{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,8,2]],"date-time":"2025-08-02T17:42:02Z","timestamp":1754156522384,"version":"3.41.2"},"reference-count":22,"publisher":"Emerald","issue":"1","license":[{"start":{"date-parts":[[2016,3,14]],"date-time":"2016-03-14T00:00:00Z","timestamp":1457913600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.emerald.com\/insight\/site-policies"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2016,3,14]]},"abstract":"\n Purpose<\/jats:title>\n \u2013 The purpose of this paper is to proposes a forward search algorithm for detecting and identifying natural structures arising in human-computer interaction (HCI) and human physiological response (HPR) data. <\/jats:p>\n <\/jats:sec>\n \n Design\/methodology\/approach<\/jats:title>\n \u2013 The paper portrays aspects that are essential to modelling and precision in detection. The methods involves developed algorithm for detecting outliers in data to recognise natural patterns in incessant data such as HCI-HPR data. The detected categorical data are simultaneously labelled based on the data reliance on parametric rules to predictive models used in classification algorithms. Data were also simulated based on multivariate normal distribution method and used to compare and validate the original data. <\/jats:p>\n <\/jats:sec>\n \n Findings<\/jats:title>\n \u2013 Results shows that the forward search method provides robust features that are capable of repelling over-fitting in physiological and eye movement data. <\/jats:p>\n <\/jats:sec>\n \n Research limitations\/implications<\/jats:title>\n \u2013 One of the limitations of the robust forward search algorithm is that when the number of digits for residuals value is more than the expected size for stack flow, it normally yields an error caution; to counter this, the data sets are normally standardized by taking the logarithmic function of the model before running the algorithm. <\/jats:p>\n <\/jats:sec>\n \n Practical implications<\/jats:title>\n \u2013 The authors conducted some of the experiments at individual residence which may affect environmental constraints. <\/jats:p>\n <\/jats:sec>\n \n Originality\/value<\/jats:title>\n \u2013 The novel approach to this method is the detection of outliers for data sets based on the Mahalanobis distances on HCI and HPR. And can also involve a large size of data with p<\/jats:italic> possible parameters. The improvement made to the algorithm is application of more graphical display and rendering of the residual plot.<\/jats:p>\n <\/jats:sec>","DOI":"10.1108\/ijicc-08-2015-0029","type":"journal-article","created":{"date-parts":[[2016,3,9]],"date-time":"2016-03-09T10:33:21Z","timestamp":1457519601000},"page":"23-41","source":"Crossref","is-referenced-by-count":4,"title":["Detection of natural structures and classification of HCI-HPR data using robust forward search algorithm"],"prefix":"10.1108","volume":"9","author":[{"given":"Fatima","family":"Isiaka","sequence":"first","affiliation":[]},{"given":"Kassim S","family":"Mwitondi","sequence":"additional","affiliation":[]},{"given":"Adamu M","family":"Ibrahim","sequence":"additional","affiliation":[]}],"member":"140","reference":[{"key":"key2020121722040467800_b2","doi-asserted-by":"crossref","unstructured":"Atkinson, A.C.\n (1994), \u201cFast very robust methods for the detection of multiple outliers\u201d, \n Journal of the American 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