dc.contributor.advisor | Parvez, Mohammad Zavid | |
dc.contributor.advisor | Rahman, Rafeed | |
dc.contributor.author | Rahman, Fahim | |
dc.contributor.author | Ahmed, Md.Istiyak | |
dc.contributor.author | Saad, Saif Shahnewaz | |
dc.contributor.author | Ashrafuzzaman, Md | |
dc.contributor.author | Mogno, Sharita Shehnaz | |
dc.date.accessioned | 2021-09-08T11:38:43Z | |
dc.date.available | 2021-09-08T11:38:43Z | |
dc.date.copyright | 2921 | |
dc.date.issued | 2021-06 | |
dc.identifier.other | ID 17101500 | |
dc.identifier.other | ID 16201021 | |
dc.identifier.other | ID 16101181 | |
dc.identifier.other | ID 16101110 | |
dc.identifier.other | ID 21141040 | |
dc.identifier.uri | http://hdl.handle.net/10361/14989 | |
dc.description | This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2021. | en_US |
dc.description | Cataloged from PDF version of thesis. | |
dc.description | Includes bibliographical references (pages 52-58). | |
dc.description.abstract | The significance and urgency of detecting cognitive load of Visually Impaired Person is essential when perception comes while designing an automated navigation
aid for them in unfamiliar indoor environments.Our paper presents a novel, robust
and multidimensional framework based on iterative feature pooling technique which
recursively selects paramount features that maintains relation with the change in
cognitive load of the brain. We have chosen to use Electroencephalogram as it is
one of the fastest imaging techniques available having a high sampling rate and analytical neuro-psychologic benchmarks of perceptive process indicated by rhythmic
activities of the brain. We took the well established ERDS method for indexing the
cognitive load and further developed the work by operating with the band power of
not only the Alpha wave but the Alpha Beta ratio band power and Alpha Theta ratio band power.The intricacy of the tasks in terms of cognitive load were quantified
considering multiple aspects to support the redemption of usability of a way finding
aid by features extraction from specific attributes, some of which were new to this
field, to support the vindication of accessibility of a way finding aid.As the machine
learning classifier the Gradient Boost outperformed all other classifiers(94% accuracy). We considered other performance parameters like the f-1 score,recall, time
delay, sensitivity and false positive rate to evaluate the performance of all available
supervised ML classifiers.This chapter marks out the estimation of based on existing literature, background, leeway, characteristics, and machine learning approaches,
cognitive load was calculated using EEG data. | en_US |
dc.description.statementofresponsibility | Fahim Rahman | |
dc.description.statementofresponsibility | Md. Istiyak Ahmed | |
dc.description.statementofresponsibility | Saif Shahnewaz Saad | |
dc.description.statementofresponsibility | Md Ashrafuzzaman | |
dc.description.statementofresponsibility | Sharita Shehnaz Mogno | |
dc.format.extent | 58 pages | |
dc.language.iso | en | en_US |
dc.publisher | Brac University | en_US |
dc.rights | Brac University theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. | |
dc.subject | Cognitive load | en_US |
dc.subject | Machine Learning | en_US |
dc.subject | Supervised Learning | en_US |
dc.subject | EEG | en_US |
dc.subject | Performance parameters | en_US |
dc.subject | Alpha Beta ratio | en_US |
dc.subject | Gradient Boost Algorithm | en_US |
dc.subject | ERDS | en_US |
dc.subject.lcsh | Machine learning | |
dc.title | Prediction and detection in change of cognitive load for VIP’s by a machine learning approach | en_US |
dc.type | Thesis | en_US |
dc.contributor.department | Department of Computer Science and Engineering, Brac University | |
dc.description.degree | B. Computer Science | |