Implementation of an Intelligent Exam Supervision System Using Deep Learning Algorithms

Examination cheating activities like whispering, head movements, hand movements, or hand contact are extensively involved, and the rectitude and worthiness of fair and unbiased examination are prohibited by such cheating activities. The aim of this research is to develop a model to supervise or cont...
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Autor*in:	Fatima Mahmood [verfasserIn] Jehangir Arshad [verfasserIn] Mohamed Tahar Ben Othman [verfasserIn] Muhammad Faisal Hayat [verfasserIn] Naeem Bhatti [verfasserIn] Mujtaba Hussain Jaffery [verfasserIn] Ateeq Ur Rehman [verfasserIn] Habib Hamam [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2022

Schlagwörter:	Regional Convolution Neural Network (RCNN) Multi-Task Cascaded Convolutional Neural Networks (MTCNN) Regional Proposal Network (RPN) Convolution Neural Network (CNN) Discriminative Deep Belief Network (DDBN)

Übergeordnetes Werk:	In: Sensors - MDPI AG, 2003, 22(2022), 17, p 6389
Übergeordnetes Werk:	volume:22 ; year:2022 ; number:17, p 6389

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/s22176389

Katalog-ID:	DOAJ023721162

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spelling	10.3390/s22176389 doi (DE-627)DOAJ023721162 (DE-599)DOAJb5d9cb4a140a40ec964130e30010f74d DE-627 ger DE-627 rakwb eng TP1-1185 Fatima Mahmood verfasserin aut Implementation of an Intelligent Exam Supervision System Using Deep Learning Algorithms 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Examination cheating activities like whispering, head movements, hand movements, or hand contact are extensively involved, and the rectitude and worthiness of fair and unbiased examination are prohibited by such cheating activities. The aim of this research is to develop a model to supervise or control unethical activities in real-time examinations. Exam supervision is fallible due to limited human abilities and capacity to handle students in examination centers, and these errors can be reduced with the help of the Automatic Invigilation System. This work presents an automated system for exams invigilation using deep learning approaches i.e., Faster Regional Convolution Neural Network (RCNN). Faster RCNN is an object detection algorithm that is implemented to detect the suspicious activities of students during examinations based on their head movements, and for student identification, MTCNN (Multi-task Cascaded Convolutional Neural Networks) is used for face detection and recognition. The training accuracy of the proposed model is 99.5% and the testing accuracy is 98.5%. The model is fully efficient in detecting and monitoring more than 100 students in one frame during examinations. Different real-time scenarios are considered to evaluate the performance of the Automatic Invigilation System. The proposed invigilation model can be implemented in colleges, universities, and schools to detect and monitor student suspicious activities. Hopefully, through the implementation of the proposed invigilation system, we can prevent and solve the problem of cheating because it is unethical. Regional Convolution Neural Network (RCNN) Multi-Task Cascaded Convolutional Neural Networks (MTCNN) Regional Proposal Network (RPN) Convolution Neural Network (CNN) Discriminative Deep Belief Network (DDBN) Chemical technology Jehangir Arshad verfasserin aut Mohamed Tahar Ben Othman verfasserin aut Muhammad Faisal Hayat verfasserin aut Naeem Bhatti verfasserin aut Mujtaba Hussain Jaffery verfasserin aut Ateeq Ur Rehman verfasserin aut Habib Hamam verfasserin aut In Sensors MDPI AG, 2003 22(2022), 17, p 6389 (DE-627)331640910 (DE-600)2052857-7 14248220 nnns volume:22 year:2022 number:17, p 6389 https://doi.org/10.3390/s22176389 kostenfrei https://doaj.org/article/b5d9cb4a140a40ec964130e30010f74d kostenfrei https://www.mdpi.com/1424-8220/22/17/6389 kostenfrei https://doaj.org/toc/1424-8220 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2111 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 22 2022 17, p 6389
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allfieldsGer	10.3390/s22176389 doi (DE-627)DOAJ023721162 (DE-599)DOAJb5d9cb4a140a40ec964130e30010f74d DE-627 ger DE-627 rakwb eng TP1-1185 Fatima Mahmood verfasserin aut Implementation of an Intelligent Exam Supervision System Using Deep Learning Algorithms 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Examination cheating activities like whispering, head movements, hand movements, or hand contact are extensively involved, and the rectitude and worthiness of fair and unbiased examination are prohibited by such cheating activities. The aim of this research is to develop a model to supervise or control unethical activities in real-time examinations. Exam supervision is fallible due to limited human abilities and capacity to handle students in examination centers, and these errors can be reduced with the help of the Automatic Invigilation System. This work presents an automated system for exams invigilation using deep learning approaches i.e., Faster Regional Convolution Neural Network (RCNN). Faster RCNN is an object detection algorithm that is implemented to detect the suspicious activities of students during examinations based on their head movements, and for student identification, MTCNN (Multi-task Cascaded Convolutional Neural Networks) is used for face detection and recognition. The training accuracy of the proposed model is 99.5% and the testing accuracy is 98.5%. The model is fully efficient in detecting and monitoring more than 100 students in one frame during examinations. Different real-time scenarios are considered to evaluate the performance of the Automatic Invigilation System. The