E-learning (EL) has emerged as one of the most valuable means for continuing education around the world,especially in the aftermath of the global pandemic that included a variety of obstacles. Real-time onlineassessments have become a significant concern for educational organizations. Instances of fraudulent behaviorduring online exams (OEs) have created considerable challenges for exam invigilators, who are unable to identifyand remove such dishonest behavior. In response to this significant issue, educational institutions have used avariety of manual procedures to alleviate the situation, but none of these measures have shown to be particularlyinnovative or effective. The current study presents a novel strategy for detecting fraudulent actions in real timeduring OEs that uses convolutional neural network (CNN) algorithms and image processing. The developmentmodel will be trained using the CK and CK++ datasets. The training procedure will use 80% of the selecteddataset, with the remaining 20% used for model testing to confirm the model’s efficacy and generalization capacity.This project intends to revolutionize the monitoring and prevention of fraudulent actions during online tests byintegrating CNN techniques and image processing. The use of CK and CK++ datasets, as well as an 80–20split for training and testing, contributes to the study’s thorough and rigorous approach. Educational institutionscan improve their assessment procedures and maintain the credibility of EL as a credible and equitable way ofcontinuing education by successfully using this unique technique.
A Multimedia Data Mining Framework for Monitoring E-Examination Environmentijma
Academic dishonesty has been a growing concern in e-learning environment due to the fact that eexamination takes place under supervised and unsupervised learning environment despite its huge advantages. The e-examination environment has faced various security breaches such as academic dishonesty (impersonation), identity theft, unauthorised access and illegal assistance as a result of inefficient measures employed. Hence, an efficient framework which will aid the monitoring of the eexamination is needed. This paper reviews the process of mining multimedia data and propose a framework for monitoring the e-examination environment in order to extract images and audio features. The framework has four major phases: data pre-processing, mining, association and post processing. The
pre-processing phases carries out the extraction and transformation of multimedia data features, the mining phase does the classification and clustering of these features, the association does pattern matching while the post processing carries out the knowledge interpretation and reporting. The approach presented in this study will allow for efficient and accurate monitoring of e-examination environment which will help provide adequate security and reduce unethical behaviour in e-examination environment.
1. The document describes an automated proctoring system developed by students at Atharva College of Engineering to address challenges with remote online exams, such as lack of proper supervision allowing for increased cheating.
2. The proposed automated proctoring system utilizes various technologies like computer vision, object detection, gaze tracking, mouth detection and head positioning to monitor students and detect any unfair practices during exams taken remotely.
3. The system is intended to mimic the key monitoring aspects of in-person exams by automatically flagging behaviors like looking away from the screen, mouth movements indicating speaking, and use of other devices, in order to reduce cheating and improve the integrity of remote online exams.
Intelligent solution for automatic online exam monitoring IJECEIAES
E-learning has shown significant growth in recent years due to its unavoidable benefits in unexpected situations such as the coronavirus disease 2019 (COVID-19) pandemic. Indeed, online exam is a very important component of an online learning program. It allows higher education institutions to assess student learning outcomes. However, cheating in exams is a widespread phenomenon worldwide, which creates several challenges in terms of integrity, reliability and security of online examinations. In this study, we propose a continuous authentication system for online exam. Our intelligent inference system based on machine learning algorithms and rules, detects continuously any inappropriate behavior in order to limit and prevent fraud. The proposed model includes several modules to enhance security, namely the registration module, the continuous students’ identity verification and control module, the live video stream and the end-to-end sessions recording.
INVESTIGATION A NEW APPROACH TO DETECT AND TRACK FRAUD IN VIRTUAL LEARNING EN...ijmnct
Virtual University is the environment that with utilizes the appropriate multimedia tools and having good communication infrastructure is a provider of e-learning services, so that usually does not have require to physical location as a traditional university and students are able in any place and at any time be willing to use a lot of services provided, such as e-courses or electronic tests. There are many solutions in order to identify and detect fraud in the online environment that use of these methods can be identified took place fraud, but still is great importance of avoid discussion and fraud detection in virtual university. In this research, we aimed are to investigate a new approach to detect and track fraud in virtual learning
environments by using decision tree. (Chayd model). The results showed that the accuracy of the model is 84.54% which is indicative of high performance and high precision in predicting fraud from the teachers, students and hackers.
Investigation of new approach to detect and track fraud in virtual learningijmnct
Virtual University is the environment that with utilizes the appropriate multimedia tools and having good communication infrastructure is a provider of e-learning services, so that usually does not have require to physical location as a traditional university and students are able in any place and at any time be willing to use a lot of services provided, such as e-courses or electronic tests. There are many solutions in order to identify and detect fraud in the online environment that use of these methods can be identified took place fraud, but still is great importance of avoid discussion and fraud detection in virtual university. In this research, we aimed are to investigate a new approach to detect and track fraud in virtual learning environments by using decision tree. (Chayd model). The results showed that the accuracy of the model is 84.54% which is indicative of high performance and high precision in predicting fraud from the teachers, students and hackers.
Investigation a New Approach to Detect and Track Fraud in Virtual Learning En...ijmnct
This document summarizes a research paper that investigated a new approach to detect and track fraud in virtual learning environments using a decision tree (Chayd) model. The researchers used a dataset of 325 students to build and test the Chayd model. The results showed the model could accurately predict fraud 84.54% of the time, identifying fraud committed by hackers, students, or teachers with 100% or 56% accuracy depending on the type of fraud. The researchers concluded the Chayd model performed well at recognizing fraud in virtual learning environments using data mining techniques.
IMBALANCED DATASET EFFECT ON CNN-BASED CLASSIFIER PERFORMANCE FOR FACE RECOGN...gerogepatton
Facial Recognition is integral to numerous modern applications, such as security systems, social media
platforms, and augmented reality apps. The success of these systems heavily depends on the performance
of the Face Recognition models they use, specifically Convolutional Neural Networks (CNNs). However,
many real-world classification tasks encounter imbalanced datasets, with some classes significantly
underrepresented. Face Recognition models that do not address this class imbalance tend to exhibit poor
performance, especially in tasks involving a wide range of faces to identify (multi-class problems). This
research examines how class imbalance in datasets impacts the creation of neural network classifiers for
Facial Recognition. Initially, we crafted a Convolutional Neural Network model for facial recognition,
integrating hybrid resampling methods (oversampling and under-sampling) to address dataset imbalances.
In addition, augmentation techniques were implemented to enhance generalization capabilities and overall
performance. Through comprehensive experimentation, we assess the influence of imbalanced datasets on
the performance of the CNN-based classifier. Using Pins face data, we conducted an empirical study,
evaluating conclusions based on accuracy, precision, recall, and F1-score measurements. A comparative
analysis demonstrates that the performance of the proposed Convolutional Neural Network classifier
diminishes in the presence of dataset class imbalances. Conversely, the proposed system, utilizing data
resampling techniques, notably enhances classification performance for imbalanced datasets. This study
underscores the efficacy of data resampling approaches in augmenting the performance of Face
Recognition models, presenting prospects for more dependable and efficient future systems.
Imbalanced Dataset Effect on CNN-Based Classifier Performance for Face Recogn...gerogepatton
Facial Recognition is integral to numerous modern applications, such as security systems, social media
platforms, and augmented reality apps. The success of these systems heavily depends on the performance
of the Face Recognition models they use, specifically Convolutional Neural Networks (CNNs). However,
many real-world classification tasks encounter imbalanced datasets, with some classes significantly
underrepresented. Face Recognition models that do not address this class imbalance tend to exhibit poor
performance, especially in tasks involving a wide range of faces to identify (multi-class problems). This
research examines how class imbalance in datasets impacts the creation of neural network classifiers for
Facial Recognition. Initially, we crafted a Convolutional Neural Network model for facial recognition,
integrating hybrid resampling methods (oversampling and under-sampling) to address dataset imbalances.
In addition, augmentation techniques were implemented to enhance generalization capabilities and overall
performance. Through comprehensive experimentation, we assess the influence of imbalanced datasets on
the performance of the CNN-based classifier. Using Pins face data, we conducted an empirical study,
evaluating conclusions based on accuracy, precision, recall, and F1-score measurements. A comparative
analysis demonstrates that the performance of the proposed Convolutional Neural Network classifier
diminishes in the presence of dataset class imbalances. Conversely, the proposed system, utilizing data
resampling techniques, notably enhances classification performance for imbalanced datasets. This study
underscores the efficacy of data resampling approaches in augmenting the performance of Face
Recognition models, presenting prospects for more dependable and efficient future systems.
A Multimedia Data Mining Framework for Monitoring E-Examination Environmentijma
Academic dishonesty has been a growing concern in e-learning environment due to the fact that eexamination takes place under supervised and unsupervised learning environment despite its huge advantages. The e-examination environment has faced various security breaches such as academic dishonesty (impersonation), identity theft, unauthorised access and illegal assistance as a result of inefficient measures employed. Hence, an efficient framework which will aid the monitoring of the eexamination is needed. This paper reviews the process of mining multimedia data and propose a framework for monitoring the e-examination environment in order to extract images and audio features. The framework has four major phases: data pre-processing, mining, association and post processing. The
pre-processing phases carries out the extraction and transformation of multimedia data features, the mining phase does the classification and clustering of these features, the association does pattern matching while the post processing carries out the knowledge interpretation and reporting. The approach presented in this study will allow for efficient and accurate monitoring of e-examination environment which will help provide adequate security and reduce unethical behaviour in e-examination environment.
1. The document describes an automated proctoring system developed by students at Atharva College of Engineering to address challenges with remote online exams, such as lack of proper supervision allowing for increased cheating.
2. The proposed automated proctoring system utilizes various technologies like computer vision, object detection, gaze tracking, mouth detection and head positioning to monitor students and detect any unfair practices during exams taken remotely.
3. The system is intended to mimic the key monitoring aspects of in-person exams by automatically flagging behaviors like looking away from the screen, mouth movements indicating speaking, and use of other devices, in order to reduce cheating and improve the integrity of remote online exams.
