-by":"crossref","unstructured":"Srishti Grover Saloni Bhartia Abhilasha Yadav and K. R. Seeja. 2018. Predicting severity of Parkinson's disease using deep learning. Procedia Comput. Sci. 132 (June 2018) 1788-1794. DOI:https:\/\/doi.org\/10.1016\/j.procs.2018.05.154 10.1016\/j.procs.2018.05.154","DOI":"10.1016\/j.procs.2018.05.154"},{"key":"#cr-split#-e_1_2_2_10_1.2","doi-asserted-by":"crossref","unstructured":"Srishti Grover Saloni Bhartia Abhilasha Yadav and K. R. Seeja. 2018. Predicting severity of Parkinson's disease using deep learning. Procedia Comput. Sci. 132 (June 2018) 1788-1794. DOI:https:\/\/doi.org\/10.1016\/j.procs.2018.05.154","DOI":"10.1016\/j.procs.2018.05.154"},{"key":"e_1_2_2_11_1","volume-title":"Optimized cuttlefish algorithm for diagnosis of Parkinson\u2019s disease. Cog. Syst. Res. 52 (Dec","author":"Gupta Deepak","year":"2018","unstructured":"Deepak Gupta , Arnav Julka , Sanchit Jain , Tushar Aggarwal , Ashish Khanna , N. Arunkumar , and Victor Hugo C. de Albuquerque . 2018. Optimized cuttlefish algorithm for diagnosis of Parkinson\u2019s disease. Cog. Syst. Res. 52 (Dec . 2018 ), 36\u201348. DOI:https:\/\/doi.org\/10.1016\/j.cogsys.2018.06.006 10.1016\/j.cogsys.2018.06.006 Deepak Gupta, Arnav Julka, Sanchit Jain, Tushar Aggarwal, Ashish Khanna, N. Arunkumar, and Victor Hugo C. de Albuquerque. 2018. Optimized cuttlefish algorithm for diagnosis of Parkinson\u2019s disease. Cog. Syst. Res. 52 (Dec. 2018), 36\u201348. DOI:https:\/\/doi.org\/10.1016\/j.cogsys.2018.06.006"},{"key":"e_1_2_2_12_1","doi-asserted-by":"publisher","DOI":"10.5555\/645529.657793"},{"key":"e_1_2_2_13_1","doi-asserted-by":"publisher","DOI":"10.1109\/MNET.2019.1800235"},{"key":"#cr-split#-e_1_2_2_14_1.1","doi-asserted-by":"crossref","unstructured":"M. S. Hossain and G. Muhammad. 2019. Emotion recognition using secure edge and cloud computing. Inf. Sci. 504 (July 2019) 589-601. DOI:https:\/\/doi.org\/10.1016\/j.ins.2019.07.040 10.1016\/j.ins.2019.07.040","DOI":"10.1016\/j.ins.2019.07.040"},{"key":"#cr-split#-e_1_2_2_14_1.2","doi-asserted-by":"crossref","unstructured":"M. S. Hossain and G. Muhammad. 2019. Emotion recognition using secure edge and cloud computing. Inf. Sci. 504 (July 2019) 589-601. DOI:https:\/\/doi.org\/10.1016\/j.ins.2019.07.040","DOI":"10.1016\/j.ins.2019.07.040"},{"key":"e_1_2_2_15_1","volume-title":"Biomed. Sig. Proc. Contr. 31 (Jan. 2017","author":"Kotsavasiloglou C.","year":"2016","unstructured":"C. Kotsavasiloglou , N. Kostikis , Dimitrios Hristu-Varsakelis , and M. Arnaoutoglou . 2017. Machine learning-based classification of simple drawing movements in Parkinson\u2019s disease . Biomed. Sig. Proc. Contr. 31 (Jan. 2017 ), 174\u2013180. DOI:https:\/\/doi.org\/10.1016\/j.bspc. 2016 .08.003 10.1016\/j.bspc.2016.08.003 C. Kotsavasiloglou, N. Kostikis, Dimitrios Hristu-Varsakelis, and M. Arnaoutoglou. 2017. Machine learning-based classification of simple drawing movements in Parkinson\u2019s disease. Biomed. Sig. Proc. Contr. 