proposed invigilation model can be implemented in colleges, universities, and schools to detect and monitor student suspicious activities. Hopefully, through the implementation of the proposed invigilation system, we can prevent and solve the problem of cheating because it is unethical. Regional Convolution Neural Network (RCNN) Multi-Task Cascaded Convolutional Neural Networks (MTCNN) Regional Proposal Network (RPN) Convolution Neural Network (CNN) Discriminative Deep Belief Network (DDBN) Chemical technology Jehangir Arshad verfasserin aut Mohamed Tahar Ben Othman verfasserin aut Muhammad Faisal Hayat verfasserin aut Naeem Bhatti verfasserin aut Mujtaba Hussain Jaffery verfasserin aut Ateeq Ur Rehman verfasserin aut Habib Hamam verfasserin aut In Sensors MDPI AG, 2003 22(2022), 17, p 6389 (DE-627)331640910 (DE-600)2052857-7 14248220 nnns volume:22 year:2022 number:17, p 6389 https://doi.org/10.3390/s22176389 kostenfrei https://doaj.org/article/b5d9cb4a140a40ec964130e30010f74d kostenfrei https://www.mdpi.com/1424-8220/22/17/6389 kostenfrei https://doaj.org/toc/1424-8220 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2111 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 22 2022 17, p 6389
allfieldsSound	10.3390/s22176389 doi (DE-627)DOAJ023721162 (DE-599)DOAJb5d9cb4a140a40ec964130e30010f74d DE-627 ger DE-627 rakwb eng TP1-1185 Fatima Mahmood verfasserin aut Implementation of an Intelligent Exam Supervision System Using Deep Learning Algorithms 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Examination cheating activities like whispering, head movements, hand movements, or hand contact are extensively involved, and the rectitude and worthiness of fair and unbiased examination are prohibited by such cheating activities. The aim of this research is to develop a model to supervise or control unethical activities in real-time examinations. Exam supervision is fallible due to limited human abilities and capacity to handle students in examination centers, and these errors can be reduced with the help of the Automatic Invigilation System. This work presents an automated system for exams invigilation using deep learning approaches i.e., Faster Regional Convolution Neural Network (RCNN). Faster RCNN is an object detection algorithm that is implemented to detect the suspicious activities of students during examinations based on their head movements, and for student identification, MTCNN (Multi-task Cascaded Convolutional Neural Networks) is used for face detection and recognition. The training accuracy of the proposed model is 99.5% and the testing accuracy is 98.5%. The model is fully efficient in detecting and monitoring more than 100 students in one frame during examinations. Different real-time scenarios are considered to evaluate the performance of the Automatic Invigilation System. The proposed invigilation model can be implemented in colleges, universities, and schools to detect and monitor student suspicious activities. Hopefully, through the implementation of the proposed invigilation system, we can prevent and solve the problem of cheating because it is unethical. Regional Convolution Neural Network (RCNN) Multi-Task Cascaded Convolutional Neural Networks (MTCNN) Regional Proposal Network (RPN) Convolution Neural Network (CNN) Discriminative Deep Belief Network (DDBN) Chemical technology Jehangir Arshad verfasserin aut Mohamed Tahar Ben Othman verfasserin aut Muhammad Faisal Hayat verfasserin aut Naeem Bhatti verfasserin aut Mujtaba Hussain Jaffery verfasserin aut Ateeq Ur Rehman verfasserin aut Habib Hamam verfasserin aut In Sensors MDPI AG, 2003 22(2022), 17, p 6389 (DE-627)331640910 (DE-600)2052857-7 14248220 nnns volume:22 year:2022 number:17, p 6389 https://doi.org/10.3390/s22176389 kostenfrei https://doaj.org/article/b5d9cb4a140a40ec964130e30010f74d kostenfrei https://www.mdpi.com/1424-8220/22/17/6389 kostenfrei https://doaj.org/toc/1424-8220 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2111 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 22 2022 17, p 6389
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callnumber-first	T - Technology
author	Fatima Mahmood
spellingShingle	Fatima Mahmood misc TP1-1185 misc Regional Convolution Neural Network (RCNN) misc Multi-Task Cascaded Convolutional Neural Networks (MTCNN) misc Regional Proposal Network (RPN) misc Convolution Neural Network (CNN) misc Discriminative Deep Belief Network (DDBN) misc Chemical technology Implementation of an Intelligent Exam Supervision System Using Deep Learning Algorithms
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topic_title	TP1-1185 Implementation of an Intelligent Exam Supervision System Using Deep Learning Algorithms Regional Convolution Neural Network (RCNN) Multi-Task Cascaded Convolutional Neural Networks (MTCNN) Regional Proposal Network (RPN) Convolution Neural Network (CNN) Discriminative Deep Belief Network (DDBN)
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title_sort	implementation of an intelligent exam supervision system using deep learning algorithms
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title_auth	Implementation of an Intelligent Exam Supervision System Using Deep Learning Algorithms
