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Monitoring Student Cheating Activities using YOLOv8

Monitoring Student Cheating Activities using YOLOv8

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International Conference on informatics and Computing (ICIC)

作者： Asep Taufik Muharram Rizki Elisa Nalawati Mera Kartika Delimayanti Nisrina Izzatul Islam Andra Rizki Pratama Dept. of Informatics and Computer Engineering Politeknik Negeri Jakarta Depok Indonesia

ISBN: (数字)9798331517601

ISBN: (纸本)9798331517618

Higher education has had tremendous growth and is starting to transition into the digital era 5.0. It’s critical to identify any unusual behavior by students, especially when it comes to student fraud. This is to guarantee graduates who are of high caliber. Although some study has been done on the recognition of cheating activities, more work needs to be done in this particular scenario because there hasn’t been much done in the way of image quality in particular. Furthermore, it is necessary to enhance the inherent precision of diverse items within the class to enable the system to adjust to a range of environmental conditions. As a result, our research creates an online tool that may identify instances of dishonesty. The YOLOv8 approach is favored for its quick identification speed and excellent accuracy. Results from testing on a database of cheating behavior recognition showed a $97 \%$ accuracy rate. This work advances the development of a web-based tool for promptly identifying instances of student cheating.

关键词： Image quality Deep learning Accuracy Image recognition Databases Education Fraud informatics Monitoring Testing

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Feature Pool Exploitation for Disease Detection in Fundus Images 17

Feature Pool Exploitation for Disease Detection in Fundus Im...

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17th International Conference on Ubiquitous Information Management and Communication, IMCOM 2023

作者： Xiang Bang, Sammy Yap Le, Kim-Ngoc Thi Le, Duc-Tai Choo, Hyunseung Sungkyunkwan University Dept. of Superintelligence Engineering Suwon Korea Republic of College of Computing and Informatics Sungkyunkwan University Suwon Korea Republic of Sungkyunkwan University Dept. of Electrical and Computer Engineering Suwon Korea Republic of

ISBN: (纸本)9781665453486

Retinal fundus diseases without immediate diagnoses and treatment may lead to serious consequences such as permanent visual impairment. Recently, many machine learning (ML) and deep learning (DL) models have been introduced for fundus image classification. However, those heavy models require high-end graphics processing units for training and testing, thus not suitable for real-case usage in fundus cameras with limited computation power. In this paper, we demonstrate the effectiveness of our proposed two-block model, Feature Exploitation Lightweight network (FEL-net) which consists of a Feature Exploitation Block (FEB) and Lightweight Classification Block (LCB) by comparing it with other DL models. The experiment was carried out on a dataset of 21, 697 fundus images and our model achieves 99% binary classification accuracy. The proposed robust FEB is used to generate a refined feature pool for fundus images to build an efficient ML classifier that can distinguish fundus images with disease from the normal case with high accuracy and low running time. © 2023 IEEE.

关键词： Feature extraction

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Sentiment Analysis on the Use of Electric Vehicles With Long Short-Term Memory

Sentiment Analysis on the Use of Electric Vehicles With Long...

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Control & Automation, Electronics, Robotics, Internet of Things, and Artificial Intelligence (CERIA), IEEE International Conference on

作者： Rizki Elisa Nalawati Dewi Yanti Liliana Ratna Widya Iswara Izzati Rozana Putri Dzakiyah Fauziyah Dept. of Informatics and Computer Engineering Politeknik Negeri Jakarta Depok Indonesia

ISBN: (数字)9798331511166

ISBN: (纸本)9798331511173

Electric vehicles, or EVs, have drawn a lot of attention lately as an eco-friendly way to cut carbon emissions and lessen reliance on fossil fuels. This work uses a neural network model called Long Short-Term Memory (LSTM), which is effective at processing sequential input, to analyze user attitudes toward electric vehicles. Opinion data were gathered from the social media network Twitter between March 1st and March 31st, 2023, using the keyword “electric vehicles”. Based on the obtained opinion data, the LSTM approach is utilized to categorize sentiments as positive or negative. Implementation of the random oversampling technique is important to be applied in increasing the accuracy of the $\mathbf{n}$ recall model for the specified class. The study's conclusions show that the LSTM model performs admirably when it comes to sentiment analysis using the test dataset. The model evaluation yields a precision of $\mathbf{99.65 \%}$ and an accuracy rating of $\mathbf{97.79 \%}$ . The modeling process is carried out with variance splitting data where the highest accuracy is obtained from a comparison of training testing data of $\mathbf{90 \%}$ : $\mathbf{10 \%}$ .