31 (Jan. 2017), 174\u2013180. DOI:https:\/\/doi.org\/10.1016\/j.bspc.2016.08.003"},{"key":"e_1_2_2_16_1","doi-asserted-by":"publisher","DOI":"10.1109\/MWC.2019.1800351"},{"key":"e_1_2_2_17_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.advengsoft.2013.12.007"},{"key":"e_1_2_2_18_1","doi-asserted-by":"publisher","DOI":"10.1109\/TII.2019.2950326"},{"key":"e_1_2_2_19_1","first-page":"1","article-title":"Accuracy improvement for predicting Parkinson\u2019s disease progression. Sci","volume":"6","author":"Nilashi Mehrbakhsh","year":"2016","unstructured":"Mehrbakhsh Nilashi , Othman Ibrahim , and Ali Ahani . 2016 . Accuracy improvement for predicting Parkinson\u2019s disease progression. Sci . Rep. 6 , 1 (Sept. 2016), 1\u201318. DOI:https:\/\/doi.org\/10.1038\/srep34181 10.1038\/srep34181 Mehrbakhsh Nilashi, Othman Ibrahim, and Ali Ahani. 2016. Accuracy improvement for predicting Parkinson\u2019s disease progression. Sci. Rep. 6, 1 (Sept. 2016), 1\u201318. DOI:https:\/\/doi.org\/10.1038\/srep34181","journal-title":"Rep."},{"key":"e_1_2_2_20_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.bbe.2017.09.002"},{"key":"e_1_2_2_21_1","volume-title":"Acharya","author":"Oh Shu Lih","year":"2018","unstructured":"Shu Lih Oh , Yuki Hagiwara , U. Raghavendra , Rajamanickam Yuvaraj , N. Arunkumar , M. Murugappan , and Rajendra U . Acharya . 2018 . A deep learning approach for Parkinson\u2019s disease diagnosis from EEG signals. Neural Comput. Applic . 32 (Aug. 2018), 1\u20137. DOI:https:\/\/doi.org\/10.1007\/s00521-018-3689-5 10.1007\/s00521-018-3689-5 Shu Lih Oh, Yuki Hagiwara, U. Raghavendra, Rajamanickam Yuvaraj, N. Arunkumar, M. Murugappan, and Rajendra U. Acharya. 2018. A deep learning approach for Parkinson\u2019s disease diagnosis from EEG signals. Neural Comput. Applic. 32 (Aug. 2018), 1\u20137. DOI:https:\/\/doi.org\/10.1007\/s00521-018-3689-5"},{"key":"e_1_2_2_22_1","first-page":"4","article-title":"SVM feature selection based rotation forest ensemble classifiers to improve computer-aided diagnosis of Parkinson disease","volume":"36","author":"Ozcift Akin","year":"2012","unstructured":"Akin Ozcift . 2012 . SVM feature selection based rotation forest ensemble classifiers to improve computer-aided diagnosis of Parkinson disease . J. Med. Syst. 36 , 4 (Mar. 2012), 2141\u20132147. DOI:https:\/\/doi.org\/10.1007\/s10916-011-9678-1 10.1007\/s10916-011-9678-1 Akin Ozcift. 2012. SVM feature selection based rotation forest ensemble classifiers to improve computer-aided diagnosis of Parkinson disease. J. Med. Syst. 36, 4 (Mar. 2012), 2141\u20132147. DOI:https:\/\/doi.org\/10.1007\/s10916-011-9678-1","journal-title":"J. Med. Syst."},{"key":"e_1_2_2_23_1","doi-asserted-by":"publisher","DOI":"10.1080\/00207721.2011.581395"},{"key":"e_1_2_2_24_1","doi-asserted-by":"publisher","DOI":"10.1109\/EBBT.2019.8741725"},{"key":"e_1_2_2_25_1","doi-asserted-by":"publisher","DOI":"10.1109\/TSP.2019.8768883"},{"key":"e_1_2_2_26_1","unstructured":"UCI ML Repository. 2008. Parkinson\u2019s Data Set. Retrieved from https:\/\/archive.ics.uci.edu\/ml\/datasets\/Parkinson%27s+Disease+Classification.  