Intelligent solution for automatic online exam monitoring IJECEIAES
E-learning has shown significant growth in recent years due to its unavoidable benefits in unexpected situations such as the coronavirus disease 2019 (COVID-19) pandemic. Indeed, online exam is a very important component of an online learning program. It allows higher education institutions to assess student learning outcomes. However, cheating in exams is a widespread phenomenon worldwide, which creates several challenges in terms of integrity, reliability and security of online examinations. In this study, we propose a continuous authentication system for online exam. Our intelligent inference system based on machine learning algorithms and rules, detects continuously any inappropriate behavior in order to limit and prevent fraud. The proposed model includes several modules to enhance security, namely the registration module, the continuous students’ identity verification and control module, the live video stream and the end-to-end sessions recording.
INVESTIGATION A NEW APPROACH TO DETECT AND TRACK FRAUD IN VIRTUAL LEARNING EN...ijmnct
Virtual University is the environment that with utilizes the appropriate multimedia tools and having good communication infrastructure is a provider of e-learning services, so that usually does not have require to physical location as a traditional university and students are able in any place and at any time be willing to use a lot of services provided, such as e-courses or electronic tests. There are many solutions in order to identify and detect fraud in the online environment that use of these methods can be identified took place fraud, but still is great importance of avoid discussion and fraud detection in virtual university. In this research, we aimed are to investigate a new approach to detect and track fraud in virtual learning
environments by using decision tree. (Chayd model). The results showed that the accuracy of the model is 84.54% which is indicative of high performance and high precision in predicting fraud from the teachers, students and hackers.
Investigation of new approach to detect and track fraud in virtual learningijmnct
Virtual University is the environment that with utilizes the appropriate multimedia tools and having good communication infrastructure is a provider of e-learning services, so that usually does not have require to physical location as a traditional university and students are able in any place and at any time be willing to use a lot of services provided, such as e-courses or electronic tests. There are many solutions in order to identify and detect fraud in the online environment that use of these methods can be identified took place fraud, but still is great importance of avoid discussion and fraud detection in virtual university. In this research, we aimed are to investigate a new approach to detect and track fraud in virtual learning environments by using decision tree. (Chayd model). The results showed that the accuracy of the model is 84.54% which is indicative of high performance and high precision in predicting fraud from the teachers, students and hackers.
Investigation a New Approach to Detect and Track Fraud in Virtual Learning En...ijmnct
This document summarizes a research paper that investigated a new approach to detect and track fraud in virtual learning environments using a decision tree (Chayd) model. The researchers used a dataset of 325 students to build and test the Chayd model. The results showed the model could accurately predict fraud 84.54% of the time, identifying fraud committed by hackers, students, or teachers with 100% or 56% accuracy depending on the type of fraud. The researchers concluded the Chayd model performed well at recognizing fraud in virtual learning environments using data mining techniques.
IMBALANCED DATASET EFFECT ON CNN-BASED CLASSIFIER PERFORMANCE FOR FACE RECOGN...gerogepatton
Facial Recognition is integral to numerous modern applications, such as security systems, social media
platforms, and augmented reality apps. The success of these systems heavily depends on the performance
of the Face Recognition models they use, specifically Convolutional Neural Networks (CNNs). However,
many real-world classification tasks encounter imbalanced datasets, with some classes significantly
underrepresented. Face Recognition models that do not address this class imbalance tend to exhibit poor
performance, especially in tasks involving a wide range of faces to identify (multi-class problems). This
research examines how class imbalance in datasets impacts the creation of neural network classifiers for
Facial Recognition. Initially, we crafted a Convolutional Neural Network model for facial recognition,
integrating hybrid resampling methods (oversampling and under-sampling) to address dataset imbalances.
In addition, augmentation techniques were implemented to enhance generalization capabilities and overall
performance. Through comprehensive experimentation, we assess the influence of imbalanced datasets on
the performance of the CNN-based classifier. Using Pins face data, we conducted an empirical study,
evaluating conclusions based on accuracy, precision, recall, and F1-score measurements. A comparative
analysis demonstrates that the performance of the proposed Convolutional Neural Network classifier
diminishes in the presence of dataset class imbalances. Conversely, the proposed system, utilizing data
resampling techniques, notably enhances classification performance for imbalanced datasets. This study
underscores the efficacy of data resampling approaches in augmenting the performance of Face
Recognition models, presenting prospects for more dependable and efficient future systems.
Imbalanced Dataset Effect on CNN-Based Classifier Performance for Face Recogn...gerogepatton
Facial Recognition is integral to numerous modern applications, such as security systems, social media
platforms, and augmented reality apps. The success of these systems heavily depends on the performance
of the Face Recognition models they use, specifically Convolutional Neural Networks (CNNs). However,
many real-world classification tasks encounter imbalanced datasets, with some classes significantly
underrepresented. Face Recognition models that do not address this class imbalance tend to exhibit poor
performance, especially in tasks involving a wide range of faces to identify (multi-class problems). This
research examines how class imbalance in datasets impacts the creation of neural network classifiers for
Facial Recognition. Initially, we crafted a Convolutional Neural Network model for facial recognition,
integrating hybrid resampling methods (oversampling and under-sampling) to address dataset imbalances.
In addition, augmentation techniques were implemented to enhance generalization capabilities and overall
performance. Through comprehensive experimentation, we assess the influence of imbalanced datasets on
the performance of the CNN-based classifier. Using Pins face data, we conducted an empirical study,
evaluating conclusions based on accuracy, precision, recall, and F1-score measurements. A comparative
analysis demonstrates that the performance of the proposed Convolutional Neural Network classifier
diminishes in the presence of dataset class imbalances. Conversely, the proposed system, utilizing data
resampling techniques, notably enhances classification performance for imbalanced datasets. This study
underscores the efficacy of data resampling approaches in augmenting the performance of Face
Recognition models, presenting prospects for more dependable and efficient future systems.
FACIAL AGE ESTIMATION USING TRANSFER LEARNING AND BAYESIAN OPTIMIZATION BASED...sipij
The document summarizes research on facial age estimation using transfer learning and Bayesian optimization based on gender information. Specifically:
1) A convolutional neural network is trained to classify gender from facial images. This gender classification CNN is then used as input for an age estimation model.
2) Bayesian optimization is applied to the pre-trained gender classification CNN to fine-tune it for the age estimation task. This reduces error on validation data.
3) Experiments on the FERET and FG-NET datasets show the proposed approach of using gender information and Bayesian optimization outperforms state-of-the-art methods, achieving a mean absolute error of 1.2 and 2.67 respectively.
A Machine Learning Ensemble Model for the Detection of Cyberbullyinggerogepatton
The pervasive use of social media platforms, such as Facebook, Instagram, and X, has significantly amplified
our electronic interconnectedness. Moreover, these platforms are now easily accessible from any location at
any given time. However, the increased popularity of social media has also led to cyberbullying.It is imperative
to address the need for finding, monitoring, and mitigating cyberbullying posts on social media platforms.
Motivated by this necessity, we present this paper to contribute to developing an automated system for
detecting binary labels of aggressive tweets.Our study has demonstrated remarkable performance compared to
previous experiments on the same dataset. We employed the stacking ensemble machine learning method,
utilizing four various feature extraction techniques to optimize performance within the stacking ensemble
learning framework. Combining five machine learning algorithms,Decision Trees, Random Forest, Linear
Support Vector Classification, Logistic Regression, and K-Nearest Neighbors into an ensemble method, we
achieved superior results compared to traditional machine learning classifier models. The stacking classifier
achieved a high accuracy rate of 94.00%, outperforming traditional machine learning models and surpassing
the results of prior experiments that utilized the same dataset. The outcomes of our experiments showcased an
accuracy rate of 0.94% in detection tweets as aggressive or non-aggressive.
A MACHINE LEARNING ENSEMBLE MODEL FOR THE DETECTION OF CYBERBULLYINGijaia
The pervasive use of social media platforms, such as Facebook, Instagram, and X, has significantly amplified
our electronic interconnectedness. Moreover, these platforms are now easily accessible from any location at
any given time. However, the increased popularity of social media has also led to cyberbullying.It is imperative
to address the need for finding, monitoring, and mitigating cyberbullying posts on social media platforms.
Motivated by this necessity, we present this paper to contribute to developing an automated system for
detecting binary labels of aggressive tweets.Our study has demonstrated remarkable performance compared to
previous experiments on the same dataset. We employed the stacking ensemble machine learning method,
utilizing four various feature extraction techniques to optimize performance within the stacking ensemble
learning framework. Combining five machine learning algorithms,Decision Trees, Random Forest, Linear
Support Vector Classification, Logistic Regression, and K-Nearest Neighbors into an ensemble method, we
achieved superior results compared to traditional machine learning classifier models. The stacking classifier
achieved a high accuracy rate of 94.00%, outperforming traditional machine learning models and surpassing
the results of prior experiments that utilized the same dataset. The outcomes of our experiments showcased an
accuracy rate of 0.94% in detection tweets as aggressive or non-aggressive.
A Machine Learning Ensemble Model for the Detection of Cyberbullyinggerogepatton
The pervasive use of social media platforms, such as Facebook, Instagram, and X, has significantly amplified
our electronic interconnectedness. Moreover, these platforms are now easily accessible from any location at
any given time. However, the increased popularity of social media has also led to cyberbullying.It is imperative
to address the need for finding, monitoring, and mitigating cyberbullying posts on social media platforms.
Motivated by this necessity, we present this paper to contribute to developing an automated system for
detecting binary labels of aggressive tweets.Our study has demonstrated remarkable performance compared to
previous experiments on the same dataset. We employed the stacking ensemble machine learning method,
utilizing four various feature extraction techniques to optimize performance within the stacking ensemble
learning framework. Combining five machine learning algorithms,Decision Trees, Random Forest, Linear
Support Vector Classification, Logistic Regression, and K-Nearest Neighbors into an ensemble method, we
achieved superior results compared to traditional machine learning classifier models. The stacking classifier
achieved a high accuracy rate of 94.00%, outperforming traditional machine learning models and surpassing
the results of prior experiments that utilized the same dataset. The outcomes of our experiments showcased an
accuracy rate of 0.94% in detection tweets as aggressive or non-aggressive.
This documentation provides a brief insight of face recognition based attendance system using neural networks in terms of product architecture which can be used for educational purpose.
Blockchain and machine learning in education: a literature reviewIAESIJAI
There is a growing influence around the use of technology in education, with many solutions already being implemented and many others being explored. Using various forms of technology to assist the educational process has increased dramatically in the previous decade in education systems in many respects. Both machine learning and the blockchain have had a significant impact on education. The purpose of this study is to conduct a literature review on the application of machine learning and blockchain technology in educational institutions. Additionally, this study examines the potential applications, benefits, and challenges those educational institutions may face as a result of using machine learning and blockchain technologies. Using machine learning and blockchain in educational systems will have a positive impact on the entire educational process and student achievement. Researchers, academics, and practitioners will benefit from this study to focus on a wider range of educational applications and solve the related issues of machine learning and blockchain technology in the education sector.
