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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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An Investigation of Running Load Comparisons of ARCore on Native Android and Unity for Outdoor Navigation System Using Smartphone 6

An Investigation of Running Load Comparisons of ARCore on Na...

引用

6th International Conference on Vocational Education and Electrical engineering, ICVEE 2023

作者： Brata, Komang Candra Funabiki, Nobuo Sukaridhoto, Sritrusta Fajrianti, Evianita Dewi Mentari, Mustika Okayama University Dept. of Information and Communication Systems Okayama Japan Universitas Brawijaya Dept. of Informatics Engineering Malang Indonesia Politeknik Elektronika Negeri Surabaya Dept. of Informatic and Computer Surabaya Indonesia State Polytechnic of Malang Dept. of Information Technology Malang Indonesia

ISBN: (纸本)9798350326642

In recent years, the utilization of location-based augmented reality (LAR) has gained popularity across various domains, including tourism, education, gaming, and advertising. The advancements in AR technology have made it easier to develop immersive LAR applications. ARCore, a widely adopted AR development library, enables the creation of location-based AR apps on Android devices. However, the implementation of ARCore in real-Time scenarios that demand significant computational resources, like outdoor pedestrian navigation, can vary depending on the chosen development environment. This research aims to explore and compare the running load performance of ARCore implementation in location-based AR applications developed with Unity and Android Native, which are two prevalent AR development platforms. The study involves building a prototype LAR application using both Unity and native Android, followed by comprehensive running load performance evaluations. The findings reveal that while both Unity and native Android apps can deliver compelling location-based AR experiences, native Android apps demonstrate superior performance and resource efficiency in outdoor scenarios, whereas Unity offers the advantage of multi-platform support and ease of development for complex 3D assets. These insights provide valuable guidance to developers in selecting the most suitable approach for developing location-based AR applications for navigation, aligning with their specific needs and requirements. © 2023 IEEE.

关键词： Augmented reality

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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...

引用

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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Causal Structure Learning from Imperfect Longitudinal Data in Healthcare 11

Causal Structure Learning from Imperfect Longitudinal Data i...

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11th IEEE International Conference on Healthcare informatics, ICHI 2023

作者： Yang, Haoyu Tourani, Roshan Li, Jia Caraballo, Pedro Steinbach, Michael Kumar, Vipin Simon, Gyorgy University of Minnesota Dept. of Computer Science and Engineering Minneapolis United States University of Minnesota Institute for Health Informatics Minneapolis United States Mayo Clinic Dept. of Internal Medicine Rochester United States

ISBN: (纸本)9798350302639

Causal Structure Discovery (CSD) is the task of learning the set of underlying causal relationships from observational data. Due to their computational scalability and flexibility, a recently developed class of CSD methods, NOTEARS, based on a formulation allowing for continuous optimization, is gaining popularity. However, this formulation can lead to incorrect edge orientations when little/no likelihood advantage is conferred upon any edge orientation, e.g., a → b → c versus c → b → a. In longitudinal data, like electronic health records (EHRs), temporal relationships are observable among many pairs of variables. Such temporal relationship is imperfect but still suggest an orientation since causal effects cannot travel backwards in time. Following this idea, we propose methods to incorporate precedence constraints into continuous optimization-based CSD methods. Experiments on both a synthetic and two real-world datasets validate the effectiveness of the proposed precedence constraints. © 2023 IEEE.

关键词： Machine learning

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Auto-Adaptive Stator Ground Fault Protection for Synchronous Generators in Diesel-Electric Locomotives 14

Auto-Adaptive Stator Ground Fault Protection for Synchronous...

引用

14th IEEE International Symposium on Diagnostics for Electrical Machines, Power Electronics and Drives, SDEMPED 2023

作者： Mahtani, Kumar Guerrero, Jose M. Beites, Luis F. Platero, Carlos A. Universidad Politécnica de Madrid Automation Electrical and Electronic Engineering and Industrial Informatics Dept. Madrid Spain Mondragon Unibertsitatea Electronics and Computer Science Dept. Arrasate-Mondragon Spain Universidad Politécnica de Madrid Automation Electrical and Electronic Engineering and Industrial Informatics Department Madrid Spain

ISBN: (纸本)9798350320770

Diesel-electric locomotives are equipped with one or more synchronous generators driven by diesel engines, that provide the necessary electrical energy for their operation. These generators are usually provided with some protection relays, depending on their rated power and their type of service. Standard stator-ground fault protection has a setting for rated conditions. In case of variable speed and voltage operation, the protection does not cover all the operation range of the machine. In addition, unwanted tripping may occur due to incorrect filtering of the third harmonic. In this paper, an auto-adaptative stator ground protection function is presented, for the operation of synchronous machines at variable voltage and frequency. A specific case study is tackled, focused on the main generators of a diesel-electric locomotive. Numerous simulations are carried out to verify the proposed protection method. The obtained results are promising, and they can be used in other synchronous machines operating at variable speed. © 2023 IEEE.