关键词： Training Sentiment analysis Analytical models Accuracy Social networking (online) Blogs Electric vehicles Data models Long short term memory Testing

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EMP-GAN: Encoder-Decoder Generative Adversarial Network for Mobility Prediction

EMP-GAN: Encoder-Decoder Generative Adversarial Network for ...

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2023 IEEE INFOCOM Conference on computer Communications Workshops, INFOCOM WKSHPS 2023

作者： Bang, Sammy Yap Xiang Raza, Syed M. Yang, Huigyu Choo, Hyunseung Sungkyunkwan University Dept. of Superintelligence Engineering Suwon Korea Republic of College of Computing and Informatics Sungkyunkwan University Suwon Korea Republic of Sungkyunkwan University Dept. of Electrical and Computer Engineering Suwon Korea Republic of

ISBN: (纸本)9781665494274

Ultra-dense cell deployments in Beyond 5G and 6G result in extensive overlapping between cells. This makes current reactive handover mechanism inadequate due to availability of multiple strong signals at a position. Moreover, recently proposed predictive mobility management schemes are also not suitable as they may lead to unnecessary handovers. A predictive path-based mobility management scheme can solve these issues, but forecasting User Equipment (UE) paths with high accuracy is a challenging task. This paper proposes Encoder-Decoder Generative Adversarial Network (EMP-GAN) for forecasting multi-step ahead UE path. EMP-GAN architecture consists of generator and discriminator neural networks, where the generator predicts mobility (next multi-step target sequence) and the discriminator classifies between the predicted target sequence and the ground truth in adversarial learning. Besides adversarial learning, feature matching and fact forcing training methods are employed for fast convergence of GAN and performance improvement. EMP-GAN is evaluated on mobility dataset collected from the wireless network of Pangyo ICT Research Center, Korea, and results show that it outperforms state-of-the-art prediction models. In particular, EMP-GAN achieves 95.55%, 94.70%, 93.50%, and 92.39% accuracies for 3, 5, 7, and 9-step predictions, respectively. © 2023 IEEE.

关键词： Generative adversarial networks

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Enhanced Driver Stress Prediction from Multiple Biosignals via CNN Encoder-Decoder Model 46

Enhanced Driver Stress Prediction from Multiple Biosignals v...

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46th Annual International Conference of the IEEE engineering in Medicine and Biology Society, EMBC 2024

作者： Singh Roha, Vishal Gaber Gendy, Maggie Ezzat Pavan, Kaveti Deserno, Thomas M. Ganapathy, Nagarajan Yuce, Mehmet R. Monash University Dept. of Electrical & Computer Systems Melbourne Australia Indian Institute of Technology Hyderabad Dept. of Biomedical Engineering Hyderabad India Technical University of Braunschweig Plri Lab for Medical Informatics Brunswick Germany

ISBN: (纸本)9798350371499

In this work, we present PhysioFuseNet, a novel framework designed to enhance driver stress state classification. PhysioFuseNet integrates a CNN-based encoder-decoder model with multimodal biosignal fusion. Using a driving simulator, different multimodal signals were acquired, namely electrocardiography, electrodermal activity, photoplethysmography, and respiration rate from (N = 25) healthy subjects. The experiment is of 35 minutes duration and contains different stress states (baseline (5 minutes), while normal, cognitive, and emotional sessions for 10 minutes). Multimodal features are extracted and employed in an encoder-decoder network. Extracted encoder features are combined through intermediate fusion and fed to support vector machine (SVM) and random forest (RF) classifiers. Experimental results demonstrate the efficacy of our approach, outperforming previous methods by achieving accuracies of 0.95 and 0.94 for SVM and RF, respectively. Notably, the framework excels in classifying emotional and cognitive stress states. In summary, the proposed framework could be useful in stress assessment in real-time and clinical conditions. © 2024 IEEE.