Social distance and face mask detector system exploiting transfer learningIJECEIAES
As time advances, the use of deep learning-based object detection algorithms has also evolved leading to developments of new human-computer interactions, facilitating an exploration of various domains. Considering the automated process of detection, systems suitable for detecting violations are developed. One such applications is the social distancing and face mask detectors to control air-borne diseases. The objective of this research is to deploy transfer learning on object detection models for spotting violations in face masks and physical distance rules in real-time. The common drawbacks of existing models are low accuracy and inability to detect in real-time. The MobileNetV2 object detection model and YOLOv3 model with Euclidean distance measure have been used for detection of face mask and physical distancing. A proactive transfer learning approach is used to perform the functionality of face mask classification on the patterns obtained from the social distance detector model. On implementing the application on various surveillance footage, it was observed that the system could classify masked and unmasked faces and if social distancing was maintained or not with accuracies 99% and 94% respectively. The models exhibited high accuracy on testing and the system can be infused with the existing internet protocol (IP) cameras or surveillance systems for real-time surveillance of face masks and physical distancing rules effectively.
Concept drift and machine learning model for detecting fraudulent transaction...IJECEIAES
The document presents a machine learning model for detecting fraudulent transactions in a streaming environment that addresses concept drift. The proposed approach uses the extreme gradient boosting (XGBoost) algorithm and employs four algorithms to continuously detect concept drift in data streams. The approach is evaluated on credit card and Twitter fraud datasets and is shown to outperform traditional machine learning models in terms accuracy, precision, and recall, and is more robust to concept drift. The proposed approach can be utilized as a real-time fraud detection system across different industries.
Proposed high level solutions to counter online examination fraud using digit...Ivans Kigwana
The document proposes digital forensic readiness techniques to counter online exam fraud. It discusses how students commonly cheat in online exams, such as accessing online content, screen sharing, opening new tabs, and inspecting HTML code. It then proposes 11 digital forensic readiness techniques that could be used to gather evidence in these situations, such as recording PC activity, using keylogging, monitoring typing patterns, deploying security cameras, logging browser activity, recording mouse activity, recording programmable device data, using webcams to verify student identity, employing biometric identification, and performing continuous authentication. The techniques were evaluated based on implementation difficulty, cost, and efficiency.
Monitoring Students Using Different Recognition Techniques for Surveilliance ...IRJET Journal
This document discusses using computer vision techniques like convolutional neural networks to monitor students and enforce dress codes in educational institutions. It proposes a system using cameras and image processing to identify whether students are properly dressed according to the dress code. The system would classify images of students as either following or not following the dress code. It also discusses related work on using technologies like biometrics and RFID cards for automated student attendance tracking and implications for security and discipline in schools.
This document summarizes a literature review that analyzed research predicting student performance and dropout rates using machine learning techniques. The review identified 78 relevant papers published between 2009-2021. These papers mostly used student data from universities and MOOC platforms to test machine learning classifiers for predicting at-risk students and dropout likelihood. The review found that machine learning methods effectively predicted student performance and helped universities develop intervention strategies to improve student outcomes.
A Systematic Literature Review Of Student Performance Prediction Using Machi...Angie Miller
This document summarizes a systematic literature review of research predicting student performance using machine learning techniques. The review examined studies from 2009 to 2021 that identified students at risk of dropping out. It found that various machine learning methods were used to understand challenges and predict performance. Most studies used data from university databases and online learning platforms. Machine learning was shown to effectively predict student risk levels and dropout rates, helping improve student outcomes.
Age and Gender Classification using Convolutional Neural NetworkIRJET Journal
This document describes a study that aims to accurately identify the gender and age range of facial images using convolutional neural networks. It begins with an introduction to age and gender classification and some of the challenges. It then discusses the system analysis and design, including the use of convolutional neural networks and machine learning techniques. The implementation section notes that Python, TensorFlow, Keras and OpenCV will be used to build a convolutional neural network model to detect faces in images and predict age and gender through training on available datasets. The overall goal is to develop an accurate system for age and gender detection from facial images.
A study of cyberbullying detection using Deep Learning and Machine Learning T...IRJET Journal
This document discusses a study on detecting cyberbullying using machine learning and deep learning techniques. Specifically, it examines using a hybrid model combining K-Nearest Neighbors, Support Vector Machine, and Random Forest algorithms, as well as a Convolutional Neural Network. The study uses a Twitter dataset to classify tweets as not bullying, racism, or sexism. It finds that the CNN model produces more accurate predictions than the hybrid stacking algorithm. The document provides background on related work applying machine and deep learning to cyberbullying detection, particularly using content-based and user-based approaches.
A study of cyberbullying detection using Deep Learning and Machine Learning T...IRJET Journal
This document summarizes a research paper that studied the detection of cyberbullying using machine learning and deep learning techniques. Specifically, it used a hybrid model combining KNN, SVM and Random Forest algorithms (stacking algorithm) and a Convolutional Neural Network (CNN) on a Twitter dataset. The stacking algorithm achieved an accuracy of X% while the CNN achieved a higher accuracy of Y%. A comparison of the two models found that CNN produced a more precise prediction of cyberbullying. The document also reviewed related work on cyberbullying detection using content-based, user-based and network-based approaches with machine learning algorithms like SVM, Naive Bayes and deep learning methods like CNN.
New hybrid ensemble method for anomaly detection in data science IJECEIAES
Anomaly detection is a significant research area in data science. Anomaly detection is used to find unusual points or uncommon events in data streams. It is gaining popularity not only in the business world but also in different of other fields, such as cyber security, fraud detection for financial systems, and healthcare. Detecting anomalies could be useful to find new knowledge in the data. This study aims to build an effective model to protect the data from these anomalies. We propose a new hyper ensemble machine learning method that combines the predictions from two methodologies the outcomes of isolation forest-k-means and random forest using a voting majority. Several available datasets, including KDD Cup-99, Credit Card, Wisconsin Prognosis Breast Cancer (WPBC), Forest Cover, and Pima, were used to evaluate the proposed method. The experimental results exhibit that our proposed model gives the highest realization in terms of receiver operating characteristic performance, accuracy, precision, and recall. Our approach is more efficient in detecting anomalies than other approaches. The highest accuracy rate achieved is 99.9%, compared to accuracy without a voting method, which achieves 97%.
This document summarizes several research papers on human face recognition using feature extraction and measurements. It discusses using face recognition for applications like surveillance, access control, and banking validation. Key steps in face recognition systems include extracting features from captured images, comparing them to known images in a training database, and identifying errors like false acceptance and false rejection rates. Methods discussed for feature extraction and dimensionality reduction include Linear Discriminant Analysis and Principal Component Analysis. The document also examines factors that affect face recognition performance like illumination changes, aging, and expressions. Quantifying uncertainty in face recognition algorithms is identified as important for evaluating system performance.
Integrated system for monitoring and recognizing students during class sessionijma
In this paper we propose a new student attendance system based on biometric authentication protocol. This
system is basically using the face detection and the recognition protocols to facilitate checking students’
attendance in the classroom. In the proposed system, the classroom’s camera is capturing the students’
photo, directly the face detection and recognition processes will be implemented to produce the instructor
attendance report. Actually, this system is more efficient than others student attendance methods since the
detection and the recognition are considered to be the best and fastest method for biometric attendance
system. Regarding to the students and instructor sides, the system is working without any preparation and
with no more effort.
SMART AGENT BASED SEARCH FOR ADMISSION IN INSTITUTIONS OF HIGHER LEARNINGIJITE
Early admission systems saw people applying to universities by filling out applications forms and placing
them in suitable envelopes and sending them through the local postal agency. This was not considered to
be cost or time effective, and this method was also not efficient. This system however needed some
improvement due to the huge workload on administrators. So researchers and software developers
improved the system so that between 1999 and 2008 application and admission was done via the Internet.
Also many Ranking system like ARWU, shanghai etc. been used for ranking the universities and colleges
around the world which would enable people choosing the universities and colleges for education on
factors like publication, funding, infrastructure and so.
The Internet has already brought the humans together in a new, exciting, and unexpected ways, and the
same is also happening to our prevalent adoption of digital mobile devices that has paved the way for the
development of many innovative applications in the commercial domain. While considering such mobile
devices for an application towards higher education in an educational institution, there has been some
amount of work done using intelligent agents. But still those agent based systems got some drawbacks
which motivated towards developing the present Agent based system to provide Smart agent based system
for higher Learning search not in Jamaican context alone but also elsewhere with these drawbacks
alleviated. The agents developed will be based on using fuzzy preference rules and heuristics, to make
accurate decisions based on the user’s criteria or specifications using JADE-LEAP on Android handset.
The system got Google map feature, intelligence in admission system and also warning for universities
with low rating. These findings of this research will be presented as screenshots.
Air quality forecasting using convolutional neural networksBIJIAM Journal
Air pollution is now one of the biggest environmental risks, which causes more than 6 million premature deathseach year from heart diseases, stroke, diabetes, respiratory disease, and so on. Protecting humans from thedamage which is caused by air pollution is one of the major issues for the global community. The prediction ofair pollution can be done by machine learning (ML) algorithms. ML combines statistics and computer science tomaximize the prediction power. ML can be also used to predict the air quality index (AQI). The aim of this researchis to develop a convolutional neural network (CNN) model to predict air quality from the unseen data set, whichincludes concentration of nitrogen dioxide (NO2), carbon monoxide (CO), and sulfur dioxide (SO2). The proposedsystem will be implemented in two steps; the first step will focus on data analysis and pre-processing, includingfiltering, feature extraction, constructing convolutional neural network layers, and optimizing the parameters ofeach layer, while the second step is used to evaluate its model accuracy. The output is predicted as AQI for thedeveloped CNN model. The developed CNN model achieves a root-mean-square error of 13.4150 and a highaccuracy of 86.585%. The overall model is implemented using MATLAB software.