关键词： Stators

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Design and Implementation of 2-DOF Arm Manipulator Robot as Robotic Kinematic Learning Platform 6

Design and Implementation of 2-DOF Arm Manipulator Robot as ...

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6th International Conference on Vocational Education and Electrical engineering, ICVEE 2023

作者： Widayaka, Parama Diptya Puspitaningayu, Pradini Alamsyah, Sayyidul Aulia Muktiadji, Rifqi Firmansyah Widodo, Arif Abdillah, Rifqi Universitas Negeri Surabaya Dept. of Electrical Engineering Surabaya Indonesia King Abdulaziz University Dept. of Electrical and Computer Engineering Jeddah Saudi Arabia Okayama University Graduate School of Interdisciplinary Science and Engineering in Health Systems Okayamas Japan Institut Teknologi Sepuluh Nopember Surabaya Dept. of Informatics Surabaya Indonesia

ISBN: (纸本)9798350326642

Technological developments have impacts and challenges in human life. Technological advances have made human life easier. The impact of technological developments is the industrial manufacturing process getting faster with the help of robotics technology. Robots can shorten manufacturing process time and efficient. On the other hand, challenges with technological developments force us to keep learning and adaptive to every changes around us, especially in the education sector. In education sector, formal educational institutions or campuses must be able to facilitate students to be able to learn comprehensively in the field of technology, especially robotics, where we can make an innovative learning platform based on robotics technology. In this study, we discussed a 2-DOF arm manipulator robot as kinematics learning platform of industrial robot to increase student competencies in preparation for facing the industrial environment in century. Inverse kinematics equation has been applied in the robot by providing input coordinates on the x-Axis and y-Axis where the accuracy value on the x-Axis is 77% and the accuracy on the y-Axis is 83.1%. © 2023 IEEE.

关键词： Machine design

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Classification Anterior and Posterior of Knee Osteoarthritis X-Ray Images Grade KL-2 Using Deep Learning with Random Brightness Augmentation

Classification Anterior and Posterior of Knee Osteoarthritis...

引用

2022 International Conference on computer engineering, Network and Intelligent Multimedia, CENIM 2022

作者： Supatman Yuniarno, Eko Mulyanto Purnomo, Mauridhi Hery Institut Teknologi Sepuluh Nopember FTI UMBY Dept. of Electrical Engineering Dept. of Informatics Surabaya60111 Indonesia Institut Teknologi Sepuluh Nopember Dept. of Electrical Engineering Dept. of Computer Engineering Surabaya60111 Indonesia

ISBN: (纸本)9781665476508

Osteoarthritis of the knee (KOA) is a narrowing of the joint space area (JSA) due to lack of fluid in the knee joint, resulting in pain when moving and when it is severe, the femur and tibia meet. Medical personnel and existing computer-based methods have been able to detect patients with X-rays but have not been able to detect where visually the posterior AJ is still wide while the anterior AJ is actually narrow, so that the patient still feels joint pain. A new approach is proposed for the classification of X-Ray Grade Kellgren-Lawrence (KL)-2 Osteoarthritis Initiative image datasets using Deep Learning Neural Networks (DCNN) by setting the Random Brightness Augmentation hyperparameter. The experimental results obtained X-Ray Grade KL-2 narrowing image classification 'Anterior View KOA' and 'Posterior View KOA' with training accuracy of 83.33% and validation accuracy of 54.69% at Random Brightness with a value of 30. © 2022 IEEE.