abstract	Examination cheating activities like whispering, head movements, hand movements, or hand contact are extensively involved, and the rectitude and worthiness of fair and unbiased examination are prohibited by such cheating activities. The aim of this research is to develop a model to supervise or control unethical activities in real-time examinations. Exam supervision is fallible due to limited human abilities and capacity to handle students in examination centers, and these errors can be reduced with the help of the Automatic Invigilation System. This work presents an automated system for exams invigilation using deep learning approaches i.e., Faster Regional Convolution Neural Network (RCNN). Faster RCNN is an object detection algorithm that is implemented to detect the suspicious activities of students during examinations based on their head movements, and for student identification, MTCNN (Multi-task Cascaded Convolutional Neural Networks) is used for face detection and recognition. The training accuracy of the proposed model is 99.5% and the testing accuracy is 98.5%. The model is fully efficient in detecting and monitoring more than 100 students in one frame during examinations. Different real-time scenarios are considered to evaluate the performance of the Automatic Invigilation System. The proposed invigilation model can be implemented in colleges, universities, and schools to detect and monitor student suspicious activities. Hopefully, through the implementation of the proposed invigilation system, we can prevent and solve the problem of cheating because it is unethical.
abstractGer	Examination cheating activities like whispering, head movements, hand movements, or hand contact are extensively involved, and the rectitude and worthiness of fair and unbiased examination are prohibited by such cheating activities. The aim of this research is to develop a model to supervise or control unethical activities in real-time examinations. Exam supervision is fallible due to limited human abilities and capacity to handle students in examination centers, and these errors can be reduced with the help of the Automatic Invigilation System. This work presents an automated system for exams invigilation using deep learning approaches i.e., Faster Regional Convolution Neural Network (RCNN). Faster RCNN is an object detection algorithm that is implemented to detect the suspicious activities of students during examinations based on their head movements, and for student identification, MTCNN (Multi-task Cascaded Convolutional Neural Networks) is used for face detection and recognition. The training accuracy of the proposed model is 99.5% and the testing accuracy is 98.5%. The model is fully efficient in detecting and monitoring more than 100 students in one frame during examinations. Different real-time scenarios are considered to evaluate the performance of the Automatic Invigilation System. The proposed invigilation model can be implemented in colleges, universities, and schools to detect and monitor student suspicious activities. Hopefully, through the implementation of the proposed invigilation system, we can prevent and solve the problem of cheating because it is unethical.
abstract_unstemmed	Examination cheating activities like whispering, head movements, hand movements, or hand contact are extensively involved, and the rectitude and worthiness of fair and unbiased examination are prohibited by such cheating activities. The aim of this research is to develop a model to supervise or control unethical activities in real-time examinations. Exam supervision is fallible due to limited human abilities and capacity to handle students in examination centers, and these errors can be reduced with the help of the Automatic Invigilation System. This work presents an automated system for exams invigilation using deep learning approaches i.e., Faster Regional Convolution Neural Network (RCNN). Faster RCNN is an object detection algorithm that is implemented to detect the suspicious activities of students during examinations based on their head movements, and for student identification, MTCNN (Multi-task Cascaded Convolutional Neural Networks) is used for face detection and recognition. The training accuracy of the proposed model is 99.5% and the testing accuracy is 98.5%. The model is fully efficient in detecting and monitoring more than 100 students in one frame during examinations. Different real-time scenarios are considered to evaluate the performance of the Automatic Invigilation System. The proposed invigilation model can be implemented in colleges, universities, and schools to detect and monitor student suspicious activities. Hopefully, through the implementation of the proposed invigilation system, we can prevent and solve the problem of cheating because it is unethical.
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The aim of this research is to develop a model to supervise or control unethical activities in real-time examinations. Exam supervision is fallible due to limited human abilities and capacity to handle students in examination centers, and these errors can be reduced with the help of the Automatic Invigilation System. This work presents an automated system for exams invigilation using deep learning approaches i.e., Faster Regional Convolution Neural Network (RCNN). Faster RCNN is an object detection algorithm that is implemented to detect the suspicious activities of students during examinations based on their head movements, and for student identification, MTCNN (Multi-task Cascaded Convolutional Neural Networks) is used for face detection and recognition. The training accuracy of the proposed model is 99.5% and the testing accuracy is 98.5%. The model is fully efficient in detecting and monitoring more than 100 students in one frame during examinations. Different real-time scenarios are considered to evaluate the performance of the Automatic Invigilation System. The proposed invigilation model can be implemented in colleges, universities, and schools to detect and monitor student suspicious activities. 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Implementation of an Intelligent Exam Supervision System Using Deep Learning Algorithms

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