Prevention of fire and hunting in forests using machine learning for sustaina...BIJIAM Journal
Deforestation, illegal hunting, and forest fires are a few current issues that have an impact on the diversity andecosystem of forests. To increase the biodiversity of species and ecosystems, it becomes imperative to preservethe forest. The conventional techniques employed to prevent these issues are costly, less effective, and insecure.The current systems are unreliable and use more energy. By utilizing an Internet of Things (IOT) system, thistechnology offers a more practical and economical method of continuously maintaining and monitoring the statusof the forest. To guarantee excellent security, this system combines a number of sensors, alarms, cameras, lights,and microphones. It aids in reducing forest loss, animal trafficking, and forest fires. In the suggested system,sensors are used for monitoring, and cloud storage is used for data storage. Through the use of machine learning,the raspberry pi camera module significantly aids in the prevention of unlawful wildlife hunting as well as thedetection and prevention of forest fires.
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FACIAL AGE ESTIMATION USING TRANSFER LEARNING AND BAYESIAN OPTIMIZATION BASED...sipij
The document summarizes research on facial age estimation using transfer learning and Bayesian optimization based on gender information. Specifically:
1) A convolutional neural network is trained to classify gender from facial images. This gender classification CNN is then used as input for an age estimation model.
2) Bayesian optimization is applied to the pre-trained gender classification CNN to fine-tune it for the age estimation task. This reduces error on validation data.
3) Experiments on the FERET and FG-NET datasets show the proposed approach of using gender information and Bayesian optimization outperforms state-of-the-art methods, achieving a mean absolute error of 1.2 and 2.67 respectively.
A Machine Learning Ensemble Model for the Detection of Cyberbullyinggerogepatton
The pervasive use of social media platforms, such as Facebook, Instagram, and X, has significantly amplified
our electronic interconnectedness. Moreover, these platforms are now easily accessible from any location at
any given time. However, the increased popularity of social media has also led to cyberbullying.It is imperative
to address the need for finding, monitoring, and mitigating cyberbullying posts on social media platforms.
Motivated by this necessity, we present this paper to contribute to developing an automated system for
detecting binary labels of aggressive tweets.Our study has demonstrated remarkable performance compared to
previous experiments on the same dataset. We employed the stacking ensemble machine learning method,
utilizing four various feature extraction techniques to optimize performance within the stacking ensemble
learning framework. Combining five machine learning algorithms,Decision Trees, Random Forest, Linear
Support Vector Classification, Logistic Regression, and K-Nearest Neighbors into an ensemble method, we
achieved superior results compared to traditional machine learning classifier models. The stacking classifier
achieved a high accuracy rate of 94.00%, outperforming traditional machine learning models and surpassing
the results of prior experiments that utilized the same dataset. The outcomes of our experiments showcased an
accuracy rate of 0.94% in detection tweets as aggressive or non-aggressive.
A MACHINE LEARNING ENSEMBLE MODEL FOR THE DETECTION OF CYBERBULLYINGijaia
The pervasive use of social media platforms, such as Facebook, Instagram, and X, has significantly amplified
our electronic interconnectedness. Moreover, these platforms are now easily accessible from any location at
any given time. However, the increased popularity of social media has also led to cyberbullying.It is imperative
to address the need for finding, monitoring, and mitigating cyberbullying posts on social media platforms.
Motivated by this necessity, we present this paper to contribute to developing an automated system for
detecting binary labels of aggressive tweets.Our study has demonstrated remarkable performance compared to
previous experiments on the same dataset. We employed the stacking ensemble machine learning method,
utilizing four various feature extraction techniques to optimize performance within the stacking ensemble
learning framework. Combining five machine learning algorithms,Decision Trees, Random Forest, Linear
Support Vector Classification, Logistic Regression, and K-Nearest Neighbors into an ensemble method, we
achieved superior results compared to traditional machine learning classifier models. The stacking classifier
achieved a high accuracy rate of 94.00%, outperforming traditional machine learning models and surpassing
the results of prior experiments that utilized the same dataset. The outcomes of our experiments showcased an
accuracy rate of 0.94% in detection tweets as aggressive or non-aggressive.
A Machine Learning Ensemble Model for the Detection of Cyberbullyinggerogepatton
The pervasive use of social media platforms, such as Facebook, Instagram, and X, has significantly amplified
our electronic interconnectedness. Moreover, these platforms are now easily accessible from any location at
any given time. However, the increased popularity of social media has also led to cyberbullying.It is imperative
to address the need for finding, monitoring, and mitigating cyberbullying posts on social media platforms.
Motivated by this necessity, we present this paper to contribute to developing an automated system for
detecting binary labels of aggressive tweets.Our study has demonstrated remarkable performance compared to
previous experiments on the same dataset. We employed the stacking ensemble machine learning method,
utilizing four various feature extraction techniques to optimize performance within the stacking ensemble
learning framework. Combining five machine learning algorithms,Decision Trees, Random Forest, Linear
Support Vector Classification, Logistic Regression, and K-Nearest Neighbors into an ensemble method, we
achieved superior results compared to traditional machine learning classifier models. The stacking classifier
achieved a high accuracy rate of 94.00%, outperforming traditional machine learning models and surpassing
the results of prior experiments that utilized the same dataset. The outcomes of our experiments showcased an
accuracy rate of 0.94% in detection tweets as aggressive or non-aggressive.
This documentation provides a brief insight of face recognition based attendance system using neural networks in terms of product architecture which can be used for educational purpose.
Blockchain and machine learning in education: a literature reviewIAESIJAI
There is a growing influence around the use of technology in education, with many solutions already being implemented and many others being explored. Using various forms of technology to assist the educational process has increased dramatically in the previous decade in education systems in many respects. Both machine learning and the blockchain have had a significant impact on education. The purpose of this study is to conduct a literature review on the application of machine learning and blockchain technology in educational institutions. Additionally, this study examines the potential applications, benefits, and challenges those educational institutions may face as a result of using machine learning and blockchain technologies. Using machine learning and blockchain in educational systems will have a positive impact on the entire educational process and student achievement. Researchers, academics, and practitioners will benefit from this study to focus on a wider range of educational applications and solve the related issues of machine learning and blockchain technology in the education sector.
Social distance and face mask detector system exploiting transfer learningIJECEIAES
As time advances, the use of deep learning-based object detection algorithms has also evolved leading to developments of new human-computer interactions, facilitating an exploration of various domains. Considering the automated process of detection, systems suitable for detecting violations are developed. One such applications is the social distancing and face mask detectors to control air-borne diseases. The objective of this research is to deploy transfer learning on object detection models for spotting violations in face masks and physical distance rules in real-time. The common drawbacks of existing models are low accuracy and inability to detect in real-time. The MobileNetV2 object detection model and YOLOv3 model with Euclidean distance measure have been used for detection of face mask and physical distancing. A proactive transfer learning approach is used to perform the functionality of face mask classification on the patterns obtained from the social distance detector model. On implementing the application on various surveillance footage, it was observed that the system could classify masked and unmasked faces and if social distancing was maintained or not with accuracies 99% and 94% respectively. The models exhibited high accuracy on testing and the system can be infused with the existing internet protocol (IP) cameras or surveillance systems for real-time surveillance of face masks and physical distancing rules effectively.
Concept drift and machine learning model for detecting fraudulent transaction...IJECEIAES
The document presents a machine learning model for detecting fraudulent transactions in a streaming environment that addresses concept drift. The proposed approach uses the extreme gradient boosting (XGBoost) algorithm and employs four algorithms to continuously detect concept drift in data streams. The approach is evaluated on credit card and Twitter fraud datasets and is shown to outperform traditional machine learning models in terms accuracy, precision, and recall, and is more robust to concept drift. The proposed approach can be utilized as a real-time fraud detection system across different industries.
Proposed high level solutions to counter online examination fraud using digit...Ivans Kigwana
The document proposes digital forensic readiness techniques to counter online exam fraud. It discusses how students commonly cheat in online exams, such as accessing online content, screen sharing, opening new tabs, and inspecting HTML code. It then proposes 11 digital forensic readiness techniques that could be used to gather evidence in these situations, such as recording PC activity, using keylogging, monitoring typing patterns, deploying security cameras, logging browser activity, recording mouse activity, recording programmable device data, using webcams to verify student identity, employing biometric identification, and performing continuous authentication. The techniques were evaluated based on implementation difficulty, cost, and efficiency.
Monitoring Students Using Different Recognition Techniques for Surveilliance ...IRJET Journal
This document discusses using computer vision techniques like convolutional neural networks to monitor students and enforce dress codes in educational institutions. It proposes a system using cameras and image processing to identify whether students are properly dressed according to the dress code. The system would classify images of students as either following or not following the dress code. It also discusses related work on using technologies like biometrics and RFID cards for automated student attendance tracking and implications for security and discipline in schools.
This document summarizes a literature review that analyzed research predicting student performance and dropout rates using machine learning techniques. The review identified 78 relevant papers published between 2009-2021. These papers mostly used student data from universities and MOOC platforms to test machine learning classifiers for predicting at-risk students and dropout likelihood. The review found that machine learning methods effectively predicted student performance and helped universities develop intervention strategies to improve student outcomes.
A Systematic Literature Review Of Student Performance Prediction Using Machi...Angie Miller
This document summarizes a systematic literature review of research predicting student performance using machine learning techniques. The review examined studies from 2009 to 2021 that identified students at risk of dropping out. It found that various machine learning methods were used to understand challenges and predict performance. Most studies used data from university databases and online learning platforms. Machine learning was shown to effectively predict student risk levels and dropout rates, helping improve student outcomes.
Age and Gender Classification using Convolutional Neural NetworkIRJET Journal
This document describes a study that aims to accurately identify the gender and age range of facial images using convolutional neural networks. It begins with an introduction to age and gender classification and some of the challenges. It then discusses the system analysis and design, including the use of convolutional neural networks and machine learning techniques. The implementation section notes that Python, TensorFlow, Keras and OpenCV will be used to build a convolutional neural network model to detect faces in images and predict age and gender through training on available datasets. The overall goal is to develop an accurate system for age and gender detection from facial images.
A study of cyberbullying detection using Deep Learning and Machine Learning T...IRJET Journal
This document discusses a study on detecting cyberbullying using machine learning and deep learning techniques. Specifically, it examines using a hybrid model combining K-Nearest Neighbors, Support Vector Machine, and Random Forest algorithms, as well as a Convolutional Neural Network. The study uses a Twitter dataset to classify tweets as not bullying, racism, or sexism. It finds that the CNN model produces more accurate predictions than the hybrid stacking algorithm. The document provides background on related work applying machine and deep learning to cyberbullying detection, particularly using content-based and user-based approaches.