关键词： computer vision

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GAN vs. LSTM: Network State Prediction 17

GAN vs. LSTM: Network State Prediction

引用

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

作者： Byun, Gyurin Raza, Syed M. Yang, Huigyu Kim, Moonseong Choo, Hyunseung Sungkyunkwan University Dept. of Superintelligence Engineering Suwon Korea Republic of College of Computing and Informatics Sungkyunkwan University Suwon Korea Republic of Seoul Theological University Dept. of It Convergence Software Bucheon Korea Republic of Sungkyunkwan University Dept. of Electrical and Computer Engineering Suwon Korea Republic of

ISBN: (纸本)9781665453486

Network Traffic prediction is the prerequisite for proactive traffic management, where a longer duration and high accuracy of prediction ensures a more effective solution. This paper exploits Generative Adversarial Network (GAN) architecture to propose a model that utilizes spatiotemporal features in network traffic data for extended prediction of future traffic. The generator in GAN consists of a Convolutional Long Short-Term Memory (ConvLSTM) model for predicting network traffic, while the discriminator uses Convolutional Neural Network (CNN) model that provides feedback to the generator for training. The experiments based on network traces show that the proposed model reduces the error by 12% compared to the baseline model while predicting next 48 mins of network traffic. © 2023 IEEE.

关键词： Generative adversarial networks

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Visual Field Prediction for Fundus Image with Generative AI 18

Visual Field Prediction for Fundus Image with Generative AI

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

作者： Kang, Honggu Ko, Seonghyeon Kim, Juchan Le, Duc-Tai Bum, Junghyun Han, Jongchul Choo, Hyunseung Dept. of AI Systems Engineering Sungkyunkwan University Suwon Korea Republic of College of Computing and Informatics Sungkyunkwan University Suwon Korea Republic of Sungkyun AI Research Institute Sungkyunkwan University Suwon Korea Republic of Dept. of Ophthalmology Samsung Medical Center Seoul Korea Republic of Dept. of Electrical and Computer Engineering Sungkyunkwan University Suwon Korea Republic of

ISBN: (纸本)9798350331011

Accurate diagnosis of glaucoma is crucial due to its high risk of blindness, but the long examination time often undermines the reliability of results by examinee's subjective factors. We propose a method to reduce examination time by generating static perimetry results with Conventional Fundus Images (CFIs) utilizing the CFI2GM technique, which leverages multimodal data. Based on data from 3,306 glaucoma patients at Samsung Medical Center in Seoul, we conducted ophthalmic image translation utilizing the Pix2Pix model. Our method, combining cGAN, L1, and SSIM loss, achieved MSE 57.9886 and PSNR 30.6057 dB. Furthermore, we received positive feedback from ophthalmologist regarding the high practical applicability of the images generated by our method. This indicates that CFI2GM can enhance the reliability of glaucoma examination results as well as reduce testing time. © 2024 IEEE.

关键词： Diagnosis

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Borneo Smart Forest Information System for Management of Dipterocarp Plants in Kalimantan Rainforest 8

Borneo Smart Forest Information System for Management of Dip...

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8th IEEE Information Technology International Seminar, ITIS 2022

作者： Wati, Masna Puspitasari, Novianti Hairah, Ummul Widians, Joan Angelina Septiarini, Anindita Tjikoa, Ade Fiqri Universitas Mulawarman Faculty of Engineering Dept. of Informatics Samarinda Indonesia Universitas Gadjah Mada Dept. of Computer Science and Electronics Yogyakarta Indonesia

ISBN: (纸本)9798350398199

Kalimantan's tropical rainforest is home to indigenous plants of the Dipterocarpaceae family (tribe), which is renowned for having the most endemic species. Dipterocarp is a family of pantropical plants, many of which are used in the wood industry. This study aims to provide information on the potential of Dipterocarp in Kalimantan forests. Education and conservation efforts for Dipterocarp trees, a prominent component of the forest ecosystem, are crucial in light of the significant harm done to Kalimantan's rainforests. The authors develop the Borneo Smart Forest information system. This system digitally organizes data on the diversity of Dipterocarp in the Kalimantan rainforest based on Web and QR-Code technology. Borneo Smart Forest (BSF) system for monitoring plant inventory and plant markers can be read by smartphones using QR codes. It gives visitors a comprehensive overview of the plants. A QR code is a printed, two-dimensional barcode with limited space for data storage. This technology allows the plant to show earlier administrator-made additions to the inventory information system. The QR code will direct the user to the BSF website, which has data about the plants. The development of this system is one of the efforts to support the achievement of Dipterocarp conservation. © 2022 IEEE.

关键词： Information systems

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