UCI ML Repository. 2008. Parkinson\u2019s Data Set. Retrieved from https:\/\/archive.ics.uci.edu\/ml\/datasets\/Parkinson%27s+Disease+Classification."},{"key":"e_1_2_2_27_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-540-87481-2_21"},{"key":"e_1_2_2_28_1","doi-asserted-by":"publisher","DOI":"10.1109\/JBHI.2013.2245674"},{"key":"e_1_2_2_29_1","first-page":"4","article-title":"Telediagnosis of Parkinson\u2019s disease using measurements of dysphonia","volume":"34","author":"Okan Sakar C.","year":"2010","unstructured":"C. Okan Sakar and Olcay Kursun . 2010 . Telediagnosis of Parkinson\u2019s disease using measurements of dysphonia . J. Med. Syst. 34 , 4 (Aug. 2010), 591\u2013599. DOI:https:\/\/doi.org\/10.1007\/s10916-009-9272-y 10.1007\/s10916-009-9272-y C. Okan Sakar and Olcay Kursun. 2010. Telediagnosis of Parkinson\u2019s disease using measurements of dysphonia. J. Med. Syst. 34, 4 (Aug. 2010), 591\u2013599. DOI:https:\/\/doi.org\/10.1007\/s10916-009-9272-y","journal-title":"J. Med. Syst."},{"key":"e_1_2_2_30_1","volume-title":"Optimized machine learning methods for prediction of cognitive outcome in Parkinson\u2019s disease. Comput. Biolo. Med. 111 (Aug","author":"Salmanpour Mohammad R.","year":"2019","unstructured":"Mohammad R. Salmanpour , Mojtaba Shamsaei , Abdollah Saberi , Saeed Setayeshi , Ivan S. Klyuzhin , Vesna Sossi , and Arman Rahmim . 2019. Optimized machine learning methods for prediction of cognitive outcome in Parkinson\u2019s disease. Comput. Biolo. Med. 111 (Aug . 2019 ), 1033\u20131047. DOI:https:\/\/doi.org\/10.1016\/j.compbiomed.2019.103347 10.1016\/j.compbiomed.2019.103347 Mohammad R. Salmanpour, Mojtaba Shamsaei, Abdollah Saberi, Saeed Setayeshi, Ivan S. Klyuzhin, Vesna Sossi, and Arman Rahmim. 2019. Optimized machine learning methods for prediction of cognitive outcome in Parkinson\u2019s disease. Comput. Biolo. Med. 111 (Aug. 2019), 1033\u20131047. DOI:https:\/\/doi.org\/10.1016\/j.compbiomed.2019.103347"},{"key":"e_1_2_2_31_1","volume-title":"Meet Vadera, Lakshminarayanan Samavedham, and Erle Chuen-Hian Lim. 2016","author":"Singh Gurpreet","year":"2016","unstructured":"Gurpreet Singh , Meet Vadera, Lakshminarayanan Samavedham, and Erle Chuen-Hian Lim. 2016 . Machine learning-based framework for multi-class diagnosis of neurodegenerative diseases: A study on Parkinson\u2019s disease. IFAC-PapersOnLine 49, 7 ( Aug. 2016 ), 990\u2013995. DOI:https:\/\/doi.org\/10.1016\/j.ifacol.2016.07.331 10.1016\/j.ifacol.2016.07.331 Gurpreet Singh, Meet Vadera, Lakshminarayanan Samavedham, and Erle Chuen-Hian Lim. 2016. Machine learning-based framework for multi-class diagnosis of neurodegenerative diseases: A study on Parkinson\u2019s disease. IFAC-PapersOnLine 49, 7 (Aug. 2016), 990\u2013995. DOI:https:\/\/doi.org\/10.1016\/j.ifacol.2016.07.331"},{"key":"e_1_2_2_32_1","doi-asserted-by":"publisher","DOI":"10.5555\/1777942.1777962"},{"key":"e_1_2_2_33_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.clinph.2018.09.018"},{"key":"e_1_2_2_34_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2017.06.015"},{"key":"e_1_2_2_35_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICMLA.2007.44"},{"key":"e_1_2_2_36_1","volume-title":"Deep