A study of cyberbullying detection using Deep Learning and Machine Learning T...IRJET Journal
This document summarizes a research paper that studied the detection of cyberbullying using machine learning and deep learning techniques. Specifically, it used a hybrid model combining KNN, SVM and Random Forest algorithms (stacking algorithm) and a Convolutional Neural Network (CNN) on a Twitter dataset. The stacking algorithm achieved an accuracy of X% while the CNN achieved a higher accuracy of Y%. A comparison of the two models found that CNN produced a more precise prediction of cyberbullying. The document also reviewed related work on cyberbullying detection using content-based, user-based and network-based approaches with machine learning algorithms like SVM, Naive Bayes and deep learning methods like CNN.
New hybrid ensemble method for anomaly detection in data science IJECEIAES
Anomaly detection is a significant research area in data science. Anomaly detection is used to find unusual points or uncommon events in data streams. It is gaining popularity not only in the business world but also in different of other fields, such as cyber security, fraud detection for financial systems, and healthcare. Detecting anomalies could be useful to find new knowledge in the data. This study aims to build an effective model to protect the data from these anomalies. We propose a new hyper ensemble machine learning method that combines the predictions from two methodologies the outcomes of isolation forest-k-means and random forest using a voting majority. Several available datasets, including KDD Cup-99, Credit Card, Wisconsin Prognosis Breast Cancer (WPBC), Forest Cover, and Pima, were used to evaluate the proposed method. The experimental results exhibit that our proposed model gives the highest realization in terms of receiver operating characteristic performance, accuracy, precision, and recall. Our approach is more efficient in detecting anomalies than other approaches. The highest accuracy rate achieved is 99.9%, compared to accuracy without a voting method, which achieves 97%.
This document summarizes several research papers on human face recognition using feature extraction and measurements. It discusses using face recognition for applications like surveillance, access control, and banking validation. Key steps in face recognition systems include extracting features from captured images, comparing them to known images in a training database, and identifying errors like false acceptance and false rejection rates. Methods discussed for feature extraction and dimensionality reduction include Linear Discriminant Analysis and Principal Component Analysis. The document also examines factors that affect face recognition performance like illumination changes, aging, and expressions. Quantifying uncertainty in face recognition algorithms is identified as important for evaluating system performance.
Integrated system for monitoring and recognizing students during class sessionijma
In this paper we propose a new student attendance system based on biometric authentication protocol. This
system is basically using the face detection and the recognition protocols to facilitate checking students’
attendance in the classroom. In the proposed system, the classroom’s camera is capturing the students’
photo, directly the face detection and recognition processes will be implemented to produce the instructor
attendance report. Actually, this system is more efficient than others student attendance methods since the
detection and the recognition are considered to be the best and fastest method for biometric attendance
system. Regarding to the students and instructor sides, the system is working without any preparation and
with no more effort.
SMART AGENT BASED SEARCH FOR ADMISSION IN INSTITUTIONS OF HIGHER LEARNINGIJITE
Early admission systems saw people applying to universities by filling out applications forms and placing
them in suitable envelopes and sending them through the local postal agency. This was not considered to
be cost or time effective, and this method was also not efficient. This system however needed some
improvement due to the huge workload on administrators. So researchers and software developers
improved the system so that between 1999 and 2008 application and admission was done via the Internet.
Also many Ranking system like ARWU, shanghai etc. been used for ranking the universities and colleges
around the world which would enable people choosing the universities and colleges for education on
factors like publication, funding, infrastructure and so.
The Internet has already brought the humans together in a new, exciting, and unexpected ways, and the
same is also happening to our prevalent adoption of digital mobile devices that has paved the way for the
development of many innovative applications in the commercial domain. While considering such mobile
devices for an application towards higher education in an educational institution, there has been some
amount of work done using intelligent agents. But still those agent based systems got some drawbacks
which motivated towards developing the present Agent based system to provide Smart agent based system
for higher Learning search not in Jamaican context alone but also elsewhere with these drawbacks
alleviated. The agents developed will be based on using fuzzy preference rules and heuristics, to make
accurate decisions based on the user’s criteria or specifications using JADE-LEAP on Android handset.
The system got Google map feature, intelligence in admission system and also warning for universities
with low rating. These findings of this research will be presented as screenshots.
Similar to Machine learning for autonomous online exam frauddetection: A concept design (20)
Air quality forecasting using convolutional neural networksBIJIAM Journal
Air pollution is now one of the biggest environmental risks, which causes more than 6 million premature deathseach year from heart diseases, stroke, diabetes, respiratory disease, and so on. Protecting humans from thedamage which is caused by air pollution is one of the major issues for the global community. The prediction ofair pollution can be done by machine learning (ML) algorithms. ML combines statistics and computer science tomaximize the prediction power. ML can be also used to predict the air quality index (AQI). The aim of this researchis to develop a convolutional neural network (CNN) model to predict air quality from the unseen data set, whichincludes concentration of nitrogen dioxide (NO2), carbon monoxide (CO), and sulfur dioxide (SO2). The proposedsystem will be implemented in two steps; the first step will focus on data analysis and pre-processing, includingfiltering, feature extraction, constructing convolutional neural network layers, and optimizing the parameters ofeach layer, while the second step is used to evaluate its model accuracy. The output is predicted as AQI for thedeveloped CNN model. The developed CNN model achieves a root-mean-square error of 13.4150 and a highaccuracy of 86.585%. The overall model is implemented using MATLAB software.
Prevention of fire and hunting in forests using machine learning for sustaina...BIJIAM Journal
Deforestation, illegal hunting, and forest fires are a few current issues that have an impact on the diversity andecosystem of forests. To increase the biodiversity of species and ecosystems, it becomes imperative to preservethe forest. The conventional techniques employed to prevent these issues are costly, less effective, and insecure.The current systems are unreliable and use more energy. By utilizing an Internet of Things (IOT) system, thistechnology offers a more practical and economical method of continuously maintaining and monitoring the statusof the forest. To guarantee excellent security, this system combines a number of sensors, alarms, cameras, lights,and microphones. It aids in reducing forest loss, animal trafficking, and forest fires. In the suggested system,sensors are used for monitoring, and cloud storage is used for data storage. Through the use of machine learning,the raspberry pi camera module significantly aids in the prevention of unlawful wildlife hunting as well as thedetection and prevention of forest fires.
Object detection and ship classification using YOLO and Amazon RekognitionBIJIAM Journal
The need for reliable automated object detection and classification in the maritime sphere has increasedsignificantly over the last decade. The increased use of unmanned marine vehicles necessitates the developmentand use of an autonomous object detection and classification systems that utilize onboard cameras and operatereliably, efficiently, and accurately without the need for human intervention. As the autonomous vehicle realmmoves further away from active human supervision, the requirements for a detection and classification suite callfor a higher level of accuracy in the classification models. This paper presents a comparative study using differentclassification models on a large maritime vessel image dataset. For this study, additional images were annotatedusing Roboflow, focusing on the types of subclasses that had the lowest detection performance, a previous workby Brown et al. (1). The present study uses a dataset of over 5,000 images. Using the enhanced set of imageannotations, models were created in the cloud using Google Colab using YOLOv5, YOLOv7, and YOLOv8 as wellas in Amazon Web Services using Amazon Rekognition. The models were tuned and run for five runs of 150 epochseach to collect efficiency and performance metrics. Comparison of these metrics from the YOLO models yieldedinteresting improvements in classification accuracies for the subclasses that previously had lower scores, but atthe expense of the overall model accuracy. Furthermore, training time efficiency was improved drastically using thenewer YOLO APIs. In contrast, using Amazon Rekognition yielded superior accuracy results across the board, butat the expense of the lowest training efficiency.
Prognostication of the placement of students applying machine learning algori...BIJIAM Journal
Placement is the process of connecting the selected candidate with the employer. Every student might have adream of having a job offer when he or she is about to complete her course. All educational institutions aim athaving their students well placed in good organizations. The reputation of any institution depends on the placementof its students. Hence, many institutions try hard to have a good placement cell. Classification using machinelearning may be utilized to retrieve data from the student-databases. A prediction model that can foretell theeligibility of the students based on their academic and extracurricular achievements is proposed. Related data wascollected from many institutions for which the placement-prediction is made. This paradigm is being weighed upwith the existing algorithms, and findings have been made regarding the accuracy of predictions. It was found thatthe proposed algorithm performed significantly better and yielded good results.
Drug recommendation using recurrent neural networks augmented with cellular a...BIJIAM Journal
Drug recommendation systems are systems that have the capability to recommend drugs. On a daily basis, a huge amount of data is being generated by the patients. All this valuable data can be properly utilized to create a reliable drug recommendation system. In this paper, we recommend a system for drug recommendations. The main scope of our system is to predict the correct medication based on reviews and ratings. Our proposed system uses natural language processing techniques (NLP), recurrent neural networks (RNN), and cellular automata (CA). We also considered various metrics like precision, recall, accuracy, F1 score, and ROC curve as measures of our system’s performance. NLP techniques are being used for gathering useful information from patient data, and RNN is a machine learning methodology that works really well in analyzing textual data. The system considers various patient data attributes like age, gender, dosage, medical history, and symptoms in order to make appropriate predictions. The proposed system has the potential to help medical professionals make informed drug recommendations.
Discrete clusters formulation through the exploitation of optimized k-modes a...BIJIAM Journal
This article focuses on the results of self-funded quantitative research conducted by social workers working inthe “refugee” crisis and social services in Greece (1). The research, among other findings, argues that front-lineprofessionals possess specific characteristics regarding their working profile. Statistical methods in the researchperformed significance tests to validate the initial hypotheses concerning the correlation between dataset variables.On the contrary of this concept, in this work, we present an alternative approach for validating initial hypothesesthrough the exploitation of clustering algorithms. Toward that goal, we evaluated several frequently used clusteringalgorithms regarding their efficiency in feature selection processes, and we finally propose a modified k-Modesalgorithm for efficient feature subset selection.