关键词： Photoplethysmography

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Big Data Analytics for Proactive Financial Fraud Detection and Prevention 2

Big Data Analytics for Proactive Financial Fraud Detection a...

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2nd International Conference on Technology Innovation and Its Applications, ICTIIA 2024

作者： Hasudungan, Yehezkeil Oganda, Fitra Putri Daeli, Ora Plane Maria Raihan Santoso, Nesti Anggraini University of Raharja Dept. of Informatics Engineering Tangerang Indonesia University of Raharja Dept. of Information System Tangerang Indonesia University of Raharja Dept. of Digital Business Tangerang Indonesia University of Raharja Dept. of Computer System Tangerang Indonesia

ISBN: (纸本)9798350351613

Financial fraud poses a significant challenge to the financial industry, with increasing complexity and scale threatening global economic stability. This paper explores the use of Big Data Analytics for proactive detection and prevention of financial fraud. By analyzing large and diverse datasets, this approach aims to identify patterns and anomalies that may indicate fraudulent activities before they harm financial institutions. In this study, Smart PLS is employed as the primary method to test the structural model and the influence of various variables on the effectiveness of fraud prevention. The findings reveal that integrating Big Data Analytics into fraud prevention strategies can enhance early detection and response to fraud threats, positively impacting financial risk management. This research contributes significantly to the field of risk management by offering a framework that can be implemented to enhance financial security through advanced data analytics. © 2024 IEEE.

关键词： Data Analytics Financial Fraud Prevention Proactive Detection Risk Management SmartPLS

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The Integration of 6G and Blockchain into an Efficient AIoT-Based Smart Education Model 6

The Integration of 6G and Blockchain into an Efficient AIoT-...

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6th World Symposium on Communication engineering, WSCE 2023

作者： Lioupa, Anna Memos, Vasileios A. Stergiou, Christos L. Ishibashi, Yutaka Psannis, Konstantinos E. School of Information Sciences University of Macedonia Dept. of Applied Informatics Thessaloniki Greece Graduate School of Engineering Nagoya Institute of Technology Dept. of Computer Science Nagoya Japan

ISBN: (纸本)9798350339505

The technological advances impact the future classrooms and the whole education system. The evolution of 6G promises faster speeds based on terahertz, wireless communication, the leverage of artificial intelligence (AL), virtually reality (VR) and generally a fully connected world. The use of applications of IoT and AIoT improves the education environment, but the intelligent network services undoubtedly gather, store and analyze big data which are vulnerable to attackers. As a solution we may consider the technology of blockchain. The trust and security of next generation intelligent networks are essential part of their design. In this article we suggest a data security system based on blockchain for AL applications in 6G networks. First, we present two Al-enabled applications, indoor positioning and mobile payment within the framework of 6G in the university area. We present the efficiency of blockchain in data security for Al-enabled systems using two case studies of indoor positioning system and mobile payment. The 6G system which is fully supported by Al and the integration of this in technology of blockchain, is ensured the security in future wireless communication. © 2023 IEEE.

关键词： Blockchain

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Deep Predictive User Path-Inclusive Service Mobility in B5G/6G Edge Clouds 18

Deep Predictive User Path-Inclusive Service Mobility in B5G/...