feature extraction from the vocal vectors using sparse autoencoders for Parkinson\u2019s classification","author":"Xiong Yanhao","year":"2020","unstructured":"Yanhao Xiong and Yaohua Lu. 2020. Deep feature extraction from the vocal vectors using sparse autoencoders for Parkinson\u2019s classification . IEEE Access 8 ( Jan. 2020 ), 27821\u201327830. DOI:https:\/\/doi.org\/10.1109\/ACCESS.2020.2968177 10.1109\/ACCESS.2020.2968177 Yanhao Xiong and Yaohua Lu. 2020. Deep feature extraction from the vocal vectors using sparse autoencoders for Parkinson\u2019s classification. IEEE Access 8 (Jan. 2020), 27821\u201327830. DOI:https:\/\/doi.org\/10.1109\/ACCESS.2020.2968177"},{"key":"e_1_2_2_37_1","doi-asserted-by":"publisher","DOI":"10.1109\/NCCCS.2012.6413034"},{"key":"e_1_2_2_38_1","volume-title":"Automated Parkinson\u2019s disease recognition based on statistical pooling method using acoustic features. Med. Hypoth. 135 (Feb","author":"Yaman Orhan","year":"2020","unstructured":"Orhan Yaman , Fatih Ertam , and Turker Tuncer . 2020. Automated Parkinson\u2019s disease recognition based on statistical pooling method using acoustic features. Med. Hypoth. 135 (Feb . 2020 ), 109483. DOI:https:\/\/doi.org\/10.1016\/j.mehy.2019.109483 10.1016\/j.mehy.2019.109483 Orhan Yaman, Fatih Ertam, and Turker Tuncer. 2020. Automated Parkinson\u2019s disease recognition based on statistical pooling method using acoustic features. Med. Hypoth. 135 (Feb. 2020), 109483. DOI:https:\/\/doi.org\/10.1016\/j.mehy.2019.109483"},{"key":"e_1_2_2_39_1","volume-title":"Nature-inspired Optimization Algorithms","author":"Yang Xin-She","unstructured":"Xin-She Yang . 2014. Nature-inspired Optimization Algorithms . Elsevier , Oxford, UK . DOI:https:\/\/doi.org\/10.1016\/C2013-0-01368-0 10.1016\/C2013-0-01368-0 Xin-She Yang. 2014. Nature-inspired Optimization Algorithms. Elsevier, Oxford, UK. DOI:https:\/\/doi.org\/10.1016\/C2013-0-01368-0"}],"container-title":["ACM Transactions on Internet Technology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3418500","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3418500","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T22:02:27Z","timestamp":1750197747000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3418500"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,6,9]]},"references-count":41,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2021,8,31]]}},"alternative-id":["10.1145\/3418500"],"URL":"https:\/\/doi.org\/10.1145\/3418500","relation":{},"ISSN":["1533-5399","1557-6051"],"issn-type":[{"value":"1533-5399","type":"print"},{"value":"1557-6051","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,6,9]]},"assertion":[{"value":"2020-01-01","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2020-08-01","order":1,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2021-06-09","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}