Emotion Recognition Based on Speech Signals by Combining Empirical Mode Decom...BIJIAM Journal
This paper proposes a novel method for speech emotion recognition. Empirical mode decomposition (EMD) is applied in this paper for the extraction of emotional features from speeches, and a deep neural network (DNN) is used to classify speech emotions. This paper enhances the emotional components in speech signals by using EMD with acoustic feature Mel-Scale Frequency Cepstral Coefficients (MFCCs) to improve the recognition rates of emotions from speeches using the classifier DNN. In this paper, EMD is first used to decompose the speech signals, which contain emotional components into multiple intrinsic mode functions (IMFs), and then emotional features are derived from the IMFs and are calculated using MFCC. Then, the emotional features are used to train the DNN model. Finally, a trained model that could recognize the emotional signals is then used to identify emotions in speeches. Experimental results reveal that the proposed method is effective.
Enhanced 3D Brain Tumor Segmentation Using Assorted Precision TrainingBIJIAM Journal
A brain tumor is a medical disorder faced by individuals of all demographics. Medically, it is described as the spread of nonessential cells close to or throughout the brain. Symptoms of this ailment include headaches, seizures, and sensory changes. This research explores two main categories of brain tumors: benign and malignant. Benign spreads steadily, and malignant express growth makes it dangerous. Early identification of brain tumors is a crucial factor for the survival of patients. This research provides a state-of-the-art approach to the early identification of tumors within the brain. We implemented the SegResNet architecture, a widely adopted architecture for threedimensional segmentation, and trained it using the automatic multi-precision method. We incorporated the dice loss function and dice metric for evaluating the model. We got a dice score of 0.84. For the tumor core, we got a dice score of 0.84; for the whole tumor, 0.90; and for the enhanced tumor, we got a score of 0.79.
Hybrid Facial Expression Recognition (FER2013) Model for Real-Time Emotion Cl...BIJIAM Journal
Facial expression recognition is a vital research topic in most fields ranging from artificial intelligence and gaming to human–computer interaction (HCI) and psychology. This paper proposes a hybrid model for facial expression recognition, which comprises a deep convolutional neural network (DCNN) and a Haar Cascade deep learning architecture. The objective is to classify real-time and digital facial images into one of the seven facial emotion categories considered. The DCNN employed in this research has more convolutional layers, ReLU activation functions, and multiple kernels to enhance filtering depth and facial feature extraction. In addition, a Haar Cascade model was also mutually used to detect facial features in real-time images and video frames. Grayscale images from the Kaggle repository (FER-2013) and then exploited graphics processing unit (GPU) computation to expedite the training and validation process. Pre-processing and data augmentation techniques are applied to improve training efficiency and classification performance. The experimental results show a significantly improved classification performance compared to state-of-the-art (SoTA) experiments and research. Also, compared to other conventional models, this paper validates that the proposed architecture is superior in classification performance with an improvement of up to 6%, totaling up to 70% accuracy, and with less execution time of 2,098.8 s.
Role of Artificial Intelligence in Supply Chain ManagementBIJIAM Journal
The term “supply chain” refers to a network of facilities that includes a variety of companies. To minimise the entire cost of the supply chain, these entities must collaborate. This research focuses on the use of Artificial Intelligence techniques in supply chain management. It includes supply chain management examples like as
demand forecasting, supply forecasting, text analytics, pricing panning, and more to help companies improve their processes, lower costs and risk, and boost revenue. It gives us a quick rundown of all the key principles of economics and how to comprehend and use them effectively.
An Internet – of – Things Enabled Smart Fire Fighting SystemBIJIAM Journal
Fire accident is a mishap that could be either man made or natural. Fire accident occurs frequently and can be controlled but may at time result in severe loss of life and property. Many a times fire fighters struggle to sort out the exact source of fire as it is continuously flammable and it spreads all over the area. On this concern, we have designed an Internet – of – Things (IoT) based device which sorts out the exact source of fire through a software and
hardware devices and also the complete detail of an area can be visualized by fire fighters which are preinstalled in the software itself. Because of our system’s intelligence in decision making during fire fighting, the proposed system is claimed as ‘Smart Fire Fighting System’. The implementation of the smart fire fighting system can make
the fire fighters to analyze the current situation immediately and make the decision quicker in an effective manner. Because of this system, fire losses in a building can be greatly reduced and many lives can be rescued immediately and moreover fire spread can also be restricted.
Evaluate Sustainability of Using Autonomous Vehicles for the Last-mile Delive...BIJIAM Journal
This study aims to determine if autonomous vehicles (AV) for last-mile deliveries are sustainable from three perspectives: social, environmental, and economic. Because of the relevance of AV application for last-mile delivery, safety was only addressed for the sake of societal sustainability. According to this study, it is rather safe to deploy AVs for delivery since current society’s speed restriction and decent road conditions give the foundation for AVs to run safely for last-mile delivery in metropolitan areas. Furthermore, AV has a distinct advantage in the event of a pandemic. The biggest worry for environmental sustainability is the emission problem. In terms of a series of emissions, it is established that AV has a substantial benefit in terms of emission reduction. This is mostly due to the differences in driving behaviour between autonomous cars and human vehicles. Because of AV’s commercial character, this research used a quantitative approach to highlight the economic sustainability of AV. The
study demonstrates the economic benefit of AV for various carrying capacities.
Automation Attendance Systems Approaches: A Practical ReviewBIJIAM Journal
Accounting for people is the first step of every manpower-based organization in today’s world. Hence, it takes up a signification amount of energy and value in the form of money from respective organizations for both
implementing a suitable system for manpower management as well as maintaining that same system. Although this amount of expenditure for big organizations is near to nothing, rather just a formality, it does not hold as much truth for small organizations such as schools, colleges, and even universities to a certain degree. This is the first point. The second point for discussion is that much work has been done to solve this issue. Various technologies like Biometrics, RFID, Bluetooth, GPS, QR Code, etc., have been used to tackle the issues of attendance collection. This study paves
the path for researchers by reviewing practical methods and technologies used for existing attendance systems.
Transforming African Education Systems through the Application of IOTBIJIAM Journal
The project sought to provide a paradigm for improving African education systems through the use of the IOT. The created IOT model for Africa will enable African countries, notably Namibia, to exchange educational content and resources with other African countries. The objective behind the IOT paradigm in Africa’s education sectors is to provide open access to knowledge and information. The study revealed that there are no recognised platforms in African education systems that are utilised by African governments to interact, communicate, and share educational material directly with African institutions. As a result, the current research developed a model for
transforming African education systems using the IOT in the Namibian context, which will serve as a centralised online platform for self-study, new skill acquisition, and self-improvement using materials provided by African institutions of higher learning. Everyone is welcome to use the platform, including students, instructors, and
members of the general public.
Deep Learning Analysis for Estimating Sleep Syndrome Detection Utilizing the ...BIJIAM Journal
Manual sleep stage scoring is frequently performed by sleep specialists by visually evaluating the patient’s neurophysiological signals acquired in sleep laboratories. This is a difficult, time-consuming, & laborious process. Because of the limits of human sleep stage scoring, there is a greater need for creating Automatic Sleep Stage Classification (ASSC) systems. Sleep stage categorization is the process of distinguishing the distinct stages of sleep & is an important step in assisting physicians in the diagnosis & treatment of associated sleep disorders. In this research, we offer a unique method & a practical strategy to predicting early onsets of sleep disorders, such as restless leg syndrome & insomnia, using the Twin Convolutional Model FTC2, based on an algorithm composed of two modules. To provide localised time-frequency information, 30 second long epochs of EEG recordings are subjected to a Fast Fourier Transform, & a deep convolutional LSTM neural network is trained for sleep stage categorization. Automating sleep stages detection from EEG data offers a great potential to tackling sleep irregularities on a
daily basis. Thereby, a novel approach for sleep stage classification is proposed which combines the best of signal
processing & statistics. In this study, we used the PhysioNet Sleep European Data Format (EDF) Database. The code
evaluation showed impressive results, reaching accuracy of 90.43, precision of 77.76, recall of 93,32, F1-score of 89.12 with the final mean false error loss 0.09. All the source code is availlable at https://github.com/timothy102/eeg.
Robotic Mushroom Harvesting by Employing Probabilistic Road Map and Inverse K...BIJIAM Journal
A collision-free path to a destination position in a random farm is determined using a probabilistic roadmap (PRM) that can manage static and dynamic obstacles. The position of ripening mushrooms is a result of picture processing. A mushroom harvesting robot is explored that uses inverse kinematics (IK) at the target
position to compute the state of a robotic hand for grasping a ripening mushroom and plucking it. The DenavitHartenberg approach was used to create a kinematic model of a two-finger dexterous hand with three degrees of freedom for mushroom picking. Unlike prior experiments in mushroom harvesting, mushrooms are not planted in a grid or design, but are randomly scattered. At any point throughout the harvesting process, no human interaction is necessary
CHINA’S GEO-ECONOMIC OUTREACH IN CENTRAL ASIAN COUNTRIES AND FUTURE PROSPECTjpsjournal1
The rivalry between prominent international actors for dominance over Central Asia's hydrocarbon
reserves and the ancient silk trade route, along with China's diplomatic endeavours in the area, has been
referred to as the "New Great Game." This research centres on the power struggle, considering
geopolitical, geostrategic, and geoeconomic variables. Topics including trade, political hegemony, oil
politics, and conventional and nontraditional security are all explored and explained by the researcher.
Using Mackinder's Heartland, Spykman Rimland, and Hegemonic Stability theories, examines China's role
in Central Asia. This study adheres to the empirical epistemological method and has taken care of
objectivity. This study analyze primary and secondary research documents critically to elaborate role of
china’s geo economic outreach in central Asian countries and its future prospect. China is thriving in trade,
pipeline politics, and winning states, according to this study, thanks to important instruments like the
Shanghai Cooperation Organisation and the Belt and Road Economic Initiative. According to this study,
China is seeing significant success in commerce, pipeline politics, and gaining influence on other
governments. This success may be attributed to the effective utilisation of key tools such as the Shanghai
Cooperation Organisation and the Belt and Road Economic Initiative.
