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      One- and Five-Ringgit Malaysia banknotes reader with counterfeit detection for visually impaired person using backlight mechanism and image processing techniques

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          Abstract

          Visually impaired persons face challenges in running business activities, especially in handling banknotes. Malaysia researchers had proposed some Ringgit banknotes recognition systems to aid visually impaired persons recognize and classify Ringgit banknotes. However, these electronic banknote readers can only recognize Malaysian Banknotes’ Ringgit value, they have no counterfeit detection features. The purpose of this study is to develop a banknote reader that not only can help visually impaired persons recognize the banknote value, but also to detect the counterfeit of the banknote, safeguarding their losses. This paper proposed a Malaysian banknote reader using backlight mechanism and image processing techniques to read and detect counterfeit for one Ringgit and five Ringgit Malaysian banknotes. The developed handheld banknote reader used visual type sensor to capture banknote image, passed to raspberry pi controller to perform image processing on banknote value and the extracted watermarks features. The developed image processing algorithm will trace out the region of interests: 1)see-thru windows, 2)Crescent and Star, 3)Perfect see though register and detect the watermarks features accordingly. The processed result will be passed back to the handheld banknote reader and broadcast on an attached mini speaker to aid the visually impaired understand the holding banknote, whether it is a real one Ringgit, real five Ringgit or none of them. The experimental result shown by this approach able to accomplish numerous round of banknote reading attempts with successful outcomes. Confusion matrix is further employed to study the performance of the banknote reader, in terms of true positive, true negative, false positive and false negative. Details analysis had been focused on the critical false positive cases (predicted real banknote and actually is fake banknote) and false negative cases (predicted fake banknote and it is actually real banknote).

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          Most cited references13

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          Roadside oral fluid testing: comparison of the results of drugwipe 5 and drugwipe benzodiazepines on-site tests with laboratory confirmation results of oral fluid and whole blood.

          Drugged drivers pose a serious threat to other people in traffic as well as to themselves. Reliable oral fluid screening devices for on-site screening of drugged drivers would be both a useful and convenient means for traffic control. In this study we evaluated the appropriateness of Drugwipe 5 and Drugwipe Benzodiazepines oral fluid on-site tests for roadside drug screening. Drivers suspected of driving under the influence of drugs were screened with the Drugwipe tests. Oral fluid and whole blood samples were collected from the drivers and tested for amphetamine-type stimulant drugs, cannabis, opiates, cocaine and benzodiazepines by immunological methods, GC and GC-MS. The performance evaluations of the tests were made by comparing the results of the Drugwipe tests with laboratory GC-MS confirmation results of oral fluid or whole blood. In addition to the performance evaluations of the Drugwipe tests based on laboratory results, a questionnaire on the practical aspects of the tests was written for the police officers who performed the tests. The aim of the questionnaire was to obtain user comments on the practicality of the tests as well as the advantages and weak points of the tests. The results of the performance evaluations were: for oral fluid (sensitivity; specificity; accuracy) amphetamines (95.5%; 92.9%; 95.3%), cannabis (52.2%; 91.2%; 85.1%), cocaine (50.0%; 99.3%; 98.6%), opiates (100%; 95.8%; 95.9%), benzodiazepines (74.4%; 84.2%; 79.2%) and for whole blood accordingly, amphetamines (97.7%; 86.7%; 95.9%), cannabis (68.3%; 87.9%; 84.9%), cocaine (50.0%; 98.5%; 97.7%), opiates (87.5%; 96.9%; 96.6%) and benzodiazepines (66.7%; 87.0%; 74.4%). Although the Drugwipe 5 successfully detected amphetamine-type stimulant drugs and the police officers were quite pleased with the current features of the Drugwipe tests, improvements must still be made regarding the detection of cannabis and benzodiazepines.
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            Ethiopian Banknote Recognition Using Convolutional Neural Network and Its Prototype Development Using Embedded Platform