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18th International Conference on Ubiquitous Information Management and Communication, IMCOM 2024

作者： Mwasinga, Lusungu J. Raza, Syed M. Le, Duc-Tai Kim, Moonseong Chung, Min Young Choo, Hyunseung Dept. of Computer Science and Engineering Sungkyunkwan University Suwon Korea Republic of Dept. of Electrical and Engineering Sungkyunkwan University Suwon Korea Republic of College of Computing and Informatics Sungkyunkwan University Suwon Korea Republic of Dept. of IT Convergence Seoul Theological University Bucheon Korea Republic of

ISBN: (纸本)9798350331011

Multi-access Edge Computing (MEC)-enabled Beyond 5G (B5G) or 6G networks require service migration to maintain service continuity and acceptable quality of experience for high-mobility User Equipment (UE). This involves migration of containerized applications to between Edge Hosts via the backhaul network as the associated UE moves away from its current gNodeB. Although crucial for both UEs and mobile network operators (MNOs), frequent migration not only prolongs service disruption time (downtime) for UEs but also increases operational expenses for the MNO. To address this problem, our study proposes a Deep Predictive User Path-Inclusive Service Mobility (DUPSM) approach for efficient management of the end-to-end migration process including deciding when and where to migrate the service. It features a deep Actor-Critic agent that learns suitable decision policy to balance cost-benefit trade-off interms of migration cost and quality of experience, respectively. As user mobility largely influences migration decisions, the proposed method incorporates user mobility path forecasting through a Generative Adversarial Network (GAN) based prediction technique to enhance ability for selecting suitable Edge Host while considering resource variations. The effectiveness of the proposed method is assessed through simulation experiments, showcasing an average reduction in service downtime of 22% when compared to a baseline scheme. © 2024 IEEE.

关键词： Reinforcement learning

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Advanced Machine Learning for Predicting Parking Availability in Urban Areas

Advanced Machine Learning for Predicting Parking Availabilit...

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IEEE International Smart Cities Conference (ISC2)

作者： Panagiotis Hadjidoukas Aikaterini Florou Dimitrios Salmas Chrysostomos Stylios Dept. of Computer Engineering and Informatics University of Patras Patras Greece Dept. of Informatics and Telecommunications University of Ioannina Arta Greece

ISBN: (数字)9798350364316

ISBN: (纸本)9798350364323

This paper examines urban parking prediction using advanced AI-based technologies like machine and deep learning, automated ML (AutoML), and Federated Learning (FL). ML and DL can provide models with high predictive performance if appropriate data processing has been applied to the raw data collected by the parking sensors. The achieved performance is also determined by the selection and fine-tuning of the proper model. AutoML tools automate this time-consuming process, delivering equivalent or better accuracy. The methodology covers data collection and preprocessing, as well as model development, and highlights the integration of FL for improved data privacy and security. The implementation utilizes open-source tools, making our work applicable to real-world scenarios.

关键词： Deep learning Data privacy Smart cities Federated learning Predictive models Data collection Automated machine learning Data models Sensors Security

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FLoW-Vision: Object Recognition and Pose Estimation System based on Three-Dimensional (3D) computer Vision 23

FLoW-Vision: Object Recognition and Pose Estimation System b...

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23rd International Electronics Symposium, IES 2021

作者： Putra, Vardyansyah Cahya Pratama Harsetya Apriandy, Kevin Ilham Pramadihanto, Dadet Barakbah, Ali Ridho Dept. of Informatics and Computer Engineering Electronic Engineering Indonesia

ISBN: (纸本)9781665443463

This paper presents a three-dimensional computer vision-based object recognition on FLoW-Vision in RoISC (formerly ER2C) has entered its second phase. Previously, the robot had a basic vision that was used to replicate 'human-like' visual skills using 2D computer vision. As a result of the above discussion, we proposed the design and implementation of an object recognition and pose estimation system based on three-dimensional computer vision to handle object recognition and pose estimation tasks in real-world environments simultaneously. In the object recognition process, a point-cloud segmentation method is used to obtain possible object clusters before starting the calculation of feature description. Then, a keypoints-based two-stage matching process is performed to speed up the computation of finding correspondences between the object clusters of the current scene and a colored point cloud model of an object. Next, a Hough voting algorithm is employed to filter out matching errors in the correspondence set and estimate the initial 3D pose of the object. Last process process the pose estimation from clustered object using RANSAC to search the largest surface as Z surface. Experimental validate the object recognition can work correctly with percentage 100% and pose estimation accuracy of the proposed system can work correctly with percentage 60% in a complex real-world scene. © 2021 IEEE.

关键词： Object detection

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