A SYSTEMATIC RISK ASSESSMENT APPROACH FOR SECURING THE SMART IRRIGATION SYSTEMSIJNSA Journal
The smart irrigation system represents an innovative approach to optimize water usage in agricultural and landscaping practices. The integration of cutting-edge technologies, including sensors, actuators, and data analysis, empowers this system to provide accurate monitoring and control of irrigation processes by leveraging real-time environmental conditions. The main objective of a smart irrigation system is to optimize water efficiency, minimize expenses, and foster the adoption of sustainable water management methods. This paper conducts a systematic risk assessment by exploring the key components/assets and their functionalities in the smart irrigation system. The crucial role of sensors in gathering data on soil moisture, weather patterns, and plant well-being is emphasized in this system. These sensors enable intelligent decision-making in irrigation scheduling and water distribution, leading to enhanced water efficiency and sustainable water management practices. Actuators enable automated control of irrigation devices, ensuring precise and targeted water delivery to plants. Additionally, the paper addresses the potential threat and vulnerabilities associated with smart irrigation systems. It discusses limitations of the system, such as power constraints and computational capabilities, and calculates the potential security risks. The paper suggests possible risk treatment methods for effective secure system operation. In conclusion, the paper emphasizes the significant benefits of implementing smart irrigation systems, including improved water conservation, increased crop yield, and reduced environmental impact. Additionally, based on the security analysis conducted, the paper recommends the implementation of countermeasures and security approaches to address vulnerabilities and ensure the integrity and reliability of the system. By incorporating these measures, smart irrigation technology can revolutionize water management practices in agriculture, promoting sustainability, resource efficiency, and safeguarding against potential security threats.
KuberTENes Birthday Bash Guadalajara - K8sGPT first impressionsVictor Morales
K8sGPT is a tool that analyzes and diagnoses Kubernetes clusters. This presentation was used to share the requirements and dependencies to deploy K8sGPT in a local environment.
DEEP LEARNING FOR SMART GRID INTRUSION DETECTION: A HYBRID CNN-LSTM-BASED MODELgerogepatton
As digital technology becomes more deeply embedded in power systems, protecting the communication
networks of Smart Grids (SG) has emerged as a critical concern. Distributed Network Protocol 3 (DNP3)
represents a multi-tiered application layer protocol extensively utilized in Supervisory Control and Data
Acquisition (SCADA)-based smart grids to facilitate real-time data gathering and control functionalities.
Robust Intrusion Detection Systems (IDS) are necessary for early threat detection and mitigation because
of the interconnection of these networks, which makes them vulnerable to a variety of cyberattacks. To
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Machine learning for autonomous online exam frauddetection: A concept design
1. BOHR International Journal of Internet of things,
Artificial Intelligence and Machine Learning
2023, Vol. 2, No. 1, pp. 31–36
DOI: 10.54646/bijiam.2023.15
www.bohrpub.com
CONCEPT DEVELOPMENT
Machine learning for autonomous online exam fraud
detection: A concept design
Abdul Cader Mohamed Nafrees1*, Sahabdeen Aysha Asra2 and RKAR. Kariapper3
1South Eastern University of Sri Lanka, Oluvil, Sri Lanka
2Department of Information Technology, South Eastern University, Oluvil, Sri Lanka
3Department of Information and Communication Technology, South Eastern University, Oluvil, Sri Lanka
*Correspondence:
Abdul Cader Mohamed Nafrees,
nafrees@seu.ac.lk
Received: 01 August 2023; Accepted: 04 August 2023; Published: 18 August 2023
E-learning (EL) has emerged as one of the most valuable means for continuing education around the world,
especially in the aftermath of the global pandemic that included a variety of obstacles. Real-time online
assessments have become a significant concern for educational organizations. Instances of fraudulent behavior
during online exams (OEs) have created considerable challenges for exam invigilators, who are unable to identify
and remove such dishonest behavior. In response to this significant issue, educational institutions have used a
variety of manual procedures to alleviate the situation, but none of these measures have shown to be particularly
innovative or effective. The current study presents a novel strategy for detecting fraudulent actions in real time
during OEs that uses convolutional neural network (CNN) algorithms and image processing. The development
model will be trained using the CK and CK++ datasets. The training procedure will use 80% of the selected
dataset, with the remaining 20% used for model testing to confirm the model’s efficacy and generalization capacity.
This project intends to revolutionize the monitoring and prevention of fraudulent actions during online tests by
integrating CNN techniques and image processing. The use of CK and CK++ datasets, as well as an 80–20
split for training and testing, contributes to the study’s thorough and rigorous approach. Educational institutions
can improve their assessment procedures and maintain the credibility of EL as a credible and equitable way of
continuing education by successfully using this unique technique.
Keywords: CNN, E-learning, image processing, machine learning, online exam
1. Introduction
Since the coronavirus disease (COVID) epidemic spreads
over the world, E-learning (EL) has become an unstoppable
strategy in the education industry, yet EL is not a novel
method in the education industry. Also, EL is a significant
advance in the field of ICT. Due to the pandemic, various
educational organizations use various methods for handling
online exams (OEs), such as via ZOOM, planning camera
systems around students with safe web pages, and a few
others; however, none of these methods prevent students
from cheating in exams, and others, such as face-to-face
investigation guidance, are ineffective due to numerous
practical problems from both students and staff. As a result,
developing an experimental solution to check the quality of
the test is critical to maintaining the quality of education.
As a result, conducting research to prevent examination
fraudulent and teaching actions during OE using ICT is
critical. As a result, integrating prior research, this study
presents a unique approach based on machine learning (ML)
to detect OE fraud by assessing candidates’ eye movement,
facial expressions, voice recognition, and facial expressions
using image processing techniques Certain ML techniques
such as selecting features, classification, and others have
been used in research to offer a specific strategy/method
for online tests.
31
2. 32 Mohamed Nafrees et al.
Machine learning (ML) has proven to be successful in a
wide range of tasks. Understanding how support from ML
techniques impacts human agency and human performance
is critical to realizing the potential of ML for enhancing
human judgments. As ML becomes more widely employed
in our society to enhance supervision, the ML community
must not only construct ML models but also increase our
knowledge of how these models are utilized and how people
interact with them during the supervisory approach (1).
In the context of the pandemic, e-proctoring technologies
have gained popularity, and some academic institutions have
employed them to identify fraud in online tests. Lecturers can
virtually watch students while they are taking exams utilizing
these methods. The professors found that their students
cheated on online exams after finding substantial variations
in scores between proctored and non-proctored students
in this study of computer engineering students (2). Fraud
detection in corridor tickets is a challenging task to combine
the various photo ingredients in a digital manner. The
suggested architecture for disconnected testing uses lobby
ticket images to provide security based on their components.
Morphological activity is used to process as well as segment
the corridor ticket images. The related component naming
method is used to identify every fragment (3). It is only logical
to employ a convolutional neural network (CNN)-based
approach to identify the phony face pictures that arise (4).
The present online exam fraud detection method for
combating online cheating is examined in part II. Part
III presents the suggested framework, whereas part IV
presents the comprehensive results and discussion. The
findings are discussed in Section V, which is followed by a
summary in Section VI.
1.1. Literature review
The research offered a framework for conducting online
exams in a safe manner in order to prevent test difficulties
and obstacles (5). Similarly, another research recommended
an OEL system for conducting tests from anywhere at any
time. This ensures the examination’s validity. (6). A group of
students suggested using an ML technique to conduct OEs,
which helps to decrease examination fraud by evaluating
every student’s activities during the test period (7).
Another study found that using live and artificial
intelligence (AI)-based remote monitoring considerably
boosts the amount of security that OEs have against
student test fraud (8, 9). To prevent examination infractions,
the authors (10) recommend using low-cost keystroke
analysis, voice recognition, facial recognition, iris reader,
and fingerprint reader as well as using technologies like
Blackboard Learning and Lockdown Browser (11).
Table 1 summarizes some other techniques used in fraud
detection in online examination too.
According to the table, the main technologies or
algorithms used for the examination fraud detection and
support to identify fake faces are LSTM, KDE, CNN,
ALEXNET, OpenPose, MATLAB, GAN, KNN, and SVM.
Most of the research papers include CNN as the technology
for their system. Usually, the uses of main technologies
in each paper do not differ much. The main uses of the
technologies are face recognition, authentication, and face
extraction. But MATLAB technology is very helpful for
deaf invigilators and visually impaired and also detects
cheating automatically.
The VGG-16 Net is used to categorize six aberrant
behavior-related poses using the picture within the generated
bounding box for each distinguished human subject. While
assessing the behavior of a testing set, the six-dimensional
output vector of VGG-16 Net is passed to LSTM to detect
unauthorized behaviors that arise instantly (17). A study
found that both the crowd-sourced and ML approaches lower
the likelihood of exam cheating (18). Similarly, tools such
as computer vision for facial recognition and ML may be
utilized to detect student participation throughout online
learning sessions (19). The spatiality reduction approach, in
which a starting dataset is separated as well as decreased to
further manageable teams, may include feature extraction
(20). The most crucial element of those big data sets is
the large number of variables required. A large amount
of computer capacity is needed to process these variables.
As a consequence, by choosing and merging variables into
alternatives, feature extraction assists in the induction of
the most effective feature from enormous data sets. These
qualities are simple to construct, yet they can correctly and
uniquely describe the data collection in issue (3).
The main aim of this concept proposal was to look at
a novel method for detecting probable cheating scenarios
utilizing the ML approach. Keeping academic integrity is one
of the most difficult tasks. The online test is an important
part of EL. Student’s tests in EL are completed virtually, with
no physical invigilators present. Because of the easiness with
which students might cheat on OE, EL colleges rely on an
examination technique in which students take a face-to-face
examination in a physical venue defined on the organization’s
facilities under supervised conditions.
To train a KNN classifier to distinguish between a
spoof and a real face, image quality measurements and
color texturing are utilized. To generate the final feature
representation, texture and quality data are obtained and
connected. After that, the characteristics are put into a
KNN classifier. KNN classifies an unknown sample by
returning the label that occurs most frequently between
the nearest training distance K and the testing dataset.
Both the unknown and training samples are measured
using Euclidean distance. KNN is more precise and faster
to perform. SVM (supervised learning model) is a data
analysis and pattern recognition model that is used for
classification and regression analysis. SVM is a method that
3. 10.54646/bijiam.2023.15 33
TABLE 1 | ML technologies used for OE fraud detection.
References Used techniques Technology used for Accuracy Limitation
Kamalov et al. (12) Long short-term memory
networks (LSTM), KDE
(Kernel density
estimation)-based outlier
detection
KDE - Recognize outliers in a
dataset. LSTM - Cope with
sequence data efficiently by
enabling prior outputs to be
used as inputs.
High level of accuracy -
Sharma et al. (13) CNN Detection of fraud during
e-exams.