            Money transactions can be performed by automated self-service machines like ATMs for money deposits and withdrawals, banknote counters and coin counters, automatic vending machines, and automatic smart card charging machines. There are four important functions such as banknote recognition, counterfeit banknote detection, serial number recognition, and fitness classification which are furnished with these devices. Therefore, we need a robust system that can recognize banknotes and classify them into denominations that can be used in these automated machines. However, the most widely available banknote detectors are hardware systems that use optical and magnetic sensors to detect and validate banknotes. These banknote detectors are usually designed for specific country banknotes. Reprogramming such a system to detect banknotes is very difficult. In addition, researchers have developed banknote recognition systems using deep learning artificial intelligence technology like CNN and R-CNN. However, in these systems, dataset used for training is relatively small, and the accuracy of banknote recognition is found smaller. The existing systems also do not include implementation and its development using embedded systems. In this research work, we collected various Ethiopian currencies with different ages and conditions and applied various optimization techniques for CNN architects to identify the fake notes. Experimental analysis has been demonstrated with different models of CNN such as InceptionV3, MobileNetV2, XceptionNet, and ResNet50. MobileNetV2 with RMSProp optimization technique with batch size 32 is found to be a robust and reliable Ethiopian banknote detector and achieved superior accuracy of 96.4% in comparison to other CNN models. Selected model MobileNetV2 with RMSProp optimization has been implemented through an embedded platform by utilizing Raspberry Pi 3 B+ and other peripherals. Further, real-time identification of fake notes in a Web-based user interface (UI) has also been proposed in the research.
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              Wearable Device for Malaysian Ringgit Banknotes Recognition Based on Embedded Decision Tree Classifier.

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                Author and article information

                Contributors
                Role: ConceptualizationRole: Data CurationRole: Formal AnalysisRole: InvestigationRole: MethodologyRole: SoftwareRole: Writing – Original Draft PreparationRole: Writing – Review & Editing
                Role: ConceptualizationRole: Funding AcquisitionRole: MethodologyRole: SupervisionRole: Writing – Review & Editing
                Role: Formal AnalysisRole: MethodologyRole: Project AdministrationRole: SupervisionRole: Writing – Review & Editing
                Role: ConceptualizationRole: Project AdministrationRole: ValidationRole: Writing – Review & Editing
                Journal
                F1000Res
                F1000Res
                F1000Research
                F1000 Research Limited (London, UK )
                2046-1402
                13 April 2022
                2021
                : 10
                : 1098
                Affiliations
                [1 ]Faculty of Engineering and Technology, Multimedia University, BKT Beruang, Melaka, 75450, Malaysia
                [1 ]Lahore Leads University, Lahore, Punjab, Pakistan
                [2 ]Computer science, University of engineering and technology lahore, Lahore, Pakistan, Pakistan
                [1 ]School of Electrical and Electronic Engineering, Universiti Sains Malaysia, Nibong Tebal, Malaysia
                [1 ]School of Electrical and Electronic Engineering, Universiti Sains Malaysia, Nibong Tebal, Malaysia
                Multimedia University, Malaysia
                [1 ]Aarhus University, Aarhus, Denmark
                Multimedia University, Malaysia
                Author notes

                No competing interests were disclosed.

                Competing interests: No competing interests were disclosed.

                Competing interests: No competing interests were disclosed.

                Competing interests: No competing interests were disclosed.

                Competing interests: No competing interests

                Competing interests: No competing interests were disclosed.

                Competing interests: No competing interests

                Author information
                https://orcid.org/0000-0003-1477-8449
                https://orcid.org/0000-0003-1681-3420
                Article
                10.12688/f1000research.58446.2
                11009568
                3a92115d-2b85-43c7-acbb-f75308c63fbd
                Copyright: © 2022 Salem TK et al.

                This is an open access article distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 7 April 2022
                Funding
                Funded by: FRGS, MOHE
                Award ID: MMUE/190246
                The supporter of this research is the Fundamental Research Grant Scheme (FRGS) under Ministry of Higher Education of Malaysia. (Grant no. MMUE/190246).
                The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Research Article
                Articles

                circuit and system,banknote reader,image processing,banknote counterfeit,ringgit detector

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