- -
Nishchal et al. (14) OpenPose, ALEXNET
model, CNN
OpenPose is used to identify
posture; Model ALEXNET is
used to identify types of fraud
77.8% of accuracy The camera should be placed
at a suitable height to detect
all the persons
Asadullah et al. (15) MATLAB Detection of cheating
automatically
The likelihood of false acceptance (FAR)
is 1%, while the probability of false
rejection (FRR) is 3%.
Unusual behaviors such as
whispering and visualizing
Mo et al. (4) Generative adversarial
network (GAN), CNN
GAN - generate realistic
looking faces, CNN - identify
fake face images
99.4% of accuracy -
Pranoto et al. (16) KNN, SVM Authentication and
recognition process
95% ± 0.2 accuracies, 84% accuracy
when the head is angled approximately
45%, 94% accuracy for small detected
faces, 75% accuracy with little occlusion
on the face, and 20% accuracy with
major occlusion.
-
uses the structural risk minimization (SRM) concept to
choose the optimum hyperplane (21). Since technological
advancement has dramatically improved, trying to capture
irregular aspects relevant to unethical activities requires
an examination of the quantity of variations that can be
investigated concurrently. This can be settled by expanding
the number of neurons and/or layers at the cost of
broader online activities of increasing sophistication neural
networks (22).
2. Methodology
2.1 Problem statement
As per the past segment, several digital solutions and manual
methods have been presented as well as employed to prevent
OEs from engaging in illegal behavior and scamming, but
no secured specific development, technique, or method has
yet been offered for executing protected automatic OEs.
This results in unjust exam outcomes for students and
also challenges in examination monitoring for staff; it also
contributes to the majority of educational sectors delaying
final tests, lengthening the course time period. As a result,
in the future, it will be important to create secured practical
solutions for OEs that will assist in avoiding all the OE
difficulties described previously.
Moreover, the mentioned practical reasons from both
students and staff of educational organizations motivated this
research study:
• Completing the course/study within the scheduled
school program
• Preserving the performance of EL as if it were face-to-
face learning
• Minimizing supervisors’ anxiety levels during OEs
• Maintaining impartial exam results to ensure student
sincerity throughout OEs.
• Important technological developments for EL practical
problems.
Most of the foregoing arguments made us to do a study
in the subject of EL on “Development of a unique machine-
learning algorithms for the automated monitoring of online
examination fraudulent activities.”
2.2. Research questions (RQ) and
Research objectives (RO)
As per prior study findings, OEs are the most difficult
activities in the EL framework for active monitoring to
prevent examination fraud. However, until now, no effective
method has been devised or supplied to address this
issue. As a result, this work provides a viable strategy
for filling this gap employing image processing approaches
using ML techniques.
This model will be trained using the provided dataset and
evaluated using real-time collected photos to corroborate the
study’s importance. Moreover, the same technique may be
used to perform OEs while using any EL tool (ZOOM, VLE,
etc.), as shown in Table 2.
4. 34 Mohamed Nafrees et al.
TABLE 2 | RQs and ROs for the proposed study.
S. No Research question Research objectives
1 What are the best ML algorithms
for detecting learners’ activity
during OE?
Examine and evaluate existing
research for the most advanced
ML approaches for voice
recognition, eye movement, and
facial recognition as well as their
performance.
2 How can I use ML methods to
create an app that captures
students’ activity through
webcam?
Build an ML-based technology to
identify students’ questionable
actions as well as alert
supervisors during OE.
3 How can the correctness of the
produced ML system be verified?
To check the correctness of the
built system, train it with
accessible picture data sets as well
as test it in a real-time context.
The suggested model will be developed using ML
techniques, and it will assess students’ facial recognition
(fear/nervousness, sorrow, and happiness), head pose, and
eye movement during OEs to detect exam cheating as well as
fraudulent activities. As a result, the CK and CK + databases
will be used to train the model, and real data gathered
from the camera will be used to evaluate the model to
determine the system’s correctness. In addition, if a graphics
processing unit (GPU) is not really available, an i7 higher
graphic general-purpose computer will be utilized to build
the overall model.
Argumentation and pre-processing of the proposed
system, design as well as implementation using ML
approaches, training the model using CK and CK + datasets,
and eventually testing the entire system with real data will all
be included in this research article.
1. Pre-processing and Argumentation
This procedure will considerably increase the accuracy
of the produced model’s findings. As a result, every image
in the training dataset and testing dataset is shrunk to
the same X ∗ X pixel size. The model’s errors will be
estimated using a sample size of 64 or 32. Furthermore,
argumentation techniques like shear, horizontal flip, and
zoom will be employed for the training data sets to decrease
erroneous predictions.
1. Design and Implementation
As once preceding steps have been performed, these
photos will be sent to the ML layers for additional processing.
1. Model Training
For the model training procedure, an equal number of
photos from each type of face expression, head stance, and
eye movements will be used. The model overall accuracy
will be determined by the greatest accuracy. In addition,
Anaconda Navigator will be utilized for the desktop user
interface, and the generated model will be trained using
TensorFlow open library software.
1. Model Testing
Just after the model has been effectively trained, this
process will be carried out with real data sets collected during
OEs. The below diagram depicts how the trained ML model
will recognize as well as inform the supervisors.
2.3. Expected outcomes
The ML-based model will assist, identify, and warn
supervisors about students’ examination fraudulent
or cheating behaviors during online tests, as stated
and illustrated above. With this technology, a trained
model will identify students’ expression in real-time
collected photographs and alert the supervisors if the
expression is suspected of being cheating or fraudulent.
Furthermore, it has been determined to publish research
publications in reputable indexing journals and IEEE or
Springer conferences.
3. Results and discussion
Researchers frequently offer various strategies to address
the problems raised by online tests. Features such as
examinee variation suspicious behavior identification, overall
system safety, question paper production, and so on are
very significant in online examinations. According to the
previous papers, there are numerous ML technologies and
algorithms that are used in the concept of autonomous
monitoring of online examination to identify examination
fraudulence. Most of the papers confess that CNN, LSTM,
KDE, ALEXNET, OpenPose, MATLAB, GAN, KNN, and
SVM are the suitable ML techniques. Also, they indicate
the accuracy and limitations as well. Almost every article’s
techniques show a high level of accuracy. Meanwhile,
some research papers discuss the limitations. The main
limitations are that covering large areas with a camera is
harder and the system could not identify unusual behaviors
(e.g., whispering).
We proposed a concept or solution to fill the gap that we
indicated previously. Especially, the proposed model would
measure students’ facial recognition (fear/nervousness, grief,
and happiness), head attitude, and eye movement to
detect exam cheating and fraudulent activities using ML
approaches. As a consequence, the CK and CK + databases
will be utilized to train the model, and real data from the
camera will be used to test the model and determine whether
the system is right. In addition, if a graphics processing unit
(GPU) is not accessible, the total model will be built on an
5. 10.54646/bijiam.2023.15 35
i7 higher visual general-purpose computer. As a result, the
training and testing datasets’ images were all downsized to
the identical X ∗ X pixel size. A sample size of 64 or 32 will
be used to estimate the model’s errors. Additionally, for the
training data sets, argumentation techniques such as shear,
horizontal flip, and zoom will be used to reduce erroneous
predictions. An equal number of photographs from each
sort of facial expression, head stance, and eye movements
will be employed in the model training phase. The greatest
accuracy will decide the model’s accuracy rate. The desktop
user interface will be Anaconda Navigator, and the resultant
model will be trained using the Tensorflow open library
software. This research article will cover the rationale and
pre-processing of the proposed system as well as the design
and implementation of the system using ML techniques,
training the model with CK and CK + datasets, and finally
testing the system with actual data.
Furthermore, training the model using CK and
CK + datasets is the most suitable and user-friendly
among other ML approaches. In addition, the webcam
will capture the student’s activity and process the image
by sending or verifying within the trained dataset. The
processed picture is then provided to an ML model to train
it to alert the examination about the students’ expression
during the online assessment. So, there is identification
of the correctness or testing in a real-time context of
the previous or recommended article. They mention the
accuracy level is high. But the article does not mention the
numeral accuracy level.
4. Conclusion
During critical events such as war, natural disasters, and
pandemics, EL has shown promise. As a result, during the
last three decades, numerous techniques and EL systems have
been created with the aim of appropriately carrying and
enhancing EL. Using ICT to avoid examination fraud and
instructional activities during OE is critical in this context.
According to prior study findings, OEs are the most
difficult activities in the EL process of active monitoring
to prevent examination fraud. However, until now, no
effective method has been devised or supplied to address
this issue. As a result, this work provides a viable strategy
for filling this gap employing image processing approaches
using ML techniques. This model will be trained using the
provided data and evaluated using real-time collected photos
to corroborate the research’s importance. Furthermore, the
same technique may be used to perform OEs while using any
EL tool (ZOOM, VLE, etc.).
Furthermore, among other ML techniques, CNN is the
most recommended because of its accuracy and processing
timing. ML approaches that train the model using CK and
CK + datasets are the most suited and user-friendly. In
the proposed system, the webcam will record the student’s
behavior and submit or verify the image inside the training
dataset. Then the processing image is sent to train the model
via an ML model to notify to the examination about the
expression of the students during the online examination. So,
the preceding or suggested article can determine the accuracy
or test it in a real-time environment. They claim that the
degree of accuracy is great. However, the numerical accuracy
level was not specified in the paper.
Therefore, during major disasters such as wars, natural
catastrophes, and pandemics over the last three decades,
EL has demonstrated its potential. Many approaches and
systems for improving EL have been created, including ICT-
based methods to prevent examination fraud during OEs.
However, successfully monitoring and eliminating fraud in
OEs continues to be difficult. This work presents a way for
monitoring students’ behavior during OEs by utilizing ML
and visual processing techniques, notably CNN. To confirm
its importance, the model will be trained and tested using
real-time data. Despite the fact that the proposed system
appears to be accurate, exact numerical findings were not
supplied. The analysis admits the possibility of some essential
contributions being overlooked due to title mismatches.
There are some limitations in this study. In this context,
it is possible that we originally overlooked a few significant
studies whose titles do not correspond to the study’s real
contributions or contents.
Author contributions
AN: concept desining, literature review, and methodology.
SA: introduction, abstract, and conclusion. RK: overall
reading and supervision. All authors contributed to the
article and approved the submitted version.
Conflict of Interest
The authors declare that the research was conducted in the
absence of any commercial or financial relationships that
could be construed as a potential conflict of interest.
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